% $Header$ % Purpose: Proposal for NSF Arctic Natural Sciences (ANS) Dirty Snow project % URLs: % http://dust.ess.uci.edu/prp/prp_ans/prp_ans.pdf % http://dust.ess.uci.edu/prp/prp_ans/prp_ans_fll.pdf % http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0714088 % CVS: cvs -d :ext:esmf.ess.uci.edu:/u/zender/cvs co -kk prp_ans % Usage (see also end of file): % cd ~/prp_ans;make -W prp_ans.tex prp_ans.pdf;cd - % 2006 NSF Arctic Research Opportunities (ARO): % NSF 06-603 % Office of Polar Programs (OPP) % Arctic Sciences Section (ASS) % Arctic Natural Sciences (ANS) Program % Program Manager, ANS: Jane V. Dionne ``die-on'', (703) 292-7427, jdionne@nsf.gov % William J. Wiseman, Jr. (703) 292-4750, wwiseman@nsf.gov % Deadlines: Full Proposal 20061208 % NSF FastLane Temporary Proposal # 6633880 PIN % NSF FastLane Proposal #0714088 (refer to as ARC-0714088) % Total 3-year budget request: $518,482 % Project duration: 20070901--20100831 % Annual progress report deadlines: 20080831, 20090831, 20081130 % Procurement control number (PCN): % UCI account number: 9-number-fund-sub-object = 9-123456-12345-1-1234 = 9-445925-12345-1-1234 % Physical Sciences budget code: 1234 % 2005 NSF OPP ARO ANS Round1: % Due December 15, 2005 % 2006 NSF OPP ARO ANS Round2: % Due December 8, 2006 % 2006 NSF OPP ARO ANS Round3: % Due November 10, 2007 % Usage: See end of file % NSF GPG (2004) formatting requirements: % Font >= 10pt; Margins >= 1 inch; Characters per inch <= 15; Lines per inch <= 6 \documentclass[10pt]{article} % Standard packages \usepackage{ifpdf} % Define \ifpdf \ifpdf % We are running PDFLaTeX \usepackage[pdftex]{graphicx} % Defines \includegraphics* \pdfcompresslevel=9 \usepackage{thumbpdf} % Generate thumbnails \usepackage{epstopdf} % Convert .eps, if found, to .pdf when required \else % We are not running PDFLaTeX \usepackage{graphicx} % Defines \includegraphics* \fi % endif PDFLaTeX \usepackage{amsmath} % \subequations, \eqref, \align \usepackage{amssymb} % amsfonts \mathbb KoD99 p. 434 \usepackage{array} % Table and array extensions, e.g., column formatting \usepackage[margin=10pt,labelfont=bf]{caption} % Caption formatting % Do not use small caption font with 10pt text %\usepackage[margin=10pt,font=small,labelfont=bf]{caption} % Caption formatting \usepackage[usenames]{color} % usenames allows, e.g., ``ForestGreen'' \usepackage{datetime} % \xxivtime, \ordinal \usepackage{floatflt} % Wrap text around floats \usepackage{longtable} % Multi-page tables, e.g., acronyms and symbols \usepackage{makeidx} % Index keyword processor: \printindex and \see \usepackage{mdwlist} % Compact list formats \itemize*, \enumerate* \usepackage{natbib} % \cite commands from aguplus \usepackage{revnum} % Reverse enumerate \usepackage{times} % Postscript Times-Roman font KoD99 p. 375 \usepackage{tocbibind} % Add Bibliography and Index to Table of Contents \usepackage{url} % Typeset URLs and e-mail addresses % hyperref is last package since it redefines other packages' commands % hyperref options, assumed true unless =false is specified: % backref List citing sections after bibliography entries % baseurl Make all URLs in document relative to this % bookmarksopen Unknown % breaklinks Wrap links onto newlines % colorlinks Use colored text for links, not boxes % hyperindex Link index to text % plainpages=false Suppress warnings caused by duplicate page numbers % pdftex Conform to pdftex conventions % Colors used when colorlinks=true: % linkcolor Color for normal internal links % anchorcolor Color for anchor text % citecolor Color for bibliographic citations in text % filecolor Color for URLs which open local files % menucolor Color for Acrobat menu items % pagecolor Color for links to other pages % urlcolor Color for linked URLs \ifpdf % We are running PDFLaTeX \usepackage[backref,breaklinks,colorlinks,citecolor=blue,linkcolor=blue,urlcolor=blue,hyperindex,plainpages=false,pdftex]{hyperref} % Hyper-references \pdfcompresslevel=9 \else % We are not running PDFLaTeX \usepackage[backref=false,breaklinks,colorlinks=false,hyperindex,plainpages=false]{hyperref} % Hyper-references \fi % endif PDFLaTeX % Personal packages \usepackage{csz} % Library of personal definitions \usepackage{abc} % Alphabet as three letter macros \usepackage{dmn} % Dimensional units \usepackage{chm} % Commands generic to chemistry \usepackage{dyn} % Commands generic to fluid dynamics \usepackage{aer} % Commands specific to aerosol physics \usepackage{psd} % Particle size distributions \usepackage{rt} % Commands specific to radiative transfer \usepackage{hyp} % Hyphenation exception list \usepackage{jrn_agu} % AGU-sanctioned journal title abbreviations % Commands which must be executed in preamble \newcounter{idxpg} % [nbr] Page number of Index %\allowdisplaybreaks[1] % AMSTeX permits eqnarray to wrap pages [1--4] %\listfiles % Print required files, versions \makeglossary % Glossary described on KoD95 p. 221 \makeindex % Index described on KoD95 p. 220 \makecompactlist{revnumerate*}{revnumerate} \newbool{tskbrk} % Break page after each task \setbool{tskbrk}{false} % Default value % Commands specific to this file \makeatletter \renewcommand{\fnum@table}{\textbf{\tablename~\thetable}} % Boldface ``Table: #'' \renewcommand{\fnum@figure}{\textbf{\figurename~\thefigure}} % Boldface ``Figure: #'' \makeatother % 1. Primary commands % 2. Derived commands % 3. Doubly-derived commands % Margins \oddsidemargin = 0.0in \evensidemargin = 0.0in \topmargin = -0.4in \textheight = 9in \textwidth = 6.5in \columnsep = 0.25in \headheight = 12pt \headsep = 12pt \footskip = 24pt % Vertical space % 20000717: None of these commands seem to have any effect here \setlength{\textfloatsep}{12pt} % Space between last top float or first bottom float and the text. \setlength{\intextsep}{12pt} % Space left on top and bottom of an in-text float. % \dbltextfloatsep is \textfloatsep for 2 column output. % \dblfloatsep is \floatsep for 2 column output. \setlength{\abovecaptionskip}{12pt} % Space above caption \setlength{\belowcaptionskip}{12pt} % Space below caption % Float placement % NB: Placement of figures is very sensitive to \textfraction \renewcommand\textfraction{0.0} % Minimum fraction of page that is text \setcounter{totalnumber}{73} % Maximum number of floats per page \setcounter{topnumber}{73} % Maximum number of floats at top of page \setcounter{dbltopnumber}{73} % Maximum number of floats at top of two-column page \setcounter{bottomnumber}{73} % Maximum number of floats at bottom of page \renewcommand\topfraction{1.0} % Maximum fraction of top of page occupied by floats \renewcommand\dbltopfraction{1.0} % Maximum fraction of top of two-column page occupied by floats \renewcommand\bottomfraction{1.0} % Maximum fraction of bottom of page occupied by floats \renewcommand\floatpagefraction{1.0} % Fraction of float page filled with floats \renewcommand\dblfloatpagefraction{1.0} % Fraction of double column float page filled with floats \begin{document} % Arctic Natural Sciences (ANS) \def\prpttl{Snow Process Studies and Modeling to Improve Arctic Climate Prediction\\} \def\prpttllikeboring{Integrating More Realistic Snow Processes into Climate Models to Improve Understanding and Prediction of Arctic Climate\\} \def\prpttlnot{Improving Understanding and Prediction of Arctic Climate by Representing Snow Lifecycle Processes\\} \def\prpttlrecent{Upscaling Snowpack Processes to Improve Detection, Attribution, and Prediction of Arctic Climate Change\\} \def\prpttlold{Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks\\} % Interdisciplinary Research (IDS) \def\prpttlids{Dirty Snow: Absorbing Aerosol Effects on Cryospheric Climate Sensitivity and Surface Hydrology\\} % Cover page \pagenumbering{roman} \setcounter{page}{1} \pagestyle{headings} \thispagestyle{empty} {\noindent% Web: \url{http://dust.ess.uci.edu/prp/prp_ans/prp_ans.pdf}\\ NSF Arctic Natural Sciences (ANS) Proposal \href{http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0714088}{ARC-0714088} \hfill Submitted: December~8, 2006\\ Last modified: \today, \xxivtime \hfill Awarded: September~5, 2007} \begin{center} \textbf{\Large\prpttl} \bigskip Dr. Charles S. Zender \hfill \\ Department of Earth System Science \hfill \\ University of California, Irvine \hfill \\ \end{center} \vskip 0.5 cm \noindent\textbf{Information for potential collaborators:} This NSF proposal responds to the 2006 NSF Arctic Research Opportunities (ARO) announcement, \href{http://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf06603}{NSF 06-603}. The proposal was submitted to the Arctic Natural Sciences (ANS) Program of the Division of Arctic Sciences (ARC) in the Office of Polar Programs (OPP). The cognizant Program Managers are Bill Wiseman \url{wwiseman@nsf.gov}, (703)~292-4750 and Jane~V. Dionne \url{jdionne@nsf.gov}, (703)~292-7427. \medskip \noindent\textbf{News/Preface:} \begin{revnumerate*} \item 20071111: Filled out IPY participant information form for Polar Field Services (NSF contractors). Identified website for initial dissemination of LGGE snow measurements as \url{http://dust.ess.uci.edu/snw}. Identified IPY sub-disciplines as Snow physics, aerosol-climate interactions, cryosphere-radiation interactions. \item 20070907: The award to UCI is official. The approved budget is $\$135634 + \$194447 + \$188401 = \$518482$. The award dates are 20070901--20100831 (pending successful annual reviews). The report due dates are 20080531, 20090531, and 20080831. The report overdue dates are 20080831, 20090831, and 20081130. \item 20070905: Received award letter describing award context and panel review. This proposal was one of 188 submitted proposals for 105 distinct projects that requested a total of \$75M from the NSF OPP/ARC/ANS in 2006. The panel review is the joint evaluation by both ANS and CLD. The available ANS budget for all awards was \$8M for three years. GEO/ATM/CLD will co-fund this grant. \item 20070806: Received word that NSF OPP/ARC/ANS and GEO/ATM/CLD (J.~Fein) jointly reviewed and recommended funding this proposal. No mention of budget constraints so grant may (knock on wood) be fully funded (\$518,482). \item Version submitted on December~8, 2006 is NSF proposal (\href{http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0714088}{ARC-0714088}) \end{revnumerate*} \noindent\textbf{Ideas for Renewals/Extensions/SGERs:} \begin{enumerate*} \item Student participation in field IPY experiments (POLARCAT/Greenland!) \end{enumerate*} \noindent\textbf{Points considered/addressed since 2006 rejection:} \begin{enumerate*} \item Respond to Panel Review Points: \begin{enumerate*} \item Evaluate against actual BC/dust-induced melt: Warren measurements \item Interpretation of satellite pixels containing water and snow: Painter \item Pan-Arctic issues: non-Greenland field measurement sites? Barrow? \end{enumerate*} \item Improve questions for BC tasks in 2007 submittal: \begin{enumerate*} \item Do industrial emissions reductions reproduce 1985--2005 Arctic BC decrease? \item When do current ramps suggest historic Arctic BC highs? \end{enumerate*} \item Improve sea-ice questions/tasks in 2007 submittal: \begin{enumerate*} \item Examine feedback between aerosol/GHG-induced warming and increases in downwelling longwave due to water vapor, atmospheric dust \item Mesh with Bruce Briegleb and Bonnie Light for sea-ice RT? (and obtain upcoming NCAR tech.~note) \item Recognize potential ocean role in causing sea-ice asymmetry \end{enumerate*} \item Needed In~situ and lab measurements in 2007 submittal: \begin{enumerate*} \item Need co-val $\tpt$, TG, $\dns$, $\rfl(\wvl)$ for, say, two weeks each season \textit{in the Arctic}? (will lack SSA without Domin\'{e}) \end{enumerate*} \item New Budget in 2007: \begin{enumerate*} \item Spring/summer Field seasons in Greenland for comprehensive SNICAR-closure measurements? \end{enumerate*} \item Letters of Support changes for 2007: \begin{enumerate*} \item Get Painter \end{enumerate*} \item Newer references and ideas to incorporate where appropriate: \begin{enumerate*} \item Francis and Hunter Eos 87(46) 20061114: Sea ice retreat \item \cite{ACH05}: dirty snow speeds up worst case scenarios presented here \item \cite{QuH05}: Partitioning surface/atmosphere contributions to polar albedo underestimates surface-mediated feedbacks \item \cite{AJM04}: SNTHERM model performance on sea-ice \item \cite{HaQ06}: Using seasonal SAF to estimate GCC SAF \item \cite{AHK06}: dust deposition on snow \item \cite{PHM02}: increasing river discharge to Arctic \item \cite{PeM95}: dust-ice sheet connections \item \cite{LaS05}: permafrost \item \cite{DTS05}: incorporate frost flowers, diamond dust, surface hoar in SNICAR \item \cite{PDT01}: snow algae \item \cite{Roe06}: AR4 snow albedo evaluation \item \cite{AnG06,HGG06}: Brown organic carbon \item \cite{TPH05}: LGM fire emissions \item \cite{KBB06}: LGM dust melts Asian ice \item \cite{BWW05}: Surface albedo over sea ice \item \cite{HWB06}: Antarctic BRDF \item McConnell et~al. recent ice core dust concentrations? \item Large-scale snow-fraction representations? \end{enumerate*} \end{enumerate*} \noindent\textbf{News/Preface:} NSF 05-979, Synthesis of Arctic System Science (SASS), due 20060316, was well suited to study integrated dirty snow impacts in Alaskan tribal environments, including: waste site incineration impacts on local snowpack/permafrost. Look at update? \tableofcontents \listoffigures %\listoftables \clearpage %\onecolumn %\tableofcontents %\listoftables %\clearpage \pagenumbering{arabic} \setcounter{page}{0} \thispagestyle{empty} %\markleft{Dirty Snow} %\markright{} %\begin{center} %\textbf{\large\prpttl} %\end{center} \begin{center} \textbf{\Large\prpttl} \bigskip Dr. Charles S. Zender \hfill \\ Department of Earth System Science, University of California, Irvine \hfill \\ Collaborators: Florent Domin\'{e}, Elizabeth Hunke, Dorothy Koch, Phil Rasch, Steve Warren \end{center} \noindent{\large{\textbf{Project Summary.}}}\label{sxn:smr} %\enlargethispage*{0.5in} \textbf{Scientific Merit:} Greenhouse gas (GHG) forcing alters the rate of snow coarsening and albedo evolution, influencing snow and sea-ice seasonality. The prevalence of bright surfaces (snow, glaciers, sea-ice, and clouds) make the Arctic vulnerable to radiatively induced effects of snow and ice impurities such as light absorbing carbon (LAC) and mineral dust (MD). Coordinated International Polar Year (IPY) activities will yield pan-Arctic measurements of surface snow properties and of impurity concentration, chemical composition, and optical properties which will help identify regions and seasons most affected by aerosol-driven ice-albedo feedback. This project uses IPY measurements to improve cryospheric models used to understand and to predict pan-Arctic climate and climate change. We will (1)~Use IPY field and lab measurements to improve representation of snowpack microphysical processes including melt scavenging, hoar formation, and impurity effects; (2)~Implement and/or refine these processes in Arctic land, atmosphere, and sea-ice components of an Earth System Model (ESM); (3)~Use the ESM to upscale and better quantify the efficacy of and response to Arctic climate forcing agents in the 20th and 21st centuries. We have integrated surface snowpack evolution and satellite-derived LAC emissions into a unified modeling framework with which we forecast and hindcast contemporary and 21st century climate with and without prescribed and predicted GHG and aerosol forcing and feedbacks. IPY measurements gathered by collaborators and by us will help constrain and evaluate multiple processes in our SNow, ICe, and Aerosol Radiative model (SNICAR) including snowpack evolution, aerosol precipitation- and melt-scavenging, and snowpack heating by impurities. We will use the ESM with self-consistent aerosol and snow lifecycles to distinguish the relative roles of aerosols and GHGs as Arctic forcing agents. Our scientific questions include: 1.~How do surface hoar and melt/freeze cycles interact in diurnal and seasonal features in Arctic snowpack specific surface area and reflectance? \csznote{ 1.~Are isothermal and temperature-gradient snow aging processes sufficient to reproduce diurnal and seasonal features in Arctic snowpack reflectance?} % end csznote 2.~What are the relative efficacies of aerosol- and GHG-driven snow forcing on land and on sea-ice? 3.~Could plausible LAC emissions reductions significantly mitigate Arctic climate change? These questions will be addressed in pre-industrial, present day, and next century contexts. The results will improve understanding of ice-albedo feedbacks crucial to Arctic climate and climate change. \csznote{ ESM experiments forced by the MODIS-derived Global Fire Emissions Database (GFED) and fossil fuel (FF) and biofuel (BF) emission record (extrapolated through the 21st century) Explaining LAC decreases from the 1980s to the present, and hindcasting the strongest IPY-observed biomass burning plumes. We hypothesize that Arctic LAC and dust cause similar ice-albedo feedback amplification over glaciers and sea-ice. We will support the IPY POLARCAT project by modeling Arctic soot concentration and snow reflectance following boreal fires. Snowpack specific surface area, albedo, crystal density, LAC concentration, LAC melt scavenging, and aging processes will (continue to) be evaluated against Arctic \textit{in~situ} measurements. } % end csznote \textbf{Broader Impacts:} Snow pervades the Arctic, and our integrated framework for snow thermodynamic, radiative, and hydrologic processes will contribute to Arctic research areas including basin and catchment hydrology, snow chemistry, snow remote sensing, sea-ice lifecycle, paleoclimate sensitivity, and glacier mass balance. Contributions to the fifth IPCC climate assessment will include more realistic snowpack processes. This project trains one graduate student and involves one post-doc in Arctic aerosol-climate interactions, opens a year-round undergraduate research position to under-represented minorities, and establishes strong international research links. PI Zender will incorporate this Arctic climate change research into a K--12 teacher training program and into presentations for lifelong learning students. \clearpage \csznote{ \begin{center} \textbf{\Large \prpttl} \end{center} } % end csznote \thispagestyle{empty} \section{Introduction}\label{sxn:ntr} The Arctic climate system is experiencing unprecedented change due to greenhouse-gas (GHG) induced warming, Arctic haze, and other factors \cite[]{ACIA05}. Global emissions of light absorbing carbon (LAC) aerosol from fossil fuel sources, a contributor to Arctic haze and ``dirty snow'', have steadily increased for decades \cite[][]{PAA01,BSY04}. LAC emissions from boreal fires are highly variable, and expected, though with less certainty, to increase as boreal forests warm and expand northward \cite[][]{RLF06}. Current understanding of Arctic LAC climate impacts derives mostly from studies which focus on LAC direct atmospheric radiative forcing and which neglect or drastically simplify surface LAC interactions. Bright surfaces (snow, glaciers, sea-ice, and clouds) make the Arctic uniquely susceptible to radiatively induced effects of surface LAC and dust such as ice-albedo feedback amplification. Such feedbacks make dirty snow more efficacious than greenhouse gases at atmospheric temperature change \cite[][]{HaN04}. Dirty snow feedbacks change throughout the aerosol lifecycle in the complex Arctic environment of snowfall, snowpack aging, snow-melt, drainage, and analogous sea-ice processes \cite[e.g.,][]{LEM98,AHH03,FlZ06}. Our goal is to assess absorbing aerosol interactions in the coupled Arctic climate system using models which represent the complex surface lifecycles of Arctic snow, LAC, and dust, and which have been evaluated against satellite, in-situ, and laboratory measurements. Many use the terms soot and BC interchangeably to denote the light absorbing component (LAC) of carbonaceous aerosol for historical reasons or for succintness \cite[e.g.,][]{BoB05}. Fossil fuel (FF) and biomass burning (BB) emissions release different ratios of Black Carbon (BC) to Organic Carbon (OC) aerosols to the atmosphere. Complicating matters further, much OC aerosol scatters brightly like sulfate, but some OC aerosols is light-absorbing and has been termed ``Brown Carbon'' \cite[]{AnG06,HGG06}. Interest in BC effects on climate, particularly Arctic climate, has increased as general circulation model (GCM) aerosol capabilities and emission inventories have improved. Recent noteworthy studies suggest that anthropogenic soot may have caused one quarter of last century's observed warming \cite[][]{HaN04}, and significant reductions in Northern hemisphere albedo and sea-ice extent \cite[][]{Jac04}. Such estimates are extremely sensitive to accurate treatment of snowpack aging and radiative transfer \cite[][]{FlZ06,FZR07}, as well as uncertainties in boundary conditions such as emissions and meteorology. This project devotes significant attention to improving model physics based on measurements, and to quantifying model uncertainties by replicating simulations in two different models. Ice-albedo feedback is arguably the most important positive feedback in the polar climate system \cite[e.g.,][]{Har94,HoB03,QuH06}. However, we are unaware of any coupled global models that account for realistic snow processes, including aerosol radiative interactions, throughout the surface Arctic. It is not premature to assemble such integrated models to assess, predict, and improve understanding of the Arctic climate, so long as the increased model complexity is justified by continuous demonstrated fidelity to laboratory and field physical process studies. Our project coordinates laboratory snow process studies with snow model development, and larger scale models with collaborator and community IPY measurements, to study the Arctic response to aerosol and GHG forcing now, in the past, and in the future. \csznote{ \begin{enumerate*} \item Interpreting historical changes in sea-ice extent and fraction \item Potential for Arctic BC to disrupt summertime sea-ice concentration \item Role of extreme Boreal fire years in Arctic reflectance \item Use coordinated suite of NASA measurements \begin{enumerate*} \item MODIS \item MISR \item AMSR-E \end{enumerate*} \end{enumerate*} } % end csznote % Dusty snowpacks provide additional meltwater on Earth and % probably other planets \cite[e.g.,]{Clo87} %\subsection{Organization}\label{sxn:org} This proposal is organized as follows. The project's historical and scientific context is in Section~\ref{sxn:bgr}. Our scientific objectives and specific hypotheses are in Section~\ref{sxn:obj}. Section~\ref{sxn:mth} describes the models and observations we will use to reach these goals. Section~\ref{sxn:crd} summarizes the project plan, personnel responsibilities, time-line, milestones, and travel. Section~\ref{sxn:prr} describes the results of our relevant, prior NSF-funded research. Projects related to ours, potential broader scientific impacts, and our education plan are in Section~\ref{sxn:mpc}. Six letters of support/collaboration and a list of acronyms and abbreviations appear at the end as supplementary documents. \section{Background}\label{sxn:bgr} \subsection{Relevance and Historic Trends}\label{sxn:hst} Bright surfaces (snow, glaciers, sea-ice and clouds) make the Arctic uniquely susceptible to ice-albedo feedbacks \cite[e.g.,][]{ClN85,HoB03,HaN04,QuH06} including those caused and amplified by radiatively induced effects of surface BC and dust (Figure~\ref{fgr:fdb_dgm}). \begin{floatingfigure}[r]{0.5\hsize} % begin Figure~\ref{fgr:fdb_dgm} %\begin{figure} %\centering % \centering uses less vertical space than center-environment \includegraphics*[width=0.95\hsize,angle=0,clip=true,trim=1.0in 1.4in 1.0in 1.0in]{/data/zender/fgr/snicar/sot_snw_fdb}% \caption[Snow-albedo feedback schematic]{ Snow- (and ice-) albedo feedbacks described in text. Pink and Red symbols denote moderate and strong positive feedback loops, respectively. Plusses and minuses indicate response of target (arrow-head) to positive change in source (arrow-tail). Absorbing aerosols amplify the grain-size feedback with gain~$\GGG$. \label{fgr:fdb_dgm}} %\end{figure} \end{floatingfigure} % end Figure~\ref{fgr:fdb_dgm} The ``classic'' snow/ice-albedo feedback refers to the snow/ice area feedback (upper left loop) that results from changes in the areal extent of snow/ice cover that conceals the underlying surface. The reflectances of snow, glacier, and sea-ice contrast enough so that significant snow/ice-area feedback occurs among these forms of frozen water \cite[][]{BWW05,FZR07}. The temperature grain-size feedback is a weaker positive feedback (center loop) that arises from temperature controls on snow aging and on the snow grain size distribution which in turn determine solar reflectance \cite[]{FlZ06}. Internally and externally mixed snowpack impurities darken snow albedo directly (top right, this is a forcing not a feedback), effectively increasing the gain $\GGG$ on the temperature-grain size feedback by heating the snowpack. Accumulation of hydrophobic impurities at the surface during melt events may cause a feedback between temperature and soot concentration (dashed lower right loop) observed by \cite{ClN85} and \cite{CGR96}. This melt-impurity feedback is thought to affect hygrophilic aerosol (e.g., sulfate-coated BC) more than hygrophobic aerosol (e.g., uncoated dust). Representing the vertical distribution of heating in a multi-layer snow or ice medium is important, not least because the penetration depths of visible and near-infrared (NIR) radiation into snow are significantly different \cite[]{WiW80}. Turbulent heat fluxes and sublimation to the atmosphere can efficiently dissipate surface-layer ($\lesssim 1$\,\cm) snowpack radiative heating but not sub-surface heating. Since snow has low thermal diffusivity that allows sub-surface heating to slowly warm the snowpack and accelerate melt onset. \cite{FlZ05} showed that representing snowpack as multi-layer, regardless of snow-aging, substantially improves snowpack simulations in the Tibetan Plateau region. Mineral dust can also play an important role in the Arctic, far from its dominant sources in the sub-tropical deserts \cite[][]{PGT02,ZBN03}. Dust is the dominant absorbing aerosol in Arctic ice cores on interglacial timescales \cite[e.g.,][]{RaK97,FWJ99}. Dust embedded in Arctic ice cores records global climate change \cite[e.g.,][]{AAG98,KoH01} and marks abrupt climate changes \cite[e.g.,][]{All00}. Numerous studies speculate that in addition to recording climate, dust changes climate by (among other things) darkening the cryosphere \cite[][]{PeM95,AWL00,HKR01,KBB06}. Present day observations in the Rockies show that dusty snow accelerates snow melt in some mid-latitude regions (Dr.~Tom Painter, NSIDC, personal communication). Net BC impacts on the Arctic will likely increase through the 21st century. BC emissions from combustion, the primary source of Arctic BC \cite[]{KoH05} have increased with fuel use over the past several decades, and are known to within a factor of about two \cite[][]{BSY04}. Some scenarios project an increase in anthropogenic BC emissions of 30--250\% in the 21st century \cite[][]{NAD00}. Biomass burning emissions may increase due changes in fire regime though this is highly uncertain \cite[]{TPH05,VRG06}. Present day dust emissions and deposition are known to no better than about a factor of four globally \cite[]{ZMT04}. Whether there is currently a trend in global mineral dust emissions is not known---increasing (from anthropogenic activities) and decreasing (due to \COd\ fertilization of vegetation) trends are both plausible \cite[]{MaL03,TWH04}. Aerosols, absorbing or not, can significantly alter cloud reflectivity and lifecycle \cite[e.g.,][]{CLV96,ATS00}. Since aerosol-cloud indirect effects are poorly constrained and are not a focus of this project, they will not be mentioned again. This does not imply that will ignore aerosol-cloud direct and semi-direct effects. Aerosol-cloud direct and semi-direct effects are analogous to the snow/ice-impurity feedbacks mentioned above, and will be treated thoroughly and consistently throughout the project. \csznote{ The increased aridity, equator-to-pole temperature gradients, exposed continental shelves, and peri-glacial dust generation in past glacial climates make dust a remarkable climate proxy \cite[][]{RSK97,MML06}. Estimates of past and future dust emissions contain large uncertainties including those due to land use change and to \COd\ fertilization of vegetative cover \cite[e.g.,][]{MaL03,TWH04} } % end csznote \csznote{ We study Arctic climate-snow-aerosol interactions within a coupled general circulation model (GCM) framework (Section~\ref{sxn:mdl}). Using this tool, we estimate Boreal fire emissions changes from 1997 to 1998 increase surface snowpack radiative forcing in the Arctic by about~50\% \cite[][]{FZR07} (Figure~\ref{fgr:frc_sfc}). \begin{floatingfigure}[r]{0.5\hsize} % Begin Figure~\ref{fgr:frc_sfc} \centering % \centering uses less vertical space than center-environment %\includegraphics[width=0.4\hsize,angle=270,clip=true,trim=0.0in 0.0in 0.0in 1.5in]{/data/zender/fgr/snicar/sotfrc_sfc_1997f11_JJA}% \includegraphics[width=0.4\hsize,angle=270,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{/data/zender/fgr/snicar/sotfrc_sfc_1998f11_JJA}% \caption[Snowpack soot direct radiative forcing]{ Summertime mean surface direct radiative forcing [\wxmS] by soot in snowpack during 1998, a strong boreal burn year. \label{fgr:frc_sfc}} \end{floatingfigure} % End Figure~\ref{fgr:frc_sfc} These estimates contain many uncertainties and potential Arctic aerosol-related biases including transport and deposition, size distribution, optical properties, aging, and cloud interactions. These longstanding issues are targets of ongoing research and International Polar Year (IPY) activities such as POLARCAT (Section~\ref{sxn:polarcat}). } % end csznote \subsection{Absorbing Arctic Aerosol in Models}\label{sxn:mdl} The fundamental physics of snowpack-aerosol radiative interactions have been understood for over 25~years \cite[][]{WiW80,WaW80,Clo87}. However, few GCMs explicitly account for these effects so the potentially important polar climate amplification of dirty snow is largely unstudied. Models which do not explicitly account for absorbing aerosol may prescribe snowpack darkening as a function of time since last snowfall \cite[e.g.,][]{ODB04}. This approach is consistent with observations that snowpacks darken with time due to decreasing snow grain specific surface area (SSA) and to increasing impurity concentration (e.g., BC deposition) \cite[e.g.,][]{WaW80,AHH03} (Figure~\ref{fgr:niwot}). \begin{floatingfigure}[r]{0.5\hsize} % begin Figure~\ref{fgr:niwot} \centering % \centering uses less vertical space than center-environment \includegraphics[width=\hsize,angle=0,clip=true,trim=0.0in 0.1in 0.0in 0.0in]{/home/zender/ppr_FlZ06/fgr_niwot2}% \caption[Niwot Ridge albedo decay]{ Observed (black) and modeled (color) albedo decay at Niwot Ridge following the January~2, 2001 snowfall event \cite[]{FlZ06}, for varying SNICAR snowpack temperature gradients. Error bars represent one standard deviation of all measurements comprising each day's albedo change. \label{fgr:niwot}} \end{floatingfigure} % end Figure~\ref{fgr:niwot} The observed 0.05 snowpack albedo change within four days at Niwot Ridge illustrates the important role of snowpack aging in controlling surface energy budgets. Prescribed snowpack aging can capture this aging effect on timescales of a few days. More sophisticated physics are required to represent longer timescale albedo evolution \cite[][]{FlZ06}. Single layer models are significantly limited in their representation of aerosol transport and removal mechanisms and in correctly representing sub-surface radiative heating and melt. \cite{FlZ05} studied the vertical distribution of solar radiant heating on snowpack. Using accurate radiative transfer methods in a multi-layer snowpack model (Section~\ref{sxn:snicar}), we showed that 20--40\% of solar heating occurs beneath the top 2\,\cm\ of typical snowpack. This sub-surface heating can cause internal snowpack melt. Normally, though not always, modeled internal melt occurs in conjunction with surface melt. Internal snowpack absorption in warm snow can melt snow more efficiently per unit absorption than surface snowpack absorption which, in the Arctic, is typically balanced by strong sensible heat fluxes due to cold overlying air \cite[]{MPB04,FlZ05}. It may take many sunny days to accumulate enough heat from the small, instantaneous solar flux divergence within snowpack \cite[]{BrW93} to warm interior snowpack to the melting point. Snow insulation keeps the internal snowpack warmer than the surface so that radiatively-induced internal warming can trigger the moderate snow-grain size-feedback loop (Figure~\ref{fgr:fdb_dgm}) until melting triggers the stronger snow-cover feedback loop. %However, all GCMs because they confine solar absorption to the top %snow layer. Today's most sophisticated snow and ice models include Arctic soot, and, less frequently, dust \cite[][]{HaN04,Jac04,KBB06,FZR07}. Significant soot concentrations in the surface snowpack strongly absorb visible radiation some of which would otherwise penetrate into the snowpack (Figure~\ref{fgr:rfl_spc_rds_ffc}). \begin{figure} % Begin Figure~\ref{fgr:rfl_spc_rds_ffc} \centering % \centering uses less vertical space than center-environment \includegraphics[width=0.50\hsize,angle=0,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{/data/zender/fgr/snicar/snw_sot_spectral_rfl}% % Rotate Mark's image by 270 degrees so baselines agree % epsffit coordinate arguments are: llx lly urx ury in Postscript units (points) % (lower-left x, lower-left y, upper-right x, upper-right y) % cd ${DATA}/fgr/snicar;fl_foo='rdsffc_1997f11-1998b11_JJA';cat ${fl_foo}.eps | epsffit -r 33 141 543 725 | epsffit -r 33 141 543 725 | epsffit -r 33 141 543 725 > ${fl_foo}_270.eps \includegraphics[width=0.50\hsize,angle=0,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{/data/zender/fgr/snicar/rdsffc_1997f11-1998b11_JJA_270}% \caption[Spectral reflectance of snow; Response of effective radius]{ Left Panel: Spectral reflectance of pure snow and snow externally mixed with 200\,\ugxkg\ BC for different snow size distributions. Vertical lines show positions of MODIS Bands 3, 4, and~5 (left-to-right). Right panel: Predicted change in summertime-mean effective radius $\rdsffc$\,[\um] of surface snowpack layer due to 1998 boreal fire BC deposition. Cross-hatching indicates statistically significant changes ($\cnfstt < 0.05$) relative to simulations without boreal fire soot \cite[][]{FZR07}. \label{fgr:rfl_spc_rds_ffc}} \end{figure} % End Figure~\ref{fgr:rfl_spc_rds_ffc} Hence surface soot concentrations can cool the lower snowpack much as atmospheric soot cools the surface by reducing insolation. The screening effect of surface soot competes with the temperature-grain-size feedback (Figure~\ref{fgr:fdb_dgm}). Clearly a modeling approach that includes thermodynamic and aerosol radiative effects on snowpack aging and heating is required to understand their combined effects on the Arctic climate system. \csznote{ Previous representations of absorbing aerosol and snowpack evolution in the Arctic have approximated or neglected crucial feedbacks. This project, in collaboration with others, will result in a more complex and interactive Arctic system where \begin{enumerate*} \item Sea-ice is sensitive to surface aerosol radiative effects \item Snow-ice-aerosol interactions are consistent across the cryosphere \item Soot is treated as fractal aggregates not spheres \item Snowpack aging reproduces observed features \item Glacial dust and boreal fire soot sources are accounted for % thermodynamic fixed Qflux sea-ice model in % slab ocean (lateral heat transport is prescribed) %\item Externally mixed soot not internally mixed soot %\item Provide improvement to global uniform snow grain size assumption % for future satellite surface reflectance retrievals. %\item Goal to monitor Arctic soot/dust concentration from satellites %No one has retrieved this yet \end{enumerate*} } % end csznote %Absorbing aerosol species like soot and dust are typically not %carried as prognostic, radiatively interactive tracers in %Arctic sea-ice models. %The Arctic Ocean Model Intercomparison Project %(\href{http://fish.cims.nyu.edu/project_aomip/overview.html}{AOMIP}) % \cite[e.g.,][]{UHM05}. \section{Scientific Objectives and Hypotheses}\label{sxn:obj} Our studies of Arctic snow-ice-aerosol processes will improve understanding and representation of ice-albedo feedbacks and polar climate amplification. Key scientific questions we will address include: \setcounter{enmrfr}{0} % Reset reference counter for this list \begin{enumerate} \item \enmrfrstp \label{idx_obj_ssa} % begin Objective~\ref{idx_obj_ssa} \textbf{Objective}: Understand and reproduce hoar formation and melt/freeze cycle effects on diurnal and seasonal snowpack specific surface area and reflectance\\ \textbf{Hypothesis}: \textit{Observed diurnal and semi-diurnal albedo cycles in polar snowpack can be explained by a spectrum of surface hoar formation interacting with temperature-driven metamorphism, including melt/freeze cycles. Zenith angle and temperature effects dominate seasonal albedo changes.}\\ Diurnal and semi-diurnal albedo cycles observed in Antarctica \cite[][]{McH85,Pir04} suggest that snow Specific Surface Area (SSA) recharges, or at least decreases more slowly, due to surface hoar formation. Hoar may darken fresh and brighten old snowpack when the SSA of hoar (often hollow prisms) is intermediate between these extremes. Whether this process can explain the measured albedo cycles will be tested in a controlled environment, with the results guiding improvements in our modeled hoar and melt/freeze formulations. Our collaboration with Dr.~Florent Domin\'{e} (LGGE/Grenoble, see attached letter of support) to measure and model SSA changes due to surface hoar formation and melt/freeze cycles in controlled laboratory experiments that mimic Arctic snow is described in Section~\ref{sxn:lgge}. \item \enmrfrstp \label{idx_obj_csn} % begin Objective~\ref{idx_obj_csn} \textbf{Objective}: Quantify Arctic climate sensitivity to timing and location of Arctic soot events\\ \textbf{Hypothesis}: \textit{Fuel combustion dominates biomass burning as sources of Arctic BC except in very strong boreal burn years. Fire BC efficacy depends strongly on burn month and location.}\\ Many of the hypotheses in this project involve the concept of Efficacy, defined as the temperature response per unit forcing relative to the temperature response due to the same forcing by \COd\ \cite[]{HSR05}. Understanding efficacy is particularly important for BC-snow studies because \cite{HSR05} and \cite{FZR07} find that \textit{soot in snow has the highest forcing efficacy of any known climate forcing agent.} Soot in snow has $2$--$4 \times$ the forcing efficiency of \COd. For example, we find that snowpack~BC heating compounded by snow-albedo feedback can exceed atmospheric~BC surface cooling of Greenland in strong fire years (Figure~\ref{fgr:mlt_Grn}). Knowing in which locations and months fires will have the greatest forcing efficacy will identify particularly valuable forest and vulnerable cryospheric regions. \csznote{ Ice core analyses (Dr.~Eric Saltzman, UCI, personal communication) and model simulations \cite[][]{KoH05,FZR07} agree that boreal fires are the primary source of BC deposition to Greenland in strong fire years. We will convolve ice core records of historic BC deposition to Greenland with present day spatially explicit BC emissions data \cite[][]{RVC05} to study maximum changes in Greenland reflectance and melt due to boreal BC over the past 1000~years. } % end csznote \item \enmrfrstp \label{idx_obj_sea_ice} % begin Objective~\ref{idx_obj_sea_ice} \textbf{Objective}: Identify snow and aerosol interactions with sea-ice formation, reflectance, seasonality, variability, and trends\\ \textbf{Hypothesis}: \textit{Rapid snow aging with warm surface temperatures in spring and summer accelerates ablation of annual sea-ice, and Arctic BC amplifies amplifies this ablation during strong burn years. Inter-hemispheric asymmetry in polar BC deposition contributes to the significant differences between Arctic and Antarctic sea-ice trends.} Despite nearly globally-uniform GHG forcing, summertime Arctic and Antarctic sea-ice show asymmetric trends over the last 25~years \cite[]{FKC01,SMS03,SSF05}. While Antarctic sea-ice has shown little trend, summertime Arctic sea-ice has retreated by more than~15\%. \cite{KoH05} speculate that asymmetry between northern and southern hemisphere polar BC deposition may explain sea-ice asymmetry. Our project will represent snow- and sea-ice-albedo feedbacks (Figures~\ref{fgr:fdb_dgm}) which when forced by the interannual variability in BC emissions (Figure~\ref{fgr:msn_bc}) and deposition, may cause some of the asymmetry and recent accelerations in Arctic sea-ice reduction. \item \enmrfrstp \label{idx_obj_ghg_aer} % begin Objective~\ref{idx_obj_ghg_aer} \textbf{Objective}: Determine relative efficacies of aerosol- and GHG-driven snow forcing on Arctic land and sea-ice\\ \textbf{Hypothesis}: \textit{Aerosol-snowpack interactions may cause as much Arctic warming as GHGs.}\\ \cite{HSR05} estimate that effective global forcing by GHGs since 1750 is about 3.0\,\wxmS, more than ten times greater than their 0.25\,\wxmS\ snow albedo forcing by soot. % Global soot+dust efficacy is 3.86 % NP2 sncpd05 change in TREFHT=1.1 K, SNOAERFRC=0.33 % ncdiff -O ${DATA}/anl_sncpd05/sncpd05_clm_NP2.nc ${DATA}/anl_sncpd06/sncpd06_clm_NP2.nc ~/sncpd05msncpd06_clm_NP2.nc % ncks -v SNOAERFRC,TREFHT ${DATA}/anl_sncpd05/sncpd05_clm_NP2.nc However, we estimate the effective snow-albedo forcing (i.e., efficacy times forcing) of soot and dust averaged over the Arctic (north of 67\,\dgrn) is 1.25\,\wxmS, about 40\% of the GHG forcing. We expect that representing snowpack impurity driven feedbacks (Figure~\ref{fgr:fdb_dgm}) over sea-ice will further increase the Arctic climate sensitivity to aerosols relative to GHGs. Hence the Arctic may be unique as region where total effective aerosol forcing is positive (not negative) and occasionally (e.g., strong burn years) exceeds GHG forcing. If true, this would bolster suggestions that targeted reductions in industrial and boreal fire emissions may achieve significant mitigation of Arctic climate change \cite[]{Jac02,Jac04,RLF06}. Our modeling scenarios will quantify the physical plausibility and robustness of these arguments. \csznote{ Multiple lines of evidence support the first hypothesis: First, representation of thin sea-ice amplifies polar climate sensitivity \cite[][]{HoB03,HBH06}. Second, internal snowpack heating amplifies mid-latitude climate sensitivity \cite[][]{FlZ05}. Third, aging and absorbing aerosol content increase polar climate sensitivity \cite[][]{Jac04,HaN04,FZR05}. We will test this hypothesis by comparing summer sea-ice retreat with and without snow aging in CICE. } % end csznote \csznote{ \item \enmrfrstp \label{idx_obj_sot_dst} % begin Objective~\ref{idx_obj_sot_dst} \textbf{Objective}: Quantify relative roles of Arctic soot and dust as polar climate amplifiers\\ \textbf{Hypothesis}: \textit{Industrial soot heats Arctic climate more/less than dust in the present/LGM climate.}\\ Soot is approximately an order of magnitude more absorptive than dust at solar wavelengths. Dust deposition to Greenland in glacial periods is 2--20 times greater than present day \cite[e.g.,][]{RaK97,MML06}, enough to compete with BC as a snow-albedo trigger. However, differences between BC and dust deposition seasonality and variability will modulate the net solar forcing of these aerosols on Arctic surfaces. Dust of Asian provenance \cite[][]{BGR97} will likely deposit more continually than North American glacial dust \cite[][]{MML06}. The springtime maximum Asian dust export \cite[][]{ZBN03} is less efficacious for Arctic forcing than summertime aerosol events. How these spatio-temporal deposition patterns affect Arctic climate sensitivity is nearly completely unexplored. } % end csznote \end{enumerate} \section{Tasks: Arctic Models and Observations}\label{sxn:mth} It is important to emphasize that this project will not develop any Arctic climate model components from scratch. Our intellectual efforts are primarily directed toward uncovering the influence of previously neglected snow-ice-aerosol interactions in the Arctic system. All model development tasks outlined below involve improving physics in our in-house snow-ice-aerosol model (SNICAR) and/or merging these physics into high-quality Arctic system component models developed and maintained at national centers. \subsection{Community Climate System Model}\label{sxn:ccsm} An integrated Earth System Model which fully couples aerosols, snow, atmosphere, ocean, and land/sea-ice is required to test our hypotheses (Section~\ref{sxn:obj}). We use the NCAR CCSM---its polar climate simulations and biases are well characterized and continually evaluated against meteorological analyses and satellite observations \cite[e.g.,][]{BrB981,HoB03,HBH06}. Arctic absorbing aerosols, soot and dust, are primarily emitted from non-frozen land surfaces at lower latitudes \cite[e.g.,][]{ZBN03,KoH05}. As such, these aerosols travel through multiple climate ``spheres'', i.e., the biosphere, atmosphere, and cryosphere before depositing to snow. This project focuses on the cryosphere and we will rely on our continuing external collaborations to obtain the most realistic aerosol distributions possible. The CCSM BC/OC aerosol transport and deposition we use come from long time collaborators Drs.~Phil Rasch (see attached letter of support) and Bill Collins (NCAR) \cite[][]{RCE01,CRE01,CRE02}. Dr.~Natalie Mahowald (NCAR) and PI~Zender are primary developers of the Dust Entrainment and Deposition (DEAD) mineral dust model \cite[][]{ZBN03,ZNT03,MLD03} which, embedded in CCSM, predicts the Arctic dust deposition fields. Part of our motivation for including dust affects in the present day Arctic stems from our preliminary equilibrium simulations of the Last Glacial Maximum (LGM) that account for glaciogenic dust. We use the method of \cite{MML06} to obtain simultaneous agreement between the model and LGM loess, ice core, and marine deposition records. Our preliminary results indicate that glaciogenic dust is very efficacious at warming LGM Arctic climate and so should not be neglected without good reasons in present day Arctic change studies. \subsection{SNICAR}\label{sxn:snicar} The project builds upon, extends, and applies our existing, state-of-the-art, SNow, ICe, and Aerosol Radiative model, SNICAR \cite[][]{FlZ05,FlZ06}. SNICAR treats snowpack hydrologic, thermodynamic, and radiative processes in a unified manner to explicitly represent feedbacks between snowpack heating, albedo evolution, densification, melt, and aerosol concentration (Figure~\ref{fgr:fdb_dgm}). For climate simulations, SNICAR runs in a host snowpack model which to date has been the Community Land Model (CLM) \cite[][]{DZD03}. The CLM uses five vertical snowpack layers \cite[]{ODB04} and itself runs off-line forced by meteorological analyses or on-line in a GCM. We nest CLM/SNICAR in the Community Atmosphere Model (CAM) \cite[][]{CRB06} modified for prognostic soot and dust emissions. SNICAR has different capabilities than, and shares some capabilities with, other the well-known snow models such as SNTHERM \cite[][]{Jor91,AJM04}. SNICAR, designed for climate modeling, is at heart a size-resolved snow aging model designed above all else to predict snow SSA, and thus snowpack optical properties \cite[]{GrW99}. SNICAR operates within a host snow-hydrology, thermal, mass-balancing model (currently CLM). SNTHERM includes many more snow and ice thermodynamic processes and states than CLM, and many fewer radiative features than SNICAR. To our knowledge, SNTHERM is alway used as a column model, and has not been embedded in an interactive GCM suitable for Arctic climate change studies. \subsubsection{Snow Aging}\label{sxn:aging} Compared to the enormous efforts and progress at improving cloud physics since \cite{CPB89} highlighted its importance, relatively little effort has gone to improve snowpack representation in climate models. It is not surprising that intercomparison of Arctic climate simulations identifies snow (mis-)treatment as a leading cause of inter-model and model-measurement discrepancy \cite[][]{HaQ06,Roe06}. Some models \cite[e.g.,][]{Jor91,ODB04} consider the role of temperature in albedo decay. To our knowledge, though, SNICAR is the only GCM snow model that considers the dominant role of temperature-gradient driven metamorphism. High-latitude snowpack can have temperature gradients well in excess of 100\,\kxm\ owing to strong radiative cooling at the surface during night, and good thermal insulation of deeper snow. Vapor-density gradients induced by these temperature gradients cause rapid snow metamorphism \cite[][]{StB97}. Using first principles, SNICAR accounts for the roles of initial size distribution, temperature, temperature gradient, snow density, and inter-particle spacing in the evolution of snow specific surface area (SSA) \cite[][]{FlZ06}. Vapor diffusion from highly-curved surfaces characteristic of fresh, dendritic snow plays a smaller, but non-negligible role in grain growth via the Kelvin Effect in low temperature-gradient environments \cite[e.g.,][]{Col80}. \csznote{ Recent SEM observations of well-sintered snow support the hypothesis that grain-boundary diffusion is an important mechanism in sintering \cite[][]{RoS06}, although the importance of this mechanism has been discounted in earlier studies. SNICAR does not yet explicitly represent sintering. } % end csznote \iftskbrk{\clearpage}{} % Break page after each task \subsubsection{Snow SSA Measurements}\label{sxn:lgge} The laboratory of Dr.~Florent Florent Domin\'{e} (LGGE/Grenoble, see attached letter of support) has produced many of the best controlled and characterized snow aging measurements. \begin{floatingfigure}[r]{0.5\hsize} % begin Figure~\ref{fgr:sdn} \centering % \centering uses less vertical space than center-environment % Rotate Mark's image by 270 degrees so baselines agree % cd ~/ppr_FlZ06;fl_foo='lggnx';cat ${fl_foo}.eps | epsffit -r 0 0 282 1020 | epsffit -r 0 0 282 1020 | epsffit -r 0 0 282 1020 > ${fl_foo}_270.eps \includegraphics[width=\hsize,angle=0,clip=true,trim=1.30in 0.0in 1.30in 0.0in]{/home/zender/ppr_FlZ06/lggnx_270}% \caption[Isothermal SSA]{ Observed \cite[]{LTD04} and modeled \cite[]{FlZ06} SSA for isothermal snowpack conditions and varying choices of $\gsd$, the standard deviation of the modeled particle-pore spacing distribution. \label{fgr:sdn}} \end{floatingfigure} % end Figure~\ref{fgr:sdn} \noindent They have characterized snow specific surface area (SSA) evolution in isothermal and temperature gradient conditions \cite[]{CLD03,LTD04,TDS07}. We use the full microphysical results of SNICAR---which runs off-line with $\sim 200$ snow grain size bins each with $\sim 40$ pore-particle spacing bins---to compute the free parameters of the compact SSA expression that they show fits their measurements \cite[]{LTD04} (Figure~\ref{fgr:sdn}). Our resulting parametric expression for SSA evolution describes isothermal and temperature gradient snow grain evolution quite well \cite[]{FlZ06,TDS07}. Zender will collaborate with Domin\'{e} at LGGE on a series of SSA measurements. Many studies show that vapor saturation can induce frost deposition of small, ornate surface hoar crystals that brighten the surface or at least retard SSA aging. This occurs diurnally and may explain daytime semi-diurnal albedo cycles observed in Antarctica \cite[][]{McH85,Pir04}, but is not yet represented in SNICAR, which predicts monotonically decreasing SSA in the absence of fresh snow. In Fall 2007 we will extend the SNICAR framework to account for different crystallographic shapes, so that the inter-particle pore spacing distribution and the ice crystal shape factor (or ``capacitance'') \cite[]{FlZ06} can account for more complex shapes. When atmospheric conditions favor surface hoar growth, the model will allow deposition to form faceted shapes such as hollow columns which have significantly higher SSA than spheres. In Winter 2008 we will measure SSA evolution from local snow to gain sufficient data to evaluate the model hoar formulation. The snow chamber can mimic a wide range of temperatures and temperature gradients found in the Arctic. We are highly interested in characterizing the SSA (and snow albedo) evolution response to melt/freeze cycles. SNICAR now employs an empirical formulation \cite[]{Bru89} outside the range of its validity. Our new model formulation for grain size evolution near the melt point will be guided by these and other observations \cite[]{RaT79,LTD04} and Ostwald ripening theory. The experiments will characterize SSA during diurnal cycles of ripening to near melt, and then to diurnally repeating melt/freeze events. The resulting parameterization will improve the grain size-albedo feedback (Figure~\ref{fgr:fdb_dgm}) which is very strong for even a single melt/freeze cycle. The influence of the surface hoar and melt/freeze processes on snowpack and climate will then be assessed in the global model, with particular attention paid to crucial Arctic transition regimes such as the ablation zone of glaciers and during melt-back of seasonal snow regions. \csznote{ In Spring 2008 we plan a series of fresh snow SSA measurements to help answer the question ``What determines the SSA of fresh snow?'' Currently by controlled amounts of snow-impurities. } % end csznote In Spring 2008 we plan a series of snow SSA measurements forced by impurities. The idea is to compare SSA evolution of snow samples mixed with well characterized soot, e.g., the Monarch~71 elemental carbon adopted by Clarke \cite[e.g.,][]{ClN85}. Impurity mass will be weighed before mixing, retrieved using reflectance fitting to the model, and can be measured with a total carbon analyzer. Standard measurement procedure will include routine visible and near infrared reflectances (450 and 1310\,\nm) to simultaneously constrain SSA and snowpack absorption. Selected measurements will be made with higher resolution spectral radiometers in collaboration with other interested LGGE investigators. It is worth noting that, to our knowledge, these will be the first simultaneous snow SSA and reflectance measurements. These data will provide novel and useful constraints for snow models such as SNICAR and SNTHERM. \iftskbrk{\clearpage}{} % Break page after each task \subsubsection{Snow Radiation and Optics}\label{sxn:opt} The off-line microphysical version of SNICAR runs at 10\,\nm\ spectral resolution in the solar spectrum from 0.3--5.0\,\um\ (470 bands). This is useful for simulating narrow-band satellite channels such as MODIS/MISR channels (Figure~\ref{fgr:rfl_spc_rds_ffc}). The vertically resolved multi-layer radiative transfer component \cite[][]{WiW80,TMA89} treats snow as a collection of hexagonal prisms based on the equivalent surface area-to-volume approximation \cite[][]{GrW99,NGW03}. SNICAR accounts for solar zenith angle, direct and diffuse incident radiation, reflectance of the underlying surface \cite[][]{DZD03}, wet snow metamorphism \cite[][]{Bru89}, and vertically-resolved effective radius (\rdsffc), snow depth, density, and concentrations of absorbing impurities \cite[][]{WaW80}. A lookup table (computed off-line) contains two-stream optical parameters (single scattering albedo, extinction coefficient, and asymmetry parameter) as functions of snow SSA and wavelength. Snow and aerosol optical properties link the snowpack microphysical properties (aerosol concentration, particle size distributions) to macroscopic net absorption (Figure~\ref{fgr:frc_sfc}), reflectances (Figures~\ref{fgr:niwot} and~\ref{fgr:rfl_spc_rds_ffc}a), and heating rates that drive the snow melt (Figure~\ref{fgr:mlt_Grn}) and temperature change which trigger snow-albedo feedback. These responses are sensitive to optical property assumptions which this project will refine, including \begin{enumerate*} \item BC indices of refraction: As per \cite{BoB05}, we use \cite{ChC90} rather than CAM-default OPAC properties \cite[][]{HKS98} for elemental carbon. We assume a BC/sulfate core/mantle for hydrophilic BC \cite[]{BHB06}. \item BC shape: Treating BC as spheres likely underestimates its single scattering albedo relative to more realistic shapes such as fractal aggregates \cite[][]{Sor01,BoB05} \item Aerosol mixing: Although existing observations are insufficient to rule out dry scavenging as the dominant removal mechanism \cite[]{KoH05}, we think BC and dust in remote regions such as the Arctic are likely deposited primarily by wet scavenging \cite[][]{NoC88,CCR01,ZBN03,CSK04,Jac04} Nucleation-scavenged aerosols will often be internally mixed within snow grains. We will try an effective medium approximation \cite[e.g.,][]{BoH83} to represent internally mixed aerosols. We will also investigate solutions for dark particles in weakly absorbing media \cite[]{MaS99} which may be more physically defensible for ice particles. All these approaches will increase snowpack absorption relative to our current externally mixed assumption. %\item Darkening by desert and peri-glacial dust sources \cite[][]{MML06}. \end{enumerate*} \subsection{In Situ Observations}\label{sxn:insitu} Arctic snowpack BC and dust concentrations and optical properties are key diagnostics that integrate aerosol source, transport, deposition, with snow aging and melt processes. We will obtain new data to evaluate and constrain our models from collaborators Drs.~Steve Warren (U.~Washington, see attached letter of support), Tom Grenfell and Tony Clarke (U.~Hawaii) who have an ANS project ``Black carbon in Arctic snow and ice, and its effect on surface albedo''. \begin{floatingfigure}[r]{0.5\hsize} % Begin Figure~\ref{fgr:frc_sfc} \centering % \centering uses less vertical space than center-environment %\includegraphics[width=0.4\hsize,angle=270,clip=true,trim=0.0in 0.0in 0.0in 1.5in]{/data/zender/fgr/snicar/sotfrc_sfc_1997f11_JJA}% \includegraphics[width=0.8\hsize,angle=270,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{/data/zender/fgr/snicar/sotfrc_sfc_1998f11_JJA}% \caption[Snowpack soot direct radiative forcing]{ Summertime mean surface direct radiative forcing [\wxmS] by soot in snowpack during 1998, a strong boreal burn year. \label{fgr:frc_sfc}} \end{floatingfigure} % End Figure~\ref{fgr:frc_sfc} Their project will sample snow and ice from pan-Arctic locations to update, improve, and extend the BC survey that Clarke and co-workers conducted in 1983--1984 \cite[][]{ClN85}. Warren's team determines BC and dust optical properties from the samples using filter absorptance techniques. They will measure in~situ snow density and spectral albedo at selected sites to enable closure studies of the instantaneous surface solar radiation field. Warren's team is already processing new aerosol-snowpack measurements from the North Pole observatory, Ellesmere, Hudson Bay, and Greenland. By summer 2007, they will have additional data from Greenland, Russia, and from Matthew Sturm's 4000\,\km\ traverse of North American tundra. The BC/dust measurements from Dr.~Konrad Steffen's Automated Weather Station (AWS) sites in Greenland \cite[]{StB01} sample strong spatial gradients in exposure to North American biomass burning plumes (Figure~\ref{fgr:frc_sfc}) and so will be particularly valuable. \csznote{ \cite{FlZ06} and \cite{FZR07} used previous measurements from Warren, Grenfell, and Clarke to evaluate and to constrain the geographic distribution of predicted BC mass concentration (Figure~\ref{fgr:fgr_ppr_sotc}) and snow spectral albedo. (Figure~\ref{fgr:rfl_spc_rds_ffc}). } % end csznote \cite{FZR07} compared CAM/SNICAR simulations to the approximately two dozen published Arctic surface snowpack BC measurements, many from Clarke's 1983--1984 survey. SNICAR captures the measurements over three orders of magnitude in concentration with little mean bias though significant RMS bias (Figure~\ref{fgr:fgr_ppr_sotc}). Greenland concentrations are typically 1--4\,\ugxkg, and as high as 30\,\ugxkg\ \cite[][]{SCD02}. \begin{floatingfigure}[r]{0.50\hsize} % begin Figure~\ref{fgr:fgr_ppr_sotc} %\begin{figure} \centering % \centering uses less vertical space than center-environment \includegraphics[width=\hsize,angle=0,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{/home/zender/ppr_FZR07/fgr_ppr_sotc_whisker2_clr}% \caption{Observed and simulated BC concentrations from \cite{FZR07}. Log correlation is 0.78. \label{fgr:fgr_ppr_sotc}} %\end{figure} \end{floatingfigure} % end Figure~\ref{fgr:fgr_ppr_sotc} Interestingly, \cite{GLS02} showed that BC concentrations measured during the SHEBA experiment had decreased significantly since the early 1980s, \textit{contrary to the increasing trend in global emissions}. Whether reduced fuel combustion in proximal regions (e.g., former Soviet Union) can explain this trend could be investigated in our modeling framework if interannually varying industrial and fire BC emissions timeseries were extended back to the 1980s. The new measurements by Warren's project will clarify the spatial extent of this intriguing result, and provide useful falsification for errant model interdecadal trends in BC deposition. In situ measurements will also provide updated constraints on scavenging coefficients using our models. Clarke will measure/estimate scavenging coefficients for removal of atmospheric BC by snow \cite[][]{NoC88} and for removal of snowpack BC by snow melt \cite[][]{ClN85}. Meltwater flushing is perhaps the most important BC removal mechanism, since preferential gravitational settling would be extremely slow for BC that is externally-mixed. Qualitative observations suggest that BC may become more concentrated in surface snow during melt events \cite[][]{WaW80,ClN85}. \cite{CGR96} spread hydrophobic and hydrophilic BC on top of snow, and noticed that hydrophobic BC remains in surface snow longer, maintaining lowered albedo for a longer time. Because of imprecise knowledge of vertical distributions of BC and meltwater formation in this experiment, we can only deduce that hydrophilic BC is scavenged about five times more efficiently than hydrophobic BC. Using a simple $\me$-folding removal model with this data, we estimate lower bounds for scavenging ratios of 0.01 and 0.002 for hydrophilic and hydrophobic BC, respectively. Even greater uncertainty exists for snow processes on sea-ice. Dr.~Tom Painter (NSIDC) is conducting studies in the Rockies that will help constrain the melt-scavenging efficiency of dust. We will perform selected closure studies to ensure that SNICAR closely reproduces the measured spectral reflectance for given snow conditions and impurity concentrations/properties (measured and estimated by Warren's team). These studies complement Warren's studies in that we also predict snow conditions and snow BC/dust concentrations. Differences between our predictions and the field measurements will help us identify and correct model physics deficiencies, e.g., melt scavenging in SNICAR, frozen precipitation scavenging in CAM. Insofar as our predictions and field measurements agree (e.g., Figure~\ref{fgr:fgr_ppr_sotc}), our model can upscale the point measurements to regional or even pan-Arctic estimates of impurity concentrations, and the consequent radiative forcing and response. \iftskbrk{\clearpage}{} % Break page after each task \subsection{Fire}\label{sxn:fire} % Section~\ref{sxn:fire} Our model uses the Global Fire Emissions Database (GFED2) which is derived from fire counts retrieved by MODIS satellite sensors. GFED2 includes more biomass burning source regions than \cite{CoW96} and \cite{KoH05} and captures the significant interannual variability of boreal fire BC \cite[]{VRG06}. \begin{floatingfigure}[r]{0.5\hsize} % begin Figure~\ref{fgr:msn_bc} \includegraphics[width=0.9\hsize,clip=true,trim=0.10in 0.0in 0.0in 0.1in]{/home/zender/ppr_FZR07/fgr_ppr_bcemis_bw} \caption{Zonal annual mean BC emissions from fossil fuel+biofuel combustion \cite[]{BSY04} and GFED2 biomass burning during 1998 and 2001 \cite[]{VRG06}.} \label{fgr:msn_bc} \end{floatingfigure} % end Figure~\ref{fgr:msn_bc} We use prescribed fossil and biofuel BC emissions \cite[][]{BSY04}, and convert GFED2 fire emissions to BC with estimated emissions factors updated from \cite{AnM01}. Using GFED2 and our central estimates of emissions factors, we estimate that biomass burning BC emissions north of 30\dgrn\ decreased from 0.8\,Tg to 0.2\,Tg between 1998, a strong fire year, and 2001, a weak fire year (Figure~\ref{fgr:msn_bc}) \cite[][]{FZR07}. Models driven by satellite-derived emissions agree that fires explains the largest component of interannual Arctic BC deposition variability although whether tropical fire BC or boreal fire BC dominates Arctic BC variability is disputed \cite[][]{KoH05,FZR07}. Our simulations suggest boreal fire soot causes seasonal net surface solar radiation forcings of 0.5--0.75\,\wxmS\ (Figure~\ref{fgr:frc_sfc}) in strong fire years. These forcings induce feedbacks such as larger snow grain size (Figure~\ref{fgr:rfl_spc_rds_ffc}) which together increase seasonal surface absorption by more than 1.5\,\wxmS\ \cite[][]{FZR07}. Soot-snow feedbacks in strong fire years appear to cause significant increases in meltwater production in Greenland snowpack (Figure~\ref{fgr:mlt_Grn}). \begin{figure} % Begin Figure~\ref{fgr:mlt_Grn} \centering % \centering uses less vertical space than center-environment \includegraphics[width=0.33\hsize,angle=270,clip=true,trim=0.5in 1.0in 0.0in 1.75in]{/data/zender/fgr/snicar/qmelt_1997f11-1998b11_JJA_GR}% \includegraphics[width=0.33\hsize,angle=270,clip=true,trim=0.5in 1.0in 0.0in 1.75in]{/data/zender/fgr/snicar/qmelt_1998f11-1998b11_JJA_GR}% \includegraphics[width=0.33\hsize,angle=270,clip=true,trim=0.5in 1.0in 0.0in 0.0in]{/data/zender/fgr/snicar/qmelt_1998g11-1998d11_JJA_GR}% \caption[Snow melt response to snowpack soot]{ Summertime mean change in Greenland snow melt [\mmxday] due to boreal soot during low (1997, left) and high (1998, middle and right) boreal burn years. Middle panel includes all feedbacks (soot in atmosphere and snowpack), while right panel includes atmospheric soot only. Cross-hatching indicates statistically significant changes ($\cnfstt < 0.05$) relative to simulations without boreal fire soot \cite[][]{FZR07}. \label{fgr:mlt_Grn}} \end{figure} % Begin Figure~\ref{fgr:mlt_Grn} Note that neglecting soot-snowpack interactions (and accounting only for atmospheric soot effects) eliminates or reverses the sign of most of the increased snow melt over Greenland. Hence, \textit{significant Arctic change is attributable to aerosol-snowpack feedbacks not represented in most GCMs}. We eagerly anticipate results in Year~2 when SNICAR is embedded in a fully interactive sea-ice model which responds to soot and dust sources. \iftskbrk{\clearpage}{} % Break page after each task \subsection{Sea-Ice}\label{sxn:cice} Sea-ice is the fulcrum of Arctic ice-albedo feedbacks. Snow conditions on and impurities in sea-ice play important roles in the solar radiation budget of the sea-ice environment \cite[]{Gre91,GLS02,BWW05}. \cite{GLS02} reported that BC mixing ratios in snow on Arctic sea-ice increased from the pole ($\sim 5$\,\ugxkg) to the coastal regions ($\sim 50$\,\ugxkg). In large-grained snow, 50\,\ugxkg\ lowers broadband snow reflectance by $ > 0.03$, a level that begins to exceed typical observational uncertainties of albedo measurements. The Los Alamos \href{http://climate.lanl.gov/Models/CICE}{CICE} model is the sea-ice component of the CCSM Earth system model. CICE contains an Ice Thickness Distribution which maintains a half dozen prognostic categories of ice thickness in each grid cell \cite[][]{HBH06}. However, CICE currently represents snow as a medium with prescribed albedo with no snow aging nor absorbing impurity representation. LANL will implement a prognostic aerosol tracer capability into CICE in early 2007 (E.~Hunke, personal communication, 2006). Our collaborator, Dr.~Elizabeth Hunke of LANL (see attached letter of support), is a CICE principle developer. \begin{floatingfigure}[r]{0.50\hsize} \centering \includegraphics[width=0.9\hsize,angle=270]{/home/zender/prp_IGPP06/fgr_rdsffc} \caption{Springtime effective grain size (\um) of snow on sea-ice predicted by SNICAR coupled to a slab-ocean version of the NCAR CSIM. The colorbar range corresponds to broadband shortwave snow albedos from $\sim 0.72$--$0.85$.} \label{fgr:rds_ffc_cice} \end{floatingfigure} \noindent With Hunke's guidance, the UCI team will merge SNICAR physics (snow aging, radiative transfer, snow-aerosol optics) into the multi-layer snowpack in CICE. Climate and aerosol conditions will cause strong regional structure in snow aging on sea-ice, resulting in a spectrum of snow grain sizes (Figure~\ref{fgr:rds_ffc_cice}) and thus ice-albedo feedbacks. The underlying sea-ice will also retain and concentrate soot and dust deposited directly from the atmosphere and scavenged from melting snow cover (Figure~\ref{fgr:fdb_dgm}). We will then test our hypotheses (Objective~\ref{idx_obj_sea_ice}) by comparing sea-ice trends with and without snow aging and aerosols. The snow/sea-ice simulations will be evaluated against available climatologies such as the National Ice Center 33-year gridded ice climatology, and satellite products described in Section~\ref{sxn:stl}. We will couple the snowpack radiative transfer and impurity physics to the new sea-ice radiative transfer physics module developed by Drs.~Bruce Briegleb (NCAR) and Bonnie Light (U.~Washington). Their sea-ice radiation model accounts for for aerosols embedded in the complex sea-ice-brine matrix (a melt pond representation is under development). Coupling the snow to sea-ice radiative transfer will require replacing the two-stream solution in SNICAR \cite[]{TMA89} with the adding-doubling method used in the sea-ice \cite[]{Bri921}. The adding-doubling method may provide superior performance to two stream methods in thick snowpacks where transmission is negligible. Simulated sea-ice reflectance, surface properties, extent and thickness will then respond to the full lifecycle of Arctic aerosols. This will be a significant improvement to current models (including ours) which remove aerosols from snow and sea-ice with rather ad~hoc mechanisms to prevent excessive concentrations from accumulating in multi-year land and sea-ice \cite[][]{Jac04,FZR07}. Also, SNICAR is sufficiently modular so that all aerosols in CAM can be easily added to the snowpack lifecycle. Future experiments will include the effects of light absorbing organic carbon aerosols termed ``Brown Carbon'' \cite[]{AnG06,HGG06}. \iftskbrk{\clearpage}{} % Break page after each task \subsection{Ice Sheets and Glaciers}\label{sxn:glc} A key shortcoming of this (and other) coupled Arctic modeling projects is the absence of realistic ice sheets and glaciers. Although crucial to the net hydrology (and stability) of the Arctic climate system, glaciers and ice sheets are not yet fully integrated into Earth system models. LANL scientists are developing an interactive ice sheet component for the CCSM based on \href{http://wiki.nesc.ac.uk/read/glimmer-project}{GLIMMER} \cite[]{Pay99}. We will provide SNICAR snow-aerosol physics for LANL's GLIMMER. Hence we anticipate the CCSM will have a glacier model component sensitive to realistic snowpack physics and BC interactions by year~3. \subsection{Satellite Observations}\label{sxn:stl} % Section~\ref{sxn:stl} NASA MODIS, MISR, and AMSR-E retrievals can constrain free model parameters and help us interpret the regional and seasonal behavior of snowpack processes. Figure~\ref{fgr:rfl_spc_rds_ffc}a shows simulated snow spectral reflectance expected in visible MODIS bands for various grain sizes. Soot concentration is most apparent in visible channels and particle sizes information is most distinguishable in the near infrared (NIR) \cite[][]{PDR03}, e.g., near MODIS channel~5. However, soot-in-snow is difficult if not impossible to retrieve from satellite. The albedo perturbation by 50\,\ugxkg\ soot (Figure~\ref{fgr:rfl_spc_rds_ffc}), a relatively high Arctic concentration (Figure~\ref{fgr:fgr_ppr_sotc}), may not exceed instrumental noise, surface variability (e.g., sastrugi, leads), and the signal of clouds and atmospheric soot (S.~Warren, personal communication, 2006). Nevertheless, detection of snow impurities may be plausible after intense pollution events or very near pollution sources. This is one goal of our POLARCAT intense modeling effort (Section~\ref{sxn:polarcat}). \csznote{ Greenland is an ideal location for evaluation of SNICAR from remote sensing platforms. Most importantly, it is free of the confounding effects of sub-gridscale vegetation. Much of the ice sheet enjoys year round sub-freezing temperatures where liquid effects are unimportant. } % csznote On the other hand, retrieving surface snowpack effective radius~$\rdsffc$ from operational satellite observations is feasible and would help identify biases in Arctic $\rdsffc$ predictions. Remote sensing of snow grain size has been demonstrated with hyperspectral airborne instruments such as AVIRIS \cite[][]{PDR03,DoP04}. The same principles apply to MODIS and/or MISR radiances, although these products currently have problems associated with large zenith angles and topography \cite[][]{ZDT03}. If and when the MODIS/MISR high-latitude spectral snow reflectance $\rfl(\wvl)$ products reach robust operational status, we will try to infer~$\rdsffc$ using a radiance ratio technique. For example, MODIS Channel~5 (1.24\,\um) to Channel 4 or~6 (0.55 and 1.64\,\um, respectively) reflectances (Figures~\ref{fgr:rfl_spc_rds_ffc}a and~b, respectively). Researchers will be welcome to use/assimilate our predicted~$\rdsffc$ to attempt to improve MODIS/MISR reflectance retrievals. In combination with temperature from meteorological analyses, retrieved~$\rfl$ and/or $\rdsffc$ could be used to evaluate SNICAR's surface snow grain size \cite[][]{FlZ06} globally, or, more precisely, wherever clouds are not contaminating the scene. AMSR-E retrieves Snow Water Equivalent (SWE) over non-ice surfaces. SWE retrievals may provide useful constraints on SNICAR simulations of continental snowpack. We will use AMSR-E sea-ice concentration and snow depth over sea-ice products to evaluate the effect of implementing SNICAR in CICE. Current AMSR-E retrieval algorithms \cite[]{MaC98,MaC00} reduce previous ice concentration biases resulting from surface glaze and layering in the snow cover and from thin ice types. Comparisons of sea-ice extent simulations between AMSR-E and CICE during and after strong BC years will help us assess climatological BC impacts on sea-ice. Daily comparisons of sea-ice snow thickness will help us evaluate BC impacts from the intense plume we will characterize for the POLARCAT event study (Section~\ref{sxn:polarcat}). The potential for our predicted snow grain size distributions to improve satellite microwave retrievals of snow properties is intriguing, though beyond the scope of this proposal. \iftskbrk{\clearpage}{} % Break page after each task \subsection{IPY POLARCAT Participation}\label{sxn:polarcat} % Section~\ref{sxn:polarcat} We will contribute to the IPY ``POLar study using Aircraft, Remote sensing, surface measurements and modeling of Climate, chemistry, Aerosols and Transport'' (\href{http://zardoz.nilu.no/~andreas/POLARCAT}{POLARCAT}) project (the attached letter from the IPY program office to POLARCAT PI~Stohl simply verifies that POLARCAT is an ``official'' IPY to those who might not be familiar with it). One of POLARCAT's main themes is the influence of biomass burning and fossil fuel pollution on the Arctic. Although many observational aspects of POLARCAT are still pending, support for regular aerosol concentration and composition measurements at Summit, Greenland appears to be in place. A number of \href{http://zardoz.nilu.no/~andreas/POLARCAT/PAAircraftExp.html#wp3d}{Proposals} for aircraft campaigns to track fire plumes from North America across the Arctic in summer 2008 are pending. Using modeled/assimilated BC deposition from NCAR collaborator and POLARCAT Steering Committee member P.~Rasch (see attached letter of support), and UCI colleague J.~Randerson, our group will simulate a boreal fire plume well-characterized during POLARCAT. Randerson will constrain emissions will be constrained using GFED techniques, Rasch will use CAM in assimilation mode to transport the plume across the arctic, and our group will characterize atmospheric and surface radiative forcing by the pollutants and compare to the in~situ observations. \csznote{ The field measurements that would most help reconcile remaining discrepancies between our SNICAR model and spectral reflectance measurements are: snow accumulation and vertical profiles of optically-effective grain size, snow density, aerosol concentration, aerosol optical properties and snowpack temperature. } % end csznote Dr.~Tom Painter (NSIDC) studies snow aging, dust-driven heating of mid-latitude snowpacks, and retrieval of snowpack reflectance, size distribution and impurities from remote sensing instruments \cite[e.g.,][]{PDT01,PDR03}. Painter has developed a quick method to measure optically effective snow grain size in situ \cite[]{PMC07}. Painter's snow reflectance, particle size, and dust concentration measurements in the Colorado Rockies have been very helpful to our snow-aerosol radiative closure experiments. Vertical profiles of snow grain size would be highly complementary to the measurements Warren's team will make at selected sites (snow density profile, spectral reflectance, and impurity concentration/optical properties) and to the POLARCAT exercise. If Painter conducts these measurements in the Arctic during IPY, we will use them for more complete closure experiments for SNICAR simulations of Arctic snow evolution. \iftskbrk{\clearpage}{} % Break page after each task \subsection{Numerical Experiment Strategy}\label{sxn:xpt} Our questions (Section~\ref{sxn:obj}) will be addressed in the context of pre-industrial, present day, and next century timescales as appropriate. \iftskbrk{\clearpage}{} % Break page after each task \begin{floatingtable}[r]{ % begin Table~\ref{tbl:mdl} \begin{tabular}{ >{\raggedright}p{8.0em}<{} llll l } \hline \rule{0.0ex}{\hlntblhdrskp}% Scenario & Source\footnote{BC/OC source options include Type (Fossil Fuel/Biofuel and/or biomass burning), Location (Tropics and/or Boreal), and Regions (North America, Asia), and prescribed burn seasons (e.g., early/late summer). Dust emissions are global.} & Interactions\footnote{Feedback options include Surface (post-deposition BC forcing feeds-back to climate), Atmosphere (Atmospheric BC forcing feeds-back), and Both.} & Climate\footnote{Climate options include Analyses (NCEP/NCAR re-analysis meteorology and SSTs), SOM (climatological deep ocean with Slab Ocean Model), IPCC (A1B transient ramp to 720\,\ppm\ \COd).} & Optics\footnote{Optics/aging options include External (soot externally mixed with clouds and snow), Internal (soot internally mixed with clouds and snow), and Coated (aged BC has spherical sulfate coating).} & GISS\footnote{Checkmark indicates D.~Koch will replicate experiments with with GISS model for intercomparison.} \\ \hline \rule{0.0ex}{\hlntblntrskp}% %\multicolumn{6}{c}{\textit{Control}} \\[0.0ex] Control & Vary & Sfc.$+$Atm. & SOM & Coated & $\checkmark$ \\[0.5ex] \multicolumn{6}{c}{\textit{Objective~\ref{idx_obj_csn}: Arctic climate sensitivity to timing and location of soot emission}} \\[0.0ex] Fire location & Vary & Sfc.$+$Atm. & SOM & Coated & \\[0.5ex] Fire seasonality & Vary & Sfc.$+$Atm. & SOM & Coated & \\[0.5ex] Forcing/Feedback & Vary & Vary & SOM & Coated & $\checkmark$ \\[0.5ex] \multicolumn{6}{c}{\textit{Objective~\ref{idx_obj_sea_ice}: Arctic BC impacts on sea-ice}} \\[0.0ex] Sea-ice feedbacks & All & Sfc.$+$Atm. & Vary & Coated & \\[0.5ex] Sea-ice asymmetry & Vary & Sfc.$+$Atm. & SOM & Coated & \\[0.5ex] \multicolumn{6}{c}{POLARCAT Event Simulation} \\[0.0ex] Control 2007/2008 & All & Sfc.$+$Atm. & Analyses & Vary & \\[0.5ex] \multicolumn{6}{c}{\textit{Objective~\ref{idx_obj_ghg_aer}: Effective Arctic forcings of GHGs and aerosols}} \\[0.0ex] \multicolumn{6}{c}{Predictions to 2100} \\[0.0ex] Equilibrium & All & Sfc.$+$Atm. & SOM & Coated & $\checkmark$ \\[0.5ex] Transient & Vary & Sfc.$+$Atm. & IPCC/SOM & Coated & $\checkmark$ \\[0.5ex] \hline \end{tabular}} \caption[CCSM/SNICAR Simulations]{\textbf{CCSM/SNICAR Simulations} \label{tbl:mdl}} \end{floatingtable} % end tbl:mdl Natural (i.e., unforced) interannual variability is quite large in the Arctic climate system \cite[e.g.,][]{BrB982,FWJ99}. Boreal fire variability is also quite large \cite[][]{VRG06} and causes much of the interannual variability in Arctic BC deposition \cite[][]{FZR07}. Detecting and assessing the relatively small (though important) signal of aerosol-induced Arctic change (Figures~\ref{fgr:rfl_spc_rds_ffc}b and~\ref{fgr:mlt_Grn}) against the noisy background of natural Arctic variability will be difficult. We will continue to employ an ensemble-based approach to increase the signal/noise ratio. The ensemble comprises multiple identical numerical experiments with slightly perturbed initial conditions. To obtain the climate responses presented in this proposal we conducted perennial 1997-, 1998-, and 2001-emissions experiments in separate 15\,yr. simulations. We used a Student's $\ttt$-test to quantify statistical significance of Arctic changes between the two ensembles. Significant ($\cnfstt < 0.05$) changes appear as cross-hatched regions in Figures~\ref{fgr:rfl_spc_rds_ffc}b and~\ref{fgr:mlt_Grn}. We plan numerous numerical experiments to systematically quantify aerosol impacts on cryospheric climate sensitivity and surface hydrology (Table~\ref{tbl:mdl}). Many of these experiments are also designed to segregate robust from model-dependent results. Our collaborator Dr.~Dorothy Koch (GISS, see attached letter of support) will replicate the subset of Table~\ref{tbl:mdl} experiments indicated by checkmarks in the GISS climate models with identical forcing scenarios to our CCSM experiments. The purpose of replicating controlled experiments in two GCMs is to understand model-dependent uncertainties, such as dry vs.~wet deposition, aerosol direct radiative forcing forcing per unit mass, and forcing efficacy. %\cite{KoH05} found that South Asia contributes 20--40\% of Arctic BC. \csznote{ \begin{floatingfigure}[r]{0.50\hsize} \centering \includegraphics[width=0.8\hsize,angle=270]{/home/zender/prp_IGPP06/fgr_sotfrc} \caption{Difference in summer surface forcing [\wxmS] from black carbon in snow on sea-ice between a strong boreal fire year (1998) and a weak year (2001).} \label{fgr:frc_cice} \end{floatingfigure} } % end csznote Using the SNICAR snowpack model, \cite{FZR07} show that microphysical feedbacks (Figure~\ref{fgr:fdb_dgm}) may double snowpack BC forcing efficacy relative to GISS estimates \cite[][]{HSR05}. This inter-model disparity is unsettling and highlights the uncertainties involved. In addition to snowpack representation, such inter-model discrepancies can be due to differences in emissions, meteorology, transport, and deposition. Quantifying the inter-model disparity associated with each process will help identify and focus optimal targets for future Arctic field and modeling studies. \cite{FZR07} found that even in years of very strong boreal biomass burning like 1998 (Figure~\ref{fgr:msn_bc}), industrial and biofuel BC exceed biomass burning as an Arctic BC source, consistent with \cite{KoH05}. However, boreal biomass BC appears to be significantly more efficacious in the Arctic than industrial/biofuel BC \cite[]{FZR07}. The proximity of boreal forest BB emissions to snow-covered regions means that a smaller fraction of hydrophobic BC has aged to hydrophilic BC before deposition to snow. Boreal forest fires also peak in summer when insolation and potential snowpack forcing are greatest, rather than winter when industrial and biofuel BC emissions peak. Hence inter-model comparison is a question of response as well as forcing. Objective~\ref{idx_obj_csn}, quantifying efficacy as a function of time and location, is important in this regard as it will show which regions and seasons are most vulnerable to BC forcing, independent of the model. In Year~3 we will test the suggestion \cite[e.g.,][]{Jac02,Jac04} that targeted reductions in soot emissions may achieve significant mitigation of Arctic climate change. Once soot effects are satisfactorily evaluated in our integrated Arctic simulations, we will conduct numerical experiments to assess the efficacy of plausible changes in BC emissions. These experiments will be designed in consultation with Koch and her GISS colleagues, and performed in parallel with them. \iftskbrk{\clearpage}{} % Break page after each task \section{Project Coordination}\label{sxn:crd} \subsection{Personnel}\label{sxn:prs} PI~Zender will coordinate all project activities. Zender has extensive experience in global-scale aerosol modeling, ice cloud physics, aerosol optics, and radiative transfer. He will work with Domin\'{e} at LGGE to improve representation of snow specific surface area changes due to surface hoar, diamond dust, and melt/freeze cycles (Section~\ref{sxn:lgge}). Zender's work as first author in Years~2 and~3 will utilize these improvements to elucidate the relative roles of GHGs and absorbing aerosols in Arctic climate change. A post-doc (to be named) will work for two years, participating in the intercomparison of Arctic climate simulations with collaborator Koch at GISS, and, with collaborator Rasch, in simulation, analysis, and evaluation of a boreal fire plume sampled by the POLARCAT team (Section~\ref{sxn:polarcat}). The post-doc will also be encouraged to pursue opportunistic and original research in Arctic aerosol-snow-climate interactions related to the project and to his/her expertise. Zender will advise an ESS graduate student in Arctic aerosol studies throughout (and beyond) the project. The student will be involved in all global modeling activities, though focused on Objectives~\ref{idx_obj_csn} and~\ref{idx_obj_sea_ice}. The student will help integrate realistic snow physics into the CICE sea-ice model, evaluate and improve snow on sea-ice simulations (Section~\ref{sxn:polarcat}), and participate on global studies of the efficacy of carbonaceous aerosol mitigation for Arctic climate. \iftskbrk{\clearpage}{} % Break page after each task \subsection{Schedule and Milestones}\label{sxn:tm_ln}\label{sxn:scd} \noindent\textbf{Year~1}. \textit{Goals}: Improve SNICAR predictions of hoar, melt/freeze, and impurity effects \begin{enumerate*} \item Summer 2007: Zender to LGGE/Grenoble to work with F.~Domin\'{e} and colleagues \item Fall 2007: Develop theory for hoar and melt/freeze processes in SNICAR \item Winter 2008: Predict/observe hoar and melt/freeze effects on \SSA, and reflectance $\rfl(\wvl)$ \item Spring 2008: Predict/observe impurity effects on \SSA, $\rfl(\wvl)$ \item Spring 2008: Submit manuscript on hoar and melt-freeze \SSA, $\rfl(\wvl)$ results \item All year: UCI student ``spins up'' on project and prepares emissions and Arctic BC concentration datasets for GCM intercomparison %\item All year: Painter and CU student isolate clear sky MODIS scenes % near surface meteorology stations and infer $\rdsffc$ from $\rfl(\wvl)$ \end{enumerate*} \textbf{Year~2}. \textit{Goals}: Represent snow effects on sea-ice, begin IPY POLARCAT project \begin{enumerate*} \item Summer/Fall 2008: Graduate student visits E.~Hunke at LANL to couple SNICAR physics to CICE sea-ice model %\item Summer 2008: Painter, Zender, and UCI graduate student to % Greenland for two-week snowpack/radiation closure measurements of % BC, $\tptsfc$, $\tptgrd$, $\tmsnw$, $\rfl(\wvl)$, and $\rdsffc(\hgt)$. %\item Painter: Compare SNICAR predictions for Arctic $\rdsffc$ with % MODIS/MISR-inferred $\rfl(\wvl)$, $\rdsffc$ \item Fall 2008: Use Clarke/Warren measurements to refine aerosol precipitation- and melt-scavenging \item Fall 2008: Present results from LGGE work at AGU \item Winter 2009: Discuss sea-ice results with CCSM PCWG \item Winter/Spring 2009: Examine impact of soot and dust on Arctic summer sea-ice extent \item Spring 2009: Manuscript on sea-ice results, manuscript on influence of snow physics changes BC/dust simulation biases \item All year: Zender and postdoc perform CCSM transient simulations with identical emissions/surface concentrations as D.~Koch and GISS colleagues. \item Undetermined: Zender to Norway/NILU for POLARCAT team meeting to identify Summer 2008 fire event for intensive modeling studies \end{enumerate*} \textbf{Year~3}. \textit{Goals}: Fully coupled IPCC-transient and LAC mitigation scenarios \begin{enumerate*} \item Summer 2009: Discuss sea-ice simulations at CCSM conference \item Summer/Fall 2009: Postdoc to NCAR to coordinate POLARCAT event simulation with P.~Rasch \item Fall 2009: Present POLARCAT and GCM intercomparison results at AGU \item Winter/Spring 2010: Efficacy of carbonaceous aerosol mitigation for Arctic climate \item All year: Integrated absorbing aerosol impact on Arctic climate sensitivity \item Spring 2010: Manuscripts on POLARCAT event simulation and historical and future Arctic simulations \csznote{ % fxm: put this as innovative new idea in main section not time-line \item Scale fire emissions from Randerson by ice core data (from Saltzman and McConnell) to estimate 1000\,year BC impacts on Greenland } % csznote \end{enumerate*} \textbf{Year~4}. (unfunded wrap-up) \begin{enumerate*} \item Fall 2010: Present results of transient IPCC/LAC scenarios at AGU \item All year: Finish manuscripts \end{enumerate*} \iftskbrk{\clearpage}{} % Break page after each task \section{Results from Prior NSF Funding on Related Projects}\label{sxn:prr} Zender was PI on \href{http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0503148}{ATM-0503148}: ``SGER: Improving CCSM Snow/Ice Radiative and Heating Processes and Assessing the Importance of the Soot Albedo Effect'', \$26828, 2/1/2005--1/31/2006. This SGER grant partially funded graduate student Mark Flanner for one year during which he published one paper \cite[]{FlZ05}, placed one manuscript in review \cite[]{FlZ06}, and gave three national meeting presentations. \cite{FlZ05} shows that resolving the vertical distribution of solar radiant heating within snowpack remediates significant climatological biases in the Tibetan Plateau region. \cite{FlZ06} study snowpack aging and its effect on albedo. This the first theoretical study to quantify the relative roles of initial size distribution, vertical temperature gradient, and snow density in snow albedo evolution (discussed further in Section~\ref{sxn:snicar}). Publications seeded by this grant include \cite{RLF06} and \cite{FZR07}. \csznote{ Zender was PI on \href{http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0321380}{ATM-0321380}: ``Acquisition of an Earth System Modeling Facility (ESMF) for Coupled Climate, Chemistry, and Biogeochemistry Studies'' \$773543, 8/1/2003--7/31/2006. The \href{http://www.ess.uci.edu/esmf}{ESMF} is a collaborative computational resource for UCI's Earth System Science (ESS) Department students and researchers. This includes eight professors, four full-time researchers, and about a dozen graduate students performing global modeling/analysis for their dissertations. In order to utilize spare cycles when ESS jobs are not running, the ESMF provides free (though lower priority) access to researchers/students from any UCI department. A summary of (about two dozen) published and in-press papers which used and acknowledged the collaborative ESMF scientific computing facility is maintained \href{http://dust.ess.uci.edu/prp/prp_mri/prp_mri_apr_smr.txt}{here}. } % end csznote \section{Related Projects, Broader Impacts and Education}\label{sxn:mpc} \subsection{Related Projects}\label{sxn:clb} Collaborations with GISS, LANL, LGGE, NCAR, and U.~Washington will benefit this project and are endorsed in attached letters of support. We plan to support these and other synergistic projects in at least the following ways: \begin{enumerate*} \item Dr.~Phil Rasch (see attached letter of support and Section~\ref{sxn:ccsm}). As originator of the BC/OC aerosol physics in CAM, Rasch is interested in incorporating the soot optical properties we are continually improving for SNICAR, back upstream into CAM. Rasch's simulations of pollution events during POLARCAT will benefit from these optical properties and from the improved snow and sea-ice response. \item Drs.~Elizabeth Hunke (see attached letter of support and Section~\ref{sxn:cice}) and Dr.~Bill Lipscomb, LANL. All CICE users will eventually benefit from the improved upper boundary condition that SNICAR to the sea-ice. Lipscomb, in the same LANL group, is concurrently adapting the \href{http://wiki.nesc.ac.uk/read/glimmer-project}{GLIMMER} ice sheet model \cite[]{Pay99} to the CCSM framework. LANL would like to use our SNICAR snow-aerosol physics as their ice sheet upper boundary condition. Assisting this effort will be relatively straightforward given the similar (CLM-ish) code structure and our close collaboration on the sea-ice. This would complete the integrated treatment of clean and dirty snow in CCSM, a major milestone for the community. \item Dr.~Tom Painter (NSIDC) has been measuring snow spectral reflectance, snow grain size, and dust concentration to understand the the influence of dust on catchment/basin hydrology in the Rockies. He is interested in simulating the direct forcing of snowpack impurities on reflectance using SNICAR snow aging and reflectance physics, coupled to, or instead of, the SNTHERM hydrology. \csznote{ Painter and Zender intend to submit a separate proposal to the upcoming NSF IPY solicitation for such a field experiment. That proposal will be to measure daily spectral surface reflectance and snow grain size, density, and temperature profiles at an Arctic site during a multi-week period in summer 2008. } % csznote \item Dr.~Jim Randerson (UC~Irvine) is primary developer of the GFED burning emissions database (see Section~\ref{sxn:fire}). Randerson is PI and Zender is a Co-PI on NSF \label{prj:fire} (\href{http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0628637}{ATM-0628637}): ``Collaborative Research: Fire at the Intersection of Global Carbon and Water Cycles'' from 10/1/2006--9/30/2010 (the ``Fire Project''). Institutional collaborations led by J.~Foley (U.~Wisconsin) and N.~Mahowald (NCAR) join us to study climate-fire interactions in the context of the carbon/nitrogen-cycles and land-use change. The Fire Project will infer historical and construct future fire emission datasets to study the interactions of biomass burning emissions with the carbon cycle and tropical interannual variability such as ENSO. The ANS project will use these datasets to study the influence of these trends in the Arctic, while the Fire Project will be an early beneficiary of improvements to SNICAR and Arctic climate response to carbonaceous aerosols developed by this ANS project. \csznote{ Two UCI graduate students (M.~Tosca and H.-W. Lin) devoted to the Fire Project will develop historical fire forcing datasets and study the interactions of tropical BB emissions with the carbon cycle and tropical interannual variability such as ENSO. } % end csznote \item Dr.~Natalie Mahowald (NCAR) is the NCAR lead on the Fire project and is Zender's long-time collaborator on dust studies. Mahowald has developed estimates of glaciogenic and anthropogenic dust sources which we may use in past (LGM) and future Arctic climate scenarios. As part of the Fire Project Mahowald and Dr.~Peter Thornton (NCAR) and Randerson are implementing a prognostic boreal fire regime in a dynamic vegetation and carbon/nitrogen cycling version of the CLM. The atmospheric and cryospheric response to fire will benefit from the improvements this ANS project will bring to SNICAR. \item Mr.~Mark Flanner is currently a graduate student with Zender at UC~Irvine and will graduate in Spring 2007. Flanner has been the primary developer of SNICAR. He has committed to a multi-year postdoctoral position at NCAR (beginning Summer 2007), funded by the Fire Project, to continue exploring BC impacts on climate, including tropical climate. He plans to remain actively involved with SNICAR development and thus would benefit from and contribute to the SNICAR improvements outlined in this ANS proposal. \item Drs.~Steve Warren (U.~Washington, see attached letter of support and Section~\ref{sxn:insitu}), Tom Grenfell and Tony Clarke (U.~Hawaii). Warren's team may benefit from using our global snow, BC, and dust predictions and hindcasts to help their search for optimal sampling locations. \csznote{ \item Dr.~Eric Saltzman (UC~Irvine) measures trace gas and aerosol concentrations in ice cores \cite[e.g.,][]{SAD04} and Co-PIs a proposed ARO project ``High-Resolution, Biomass-Burning-Specific Tracers in Greenland Ice Cores over the Past 1000~Years''. In conjunction with Randerson's fire emission database, our project provides a method to quantify the impact of Saltzman's proxy measurements of biomass burning aerosol variability in Greenland over the last 1000\,yr. } % end csznote \end{enumerate*} \subsection{Improved Community Modeling Capabilities}\label{sxn:mdl_mpr} Snow pervades the Arctic, and improved snowpack models can and probably will improve understanding of Arctic hydrology. Zender has provided many useful research models to various communities including the Column Radiation Model (\href{http://www.cgd.ucar.edu/cms/crm}{CRM}), the Dust Entrainment and Deposition Model (\href{http://dust.ess.uci.edu/dead}{DEAD}), and the netCDF Operators (\href{http://nco.sf.net}{NCO}). SNICAR is and will be freely available for use in land, atmosphere, and sea-ice components (CLM, CAM, and CICE, respectively) of the CCSM. All the snow model developments will translate directly to the new ice sheet model being built at LANL and intended for use in CCSM global climate experiments. Hence extending and improving SNICAR's physics will contribute to Arctic research areas including glacier mass balance (through realistic upper boundary radiation and melt conditions), basin and catchment hydrology (improved representation of snowpack sublimation vs.\ internal melt and surface percolation), physical impact of algae on snow, paleoclimate sensitivity (through improved accuracy of Arctic responsiveness to orbital and aerosol forcing), and snow chemistry improved representation of snowpack SSA \cite[]{DoS02}. % DoS02 review air-snow chemistry \subsection{Education}\label{sxn:edc} This project trains one graduate student in Arctic aerosol-climate interactions. UC Irvine is a US Department of Education Minority Serving Institutions with large pools of under-represented minorities (URMs) potentially interested in pursuing undergraduate research projects. The UCI ESS department where Zender teaches has three programs which pipeline URMs to ESS research opportunities: (1) CAMP (Campus Alliance for Minority Participation) in Science, Engineering and Math, (2) an NSF \href{http://www.ess.uci.edu/reu}{REU in ESS} (2006--2009, PI J.~K. Moore of ESS), and (3) a NASA Education Grant (2006--2009, PI J.~Randerson of ESS). With these resources Zender will open a year-round undergraduate research position in his group at no additional cost to NSF. The student will assist inverting snowpack spectral measurements for aerosol optical properties and mixing state given measured aerosol concentrations. Moreover, PI~Zender helps train Orange County K--12 teachers in Earth Science curricula. An NSF Math Science Partnership project called \href{http://focus.mspnet.org}{FOCUS} (Faculty Outreach Collaborations Uniting Scientists, Students and Schools) brings the teachers to UCI. Zender will incorporate Arctic climate change materials into \href{http://dust.ess.uci.edu/smn/smn_sun_focus_200508.pdf}{his FOCUS seminars}. Zender will also incorporate this Arctic research into his \href{http://dust.ess.uci.edu/smn/smn_arc_mlt_olli_200610.pdf}{seminars} to students of the Osher Lifelong Learning Institute (\href{http://unex.uci.edu/community/olli/index.asp?}{OLLI}). \newpage \section*{References}\label{sxn:rfr} % \section* does not number section \setcounter{page}{1} \thispagestyle{empty} % Bibliography \renewcommand\refname{(blue numbers show proposal section where citation occurs)} \vspace{-24.0pt} \newlength{\oldbaselineskip} \setlength{\oldbaselineskip}{\baselineskip} %\setlength{\baselineskip}{13.574pt} % 1.234 X 12pt \setlength{\baselineskip}{12.0pt} % 1.234 X 11pt \setlength{\bibsep}{4pt} % Space between natbib bibliography items %\bibliographystyle{ieeetr} \bibliographystyle{agu04} \bibliography{bib} \setlength{\baselineskip}{\oldbaselineskip} % 1.851 X 11pt \newpage \printindex % Requires makeidx KoD95 p. 221 \addcontentsline{toc}{section}{Index} \newpage \subsection{Budget Justification}\label{sxn:bdg_jst} \setcounter{page}{1} \thispagestyle{empty} \subsubsection{Salaries and Wages} One month of summer support for three years is requested for Prof.\ C.~Zender, the PI, who will have primary responsibility for the proposed research. Zender requests his sabbatical differential salary in Year~1 (\$25,542), when he will be in residence at LGGE/Grenoble performing controlled snow studies with collaborator F.~Domin\'{e}. In Year~2 the project will focus more on solar energy distribution and snow aging evaluation in the Arctic sea-ice regime. In Year~3 the project will elucidate the relative roles of GHGs and absorbing aerosols in Arctic climate change. A postdoctoral scholar is requested in Years~2 and~3. The postdoc will perform original research applying our snow aging and aerosol semi-direct forcing techniques to the IPY POLARCAT field exercise (in collaboration with P.~Rasch) and climate simulations that quantify the efficacy of Arctic aerosol forcing in various IPCC scenarios (in collaboration with D.~Koch). The PI thinks that the proposed CCSM-GISS inter-model comparison is better suited to a post-doc than a graduate student because GCM intercomparison is best done by someone already comfortable and familiar with the inner workings of at least one global model. To be Named---Graduate Student Researcher~III.  Funds are requested to support one non-resident graduate student each year of the project. The graduate student support is requested at 50\% for 9~months during the academic year and 100\% for 3~months during the summer. The student will be involved in most global modeling activities: integrating realistic snow physics into the CICE sea-ice model, evaluate and improve snow/sea-ice simulations (in collaboration with E.~Hunke), and participating in global studies of the efficacy of carbonaceous aerosol mitigation for Arctic climate. All salaries and wages were estimated using UCI's academic and staff salary scales. A 2\% cost-of-living increase was applied each year of this proposal as well as a 5\% merit, where applicable. \subsubsection{Employee Benefits} Fringe Benefits were estimated using the composite rates agreed upon by the University of California Office of the President and the DHHS Audit Agency, the Cognizant Audit Agency for the University of California. Benefit rates used in this proposal are:\\ Faculty --- academic year --- 17\%\\ Faculty --- summer --- 12.7\%\\ Postdoc --- 17\%\\ Student employees --- summer --- 3\%\\ Student employees --- academic year --- 1.3\%\\ Fees and tuition are requested for one nonresident student for the duration of the project. University of California policy requires award payment of fees for any student with more than 25\% support from a grant. In the first year, \$26,951 is requested, \$29,511 in the second year, and \$32,338 in the third year. Fees and tuition are excluded from indirect cost assessment. \subsubsection{Equipment} Equipment funds are requested for two Linux scientific workstations (dual or quad CPU, 4\,GB RAM) at \$5,000 each. One in year one (for the graduate student) and one in year two (for the postdoc). These workstations will include adequate RAID'ed disk space (2--4\,TB) for the researchers to store and analyze CCSM model output and satellite MODIS, MISR, and AMSR-E datasets. \subsubsection{Travel} Domestic:  Round-trip travel at \$2000 per trip is requested for the PI and graduate student to travel to national meetings (primarily AGU) to present results in Years 2 and~3. Each trip includes round-trip travel from Irvine to San Francisco, one-week hotel and per diem. Round-trip travel at \$2,000 per trip is requested for the postdoc (Year~2) and PI (Year~3) to travel to NASA GISS in New York City to intercompare models with collaborator D. Koch. This includes round-trip, lodging and per diem for one week. Round-trip travel is requested for the graduate student and PI to travel to Los Alamos NM to work on sea-ice modeling with LANL collaborator E.~Hunke. One month housing is requested for the graduate student who will work closely with Hunke. One week lodging is requested for the PI. Travel expenses for the graduate student are estimated at \$3,500 (for one month) and \$1,000 (for one week) for the PI. These trips include estimated conference registration, abstract submission fees, RT airfare, lodging, meals and ground transportation. Travel estimates are based on historical usage. International:  One round-trip at \$1,500 is requested for the PI to travel to Grenoble, France in Year~1 to collaborate with F.~Domine while on sabbatical. This trip includes only transportation. One round-trip at \$3,000 is requested for the PI to travel to Norway in Year~2 to participate in IPY POLARCAT team meeting activities and to present results. This trip includes roundtrip travel from Irvine to Oslo, one-week hotel and per diem. These trips include estimated conference registration, abstract submission fees, RT airfare, lodging, meals and ground transportation. Travel estimates are based on historical usage. \subsubsection{Other Direct Costs} Charges for journals, photocopying, long distance phone, FAX and postage charges pursuant to this project are requested. Included in these expenses are long-distance charges for usage directly related to the project, such as communication with colleagues, journals, and vendors. Photocopying of research materials including publications and results of this sponsored research project are requested as well as mail and shipping for materials related to this project. These costs are estimated at \$500 per year. Support is requested for publication costs (\$2,000 in Year~1, \$4,000 in Year~2 and \$6,000 in Year~3) pursuant to this project, which include utilization of expensive color figures. Costs were estimated according to historical usage. \subsubsection{Indirect Costs} Facilities and Administrative costs were estimated in accordance with UCI's approved indirect cost rate agreement. The indirect cost rate of 52.5\% of MTDC effective 7/1/05 was based upon the nature and location of the work proposed. Graduate student fees and tuition and equipment are excluded from indirect cost assessment. UCI's indirect cost rate agreement was approved by DHHS, the Federal Cognizant Audit Agency for UCI on 12/5/01. \newpage \section{Facilities, Equipment, and Other Resources} \subsection{Computational Resources} \setcounter{page}{1} \thispagestyle{empty} The computational activities in this project will take place at one of three computing facilities based on the simulation scale. PI~Zender directs the UCI Earth System Modeling Facility (\href{http://www.ess.uci.edu/esmf}{ESMF}), an NSF-supported MRI facility. The ESMF flagship machine is an 88-CPU Power4+ IBM supercomputer with 192\,GB RAM and 16\,TB of RAID storage. In summer 2006 ESMF acquired a new Beowulf cluster (named IPCC) comprising twenty-seven two-way dual core Opteron nodes (108~CPUs) and 5\,TB of RAID storage. This ANS proposal fits squarely within the ESMF mission, and the ESMF will host the initial modeling development and shorter simulations. Coupling, testing, and shorter evaluations of SNICAR with CICE will occur at UCI. Longer sea-ice simulations with collaborator E.~Hunke will be performed at LANL or NCAR, which both support the CCSM on supercomputer facilities designed for long, ``production'' simulations. We will request supplementary time at NCAR for the ensemble of equilibrium and transient simulations of the fully coupled CCSM/SNICAR code outlined in Table~\ref{tbl:mdl}. This will be done under the auspices of the Polar Climate Working Group, which is co-chaired by collaborator E.~Hunke. \newpage \section{Acronyms and Abbreviations}\label{sxn:abb} \setcounter{page}{1} \thispagestyle{empty} %\begin{longtable}{ >{\raggedright}p{7.0em}<{} >{\raggedright}p{8.0em}<{} } \begin{longtable}{ r >{\raggedright}p{25.0em}<{} l } & \kill % NB: longtable requires caption as table entry \caption[Acronyms and Abbreviations]{\textbf{Acronyms and Abbreviations}% \label{tbl:abb}} \\ \hline\hline \rule{0.0ex}{\hlntblhdrskp}% Abbreviation & Description & \\[0.0ex] \hline \rule{0.0ex}{\hlntblntrskp}% \endfirsthead % Lines between and \endfirsthead appear at top of table \caption[]{(continued)} \\ % Set label for following pages Abbreviation & Description & \\[0.0ex] \hline \rule{0.0ex}{\hlntblntrskp}% \endhead % Previous block appears at top of every page \endlastfoot % Previous block appears at end of table AGU & American Geophysical Union (meets in San Francisco in Fall) & \\ AMSR-E & Advanced Microwave Scanning Radiometer (satellite instrument) & \\ ANS & Arctic Natural Sciences & \\ AR4 & Fourth Assessment Report & \\ ARF & Aerosol Radiative Forcing & \\ ATSR & Along Track Scanning Radiometer and Microwave Sounder & \\ AVIRIS & Airborne Visible/Infrared Imaging Spectrometer & \\ AWS & Automated Weather Station & \\ BB & Biomass Burning & \\ BC & Black Carbon (light-absorbing component of carbonaceous aerosol) & \\ BF & Biofuel & \\ BRDF & Bi-directional Reflectance Distribution Function & \\ CAM & Community Atmosphere Model (atmosphere component of CCSM) & \\ CCSM & Community Climate System Model & \\ CICE & Los Alamos sea-ice model (sea-ice component of CCSM) & \\ CLM & Community Land Model (land component of CCSM) & \\ CRM & Column Radiation Model & \\ DEAD & Dust Entrainment And Deposition Model & \\ EMA & Effective Medium Approximation & \\ ESM & Earth System Model & \\ ESMF & Earth System Modeling Facility & \\ ESS & Earth System Science (Department) & \\ FF & Fossil Fuel & \\ FOCUS & Faculty Outreach Collaborations Uniting Scientists, Students and Schools & \\ GCM & General Circulation Model & \\ GFED & Global Fire Emissions Database & \\ GHG & Greenhouse Gas & \\ GISS & Goddard Institute for Space Studies & \\ GLIDE & General Land Ice Dynamic Elements (core of GLIMMER) & \\ GLIMMER & Ice Sheet Model of Payne et~al. & \\ GSFC & Goddard Space Flight Center & \\ IPCC & Intergovernmental Panel on Climate Change & \\ IPY & International Polar Year & \\ LAC & Light Absorbing Carbon (light-absorbing component of carbonaceous aerosol) & \\ LANL & Los Alamos National Laboratory & \\ LGGE & Laboratoire de Glaciologie G\'{e}ophysique de l'Environnement (Grenoble, France) & \\ LGM & Last Glacial Maximum & \\ MISR & Multi-angle Imaging Spectro-Radiometer (satellite instrument) & \\ MODIS & Moderate Resolution Imaging Spectroradiometer (satellite instrument) & \\ NASA & National Aeronautic and Space Administration & \\ NCAR & National Center for Atmospheric Research (Boulder, Colorado) & \\ NCO & netCDF Operators & \\ NIC & National Ice Center & \\ NILU & Norwegian Institute for Air Research (Kjeller, Norway) & \\ NIR & Near InfraRed & \\ NSIDC & National Snow and Ice Data Center & \\ OC & Organic Carbon & \\ OLLI & Osher Lifelong Learning Institute & \\ OPAC & Optical Properties of Aerosols and Clouds (\cite{HKS98}) & \\ PCWG & (CCSM) Polar Climate Working Group & \\ PI & Principle Investigator & \\ POLARCAT & POLar study using Aircraft, Remote sensing, surface measurements and modeling of Climate, chemistry, Aerosols and Transport (IPY project organized by Andreas Stohl of NILU) & \\ RT & Radiative Transfer & \\ SEI & Science and Engineering Informatics & \\ SEM & Scanning Electron Microscopy & \\ SGER & Small Grant for Exploratory Research & \\ SHEBA & Surface Heat Budget of the Arctic (field campaign on Arctic sea-ice) & \\ SNICAR & SNow, ICe, and Aerosol Radiative model & \\ SNTHERM & Snow Melt model & \\ SOM & Slab Ocean Model & \\ SSA & Specific Surface Area & \\ SWE & Snow Water Equivalent & \\ TG & Temperature Gradient & \\ UCI & University of California, Irvine & \\ \end{longtable} % end tbl:abb \newpage \section{Project-Wide Combined Collaborator and Advisor List}\label{sxn:prs_lst} \setcounter{page}{1} \thispagestyle{empty} % Currently includes: Zender All Personnel Associated with Proposal, Collaborators and Co-Editors of Project Senior Personnel, their Post-docs, and their Thesis Advisors: \begin{enumerate*} \item[] Ammann, C.~A. (NCAR) \item[] Bian,~H. (NASA/UMBC) \item[] Bonan, G.~B. (NCAR) \item[] Busacca,~A. (WSU) \item[] Cakmur,~R. (NASA GISS) \item[] Colarco,~P. (GSFC) \item[] Collins, W.~D. (NCAR) \item[] Cooper, W.~A. (NCAR) \item[] Famiglietti,~J. (UCI) \item[] Gaylord,~D. (WSU) \item[] Grini,~A. (U.~Oslo) \item[] Jenks,~S. (UCI) \item[] Khalsa, S.~J.~S. (NSIDC) \item[] Kiehl, J.~T. (NCAR) \item[] Kuester,~F. (UCI) \item[] Levin,~Z. (TAU, Israel) \item[] Mahowald, N.~M. (NCAR) \item[] Miller,~R. (NASA GISS) \item[] Moore, J.~K. (UCI) \item[] Okin,~G. (U.~Virginia) \item[] Painter,~T. (NSIDC) \item[] Pajarola,~R. (UCI) \item[] Papadopoulos,~P. (UCI) \item[] Rasch, P.~J. (NCAR) \item[] Tegen,~I. (IfT, Germany) \item[] Thomas, G.~T. (CU) \item[] Torres,~O. (NASA GSFC) \item[] Valero, F.~P.~J. (Scripps) \item[] Yu,~S. (Duke) \end{enumerate*} \newpage List is Alphabetical by Surname. \begin{enumerate*} \item[] Collaborators of Zender: \begin{enumerate*} \item[] Ammann, C.~A. (NCAR) \item[] Bian,~H. (NASA/UMBC) \item[] Bonan, G.~B. (NCAR) \item[] Busacca,~A. (WSU) \item[] Cakmur,~R. (NASA GISS) \item[] Colarco,~P. (GSFC) \item[] Collins, W.~D. (NCAR) \item[] Cooper, W.~A. (NCAR) \item[] Famiglietti,~J. (UCI) \item[] Gaylord,~D. (WSU) \item[] Grini,~A. (U.~Oslo) \item[] Jenks,~S. (UCI) \item[] Khalsa, S.~J.~S. (NSIDC) \item[] Kiehl, J.~T. (NCAR) \item[] Kuester,~F. (UCI) \item[] Levin,~Z. (TAU, Israel) \item[] Mahowald, N.~M. (NCAR) \item[] Miller,~R. (NASA GISS) \item[] Moore, J.~K. (UCI) \item[] Okin,~G. (U.~Virginia) \item[] Painter,~T. (NSIDC) \item[] Pajarola,~R. (UCI) \item[] Papadopoulos,~P. (UCI) \item[] Rasch, P.~J. (NCAR) \item[] Tegen,~I. (IfT, Germany) \item[] Thomas, G.~T. (CU) \item[] Torres,~O. (NASA GSFC) \item[] Valero, F.~P.~J. (Scripps) \item[] Yu,~S. (Duke) \end{enumerate*} \item[] Collaborators of Co-PI Dr.~Who: \begin{enumerate*} \item Hi \end{enumerate*} \end{enumerate*} \newpage \subsection{Supplementary Documents}\label{sxn:spl_doc} \setcounter{page}{1} \thispagestyle{empty} \begin{enumerate} \item \${DATA}/prp/prp\_ans/prp\_ans\_abb.pdf \item \${DATA}/prp/prp\_ans/prp\_ans\_clb.pdf \item \${DATA}/prp/prp\_ans/prp\_ans\_ltr\_domine.pdf \item \${DATA}/prp/prp\_ans/prp\_ans\_ltr\_hunke.pdf \item \${DATA}/prp/prp\_ans/prp\_ans\_ltr\_koch.pdf \item \${DATA}/prp/prp\_ans/prp\_ans\_ltr\_rasch.pdf \item \${DATA}/prp/prp\_ans/prp\_ans\_ltr\_warren.pdf \item \${DATA}/prp/prp\_ans/prp\_ans\_ltr\_polarcat.pdf \item \${DATA}/prp/prp\_ans/prp\_ans\_ltr\_polarcat.pdf \end{enumerate} \clearpage \csznote{ % E-mail to collaborators % Letter sent to Liz Ford 20061130 Hi Liz, I am submitting a single PI proposal to the 2006 NSF Arctic Research Opportunities (ARO) announcement, NSF 06-603: ``Integrating Snow Processes to Improve Understanding and Prediction of Arctic Climate Change'' The one-page summary is attached. Please draft me a three year budget 7/1/07-6/30/10 that includes: 1. 100% sabbatical differential for year 1 2. 1 month summer salary years 1-3 3. 1 international graduate student years 1-3 4. International travel to Grenoble, France in year 1, airfare only 5. Domestic travel to AGU for me and grad student years 2 and 3 6. International travel for me to Oslo, Norway in year 2 one week expeses 7. 1 month visit for grad student to LANL year 2 8. 1 week visit for me to LANL year 2 9. 2 Linux scientific workstations at $5k each 10. Publication expenses years 1/2/3 = $2000/4000/4000 Thanks, Charlie % Letter sent to Liz Ford 20061202 Hi Liz, Please increase the draft budget of my NSF ANS proposal by adding support for a postdoc in years 2 and 3. P.S. The title has changed to Snow Process Studies and Modeling to Improve Arctic Climate Prediction and a better one page summary has been uploaded. Thanks, Charlie % Letter sent to Florent Domine 20061130 Hello Florent, I have been (re-)writing an NSF IPY proposal due Friday 12/8. This is one of two proposals I am writing which would fund my sabbatical stay at LGGE. I hope you are still interested in collaborating. Attached is the one page proposal summary and the schedule. The full proposal, a draft that improves daily, is online at http://dust.ess.uci.edu/prp/prp_ans/prp_ans.pdf I have not finished writing more details on the first year when I would be at LGGE. I plan to do this tomorrow and I want to give you as much advance notice as possible. I plan to describe three experiments (two that we have already discussed), and I wanted to double-check with you on whether this is realistic: 1. Fall 2007: Modeling/measurement of SSA changes from surface hoar 2. Winter 2008: Modeling/measurement of SSA changes melt/freeze cycles 3. Spring 2008: Modeling/measurement of impurity effects on snow A letter of support from you would be very helpful because I intend to collaborate with your group at LGGE in Year 1 on projects to improve our models, and the proposal explicitly mentions this. If you choose to provide a letter of support, it would be most helpful to receive a PDF on your letterhead, or an e-mail, by Thursday 12/7. Best Regards, Charlie Hi Florent, Attached please find a draft one page summary of Snow SSA measurements. I welcome your comments. You'll note that I left out the fresh snow SSA measurements to help parameterize initial snow SSA as a function of atmospheric conditions. I can add that in but I thought that the experiements mentioned were already more than enough for one year. This proposal has to make the case that it is solving Arctic problems so there is text on "mimicking" Arctic conditions once sample are in the lab. Is it possible to do what I wrote? I am excited by the potential to do controlled impurity studies. However, is "controlled" an exaggeration? Is it possible to somehow distribute known concentrations of impuritities into snow? Or must one just measure what one finds in the field? And the melt/freeze experiments---I think there is a crucial gap in our knowledge here but again, I'm not sure whether your methane technique will work when liquid is present. Please clarify if I should change the description from melt/freeze to warm/freeze, or drop it entirely. Let me know what you think. Will you be able to send me a PDF letter of support sometime Thursday? Thanks, Charlie % Letter sent to Elizabeth Hunke 20061130 Hi Elizabeth, I am submitting a revised NSF ANS proposal due 12/8. Similar to our previous proposals, it requests funding for a graduate student who will work to improve snow physics in CICE sea-ice. The proposal describes the rationale of porting SNICAR to CICE, and allocates funds for the student to work at LANL for one-month with your guidance (and a one-week visit by me). I apologize for not having called you earlier to discuss this project. Just say "No!" and disregard the rest of this message if you do not wish to participate. I understand that plans change and new opportunities arise. Good. You're still reading :) Attached is a one page summary, the portion that most involves you, and the schedule. The full proposal, a moving target that improves daily, is online at http://dust.ess.uci.edu/prp/prp_ans/prp_ans.pdf A letter of support from you would be very helpful because the proposal explicitly mentions the sea-ice studies numerous times. If you choose to provide a letter of support, it would be most helpful to receive a PDF on your letterhead, or an e-mail, by Thursday 12/7. Your support letter from last year would be a fine template because, from your point of view, not much has changed. A UCI student would work remotely to spin up on the project and then show up at LANL for a month or so when ready to extend CICE with SNICAR. The project involves other elements which are more or less independent of the CICE component. These include: my sabbatical at LGGE/Grenoble to work on hoar, diamond dust, and melt freeze improvements to SNICAR, intercomparison with GISS GCM simulations by Koch et al., continuing to work with Flanner, Rasch, and Randerson on the physics, transport, and fire components, and performing IPCC-ish future simulations. Best Regards, Charlie % Letter sent to Dorothy Koch 20061130 Hi Dorothy, Great! I'm glad you've elected to request a postdoc to work on this. It will be hard to lure Mark to GISS---though it never hurts to try. He's received one attractive postdoc offer already. My letter of support is attached. Let me know if you want changes. Your text might clarify that Flanner and Zender (2006) describe the SNICAR model, whereas Flanner et al. (2006, submitted) describe its global results driven by BC aerosol. Attached is a one page summary of my NSF ANS proposal, and the portion that most involves you. A letter of support from you would be very helpful. Mentioning (if true) that you believe SNICAR development will benefit the Arctic modeling community would be helpful. If you choose to provide a letter of support, it would be most helpful to receive a PDF on your letterhead, or an e-mail, by Thursday 12/7. Good luck! Charlie % Letter sent to Tom Painter 20061130: Hi Tom, As you know I am (re-)writing an NSF Arctic proposal now called "Snow Process Studies and Modeling to Improve Arctic Climate Prediction". Given the feedback from the program manager, and knowing what Warren is funded for, I felt that your field measurements would be hard for NSF to support in this proposal. The proposal would fund a grad student, and a two year postdoc to continue studying dirty snow, both BC and dust, and to move to the next level of feedbacks by including feedbacks with sea-ice. The proposal will also fund new snow process studies in Florent Domine's lab at LGGE/Grenoble where I hope to spend my sabbatical. The results would be integrated into an intense modeling study of a POLARCAT-characterized plume, and into IPCC-style GCM simulations. I would very much like to collaborate with you on solar radiative budget and snowpack evolution experiments anywhere. However, I think it would be best if you PI'd such a proposal. I wrote, and correct me if I'm wrong or should not mention this, that you have pending proposals to measure spectral reflectance and grain size in the Arctic during IPY, and that we would find your data valuable for closure on snow evolution experiments. I would like to verify that it this OK with you. Attached is the one page proposal summary, the POLARCAT section (where the stuff most relevant to you is), and the schedule. The full proposal, a bit of a moving target, is online at http://dust.ess.uci.edu/prp/prp_ans/prp_ans.pdf Would you consider writing a short letter of support? If yes, it would be most helpful to receive a PDF on your letterhead, or an e-mail, by Thursday 12/7. Thanks, Charlie P.S. Sorry for the lateness of this, and for continually missing calls. Hope to see you in S.F.! % Letter sent to Phil Rasch 20061130: Hi Phil, We are (re-)writing an NSF Arctic proposal called "Snow Process Studies and Modeling to Improve Arctic Climate Prediction". The proposal would fund a grad student, and a two year postdoc to continue studying dirty snow, both BC and dust, and to move to the next level of feedbacks by including feedbacks with sea-ice. We will work with Elizabeth Hunke on coupling SNICAR to the sea-ice. And possibly with Briegleb's sea-ice physics if that is ready. The proposal will also fund new snow process studies in Florent Domine's lab at LGGE/Grenoble where I hope to spend my sabbatical. The results would be integrated into an intense modeling study of a POLARCAT-characterized plume, and into IPCC-style GCM simulations. We would be very interested in collaborating with you on a boreal fire plume event simulation for POLARCAT. With Randerson's fire emissions, your transport, and our radiation and snow, we could do an integrated end-to-end exercise. What do you think? May I mention this in the proposal? This proposal will also rely heavily on continued use of your BC aerosol model in CAM, with the holiday trimmings that we have added. I am verifying that we can rely on you for continued guidance on the BC/OC component, and that I can mention this too. Attached is the one page proposal summary, the POLARCAT section (that most involves you), and the schedule. The full proposal, a bit of a moving target, is online at http://dust.ess.uci.edu/prp/prp_ans/prp_ans.pdf Would you consider writing a short letter of support? If yes, it would be most helpful to receive a PDF on your letterhead, or an e-mail, by Thursday 12/7. Thanks, Charlie P.S. Sorry for the lateness. Hope to see you in S.F. % Letter sent to Steve Warren 20061130 Dear Steve, Attached is a one page summary of my NSF ANS proposal, the portion that most involves you, and the schedule. The full proposal, in flux and improving daily, is online at http://dust.ess.uci.edu/prp/prp_ans/prp_ans.pdf A letter of support from you would be very helpful because the data your and Tony Clarke's groups collecting are crucial to evaluating and improving our models, and the proposal explicitly mentions this. Numerous times :) If you choose to provide a letter of support, it would be most helpful to receive a PDF on your letterhead, or an e-mail, by Thursday 12/7. Your support letter from last year would be a fine template. Briefly, I plan to adopt a graduate student who will pursue a PhD in aerosol-cryosphere-climate interactions. In year 1, I'll (hopefully) be with Florent Domine et al. (including Fily) at LGGE in Grenoble. We'll collaborate on SSA modeling and measurements in lab environments relevant to Arctic snowpack. We'll try to improve SNICAR by explicitly representing SSA of non-precipitation snowpack changes especially surface hoar and possibly diamond dust. Also hope to try some melt/freeze experiments and some controlled impurity experiments. In Year 2 the graduate student has ``spun up'' and commences studies with impurities in snow on sea-ice, in collaboration with Hunke/LANL. In Year 3 we'll hindcast the most significant biomass burning plume characterized during IPY, and apply what we have learned from the microphysics and sea-ice work to integrated Arctic scenarios. Thanks! Charlie } % end csznote \csznote{ % E-mail from Steve Warren 20061110 Hi Charlie. I enjoyed our phone conversation today. 1. Below is the response I wrote last March to Marco Tedesco, who had proposed satellite detection of the effect of BC on snow albedo. 2. You can fetch our paper on spectral BRDF (Hudson et al 2006) from my website http://www.atmos.washington.edu/~sgw/s_warren_pub.html. This is the paper that shows that wavelengths with the same albedo have the same BRDF (compare Figures 5 and 7). I look forward to meeting with you at AGU. Best regards, - Steve > Date: Wed, 22 Mar 2006 18:41:39 -0800 > To: mtedesco@umbc.edu > From: Stephen Warren > Subject: Re: Snow impurities from satellite > Cc: James Hansen ,Tom Grenfell , Armond Cohen , Ellen Baum ,yoram.j.kaufman@nasa.gov, Warren Wiscombe ,dorothy.k.hall@nasa.gov > > Dear Dr. Tedesco, > > Thanks for sending me your outline for a proposal. I think that satellite observations will not be useful to quantify soot in Arctic snow, for the following reasons. > > 1. Satellites still have difficulty detecting clouds over snow, so we may not know whether we're looking at snow or clouds-over-snow. Thin near-surface layers of atmospheric ice crystals ("diamond-dust") are common in the Arctic. > > 2. If we can be sure we are looking at a cloud-free pixel, then as you indicate we would choose two channels, one in the near-infrared to get the snow grain size, and one in the visible to get the soot (because the albedo-reduction by soot depends on the snow grain size). Actually we would need two channels in the near-infrared, and would use their ratio to get grain size, because what the satellite measures is not albedo but rather radiance into a particular direction. MODIS does have enough channels. However, the anisotropic reflectance factor (ARF) varies with wavelength, and this variation with wavelength itself depends on grain size. The presence of soot in the snow will also alter its visible ARF (relatively more forward-scattering, less scattering to zenith), so an iterative procedure would be needed. > > 3. The ARF is very much affected by small-scale surface roughness such as ripples, sastrugi, and suncups. On sea ice there are also pressure-ridges, which are much larger. The height and direction of sastrugi are altered after each strong windstorm, and the height-to-width ratios are unpredictable. The effects of sastrugi on ARF are different at the different wavelengths, because they depend on the ratio of sastrugi width to flux-penetration depth (which varies with wavelength). When a thin ground-fog (or diamond-dust layer) covers the snow and thus partially hides the roughness, our measurements show that the forward peak is enhanced and the nadir view is darker, even though the albedo is probably unchanged or slightly higher. This darkening at nadir could be mistaken for soot-contamination. > > 4. Soot can be mimicked. > (a) Sooty snow has the same spectral signature as thin snow: both soot and thinness reduce albedo in the visible but not in the near-infrared. So we can't get the soot content from space unless we know independently the snow thickness. > (b) If there is any vegetation in the satellite pixel, this will cause additional difficulty. Snow on the tundra is often thin enough that grass is sticking up through it. > (c) In the Arctic Ocean, sub-grid-scale leads will cause a similar difficulty. So the method appears not to be useful for the forest, the tundra, and the Arctic Ocean; its use would probably be limited to flat areas of the Greenland Ice Sheet. > > 5. MODIS will probably not be able to distinguish between (a) soot in the snow and (b) soot in the atmosphere above the snow ("Arctic haze"). Both will give similar spectral signatures when viewed from above, but their climatic consequences are different: soot in the atmosphere shields the surface from sunlight, stabilizes the atmospheric temperature profile, etc., as shown in the publications about Arctic haze and "nuclear winter". > > 6. As Jim Hansen pointed out, significant climatic effects can >result from changing snow albedo by only 1\%. If the directional >radiance measured by MODIS is 1\% lower than your predicted directional radiance for clean snow of some specified roughness, will you really be sure that soot is responsible? > > Sincerely, > - Stephen Warren > > > At 02:53 PM 3/14/2006, mtedesco@umbc.edu wrote: > >> I have been contacting already colleagues here at Goddard for receiving >> some support. So far, Yoram Kaufman and Warren Wiscombe have expressed >> their interest in the idea and they could provide letters of support to >> the proposal. Our estimeed colleagues in Europe (Kokhanovsky and Zege) >> agreed about being unfunded collaborators on the proposal to support the >> theoretical background of the study. Also, the members of the Goddard Snow >> Team (Ed Kim, Dorothy Hall and Jim Foster) will support the proposal with >> a letter to NASA. I wrote yesterday to Jim Hansen and I have been talking >> to his assistant for a meeting. I think he might be interested in the >> study and I am waiting for his reply. >> >> >> >> >>We will make use of MODIS observations to derive grain size and soot >> >>concentration by means of a recently developed snow optical model based >> >> on >> >>the representation of snow grains as fractal particles. Grain size values >> >>will be extracted from MODIS reflectance collected at near-infrared (e.g. >> >>1245 nm). Soot concentration will be then derived from the reflectance in >> >>the visible region (e.g. 545 nm) by using the values of grain size >> >>previously retrieved. Passive microwave space-borne data at low >> >> resolution >> >>will be also eventually used to study the relationships between melting >> >>onset of snow and soot concentration. > > > Stephen G. Warren > Department of Atmospheric Sciences tel 206-543-7230 > University of Washington, Box 351640 fax 206-543-0308 > Seattle WA 98195 USA sgw@atmos.washington.edu > http://www.atmos.washington.edu/warren.html } % end csznote Warren's concerns \csznote{ % Talks with Steve Warren and Tom Painter By summer 2007, beginning of my year 1, Warren will have 1. spectral and concentration measurements from northeast greenland, russia, sturm's traverse By summer 2008, beginning of my year 2, Warren will have 1. 1st year of air scavenging measurements 2. melt scavenging Warren already has new data from NPO, Ellesmere, Hudson Bay, Greenland Summit and the 2000m line. Collaborating with Sebastian Gerlard from Norway in Svalbard } % end csznote \csznote{ % Award notification letter received 20070905 Dear Dr. Zender: I am happy to inform you that your recent proposal to the Arctic Natural Sciences Program at NSF has been recommended for funding. Congratulations on a well-written proposal! A total of 188 proposals were received for the recent Arctic Systems Science/Arctic Natural Sciences target date representing approximately $75 million in requested funding. These 188 proposals represented 105 different projects. Additional requests for support of REUs, SGERs, and for support of proposals submitted to, co-reviewed with, and co-funded with other programs have also been considered. Each proposal was reviewed by experts outside the National Science Foundation and, where appropriate, by panels. Approximately $8 million were available to fund projects submitted to the present competition. A variety of factors besides the ratings and, more importantly, content of reviews of the proposal enter the funding decision: program goals, the balance of disciplines in the program, logistics capabilities, and the appropriateness of the work for the Arctic are very prominent ones, but we must also consider issues of diversity, recruitment of new scientists, and the broader impacts of awards on society. Given these multiple constraints on funding decisions, usually only those proposals that generate significant enthusiasm amongst the reviewers rise above the level required for funding. Please remember that program officers do not make awards. Only the NSF Division of Grants and Agreements (DGA) makes awards. If you have not yet heard from DGA, you should hear soon, but any expenditures prior to notification by DGA are made at your own risk. The reviews of your proposal and panel review have been released for your perusal. I hope that you will read them carefully and take their suggestions to heart wherever appropriate. Your proposal was reviewed by, and is being co-funded by, both the Arctic Natural Sciences program of the Office of Polar Programs and the Climate Dynamics program of the Division of Atmospheric Sciences. The following paragraphs summarize the Programs' assessment of your proposal. Intellectual Merit: The Programs believe that a talented scientist has proposed a project that is a logical continuation of his prior efforts. The data sets to be collected during this project will form a significant community legacy once they are archived. The anticipated physics-based modeling of snow-related processes has the potential to make a significant improvement to future climate models. The Programs find merit in many of the constructive criticisms provided by the mail reviewers. The PI is urged to consider these carefully and to modify his approach where this seems appropriate and possible. Nevertheless, the Programs concur with the mail reviewer who noted that the project will make a strong improvement to current understanding if only a portion of the proposed work is successful. Broader Impacts: The Programs believe that the proposed broader impacts are good. The PI's institution is a minority-serving institution and he will use departmental resources to recruit an undergraduate student from an under-represented minority to work on the project as a year-round assistant. The project also will provide support for the training of a graduate student and a post-doctoral associate. Finally, the PI will incorporate results from this project into his lectures for both K-12 teacher education and outreach to informal learning communities. After you have digested the content of the reviews and the comments above, please feel free to contact me, if you have any questions or concerns. Again, congratulations on a successful proposal. Sincerely, Bill Wiseman } % end award notification letter \csznote{ % Reviews of 2006 submission: Proposal Number: 0714088 Performing Organization: U of Cal Irvine NSF Program: Arctic Natural Sciences Principal Investigator: Zender, Charles S Proposal Title: Snow Process Studies and Modeling to Improve Arctic Climate Prediction Panel review is contained in above award notification letter Rating distribution: 2 Excellent (#s 2, 5), 3 Very Good (#s 1, 3, 7), 2 Good (#s 4, 6) Review #1 Rating: Very Good REVIEW: What is the intellectual merit of the proposed activity? The proposed work addresses one of the most important science problems over theArctic; i.e., the impact of aerosols on snow/ice and the subsequent effect on Arctic climate system. The proposer is well qualified for the proposed work, and is well connected in the broad modeling and data community. For the prior work supported by a NSF SGER, the progress is impressive. The aerosol-snow interaction is a complicated issue. The proposer intends to use IPY measurements to improve the representation of snowpack microphysical processes including melt scavenging, hoar formation, and impurity effects. Then the improved parameterization will be implemented in climate models to address the aerosol-snow interaction in the context of Arctic climate system. While no points in the proposal are necessarily original, integrating the data and model together still represents a useful and difficult task that is worth support. The real weakness of this proposal is the organization of the proposed activity. After carefully reading the proposal, it is still unclear exactly what tasks will be carried out. Section 3 discusses four goals and associated hypotheses, but it is unclear how each of them will be addressed by specific tasks. Section 4 (Tasks: Arctic models and observations) contains nine subsections (or tasks), but it is unclear what tasks will actually be done by the proposer's team. By mixing collaborations with the actual tasks that will be carried out by the proposer throughout the proposal, and by including too many materials using the minimum type size, the organization of the proposed activity is actually weakened. The proposer has sufficient access to the necessary resources. Furthermore, the proposed work is closely related to the IPY activities, which is a plus. Overall, with the proposer's experience and active collaboration with researcers involved in the Arctic science, some interesting results are expected. Rating for intellectual merit is: Very Good. What are the broader impacts of the proposed activity? The proposed project will train one graduate student; the PI will participate in three programs that provide opportunities to under-represented minorities; and the PI will participate in K-12 Outreach activities The PI is well-connected in the broad data and modeling community. The proposer will use results from other groups, while results from this project will be used in some of the major centers/institutions (NCAR, LANL, GISS). Rating for the broad impacts is: Excellent Summary Statement The proposer intends to use IPY measurements to improve snow/ice models used to understand and to predict pan-Arctic climate and climate change. The proposer is experienced in the proposed research area and well connected in the community. While it is certain that some work on data analysis, model revision, and climate modeling sensitivity will be done, it is unclear exactly what tasks will be carried out and how these tasks will address each of the four objectives and associated hypotheses in Section 3. The proposed work has broad impacts, as indicated by the interest of large number of collaborators on the proposed work as well as the integration of research results with educational activities. Based on equal weighting of the two criteria, the overall rating is: Very good/Excellent %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #2 Rating: Excellent REVIEW: What is the intellectual merit of the proposed activity? This is an ambitious proposal well timed with other IPY efforts. The proposal offers to examine the influence of ice and snow on the albedo feedback across the Arctic. The work makes use of measurements gathered for IPY, existing datasets and advanced techniques. The PI is well prepared to address these issues. The hypotheses and work plan are well developed. A number of letters of support indicate the importance of this work. What are the broader impacts of the proposed activity? The work will include one graduate student. The subject matter is of broad importance to understanding Arctic climate and global climate change. Summary Statement This project is an ambitious one, well suited to the PI. If only a portion of the work gets done, the contribution to our current understanding will be very strong. The broader impacts lie most heavily on the importance of the subject matter and the likelihood of the PI to succeed. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #3 Rating: Very Good REVIEW: What is the intellectual merit of the proposed activity? 1) What is the intellectual merit of the proposed activity? * The scientific and intellectual merit of the proposed work is strong. * The focus is on an important problem û the ice-albedo feedback in arctic regions. Snow impurities may significantly alter snowmelt and surface energy fluxes and if trends in energy consumption continue, LAC and MD may play an ever-larger role in determining the SSA. * Significant progress towards Objectives 1 and 3 are in reach during the lifetime of this proposal. Considering the other processes that play a role with respect to landscape level fluxes, Objectives 2 and 4 and more questionable. What are the broader impacts of the proposed activity? 2) What are the broader impacts of the proposed activity? The broader impact of this work will not be felt in the short-term. Until issues as snow heterogeneity have been dealt with û and the impact that heterogeneity has on landscape level melt and surface water/energy fluxes û this work may not lead to 'better' Arctic GCM simulations. However, of those processes that need addressing so that better simulations can be achieved - within the next decade û dealing with the deposition and impact of snow impurities should be a high priority Summary Statement Snow Process Studies and Modeling to Improve Arctic Climate Prediction Zender Assessment: Very Good Key words: Greenhouse gas (GHG) forcing Light absorbing carbon (LAC) Mineral dust (MD) Proposal Summary and Motivation Statements This project uses IPY measurements to improve cryospheric models used to understand and to predict pan-Arctic climate and climate change. We will (1) Use IPY field and lab measurements to improve representation of snowpack microphysical processes including melt scavenging, hoar formation, and impurity effects; (2) Implement and/or refine these processes in Arctic land, atmosphere, and sea-ice components of an Earth System Model (ESM); (3) Use the ESM to upscale and better quantify the efficacy of and response to Arctic climate forcing agents in the 20th and 21st centuries. Ice-albedo feedback is arguably the most important positive feedback in the polar climate system (e.g., Hartmann, 1994; Holland and Bitz, 2003; Qu and Hall, 2006). However, we are unaware of any coupled global models that account for realistic snow processes, including aerosol radiative interactions, throughout the surface Arctic Net BC [soot] impacts on the Arctic will likely increase through the 21st century. BC emissions from combustion, the primary source of Arctic BC (Koch and Hansen, 2005) have increased with fuel use over the past several decades, and are known to within a factor of about two (Bond et al., 2004). Some scenarios project an increase in anthropogenic BC emissions of 30-250 in the 21st century (Nakicenovic et al., 2000). Biomass burning emissions may increase due changes in fire regime though this is highly uncertain (Thonicke et al., 2005; van der Werf et al., 2006). Present day dust emissions and deposition are known to no better than about a factor of four globally (Zender et al., 2004). Whether there is currently a trend in global mineral dust emissions is not knownùincreasing (from anthropogenic activities) and decreasing (due to CO2 fertilization of vegetation) trends are both plausible (Mahowald and Luo, 2003; Tegen et al., 2004). Strengths: The proposed work identifies an important problem; that snow impurities, especially light absorbing carbon (LAC) and mineral dust (MD), can have a large impact on snow surface aging (SSA) - and that this impact has not been explored in any detail. Most snow models used in GCMs employ relatively simple parameterizations to capture snow surface aging, and the associated changes in the surface albedo. The most common parameterization is based on Army Corps of Engineering data taken a half century ago, where the snow surface albedo exponentially declines from ~.85 to ~0.5 after a number of days without snow freshening. The albedo is reset to 0.85 after a new snow event. This new work proposes to employ more physically robust algorithms to account for the surface albedo changes due to the effects of LAC and MD. The researches have the expertise to follow through on most of the objectives and present a logical progression of research: from incorporating SINCAR with the CSM, to using in-situ measurements, to incorporating remote sensing (MODIS). Weakness: There is no specific scientific weakness. However, the notion that this will significantly improve arctic simulations in the near term is debatable: 1) Contrary to the notion presented here, multi-layer models have been developed and are currently in use in GCMs û many owe much of their physics to SNOTHERM. They allow thermal profiles to develop, which in turn, impact internal thermal properties, etc. Offline testing has shown these models can reproduce the thermal profile within the snow, the insulation provided by the snow (by monitoring of ground temperatures), and can achieve the correct seasonal timing of snow melt. When coupled to GCMs, these models have also been able to reproduce simulate the aerial extent of seasonal snowcover. 2) Snow heterogeneity in the arctic is significant, and in some regions, show strong year-to-year variability. More to the point, this heterogeneity is below the MODIS length scale. Until this is resolved, it is questionable whether, inclusion of dirty snow will improve simulations significantly. 3) A still unresolved issue is that of the snow surface sublimation-condensation and the impact this has on the snow surface albedo and surface energy exchange. Will incorporating for the effects of LAC and MD significantly improve the surface and internal snow processes above and beyond what has currently been achieved? My own view is that until the heterogenity issue is properly dealt with, there will not be significant progress in representing landscape level surface water and energy fluxes at high latitude systems. Assessment: The two Merit Review Criteria used are: 1) What is the intellectual merit of the proposed activity? * The scientific and intellectual merit of the proposed work is strong. * The focus is on an important problem û the ice-albedo feedback in arctic regions. Snow impurities may significantly alter snowmelt and surface energy fluxes and if trends in energy consumption continue, LAC and MD may play an ever-larger role in determining the SSA. * Significant progress towards Objectives 1 and 3 are in reach during the lifetime of this proposal. Considering the other processes that play a role with respect to landscape level fluxes, Objectives 2 and 4 and more questionable. 2) What are the broader impacts of the proposed activity? * The broader impact of this work will not be felt in the short-term. Until issues as snow heterogeneity have been dealt with û and the impact that heterogeneity has on landscape level melt and surface water/energy fluxes û this work may not lead to 'better' Arctic GCM simulations. However, of those processes that need addressing so that better simulations can be achieved - within the next decade û dealing with the deposition and impact of snow impurities should be a high priority %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #4 Rating: Good REVIEW: What is the intellectual merit of the proposed activity? The proposer has excellent expertise in aerosol modeling, black carbon cloud physics, and radiative transfer modeling. There appear to be some knowledge gaps in the proposal regarding arctic snow, sea ice, and albedo. There is prior work on snow stratigraphy and snow albedo evolution that is relevant to the author's approach concerning the importance of metamorphism on large-scale albedo. Several excellent partnership are proposed (Domine, LANL, CCSM) that should be quite productive. Other partnerships beyond the laboratory and modeling communities might be useful. The proposal focuses on the impact of black carbon and of snow metamorphism on the albedo of polar snow. The case for the black carbon studies is strong. A little bit of black carbon goes a long way in reducing snow albedo. This could have significant impact on large-scale polar snow albedo, particularly in the Arctic with abundant northern hemisphere BC sources. The motivation for the metamorphism studies is less evident. A highly detailed approach to modeling temperature gradient metamorphism, surface hoar formation, and diurnal cycling and the consequence changes in the specific surface area of the snow is proposed. However, the impact of wind blown snow on snow microstructure is not considered. The proposal does a good job of identifying the large temperature gradients present in polar snowpacks and considering snow metamorphism. But what about the impact of wind and blowing snow on snow structure? This is a major factor in the small scale structure of polar snowpacks. More discussion of the observed properties of polar snowpacks would be helpful. Is extensive TG metamorphism evident? What do the observations of snowpack stratigraphy show? A few minor comments: 1. More explanation of "efficacy" would be helpful. In particular the idea of per unit forcing: how is a unit of CO2 compared to a unit of BC. What are the units used for comparison? 2. Extend the spectral measurements beyond 1310 all the way to 2500 nm. Good instruments are available over this spectral range. 3. Many other explanations, besides black carbon, have been given for the polar asymmetry in sea ice change. 4. A sentence describing Painter's method for quickly determining SSA would be useful. 5. Additional information on the optical measurements made in conjunction with the SSA observations would be useful. Spectral reflectance measurements are mentioned, but at what angle and instrument field of view? Will a suite of reflectances at different zenith and azimuth angles be measured? How about transmission measurements? What are the broader impacts of the proposed activity? The scientific broader impacts are very clearly defined. SNICAR model seems to be a powerful tool and a vital part of this proposal. There is excellent integration of models on different scales from SNICAR to CICE to CCSM. Outreach activities include a graduate student and a post-doc. Other broader impacts are limited. Summary Statement %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #5 Rating: Excellent REVIEW: What is the intellectual merit of the proposed activity? Snow cover strongly interacts with Arctic climate through snow-albedo feedback. Snow aging due to deposition of dust and black carbon (soot) may strengthen the feedback. The proposed work addresses very important questions with regard to the impacts of various aerosols on snow surface albedo, Arctic sea ice ablation, and Arctic warming. The proposed activities will certainly advance our understandings of the relative contributions of snow-albedo feedback and aerosols in snow to Artic warming, compared to the contributions of GHG forcings. The PI of the proposal is well qualified for leading the project as reflected from his extensive experience in global aerosol modeling, ice cloud physics, and aerosol optics as well as his previous NSF SGER projects, which developed the SNICAR. The proposed activities include lab experiments, field experiments, model simulations, and extensive uses of satellite data through collaborations with national and international scientists. The activities are well conceived and organized as evidenced from letters of his collaborators. However, I would like to suggest the proposer acknowledge the efforts in snow model developments during the past decade and developments in representing other important snow cover processes, such as, subgrid distributions of snow cover, vegetation sheltering effects on snow cover, blowing snow, etc.. The snow model in CLM is a multi-layer and physically-based model and represents one of the best snow models in current GCMs. What are the broader impacts of the proposed activity? The proposed studies will have broader impacts on researches including Arctic climate, terrestrial hydrology, wetland and CH4 emissions, and biology. It may contribute to IPCC assessments of various anthropogenic forcings. I am certain that the advanced SNICAR will be incorporated into the NCAR community atmosphere and land models and also be released to broader users. Additionally, the project will provide trainings to a undergraduate student from underrepresented minorities, post-doc, and K-12 teachers. Summary Statement The proposed work addresses very important questions with regard to the impacts of various aerosols on snow surface albedo, Arctic sea ice ablation, and Arctic warming. Overall, the proposal is very well written and the proposed research activities are well organized. The PI has actively interacted with various scientific communities and is well qualified for leading the proposed activities. I suggest the PI be aware of activities in snow modeling communities during the proposed period if it is funded. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #6 Rating: Good REVIEW: The proposal is not written in accord with NSF guidelines. The PI missed to include full citations of publications and presentations resulting from awarded or funded NSF project. The proposal makes the impression being written with a hot pen. Abbreviations are used before being explained some paragraphs later; the formatting is very messy and the small hard to read. Furthermore, asking for an unfunded wrap-up to begin with means that the work cannot be done in the time funding is asked for. Where does the funding for the unfunded wrap-up come from? Is here already the supplemental proposal thought off and planned for because the call does not permit him to ask for 4 years or the cost becomes too high that he thinks it would reduce the chances for getting funded at all if funds are asked for 4 years? Over wide ranges of the proposal it remains unclear whether the main purpose is to further develop SNICAR or develop a better snow module for CCSM. According to the proposal title the PI obviously is not a climate modeler also he claims so. In climate modeling, one talks about climate projections or scenarios, but not climate prediction. The research on the scientific background is well done with respect to snow physics models, but not with respect to CCSM. To my best knowledge CCSM uses CSIM as sea-ice model, not CICE. CCSM has no glacier model component. It treats glaciers as one of four land-cover types within the framework of the Common Land Model. Despite reading the proposal several times it remains foggy whether the intent is to develop an improved snow module for CCSM or implementing SNICAR with CICE. Despite these technical writing aspects are not NSF criteria, I think that they are worth mentioning. My following evaluation bases on NSF criteria only. However, I may have missed some important points/aspects due to the way the proposal is written. Scientific merit: The proposed work will integrate observational studies, high resolution snow pack modeling and climate modeling to improve snow process modeling in Arctic climate predictions and to investigate the snow-aerosol feedback. Including temperature gradient driven snow metamorphism in CCSM is of high scientific merit because the current snow physics are very simple. It will provide a more realistic description of snow processes in Polar Regions. The PI intends to identify the contributions of aerosols and greenhouse gases as Arctic forcing agents and to identify their impact on snow-pack life cycle. Doing so is of high merit for any snow physics models. The proposed work would provide the first dataset that holds simultaneous SSA and reflectance measurements. Such a dataset may be of importance for better understanding snow processes and development of new parameterizations. The methods suggested remain very foggy to unclear with respect to how the different scales are to be bridged and how the different data sources and models are to be linked. Furthermore, there are several unaddressed issues. Most snow packs in the Arctic do not experience melt/freeze cycles before spring. May be it is just misunderstandably written, but except for high elevation, snow on the Arctic sea-ice and Greenland there is no snow in the Arctic that can be affected by particles from boreal forest biomass burning that occurs in summer only. Other shortcomings are that no sea-ice retreat effects and interaction with soot will be considered because the PI intends to use sea-ice from data. This means that in simulations without soot effect the soot is indirectly included in the forcing data for the lower boundary condition. The observed sea-ice distribution as is/was evolved with the soot. Moreover, recent studies showed that the sea-ice retreat is dynamically driven. The model physics of CLM and SNICR differ. I am worried about inconsistencies in the radiative transfer as treated in the two models and their impacts on the results. I am well familiar with Domine s data, they are excellent and well documented. They are suitable for snow physics models, but I strongly believe that they are unsuitable for the purpose of the proposed study. A snow physics model is too complex to be run in CCSM with today s computational resources. In summary, the idea is good, but not achievable in the anticipated timeframe with the group size in mind. It is high risk because the PI depends on so many other people to get funding to provide elements he needs to do the proposed work. I highly recommend cleaning the proposal up, adding more human power and resubmitting a revised proposal as a collaborative proposal. I would rate the proposal good if all the others were funded, undoable if not. Rating: average Broader impact: Broader impacts come more like an after thought than being a real part of the proposal. Having a graduate student and a undergraduate student will mean great broad impact on the society. He is also involved in school curricula. Improving the education at the high school level is an urgent need for staying competitive internationally. Thus, his work has a broad impact along this line. Rating: good %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #7 Rating: Very Good REVIEW: What is the intellectual merit of the proposed activity? I believe this proposed work would represent a significant contribution in advancing our knowledge regarding light absorbing carbon, dust, etc. in snow-covered regions. Indeed, these processes are currently not being considered in general circulation models (GCMs), which represents a significant shortcoming as these are crucial processes. In fact, most crucial high-latitude processes are not currently considered in GCMs, therefore any activity to improve these shortcomings is highly commendable. The proposer and his collaborators are certainly highly qualified to conduct this work, as it represents their primary specialty. This is reflected in both their previous work, as well as their overall 'reputation.' This represents the 'strengths' of the proposal. In terms of potential weaknesses, it seems some of the observational data that will be used to improve modeling capabilities will be based on laboratory experiments (section 4.2)? While this is certainly useful, I would prefer to see more emphasis placed on actual real world observations. This is a minor weakness, since obviously it is not feasible or even possible to come by actual observations for many of the parameters to be evaluated, and the proposers will use actual in situ observations as well, as outlined in sections 4.3-4.8. Lastly, a concern I have with the proposal is that there is no statement regrading the proposer's data management/archiving plans (I thought this is now required by NSF?). Hopefully any/all data sets and data products to come out of this investigation would be properly archived at a place like the National Snow and Ice Data Center, and therefore freely available to the scientific community? What are the broader impacts of the proposed activity? The broader impacts of this proposed project are also highly commendable. Not only does much of the proposal utilize work done by a student (SNICAR), but an additional student as well as a postdoc will be trained as a result of this work. The PI trains K-12 teachers in Earth Sciences, and including the type of science that is considered in this proposal would certainly contribute greatly to the training of teachers and hence K-12 students. As also stated in the proposal, UC-Irvine is known for its minority participation and involvement. In addition to these obvious educational impacts of the work, as stated above in the intellectual merit section, this work has the potential to significantly improve modeling capabilities, and hence our abilities to predict future high-latitude changes. I believe this proposal to have excellent broader impacts. Summary Statement In determining my overall evaluation of this proposal, I placed ~2/3 emphasis on the intellectual merit, and ~1/3 on the broader impacts. Overall, I find this proposal to be very good given the potential impact it may have on improving modeling capabilities. Furthermore, this proposal ranks very highly in terms of its broader impacts. I believe this proposed work should be supported if at all possible. } % end csznote \csznote{ % Reviews of 2005 submission: Proposal Number: 0612954 Performing Organization: U of Cal Irvine NSF Program: Arctic Natural Sciences Principal Investigator: Zender, Charles S Proposal Title: Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks Rating distribution: 3 Excellent (#s 1, 2, 5), 1 Very Good (#s 3), 1 Good (#s 4) (Review #3 was most informative, not counted due to conflict of interest) Panel Summary #1 The overall objective of this proposal is to estimate the influence of absorbing aerosol (biomass burning, soot, and dust) deposition to Arctic snow and sea ice on Arctic climate. The proposal seeks to use a GCM coupled snow model (SNICAR) developed by the PI that will estimate the deposition and transformation of absorbing aerosols to snow. As pointed out by the PI, snow chemical/physical dynamical properties are extremely important and the model does take these processes into consideration. Although a weakness is that there are probably many parameterizations that need to be validated (i.e. the link between snow chemistry and grain growth). The proposal will also bring satellite retrieval of grain size and absorbing aerosol into play, and will couple with other IPY proposals (Very Good). The panel had a chance to review the proposal and the mail reviews. The mail reviews were largely positive (3 Excellent, 1 Good) which cites the potentially significant impact of this work on climate feedbacks related to black carbon. This also brings a new researcher into the study of arctic research and modeling. The Good review cites the limitations of obtaining the sensitivity of albedo from satellite data based on a small change in snow from black carbon. However, the panel sees this as an improved step to attempt to validate this BC effect over what previous GCMs have modeled in overly simple methods. The proposed PI has a good record of making connections and tools for the GCM community that will help in the integration of this physics into GCMs. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #1 Rating: Excellent REVIEW: What is the intellectual merit of the proposed activity? Will clarify a role of black carbon on the Arctic snow and ice and on the Greenland ice sheet. What are the broader impacts of the proposed activity? Improved understanding of the role of black carbon on climate. This is essential to understand many questions connected to Arctic climate; for example why is the melt area of Greenland ice sheet increasing? Summary Statement Very well written proposal that will impact our knowledge concerning Arctic climate. PI is well qualified to accomplish proposed research. The results will be important for future AOGCM modeling. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #2 Proposal Number: 0612954 Performing Organization: U of Cal Irvine NSF Program: Arctic Natural Sciences Principal Investigator: Zender, Charles S Proposal Title: Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks Rating: Excellent REVIEW: What is the intellectual merit of the proposed activity? 1.) Intellectual Merit: This proposal addresses an important element of Arctic System energy exchange, the effect of soot and dust on the albedo of snow and ice. It is a model improvement proposal (i.e., there is no field work) and the outcome is a diagnostic or process model (SNICAR) that will inform (but not be directly incorporated into) large climate system models. That is probably a good thing as the topic is complex and the larger models are not yet ready to for the level of sophistication that the investigators should be able to achieve in their work (and which is needed to understand the effect of the soot and dust). One of the strengths of the proposed work is that the investigators have been working on the topic of soot, dust, and snow for some time. They have produced a solid string of papers on the topic, are well connected to other modeling efforts, and have developed a structure through which they can disseminate their findings. In short, the proposed work is likely to be effective in producing results on a topic of importance. It should be funded. What are the broader impacts of the proposed activity? 2.) Broader Impact: There is some evidence to suggest that fires and dust storms could have as great, or a greater impact, on the arctic climate than greenhouse gases. Given the intense national and international efforts to identify and quantify the impact of greenhouse gases on climate, it is equally important to investigate these other sources of climate forcing. It is also important that we understand how these forcings work: the devil may be in the details. The investigators have a reasonable chance of producing an advance in this area by focusing on the topic, producing a stand-alone model, and incorporating the results into the larger model CCSM. Summary Statement 3.) Recommendation: Excellent proposal. Fund it. But here are some suggestions the investigators should consider: a. Snow grain size and soot: What is the evidence that soot or dust change the process and growth rate of equilibrium snow grains? My sense is that this is a first order thermal process driven by spring weather with percolation of melt water far more important than microscopic amounts of soot. By all means, the investigators should look at whether the soot produces enough radiation heating to materially affect the grain size, but I doubt it. They should keep in mind that on the sea ice and on the tundra, the melt comes on so fast, there isn't that much time for minor differences in albedo to have much of an impact. b. Mixed Pixels: How do the investigators plan to deal with this issue? We know that for sea ice the distribution of melt ponds and open water has a profound impact of the 'scene albedo'. Similarly, for tundra and taiga, bare patches and exposed vegetation lowers the scene albedo more than soot or dust. Only in Greenland is the mixed pixel problem less important. The proposal has a major focus on Greenland, but the investigators should not neglect the rest of the Arctic terrestrial and marine area. c. Where is the soot and when? The snow aging section of the proposal seems overly simplistic to me (and I have watched a lot of arctic snow melt, on land, on sea ice, and on glaciers). My suggestion is to go at this complex set of events using time as a weighting factor. O.K., each melt season is different, but broad generalizations can be made. How much time does the snow spend in the pre-melt period? How much solar energy is available during that phase? Does downward percolation remove (or concentrate) the soot quickly? Once scrubbed of soot, how long is the surface snow clearer and therefore higher in albedo? I urge the investigators to confirm with experts on the melt and keep an open mind as to what is and isn't important. For example, why is sintering important with respect to soot/dust and albedo? Seems like a less important process than many others. There are also scaling issues inherent in this issue. The processes take place locally, at scales that are order meters. The modeling seems to be at scales that examine tens of kilometers. How to ensure that the latter aggregate linearly at the latter scales? The collaboration with the Warren-Grenfell group is a step in the right direction, but even a closer connection with snow process researchers would help guide the proposed research better. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #3 Proposal Number: 0612954 Performing Organization: U of Cal Irvine NSF Program: Arctic Natural Sciences Principal Investigator: Zender, Charles S Proposal Title: Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks Rating: Not Available This review was determined to represent a Conflict of Interest and was NOT used in the decision-making process. REVIEW: What is the intellectual merit of the proposed activity? The goal of this project is "to use a theoretical radiative model for snow, ice, and aerosols to assess absorbing aerosol interactions in the coupled Arctic climate system using models which represent the complex surface lifecycles of Arctic snow, black carbon, and dust." Intellectual merits of the proposed activity are listed as follows: a) Potential for Arctic BC to disrupt summertime sea-ice formation. b) Role of boreal fire variability in Arctic reflectance, particularly Greenland c) Relative roles of Arctic soot and dust in inducing surface melt, heating and darkening. d) BC content is to be addressed in the context of preindustrial, present day, and next century time scales. Strengths û This study would contribute to the development of a significant model for understanding small scale snowpack metamorphism processes affecting grain growth, their interaction with BC deposited in the snow, and the implications for snowpack albedo. Zender and colleagues have already done a significant amount of development on the basic snow model. While there are still significant issues to check with respect to equi-temperature and temperature-gradient metamorphism, I feel that this model is coming along nicely and would be quite useful for (i) interpreting surface-based and satellite observations of BC-laden snow and (ii) coupling the evolution of snowpack properties driven by radiative and for convective forcing with wavelength integrated and spectral albedo. Including better physics into GCM models is critical if we expect them to actually work well one day. Weaknesses û I found the proposal somewhat diffuse and lacking in clarity in a number of places. Based on my field and theoretical experiences, I feel that there are also a number of misconceptions about the physics of sea ice and its snow cover that will need to be addressed. These are not critical for the work done to date on the model, but they will need to be handled properly. My concerns are covered in more detail in the summary statement. The proposer should know about two existing surface based observational data sets of BC in the Arctic snow pack which indicate that the BC levels in the Arctic are very likely lower now than in the early 1980's. The statement that the BC levels in the snow are increasing steadily thus needs to be clarified. I don't see any mention of the SNOWTHERM model, which I suspect has large areas of overlap with SNTHERM could be made to host SNICAR. the proposed UCI model. Specific comments: Summary and in the Project Description: "to disrupt summertime sea-ice formation û I'm wondering what the deal is here. Summertime in the Arctic Basin is broadly defined as the season when the sea ice is melting, and it's well documented that melting is by far the dominant mode during June, July, and the first half of August. The potential for BC would be to extend the length of the melt season rather than inhibit ice formation during that time. 'BC is slowly increasing in the Arctic'. The author appears to be unaware of two surface-based studies of BC (Grenfell, Light, & Sturm, JGR, 2002; Grenfell & Perovich, JGR, 109, 2004) showing that present BC levels on certain areas of Arctic sea ice are significantly lower than in the early 1980s. Lowering the albedo of the ice grains themselves û unclear. Ambiguous use of albedo? Single scattering albedo is not the same as surface albedo. Figure 1 is obscure and confusing. You should at least explain the symbols and expand the description in the caption. What does it mean to "mediate" a feedback. Does this mean 'cause' or 'suppress'? Figure 2 û In JJA there isn't much snow on the Arctic sea ice. In addition to the fact that the snow cover melted away in early to mid June (this was well documented during the SHEBA experiment in 1998), there were large areas of open water at the margins of the basin during most of the JJA period in both 1997 and 1998 where the model shows large BC forcing. I could see large forcing from forest fires in say AMJ, but after that, in the open water, the albedo is already 0.067 or so, all due to surface reflectance. So adding soot, even in rather large quantities, should make very little difference. If the model simply assumed the presence of snow and calculated forcing on that basis, the numbers aren't particularly meaningful. The plots in figure 2 thus needs explanation. Internal snowpack melt. Brandt and Warren studied this in detail, JGR, 39, 1993, and concluded that the solid state greenhouse effect in snow is very small at best. They showed that a detailed spectral radiative transfer model must be used in an analysis of this effect. Personally, I have never observed significant internal snowpack melt that wasn't accompanied by surface melting. Although one can no doubt construct plausible scenarios, I don't think it is an important medium to large-scale effect 'Previous effortsà disallow some important feedbacks that occur in Nature (sic)' û What do you have in mind here? 'will result in a more complex and interactive Arctic system whereà' û it's not the system but the model that will be more complex. By 'boreal soot' I assume you mean soot from boreal fires. Boreal simply means "northern", of course, which could be the meaning here, but it would be redundant since your focus is limited to boreal areas. It would be good to be a bit more precise about what would actually be studied. Figure 4 û what's all the nomenclature at the top of the right-hand panel. "due to 1998" û what is it about the year 1998 that affected r(eff)? Atmospheric BC cools the surface by backscatteringà - I think backscattering from soot is negligible compared with the cloud droplets that are quite ubiquitous in the Arctic summer atmosphere. I'm sure any cooling of the surface is virtually all due to absorption. Artic haze looks dark. Thinnest ice category is the most susceptible.. û the thinnest ice has very little snow on it as a rule. Significant changes appear as cross hatched regions in Figures 4 and 5 û Be careful here. It is improper statistical technique to say that part of a distribution is significant in the context of analyzing the whole data set, i.e. that there are certain areas in a pattern with "significant variations" relative to other areas. The statistical test tells you whether you can believe your hypothesis for the entire distribution, not just for a part of it. In a noisy comparison, one can almost always find a big variation that are apparent, but its significance isn't a local matter. The analog in one dimension is does a linear regression match the data to say the 95\% confidence level, for example. In that context you can't say anything meaningful about a subset of the regression that matches particularly well (or poorly). Pg 12 û Since soot concentrations rarely exceed 5 micrograms/kg in Greenland û this is probably true, but how do you know what the value is? This is pretty close to the background value for the snow on sea ice in the central Beaufort Sea. Are these relatively low values rarely exceeding 5 consistent with a steady increase with time? What are the broader impacts of the proposed activity? Broader impacts of the proposed activity. Listed are: a) Understanding of Arctic hydrology b) SNICAR model would be made widely available to the scientific community û in the CCSM and in independent form for process study applications. c) Contributions to glacier mass balance, basin and catchment hydrology, sea-ice lifecycle, paleoclimate sensitivity, and snow chemistry. d) K-12 teacher training program and presentations for lifelong learning students. These are all valid broader impacts, and I think the proposer would make a serious effort on the outreach component of the project. However, I think that more important than any of these are the implications of BC for large scale radiative forcing of the arctic atmosphere and the surface snow cover. I note that a BC content of 5 micrograms/kg is not enough to lower the snow albedo by 0.01, a level that is comparable to the observational uncertainties of the albedo of snow-covered surfaces. Summary Statement I rate this proposal as VG minus. I think it would be very valuable to have a working model of this sort available to the geophysical community at large, and I believe this snow model makes a serious effort to include the physics of snow metamorphism correctly. I have some reservations related to the comments I provided in the intellectual merit section that need to be addressed. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #4 Proposal Number: 0612954 Performing Organization: U of Cal Irvine NSF Program: Arctic Natural Sciences Principal Investigator: Zender, Charles S Proposal Title: Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks Rating: Good REVIEW: What is the intellectual merit of the proposed activity? This proposal focuses on absorbing aerosol interactions in the Arctic climate system using the SNICAR model for the complex surface lifecycle of Arctic snow, black carbon, and dust. The study field is limited within Greenland, which is not representative of the pan-Arctic. The PI addresses the effect of dirty snow on land, glaciers and sea-ice and the role of the Arctic BC; however, the proposal is insufficient in its the presentation on land and glaciers. For example, the snow density, snowpack temperature and snow water equivalent (SWE) along the tundra and boreal forest of Alaska during the winter change remarkably with time, and AMSR-E retrieval is quite different from in-situ data for the representation of SWE, which is due to thicker wind clusters (> 10 cm) and well-developed depth hoar at bottom snowpack despite less than 40 cm snow depth in the tundra of Alaska. For these reasons, I questioned the SNICAR is questioned model can reproduce the Arctic climate system sufficiently. What are the broader impacts of the proposed activity? Because this proposal includes unpublished citations, such as AGU abstracts and submitted manuscripts that may play significant roles in assessing the Arctic climate system, it was difficult to review. The objectives and hypotheses in the proposal regard the fate of Arctic BC related to boreal fires. In fact, many scientists have described and demonstrated the importance of anthropogenic BC rather than wildfire BC, which makes up a much smaller fraction than the fossil fuel and biofuel BC in Asia. According to Koch and Hansen (2004), the BC deposition in Greenland is dry, not wet scavenging as the PI described. The SNICAR model that merged sea-ice and ice-sheet models is in the PI's responsibility, not graduate students'. Lacking are any innovative programs with broader impacts that involve recruiting minority students or related activities. Summary Statement Overall, I found the goal to be admirable, but the project to be unfundable. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Review #5 Proposal Number: 0612954 Performing Organization: U of Cal Irvine NSF Program: Arctic Natural Sciences Principal Investigator: Zender, Charles S Proposal Title: Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks Rating: Excellent REVIEW: What is the intellectual merit of the proposed activity? The proposal addresses determination of the spectral snow albedo dependence on snow physical parameters such as snow age, depth, density, temperature, temperature gradients, grain size, and more importantly, absorbing (soot) inclusions. Since the interaction of the different physical parameters is decidedly non-linear, a comprehensive radiative transfer model (that also can model the atmospheric spectral dependence) is required to properly analyze the interactions. The modeling of snow over sea ice will also be addressed in the proposed research. What are the broader impacts of the proposed activity? Accurate simulation of snow albedo over different surface types is important for accurate climate change modeling, especially in the polar regions where the local surface heating and cooling rates have a strong influence on snow-ice feedback effects. A good benchmark model is needed before effective parameterizations for snow albedo dependence on snow physical properties and soot inclusions can be accurately included in GCM simulations. A good snow albedo model is also needed to effectively interpret satellite (MODIS, MISR) retrievals of snow covered areas. Summary Statement The investigator has previously demonstrated expertise in modeling and interpreting spectral measurements of snow albedo, as well as expertise in radiative transfer modeling of aerosol and cloud effects on the downwelling radiation field. A quantitative determination of soot inclusions on snow albedo is important for improved climate change modeling in the polar regions. There is adequate post-doc and graduate student participation in the proposed research project. The proposed research tasks are well defined. I therefore recommend that the proposal be funded. } % end csznote \csznote{ # Dozier's pubs http://www.snow.ucsb.edu/REASoN-Publications.htm # Email distribution list Siri Jodha Singh Khalsa , Tom Painter , Jim Randerson , Mark Flanner , Jorge Talamantes , Charlie Zender % Letter sent to Bill Lipscomb 20051215: Dear Bill, Thanks for discussing collaborations on the phone yesterday. As I said, I am following up with a one page summary of our proposed NSF project, and some sample text should you choose to endorse our collaboration more formally with NSF. Please feel free to ruthlessly alter/edit! The below text is meant to save you time, not to constrain you or to obligate you to do work you're not already interested in. If you choose to write such a letter, it would be most helpful if you could e-mail me a PDF on your letterhead by Wednesday morning 12/21, although a simple e-mail will suffice if you are too busy for the deluxe PDF option. Thanks! Charlie "Dear Charlie, As one of the principle developers of the Los Alamos sea-ice model (CICE), a primary component of the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM), I have a great interest in understanding and correctly representing the coupled cryosphere in Earth System Models. Your proposed NSF project "Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks" would continue to advance the state-of-the-art in snowpack radiative transfer which provides an important upper boundary condition in the cryosphere. We are happy to collaborate with you and Mark Flanner in two related areas during the NSF project. First, we are implementing a multi-layer snow thermodynamic model to improve the current single layer snow model in CICE. We will provide source code access and advice on technical details so that your project can merge SNICAR physics for snowpack aging and radiative processes into CICE. Second, we are developing an interactive ice sheet component, based on GLIMMER, for the CCSM. We envisage using the a form of the Community Land Model (CLM) and its snow model with SNICAR as the upper boundary condition. The integrated treatment of clean and dirty snow in the cryosphere which will result from our complementary efforts will provide a state-of-the-art toolkit with which to study the scientific questions posed in your proposal." % Letter sent to Steve Warren 20051215: Dear Steve, As I said, I am following up with some sample text should you choose to endorse our collaboration more formally with NSF. Please feel free to ruthlessly alter/edit! The below text is meant to save you time, not to constrain you or to obligate you to do work you're not already interested in. If you choose to write such a letter, it would be most helpful if you could e-mail me a PDF on your letterhead by Wednesday morning 12/21, although a simple e-mail will suffice if you are too busy for the deluxe PDF option. P.S. As I think you already know, SNICAR uses optical properties based on your and Grenfell's work on the surface-area-to-volume Mie approximation. Thanks again for developing this useful approximation! Charlie "Dear Charlie, As you know I have a great interest in understanding snow, ice, and Arctic aerosol properties and I approach these topics from a combination of laboratory and in situ measurements combined with radiative transfer modeling. Your proposed NSF project "Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks" would continue to advance the state-of-the-art in snowpack radiative transfer which provides an important upper boundary condition in the cryosphere. Moreover, your coupled snowpack aging and radiation model, SNICAR, is a very suitable tool for hind-casting the snowpack BC concentrations in Greenland which our group continues to measure. Our newly proposed NSF project "title goes here" will fund us to measure BC from snow samples collected at all Koni Steffen's AWS sites, which will provide good coverage of the Greenland ice sheet. We will be pleased to provide your project with these measurements and to collaborate on their interpretation. The synergy between our measurements and your modeling project to understand and represent Arctic snow-ice-aerosol feedbacks is exciting." % Letter sent to Phil Rasch 20051215: Hi Phil, We are writing an NSF Arctic proposal called "Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks". The proposal would fund a grad student, and a one year postdoc which Mark Flanner can use as bridge funding. The goal is to continue studying dirty snow, both BC and dust, and to move to the next level of feedbacks by really including sea-ice and glacier effects. We think we have land pretty well covered, and will work with Bill Lipscomb (and probably Bruce Briegleb, although I'm a bit unsure where that stands) on the sea-ice and glacier coupling to SNICAR. Mark is beginning to write a boreal soot manuscript on which you will be offered co-authorship. Not making you an official co-author on the study Mark presented at AGU was an oversight! Although we fixed it in the title slide your name was not in the program. Apologies. Back to the present. This proposal is due Thursday 12/22 (I got an extension to improve the collaboratory aspects of the proposal). This proposal will rely heavily on continued use of your BC aerosol model in CAM. I am verifying that we can rely on you for this, and that I can mention this in the proposal's collaborative portion. Is there anything I should know or that you'd like to add? Attached is the current one-page summary FYI. Thanks, Charlie % Letter sent to Natalie Mahowald 20051215: Hi Natalie, We are writing an NSF Arctic proposal called "Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks". The proposal would fund a grad student, and a one year postdoc which Mark Flanner can use as bridge funding. The goal is to continue studying dirty snow, both BC and dust, and to move to the next level of feedbacks by really including sea-ice and glacier effects. We think we have dirty land-snow pretty well covered, and will work with Bill Lipscomb (and probably Bruce Briegleb, although I'm a bit unsure where that stands) on the sea-ice and glacier coupling to SNICAR. This proposal is due Thursday 12/22 (I got an extension to improve the collaborative aspects of the proposal). This proposal will rely heavily on continued use of Phil's BC aerosol model in CAM and on your dust code, if you agree. May we rely on you for code access and advice as has been our habit? It would be especially cool to include your glaciogenic dust sources (pun intended). Of course, we'll offer you co-authorship on any of the dust-related results. If you agree, may I mention this in the proposal's collaborative portion? Is there anything I should know or that you'd like to add? Attached is the current one-page summary FYI. Thanks, Charlie } % end csznote \csznote{ % Proposal preparation: /bin/cp ${DATA}/ps/bio_nsf.pdf ${DATA}/prp/prp_ans/prp_ans_cv_zender.pdf # Summary + Project Description + References pdftk A=${DATA}/ps/prp_ans.pdf cat A4-33 output ${DATA}/prp/prp_ans/prp_ans.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A22 output ${DATA}/prp/prp_ans/prp_ans_scd.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A4 output ${DATA}/prp/prp_ans/prp_ans_smr.pdf # Collaborator tasks pdftk A=${DATA}/ps/prp_ans.pdf cat A14 output ${DATA}/prp/prp_ans/prp_ans_tsk_domine.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A17 output ${DATA}/prp/prp_ans/prp_ans_tsk_hunke.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A19 output ${DATA}/prp/prp_ans/prp_ans_tsk_koch.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A20 output ${DATA}/prp/prp_ans/prp_ans_tsk_painter.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A20 output ${DATA}/prp/prp_ans/prp_ans_tsk_rasch.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A21 output ${DATA}/prp/prp_ans/prp_ans_tsk_warren.pdf # Divvy up master PDF into FastLane components pdftk A=${DATA}/ps/prp_ans.pdf cat A4 output ${DATA}/prp/prp_ans/prp_ans_smr.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A5-19 output ${DATA}/prp/prp_ans/prp_ans_dsc.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A20-25 output ${DATA}/prp/prp_ans/prp_ans_rfr.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A26-27 output ${DATA}/prp/prp_ans/prp_ans_bdg_jst.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A28 output ${DATA}/prp/prp_ans/prp_ans_fcl.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A29-30 output ${DATA}/prp/prp_ans/prp_ans_abb.pdf pdftk A=${DATA}/ps/prp_ans.pdf cat A31 output ${DATA}/prp/prp_ans/prp_ans_clb.pdf # Add letters of support in one command rather than loop # pdftk does not allow input file to be output file pdftk \ A=${DATA}/prp/prp_ans/prp_ans_ltr_domine.pdf \ B=${DATA}/prp/prp_ans/prp_ans_ltr_hunke.pdf \ C=${DATA}/prp/prp_ans/prp_ans_ltr_koch.pdf \ D=${DATA}/prp/prp_ans/prp_ans_ltr_rasch.pdf \ E=${DATA}/prp/prp_ans/prp_ans_ltr_warren.pdf \ F=${DATA}/prp/prp_ans/prp_ans_ltr_polarcat.pdf \ cat A B C D E F output ${DATA}/prp/prp_ans/prp_ans_ltr.pdf # Add supplementary files in one command rather than loop # pdftk does not allow input file to be output file pdftk A=${DATA}/ps/prp_ans.pdf \ B=${DATA}/prp/prp_ans/prp_ans_ltr.pdf \ C=${DATA}/prp/prp_ans/prp_ans_cv_zender.pdf \ D=${DATA}/prp/prp_ans/prp_ans_cp_zender.pdf \ cat A B C D output ${DATA}/ps/prp_ans_fll.pdf # Suggested reviewers: Bruce Briegleb Jeff Dozier Alex Hall Mark Jacobson Bonnie Light } % end csznote on proposal preparation \csznote{ % Usage: Place usage here at end of file so comment character % not needed cd ~/prp_ans;make -W prp_ans.tex prp_ans.dvi prp_ans.ps prp_ans.pdf prp_ans.txt;cd - scp ${HOME}/prp_ans/prp_ans.dvi ${DATA}/ps/prp_ans.pdf ${DATA}/ps/prp_ans.ps ${HOME}/prp_ans/prp_ans.tex ${HOME}/prp_ans/prp_ans.txt dust.ess.uci.edu:/var/www/html/prp/prp_ans # NB: latex2html works well on prp_ans.tex latex2html -dir /var/www/html/prp/prp_ans prp_ans.tex # NB: tth chokes on prp_ans.tex cd ${HOME}/prp_ans;tth -a -Lprp_ans -p./:${TEXINPUTS}:${BIBINPUTS} < ${HOME}/prp_ans/prp_ans.tex > prp_ans.html scp prp_ans.html dust.ess.uci.edu:/var/www/html/prp/prp_ans # NB: tex4ht works well on prp_ans.tex cd ${HOME}/prp_ans;htlatex prp_ans.tex scp prp_ans*.css prp_ans*.html dust.ess.uci.edu:/var/www/html/prp/prp_ans # NB: tex4moz works well on prp_ans.tex cd ${HOME}/prp_ans;/usr/share/tex4ht/mzlatex prp_ans.tex scp prp_ans*.css prp_ans*.html prp_ans*.xml dust.ess.uci.edu:/var/www/html/prp/prp_ans } % end csznote on usage \end{document}