% $Header$

% Purpose: Proposal for NASA Interdisciplinary Science (IDS) Dirty Snow project
% Funded as NASA International Polar Year (IPY) Dirty Snow project
% Re-write of NSF ANS 2005 proposal prp_ans.tex with new Hydrology component

% URLs:
% http://dust.ess.uci.edu/prp/prp_ids/prp_ids.pdf
% http://dust.ess.uci.edu/prp/prp_ids/prp_ids_fll.pdf
% http://www.nasa.gov/mission_pages/IPY
% NASA NRA Guidebook: http://www.hq.nasa.gov/office/procurement/nraguidebook/
% CVS: cvs -d :ext:esmf.ess.uci.edu:/u/zender/cvs co -kk prp_ids

% Usage (see also end of file):
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% Submitted 2006 as NASA IDS in response to Appendix 14 of ROSES announcement
% IDS Subelement 5: Aerosol Impacts on Clouds, Precipitation, and the Hydrologic Cycle
% Program Manager, IDS subelement 5: Phil DeCola (202) 358-0768, pdecola@nasa.gov

% Awarded 2006 as NASA IPY in response to Appendix 15(?) of ROSES announcement
% Program Manager, IDS/IPY: Hal Maring <hal.maring@nasa.gov>
% Program Manager, IPY: Seelye Martin <seelye.martin-1@nasa.gov>

% Total 3-year budget request: $677,725
% Total 3-year budget award: $607,000
% NASA URL: 
% NASA Proposal number: 06-IDS06-0052
% NASA PR number: 4200213998
% Grant number: NNX07AR23G <-- Acknowledge this number in publications
% Project duration: 20070802--20100801
% Annual progress report deadlines: 20080801, 20090801, 20101101 (I made these up)
% UCI account number: 9-number-fund-sub-object = 9-123456-12345-1-1234 = 9-445925-23227-1-1234
% Physical Sciences budget code: 2019

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\begin{document}

\def\prpttl{Black Carbon Impacts on Cryospheric Climate Sensitivity and Surface Hydrology\\}
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{\noindent%
Web: \url{http://dust.ess.uci.edu/prp/prp_ids/prp_ids.pdf}\\
NASA International Polar Year (IPY) Proposal \hfill Submitted: April~17, 2006\\
Last modified: \today, \xxivtime \hfill Funded: May~17, 2007}
\begin{center}
\textbf{\Large\prpttl}
\bigskip
Dr.~Charles Zender, Dr.~Jay Famiglietti, Dr.~James Randerson\hfill \\
Department of Earth System Science, University of California, Irvine \hfill \\
Dr.~Siri Jodha Khalsa\hfill \\
National Snow and Ice Data Center, Boulder, Colorado\hfill \\
\end{center}
\vskip 0.5 cm

\noindent\textbf{News/Preface:} 
\begin{revnumerate*}
\item 20070914: Received award contract.
      This is NASA Grant number NNX07AR23G.
      Acknowledge this number in publications with, e.g.,
      ``Supported by NASA NNX07AR23G''.
\item 20070907: Award package completed. 
      Award performance dates are 20070802--20100801.
      Annual Progress Report (APR) deadlines are 20080601, 20090601, 20101101.
      Submit APRs to Technical Officer (TO) Hal Maring and to NASA Grant Officer  
      (GO) via e-mail (\url{nssc-contactcenter@nasa.gov}).
      Include the grant number (NNX07AR23G) in the subject line.
\item 20070805: Posted opportunities for Graduate Student Researchers
  and Postdocs on \href{cryolist@lists.colorado.edu}{CRYOLIST}
  and on \href{http://www.ess.uci.edu/employment}{ESS Website}.
\item 20070723: Registered for 
  \href{http://niflheim.nilu.no/spac}{SPAC Workshop} in Kjeller (stay
  in Lillestrom).
\item 20070709--20070712: Attended cryospheric sessions at IUGG/Perugia.
\item 20070614: Posted opportunities for Graduate Student Researchers
  and Postdocs on  
  \href{http://www.ess.uci.edu/~zender#jobs}{group website}. 
\item POLARCAT organization and planning at Paris meeting.
  Online 
  \href{http://www.polarcat.no/coordination/meeting-paris/meeting-summaries}{presentations}. 
  Need to register project/join.
\item 20070517: This proposal was one of 33 funded (of 92 submitted) 
  by the NASA
  \href{https://nspires.nasaprs.com/external/solicitations/summary.do?method=init&solId={9229C302-4B94-263B-290A-7F7F97BC0404}&path=past}{IPY06} program. 
  The public announcment of these NASA IPY awards is
  \href{http://www.nasa.gov/mission_pages/IPY/main/PolarExploration.html}{here}. 
  The entire IPY06 budget is \$18M for about 33~awards, which averages  
  out to \$545k per award, and to \$182k per award-year.
  Our request for \$677725 was reduced $\sim 10$\% to \$607k (\$200k, \$202k, \$205k).
\item Program manager is Hal Maring.
\end{revnumerate*}

\noindent\textbf{Background:} 
\begin{enumerate*}
\item 20061206: Proposal was not selected by IDS program.
  IDS program managers thoughtfully reclassified this as an IPY06
  proposal in response to NNH06ZDA001N-IPY (i.e., ROSES 2006 appendix A-16).

\item 20060417: Proposal was submitted to IDS program in rough form. 
  Still needs work. May re-write and submit proposal to ROSES A-15,
 ``Earth System Science Research using Data and Products from Terra,
 Aqua, and ACRIMSAT satellites''.
 Letters of intent due 5/1/2006, full proposals due 7/18/2006.

\item 20060312: This NASA proposal originally responded to the 2006
  NASA Research Opportunities in Space and Earth Sciences (ROSES)
  announcement, NNH06ZDA001N-IDS, ROSES 2006 appendix A-16.
  The annual IDS-wide budget was planned to be \$11M for about
  35~awards, or \$314k per award per year.
  I think this was later cut (to \$8M?).
  The proposal was submitted to the Interdisciplinary Science (IDS)
  Program subelement~5: Aerosol Impacts on Clouds, Precipitation, and
  the Hydrologic Cycle.
  The cognizant Program Manager is Phil DeCola \url{pdecola@nasa.gov},
  (202)~358-0768. 
\end{enumerate*}

\noindent\textbf{Information for potential collaborators/contributors:}
\begin{enumerate*}
\item Use CVS to obtain source to this proposal:\\
  \verb'cvs -d :ext:esmf.ess.uci.edu:/u/zender/cvs co -kk prp_ids'
\item Use instructions 
\href{http://dust.ess.uci.edu/doc/tex/index.shtml}{here}
(http://dust.ess.uci.edu/doc/tex/index.shtml) to build proposal
\end{enumerate*}

\noindent\textbf{Suggestions for current proposal:}
\begin{enumerate*}
\item Beef up specific hypotheses to test with satellite data
\item Zong-Liang Yang for snow extent and vegetation interactions?
\item New Science questions:
\begin{enumerate*}
\item Quantify ``dangerous'' BC levels for polar regions
\item Learn about snow extent/melt by combining AMSR-E and MODIS/MISR
\item BC vs. GHG impact on permafrost
\item Ghan Barrow ARM/IOP for arctic haze
\end{enumerate*}
\item Incorporate new references:
\begin{enumerate*}
\item \cite{HSR05}: dirty snow has greatest efficacy of all forcing agents
\item \cite{ACH05}: dirty snow speeds up worst case scenarios presented here
\item \cite{HaQ06}: Using seasonal SAF to estimate GCC SAF
\item \cite{AHK06}: dust deposition on snow
\item \cite{PeM95}: dust-ice sheet connections
\item \cite{LaS05}: permafrost
\item \cite{SBG05}: MODIS-albedo biases
\item \cite{Pir04}: Antarctic station albedo measurements
\item \cite{GWM94}: Antarctic reflectance albedo-modeling
\item \cite{GDR02}: Role of liquid water in surface reflectance
\item \cite{BAL05}: Global warming and water availability
\item \cite{SFZ07}: Arctic freshwater discharge
\item Large-scale snow-fraction representations: Yang
\end{enumerate*}
\end{enumerate*}
\clearpage

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\subsection{Summary of Proposal Personnel and Work Efforts}\label{sxn:wrk_frc}
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Percentage of nominal work years 1, 2, and~3 spent on project.
(Percentages differ from budget request).
\begin{enumerate*}
\item PI Zender: 25\%, 25\%, 25\%
\item Co-PI Famiglietti: 10\%, 10\%, 10\%
\item Co-PI Randerson: 10\%, 10\%, 10\%
\item Graduate Student~I (initially Mark Flanner): 100\%, 100\%, 100\% 
\item Graduate Student~II (TBD): 100\%, 100\%, 100\%
\item Scientific Programmer/Analyst Chao Luo: 25\%, 25\%, 25\%
\end{enumerate*}
\newpage

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%\markleft{Dirty Snow}
%\markright{}

%\begin{center}
%\textbf{\large\prpttl}
%\end{center}

\begin{center}
\textbf{\Large\prpttl}
\bigskip
Dr.~Charles~S. Zender, Dr.~Jay Famiglietti, Dr.~James Randerson\hfill \\
Department of Earth System Science, University of California, Irvine \hfill \\
Dr.~Siri Jodha Khalsa\hfill \\
National Snow and Ice Data Center, Boulder, Colorado\hfill \\
\end{center}

\subsection{Project Summary}\label{sxn:smr}
%\noindent{\large{\textbf{Project Summary.}}}\label{sxn:smr}
%\enlargethispage*{0.5in}

\csznote{
Project Summary must include in 4000 characters or less:
1. A description of the key, central objectives of the proposal in
   terms understandable to a nonspecialist; 
2. A concise statement of the methods/techniques proposed to
   accomplish the stated research objectives; 
3. A statement of the perceived significance of the proposed work
   to the objectives of the solicitation and to NASA interests and
   programs in general. 
} % end csznote

The prevalence of bright surfaces (snow, glaciers, sea-ice, and
clouds) make the cryosphere uniquely susceptible to radiatively 
induced effects of black carbon (BC) such as ice-albedo feedback
amplification. 
We will advance current understanding of cryospheric BC climate
impacts by integrating effects of post-deposition BC (i.e., dirty
snow) with the direct effects of atmospheric BC.
This project's primary objective is to understand BC effects on
cryospheric climate sensitivity and surface hydrology. 

We have integrated satellite-derived BC emissions into a unified
modeling framework, where we will forecast and hindcast contemporary
and 21st century climate with and without atmospheric and surface BC
effects.  
These simulations rely on our SNow, ICe, and Aerosol Radiative model  
(SNICAR) embedded in the Community Climate System Model (CCSM) forced
by the MODIS-derived Global Fire Emissions Database (GFED).
We ask three types of questions:

First, how do timing and location of BC emissions affect Arctic
surface reflectance and atmospheric processes? 
BC increases atmospheric absorptance in clear and cloudy conditions 
and this helps warm and thus darken snowpack.
However, snowpack is also very sensitive to temperature feedbacks
triggered by the vertical distribution of soot in the snowpack itself.
Using alternating years of high and low boreal soot emissions from the
GFED, we will test how atmospheric and surface soot contribute to
improving model agreement with MODIS-derived spectral surface
reflectance. 

Second, what are the relative roles of surface and atmospheric BC
forcing on Arctic climate sensitivity including sea-ice? 
Atmospheric BC cools the surface by backscattering and absorbing
incident sunlight.
Snowpack BC heating compounded by snow-albedo feedback can exceed
atmospheric BC surface cooling in strong fire years.
We will assess how BC mixing state affects top-of-atmosphere albedo
(from CERES), surface spectral reflectance (from MODIS), and
sea-ice extent (from AMSR-E). 

Third, how does BC alter surface water seasonality such as soil
moisture, snowpack depth and extent, depth to permafrost, and 
runoff to the Arctic? 
Concentration and scavenging of seasonally deposited BC within
snowpack can significantly alter partitioning of spring thaw processes 
between sublimation to the atmosphere and melt/percolation to surface
water.
We will use in situ snowpack BC profiles measured during IPY
activities to improve BC scavenging in SNICAR and CCSM. 
Snow water equivalent, extent, and liquid surface soil moisture
(from AMSR) and spring discharge to the Arctic Ocean
(from gauge data and GRACE) will test our global simulations.

\textbf{Relevance to NASA's Strategic Objectives:}
% NB: Repeat statement in Summary and in Introduction?
% Summary version is shorter than Introduction version:
The project outcomes meets NASA Strategic Goal 3.1 
(``Study planet Earth from space to advance scientific understanding
and meet societal needs'') and IDS Subelement 5 objectives by using 
\textbf{space-based remote sensing} and \textbf{global models} 
to improve understanding and prediction of the 
\textbf{role of black carbon in affecting clouds, precipitation, and 
  the hydrologic cycle}. 
Our improved understanding and predictions of the cryospheric
hydrologic cycle will be incorporated via CCSM into the IPCC AR5
report to help society understand, plan for, and mitigate BC effects
on cryospheric climate.
\clearpage

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\begin{center}
\textbf{\Large \prpttl}
\end{center}

\section{Introduction}\label{sxn:ntr}

Surface and atmospheric concentrations of black carbon (BC) are highly
variable and slowly increasing in the Arctic \cite[][]{PAA01,ACIA05}.   
Bright surfaces (snow, glaciers, sea-ice, and clouds) make
the Arctic uniquely susceptible to radiatively induced effects of
BC such as ice-albedo feedback amplification
\cite[]{WaW80,ClN85,HoB03}.
Understanding both surface and atmospheric BC effects is important 
in the Arctic because surface albedo variability dominates planetary 
albedo variability there \cite[]{QuH05}, and ice-albedo feedbacks
arguably dominate long-term Arctic climate sensitivity, e.g., to
greenhouse gas forcing.
This project will advance current understanding of cryospheric BC
climate impacts by integrating effects of post-deposition BC (i.e.,
dirty snow) with the direct effects of atmospheric~BC in coupled
models which can quantify, test, and evaluate hypotheses against
satellite, in-situ, and laboratory measurements.    

Soot is an important component of Arctic haze \cite[]{TSJ89} which
interacts with clouds and snowfall \cite[]{NoC88}, and thus has the 
potential for causing significant direct and indirect effects
\cite[]{VAG89,ATS00}. 
Dirty snow/ice feedbacks (described in Section~\ref{sxn:iaf}) change
throughout the aerosol lifecycle in the complex Arctic environment of
cloud, snowfall, snowpack aging, snow-melt, drainage, and analogous
sea-ice processes \cite[e.g.,][]{LEM98,AHH03,FlZ06}.   
Ice-albedo feedbacks make dirty snow more efficacious (per unit
forcing) than greenhouse gases at changing atmospheric temperature
\cite[][]{HaN04}.
Large scale interannual variability in BC emissions related to ENSO
and boreal fires modulate BC delivery to the Arctic
\cite[]{VRC04,KoH05}. 
Our project uses models to integrate BC processes across these
spatial and temporal scales, and NASA satellite and IPY in~situ 
observations to help constrain and evaluate model fidelity.

We use the terms soot and BC interchangeably to denote the light
absorbing component of carbonaceous aerosol \cite[][]{BoB05}.
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}. 
Our mid-latitude and polar snow
studies show that such estimates are  
extremely sensitive to accurate treatment of snowpack aging and soot
optical properties \cite[][]{FlZ05,FZR05}, two areas where this
project will  devote significant attention. 
Our interdisciplinary research team includes experts in aerosols and
clouds, surface hydrology and remote sensing, snowpack radiation and
aging, and biomass burning emissions and variability.

\textbf{Relevance to NASA's Strategic Objectives:}
% NB: Repeat statement in Summary and in Introduction
% Introduction version is longer than Summary version:
The project outcomes meet NASA Strategic Goal~3.1, 
``Study planet Earth from space to advance scientific understanding
and meet societal needs''.
The direct and indirect effects of BC on climate are mediated by
sunlight, whether in the atmosphere, clouds, or surface snowpack. 
Annual runoff north of 40\,\dgrn\ is predominantly snowfall-generated 
\cite[][]{BAL05}.
Hence improved understanding and predictions of the cryospheric
hydrologic cycle will help society understand, plan for, and mitigate
the effects of BC on high latitude climate change.

Note that four letters of support/collaboration and a complete list of
acronyms and abbreviations appear as supplementary documents to the
main proposal. 

\subsection{BC Role in Ice-Albedo Feedback}\label{sxn:iaf} % Section~\ref{sxn:iaf}
Snow-albedo feedback is triggered by any forcing mechanism (e.g.,
solar absorption by soot) which changes the areal extent of snow cover
(Figure~\ref{fgr:frc_fdb}). 
%\begin{floatingfigure}[r]{0.50\hsize} % begin Figure~\ref{fgr:frc_fdb}
\begin{figure}
\centering % \centering uses less vertical space than center-environment
% 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='sotfrc_sfc_1998f11_JJA';cat ${fl_foo}.eps | epsffit -r 33 141 543 725 | epsffit -r 33 141 543 725 | epsffit -r 33 141 543 725 > ${fl_foo}_270.eps
\includegraphics[width=0.4\hsize,angle=0,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{/data/zender/fgr/snicar/sotfrc_sfc_1998f11_JJA_270}%
%\includegraphics[width=0.4\hsize,angle=270,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{/data/zender/fgr/snicar/sotfrc_sfc_1998f11_JJA}%
\includegraphics[width=0.5\hsize,angle=0,clip=true,trim=1.0in 1.0in 1.0in 1.0in]{/data/zender/fgr/snicar/sot_snw_fdb}%
\caption[Snow-albedo radiative forcing and feedback amplification]{
(a)~Summer-mean surface direct radiative forcing [\wxmS] by soot in
snowpack during 1998, a strong boreal burn year. 
(b)~Soot amplifies snow-albedo feedback via multiple paths.  
Analogous feedbacks occur in clouds and sea-ice.
\label{fgr:frc_fdb}}
\end{figure}
%\end{floatingfigure} % end Figure~\ref{fgr:frc_fdb}
A weaker, positive feedback associated with changes in net surface
radiation is the change in growth rate of snow grains.
Soot in the snowpack directly lowers snow albedo and increases the
growth rate of snow grains, lowering albedo of the ice grains themselves.  
Furthermore, the instantaneous perturbation of soot is greater in
larger-grained snowpack, effectively increasing the gain ($\GGG$) on 
feedback involving grain growth.
Finally, a fourth mechanism of perturbation may result from
accumulation of hydrophobic impurities at the surface during melt
events \cite[]{ClN85,CGR96}. 

Of course, BC in clouds and sea-ice causes direct and indirect
effects too \cite[e.g.,][]{VAG89,CLV96,ATS00}. 
The feedbacks are analogous to Figure~\ref{fgr:frc_fdb}, with
additional complexities introduced by the dynamic nature of clouds and
sea-ice. 
In polluted marine environments, for example, soot solar absorption
appears to reduce cloud albedo and lifetime by reducing net cloud top
radiative cooling, boundary layer mixing, and cloud moisture supply
\cite[]{ATS00}. 

\section{Scientific Objectives and Hypotheses}\label{sxn:obj} % Section~\ref{sxn:obj}

Our studies of BC effects on cryospheric climate and surface hydrology
will utilize NASA satellite observations to improve understanding and
simulation of BC effects on polar climate amplification in Nature, and
thus improve the potential for more informed mitigation of such effects. 
Key scientific questions we will address include:
%\begin{wrapfigure}{r}{0.50\hsize} % begin Figure~\ref{fgr:rfl_sfc}

\setcounter{enmrfr}{0} % Reset reference counter for this list
%\begin{enumerate}
%\item \enmrfrstp \label{idx_obj_csn} % begin Objective~\ref{idx_obj_csn}
\enmrfrstp \label{idx_obj_csn} % begin Objective~\ref{idx_obj_csn}
\noindent \textbf{Objective~\ref{idx_obj_csn}}: Discover Arctic climate sensitivity to timing 
and location of Arctic soot events\\ 
\textbf{Hypothesis}: \textit{Boreal fires outweigh tropical BC effects
  on Arctic climate sensitivity. 
  Both amplify the ice-albedo feedback.}\\ 
%\begin{wrapfigure}{t}{3.0in} % begin Figure~\ref{fgr:rfl_sfc}
%\begin{wrapfigure}{r}{0.50\hsize} % begin Figure~\ref{fgr:rfl_sfc}
\begin{floatingfigure}[r]{0.50\hsize} % begin Figure~\ref{fgr:rfl_sfc}
%\begin{figure}
\centering % \centering uses less vertical space than center-environment
\includegraphics[width=\hsize,angle=0,clip=true,trim=0.1in 0.0in 0.0in 0.0in]{./albs_GR2}%
%\includegraphics[width=0.5\hsize,angle=0,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{./albs_AN}%
\caption[Greenland surface albedo]{
Seasonal cycle of modeled \cite[]{FZR05} and retrieved surface albedo
in Greenland. 
Experiments clm23a and clm23b include soot and snow-aging effects
neglected by experiment clm01c.
\label{fgr:rfl_sfc}}
%\end{figure}
\end{floatingfigure} % end Figure~\ref{fgr:rfl_sfc}
%\end{wrapfigure} % end Figure~\ref{fgr:rfl_sfc}
Seasonality and location modulate the net solar forcing of Arctic BC.
BC of tropical and sub-tropical provenance \cite[][]{VRC03} deposits
more continually than mid-lat\-i\-tude and sub-arctic boreal fire BC
\cite[][]{KoH05}. 
Low zenith angles reduce the Arctic forcing efficacy (response per
unit mass BC) of winter relative to summer BC.
How spatio-temporal soot emission patterns affect Arctic climate
sensitivity is important in the context of wildfire management and
changing fire regimes, yet is nearly completely unexplored.  
We will inventory relative effects of Asian, American, and tropical,
and fossil fuel BC sources on Arctic climate sensitivity.

We expect soot to amplify the positive ice-albedo feedback and 
accelerate Arctic albedo change during Spring and Fall transitions,
especially during strong boreal fire years.  
Models currently overestimate surface reflectance relative to
satellite retrievals all year, even at relatively high zenith angles  
(i.e., summer) (Figure~\ref{fgr:rfl_sfc}). 

Since Arctic albedo change during spring is dominated by melt
processes \cite[]{QuH05,QuH06} so the efficacy of winter deposition
will depend strongly on meltwater scavenging of soot in snowpack. 
During spring thaw weak scavenging may concentrate hygrophobic soot at
the surface \cite[]{ClN85,NoC88} and cause additional melt. 
Our preliminary investigations (Figure~\ref{fgr:rfl_sfc}) show that
representing snow aging and soot deposition improves springtime albedo 
response.
Scavenging measurements to be made during IPY will help reduce the
uncertainty in these processes
(Sections~\ref{sxn:insitu}--\ref{sxn:arm}).\\ 

%\item \enmrfrstp \label{idx_obj_atm_sfc} % begin Objective~\ref{idx_obj_atm_sfc}
\enmrfrstp \label{idx_obj_atm_sfc} % begin Objective~\ref{idx_obj_atm_sfc}
\noindent \textbf{Objective~\ref{idx_obj_atm_sfc}}: Relative roles of
surface and atmospheric BC forcing on Arctic climate sensitivity.\\
\textbf{Hypothesis}: \textit{BC warms Greenland in strong boreal fire
  years and cools Greenland in weak fire years. 
  Increasing soot will amplify 21st century polar climate sensitivity}.\\
Atmospheric BC cools the surface by backscattering and absorbing
incident sunlight.
Snowpack~BC heating compounded by snow-albedo feedback can exceed
atmospheric~BC surface cooling in strong fire years \cite[][]{FZR05}  
(cf.\ Figure~\ref{fgr:mlt_Grn}).   
The net effect of BC on Greenland surface will depend on the balance 
of atmospheric and surface BC forcing.

Surface and atmospheric BC concentrations are highly variable and
slowly increasing in the Arctic.
Most emission scenarios project anthropogenic BC emissions will
increase 30--250\% in the 21st century \cite[][]{NAD00,KoH05}.
The seasonal cycle of surface albedo is, in models at least, a good
proxy for Arctic climate sensitivity to 21st century GHG forcing
\cite[]{HaQ06}. 
Hence reducing model biases with current observed albedo variability
will also reduce uncertainty in 21st century climate forecasts.

Ice core analyses and model simulations \cite[][]{KoH05,FZR05} agree 
that boreal fires are the primary source of BC deposition to Greenland 
in strong fire years.
BC preserved in snow and ice records will allow us to ask how the
strongest Boreal events may have affected Greenland on longer
timescales.\\

%\item \enmrfrstp \label{idx_obj_sea_ice} % begin Objective~\ref{idx_obj_sea_ice}
\enmrfrstp \label{idx_obj_sea_ice} % begin Objective~\ref{idx_obj_sea_ice}
\noindent \textbf{Objective~\ref{idx_obj_sea_ice}}: Assess Arctic BC impacts on sea-ice\\
\textbf{Hypothesis}: \textit{Arctic BC amplifies polar climate
  sensitivity by reducing summer sea-ice thickness and extent during 
  strong burn years. 
  Inter-hemispheric asymmetry in polar BC deposition contributes 
  to the significant differences between Arctic and Antarctic sea-ice 
  trends.}\\ 
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}. 
Moreover our preliminary investigations with slab ocean models and
simple sea-ice models suggest a summertime Arctic sea-ice response
to boreal soot in strong fire years.

In spite of globally-uniform greenhouse forcing, summertime Arctic and 
Antarctic sea-ice show asymmetric trends over the last 25 years
\cite[]{FKC01}, likely related to greenhouse gas-induced warming
\cite[][]{SMS03,SSF05}. 
While Antarctic sea-ice has shown little trend, summertime Arctic
sea-ice has retreated by more than 15\%.  
Has non-GHG forcing such as snow-aerosol interactions contributed to 
this trend?
To what extent does the asymmetry between northern and southern
hemisphere polar BC deposition explain this phenomena?
We will search for connections between BC emissions \cite[]{RVC05} 
and recent accelerations in Arctic sea-ice reduction.\\

%\item \enmrfrstp \label{idx_obj_hyd} % begin Objective~\ref{idx_obj_hyd}
\enmrfrstp \label{idx_obj_hyd} % begin Objective~\ref{idx_obj_hyd}
\noindent \textbf{Objective~\ref{idx_obj_hyd}}: Role of BC forcing on Arctic surface hydrology.\\ 
\textbf{Hypothesis}: \textit{BC-induced positive temperature feedbacks
  alter Arctic surface hydrology in strong fire years. 
  Changes include wetter, moister soil beneath snowpack, accelerated
  spring melt, and increased active layer depth to permafrost.}\\
Snow insulates the underlying surface Arctic from the atmosphere for
much of the year so BC-induced changes in snow extent and melt alter
surface hydrology.
Snowpack thickness and seasonal phasing respond strongly to snowpack
opacity \cite[]{FlZ05}.
Our preliminary investigations show that soil moisture, active layer
depth to permafrost (not shown), and phasing of freshwater drainage to
the Arctic are also sensitive to snowpack opacity
(Figure~\ref{fgr:rvr_Yen}). 
%\begin{floatingfigure}[r]{\hsize} % begin Figure~\ref{fgr:rvr_Yen}
\begin{figure} % begin Figure~\ref{fgr:rvr_Yen}
\centering % \centering uses less vertical space than center-environment
%\includegraphics[width=0.50\hsize,angle=0,clip=true,trim=1.9in 6.95in 1.5in 2.25in]{./clm11_clm10_amsre_clm_0112_TiP_vlwcsfc_NS}%
\includegraphics[width=0.50\hsize,angle=0,clip=true,trim=0.0in 5.95in 0.75in 0.2in]{./clm11_clm10_amsre_clm_0112_TiP_vlwcsfc_NS}%
\includegraphics[width=0.50\hsize,angle=0,clip=true,trim=1.0in 9.3in 4.1in 0.0in]{./runoff_comp_clm}%
\caption[Soil moisture and River Discharge]{
(a)~Seasonal cycle of surface soil moisture in the Tibetan Plateau
from models \cite[]{FlZ05} and AMSR-E retrievals.
(b)~Impact of SNICAR snow-aerosol treatment on predicted seasonal runoff
from Yenisey basin.
Earlier spring thaw due to SNICAR improves agreement with observations.
\label{fgr:rvr_Yen}}
\end{figure} % begin Figure~\ref{fgr:rvr_Yen}
%\end{floatingfigure} % begin Figure~\ref{fgr:rvr_Yen}
Since BC alters snowpack opacity, we will examine how BC events affect
Arctic surface hydrology.
If this hypothesis is true, then recent projections of 21st century
permafrost degradation \cite[]{LaS05} may be too conservative. 
%\end{enumerate}

\section{Methods: Arctic Models and Observations}\label{sxn:mth}

To achieve our objectives we will use NASA satellite products, in~situ
measurements, and community models.
We will also create products useful to NASA in validation and
development of satellite retrieval algorithms. 
This project will not develop any Arctic climate model components from 
scratch. 

\csznote{
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.  
} % end csznote

\subsection{Climate sensitivity to timing and location of Arctic soot events}\label{sxn:idx_obj_csn} 

\subsubsection{BC/OC emissions}\label{sxn:ems} % Section~\ref{sxn:ems}

The principle sources of BC and Organic Carbon (OC), biomass burning
and combustion of fossil- and bio-fuels, have distinct spatial
distributions, annual cycles, and interannual variability.
We incorporate BC/OC distributions into our models \cite[]{FZR05}
based on two main sources.
Fossil and biofuel BC and OC sources are from \cite{BSY04}.
Co-PI Randerson's group assembled the Global Fire Emissions Database
(\href{http://ess1.ess.uci.edu/~jranders/data/GFED2/readme.pdf}{GFEDv2})
including extra-tropical BC/OC fire emissions based on MODIS-derived
fire counts \cite[]{VRC03,VRC04,RVC05} from 1997--2005.   
Randerson's group will continue to improve, interpret, and update
GFEDv2. 

Using emissions factors \cite{AnM01} to obtain BC/OC aerosols, we
estimate that biomass burning BC emissions north of 30\dgrn\ increased
from 0.29 to 1.2\,\TgBC\ between 1997, a weak boreal fire year, and
1998, a strong fire year. 
The end-member years for tropical fire BC emissions from 1997--2005
were 2000 (2.1\,\TgBC) and 1997 (7.8\,\TgBC).
Hence, the recent decade exhibited interannual emissions variability
of approximately a factor of four in both tropical and boreal sources.

We estimate Boreal fire emissions changes from 1997 to 1998 increase
surface snowpack radiative forcing in the Arctic by about~50\%
\cite[][]{FZR05} (Figure~\ref{fgr:frc_fdb}a).
These estimates contain many uncertainties and potential Arctic
aerosol-related biases including transport and deposition, size
distribution, optical properties, aging, and cloud interactions.
As part of Objective~\ref{idx_obj_csn}, we will systematically
inventory how sensitive Arctic climate response is to BC emission
timing (e.g., early vs.\ late summer boreal fires) and location. 
Section~\ref{sxn:xpt} describes our numerical strategies for this.


\subsubsection{Snow and Ice Aging and BC Removal}\label{sxn:aging}
We comprehensively describe dry snow aging in \cite{FlZ06}.
BC heating increases ice crystal size (Figure~\ref{fgr:frc_fdb}b).
This can cause remarkable growth in snow grain size following soot
events (cf.\ Figure~\ref{fgr:rfl_spc_rds_ffc}b), with corresponding
decreases in broadband surface reflectance (not shown).

In addition to BC effects, our microphysical model, SNICAR,
incorporates the roles of snow temperature, temperature gradient,
density, initial size distribution, and irregularity in particle
spacing to predict snow albedo evolution.  
Temperature gradient can have the most profound influence on snow
albedo evolution, but is modulated by snow temperature and density. 
We account for enhanced aging with liquid water in the snowpack using
empirical growth rates \cite{Bru89}. 
Research funded from other sources will also quantify the effects of
melt-freeze cycles, sintering \cite[]{Ros06}, and wind.

Meltwater flushing is the most important surface BC removal mechanism,
since preferential gravitational settling only operates on external
mixtures, and is likely extremely slow.
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.
Even greater uncertainty exists for snow processes on sea-ice.
Planned IPY field studies by Warren and Grenfell
(Section~\ref{sxn:insitu}) will help us constrain these scavenging 
factors (see attached letter of collaboration).

Our simulations suggest boreal soot in snowpack causes seasonal net
surface solar radiation forcings of 0.5--0.75\,\wxmS\
(Figure~\ref{fgr:frc_fdb}a) 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[][]{FZR05}.
Soot-snow feedbacks in strong fire years appear to cause significant
increases in meltwater production in Greenland snowpack
(Figure~\ref{fgr:mlt_Grn}).
%\begin{floatingfigure}[t]{\hsize}
\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 soot
\cite[][]{FZR05}. 
\label{fgr:mlt_Grn}}
\end{figure} % end Figure~\ref{fgr:mlt_Grn}
%\end{floatingfigure} % end 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 which only
account for atmospheric soot or prescribe surface soot effects}.
This makes us eagerly anticipate results in year~3 when SNICAR is 
embedded in fully interactive sea-ice and glacier models which
can fully respond to soot sources.

\subsubsection{Satellite-Retrieved Surface Albedo}\label{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
and BC concentrations.
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.

We will use current and near-future NASA reflectance products
to characterize observed surface and TOA albedos.
MODIS reflectance retrievals (Figure~\ref{fgr:rfl_sfc}) have known
biases \cite[e.g.,][]{SBG05} over vegetation-free surfaces such as
Greenland. 
Understanding and reducing the discrepancy between the MODIS-retrieved  
and ISCCP-inferred snow reflectance and models
(Figure~\ref{fgr:rfl_sfc}) is part of Objectives~\ref{idx_obj_csn}
and~\ref{idx_obj_atm_sfc}.  

Potential contributors to the model-observed surface albedo
discrepancy include zenith angle effects, snow grain size and
surface impurities such as soot.
Retrieved reflectance biases have been associated with large zenith 
angles and topography \cite[]{StN02,SBG05}. 
While the annual cycle of zenith angle supports the modeled ``happy
face'' shape in (Figure~\ref{fgr:rfl_sfc}), biases in summer are
much more important than winter from energetic considerations.
Spring and summer are the periods when soot and snow grain size
effects are largest. 
Accounting for these effects brings the CCSM/SNICAR into good
agreement with MODIS and ISCCP surface albedo slopes, although
a significant offset still exists.
We will explore whether and how much of this discrepancy may be due to 
snow grain size, to which albedo retrievals over snow surfaces are
extremely sensitive \cite[][]{NoD00,GDR02}.

\subsubsection{Optics}\label{sxn:opt}
Aerosol, cloud, and snowpack optical processes will be refined to
attempt to improve satellite-model reflectance agreement
(Figure~\ref{fgr:rfl_sfc}).
Snow and aerosol optical properties link the snowpack microphysical 
properties (aerosol concentration, particle size distributions)
to macroscopic net absorption (Figure~\ref{fgr:frc_fdb}a), 
reflectances (Figure~\ref{fgr:rfl_spc_rds_ffc}a),  
and heating rates that drive the snow melt and temperature change
which trigger snow-albedo feedback.
These responses are sensitive to optical property assumptions which
this project will explore and improve, including  
\begin{enumerate*}
\item BC indices of refraction: 
  \cite{BoB05} question the OPAC properties \cite[][]{HKS98}  
  (which we use) and recommend other measurements including
  \cite{ChC90}    
\item BC shape: 
  Treating BC as spheres likely underestimates single scattering
  albedo relative to more realistic fractal aggregates \cite[][]{Sor01,BoB05}
\item Aerosol mixing: 
  BC and dust in remote regions such as the Arctic are primary
  deposited via wet scavenging \cite[][]{CCR01,CSK04,ZBN03} and so will
  often be internally mixed within snow grains.
  We will treat aged BC as internally mixed coated aerosols
  \cite[e.g.,][]{BoH83,BHB06}.  
  We will also investigate solutions for dark particles in weakly
  absorbing media \cite[]{MaS99} which may be more physically
  defensible for ice particles.
\item Resonance effects:
  Optical properties will be computed at high spectral resolution
  following to resolve resonance effects \cite[]{ZeT06}.
%\item Darkening by desert and peri-glacial dust sources \cite[][]{MML06}. 
\end{enumerate*}
These optical improvements will, in the net, increase clear sky,
cloudy sky, and snowpack absorption relative to our current externally
mixed assumption.

\subsection{Relative roles of surface and atmospheric BC forcing on
  Arctic climate sensitivity}\label{sxn:idx_obj_atm_sfc}  

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{floatingfigure}[t]{\hsize} % begin Figure~\ref{fgr:rfl_spc_rds_ffc}
\begin{figure}
\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: 
Change in summertime-mean effective radius $\rdsffc$\,[\um] of surface  
snowpack layer due to 1998 soot.
Cross-hatching indicates statistically significant changes 
($\cnfstt < 0.05$) relative to simulations without boreal soot
\cite[][]{FZR05}.
\label{fgr:rfl_spc_rds_ffc}}
\end{figure}
%\end{floatingfigure} % 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:frc_fdb}b).
Interestingly, our preliminary simulations reveal conditions of
where soot appears to reduce total snow melt. 
We will numerically assess the relative influences of these 
competing feedbacks for Arctic climate (Section~\ref{sxn:xpt}).
This will require closure experiments based on in~situ data
from sooty, clear, and cloudy Arctic locations.

\subsubsection{In Situ Observations}\label{sxn:insitu}
Arctic atmospheric and snowpack BC measurements span a wide range of
concentrations \cite[][]{ClN85,NoC88,HaN04}. 
Snowpack BC concentration is the key diagnostic which integrates
aerosol source, transport, deposition, and melt processes. 
Greenland concentrations are typically 1--4\,\ugxkg, and as high
as 30\,\ugxkg\ \cite[][]{SCD02}.  
Acquiring and assembling updated and improved (e.g., vertically and
size resolved) BC measurements for event evaluation will be an 
ongoing activity for this project.
The most important measurements we need to help reconcile our
model discrepancies with observations (e.g., Figure~\ref{fgr:rfl_sfc})
are vertical profiles of aerosol concentration, snow
accumulation/melt, snowpack temperature, and spectral or broadband
fluxes. 

Drs.~Steve Warren (U.~Washington, see attached letter of support), Tom  
Grenfell and Tony Clarke (U.~Hawaii) proposed an NSF project ``Black
carbon in Arctic snow and ice, and its effect on surface albedo''
to measure BC in snow and ice in tundra regions of Alaska, Canada, and
Russia, as well as on the Greenland Ice Sheet and the Arctic Ocean to
update and improve the BC survey that Clarke conducted in 1983--1984. 
They will (continue) to share their measurements with us,
including, potentially, BC measurements collected at Dr.~Konrad
Steffen's automated meteorological sites in Greenland. 

Warren et~al.\ will also measure/estimate scavenging coefficients for
removal of atmospheric BC by snow and removal of surface BC during
snow melt (Section~\ref{sxn:aging}).
These scavenging coefficients will provide important constraints for
BC scavenging relevant to Hypothesis~\ref{idx_obj_atm_sfc}.
In addition to Warren and Clarke's earlier measurements,
\cite{FlZ06} evaluated SNICAR against \textit{in situ} and laboratory
measurements of snowpack specific surface area, crystal density,
albedo, and curvature- and temperature-gradient growth processes. 
This project will enable us to continue these comparisons as new data
become available.

\subsubsection{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}) 
(see attached letter from IPY program office to POLARCAT PI~Stohl). 
Our contribution is to one of POLARCAT's main themes---the influence
of boreal fire aerosol on Arctic surface properties.
Although many observational aspects of POLARCAT are still pending,
support for regular aerosol observations at Summit, Greenland appear
to be in place.
Using modeled/assimilated BC deposition from NCAR collaborator 
and POLARCAT Steering Committee member P.~Rasch, our group will 
estimate surface reflectance changes at Summit from significant boreal  
events upwind, and compare them to in~situ observations.

\subsubsection{ARM IOP at Barrow}\label{sxn:arm} % Section~\ref{sxn:arm}
Dr.~Steve Ghan (PNNL) (personal communication, 2008) is proposing a
DOE Atmospheric Radiation Measurement (ARM) Program intensive
observing period (IOP) at the North Slope of Alaska (NSA) facility in
2008. 
The NSA facility at Barrow has been a premier repository of
radiometric data in Arctic cloudy and (infrequently) clear skies since
about 2001.  
The IPY IOP would augment these with additional aerosol measurements
suitable for assessing effects of Arctic haze.  
A BC event (upwind fire) during the IOP would be very fortuitous.
We plan to join this IOP in Barrow for one week, and use IOP data to
calibrate and validate Arctic BC effects.

\subsubsection{Greenland Ice Core}\label{sxn:core} % Section~\ref{sxn:core}
Part of Objective~\ref{idx_obj_atm_sfc} is to place current BC forcing
of Greenland in a longer term historical perspective.
Dr.~Eric Saltzman (UC~Irvine) measures trace gas and aerosol
concentrations in ice cores \cite[e.g.,][]{SAD04} and Co-PIs a  
pending NSF project ``High-Resolution, Biomass-Burning-Specific
Tracers in Greenland Ice Cores over the Past 1000~Years''.
In conjunction with Co-PI 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. 
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.  

Microwave brightness temperatures measured by AMSR-E can be used to
retrieve several useful cryospheric parameters.
Currently, Snow Water Equivalent (SWE) is produced on a 25~\km\
grid over non-ice surfaces.
The standard product, archived at NSIDC (by Co-PI Khalsa), uses a
static, semi-empirical approach based on \cite{CFH87} subject to
errors due to variable snow crystal size, inadequate wet snow
discrimination, and difficulty mapping snow in densely forested 
areas. 
A more dynamic SWE retrieval algorithm that incorporates estimates of 
snow properties is in development. 
We will compare AMSR-E estimates of mean snowpack grain size with
SNICAR predictions (cf.\ Figure~\ref{fgr:rfl_spc_rds_ffc}b).
In particular, we are interested in assessing the influence of extreme
aerosol events on the duration of snow cover in seasonally snow
covered regions which are most susceptible to snow-albedo feedback
\cite[]{FlZ05}.

Once MISR-inferred spectral snow reflectance and monthly CERES-derived 
broadband surface reflectance products reach robust operational
status, we will also use them to evaluate SNICAR reflectance, and to 
try to infer~$\rdsffc$   
(Figures~\ref{fgr:rfl_spc_rds_ffc}a and~b, respectively).
(We will happily provide our predicted~$\rdsffc$ to any retrieval
experts attempting to improve MODIS/MISR surface reflectance). 
In combination with temperature from meteorological analyses,
retrieved~$\rfl$ and/or $\rdsffc$ will be used to evaluate SNICAR's
snow aging physics \cite[][]{FlZ06} which predict significant
temperature dependence for~$\rdsffc$.

Greenland is an ideal location for evaluation of SNICAR from remote
sensing platforms.
Much of the ice sheet enjoys year round sub-freezing temperatures 
which remove the potentially confounding influence of liquid effects.
Since soot concentrations rarely exceed 5\,\ugxkg\ in Greenland, 
surface snowpack effective radius~$\rdsffc$ is the most promising
model parameters to retrieve and constrain.

\subsection{Assess Arctic BC impacts on sea-ice}\label{sxn:idx_obj_sea_ice}

Accounting for impurities in sea-ice is important to accurate
radiative transfer throughout the atmosphere/ice/ocean system
\cite[]{Gre91}. 
Our sea-ice model, CICE (described in Section~\ref{sxn:cice})
uses a single layer snowpack upper boundary condition
and neither the sea-ice nor the snowpack tracks absorbing impurities 
such as soot as prognostic tracers.
A multi-layer snowpack and prognostic aerosol tracer capability will
be in CICE in 2006 (W.~Lipscomb, personal communication). 
Graduate student Mark Flanner will merge SNICAR physics (snow aging,
radiative transfer, snow-aerosol optics) into this CICE configuration.

The resulting CICE simulations will retain soot deposited directly on
bare sea-ice from the atmosphere and from melting snow cover.
We will use improved sea-ice radiative transfer physics as available
(e.g., Briegleb and Light, personal communication, 2005) to account
for aerosols embedded in the complex sea-ice-brine-pond matrix. 
Sea-ice extent and thickness may then respond to the full lifecycle of
Arctic~BC.  
This will be a significant improvement to current models which
prescribe BC albedo alterations or remove BC from snow and sea-ice
with rather ad~hoc mechanisms to prevent excessive concentrations from
accumulating in multi-year land and sea-ice
\cite[][]{HaN04,Jac04,FZR05}. 
Residence time estimates for optically active Arctic soot will improve. 
This may play an important role in studies of
Objective~\ref{idx_obj_sea_ice} (Section~\ref{sxn:obj}).

We will incorporate the AMSR-E sea-ice concentration and snow 
depth over sea-ice products into our investigations. 
The 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 BC impacts on
sea-ice. 

Lipscomb and Hunke (see attached letter of support) 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{Role of BC forcing on Arctic surface hydrology}\label{sxn:idx_obj_hyd}
AMSR-E estimates surface soil liquid water content for snow free
surfaces on a daily 25\,\km\ grid \cite[]{NJL03,NCC04}.
The product seems to underestimate spatial and temporal soil moisture
variability (Figure~\ref{fgr:rvr_Yen}a).
Figure~\ref{fgr:rvr_Yen}b shows that if BC alters snowpack opacity,
it can potentially alter integrated downstream measures of surface
hydrology such as river discharge to the Arctic.
To tease out the potential links between BC and surface hydrology,
which are likely difficult to discern on large spatial scales, 
we will first simulate hydrologic effects of extreme BC events.
Then we will search for similar patterns following strong BC years
in available satellite data.

\cite{SFC05} recently developed a method for estimating total
basin discharge (both surface and groundwater) using data from the
Gravity Recovery and Climate Experiment (GRACE) mission
\cite[]{TBW04}.
This method is currently being implemented globally within
Famiglietti's research group, with an important focus on quantifying
basin scale variations in surface water and flow to continental
margins.

\csznote{
%\begin{floatingfigure}[t]{\hsize} % begin fgr:prm_frs
\begin{figure} % begin fgr:prm_frs
\centering % \centering uses less vertical space than center-environment
\includegraphics*[width=0.40\hsize,angle=270,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{./clm01d}%
%\includegraphics*[width=0.40\hsize,angle=270,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{./clm21}%
\includegraphics*[width=0.40\hsize,angle=270,clip=true,trim=0.0in 0.0in 0.0in 0.0in]{./clm24-clm01d_permafrost}%
\caption[Active Layer Depth to Permafrost]{
Simulated active layer depth (ALD) to Arctic permafrost in (a)~vanilla
CCSM and (b)~change in ALD due to SNICAR aerosol and snow aging
treatment. 
\label{fgr:prm_frs}}
\end{figure} % end fgr:prm_frs
%\end{floatingfigure} % end fgr:prm_frs
} % end csznote

\section{Earth System Model Description}\label{sxn:esm}

Ice-albedo feedback is arguably the most important positive feedback
in the polar climate system \cite[e.g.,][]{Har94,HoB03,QuH06}.
Hence 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}) against global scale observations.
The NCAR CCSM \cite[][]{CBB06} is that model.
CCSM polar climate simulations have been continually evaluated against
meteorological analyses and satellite observations
\cite[e.g.,][]{BrB981,HoB03,HBH06}.
This project will use CCSM component models with our 
snow-ice-aerosol physics \cite[][]{FlZ05,FlZ06} to ask questions about  
the integrated impacts of BC aerosol on Arctic climate. 

Arctic BC primarily originates from non-frozen land surfaces at lower 
latitudes \cite[][]{KoH05} so it traverses multiple climate ``spheres''
to the Arctic (biosphere-atmosphere-cryosphere). 
The project relies on our continuing external collaborations for
realistic aerosol distributions and simulation codes (discussed in
Section~\ref{sxn:esm}). 
The initial CCSM BC/OC aerosol transport, deposition, and optics we
use (and modify) come from long time collaborators Drs.~Phil Rasch and
Bill Collins (NCAR) \cite[][]{CRE01,CRE02}.

\subsection{SNow, ICe, and Aerosol Interactions}\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 radiative transfer, aging, and aerosol 
interactions in a unified manner that allows for realistic 
feedbacks between solar radiation, snowpack temperature gradients,
and aerosol concentration.
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}.
A lookup table (computed off-line) contains Mie parameters (single
scattering albedo, extinction coefficient, and asymmetry parameter)
for any lognormal size distribution of snow.
SNICAR accounts for solar zenith angle, 
direct and diffuse incident radiation, 
bare surface reflectance \cite[][]{DZD03}, and 
vertically-resolved effective radius (\rdsffc),
snow depth, density, and concentrations of absorbing impurities
\cite[][]{WaW80}.  

An off-line version of SNICAR runs at high spectral resolution,
10\,\nm\ 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}).
For climate simulations, SNICAR runs in a host snowpack model,
typically 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 
the Community Atmosphere Model (CAM) \cite[][]{CRB06}.

\subsection{Sea-Ice and Ice Sheets}\label{sxn:cice} % Section~\ref{sxn:cice}
Sea-ice is the fulcrum of ice-albedo feedbacks in the Arctic Ocean.
Drs.~Bill Lipscomb and Elizabeth Hunke of LANL (see attached
letter of support) are the principle developers of the Los Alamos
sea-ice model 
(\href{http://climate.lanl.gov/Models/CICE}{CICE}), a primary
component of the National Center for Atmospheric Research (NCAR)
Community Climate System Model (CCSM). 
The CICE model contains an Ice Thickness Distribution (ITD) which
maintains a half dozen prognostic categories of ice thickness in
each grid cell \cite[][]{HBH06}. 
The thinnest ice category is most susceptible to changes in net solar 
radiation due to snowpack aging and aerosol concentration.

\subsection{Numerical Experiment Strategy}\label{sxn:xpt} % Section~\ref{sxn:xpt}
Our objectives (Section~\ref{sxn:obj}) will be approached in the
context of pre-industrial, present day, and next century timescales
when appropriate.  
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[e.g.,][]{RVC05}
and seems to explain most of the variability in Arctic BC deposition
\cite[][]{FZR05}. 
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 is 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- and 1998-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 BC
impacts on cryospheric climate sensitivity and surface hydrology
(Table~\ref{tbl:mdl}).
\begin{table} % begin tbl:mdl
\centering % \centering uses less vertical space than center-environment
\begin{minipage}{\hsize}
\renewcommand{\footnoterule}{\rule{\hsize}{0.0cm}\vspace{-0.0cm}} % KoD95 p. 111
\caption[CCSM/SNICAR Simulations]{\textbf{CCSM/SNICAR Simulations}
\label{tbl:mdl}}
\vspace{\cpthdrhlnskp}
\begin{tabular}{ >{\raggedright}p{12.0em}<{} llll l }
\hline \rule{0.0ex}{\hlntblhdrskp}%
Scenario &
Sources\footnote{BC/OC source options include Type (Fossil Fuel/Biofuel
  and/or Fires), Location (Tropics and/or Boreal), and Regions (North
  America, Asia), and prescribed burn seasons (e.g., early/late summer).} &
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 water coating).} & 
 \\
\hline \rule{0.0ex}{\hlntblntrskp}%
%\multicolumn{6}{c}{\textit{Control}} \\[0.0ex]
Control & All & Sfc.$+$Atm. & SOM & Coated & \\[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]
\multicolumn{6}{c}{\textit{Objective~\ref{idx_obj_atm_sfc}: Relative roles of surface and atmospheric BC forcing}} \\[0.0ex]
Forcing/Feedback & All & Vary & SOM & Coated & \\[0.5ex]
Aging/Scavenging & All & Sfc.$+$Atm. & SOM & Vary & \\[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}{Predictions to 2100} \\[0.0ex]
Equilibrium & All & Sfc.$+$Atm. & SOM & Coated & \\[0.5ex]
Transient & All & Sfc.$+$Atm. & IPCC/SOM & Coated & \\[0.5ex]
\hline
\end{tabular}
\end{minipage}
\end{table} % end tbl:mdl

\section{Impact and Relevance}\label{sxn:mpc}
Our improved understanding and predictions of snow, sea-ice, and polar
climate sensitivity to BC trends will be incorporated via CCSM into
the IPCC AR5 report to help society understand, plan for, and,
potentially, mitigate cryospheric climate change.
At the rate the Arctic is changing, e.g., summer sea-ice retreat
\cite{SSF05},  accounting for all known significant polar climate
amplifiers in AR5 is critical.
Our specific advances in our field include
\begin{enumerate*}
\item Inventory of Arctic forcing efficacies of important BC sources
\item State-of-the-art climatology and evaluation of global snowpack 
  grain size and BC concentration. 
\item Methodology to improve prescribed surface boundary conditions
  (snow reflectance, snow grain size) used in satellite retrievals
\item Improved representation of BC impacts in past, present, and
  future climate
\end{enumerate*}

The project outcomes meets NASA Strategic Goal~3.1 
(``Study planet Earth from space to advance scientific understanding
and meet societal needs'') and IDS Subelement~5 objectives by using 
\textbf{space-based remote sensing} and \textbf{global models} 
to improve understanding and prediction of the 
\textbf{role of black carbon in affecting clouds, precipitation, and 
  the hydrologic cycle}. 
Our improved understanding and predictions of the cryospheric
hydrologic cycle will be incorporated via CCSM into the IPCC AR5
report to help society understand, plan for, and mitigate BC effects
on cryospheric climate.

\section{Mangagement}\label{sxn:mgt}

\subsection{Personnel}\label{sxn:prs}
Zender will develop, test, and implement BC/OC optical and scavenging
property improvements in CAM and SNICAR, lead IPY collaborations, and
coordinate design and interpretation of hypothesis testing outlined
above. 
Co-PI Famiglietti and his group will perform and interpret hydrologic
evaluations against GRACE and AMSR-E satellite data. 
Co-PI Randerson will provide and update GFED fire emissions and MODIS
reflectance time-series and help perform and interpret 
Co-PI Khalsa will obtain, screen, regrid, and average AMSR-E products
suitable for input to and comparison with model simulations.  

UCI graduate student Mark Flanner will merge SNICAR physics into the
CICE sea-ice model and, in Year~3, into the CCSM-compatible version of
the GLIMMER ice sheet model provided by LANL.  
Scientific specialist Dr.~Chao Luo has specialized in atmospheric
dust transport \cite[][]{LMD03,MaL03,LMJ04} and, more recently, 
cryospheric hydrology based on AMSR-E, ISCCP, and CERES retrievals.
Luo will assist with satellite data analysis and model simulations. 

Zender will advise an ESS graduate student in Arctic BC processes.
We will focus on using satellite products, optical models, and
microphysics to improve model-observation agreement in surface albedo
and climate sensitivity (Section~\ref{sxn:idx_obj_csn}).   
A second student graduate student will focus on larger spatial- and
temporal-scale effects of BC on Arctic climate and surface hydrology. 
This student will pick Famiglietti, Randerson, and/or Zender as
primary advisor(s) according to their interests and project needs.

\subsection{Schedule and Milestones}\label{sxn:tm_ln}
\noindent\textbf{Year~1}. \textit{Milestones}: 
1a.~SNICAR interactive with CICE;
1b.~Fractal aggregate soot mixtures;
1c.~Global snow grain size climatology. 
\textit{Tasks and Meetings}:
\begin{enumerate*}
\item Couple SNICAR physics to sea-ice, examine impact on summer melt
  and extent 
\item Update soot optical properties to fractal aggregates
\item Convert/rebin MODIS, AMSR-E SWE ice extent products to model grid
\item Test tropical/boreal/seasonal hypothesis
\item Zender visits Norway/NILU for POLARCAT team meeting
\item Khalsa visits Irvine for IDS team meeting and AMSR-E coordination
\item Flanner visits LANL to coordinate sea-ice simulations
\item Presentations to CCSM PCWG and AGU on Objective~\ref{idx_obj_csn} and tropical/boreal/seasonal hypothesis 
\end{enumerate*}
\textbf{Year~2}. \textit{Milestones}: 
2a.~Sensitivity matrix to BC source/timing;
2b.~DOE ARM IOP participation;
2c.~IPY POLARCAT participation. 
\textit{Tasks and Meetings}:
\begin{enumerate*}
\item Assemble GRACE Arctic water budgets to model grid
\item Produce global snow grain size database for MODIS and AMSR-E retrievals
\item Test sea-ice asymmetry hypothesis, write up results
\item Refine scavenging with Warren et~al.'s Greenland BC measurements;
\item Compare SNICAR predictions with AMSR-E inferred $\rfl$, $\rdsffc$
\item Hunke visits Irvine for sea-ice coordination (separate funding)
\item Zender visits Barrow for DOE ARM NSA IOP
\item Graduate student to LANL to merge SNICAR into GLIMMER
\item Presentations to CCSM PCWG and AGU on
  Objective~\ref{idx_obj_sea_ice} and sea-ice asymmetry hypothesis  
\end{enumerate*}
\textbf{Year~3}. \textit{Milestones}: 
3a.~SNICAR coupled to LANL glacier-model;
3b.~Fully coupled land/sea-ice simulations;
3c.~Millennial Greenland BC extrema.
\textit{Tasks and Meetings}:
\begin{enumerate*}
\item POLARCAT Greenland event simulation
\item Hindcast ARM IOP TOA and surface reflectance
\item Re-visit Hypothesis~\ref{idx_obj_csn} based on outcome of
  Objective~\ref{idx_obj_sea_ice}  
\item If CCSM/GLIMMER ready, estimate soot effect on Greenland ablation
\item Millennial BC impacts on Greenland from ice core data and GFED
\item Presentations to CCSM PCWG and AGU (likely in year~4 also) on 
  Objectives~\ref{idx_obj_atm_sfc} and~\ref{idx_obj_csn}
\end{enumerate*}
\newpage

\section{Acronyms and Abbreviations}\label{sxn:abb}
%\setcounter{page}{1}
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%\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
ALD & Active Layer Depth (of permafrost) & \\
AMSR-E & Advanced Microwave Scanning Radiometer (satellite instrument) & \\
AOMIP & Arctic Ocean Model Intercomparison Project & \\
AR4 & IPCC Fourth Assessment Report & \\
AR5 & IPCC Fifth Assessment Report & \\
ARF & Aerosol Radiative Forcing & \\
ARM & Atmospheric Radiation Measurement & \\
ATSR & Along Track Scanning Radiometer and Microwave Sounder & \\
AVIRIS & Airborne Visible/Infrared Imaging Spectrometer & \\
BC & Black Carbon (light-absorbing component of carbonaceous aerosol) & \\
BRDF & Bi-directional Reflectance Distribution Function & \\
CAM & Community Atmosphere Model & \\
CCSM & Community Climate System Model & \\
CICE & Los Alamos sea-ice model & \\
CLM & Community Land Model & \\
CRM & Column Radiation Model & \\
EMA & Effective Medium Approximation & \\
ESM & Earth System Model & \\
ESMF & Earth System Modeling Facility & \\
ESS & Earth System Science (Department) & \\
GCM & General Circulation Model or Global Climate Model & \\
GFED & Global Fire Emissions Database & \\
GHG & Greenhouse Gas & \\
GLIDE & General Land Ice Dynamic Elements (core of GLIMMER) & \\
GLIMMER & Ice Sheet Model of Payne et~al. & \\
GSFC & Goddard Space Flight Center & \\
ISDAC & Indirect and Semi-Direct Aerosol Campaign & \\
IOP & Intensive Observing Period & \\
IPCC & Intergovernmental Panel on Climate Change & \\
IPY & International Polar Year & \\
ITD & Ice Thickness Distribution & \\
LANL & Los Alamos National Laboratory & \\
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 & \\
NCCS & NASA Center for Computational Sciences & \\
NCO & netCDF Operators & \\
NILU & Norwegian Institute for Air Research & \\
NIR & Near InfraRed & \\
NSA & North Slope of Alaska & \\
NSIDC & National Snow and Ice Data Center & \\
OC & Organic Carbon & \\
OPAC & Optical Properties of Aerosols and Clouds & \\
PI & Principle Investigator & \\
POLARCAT & POLar study using Aircraft, Remote sensing, surface measurements and modeling of Climate, chemistry, Aerosols and Transport (IPY project) & \\
RT & Radiative Transfer & \\
SEM & Scanning Electron Microscopy & \\
SGER & Small Grant for Exploratory Research & \\
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{References}\label{sxn:rfr}
%\section*{References}\label{sxn:rfr} % \section* does not number section
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\csznote{ % NASA proposals require a 1-page table of contents---skip fluff
\printindex % Requires makeidx KoD95 p. 221
\addcontentsline{toc}{section}{Index}
\newpage
} % end csznote

\section{Facilities, Equipment, and Other Resources}
\subsection{Computational Resources}
%\setcounter{page}{1}
%\thispagestyle{empty}
Our IDS project is well-situated to take advantage of UCI's fast
computing capabilities for data analysis and relatively short model
simulations. 
PI~Zender directs the UCI Earth System Modeling Facility (ESMF), 
an NSF-supported MRI facility dedicated to coupled global climate,
chemistry, and biogeochemistry simulations.
The ESMF flagship machines is an 88-CPU Power4+ IBM supercomputer with
192\,\GB\ RAM and 16\,\TB\ of RAID storage.
In Spring 2006, ESMF anticipates acquiring a new Beowulf cluster
comprising approximately twenty two-way dual core Opteron nodes
(80~CPUs) and about 5\,\TB\ of RAID storage.

Since this IDS proposal is squarely fits the ESMF mission, the ESMF
will host the primary modeling development and shorter simulations.
However, the ESMF is inadequate to perform the bulk of the climate
simulations so if funded we will request computing time from the 
NASA Center for Computational Sciences (NCCS) for the fully coupled
CCSM/SNICAR code. 
\newpage

% NASA Proposals: Insert Biographical Sketches Here

% NASA Proposals: Insert Current and Pending Statements Here

\section*{Budget Justification}\label{sxn:bdg_jst} % \section* does not number section
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%\begin{verbatim}
% NB: Do not use LaTeX formatting in Budget Justification since must
% upload into Liz's Word document 
Salaries and Wages:

One month of summer support is requested for the PI, Charlie Zender,
for each year of the project. Prof. Zender will coordinate the overall
project, and take responsibility for modeling aerosol, cloud, and
snowpack interactions. One month of summer salary for each year is
requested for Co-PI Jay Famiglietti and one-half month of summer
salary is requested for Co-PI James Randerson. Prof. Famiglietti will
perform hydrologic evaluations against GRACE and AMSR satellite
data. Prof. Randerson will provide and update GFED fire emissions and
MODIS time-series. 

Funds are requested to support Dr. Chao Luo, Associate Specialist Step
II, at a rate of 0.25 FTE for the first three years of the
project. Dr. Luo is the principal scientific programmer associated
with UCI's Earth System Modeling facility. He has experience with all
the complex models involved in this project (SNICAR, CLM, CAM, CCSM),
as well as CERES, ISCCP, and AMSR datasets. Dr. Luo will assist with
running the models and analyzing model output. 

Salary support is requested for two nonresident PhD graduate students
for all three years. One graduate student dissertation will advance
understanding of BC, cloud, snowpack interactions through
microphysical approaches primarily under the direction of PI
Zender. The other graduate dissertation will ellucidate large spatial
and temporal scale processes which control BC climate effects. 

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 increase, where applicable. 

Employee Benefits:

Fringe Benefits are actual benefits for named employees based on
current financial records. Benefit rates used in this proposal are:
Faculty - summer - 12.7\% Programmer ­ 31\% Student employees - summer
- 3\% Student employees - academic year - 1.3\%.
Fees and tuition are requested for two nonresident
students. University of California policy requires award payment of
fees and tuition for any student with more than 25\% support from a
grant. Nonresident graduate student fees and tuition are \$24,755 for
each student in year 1, \$27,091 per student in year 2, and \$29, 669
per student in year 3. Fees and tuition are excluded from indirect
cost assessment.  

Equipment:

Equipment funds are requested in the first year for three scientific
workstations for model development and data analysis. Workstations
will have storage and software to analyze 1 TB of data with GIS and
statistical software. 

Travel Domestic: 

In years 1, 2 and 3 travel funds are requested for the two members of
the scientific team to attend the 5-day NCAR CCSM Workshop in
Brekenridge. Costs are estimated at \$1,500 per person include
roundtrip air travel, conference registration, hotel, per diem
expenses, and local transportation. 
Support is also requested in each year for two members of the team to
attend the AGU San Francisco meeting for one week. 
Costs are estimated at \$1,500 per person include roundtrip air
travel, conference registration, hotel, per diem expenses, and local
transportation. 

One round-trip at \$3000 is requested for the PI to travel to Barrow,
Alaska in Year 2 to participate in the DOE ARM IOP proposed for the
North Slope of Alaska (NSA) facility. Costs include roundtrip air
travel, hotel, per diem expenses, and local transportation. 
PI will participate in IOP for one week to help characterize arctic
haze interactions with clouds. 

In year 1, support is requested for Dr. Khalsa to visit Irvine and
meet with collaborators for a one-week visit. Costs are estimated at
\$1,500 per person include roundtrip air travel, conference
registration, hotel, per diem expenses, and local ransportation. 
In year 2, travel support is requested for one person to attend the
IGARS conference. Costs are estimated at \$1,500 per person include
roundtrip air travel, conference registration, hotel, per diem
expenses, and local transportation. 

Travel International: 

One round-trip at \$3000 is requested for the PI to
travel to Norway in Year 1 to participate in the IPY POLARCAT team
meeting activities and to present results. This trip includes
roundtrip travel from Irvine to Oslo, one-week hotel and per diem. 
Travel estimates are based on historical usage. 

Other Direct Costs:

Publication Costs: \$2,000 in year 1 and \$4,000 in years 2 and 3
is requested for publication costs pursuant to this project, which
typically include the utilization of expensive color
figures. . 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. Costs were estimated according to historical
usage. 

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. 
%\end{verbatim}
\newpage

\subsection*{Supplementary Documents}\label{sxn:spl_doc} % \section* does not number section
\setcounter{page}{1}
\thispagestyle{empty}
\begin{verbatim}
1. ${DATA}/prp/prp_ids/prp_ids_wrk_frc.pdf
1. ${DATA}/prp/prp_ids/prp_ids_ltr_warren.pdf
2. ${DATA}/prp/prp_ids/prp_ids_ltr_lanl.pdf
3. ${DATA}/prp/prp_ids/prp_ids_ltr_polarcat.pdf
4. ${DATA}/prp/prp_ids/prp_ids_cmt_khalsa.pdf
5. ${DATA}/prp/prp_ids/prp_ids_abb.pdf
6. ${DATA}/prp/prp_ids/prp_ids_fcl.pdf
\end{verbatim}

\clearpage

\csznote{
% Text snippets for future proposals...
} % end csznote

\csznote{
# Dozier's pubs
http://www.snow.ucsb.edu/REASoN-Publications.htm

# Email distribution list
Jay Famiglietti <jfamigli@uci.edu>, Jim Randerson <jranders@uci.edu>, Siri Jodha Singh Khalsa <sjsk@nsidc.org>, Mark Flanner <mflanner@uci.edu>, Charlie Zender <zender@uci.edu>

List all participants in this investigation, both requesting funding
and not requesting funding, who do not appear on the proposal's cover
page as a Co-investigator, Collaborator, or any other category of
participant. Include name, institution, city, state or country, and a
description of the role in five words or less (e.g. data analyst,
facility provider, support technician).

Professor Eric Saltzman
University of California at Irvine
Irvine, CA
Provide ice core BC measurements

Dr. Chao Luo
University of California at Irvine
Irvine, CA
Scientific programmer/analyst

Professor Steve Warren
University of Washington
Seattle, WA
Provides soot/scavenging measurements

Drs. Elizabeth Hunke and Bill Lipscomb
Los Alamos National Laboratory
Los Alamos, NM
Supervise soot and sea-ice integration

Dr. Phil Rasch
National Center for Atmospheric Research
Boulder, CO
Collaborate on BC in CAM

Dr. Jorge Talamantes
California State University, Bakersfield
Bakersfield, CA
Assist with BC optical calculations

% Text from Jay Famiglietti in prp_MZF06:
We will also pay particular attention to the role of groundwater in
freshwater and nutrient transport. Syed et al. (2005) recently
developed a method for estimating total basin discharge (both surface
and groundwater) using data from the Gravity Recovery and Climate
Experiment (GRACE) mission (Tapley et al., 2004).  This method is
currently being implemented globally within Famiglietti's research
group, with an important focus on quantifying rates of surface water
and submarine groundwater discharge (SGD).  In regions where these and
other results point to the importance of SGD in global land-ocean
freshwater and nutrient transfers, we will incorporate an additional
groundwater module within the CLM-CN (e.g. Yeh and Eltahir,
2005a,b). Postdoctoral associate Pat Yeh, the groundwater model
developer for the above reference, is currently a member of
Famiglietti's group, and will supervise this component of model
development.  Dr. Yeh is supported by other external sources.

% Text received from Siri Jodha Khalsa:
Below are some words for section 5.5.

In reading the last draft the distinctions between "aerosol," dust, and BC get blurred.  First sentence - is there an implied "that is" between "aerosols" and "soot and dust" or is it a 3-item list?

Do you want a letter from me that spells out the last sentence of the text below?

Microwave brightness temperatures measured by AMSR-E can be used to retrieve several geophysical parameters that will be used to constrain and evaluate model performance.  Currently, Snow Water Equivalent (SWE) is produced on 25 km Northern and Southern Hemisphere azimuthal equal area grids over non-ice surfaces for daily, five day and monthly time periods. The standard product, archived at NSIDC, uses a static, semi-empirical approach based on Chang, Foster & Hall, (1987) that is subject to errors due to variable snow crystal size, inadequate wet snow discrimination, and difficulty mapping snow in densely forested areas. Soon, a new, more dynamic algorithm that incorporates estimates of snow properties in the retrieval process will be implemented. We will use this new product to constrain and evaluate SNICAR simulations of continental snowpack. In particular, we are interested in assessing the influence of extreme aerosol events on the duration of snow cover in seasonally snow covered regions which are most susceptible to snow-albedo feedback (Flanner and Zender, 2005). In addition, we will compare AMSR-E predictions of mean snowpack grain size with model predictions.

Sea ice concentration, as well as snow depth over sea ice, are other AMSR-E derived standard products that we will incorporate into our investigations. The 12.5 km sea ice concentration product is generated using an algorithm based on techniques described in Cavalieri et al. (1984) and Gloersen and Cavalieri (1986). An improved algorithm (Markus and Cavalieri 2000) was developed to reduce ice concentration biases resulting from surface glaze and layering in the snow cover and from thin ice types. The algorithm also quantifies atmospheric effects by calculating TBs for each channel using a forward atmospheric radiative transfer model for first-year ice, multiyear ice, total ice concentration, and open water. The method for deriving snow depth over sea ice is described in Markus and Cavalieri (1998). We will compare sea-ice extent simulations to AMSR-E measurement... (is snow depth a prognostic variable of CICE?)

Finally, AMSR-E is capable of estimating surface soil moisture content. The proposed at-launch algorithm, described in Njoku, et al. (2003), used an interative scheme to solve simultaneously for soil moisture, vegetation water content and surface temperature using a forward radiative transfer model. Due to radio frequency interference in the 6.9GHz channel (Njoku et al., 2004) this method could not be applied globally, and so a regression approach based the 10.7 and 18.7 GHz channels was developed. The standard product is generated daily on a 25-km cylindrical equal area grid. Surface soil moisture is a prognostic variable in SNICAR (see Fig. 4) and we will compare with in situ (will we have?) and AMSR-E data with it.

Dr. Siri Jodha S. Khalsa will be responsible to obtaining AMSR-E products, screening, regridding and averaging to produce parameters suitable for input to or comparison with model simulations.

Chang, A.T.C., J.L. Foster, and D.K. Hall. 1987. Nimbus-7 derived global snow cover parameters. Annals of Glaciology 9: 39-44.

Cavalieri, D.J., P. Gloersen, and W.J. Campbell. 1984. Determination of sea ice parameters with the NIMBUS-7 SMMR. Journal of Geophysical Research 89(D4):5355-5369.

Gloersen P. and D.J. Cavalieri. 1986. Reduction of weather effects in the calculation of sea ice concentration from microwave radiances. Journal of Geophysical Research 91(C3):3913-3919.

Markus, T. and D. Cavalieri. 1998. Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data. Antarctic Sea Ice: Physical Processes, Interactions, and Variability. Antarctic Research Series 74:19-39. Washington, DC, USA: American Geophysical Union.

Markus, T., and D. Cavalieri. 2000. An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on Geoscience and Remote Sensing 38: 1387-1398.
x

Njoku, E. G., T. L. Jackson, V. Lakshmi, T. Chan, and S. V. Nghiem. 2003. Soil moisture retrieval from AMSR-E. IEEE Transactions on Geoscience and Remote Sensing 41 (2): 215-229.

Njoku, E., T. Chan, W. Crosson, and A. Limaye. 2004. Evaluation of the AMSR-E data calibration over land. Italian Journal of Remote Sensing 29 (4): 19-37.

Good luck,

Siri Jodha

home: 303-494-4487, cell phone: 720-231-3699,  cabin 303-747-2608.
don't hesitate to call.
} % end csznote

\csznote{
cd ~/prp_ids
/bin/rm runoff_comp_clm.*
wget http://dust.ess.uci.edu/chaoluo/runoff/runoff_comp_clm.ps
ps2pdf runoff_comp_clm.ps
ln -s -f runoff_comp_clm.ps runoff_comp_clm.eps

cd ~/prp_ids
/bin/rm clm01d*
wget http://dust.ess.uci.edu/mflanner/permafrost/clm01d.eps

cd ~/prp_ids
/bin/rm clm21*
wget http://dust.ess.uci.edu/mflanner/permafrost/clm21.eps

cd ~/prp_ids
/bin/rm clm24*
wget http://dust.ess.uci.edu/mflanner/permafrost/clm24-clm01d_permafrost.eps

cd ~/prp_ids
/bin/rm clm11_clm10*
wget http://dust.ess.uci.edu/chaoluo/amsre/climatology/seasonal/clm11_clm10_amsre_clm_0112_TiP_vlwcsfc_NS.ps
ln -s -f clm11_clm10_amsre_clm_0112_TiP_vlwcsfc_NS.ps clm11_clm10_amsre_clm_0112_TiP_vlwcsfc_NS.eps

cd ~/prp_ids
/bin/rm albs_GR*
wget http://dust.ess.uci.edu/mflanner/alb/albs_GR2.eps
} % end csznote on figures

\csznote{ % Proposal preparation: 
/bin/cp ${DATA}/ps/bio_nsf.pdf ${DATA}/prp/prp_ids/prp_ids_cv_zender.pdf

# Divvy up master PDF into FastLane components
pdftk A=${DATA}/ps/prp_ids.pdf cat A4 output ${DATA}/prp/prp_ids/prp_ids_wrk_frc.pdf
pdftk A=${DATA}/ps/prp_ids.pdf cat A5-6 output ${DATA}/prp/prp_ids/prp_ids_smr.pdf
pdftk A=${DATA}/ps/prp_ids.pdf cat A3-29 output ${DATA}/prp/prp_ids/prp_ids_dsc.pdf
pdftk A=${DATA}/ps/prp_ids.pdf cat A22-23 output ${DATA}/prp/prp_ids/prp_ids_abb.pdf
pdftk A=${DATA}/ps/prp_ids.pdf cat A24-28 output ${DATA}/prp/prp_ids/prp_ids_rfr.pdf
pdftk A=${DATA}/ps/prp_ids.pdf cat A29 output ${DATA}/prp/prp_ids/prp_ids_fcl.pdf
pdftk A=${DATA}/ps/prp_ids.pdf cat A30-31 output ${DATA}/prp/prp_ids/prp_ids_bdg_jst.pdf

cd ${DATA}/prp/prp_ids; pdftotext ${DATA}/prp/prp_ids/prp_ids_smr.pdf

# Add letters of support in one command rather than loop
# pdftk does not allow input file to be output file
pdftk A=${DATA}/prp/prp_ids/prp_ids_ltr_warren.pdf \
B=${DATA}/prp/prp_ids/prp_ids_ltr_lanl.pdf \
C=${DATA}/prp/prp_ids/prp_ids_ltr_polarcat.pdf \
D=${DATA}/prp/prp_ids/prp_ids_cmt_khalsa.pdf \
cat A B C D output ${DATA}/prp/prp_ids/prp_ids_ltr.pdf

pdftk A=${DATA}/prp/prp_ids/prp_ids_cv_randerson.pdf cat A1-2 output ${DATA}/prp/prp_ids/prp_ids_cv_randerson_2pg.pdf
/bin/mv ${DATA}/prp/prp_ids/prp_ids_cv_randerson_2pg.pdf ${DATA}/prp/prp_ids/prp_ids_cv_randerson.pdf

# Add supplementary files in one command rather than loop
# pdftk does not allow input file to be output file
pdftk A=${DATA}/prp/prp_ids/prp_ids_dsc.pdf \
C=${DATA}/prp/prp_ids/prp_ids_cv_zender.pdf \
D=${DATA}/prp/prp_ids/prp_ids_cv_famiglietti.pdf \
E=${DATA}/prp/prp_ids/prp_ids_cv_randerson.pdf \
F=${DATA}/prp/prp_ids/prp_ids_cv_khalsa.pdf \
G=${DATA}/prp/prp_ids/prp_ids_cp_zender.pdf \
H=${DATA}/prp/prp_ids/prp_ids_cp_famiglietti.pdf \
I=${DATA}/prp/prp_ids/prp_ids_cp_randerson.pdf \
J=${DATA}/prp/prp_ids/prp_ids_ltr_warren.pdf \
K=${DATA}/prp/prp_ids/prp_ids_ltr_lanl.pdf \
L=${DATA}/prp/prp_ids/prp_ids_ltr_polarcat.pdf \
M=${DATA}/prp/prp_ids/prp_ids_cmt_khalsa.pdf \
cat A C D E F G H I J K L M output ${DATA}/ps/prp_ids_fll.pdf
} % end csznote on usage

\csznote{
% Usage: Place usage here at end of file so comment character % not needed
cd ~/prp_ids;make -W prp_ids.tex prp_ids.dvi prp_ids.ps prp_ids.pdf prp_ids.txt;cd -
scp ${HOME}/prp_ids/prp_ids.dvi ${DATA}/ps/prp_ids.pdf ${DATA}/ps/prp_ids.ps ${HOME}/prp_ids/prp_ids.tex ${HOME}/prp_ids/prp_ids.txt dust.ess.uci.edu:/var/www/html/prp/prp_ids

# NB: latex2html works well on prp_ids.tex
latex2html -dir /var/www/html/prp/prp_ids prp_ids.tex
# NB: tth chokes on prp_ids.tex
cd ${HOME}/prp_ids;tth -a -Lprp_ids -p./:${TEXINPUTS}:${BIBINPUTS} < ${HOME}/prp_ids/prp_ids.tex > prp_ids.html
scp prp_ids.html dust.ess.uci.edu:/var/www/html/prp/prp_ids
# NB: tex4ht works well on prp_ids.tex
cd ${HOME}/prp_ids;htlatex prp_ids.tex
scp prp_ids*.css prp_ids*.html dust.ess.uci.edu:/var/www/html/prp/prp_ids
# NB: tex4moz works well on prp_ids.tex
cd ${HOME}/prp_ids;/usr/share/tex4ht/mzlatex prp_ids.tex
scp prp_ids*.css prp_ids*.html prp_ids*.xml dust.ess.uci.edu:/var/www/html/prp/prp_ids
} % end csznote on usage

\end{document}