On the Web at http://dust.ess.uci.edu/prp/prp ans-1.0/prp ans.pdf NSF Arctic Natural Sciences (ANS) Proposal Submitted: December 22, 2005 Last modified: Friday 10th August, 2007, 09:55 Next Round Due: November 10, 2006

Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks
Dr. Charles S. Zender Department of Earth System Science University of California, Irvine News/Preface: This is NSF proposal 0612954. Information for potential collaborators: This NSF proposal responds to the 2005 NSF Arctic Research Opportunities (ARO) announcement, NSF 05-618. The proposale was submitted to the Arctic Natural Sciences (ANS) Program of the Arctic Sciences Section (ASS) in the Office of Polar Programs (OPP). The cognizant Program Manager is Jane V. Dionne jdionne@nsf.gov, (703) 292-7427. Suggestions for next round: 1. Don’t write it in an all-nighter three days before Christmas 2. Make satellite section less cheesey for NASA—e.g., include AMSR-E comparison 3. Contact Dorothy Koch for NASA-GISS snow physics collaboration?

Contents
Contents List of Figures 1 Introduction 1.1 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results from Prior NSF Funding on Related Projects Background 3.1 Relevance and Historic Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Absorbing Arctic Aerosol in Models . . . . . . . . . . . . . . . . . . . . . . . . . Scientific Objectives and Hypotheses i ii 1 2 2 2 2 4 6

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LIST OF FIGURES 5 Methods: Arctic Models and Observations 5.1 Community Climate System Model . . 5.2 SNICAR . . . . . . . . . . . . . . . . . 5.2.1 Snow Aging . . . . . . . . . . 5.2.2 Optics . . . . . . . . . . . . . . 5.3 Sea-Ice and Ice Sheets . . . . . . . . . 5.4 Numerical Experiment Strategy . . . . 5.5 Satellite Observations . . . . . . . . . . 5.6 In Situ Observations . . . . . . . . . . . 5.7 IPY POLARCAT Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ii 7 7 8 9 10 10 11 11 12 12

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Project Coordination 13 6.1 Personnel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.2 Schedule and Milestones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Related Projects, Broader Impacts and Education 14 7.1 Related Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 7.2 Improved Community Modeling Capabilities . . . . . . . . . . . . . . . . . . . . 15 7.3 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1 1 1 1 1 1 1 1

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Bibliography Index 7.4 8

Budget Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Facilities, Equipment, and Other Resources 8.1 Computational Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acronyms and Abbreviations

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10 Project-Wide Combined Collaborator and Advisor List 10.1 Supplementary Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

List of Figures
1 2 3 4 5 Snow-albedo feedback schematic . . . . . . . . . . . . . . Snowpack soot direct radiative forcing . . . . . . . . . . . Niwot Ridge albedo decay, Isothermal specific surface area Spectral reflectance of snow; Response of effective radius . Snow melt response to snowpack soot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 4 5 6 9

Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks
Dr. Charles S. Zender Department of Earth System Science, University of California, Irvine

Project Summary.
Surface and atmospheric concentrations of black carbon (BC), i.e, absorbing carbonaceous aerosol, are highly variable and slowly increasing in the Arctic. Current understanding Arctic BC climate impacts derives mostly from studies which focus on BC direct atmospheric radiative forcing and which neglect or drastically simplify surface BC interactions. However, the prevalence of bright surfaces (snow, glaciers, sea-ice, and clouds) make the Arctic uniquely susceptible to radiatively induced effects of surface BC and dust such as ice-albedo feedback amplification. 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. 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, BC, and dust, and which have been evaluated against satellite, in-situ, and laboratory measurements. The project builds upon, extends, and applies our existing, SNow, ICe, and Aerosol Radiative model, SNICAR. SNICAR has already helped us show that snowpack aging processes can trigger ice-albedo feedbacks which amplify direct surface forcing by an order of magnitude in mid-latitude regions. We hypothesize that Arctic BC and dust cause similar, or possibly greater, ice-albedo feedback amplification over glaciers and sea-ice. However, current coupled model systems cannot test this hypothesis. Hence, our project integrates BC, dust and snow lifecycles within SNICAR, and places SNICAR in existing coupled snow, ice sheet, and sea ice models. NASA MODIS, MISR, and AMSR-E retrievals will help us constrain model parameters, evaluate predictions, and interpret the regional and seasonal behavior of snowpack processes. 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, BC concentration, BC melt scavenging, and aging processes will (continue to) be evaluated against Arctic in situ measurements. Scientific Merit: Our studies of Arctic snow-ice-aerosol processes will improve understanding of ice-albedo feedbacks and polar climate amplification. Key scientific questions we will address include: 1) Potential for Arctic BC to disrupt summertime sea-ice formation; 2) Role of Boreal fire variability in Arctic reflectance, particularly Greenland; 3) Relative roles of Arctic soot and dust in inducing surface melt, heating, and darkening. These questions will be addressed in the context of pre-industrial, present day, and next century timescales. Broader Impacts: Snow pervades the Arctic, and our improved snowpack model can and probably will improve understanding of Arctic hydrology. SNICAR will be freely available to run in both off-line and Community Climate System Model modes. We anticipate it will contribute to Arctic research areas including glacier mass balance, basin and catchment hydrology, sea-ice lifecycle, paleoclimate sensitivity, and snow chemistry. This project trains one graduate student in Arctic aerosol-climate interactions. PI Zender will incorporate this Arctic climate change research into a K–12 teacher training program and into presentations for lifelong learning students.

Dirty Snow on Land, Glaciers, and Sea-Ice: Understanding Arctic Absorbing Aerosol Forcing and Feedbacks 1 Introduction

Surface and atmospheric concentrations of black carbon (BC), i.e, absorbing carbonaceous aerosol, are highly variable and slowly increasing in the Arctic (Penner et al., 2001). Current understanding of Arctic BC climate impacts derives mostly from studies which focus on BC direct atmospheric radiative forcing and which neglect or drastically simplify surface BC interactions. However, bright surfaces (snow, glaciers, sea-ice, and clouds) make the Arctic uniquely susceptible to radiatively induced effects of surface BC and dust such as ice-albedo feedback amplification. Such feedbacks make dirty snow more efficacious than greenhouse gases at atmospheric temperature change (Hansen and Nazarenko, 2004). 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 (e.g., Light et al., 1998; Aoki et al., 2003; Flanner and Zender, 2006). 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, BC, and dust, and which have been evaluated against satellite, in-situ, and laboratory measurements. We use the terms soot and BC interchangeably to denote the light absorbing component of carbonaceous aerosol (Bond and Bergstrom, 2005). 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 (Hansen and Nazarenko, 2004), and significant reductions in Northern hemisphere albedo and sea-ice extent (Jacobson, 2004). A lesson learned from our mid-latitude snow studies is that such estimates are extremely sensitive to accurate treatment of snowpack aging and radiative transfer (Flanner and Zender, 2005), two areas where this project will devote significant attention. Dust can also play an important role in the Arctic, far from its dominant sources in the subtropical deserts (Prospero et al., 2002; Zender et al., 2003a). Dust embedded in Arctic ice cores records global climate change (e.g., Andersen et al., 1998; Ram and Koenig, 1997; Fuhrer et al., 1999; Kohfeld and Harrison, 2001) and marks periods of abrupt climate change (e.g., Alley, 2000). The extent to which this embedded dust may change Arctic and global climate is still uncertain, and potentially significant for understanding abrupt climate change mechanisms (Archer et al., 2000; Harrison et al., 2001; Mahowald et al., 2006). 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 fully interactive atmosphere-land-ocean-glacier-sea-ice model which accounts for soot and dust radiative interactions in the surface Arctic. Fortunately the necessary component models for an integrated model to assess, predict, and improve understanding of Arctic absorbing aerosol exist (e.g., Collins et al., 2006a). Coupled component models are commonly used to study Arctic climate (e.g., Holland and Bitz, 2003; Holland et al., 2006). This project uses high quality component models with realistic snow-ice-aerosol physics (Flanner and Zender, 2005, 2006) to ask questions about the integrated impacts of absorbing aerosols on Arctic climate.

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RESULTS FROM PRIOR NSF FUNDING ON RELATED PROJECTS

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1.1 Organization
This proposal is organized as follows. Section 2 describes the results of our relevant, prior NSFfunded research. The project’s historical and scientific context is in Section 3. Our scientific objectives and specific hypotheses are in Section 4. Section 5 describes the models and observations we will use to reach these goals. Section 6 summarizes the project plan, personnel responsibilities, time-line, milestones, and travel. Projects related to ours, potential broader scientific impacts, and our education plan are in Section 7. Four letters of support/collaboration and a list of acronyms and abbreviations appear as supplementary documents.

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Results from Prior NSF Funding on Related Projects

PI Zender is PI on ATM-0321380: “Acquisition of an Earth System Modeling Facility (ESMF) for Coupled Climate, Chemistry, and Biogeochemistry Studies”. The ESMF opened in February 2004 and supports about thirty users. The ESMF is the main computational resource for UCI’s Earth System Science (ESS) Department. All ESS modeling groups use the ESMF. This includes eight professors, four full-time researchers, and about a dozen graduate students performing global modeling/analysis as part of their dissertations. In order to utilize spare cycles when ESS jobs are not running, the ESMF provides free (but lower priority) access to researchers/students from any UCI department. A summary of (about one dozen) published and in-press papers which used and acknowledged the collaborative ESMF scientific computing facility is maintained here. Zender is/was PI on ATM-0503148: “SGER: Improving CCSM Snow/Ice Radiative and Heating Processes and Assessing the Importance of the Soot Albedo Effect”. This SGER grant funded graduate student Mark Flanner for one year, during which he published one paper (Flanner and Zender, 2005), placed one manuscript in review (Flanner and Zender, 2006), and gave three national meeting presentations. Flanner and Zender (2005) shows that resolving the vertical distribution of solar radiant heating within snowpack remediates significant climatological biases in the Tibetan Plateau region. Flanner and Zender (2006) 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 5.2). This proposal requests one year of postdoctoral funding for Flanner.

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Background

3.1 Relevance and Historic Trends
Bright surfaces (snow, glaciers, sea-ice and clouds) make the Arctic uniquely susceptible to radiatively induced effects of surface BC and dust such as ice-albedo feedback amplification (e.g., Holland and Bitz, 2003; Hansen and Nazarenko, 2004; Qu and Hall, 2006) (Figure 1). Snowalbedo feedback is triggered by any forcing mechanism which changes the areal extent of snow cover. 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

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BACKGROUND

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Figure 1: Absorbing aerosols like soot and dust mediate snow-albedo feedback via multiple paths.

gain (G) on feedback involving grain growth. Finally, a fourth mechanism perturbation may result from accumulation of hydrophobic impurities at the surface during melt events, as supported by observations from Clarke and Noone (1985) and Conway et al. (1996). Absorbing aerosol y also alters cloud reflectivity and lifecycle (Ch´ lek et al., 1996; Ackerman et al., 2000). However, cloud-aerosol effects are not a focus of this project and will not be further mentioned. Surface and atmospheric concentrations of black carbon (BC), i.e, absorbing carbonaceous aerosol, are highly variable and slowly increasing in the Arctic. Most emission scenarios project an increase in anthropogenic BC emissions of 30–250% in the 21st century (Naki´ enovi´ et al., c c 2000; Koch and Hansen, 2005). Changes in fire regime also affect total BC emissions (van der Werf et al., 2004). Hence BC impacts on the Arctic will likely increase through the 21st century. Dust plays an important role in accelerating snow melt in some mid-latitude regions (Dr. Tom Painter, NSIDC, personal communication). Dust-snow interactions may play a very important role in glacial climates due to increased aridity, equator-to-pole temperature gradients, exposed continental shelves, and peri-glacial dust generation (Ram et al., 1997; Mahowald et al., 2005, 2006). Estimates of past and future dust emissions contain large uncertainties including those due to land use change and to CO2 fertilization of vegetative cover (e.g., Mahowald and Luo, 2003; Tegen et al., 2004). Arctic atmospheric and snowpack BC measurements span a wide range of concentrations (Clarke and Noone, 1985; Noone and Clarke, 1988; Hansen and Nazarenko, 2004). Greenland concentrations are typically 1–4 µg kg−1 , and as high as 30 µg kg−1 (Slater et al., 2002). Models driven by satellite-derived emissions estimates suggest that boreal fires explain the largest component of interannual Arctic BC deposition variability (Koch and Hansen, 2005). Our model (Flanner et al., 2005) uses prescribed fossil and biofuel BC emissions (Bond et al., 2004), and converts fire emissions (van der Werf et al., 2003, 2004; Randerson et al., 2005b) to BC with estimated emissions factors (Andreae and Merlet, 2001). We estimate that biomass burning BC emissions north of 40◦ N increased from 0.1 Tg to 0.58 Tg between 1997, a weak fire year, and 1998, a strong fire year. We study Arctic climate-aerosol interactions within a coupled general circulation model (GCM) framework (Section 3.2). Using this tool, we estimate Boreal fire emissions changes from 1997

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Figure 2: Summertime mean surface direct radiative forcing [W m−2 ] by soot in snowpack during weak (1997) and strong (1998) boreal burn years. to 1998 increase surface snowpack radiative forcing in the Arctic by about 50% (Flanner et al., 2005) (Figure 2). 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 5.7). More to the point, recent investigations by us and others suggest that aerosol-snow interactions play a significant role in Arctic climate sensitivity via snow-ice-albedo feedback mechanisms on which rapid progress can be made.

3.2 Absorbing Arctic Aerosol in Models
The fundamental physics of snowpack-aerosol radiative interactions have been understood for over 25 years (Wiscombe and Warren, 1980; Warren and Wiscombe, 1980; Clow, 1987). 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 (e.g., Oleson et al., 2004). 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) (e.g., Warren and Wiscombe, 1980; Aoki et al., 2003) (Figure 3). 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 (Flanner and Zender, 2006). 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. Flanner and Zender (2005) studied the vertical distribution of solar radiant heating on snowpack. Using accurate radiative transfer methods in a multi-layer snowpack model (Section 5.2), we showed that 20–40% of solar heating occurs beneath the top 2 cm of typical snowpack. This sub-surface heating

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BACKGROUND
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Figure 3: Left Panel: Observed and modeled albedo decay at Niwot Ridge following the January 2, 2001 snowfall event. Error bars represent on standard deviation of all measurements comprising each day’s albedo change. Right Panel: Observed and modeled (SNICAR) isothermal snowpack specific surface area (SSA) (Legagneux et al., 2004; Flanner and Zender, 2006). leads to internal snowpack melt. Internal snowpack absorption 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 (Molotch et al., 2004; Flanner and Zender, 2005). Snow insulation keeps the internal snowpack warmer than the surface so that radiativelyinduced internal warming can trigger the moderate snow-grain size-feedback loop (Figure 1) until melting triggers the stronger snow-cover feedback loop. Today’s most sophisticated snow and ice models include Arctic soot, and, less frequently, dust (Jacobson, 2004; Hansen and Nazarenko, 2004; Flanner et al., 2005). Interestingly, our preliminary simulations of Greenland show that soot can reduce total snow melt. Significant soot concentrations in the surface snowpack strongly absorb visible radiation some of which would otherwise penetrate into the snowpack (Figure 4). 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 1). The ramifications of these competing feedbacks for Arctic climate remain unknown. Nevertheless, it is clear that a prognostic approach which de-convolves SSA and aerosol effects on snowpack aging and heating is required for Arctic aerosol-climate impact studies such as this project. Previous efforts to understand absorbing aerosol impacts on the Arctic have made many approximations which disallow some important feedbacks that occur in Nature. This project, in collaboration with others, will result in a more complex and interactive Arctic system where 1. 2. 3. 4. 5. 6. Prognostic glaciers allow determination of net hydrology Sea-ice is sensitive to surface aerosol radiative effects Snow-ice-aerosol interactions are consistent across the cryosphere Soot is treated as fractal aggregates not spheres Snowpack aging reproduces observed features Glacial dust and boreal soot sources are accounted for

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SCIENTIFIC OBJECTIVES AND HYPOTHESES
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Figure 4: Left Panel: Spectral reflectance of pure snow and snow externally mixed with 200 µg kg−1 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 re [µm] of surface snowpack layer due to 1998. Cross-hatching indicates statistically significant changes (p < 0.05) relative to simulations without boreal soot (Flanner et al., 2005).

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Scientific Objectives and Hypotheses

Our studies of Arctic snow-ice-aerosol processes will improve understanding of ice-albedo feedbacks and polar climate amplification in Nature, and their representations in models. Key scientific questions we will address include: 1. Objective: Arctic absorbing aerosol impacts on polar climate sensitivity mediated by sea-ice Hypothesis: Arctic BC can significantly disrupt summer sea-ice formation and extent during strong burn years Multiple lines of evidence support this hypothesis: First, representation of thin sea-ice amplifies polar climate sensitivity (Holland and Bitz, 2003; Holland et al., 2006). Second, internal snowpack heating amplifies mid-latitude climate sensitivity (Flanner and Zender, 2005). Third, aging and absorbing aerosol content increase polar climate sensitivity (Jacobson, 2004; Hansen and Nazarenko, 2004; Flanner et al., 2005). 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. Summertime Arctic sea-ice extent has been declining in recent decades, likely related to greenhouse gas-induced warming (Serreze et al., 2003; Stroeve et al., 2004). We will quantify the extent to which snow-aerosol interactions may contribute to this trend. Boreal emissions estimates for recent strong fire years (Randerson et al., 2005b) will allow us to search for connections to recent accelerations in Arctic sea-ice reduction (g.g., Stroeve et al., 2004). 2. Objective: Quantify relative roles of Arctic soot and dust as polar climate amplifiers Hypothesis: Boreal soot amplifies Arctic climate sensitivity more/less than dust in the

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present/LGM climate. Soot is approximately an order of magnitude more absorptive than dust at solar wavelengths (assuming single scattering albedos of 0.5 and 0.95 for soot and dust, respectively). Dust deposition to Greenland in glacial periods is 2–20 times greater than present day (e.g., Ram and Koenig, 1997; Mahowald et al., 2006), 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 (Biscaye et al., 1997) will likely deposit more continually than North American glacial dust (Mahowald et al., 2006). The springtime maximum Asian dust export (Zender et al., 2003a) is less efficacious for Arctic forcing than summertime aerosol events. How these spatio-temporal deposition patterns affect Arctic climate sensitivity is nearly completely unexplored. 3. Objective: Role of Boreal fire variability in Arctic reflectance, particularly Greenland Hypothesis: BC warms Greenland in strong fire years and cools Greenland in weak fire years. 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 (Flanner et al., 2005) (Figure 5). Ice core analyses (Dr. Eric Saltzman, UCI, personal communication) and model simulations (Koch and Hansen, 2005; Flanner et al., 2005) 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 (Randerson et al., 2005b) to study maximum changes in Greenland reflectance and melt due to boreal BC over the past 1000 years.

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Methods: Arctic Models and Observations

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.

5.1 Community Climate System Model
An integrated Earth System Model which fully couples aerosols, snow, atmosphere, ocean, and land/sea-ice is required to test our hypotheses (Section 4). The NCAR CCSM is that model. CCSM polar climate simulations have been continually evaluated against meteorological analyses and satellite observations (e.g., Briegleb and Bromwich, 1998b; Holland and Bitz, 2003; Holland et al., 2006). Arctic absorbing aerosols, soot and dust, are primarily emitted from non-frozen land surfaces at lower latitudes (e.g., Zender et al., 2003a; Koch and Hansen, 2005), and, as such, cut across mul-

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tiple climate “spheres” (biosphere-atmosphere-cryosphere). The project relies on our continuing external collaborations for realistic aerosol distributions and simulation codes. The CCSM BC/OC aerosol transport, deposition, and optics we use come from long time collaborators Drs. Phil Rasch and Bill Collins (NCAR) (Collins et al., 2001, 2002). Long time collaborator Dr. Natalie Mahowald (NCAR, see attached letter of support) studies atmospheric biogeochemistry and dust-carbon-climate interactions on multiple timescales. Mahowald and PI Zender are primary developers of the Dust Entrainment and Deposition (DEAD) mineral dust model (Zender et al., 2003a,b; Mahowald et al., 2003). Ice core measurements are consistent with peri-glacial dust production during glacial periods (Mahowald et al., 2006). Our simulations will account for this glacial dust production using the method and model of Mahowald et al. (2006).

5.2 SNICAR
The project builds upon, extends, and applies our existing, state-of-the-art, SNow, ICe, and Aerosol Radiative model, SNICAR (Flanner and Zender, 2005, 2006). 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 (Wiscombe and Warren, 1980; Toon et al., 1989) treats snow as a collection of hexagonal prisms based on the equivalent surface area-to-volume approximation (Grenfell and Warren, 1999; Neshyba et al., 2003). 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 (Dai et al., 2003), and verticallyresolved effective radius (re ), snow depth, density, and concentrations of absorbing impurities (Warren and Wiscombe, 1980). An off-line version of SNICAR runs at high spectral resolution, 10 nm resolution in the solar spectrum from 0.3–5.0 µm (470 bands). This is useful for simulating narrow-band satellite channels such as MODIS/MISR channels (Figure 4). For climate simulations, SNICAR runs in a host snowpack model, typically the Community Land Model (CLM) (Dai et al., 2003). The CLM uses five vertical snowpack layers (Oleson et al., 2004) 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) (Collins et al., 2006b) with modifications for prognostic soot driven by recent fossil fuel and biofuel emissions estimates (Bond et al., 2004), and biomass burning emissions (van der Werf et al., 2003, 2004; Randerson et al., 2005b). Our simulations suggest boreal soot causes seasonal net surface solar radiation forcings of 0.5–0.75 W m−2 (Figure 2) in strong fire years. These forcings induce feedbacks such as larger snow grain size (Figure 4) which together increase seasonal surface absorption by more than 1.5 W m−2 (Flanner et al., 2005). Soot-snow feedbacks in strong fire years appear to cause significant increases in meltwater production in Greenland snowpack (Figure 5). 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, significant Arctic change is attributable to aerosol-snowpack feedbacks not represented in most GCMs. This makes us eagerly anticipate results in year 3 when SNICAR is embedded in fully interactive sea-ice and glacier models which can respond to soot and to glacial dust sources (Mahowald et al., 2006).

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Figure 5: Summertime mean change in Greenland snow melt [mm d−1 ] 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 (p < 0.05) relative to simulations without boreal soot (Flanner et al., 2005). 5.2.1 Snow Aging

Existing representation of snow aging in Arctic climate models is crude and empirical. Some models consider the role of temperature in albedo decay (Jordan, 1991; Oleson et al., 2004) though none consider the dominant role of temperature gradient (TG), which requires a multi-layer snow model such as SNICAR. High-latitude snowpack can have temperature gradients well in excess of 100 K m−1 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 (Sturm and Benson, 1997). Using first principles, we account 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) (Flanner and Zender, 2006). 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 TG environments (e.g., Colbeck, 1980). Recent SEM observations of well-sintered snow support the hypothesis that grain-boundary diffusion is an important mechanism in sintering (Robock et al., 2006), although the importance of this mechanism has been discounted in earlier studies. Meltwater flushing is the most important 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 (Warren and Wiscombe, 1980; Clarke and Noone, 1985). Conway et al. (1996) 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 e-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. Planned field studies from Warren and Grenfell (Section 5.6) will help constrain

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these scavenging factors, while current studies by Dr. Tom Painter (NSIDC) will help estimate scavenging efficiency for dust. In summary, this project will improve representation of these snowpack aging processes in SNICAR: 1. Sintering and melting/re-freezing in temporal SSA decay. Mark Flanner and Dr. Tom Painter (NSIDC) will collaborate on representing this process which may be very important for surface albedo in low-TG environments 2. Frost deposition that brightens snow surfaces (Pirazzini, 2004). We will explore means of parameterizing diurnal SSA increases due to hoar frost. 3. Meltwater scavenging of soluble and insoluble aerosols 5.2.2 Optics

Snow and aerosol optical properties link the snowpack microphysical properties (aerosol concentration, particle size distributions) to macroscopic net absorption (Figure 2a), reflectances (Figures 3 and 4a), 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 1. BC indices of refraction: Bond and Bergstrom (2005) question the OPAC properties (Hess et al., 1998) (which we use) and recommend other measurements including Chang and Charalampopoulos (1990) 2. BC shape: Treating BC as spheres likely underestimates its single scattering albedo relative to more realistic shapes such as fractal aggregates (Sorensen, 2001; Bond and Bergstrom, 2005) 3. Aerosol mixing: BC and dust in remote regions such as the Arctic are primary deposited via wet scavenging (Clarke et al., 2001, 2004; Zender et al., 2003a) and so will often be internally mixed within snow grains. We will adopt an effective medium approximation (e.g., Bohren and Huffman, 1983) to represent internally mixed aerosols. This will increase snowpack absorption relative to our current externally mixed assumption.

5.3 Sea-Ice and Ice Sheets
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 (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 (Holland et al., 2006). The thinnest ice category is most susceptible to changes in net solar radiation due to snowpack aging and aerosol concentration. Accounting for impurities in sea-ice is important to accurate radiative transfer throughout the atmosphere/ice/ocean system (Grenfell, 1991). Currently, CICE uses a single layer snowpack upper boundary condition and neither the sea-ice nor the snowpack tracks absorbing impurities such as soot and dust as prognostic tracers. A multi-layer snowpack and prognostic aerosol tracer capability will be in CICE in 2006 (W. Lipscomb, personal communication). Postdoc Mark Flanner

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will merge SNICAR physics (snow aging, radiative transfer, snow-aerosol optics) into this CICE configuration. The resulting CICE simulations will retain soot and dust deposited directly on bare sea-ice from the atmosphere and from melting snow cover. We hope to use sea-ice radiative transfer physics developed by Bruce Briegleb (NCAR) 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 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 (Jacobson, 2004; Flanner et al., 2005). Residence time estimates for optically active Arctic soot will improve. This may play an important role in studies of Objective 1 (Section 4). Lipscomb and Hunke are developing an interactive ice sheet component for the CCSM. This ice sheet model is based on GLIMMER (Payne, 1999). The snow and energy balance model which sits atop GLIMMER will be based on the Community Land Model (CLM). Once postdoc Flanner merges the SNICAR snow-aerosol physics into LANL’s GLIMMER, the CCSM will have a glacier model component sensitive to realistic snowpack physics and aerosol interactions. This will complete the integrated treatment of clean and dirty snow in the cryosphere. The ice sheet-aerosol component is scheduled for completion in project year 3 but may lag due to its complexity and resource issues beyond our control. In any case, our numerical studies of the coupled Arctic land-ocean-sea-ice system with fixed ice sheets will proceed apace toward Objectives 1 and 2 (Section 4).

5.4 Numerical Experiment Strategy
Our questions (Section 4) will be addressed 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 (e.g., Briegleb and Bromwich, 1998a; Fuhrer et al., 1999). Boreal fire variability is also quite large (e.g., Randerson et al., 2005b) and seems to explain most of the variability in Arctic BC deposition (Flanner et al., 2005). Detecting and assessing the relatively small (though important) signal of aerosol-induced Arctic change (Figures 4b and 5) 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 ttest to quantify statistical significance of Arctic changes between the two ensembles. Significant (p < 0.05) changes appear as cross-hatched regions in Figures 4b and 5.

5.5 Satellite Observations
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 4a 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) (Painter et al., 2003), e.g., near MODIS channel 5.

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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 µg kg−1 in Greenland, surface snowpack effective radius re is the most promising model parameters to retrieve and constrain. Remote sensing of snow grain size has been demonstrated with hyperspectral airborne instruments such as AVIRIS (Painter et al., 2003; Dozier and Painter, 2004). The same principles apply to MODIS and/or MISR radiances. However, these satellite products currently have problems associated with large zenith angles and topography. Once the MODIS/MISR spectral snow reflectance (R) products reach robust operational status, we will use them to evaluate SNICAR spectral reflectance, and to try to infer re (Figures 4a and b, respectively). (We will happily provide our predicted re to any retrieval experts attempting to improve MODIS/MISR surface reflectance). In combination with temperature from meteorological analyses, retrieved R and/or re will be used to evaluate SNICAR’s snow aging physics (Flanner and Zender, 2006) which predict significant temperature dependence for re (Figure 3). AMSR-E retrieves Snow Water Equivalent (SWE) over non-ice surfaces. SWE retrievals may provide useful constraints on 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).

5.6 In Situ Observations
Arctic snowpack BC concentration is the key diagnostic which integrates aerosol source, transport, deposition, and melt processes. Drs. Steve Warren (U. Washington, see attached letter of support), Tom Grenfell and Tony Clarke (U. Hawaii) proposed an ARO project “Black carbon in Arctic snow and ice, and its effect on surface albedo” which will 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 5.2.1). These scavenging coefficients will provide important constraints for BC scavenging in CCSM and SNICAR, respectively. In addition to Warren and Clarke’s earlier measurements, Flanner and Zender (2006) evaluated SNICAR against 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.

5.7 IPY POLARCAT Participation
We are contributing to the IPY “POLar study using Aircraft, Remote sensing, surface measurements and modeling of Climate, chemistry, Aerosols and Transport” (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

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many observational aspects of POLARCAT are still pending, support for regular aerosol observations at Summit, Greenland appear to be in place. Commitments for aircraft campaigns extending from boreal forests to Greenland are likely in summer 2008. 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. The most important measurements we need to help reconcile discrepancies between our SNICAR model and broadband reflectance measurements are snow accumulation and vertical profiles of aerosol concentration, and snowpack temperature. Spectral reflectivity would be very valuable but is less likely to be measured. We will consider investigating other targets of opportunity which may arise during POLARCAT as instrument availability and fire events allow.

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Project Coordination

6.1 Personnel
PI Zender will coordinate all project activities. Zender has extensive experience in global-scale aerosol modeling and ice cloud physics and radiative transfer. Zender will use DEAD (Zender et al., 2003a) embedded in the CCSM/SNICAR with peri-glacial sources (Mahowald et al., 2006) to provide Arctic dust deposition fields. In addition, Zender will develop, test, and implement optical property improvements in SNICAR, including soot fractal aggregates (Sorensen, 2001), improved refractive indices (Chang and Charalampopoulos, 1990), and internally mixed dust/soot/snow properties. Postdoc Mark Flanner (currently UCI graduate student in ESS) will merge SNICAR physics into the CICE sea-ice model and 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 (Luo et al., 2003; Mahowald and Luo, 2003; Luo et al., 2004) and, more recently, cryospheric hydrology based on AMSR-E retrievals. Luo will assist with coupled model simulations and satellite data analysis. Zender will advise an ESS graduate student in Arctic aerosol studies. We will initially focus on satellite data analysis of Arctic surface reflectance. In the second and third years, we will use satellite products to help evaluate and constrain SNICAR, as described in Section 5.5.

6.2 Schedule and Milestones
Year 1. Milestones: 1a. SNICAR interactive with CICE; 1b. Fractal aggregate soot; 1c. Snowpack can brighten diurnally Tasks: 1. Couple SNICAR physics to sea-ice, examine impact on summer melt and extent 2. Update soot optical properties to fractal aggregates 3. Represent hoar frost, diamond dust snowpack brightening Travel:

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1. Zender and Postdoc Flanner to Boulder (supported by NCAR affiliate scientist and visitor funds) to collaborate with Mahowald and Rasch (NCAR) and Painter (NSIDC) 2. Zender and Postdoc Flanner present results at AGU Year 2. Milestones: 2a. Diurnal snowpack R matches observations; 2b. SNICAR coupled to LANL glacier-model; 2c. IPY POLARCAT participation Tasks: 1. 2. 3. 4. Represent sintering, especially in non-TG snowpacks Refine scavenging with Warren et al.’s Greenland BC measurements; Compare SNICAR predictions with MODIS/MISR-inferred R, re Quantify Arctic sea-ice sensitivity to soot and dust separately and together

Travel: 1. Zender to Norway/NILU for IPY POLARCAT team meeting 2. Graduate student to Los Alamos to merge SNICAR into GLIMMER 3. Zender presents results at AGU Year 3. Milestones: 3a. SNICAR fully coupled in land/sea/glaciers; 3b. POLARCAT event simulation Tasks: 1. Estimate soot/dust effect on Greenland accumulation/ablation budgets 2. Integrated absorbing aerosol impact on Arctic climate sensitivity 3. Scale fire emissions from Randerson by ice core data (from Saltzman and McConnell) to estimate 1000 year BC impacts on Greenland Travel: 1. PI Zender and graduate student present results at AGU

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Related Projects, Broader Impacts and Education

7.1 Related Projects
This project is synergistic with multiple existing and newly-proposed projects. In addition to collaborative exchanges with LANL, NCAR, and U. Washington described within the proposal text and endorsed in attached letters of support, the following projects will benefit from ours: 1. Drs. Steve Warren (U. Washington, see attached letter of support and Section 5.6), Tom Grenfell and Tony Clarke (U. Hawaii). We will continue to provide Warren’s group with BC deposition simulations to aid them in choosing BC measurement site locations. 2. Drs. Natalie Mahowald (see attached letter of support and Section 5.1) and Phil Rasch (NCAR). We will continue to make SNICAR code improvements available to NCAR CCSM component models. Mahowald and Dr. Peter Thornton are investigating changes in boreal fire regime in a dynamic vegetation and carbon/nitrogen cycling framework. The more realistic lower boundary condition SNICAR provides will improve these studies’ realism.

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3. Drs. Bill Lipscomb and Elizabeth Hunke, LANL (see attached letter of support and Section 5.3). 4. 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. Painter will use SNICAR physics within the SNTHERM snowpack model to account for dust-snowpack interactions on catchment/basin scale hydrology. 5. Drs. Jim Randerson and Yufang Jin (UC Irvine) are our primary in-house collaborators on boreal soot impacts. Their integrated studies of C-cycling along boreal fire chronosequences provide the fire emissions estimates which drive our simulations (e.g., Randerson et al., 2005b). We will continue to collaborate with them on integrated forcing estimates from boreal fires and to use CCSM/SNICAR to quantify soot indirect effects such snowpackmediated radiative forcing (Flanner et al., 2005; Randerson et al., 2005a). 6. Dr. Eric Saltzman (UC Irvine) measures trace gas and aerosol concentrations in ice cores (e.g., Saltzman et al., 2004) and Co-PIs a proposed ARO project “High-Resolution, BiomassBurning-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.

7.2 Improved Community Modeling Capabilities
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 (CRM), the Dust Entrainment and Deposition Model (DEAD), and the netCDF Operators (NCO). SNICAR will be freely available both in offline and in Community Climate System Model modes. We anticipate SNICARS and its 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), sea-ice lifecycle (improved upper boundary condition), paleoclimate sensitivity (through improved accuracy of Arctic responsiveness to orbital and aerosol forcing), and snow chemistry (through improved representation of snowpack specific surface area).

7.3 Education
This project trains one graduate student in Arctic aerosol-climate interactions. Moreover, PI Zender helps train Orange County K–12 teachers in Earth Science curricula. An NSF Math Science Partnership project called FOCUS (Faculty Outreach Collaborations Uniting Scientists, Students and Schools) brings the teachers to UCI. Zender will incorporate Arctic climate change materials into his FOCUS seminars. Zender will also incorporate this Arctic research into his seminars to students of the Osher Lifelong Learning Institute (OLLI).

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7.4 Budget Justification
% NB: Do not use LaTeX formatting in Budget Justification since must % upload into Liz’s Word document Salaries and Wages One month of summer salary support for three years is requested for Prof. Charles Zender, the PI, who has primary responsibility for the proposed research. Salary support for Mark Flanner, a postdoctoral scholar is requested in year 1. Dr. Flanner is responsible for fully coupling the SNICAR aerosol/snowpack model to the glacier dynamics model and to the sea-ice model, and for performing initial fully coupled studies. Funds are requested to to support Dr. Chao Luo, Associate Specialist Step II, at a rate of 0.2 FTE for the duration of the project. Dr. Luo is the principal scientific programmer associated with UCI’s Earth System Modeling facility. Dr. Luo has run the complex models involved in this project (SNICAR, DEAD, CLM, CAM, CCSM) and will devote 20% of his time to performing and to analyzing coupled model studies. A 2% cost of living increase was applied each year of this proposal as well as a 5% merit, where applicable. 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 graduate student will work on isolating and evaluating dirty snowpack signatures in satellite data, modeling soot transport events in support of IPY POLARCAT, and, in year 3, performing fully coupled model studies. All salaries and wages were estimated using UCI’s academic and staff salary scales. 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 - summer - 12.7% Academic (Specialist) - 17% Student employees - summer - 3% Student employees - academic year - 1.3%

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Fees 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, $26,690 is requested for non-resident fees and tuition, $29,258 in the second year. It is anticipated that the graduate student will advance to candidacy at the beginning of the third year. University policy provides a 75% reduction in nonresident tuition post advancement. Therefore, fees and tuition are reduced to $16,748 in year 3. Fees and tuition are excluded from indirect cost assessment. Equipment Equipment funds are requested for the first year only for one dual Opteron, dual core workstation at $6,000. This workstation will include adequate RAID’ed disk space (1 TB) for the graduate student to store and analyze satellite MODIS, MISR, and AMSR-E datasets. Travel Domestic: Round-trip travel at $1500 per trip is requested for the PI and Postdoc (year 1) or graduate student (years 2 and 3) to travel to national meetings (primarily AGU) to present results. Each trip includes roundtrip travel from Irvine to San Francisco or the East Coast, 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. International: One round-trip at $3000 is requested for the PI to travel to Norway in Year 2 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. These trips include estimated conference registration, abstract submission fees, RT airfare, lodging, meals and ground transportation. Travel estimates are based on historical usage. Other Direct Costs Charges for journals, photocopying, long distance phone, fax and postage charges pursuant to this project are requested each year. 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. Support is requested in years 2 and

BIBLIOGRAPHY 3 for publication costs pursuant to this project, which include utilization of expensive color figures. Costs were estimated according to historical usage.

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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.

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Facilities, Equipment, and Other Resources

8.1 Computational Resources
Our ANS project is well-situated to take advantage of UCI’s fast computing capabilities previously funded by NSF and other agencies. PI Zender directs the 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 ANS proposal is squarely fits the ESMF mission, the ESMF will host the primary modeling development and shorter simulations. Once this project is funded, we will request supplementary time at NCAR for long production simulations of the fully coupled CCSM/SNICAR code.

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Acronyms and Abbreviations
Table 1: Acronyms and Abbreviations Abbreviation Description AAA Arctic Absorbing Aerosol AMSR-E Advanced Microwave Scanning Radiometer (satellite instrument) ANS Arctic Natural Sciences AOMIP Arctic Ocean Model Intercomparison Project AR4 Fourth Assessment Report ARF Aerosol Radiative Forcing 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 CFEP Center for Educational Partnerships CICE Los Alamos sea-ice model CLM Community Land Model 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) FOCUS Faculty Outreach Collaborations Uniting Scientists, Students and Schools GCM General Circulation 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 IPCC Intergovernmental Panel on Climate Change IPY International Polar Year ITD Ice Thickness Distribution LANL Los Alamos National Laboratory LGM Last Glacial Maximum MISR Multi-angle Imaging Spectro-Radiometer (satellite instrument)

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ACRONYMS AND ABBREVIATIONS Table 1: (continued) Abbreviation Description MODIS Moderate Resolution Imaging Spectroradiometer (satellite instrument) NASA National Aeronautic and Space Administration NCAR National Center for Atmospheric Research NCO netCDF Operators NILU Norwegian Institute for Air Research 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 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 SEI Science and Engineering Informatics 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

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Project-Wide Combined Collaborator and Advisor List

All Personnel Associated with Proposal, Collaborators and Co-Editors of Project Senior Personnel, their Post-docs, and their Thesis Advisors: Ammann, C. A. (NCAR) Bian, H. (NASA/UMBC) Bonan, G. B. (NCAR) Busacca, A. (WSU) Cakmur, R. (NASA GISS) Colarco, P. (GSFC) Collins, W. D. (NCAR) Cooper, W. A. (NCAR) Famiglietti, J. (UCI) Gaylord, D. (WSU) Grini, A. (U. Oslo) Jenks, S. (UCI) Khalsa, S. J. S. (NSIDC) Kiehl, J. T. (NCAR) Kuester, F. (UCI) Levin, Z. (TAU, Israel) Mahowald, N. M. (NCAR) Miller, R. (NASA GISS) Moore, J. K. (UCI) Okin, G. (U. Virginia) Painter, T. (NSIDC) Pajarola, R. (UCI) Papadopoulos, P. (UCI) Rasch, P. J. (NCAR) Tegen, I. (IfT, Germany) Thomas, G. T. (CU) Torres, O. (NASA GSFC) Valero, F. P. J. (Scripps) Yu, S. (Duke)

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PROJECT-WIDE COMBINED COLLABORATOR AND ADVISOR LIST List is Alphabetical by Surname. Collaborators of Zender: Ammann, C. A. (NCAR) Bian, H. (NASA/UMBC) Bonan, G. B. (NCAR) Busacca, A. (WSU) Cakmur, R. (NASA GISS) Colarco, P. (GSFC) Collins, W. D. (NCAR) Cooper, W. A. (NCAR) Famiglietti, J. (UCI) Gaylord, D. (WSU) Grini, A. (U. Oslo) Jenks, S. (UCI) Khalsa, S. J. S. (NSIDC) Kiehl, J. T. (NCAR) Kuester, F. (UCI) Levin, Z. (TAU, Israel) Mahowald, N. M. (NCAR) Miller, R. (NASA GISS) Moore, J. K. (UCI) Okin, G. (U. Virginia) Painter, T. (NSIDC) Pajarola, R. (UCI) Papadopoulos, P. (UCI) Rasch, P. J. (NCAR) Tegen, I. (IfT, Germany) Thomas, G. T. (CU) Torres, O. (NASA GSFC) Valero, F. P. J. (Scripps) Yu, S. (Duke) Collaborators of Co-PI Dr. Who: (a) Hi

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10.1 Supplementary Documents
1. 2. 3. 4. 5. 6. ${DATA}/prp/prp_ans/prp_ans_ltr_warren.pdf ${DATA}/prp/prp_ans/prp_ans_ltr_lanl.pdf ${DATA}/prp/prp_ans/prp_ans_ltr_mahowald.pdf ${DATA}/prp/prp_ans/prp_ans_ltr_polarcat.pdf ${DATA}/prp/prp_ans/prp_ans_clb.pdf ${DATA}/prp/prp_ans/prp_ans_abb.pdf