Web: http://dust.ess.uci.edu/prp/prp_ans/prp_ans.pdf NSF Arctic Natural Sciences (ANS) Proposal ARC-0714088 Last modified: Sunday 11 November, 2007, 13:34 Submitted: December 8, 2006 Awarded: September 5, 2007 Snow Process Studies and Modeling to Improve Arctic Climate Prediction Dr. Charles S. Zender Department of Earth System Science University of California, Irvine Information for potential collaborators: This NSF proposal responds to the 2006 NSF Arctic Research Opportunities (ARO) announcement, NSF 06-603. The proposal was submitted to the Arctic Natural Sciences (ANS) Program of the Division of Arctic Sciences (ARC) in the Office of Polar Programs (OPP). The cognizant Program Managers are Bill Wiseman wwiseman@nsf.gov, (703) 292-4750 and Jane V. Dionne jdionne@nsf.gov, (703) 292-7427. News/Preface: 5. 20071111: Filled out IPY participant information form for Polar Field Services (NSF contractors). Identified website for initial dissemination of LGGE snow measurements as http://dust.ess.uci.edu/snw. Identified IPY sub-disciplines as Snow physics, aerosol-climate interactions, cryosphere-radiation interactions. 4. 20070907: The award to UCI is official. The approved budget is $135634+$194447+$188401 = $518482. The award dates are 20070901–20100831 (pending successful annual reviews). The report due dates are 20080531, 20090531, and 20080831. The report overdue dates are 20080831, 20090831, and 20081130. 3. 20070905: Received award letter describing award context and panel review. This proposal was one of 188 submitted proposals for 105 distinct projects that requested a total of $75M from the NSF OPP/ARC/ANS in 2006. The panel review is the joint evaluation by both ANS and CLD. The available ANS budget for all awards was $8M for three years. GEO/ATM/CLD will co-fund this grant. 2. 20070806: Received word that NSF OPP/ARC/ANS and GEO/ATM/CLD (J. Fein) jointly reviewed and recommended funding this proposal. No mention of budget constraints so grant may (knock on wood) be fully funded ($518,482). 1. Version submitted on December 8, 2006 is NSF proposal (ARC-0714088) Ideas for Renewals/Extensions/SGERs: 1. Student participation in field IPY experiments (POLARCAT/Greenland!) Points considered/addressed since 2006 rejection: 1. Respond to Panel Review Points: (a) Evaluate against actual BC/dust-induced melt: Warren measurements (b) Interpretation of satellite pixels containing water and snow: Painter (c) Pan-Arctic issues: non-Greenland field measurement sites? Barrow? 2. Improve questions for BC tasks in 2007 submittal: (a) Do industrial emissions reductions reproduce 1985–2005 Arctic BC decrease? (b) When do current ramps suggest historic Arctic BC highs? 3. Improve sea-ice questions/tasks in 2007 submittal: (a) Examine feedback between aerosol/GHG-induced warming and increases in downwelling longwave due to water vapor, atmospheric dust (b) Mesh with Bruce Briegleb and Bonnie Light for sea-ice RT? (and obtain upcoming NCAR tech. note) (c) Recognize potential ocean role in causing sea-ice asymmetry 4. Needed In situ and lab measurements in 2007 submittal: (a) Need co-val T , TG, ρ, R(λ) for, say, two weeks each season in the Arctic? (will lack SSA without Domin´ ) e 5. New Budget in 2007: CONTENTS (a) Spring/summer Field seasons in Greenland for comprehensive SNICAR-closure measurements? 6. Letters of Support changes for 2007: (a) Get Painter 7. Newer references and ideas to incorporate where appropriate: ii (a) Francis and Hunter Eos 87(46) 20061114: Sea ice retreat (b) Alley et al. (2005): dirty snow speeds up worst case scenarios presented here (c) Qu and Hall (2005): Partitioning surface/atmosphere contributions to polar albedo underestimates surfacemediated feedbacks (d) Andreas et al. (2004): SNTHERM model performance on sea-ice (e) Hall and Qu (2006): Using seasonal SAF to estimate GCC SAF (f) ?: dust deposition on snow (g) Peterson et al. (2002): increasing river discharge to Arctic (h) Peltier and Marshall (1995): dust-ice sheet connections (i) Lawrence and Slater (2005): permafrost (j) Domin´ et al. (2005): incorporate frost flowers, diamond dust, surface hoar in SNICAR e (k) Painter et al. (2001): snow algae (l) Roesch (2006): AR4 snow albedo evaluation (m) Andreae and Gelencs´ r (2006); Hoffer et al. (2006): Brown organic carbon e (n) Thonicke et al. (2005): LGM fire emissions (o) Krinner et al. (2006): LGM dust melts Asian ice (p) Brandt et al. (2005): Surface albedo over sea ice (q) Hudson et al. (2006): Antarctic BRDF (r) McConnell et al. recent ice core dust concentrations? (s) Large-scale snow-fraction representations? News/Preface: NSF 05-979, Synthesis of Arctic System Science (SASS), due 20060316, was well suited to study integrated dirty snow impacts in Alaskan tribal environments, including: waste site incineration impacts on local snowpack/permafrost. Look at update? Contents Contents List of Figures 1 2 Introduction Background 2.1 Relevance and Historic Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Absorbing Arctic Aerosol in Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii iii 1 1 1 2 3 5 5 5 6 6 7 8 9 9 3 Scientific Objectives and Hypotheses 4 Tasks: Arctic Models and Observations 4.1 Community Climate System Model 4.2 SNICAR . . . . . . . . . . . . . . . 4.2.1 Snow Aging . . . . . . . . 4.2.2 Snow SSA Measurements . 4.2.3 Snow Radiation and Optics . 4.3 In Situ Observations . . . . . . . . . 4.4 Fire . . . . . . . . . . . . . . . . . 4.5 Sea-Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LIST OF FIGURES 4.6 4.7 4.8 4.9 Ice Sheets and Glaciers . . . . . Satellite Observations . . . . . . IPY POLARCAT Participation . Numerical Experiment Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 11 11 11 12 13 13 13 14 14 14 15 15 1 1 1 1 1 1 1 2 2 1 1 1 1 1 5 Project Coordination 5.1 Personnel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Schedule and Milestones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Results from Prior NSF Funding on Related Projects 7 Related Projects, Broader Impacts and Education 7.1 Related Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Improved Community Modeling Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography Index 7.4 Budget Justification . . . . 7.4.1 Salaries and Wages 7.4.2 Employee Benefits 7.4.3 Equipment . . . . 7.4.4 Travel . . . . . . . 7.4.5 Other Direct Costs 7.4.6 Indirect Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Facilities, Equipment, and Other Resources 8.1 Computational Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acronyms and Abbreviations 9 10 Project-Wide Combined Collaborator and Advisor List 10.1 Supplementary Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List of Figures 1 2 3 4 5 6 7 8 9 Snow-albedo feedback schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Niwot Ridge albedo decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spectral reflectance of snow; Response of effective radius . . . . . . . . . . . . . . . . . . . . . . . . Isothermal SSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Snowpack soot direct radiative forcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Observed and simulated BC concentrations from Flanner et al. (2007). Log correlation is 0.78. . . . Zonal annual mean BC emissions from fossil fuel+biofuel combustion (Bond et al., 2004) and GFED2 biomass burning during 1998 and 2001 (van der Werf et al., 2006). . . . . . . . . . . . . . . . . . . . Snow melt response to snowpack soot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Springtime effective grain size (µm) of snow on sea-ice predicted by SNICAR coupled to a slab-ocean version of the NCAR CSIM. The colorbar range corresponds to broadband shortwave snow albedos from ∼ 0.72–0.85. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 4 6 8 8 9 10 10 Snow Process Studies and Modeling to Improve Arctic Climate Prediction Dr. Charles S. Zender Department of Earth System Science, University of California, Irvine Collaborators: Florent Domin´ , Elizabeth Hunke, Dorothy Koch, Phil Rasch, Steve Warren e Project Summary. Scientific Merit: Greenhouse gas (GHG) forcing alters the rate of snow coarsening and albedo evolution, influencing snow and sea-ice seasonality. The prevalence of bright surfaces (snow, glaciers, sea-ice, and clouds) make the Arctic vulnerable to radiatively induced effects of snow and ice impurities such as light absorbing carbon (LAC) and mineral dust (MD). Coordinated International Polar Year (IPY) activities will yield pan-Arctic measurements of surface snow properties and of impurity concentration, chemical composition, and optical properties which will help identify regions and seasons most affected by aerosol-driven ice-albedo feedback. This project uses IPY measurements to improve cryospheric models used to understand and to predict pan-Arctic climate and climate change. We will (1) Use IPY field and lab measurements to improve representation of snowpack microphysical processes including melt scavenging, hoar formation, and impurity effects; (2) Implement and/or refine these processes in Arctic land, atmosphere, and sea-ice components of an Earth System Model (ESM); (3) Use the ESM to upscale and better quantify the efficacy of and response to Arctic climate forcing agents in the 20th and 21st centuries. We have integrated surface snowpack evolution and satellite-derived LAC emissions into a unified modeling framework with which we forecast and hindcast contemporary and 21st century climate with and without prescribed and predicted GHG and aerosol forcing and feedbacks. IPY measurements gathered by collaborators and by us will help constrain and evaluate multiple processes in our SNow, ICe, and Aerosol Radiative model (SNICAR) including snowpack evolution, aerosol precipitation- and melt-scavenging, and snowpack heating by impurities. We will use the ESM with self-consistent aerosol and snow lifecycles to distinguish the relative roles of aerosols and GHGs as Arctic forcing agents. Our scientific questions include: 1. How do surface hoar and melt/freeze cycles interact in diurnal and seasonal features in Arctic snowpack specific surface area and reflectance? 2. What are the relative efficacies of aerosol- and GHG-driven snow forcing on land and on sea-ice? 3. Could plausible LAC emissions reductions significantly mitigate Arctic climate change? These questions will be addressed in pre-industrial, present day, and next century contexts. The results will improve understanding of ice-albedo feedbacks crucial to Arctic climate and climate change. Broader Impacts: Snow pervades the Arctic, and our integrated framework for snow thermodynamic, radiative, and hydrologic processes will contribute to Arctic research areas including basin and catchment hydrology, snow chemistry, snow remote sensing, sea-ice lifecycle, paleoclimate sensitivity, and glacier mass balance. Contributions to the fifth IPCC climate assessment will include more realistic snowpack processes. This project trains one graduate student and involves one post-doc in Arctic aerosol-climate interactions, opens a year-round undergraduate research position to under-represented minorities, and establishes strong international research links. PI Zender will incorporate this Arctic climate change research into a K–12 teacher training program and into presentations for lifelong learning students. 1 Introduction The Arctic climate system is experiencing unprecedented change due to greenhouse-gas (GHG) induced warming, Arctic haze, and other factors (ACIA, 2005). Global emissions of light absorbing carbon (LAC) aerosol from fossil fuel sources, a contributor to Arctic haze and “dirty snow”, have steadily increased for decades (Penner et al., 2001; Bond et al., 2004). LAC emissions from boreal fires are highly variable, and expected, though with less certainty, to increase as boreal forests warm and expand northward (Randerson et al., 2006). Current understanding of Arctic LAC climate impacts derives mostly from studies which focus on LAC direct atmospheric radiative forcing and which neglect or drastically simplify surface LAC interactions. Bright surfaces (snow, glaciers, sea-ice, and clouds) make the Arctic uniquely susceptible to radiatively induced effects of surface LAC and dust such as ice-albedo feedback amplification. Such feedbacks make dirty snow more efficacious than greenhouse gases at atmospheric temperature change (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, LAC, and dust, and which have been evaluated against satellite, in-situ, and laboratory measurements. Many use the terms soot and BC interchangeably to denote the light absorbing component (LAC) of carbonaceous aerosol for historical reasons or for succintness (e.g., Bond and Bergstrom, 2005). Fossil fuel (FF) and biomass burning (BB) emissions release different ratios of Black Carbon (BC) to Organic Carbon (OC) aerosols to the atmosphere. Complicating matters further, much OC aerosol scatters brightly like sulfate, but some OC aerosols is light-absorbing and has been termed “Brown Carbon” (Andreae and Gelencs´ r, 2006; Hoffer et al., 2006). e 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). Such estimates are extremely sensitive to accurate treatment of snowpack aging and radiative transfer (Flanner and Zender, 2006; Flanner et al., 2007), as well as uncertainties in boundary conditions such as emissions and meteorology. This project devotes significant attention to improving model physics based on measurements, and to quantifying model uncertainties by replicating simulations in two different models. Ice-albedo feedback is arguably the most important positive feedback in the polar climate system (e.g., Hartmann, 1994; Holland and Bitz, 2003; Qu and Hall, 2006). However, we are unaware of any coupled global models that account for realistic snow processes, including aerosol radiative interactions, throughout the surface Arctic. It is not premature to assemble such integrated models to assess, predict, and improve understanding of the Arctic climate, so long as the increased model complexity is justified by continuous demonstrated fidelity to laboratory and field physical process studies. Our project coordinates laboratory snow process studies with snow model development, and larger scale models with collaborator and community IPY measurements, to study the Arctic response to aerosol and GHG forcing now, in the past, and in the future. This proposal is organized as follows. The project’s historical and scientific context is in Section 2. Our scientific objectives and specific hypotheses are in Section 3. Section 4 describes the models and observations we will use to reach these goals. Section 5 summarizes the project plan, personnel responsibilities, time-line, milestones, and travel. Section 6 describes the results of our relevant, prior NSF-funded research. Projects related to ours, potential broader scientific impacts, and our education plan are in Section 7. Six letters of support/collaboration and a list of acronyms and abbreviations appear at the end as supplementary documents. 2 Background 2.1 Relevance and Historic Trends Bright surfaces (snow, glaciers, sea-ice and clouds) make the Arctic uniquely susceptible to ice-albedo feedbacks (e.g., Clarke and Noone, 1985; Holland and Bitz, 2003; Hansen and Nazarenko, 2004; Qu and Hall, 2006) including those caused and amplified by radiatively induced effects of surface BC and dust (Figure 1). The “classic” snow/ice-albedo feedback refers to the snow/ice area feedback (upper left loop) that results from changes in the areal extent of snow/ice cover that conceals the underlying surface. The reflectances of snow, glacier, 2 BACKGROUND 2 and sea-ice contrast enough so that significant snow/ice-area feedback occurs among these forms of frozen water (Brandt et al., 2005; Flanner et al., 2007). The temperature grain-size feedback is a weaker positive feedback (center loop) that arises from temperature controls on snow aging and on the snow grain size distribution which in turn determine solar reflectance (Flanner and Zender, 2006). Internally and externally mixed snowpack impurities darken snow albedo directly (top right, this is a forcing not a feedback), effectively increasing the gain G on the temperaturegrain size feedback by heating the snowpack. Accumulation of hydrophobic impurities at the surface during melt events may cause a feedback between temperature and soot concentration (dashed lower right loop) observed by Clarke and Noone (1985) and Conway et al. (1996). This melt-impurity feedback is thought to affect hygrophilic aerosol (e.g., sulfate-coated BC) more than hygrophobic aerosol (e.g., uncoated dust). Representing the vertical distribution of heating in a multi-layer snow or ice medium is important, not least because the penetration depths of visible and near-infrared (NIR) radiation into snow are significantly different (Wiscombe and Warren, 1980). Turbulent heat fluxes and sublimation to the atmosphere can efficiently dissipate surface-layer ( 1 cm) snowpack radiative heating but not sub-surface heating. Since snow has low thermal diffusivity that allows sub-surface heating to slowly warm the snowpack and accelerate melt onset. Flanner and Zender (2005) showed that representing snowpack as multi-layer, regardless of snow-aging, substantially improves snowpack simulations in the Tibetan Plateau region. Figure 1: Snow- (and ice-) albedo feedbacks deMineral dust can also play an important role in the scribed in text. Pink and Red symbols denote modArctic, far from its dominant sources in the sub-tropical erate and strong positive feedback loops, respecdeserts (Prospero et al., 2002; Zender et al., 2003a). tively. Plusses and minuses indicate response of tarDust is the dominant absorbing aerosol in Arctic ice get (arrow-head) to positive change in source (arrowcores on interglacial timescales (e.g., Ram and Koenig, tail). Absorbing aerosols amplify the grain-size feed1997; Fuhrer et al., 1999). Dust embedded in Arctic back with gain G. ice cores records global climate change (e.g., Andersen et al., 1998; Kohfeld and Harrison, 2001) and marks abrupt climate changes (e.g., Alley, 2000). Numerous studies speculate that in addition to recording climate, dust changes climate by (among other things) darkening the cryosphere (Peltier and Marshall, 1995; Archer et al., 2000; Harrison et al., 2001; Krinner et al., 2006). Present day observations in the Rockies show that dusty snow accelerates snow melt in some mid-latitude regions (Dr. Tom Painter, NSIDC, personal communication). Net BC impacts on the Arctic will likely increase through the 21st century. BC emissions from combustion, the primary source of Arctic BC (Koch and Hansen, 2005) have increased with fuel use over the past several decades, and are known to within a factor of about two (Bond et al., 2004). Some scenarios project an increase in anthropogenic BC emissions of 30–250% in the 21st century (Naki´ enovi´ et al., 2000). Biomass burning emissions may increase c c due changes in fire regime though this is highly uncertain (Thonicke et al., 2005; van der Werf et al., 2006). Present day dust emissions and deposition are known to no better than about a factor of four globally (Zender et al., 2004). Whether there is currently a trend in global mineral dust emissions is not known—increasing (from anthropogenic activities) and decreasing (due to CO2 fertilization of vegetation) trends are both plausible (Mahowald and Luo, 2003; Tegen et al., 2004). Aerosols, absorbing or not, can significantly alter cloud reflectivity and lifecycle (e.g., Ch´ lek et al., 1996; Acky erman et al., 2000). Since aerosol-cloud indirect effects are poorly constrained and are not a focus of this project, they will not be mentioned again. This does not imply that will ignore aerosol-cloud direct and semi-direct effects. Aerosol-cloud direct and semi-direct effects are analogous to the snow/ice-impurity feedbacks mentioned above, and will be treated thoroughly and consistently throughout the project. 2.2 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 3 SCIENTIFIC OBJECTIVES AND HYPOTHESES 3 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 2). The observed 0.05 snowpack albedo change within 0 four days at Niwot Ridge illustrates the important role of −0.02 snowpack aging in controlling surface energy budgets. Prescribed snowpack aging can capture this aging effect −0.04 on timescales of a few days. More sophisticated physics are required to represent longer timescale albedo evolu−0.06 tion (Flanner and Zender, 2006). −0.08 Single layer models are significantly limited in their representation of aerosol transport and removal mech−0.1 anisms and in correctly representing sub-surface radiative heating and melt. Flanner and Zender (2005) stud−0.12 SNICAR, dT/dz=20 K m−1 ied the vertical distribution of solar radiant heating on SNICAR, dT/dz=40 K m−1 Niwot, Jan2−Jan12, 11:00 SNICAR, dT/dz=80 K m−1 Niwot, Jan2−Jan12, 12:00 −0.14 snowpack. Using accurate radiative transfer methods in NCAR CLM Niwot, Jan2−Jan12, 13:00 Verseghy, 1991 Niwot, Jan2−Jan12, 14:00 a multi-layer snowpack model (Section 4.2), we showed −0.16 that 20–40% of solar heating occurs beneath the top 0 1 2 3 4 5 6 7 8 9 10 Time (days) 2 cm of typical snowpack. This sub-surface heating can cause internal snowpack melt. Normally, though not alFigure 2: Observed (black) and modeled (color) ways, modeled internal melt occurs in conjunction with albedo decay at Niwot Ridge following the January 2, surface melt. Internal snowpack absorption in warm 2001 snowfall event (Flanner and Zender, 2006), for snow can melt snow more efficiently per unit absorption varying SNICAR snowpack temperature gradients. than surface snowpack absorption which, in the Arctic, Error bars represent one standard deviation of all meais typically balanced by strong sensible heat fluxes due surements comprising each day’s albedo change. to cold overlying air (Molotch et al., 2004; Flanner and Zender, 2005). It may take many sunny days to accumulate enough heat from the small, instantaneous solar flux divergence within snowpack (Brandt and Warren, 1993) to warm interior snowpack to the melting point. Snow insulation keeps the internal snowpack warmer than the surface so that radiatively-induced internal warming can trigger the moderate snow-grain size-feedback loop (Figure 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 (Hansen and Nazarenko, 2004; Jacobson, 2004; Krinner et al., 2006; Flanner et al., 2007). Significant soot concentrations in the surface snowpack strongly absorb visible radiation some of which would otherwise penetrate into the snowpack (Figure 3). 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). Clearly a modeling approach that includes thermodynamic and aerosol radiative effects on snowpack aging and heating is required to understand their combined effects on the Arctic climate system. 3 Scientific Objectives and Hypotheses Our studies of Arctic snow-ice-aerosol processes will improve understanding and representation of ice-albedo feedbacks and polar climate amplification. Key scientific questions we will address include: 1. Objective: Understand and reproduce hoar formation and melt/freeze cycle effects on diurnal and seasonal snowpack specific surface area and reflectance Hypothesis: Observed diurnal and semi-diurnal albedo cycles in polar snowpack can be explained by a spectrum of surface hoar formation interacting with temperature-driven metamorphism, including melt/freeze cycles. Zenith angle and temperature effects dominate seasonal albedo changes. Diurnal and semi-diurnal albedo cycles observed in Antarctica (McGuffie and Henderson-Sellers, 1985; Pirazzini, 2004) suggest that snow Specific Surface Area (SSA) recharges, or at least decreases more slowly, due to surface hoar formation. Hoar may darken fresh and brighten old snowpack when the SSA of hoar (often hollow Albedo Change 3 SCIENTIFIC OBJECTIVES AND HYPOTHESES 4 1 0.9 re=50µm re=200µm re=500µm re=1000µm 200 ng g−1 soot Hemispheric Snow Reflectance 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 Wavelength (µm) 2.1 2.3 2.5 Figure 3: 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: Predicted change in summertime-mean effective radius re [µm] of surface snowpack layer due to 1998 boreal fire BC deposition. Cross-hatching indicates statistically significant changes (p < 0.05) relative to simulations without boreal fire soot (Flanner et al., 2007). prisms) is intermediate between these extremes. Whether this process can explain the measured albedo cycles will be tested in a controlled environment, with the results guiding improvements in our modeled hoar and melt/freeze formulations. Our collaboration with Dr. Florent Domin´ (LGGE/Grenoble, see attached letter of e support) to measure and model SSA changes due to surface hoar formation and melt/freeze cycles in controlled laboratory experiments that mimic Arctic snow is described in Section 4.2.2. 2. Objective: Quantify Arctic climate sensitivity to timing and location of Arctic soot events Hypothesis: Fuel combustion dominates biomass burning as sources of Arctic BC except in very strong boreal burn years. Fire BC efficacy depends strongly on burn month and location. Many of the hypotheses in this project involve the concept of Efficacy, defined as the temperature response per unit forcing relative to the temperature response due to the same forcing by CO2 (Hansen et al., 2005). Understanding efficacy is particularly important for BC-snow studies because Hansen et al. (2005) and Flanner et al. (2007) find that soot in snow has the highest forcing efficacy of any known climate forcing agent. Soot in snow has 2–4× the forcing efficiency of CO2 . For example, we find that snowpack BC heating compounded by snow-albedo feedback can exceed atmospheric BC surface cooling of Greenland in strong fire years (Figure 8). Knowing in which locations and months fires will have the greatest forcing efficacy will identify particularly valuable forest and vulnerable cryospheric regions. 3. Objective: Identify snow and aerosol interactions with sea-ice formation, reflectance, seasonality, variability, and trends Hypothesis: Rapid snow aging with warm surface temperatures in spring and summer accelerates ablation of annual sea-ice, and Arctic BC amplifies amplifies this ablation during strong burn years. Inter-hemispheric asymmetry in polar BC deposition contributes to the significant differences between Arctic and Antarctic sea-ice trends. Despite nearly globally-uniform GHG forcing, summertime Arctic and Antarctic sea-ice show asymmetric trends over the last 25 years (Folland et al., 2001; Serreze et al., 2003; Stroeve et al., 2004). While Antarctic sea-ice has shown little trend, summertime Arctic sea-ice has retreated by more than 15%. Koch and Hansen (2005) speculate that asymmetry between northern and southern hemisphere polar BC deposition may explain sea-ice asymmetry. Our project will represent snow- and sea-ice-albedo feedbacks (Figures 1) which when forced by the interannual variability in BC emissions (Figure 7) and deposition, may cause some of the 4 TASKS: ARCTIC MODELS AND OBSERVATIONS asymmetry and recent accelerations in Arctic sea-ice reduction. 5 4. Objective: Determine relative efficacies of aerosol- and GHG-driven snow forcing on Arctic land and sea-ice Hypothesis: Aerosol-snowpack interactions may cause as much Arctic warming as GHGs. Hansen et al. (2005) estimate that effective global forcing by GHGs since 1750 is about 3.0 W m−2 , more than ten times greater than their 0.25 W m−2 snow albedo forcing by soot. However, we estimate the effective snow-albedo forcing (i.e., efficacy times forcing) of soot and dust averaged over the Arctic (north of 67 ◦ N) is 1.25 W m−2 , about 40% of the GHG forcing. We expect that representing snowpack impurity driven feedbacks (Figure 1) over sea-ice will further increase the Arctic climate sensitivity to aerosols relative to GHGs. Hence the Arctic may be unique as region where total effective aerosol forcing is positive (not negative) and occasionally (e.g., strong burn years) exceeds GHG forcing. If true, this would bolster suggestions that targeted reductions in industrial and boreal fire emissions may achieve significant mitigation of Arctic climate change (Jacobson, 2002, 2004; Randerson et al., 2006). Our modeling scenarios will quantify the physical plausibility and robustness of these arguments. 4 Tasks: 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 inhouse snow-ice-aerosol model (SNICAR) and/or merging these physics into high-quality Arctic system component models developed and maintained at national centers. 4.1 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 3). We use the NCAR CCSM—its polar climate simulations and biases are well characterized and continually evaluated against meteorological analyses and satellite observations (e.g., Briegleb and Bromwich, 1998a; 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). As such, these aerosols travel through multiple climate “spheres”, i.e., the biosphere, atmosphere, and cryosphere before depositing to snow. This project focuses on the cryosphere and we will rely on our continuing external collaborations to obtain the most realistic aerosol distributions possible. The CCSM BC/OC aerosol transport and deposition we use come from long time collaborators Drs. Phil Rasch (see attached letter of support) and Bill Collins (NCAR) (Rasch et al., 2001; Collins et al., 2001, 2002). Dr. Natalie Mahowald (NCAR) 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) which, embedded in CCSM, predicts the Arctic dust deposition fields. Part of our motivation for including dust affects in the present day Arctic stems from our preliminary equilibrium simulations of the Last Glacial Maximum (LGM) that account for glaciogenic dust. We use the method of Mahowald et al. (2006) to obtain simultaneous agreement between the model and LGM loess, ice core, and marine deposition records. Our preliminary results indicate that glaciogenic dust is very efficacious at warming LGM Arctic climate and so should not be neglected without good reasons in present day Arctic change studies. 4.2 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 hydrologic, thermodynamic, and radiative processes in a unified manner to explicitly represent feedbacks between snowpack heating, albedo evolution, densification, melt, and aerosol concentration (Figure 1). For climate simulations, SNICAR runs in a host snowpack model which to date has been 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., 2006) modified for prognostic soot and dust emissions. 4 TASKS: ARCTIC MODELS AND OBSERVATIONS 6 SNICAR has different capabilities than, and shares some capabilities with, other the well-known snow models such as SNTHERM (Jordan, 1991; Andreas et al., 2004). SNICAR, designed for climate modeling, is at heart a size-resolved snow aging model designed above all else to predict snow SSA, and thus snowpack optical properties (Grenfell and Warren, 1999). SNICAR operates within a host snow-hydrology, thermal, mass-balancing model (currently CLM). SNTHERM includes many more snow and ice thermodynamic processes and states than CLM, and many fewer radiative features than SNICAR. To our knowledge, SNTHERM is alway used as a column model, and has not been embedded in an interactive GCM suitable for Arctic climate change studies. 4.2.1 Snow Aging Compared to the enormous efforts and progress at improving cloud physics since Cess et al. (1989) highlighted its importance, relatively little effort has gone to improve snowpack representation in climate models. It is not surprising that intercomparison of Arctic climate simulations identifies snow (mis-)treatment as a leading cause of inter-model and model-measurement discrepancy (Hall and Qu, 2006; Roesch, 2006). Some models (e.g., Jordan, 1991; Oleson et al., 2004) consider the role of temperature in albedo decay. To our knowledge, though, SNICAR is the only GCM snow model that considers the dominant role of temperature-gradient driven metamorphism. High-latitude snowpack can have temperature gradients well in excess of 100 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, SNICAR accounts for the roles of initial size distribution, temperature, temperature gradient, snow density, and inter-particle spacing in the evolution of snow specific surface area (SSA) (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 temperature-gradient environments (e.g., Colbeck, 1980). 4.2.2 Snow SSA Measurements The laboratory of Dr. Florent Florent Domin´ (LGGE/Grenoble, see attached letter of support) has produced many of e the best controlled and characterized snow aging measurements. They90 have characterized snow specific surface area (SSA) 110 Legagneux et al. [2004], Sample Legagneux et al. [2004], Sample 2 evolution in isothermal and temperature gradient con-1 Model, σg = 1.8 Model, σg = 1.8 100 ditions (Cabanes et al., 2003; Legagneux et al., 2004; Model, σ = 2.3 Model, σ = 2.3 80 g g Taillandier et al., 2007). We use the full microphysiModel, σ = 2.7 Model, σ = 2.7 g g 90 cal results of SNICAR—which runs off-line with ∼ 200 snow70 grain size bins each with ∼ 40 pore-particle spac80 ing bins—to compute the free parameters of the compact 60 SSA expression that they show fits their measure70 ments (Legagneux et al., 2004) (Figure 4). Our result60 ing parametric expression for SSA evolution describes 50 isothermal and temperature gradient snow grain evolu50 tion quite well (Flanner and Zender, 2006; Taillandier 402007). et al., 40 Zender will collaborate with Domin´ at LGGE on e a series of SSA measurements. Many studies show that 30 30 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 vapor saturation can induce Time (days) frost deposition of small, orTime (days) nate surface hoar crystals that brighten the surface or at least retard SSA aging. This occurs diurnally and may Figure 4: Observed (Legagneux et al., 2004) and explain daytime semi-diurnal albedo cycles observed in modeled (Flanner and Zender, 2006) SSA for isotherAntarctica (McGuffie and Henderson-Sellers, 1985; Pimal snowpack conditions and varying choices of σg , razzini, 2004), but is not yet represented in SNICAR, the standard deviation of the modeled particle-pore which predicts monotonically decreasing SSA in the abspacing distribution. sence of fresh snow. In Fall 2007 we will extend the SNICAR framework to account for different crystallographic shapes, so that the inter-particle pore spacing distribution and the ice crystal shape factor (or “capacitance”) (Flanner and Zender, 2006) can account for more complex shapes. When atmospheric conditions favor surface hoar Specific Surface Area (m2 kg−1) 60 55 Specific Surface Area (m kg ) Specific Surface Area (m kg ) −1 −1 2 2 50 45 40 35 30 25 0 1 2 3 4 TASKS: ARCTIC MODELS AND OBSERVATIONS 7 growth, the model will allow deposition to form faceted shapes such as hollow columns which have significantly higher SSA than spheres. In Winter 2008 we will measure SSA evolution from local snow to gain sufficient data to evaluate the model hoar formulation. The snow chamber can mimic a wide range of temperatures and temperature gradients found in the Arctic. We are highly interested in characterizing the SSA (and snow albedo) evolution response to melt/freeze cycles. SNICAR now employs an empirical formulation (Brun, 1989) outside the range of its validity. Our new model formulation for grain size evolution near the melt point will be guided by these and other observations (Raymond and Tusima, 1979; Legagneux et al., 2004) and Ostwald ripening theory. The experiments will characterize SSA during diurnal cycles of ripening to near melt, and then to diurnally repeating melt/freeze events. The resulting parameterization will improve the grain size-albedo feedback (Figure 1) which is very strong for even a single melt/freeze cycle. The influence of the surface hoar and melt/freeze processes on snowpack and climate will then be assessed in the global model, with particular attention paid to crucial Arctic transition regimes such as the ablation zone of glaciers and during melt-back of seasonal snow regions. In Spring 2008 we plan a series of snow SSA measurements forced by impurities. The idea is to compare SSA evolution of snow samples mixed with well characterized soot, e.g., the Monarch 71 elemental carbon adopted by Clarke (e.g., Clarke and Noone, 1985). Impurity mass will be weighed before mixing, retrieved using reflectance fitting to the model, and can be measured with a total carbon analyzer. Standard measurement procedure will include routine visible and near infrared reflectances (450 and 1310 nm) to simultaneously constrain SSA and snowpack absorption. Selected measurements will be made with higher resolution spectral radiometers in collaboration with other interested LGGE investigators. It is worth noting that, to our knowledge, these will be the first simultaneous snow SSA and reflectance measurements. These data will provide novel and useful constraints for snow models such as SNICAR and SNTHERM. 4.2.3 Snow Radiation and Optics The off-line microphysical version of SNICAR runs at 10 nm spectral 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 3). 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). SNICAR accounts for solar zenith angle, direct and diffuse incident radiation, reflectance of the underlying surface (Dai et al., 2003), wet snow metamorphism (Brun, 1989), and vertically-resolved effective radius (re ), snow depth, density, and concentrations of absorbing impurities (Warren and Wiscombe, 1980). A lookup table (computed off-line) contains two-stream optical parameters (single scattering albedo, extinction coefficient, and asymmetry parameter) as functions of snow SSA and wavelength. Snow and aerosol optical properties link the snowpack microphysical properties (aerosol concentration, particle size distributions) to macroscopic net absorption (Figure 5), reflectances (Figures 2 and 3a), and heating rates that drive the snow melt (Figure 8) and temperature change which trigger snow-albedo feedback. These responses are sensitive to optical property assumptions which this project will refine, including 1. BC indices of refraction: As per Bond and Bergstrom (2005), we use Chang and Charalampopoulos (1990) rather than CAM-default OPAC properties (Hess et al., 1998) for elemental carbon. We assume a BC/sulfate core/mantle for hydrophilic BC (Bond et al., 2006). 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: Although existing observations are insufficient to rule out dry scavenging as the dominant removal mechanism (Koch and Hansen, 2005), we think BC and dust in remote regions such as the Arctic are likely deposited primarily by wet scavenging (Noone and Clarke, 1988; Clarke et al., 2001; Zender et al., 2003a; Clarke et al., 2004; Jacobson, 2004) Nucleation-scavenged aerosols will often be internally mixed within snow grains. We will try an effective medium approximation (e.g., Bohren and Huffman, 1983) to represent internally mixed aerosols. We will also investigate solutions for dark particles in weakly absorbing media (Markel and Shalaev, 1999) which may be more physically defensible for ice particles. All these approaches will increase snowpack absorption relative to our current externally mixed assumption. 4 TASKS: ARCTIC MODELS AND OBSERVATIONS 8 4.3 In Situ Observations Arctic snowpack BC and dust concentrations and optical properties are key diagnostics that integrate aerosol source, transport, deposition, with snow aging and melt processes. We will obtain new data to evaluate and constrain our models from collaborators Drs. Steve Warren (U. Washington, see attached letter of support), Tom Grenfell and Tony Clarke (U. Hawaii) who have an ANS project “Black carbon in Arctic snow and ice, and its effect on surface albedo”. Their project will sample snow and ice from panArctic locations to update, improve, and extend the BC survey that Clarke and co-workers conducted in 1983– 1984 (Clarke and Noone, 1985). Warren’s team determines BC and dust optical properties from the samples using filter absorptance techniques. They will measure in situ snow density and spectral albedo at selected sites to enable closure studies of the instantaneous surface solar radiation field. Warren’s team is already processing new aerosolsnowpack measurements from the North Pole observatory, Ellesmere, Hudson Bay, and Greenland. By summer 2007, they will have additional data from Greenland, Russia, and from Matthew Sturm’s 4000 km traverse of North American tundra. The BC/dust measurements from Dr. Konrad Steffen’s Automated Weather Station (AWS) sites in Greenland (Steffen and Box, 2001) sample strong spatial gradients in exposure to North Amer- Figure 5: Summertime mean surface direct radiative ican biomass burning plumes (Figure 5) and so will be forcing [W m−2 ] by soot in snowpack during 1998, a particularly valuable. strong boreal burn year. Flanner et al. (2007) compared CAM/SNICAR simulations to the approximately two dozen published Arctic surface snowpack BC measurements, many from Clarke’s 1983–1984 survey. SNICAR captures the measurements over three orders of magnitude in concentration with little mean bias though significant RMS bias (Figure 6). Greenland concentrations are typically 1–4 µg kg−1 , and as high as 30 µg kg−1 (Slater et al., 2002). Interestingly, Grenfell et al. (2002) showed that BC 3 10 concentrations measured during the SHEBA experiment Greenland had decreased significantly since the early 1980s, conArctic Continental trary to the increasing trend in global emissions. Whether 2 Antarctica 10 reduced fuel combustion in proximal regions (e.g., former Soviet Union) can explain this trend could be investigated in our modeling framework if interannually 1 10 varying industrial and fire BC emissions timeseries were extended back to the 1980s. The new measurements by Warren’s project will clarify the spatial extent of this in0 10 triguing result, and provide useful falsification for errant model interdecadal trends in BC deposition. In situ measurements will also provide updated constraints on scavenging coefficients using our models. Clarke 10−1 −1 0 1 2 3 will measure/estimate scavenging coefficients for removal 10 10 10 10 10 −1 Observed BC in Snow (ng g ) of atmospheric BC by snow (Noone and Clarke, 1988) and for removal of snowpack BC by snow melt (Clarke and Noone, 1985). Meltwater flushing is perhaps the Figure 6: Observed and simulated BC concentrations most important BC removal mechanism, since preferenfrom Flanner et al. (2007). Log correlation is 0.78. tial gravitational settling would be extremely slow for BC that is externally-mixed. Qualitative observations suggest that BC may become more concentrated in surface snow during melt events (Warren and Wiscombe, 1980; Clarke and Noone, 1985). Conway et al. (1996) spread hydrophobic and hydrophilic BC on top of snow, and noticed Modeled BC in Snow (ng g−1) 4 TASKS: ARCTIC MODELS AND OBSERVATIONS 9 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. Dr. Tom Painter (NSIDC) is conducting studies in the Rockies that will help constrain the melt-scavenging efficiency of dust. We will perform selected closure studies to ensure that SNICAR closely reproduces the measured spectral reflectance for given snow conditions and impurity concentrations/properties (measured and estimated by Warren’s team). These studies complement Warren’s studies in that we also predict snow conditions and snow BC/dust concentrations. Differences between our predictions and the field measurements will help us identify and correct model physics deficiencies, e.g., melt scavenging in SNICAR, frozen precipitation scavenging in CAM. Insofar as our predictions and field measurements agree (e.g., Figure 6), our model can upscale the point measurements to regional or even pan-Arctic estimates of impurity concentrations, and the consequent radiative forcing and response. 4.4 Fire Our model uses the Global Fire Emissions Database (GFED2) which is derived from fire counts retrieved by MODIS satellite sensors. GFED2 includes more biomass burning source regions than Cooke and Wilson (1996) and Koch and Hansen (2005) and captures the significant interannual variability of boreal fire BC (van der Werf et al., 2006). −5 We use prescribed fossil and biofuel BC emissions x 10 5 (Bond et al., 2004), and convert GFED2 fire emissions Fossil+Biofuel 4.5 1998 Biomass to BC with estimated emissions factors updated from 2001 Biomass 4 Andreae and Merlet (2001). Using GFED2 and our central estimates of emissions factors, we estimate that biomass 3.5 burning BC emissions north of 30◦ N decreased from 3 0.8 Tg to 0.2 Tg between 1998, a strong fire year, and 2.5 2001, a weak fire year (Figure 7) (Flanner et al., 2007). 2 Models driven by satellite-derived emissions agree that 1.5 fires explains the largest component of interannual Arc1 tic BC deposition variability although whether tropical 0.5 fire BC or boreal fire BC dominates Arctic BC variability is disputed (Koch and Hansen, 2005; Flanner et al., 0 −50 −40 −30 −20 −10 0 10 20 30 40 50 60 70 2007). Latitude Our simulations suggest boreal fire soot causes seasonal net surface solar radiation forcings of 0.5–0.75 W m−2 Figure 7: Zonal annual mean BC emissions from (Figure 5) in strong fire years. These forcings induce fossil fuel+biofuel combustion (Bond et al., 2004) feedbacks such as larger snow grain size (Figure 3) which and GFED2 biomass burning during 1998 and 2001 together increase seasonal surface absorption by more (van der Werf et al., 2006). −2 than 1.5 W m (Flanner et al., 2007). Soot-snow feedbacks in strong fire years appear to cause significant increases in meltwater production in Greenland snowpack (Figure 8). 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. We eagerly anticipate results in Year 2 when SNICAR is embedded in a fully interactive sea-ice model which responds to soot and dust sources. 4.5 Sea-Ice Sea-ice is the fulcrum of Arctic ice-albedo feedbacks. Snow conditions on and impurities in sea-ice play important roles in the solar radiation budget of the sea-ice environment (Grenfell, 1991; Grenfell et al., 2002; Brandt et al., 2005). Grenfell et al. (2002) reported that BC mixing ratios in snow on Arctic sea-ice increased from the pole (∼ 5 µg kg−1 ) to the coastal regions (∼ 50 µg kg−1 ). In large-grained snow, 50 µg kg−1 lowers broadband snow reflectance by > 0.03, a level that begins to exceed typical observational uncertainties of albedo measurements. BC Emissions (kg m−2 yr−1) 4 TASKS: ARCTIC MODELS AND OBSERVATIONS 10 Figure 8: 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 fire soot (Flanner et al., 2007). The Los Alamos CICE model is the sea-ice component of the CCSM Earth system model. CICE contains an Ice Thickness Distribution which maintains a half dozen prognostic categories of ice thickness in each grid cell (Holland et al., 2006). However, CICE currently represents snow as a medium with prescribed albedo with no snow aging nor absorbing impurity representation. LANL will implement a prognostic aerosol tracer capability into CICE in early 2007 (E. Hunke, personal communication, 2006). Our collaborator, Dr. Elizabeth Hunke of LANL (see attached letter of support), is a CICE principle developer. With Hunke’s guidance, the UCI team will merge SNICAR physics (snow aging, radiative transfer, snow-aerosol optics) into the multi-layer snowpack in CICE. Climate and aerosol conditions will cause strong regional structure in snow aging on sea-ice, resulting in a spectrum of snow grain sizes (Figure 9) and thus ice-albedo feedbacks. The underlying sea-ice will also retain and concentrate soot and dust deposited directly from the atmosphere and scavenged from melting snow cover (Figure 1). We will then test our hypotheses (Objective 3) by comparing sea-ice trends with and without snow aging and aerosols. The snow/sea-ice simulations will be evaluated against available climatologies such as the National Ice Center 33-year gridded ice climatology, and satellite products described in Section 4.7. We will couple the snowpack radiative transfer and impurity physics to the new sea-ice radiative transfer physics module developed by Drs. Bruce Briegleb (NCAR) and Bonnie Light (U. Washington). Their sea-ice radiation model accounts for for aerosols embedded in Figure 9: Springtime effective grain size (µm) of the complex sea-ice-brine matrix (a melt pond represnow on sea-ice predicted by SNICAR coupled to a sentation is under development). Coupling the snow to slab-ocean version of the NCAR CSIM. The colorbar sea-ice radiative transfer will require replacing the tworange corresponds to broadband shortwave snow albestream solution in SNICAR (Toon et al., 1989) with the dos from ∼ 0.72–0.85. adding-doubling method used in the sea-ice (Briegleb, 1992). The adding-doubling method may provide superior performance to two stream methods in thick snowpacks where transmission is negligible. Simulated sea-ice reflectance, surface properties, extent and thickness will then respond to the full lifecycle of 4 TASKS: ARCTIC MODELS AND OBSERVATIONS 11 Arctic aerosols. This will be a significant improvement to current models (including ours) which remove aerosols from snow and sea-ice with rather ad hoc mechanisms to prevent excessive concentrations from accumulating in multi-year land and sea-ice (Jacobson, 2004; Flanner et al., 2007). Also, SNICAR is sufficiently modular so that all aerosols in CAM can be easily added to the snowpack lifecycle. Future experiments will include the effects of light absorbing organic carbon aerosols termed “Brown Carbon” (Andreae and Gelencs´ r, 2006; Hoffer et al., 2006). e 4.6 Ice Sheets and Glaciers A key shortcoming of this (and other) coupled Arctic modeling projects is the absence of realistic ice sheets and glaciers. Although crucial to the net hydrology (and stability) of the Arctic climate system, glaciers and ice sheets are not yet fully integrated into Earth system models. LANL scientists are developing an interactive ice sheet component for the CCSM based on GLIMMER (Payne, 1999). 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. 4.7 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 3a 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. However, soot-in-snow is difficult if not impossible to retrieve from satellite. The albedo perturbation by 50 µg kg−1 soot (Figure 3), a relatively high Arctic concentration (Figure 6), may not exceed instrumental noise, surface variability (e.g., sastrugi, leads), and the signal of clouds and atmospheric soot (S. Warren, personal communication, 2006). Nevertheless, detection of snow impurities may be plausible after intense pollution events or very near pollution sources. This is one goal of our POLARCAT intense modeling effort (Section 4.8). On the other hand, retrieving surface snowpack effective radius re from operational satellite observations is feasible and would help identify biases in Arctic re predictions. 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, although these products currently have problems associated with large zenith angles and topography (Zhou et al., 2003). If and when the MODIS/MISR high-latitude spectral snow reflectance R(λ) products reach robust operational status, we will try to infer re using a radiance ratio technique. For example, MODIS Channel 5 (1.24 µm) to Channel 4 or 6 (0.55 and 1.64 µm, respectively) reflectances (Figures 3a and b, respectively). Researchers will be welcome to use/assimilate our predicted re to attempt to improve MODIS/MISR reflectance retrievals. In combination with temperature from meteorological analyses, retrieved R and/or re could be used to evaluate SNICAR’s surface snow grain size (Flanner and Zender, 2006) globally, or, more precisely, wherever clouds are not contaminating the scene. AMSR-E retrieves Snow Water Equivalent (SWE) over non-ice surfaces. SWE retrievals may provide useful constraints on SNICAR simulations of continental snowpack. We will use AMSR-E sea-ice concentration and snow depth over sea-ice products to evaluate the effect of implementing SNICAR in CICE. Current AMSR-E retrieval algorithms (Markus and Cavalieri, 1998, 2000) reduce previous ice concentration biases resulting from surface glaze and layering in the snow cover and from thin ice types. Comparisons of sea-ice extent simulations between AMSR-E and CICE during and after strong BC years will help us assess climatological BC impacts on sea-ice. Daily comparisons of seaice snow thickness will help us evaluate BC impacts from the intense plume we will characterize for the POLARCAT event study (Section 4.8). The potential for our predicted snow grain size distributions to improve satellite microwave retrievals of snow properties is intriguing, though beyond the scope of this proposal. 4.8 IPY POLARCAT Participation We will contribute to the IPY “POLar study using Aircraft, Remote sensing, surface measurements and modeling of Climate, chemistry, Aerosols and Transport” (POLARCAT) project (the attached letter from the IPY program office to POLARCAT PI Stohl simply verifies that POLARCAT is an “official” IPY to those who might not be familiar with it). One of POLARCAT’s main themes is the influence of biomass burning and fossil fuel pollution on the Arctic. Although many observational aspects of POLARCAT are still pending, support for regular aerosol concentration and 4 TASKS: ARCTIC MODELS AND OBSERVATIONS 12 composition measurements at Summit, Greenland appears to be in place. A number of Proposals for aircraft campaigns to track fire plumes from North America across the Arctic in summer 2008 are pending. Using modeled/assimilated BC deposition from NCAR collaborator and POLARCAT Steering Committee member P. Rasch (see attached letter of support), and UCI colleague J. Randerson, our group will simulate a boreal fire plume well-characterized during POLARCAT. Randerson will constrain emissions will be constrained using GFED techniques, Rasch will use CAM in assimilation mode to transport the plume across the arctic, and our group will characterize atmospheric and surface radiative forcing by the pollutants and compare to the in situ observations. 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 (e.g., Painter et al., 2001, 2003). Painter has developed a quick method to measure optically effective snow grain size in situ (Painter et al., 2007). Painter’s snow reflectance, particle size, and dust concentration measurements in the Colorado Rockies have been very helpful to our snow-aerosol radiative closure experiments. Vertical profiles of snow grain size would be highly complementary to the measurements Warren’s team will make at selected sites (snow density profile, spectral reflectance, and impurity concentration/optical properties) and to the POLARCAT exercise. If Painter conducts these measurements in the Arctic during IPY, we will use them for more complete closure experiments for SNICAR simulations of Arctic snow evolution. 4.9 Numerical Experiment Strategy Our questions (Section 3) will be addressed in the context of pre-industrial, present day, and next century timescales as appropriate. Natural (i.e., unforced) inScenario Source6 Interactions7 Climate8 Optics9 GISS10 terannual variability is quite Control Vary Sfc.+Atm. SOM Coated large in the Arctic climate system (e.g., Briegleb and Bromwich, Objective 2: Arctic climate sensitivity to timing and location of soot emission Fire location Vary Sfc.+Atm. SOM Coated 1998b; Fuhrer et al., 1999). Boreal fire variability is also Fire seasonality Vary Sfc.+Atm. SOM Coated quite large (van der Werf et al., Forcing/Feedback Vary Vary SOM Coated 2006) and causes much of the Objective 3: Arctic BC impacts on sea-ice interannual variability in ArcSea-ice feedbacks All Sfc.+Atm. Vary Coated tic BC deposition (Flanner et al., Sea-ice asymmetry Vary Sfc.+Atm. SOM Coated 2007). Detecting and assessPOLARCAT Event Simulation ing the relatively small (though Control 2007/2008 All Sfc.+Atm. Analyses Vary important) signal of aerosolinduced Arctic change (FigObjective 4: Effective Arctic forcings of GHGs and aerosols ures 3b and 8) against the noisy Predictions to 2100 background of natural Arctic Equilibrium All Sfc.+Atm. SOM Coated variability will be difficult. Transient Vary Sfc.+Atm. IPCC/SOM Coated We will continue to employ an ensemble-based approach Table 1: CCSM/SNICAR Simulations to increase the signal/noise ratio. The ensemble comprises multiple identical numerical experiments with slightly perturbed initial conditions. To obtain the climate responses presented in this proposal we conducted perennial 1997-, 1998-, and 2001-emissions experiments in separate 15 yr. simulations. We used a Student’s t-test to quantify statistical significance of Arctic changes between the two ensembles. Significant (p < 0.05) changes appear as cross-hatched regions in Figures 3b and 8. We plan numerous numerical experiments to systematically quantify aerosol impacts on cryospheric climate sensitivity and surface hydrology (Table 1). Many of these experiments are also designed to segregate robust from model-dependent results. Our collaborator Dr. Dorothy Koch (GISS, see attached letter of support) will replicate the subset of Table 1 experiments indicated by checkmarks in the GISS climate models with identical forcing scenarios to our CCSM experiments. The purpose of replicating controlled experiments in two GCMs is to understand model-dependent uncertainties, such as dry vs. wet deposition, aerosol direct radiative forcing forcing per unit mass, and forcing efficacy. 5 PROJECT COORDINATION 13 Using the SNICAR snowpack model, Flanner et al. (2007) show that microphysical feedbacks (Figure 1) may double snowpack BC forcing efficacy relative to GISS estimates (Hansen et al., 2005). This inter-model disparity is unsettling and highlights the uncertainties involved. In addition to snowpack representation, such inter-model discrepancies can be due to differences in emissions, meteorology, transport, and deposition. Quantifying the inter-model disparity associated with each process will help identify and focus optimal targets for future Arctic field and modeling studies. Flanner et al. (2007) found that even in years of very strong boreal biomass burning like 1998 (Figure 7), industrial and biofuel BC exceed biomass burning as an Arctic BC source, consistent with Koch and Hansen (2005). However, boreal biomass BC appears to be significantly more efficacious in the Arctic than industrial/biofuel BC (Flanner et al., 2007). The proximity of boreal forest BB emissions to snow-covered regions means that a smaller fraction of hydrophobic BC has aged to hydrophilic BC before deposition to snow. Boreal forest fires also peak in summer when insolation and potential snowpack forcing are greatest, rather than winter when industrial and biofuel BC emissions peak. Hence inter-model comparison is a question of response as well as forcing. Objective 2, quantifying efficacy as a function of time and location, is important in this regard as it will show which regions and seasons are most vulnerable to BC forcing, independent of the model. In Year 3 we will test the suggestion (e.g., Jacobson, 2002, 2004) that targeted reductions in soot emissions may achieve significant mitigation of Arctic climate change. Once soot effects are satisfactorily evaluated in our integrated Arctic simulations, we will conduct numerical experiments to assess the efficacy of plausible changes in BC emissions. These experiments will be designed in consultation with Koch and her GISS colleagues, and performed in parallel with them. 5 Project Coordination 5.1 Personnel PI Zender will coordinate all project activities. Zender has extensive experience in global-scale aerosol modeling, ice cloud physics, aerosol optics, and radiative transfer. He will work with Domin´ at LGGE to improve representation of e snow specific surface area changes due to surface hoar, diamond dust, and melt/freeze cycles (Section 4.2.2). Zender’s work as first author in Years 2 and 3 will utilize these improvements to elucidate the relative roles of GHGs and absorbing aerosols in Arctic climate change. A post-doc (to be named) will work for two years, participating in the intercomparison of Arctic climate simulations with collaborator Koch at GISS, and, with collaborator Rasch, in simulation, analysis, and evaluation of a boreal fire plume sampled by the POLARCAT team (Section 4.8). The post-doc will also be encouraged to pursue opportunistic and original research in Arctic aerosol-snow-climate interactions related to the project and to his/her expertise. Zender will advise an ESS graduate student in Arctic aerosol studies throughout (and beyond) the project. The student will be involved in all global modeling activities, though focused on Objectives 2 and 3. The student will help integrate realistic snow physics into the CICE sea-ice model, evaluate and improve snow on sea-ice simulations (Section 4.8), and participate on global studies of the efficacy of carbonaceous aerosol mitigation for Arctic climate. 5.2 1. 2. 3. 4. 5. 6. Schedule and Milestones Summer 2007: Zender to LGGE/Grenoble to work with F. Domin´ and colleagues e Fall 2007: Develop theory for hoar and melt/freeze processes in SNICAR Winter 2008: Predict/observe hoar and melt/freeze effects on SSA, and reflectance R(λ) Spring 2008: Predict/observe impurity effects on SSA, R(λ) Spring 2008: Submit manuscript on hoar and melt-freeze SSA, R(λ) results All year: UCI student “spins up” on project and prepares emissions and Arctic BC concentration datasets for GCM intercomparison Year 1. Goals: Improve SNICAR predictions of hoar, melt/freeze, and impurity effects Year 2. Goals: Represent snow effects on sea-ice, begin IPY POLARCAT project 6 RESULTS FROM PRIOR NSF FUNDING ON RELATED PROJECTS 14 1. Summer/Fall 2008: Graduate student visits E. Hunke at LANL to couple SNICAR physics to CICE sea-ice model 2. Fall 2008: Use Clarke/Warren measurements to refine aerosol precipitation- and melt-scavenging 3. Fall 2008: Present results from LGGE work at AGU 4. Winter 2009: Discuss sea-ice results with CCSM PCWG 5. Winter/Spring 2009: Examine impact of soot and dust on Arctic summer sea-ice extent 6. Spring 2009: Manuscript on sea-ice results, manuscript on influence of snow physics changes BC/dust simulation biases 7. All year: Zender and postdoc perform CCSM transient simulations with identical emissions/surface concentrations as D. Koch and GISS colleagues. 8. Undetermined: Zender to Norway/NILU for POLARCAT team meeting to identify Summer 2008 fire event for intensive modeling studies Year 3. Goals: Fully coupled IPCC-transient and LAC mitigation scenarios 1. 2. 3. 4. 5. 6. Summer 2009: Discuss sea-ice simulations at CCSM conference Summer/Fall 2009: Postdoc to NCAR to coordinate POLARCAT event simulation with P. Rasch Fall 2009: Present POLARCAT and GCM intercomparison results at AGU Winter/Spring 2010: Efficacy of carbonaceous aerosol mitigation for Arctic climate All year: Integrated absorbing aerosol impact on Arctic climate sensitivity Spring 2010: Manuscripts on POLARCAT event simulation and historical and future Arctic simulations Year 4. (unfunded wrap-up) 1. Fall 2010: Present results of transient IPCC/LAC scenarios at AGU 2. All year: Finish manuscripts 6 Results from Prior NSF Funding on Related Projects Zender was PI on ATM-0503148: “SGER: Improving CCSM Snow/Ice Radiative and Heating Processes and Assessing the Importance of the Soot Albedo Effect”, $26828, 2/1/2005–1/31/2006. This SGER grant partially funded graduate student Mark Flanner for one year during which he published one paper (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 4.2). Publications seeded by this grant include Randerson et al. (2006) and Flanner et al. (2007). 7 Related Projects, Broader Impacts and Education 7.1 Related Projects Collaborations with GISS, LANL, LGGE, NCAR, and U. Washington will benefit this project and are endorsed in attached letters of support. We plan to support these and other synergistic projects in at least the following ways: 1. Dr. Phil Rasch (see attached letter of support and Section 4.1). As originator of the BC/OC aerosol physics in CAM, Rasch is interested in incorporating the soot optical properties we are continually improving for SNICAR, back upstream into CAM. Rasch’s simulations of pollution events during POLARCAT will benefit from these optical properties and from the improved snow and sea-ice response. 2. Drs. Elizabeth Hunke (see attached letter of support and Section 4.5) and Dr. Bill Lipscomb, LANL. All CICE users will eventually benefit from the improved upper boundary condition that SNICAR to the sea-ice. Lipscomb, in the same LANL group, is concurrently adapting the GLIMMER ice sheet model (Payne, 1999) to the CCSM framework. LANL would like to use our SNICAR snow-aerosol physics as their ice sheet upper boundary condition. Assisting this effort will be relatively straightforward given the similar (CLM-ish) code 7 RELATED PROJECTS, BROADER IMPACTS AND EDUCATION 15 3. 4. 5. 6. 7. structure and our close collaboration on the sea-ice. This would complete the integrated treatment of clean and dirty snow in CCSM, a major milestone for the community. Dr. Tom Painter (NSIDC) has been measuring snow spectral reflectance, snow grain size, and dust concentration to understand the the influence of dust on catchment/basin hydrology in the Rockies. He is interested in simulating the direct forcing of snowpack impurities on reflectance using SNICAR snow aging and reflectance physics, coupled to, or instead of, the SNTHERM hydrology. Dr. Jim Randerson (UC Irvine) is primary developer of the GFED burning emissions database (see Section 4.4). Randerson is PI and Zender is a Co-PI on NSF (ATM-0628637): “Collaborative Research: Fire at the Intersection of Global Carbon and Water Cycles” from 10/1/2006–9/30/2010 (the “Fire Project”). Institutional collaborations led by J. Foley (U. Wisconsin) and N. Mahowald (NCAR) join us to study climate-fire interactions in the context of the carbon/nitrogen-cycles and land-use change. The Fire Project will infer historical and construct future fire emission datasets to study the interactions of biomass burning emissions with the carbon cycle and tropical interannual variability such as ENSO. The ANS project will use these datasets to study the influence of these trends in the Arctic, while the Fire Project will be an early beneficiary of improvements to SNICAR and Arctic climate response to carbonaceous aerosols developed by this ANS project. Dr. Natalie Mahowald (NCAR) is the NCAR lead on the Fire project and is Zender’s long-time collaborator on dust studies. Mahowald has developed estimates of glaciogenic and anthropogenic dust sources which we may use in past (LGM) and future Arctic climate scenarios. As part of the Fire Project Mahowald and Dr. Peter Thornton (NCAR) and Randerson are implementing a prognostic boreal fire regime in a dynamic vegetation and carbon/nitrogen cycling version of the CLM. The atmospheric and cryospheric response to fire will benefit from the improvements this ANS project will bring to SNICAR. Mr. Mark Flanner is currently a graduate student with Zender at UC Irvine and will graduate in Spring 2007. Flanner has been the primary developer of SNICAR. He has committed to a multi-year postdoctoral position at NCAR (beginning Summer 2007), funded by the Fire Project, to continue exploring BC impacts on climate, including tropical climate. He plans to remain actively involved with SNICAR development and thus would benefit from and contribute to the SNICAR improvements outlined in this ANS proposal. Drs. Steve Warren (U. Washington, see attached letter of support and Section 4.3), Tom Grenfell and Tony Clarke (U. Hawaii). Warren’s team may benefit from using our global snow, BC, and dust predictions and hindcasts to help their search for optimal sampling locations. 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 is and will be freely available for use in land, atmosphere, and sea-ice components (CLM, CAM, and CICE, respectively) of the CCSM. All the snow model developments will translate directly to the new ice sheet model being built at LANL and intended for use in CCSM global climate experiments. Hence extending and improving SNICAR’s physics will contribute to Arctic research areas including glacier mass balance (through realistic upper boundary radiation and melt conditions), basin and catchment hydrology (improved representation of snowpack sublimation vs. internal melt and surface percolation), physical impact of algae on snow, paleoclimate sensitivity (through improved accuracy of Arctic responsiveness to orbital and aerosol forcing), and snow chemistry improved representation of snowpack SSA (Domin´ and Shepson, 2002). e 7.3 Education This project trains one graduate student in Arctic aerosol-climate interactions. UC Irvine is a US Department of Education Minority Serving Institutions with large pools of under-represented minorities (URMs) potentially interested in pursuing undergraduate research projects. The UCI ESS department where Zender teaches has three programs which pipeline URMs to ESS research opportunities: (1) CAMP (Campus Alliance for Minority Participation) in Science, Engineering and Math, (2) an NSF REU in ESS (2006–2009, PI J. K. Moore of ESS), and (3) a NASA Education Grant (2006–2009, PI J. Randerson of ESS). With these resources Zender will open a year-round undergraduate research position in his group at no additional cost to NSF. The student will assist inverting snowpack spectral measurements for aerosol optical properties and mixing state given measured aerosol concentrations. 7 RELATED PROJECTS, BROADER IMPACTS AND EDUCATION 16 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. 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(2003), Comparison of seasonal and spatial variations of albedos from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Common Land Model, J. Geophys. Res., 108(D15), 4488, doi:10.1029/2002JD003,326. 4.7 7.4 7.4.1 Budget Justification Salaries and Wages One month of summer support for three years is requested for Prof. C. Zender, the PI, who will have primary responsibility for the proposed research. Zender requests his sabbatical differential salary in Year 1 ($25,542), when he will be in residence at LGGE/Grenoble performing controlled snow studies with collaborator F. Domin´ . In Year 2 the e project will focus more on solar energy distribution and snow aging evaluation in the Arctic sea-ice regime. In Year 3 the project will elucidate the relative roles of GHGs and absorbing aerosols in Arctic climate change. A postdoctoral scholar is requested in Years 2 and 3. The postdoc will perform original research applying our snow aging and aerosol semi-direct forcing techniques to the IPY POLARCAT field exercise (in collaboration with P. Rasch) and climate simulations that quantify the efficacy of Arctic aerosol forcing in various IPCC scenarios (in collaboration with D. Koch). The PI thinks that the proposed CCSM-GISS inter-model comparison is better suited to a post-doc than a graduate student because GCM intercomparison is best done by someone already comfortable and familiar with the inner workings of at least one global model. To be Named—Graduate Student Researcher III. Funds are requested to support one non-resident graduate student each year of the project. The graduate student support is requested at 50% for 9 months during the academic year and 100% for 3 months during the summer. The student will be involved in most global modeling activities: integrating realistic snow physics into the CICE sea-ice model, evaluate and improve snow/sea-ice simulations (in collaboration with E. Hunke), and participating in global studies of the efficacy of carbonaceous aerosol mitigation for Arctic climate. All salaries and wages were estimated using UCI’s academic and staff salary scales. A 2% cost-of-living increase was applied each year of this proposal as well as a 5% merit, where applicable. 7.4.2 Employee Benefits Fringe Benefits were estimated using the composite rates agreed upon by the University of California Office of the President and the DHHS Audit Agency, the Cognizant Audit Agency for the University of California. Benefit rates used in this proposal are: Faculty — academic year — 17% Faculty — summer — 12.7% Postdoc — 17% Student employees — summer — 3% Student employees — academic year — 1.3% Fees and tuition are requested for one nonresident student for the duration of the project. University of California policy requires award payment of fees for any student with more than 25% support from a grant. In the first year, $26,951 is requested, $29,511 in the second year, and $32,338 in the third year. Fees and tuition are excluded from indirect cost assessment. 7.4.3 Equipment Equipment funds are requested for two Linux scientific workstations (dual or quad CPU, 4 GB RAM) at $5,000 each. One in year one (for the graduate student) and one in year two (for the postdoc). These workstations will include adequate RAID’ed disk space (2–4 TB) for the researchers to store and analyze CCSM model output and satellite MODIS, MISR, and AMSR-E datasets. 7.4.4 Travel Domestic: Round-trip travel at $2000 per trip is requested for the PI and graduate student to travel to national meetings (primarily AGU) to present results in Years 2 and 3. Each trip includes round-trip travel from Irvine to San Francisco, one-week hotel and per diem. Round-trip travel at $2,000 per trip is requested for the postdoc (Year 2) and PI (Year 3) to travel to NASA GISS in New York City to intercompare models with collaborator D. Koch. This includes roundtrip, lodging and per diem for one week. Round-trip travel is requested for the graduate student and PI to travel to Los Alamos NM to work on sea-ice modeling with LANL collaborator E. Hunke. One month housing is requested for the BIBLIOGRAPHY 2 graduate student who will work closely with Hunke. One week lodging is requested for the PI. Travel expenses for the graduate student are estimated at $3,500 (for one month) and $1,000 (for one week) for the PI. These trips include estimated conference registration, abstract submission fees, RT airfare, lodging, meals and ground transportation. Travel estimates are based on historical usage. International: One round-trip at $1,500 is requested for the PI to travel to Grenoble, France in Year 1 to collaborate with F. Domine while on sabbatical. This trip includes only transportation. One round-trip at $3,000 is requested for the PI to travel to Norway in Year 2 to participate in IPY POLARCAT team meeting activities and to present results. This trip includes roundtrip travel from Irvine to Oslo, one-week hotel and per diem. These trips include estimated conference registration, abstract submission fees, RT airfare, lodging, meals and ground transportation. Travel estimates are based on historical usage. 7.4.5 Other Direct Costs Charges for journals, photocopying, long distance phone, FAX and postage charges pursuant to this project are requested. Included in these expenses are long-distance charges for usage directly related to the project, such as communication with colleagues, journals, and vendors. Photocopying of research materials including publications and results of this sponsored research project are requested as well as mail and shipping for materials related to this project. These costs are estimated at $500 per year. Support is requested for publication costs ($2,000 in Year 1, $4,000 in Year 2 and $6,000 in Year 3) pursuant to this project, which include utilization of expensive color figures. Costs were estimated according to historical usage. 7.4.6 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. 8 Facilities, Equipment, and Other Resources 8.1 Computational Resources The computational activities in this project will take place at one of three computing facilities based on the simulation scale. PI Zender directs the UCI Earth System Modeling Facility (ESMF), an NSF-supported MRI facility. The ESMF flagship machine is an 88-CPU Power4+ IBM supercomputer with 192 GB RAM and 16 TB of RAID storage. In summer 2006 ESMF acquired a new Beowulf cluster (named IPCC) comprising twenty-seven two-way dual core Opteron nodes (108 CPUs) and 5 TB of RAID storage. This ANS proposal fits squarely within the ESMF mission, and the ESMF will host the initial modeling development and shorter simulations. Coupling, testing, and shorter evaluations of SNICAR with CICE will occur at UCI. Longer sea-ice simulations with collaborator E. Hunke will be performed at LANL or NCAR, which both support the CCSM on supercomputer facilities designed for long, “production” simulations. We will request supplementary time at NCAR for the ensemble of equilibrium and transient simulations of the fully coupled CCSM/SNICAR code outlined in Table 1. This will be done under the auspices of the Polar Climate Working Group, which is co-chaired by collaborator E. Hunke. 9 Acronyms and Abbreviations Table 2: Acronyms and Abbreviations Abbreviation AGU AMSR-E ANS AR4 ARF ATSR AVIRIS AWS BB BC BF BRDF CAM CCSM CICE CLM CRM DEAD EMA ESM ESMF ESS FF FOCUS GCM GFED GHG GISS GLIDE GLIMMER GSFC IPCC IPY LAC LANL LGGE LGM MISR Description American Geophysical Union (meets in San Francisco in Fall) Advanced Microwave Scanning Radiometer (satellite instrument) Arctic Natural Sciences Fourth Assessment Report Aerosol Radiative Forcing Along Track Scanning Radiometer and Microwave Sounder Airborne Visible/Infrared Imaging Spectrometer Automated Weather Station Biomass Burning Black Carbon (light-absorbing component of carbonaceous aerosol) Biofuel Bi-directional Reflectance Distribution Function Community Atmosphere Model (atmosphere component of CCSM) Community Climate System Model Los Alamos sea-ice model (sea-ice component of CCSM) Community Land Model (land component of CCSM) Column Radiation Model Dust Entrainment And Deposition Model Effective Medium Approximation Earth System Model Earth System Modeling Facility Earth System Science (Department) Fossil Fuel Faculty Outreach Collaborations Uniting Scientists, Students and Schools General Circulation Model Global Fire Emissions Database Greenhouse Gas Goddard Institute for Space Studies General Land Ice Dynamic Elements (core of GLIMMER) Ice Sheet Model of Payne et al. Goddard Space Flight Center Intergovernmental Panel on Climate Change International Polar Year Light Absorbing Carbon (light-absorbing component of carbonaceous aerosol) Los Alamos National Laboratory Laboratoire de Glaciologie G´ ophysique de l’Environnement e (Grenoble, France) Last Glacial Maximum Multi-angle Imaging Spectro-Radiometer (satellite instrument) 9 ACRONYMS AND ABBREVIATIONS Table 2: (continued) Abbreviation MODIS NASA NCAR NCO NIC NILU NIR NSIDC OC OLLI OPAC PCWG PI POLARCAT Description Moderate Resolution Imaging Spectroradiometer (satellite instrument) National Aeronautic and Space Administration National Center for Atmospheric Research (Boulder, Colorado) netCDF Operators National Ice Center Norwegian Institute for Air Research (Kjeller, Norway) Near InfraRed National Snow and Ice Data Center Organic Carbon Osher Lifelong Learning Institute Optical Properties of Aerosols and Clouds (Hess et al. (1998)) (CCSM) Polar Climate Working Group Principle Investigator POLar study using Aircraft, Remote sensing, surface measurements and modeling of Climate, chemistry, Aerosols and Transport (IPY project organized by Andreas Stohl of NILU) Radiative Transfer Science and Engineering Informatics Scanning Electron Microscopy Small Grant for Exploratory Research Surface Heat Budget of the Arctic (field campaign on Arctic sea-ice) SNow, ICe, and Aerosol Radiative model Snow Melt model Slab Ocean Model Specific Surface Area Snow Water Equivalent Temperature Gradient University of California, Irvine 2 RT SEI SEM SGER SHEBA SNICAR SNTHERM SOM SSA SWE TG UCI 10 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) 10 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 2 10.1 Supplementary Documents 1. $DATA/prp/prp ans/prp ans abb.pdf 2. $DATA/prp/prp ans/prp ans clb.pdf 3. $DATA/prp/prp ans/prp ans ltr domine.pdf 4. $DATA/prp/prp ans/prp ans ltr hunke.pdf 5. $DATA/prp/prp ans/prp ans ltr koch.pdf 6. $DATA/prp/prp ans/prp ans ltr rasch.pdf 7. $DATA/prp/prp ans/prp ans ltr warren.pdf 8. $DATA/prp/prp ans/prp ans ltr polarcat.pdf 9. $DATA/prp/prp ans/prp ans ltr polarcat.pdf