Welcome to CGD

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Summary of achievements

In my position as CCSM Polar Climate Working Group (PCWG) and sea ice model liaison, I am responsible for supporting the scientific projects of interest to the PCWG, generating sea ice diagnostic plots for simulations for the general CCSM community, aiding new users of CCSM with experimental setup and design, and ongoing maintenance and development of the Community Ice CodE (CICE), the sea ice component of the CCSM. Some recent examples of PCWG projects that I've been involved in are: providing sea ice concentration from IPCC 21st century projections for use in polar bear habitat prediction models; performing ensemble experiments using the CCSM to investigate possible sea ice response under idealized greenhouse gas commitment scenarios and predictability of September sea ice extent. Earlier this year I had participated in a workshop sponsored by NSF Arctic System Science (ARCSS) with particular focus on the 2007 September ice minimum. I was an active participant in the working group on predictability of the sea ice, which eventually led to an open community September sea ice outlook. I have also been involved with 20th century hindcast runs using the CCSM as a basis for short-term prediction studies. I am currently testing and developing new parameterizations in the sea ice for melt ponds, shortwave radiation, and aerosol deposition on sea ice. I am also peripherally involved in the grand challenge project to run the CCSM at very high resolution.

 

Publications

G.M. Durner, D.C. Douglas, R.M. Nielson, S.C. Amstrup, T.L. McDonald, I. Stirling, M. Mauritzen, E.W. Born, Ø. Wiig, E. DeWeaver, M.C. Serreze, S.E. Belikov, M.M. Holland, J. Maslanik, J. Aars, D.A. Bailey and A.E. Derocher. 2009: Predicting 21st-century polar bear habitat distribution from global climate models. Ecological Monographs, 79, 25-58, doi:10.1890/07-2089.1.

Figure 1: High resolution figure

Abstract: Projections of polar bear (Ursus maritimus) sea ice habitat distribution in the polar basin during the 21st century were developed to understand the consequences of anticipated sea ice reductions on polar bear populations. We used location data from satellite-collared polar bears and environmental data (e.g., bathymetry, distance to coastlines, and sea ice) collected from 1985 to 1995 to build resource selection functions (RSFs). RSFs described habitats that polar bears preferred in summer, autumn, winter, and spring. When applied to independent data from 1996 to 2006, the RSFs consistently identified habitats most frequently used by polar bears. We applied the RSFs to monthly maps of 21st-century sea ice concentration projected by 10 general circulation models (GCMs) used in the Intergovernmental Panel of Climate Change Fourth Assessment Report, under the A1B greenhouse gas forcing scenario. Despite variation in their projections, all GCMs indicated habitat losses in the polar basin during the 21st century. Losses in the highest-valued RSF habitat (optimal habitat) were greatest in the southern seas of the polar basin, especially the Chukchi and Barents seas, and least along the Arctic Ocean shores of Banks Island to northern Greenland. Mean loss of optimal polar bear habitat was greatest during summer; from an observed 1.0 million km² in 1985–1995 (baseline) to a projected multi-model mean of 0.32 million km² in 2090–2099 (-68% change). Projected winter losses of polar bear habitat were less: from 1.7 million km² in 1985–1995 to 1.4 million km² in 2090–2099 (-17% change). Habitat losses based on GCM multi-model means may be conservative; simulated rates of habitat loss during 1985–2006 from many GCMs were less than the actual observed rates of loss. Although a reduction in the total amount of optimal habitat will likely reduce polar bear populations, exact relationships between habitat losses and population demographics remain unknown. Density and energetic effects may become important as polar bears make long-distance annual migrations from traditional winter ranges to remnant high-latitude summer sea ice. These impacts will likely affect specific sex and age groups differently and may ultimately preclude bears from seasonally returning to their traditional ranges.

Figure caption: (a) Example of two consecutive locations of polar bear number 20224 on the 8th (yellow point) and 14th (red point) of September 2005 and the probable extent of available habitat (red circle), had the bear sustained a maximum rate of travel for the six-day period. (b) An example of habitat pixels available to a polar bear as it moved from one location (yellow point) to a subsequent location (red point and black pixel). All pixels within the red circle were considered available for selection by the bear, but only the black pixel containing the second bear location (red point) was coded as a used point. Each pixel in the availability circle included covariates totcon, bath, dist2land, dist15, dist50, and dist75. Key to variables: totcon is a monthly estimate of the aerial extent of sea ice within each 25 × 25 km pixel; totcon2 is the second-order effect (quadratic) of totcon; dist15 is the distance from the center of each 25 × 25 km pixel to the nearest pixel on the =15% sea ice concentration boundary; dist50 is the distance from the center of each 25 × 25 km pixel to the nearest pixel on the =50% sea ice concentration boundary; dist75 is the distance from the center of each 25 × 25 km pixel to the nearest pixel on the =75% sea ice concentration boundary; bath is ocean depth; dist2land is the distance from the center of each 25 × 25 km pixel to the nearest coastline.

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Gent, P.R., S.G. Yeager, R.B. Neale, S. Levis and D.A. Bailey. 2009: Improvements in a half degree atmosphere/land version of the CCSM. Climate Dynamics, 79, 25-58, doi:10.1007/s00382-009-0614-8.

Figure 2: High resolution figure

Abstract: A decadal climate projection between 1980 and 2030 using a nominal 0.5° resolution in the atmosphere and land components has been performed using the Community Climate System Model, version 3.5. The mean climate is compared to a companion simulation using a nominal 2° resolution in the atmosphere and land components. The increased atmosphere resolution has several benefits, and produces a significantly better mean climate. The maximum sea surface temperature biases in the major upwelling regions, including the West Coast of the USA, are reduced by more than 60%. Precipitation patterns are improved in the summer Asian monsoon, mostly due to the better resolved orography, and in the eastern tropical Pacific Ocean south of the equator. The improved precipitation patterns lead to better river flows in many rivers worldwide. The atmospheric circulation in the Arctic also improves, which leads to a better regional sea ice thickness distribution in the Arctic Ocean.

Figure caption: Annual mean Arctic sea ice thickness in meter from (a) 0.5° run, and (b) 2° run.