Global Modeling Status Thomas Lachlan-Cope 1 and Keith M. Hines 2 1 British Antarctic Survey Cambridge, UK 2 Polar Meteorology Group Byrd Polar Research Center The Ohio State University

A Short History of the World (with emphasis on cloud modeling in GCMs) It had been common for GCMs to parameterize the radiative large-scale clouds. Cloud fraction was based upon factors such as relative humidity, static stability, … (e.g., Slingo-type schemes) This decade GCMs have moved toward prognostic radiative clouds (can include water and ice clouds). The radiative clouds are being made more consistent with the precipitating clouds. More advanced cloud prognostic schemes (e.g., two-moment microphysics) are recently being implemented.

Sea Level Pressure Combination Anomalies > 0.5 Standard Deviations NCAR CAM3.5 AMIP-type Simulation

An evaluation of Antarctic near-surface temperature and snowfall in IPCC AR4 GCMs Andrew J. Monaghan, David H. Bromwich, Ryan L. Fogt Presented by Keith Hines Polar Meteorology Group Byrd Polar Research Center The Ohio State University Columbus, Ohio, USA David Schneider NCAR Boulder, Colorado, USA Research funded by NSF-OPP and NASA Annual mean near-surface temperature (from AMPS Polar MM5) Overlain on RAMP DEM Annual mean snowfall (from AMPS Polar MM5) Overlain on RAMP DEM

Background Due to strong natural multidecadal climate variability in Antarctica, recent work has focused on spatially and temporally extending Antarctic snowfall and temperature records, as well as temporally extending the SAM index. These new records allow us to assess current Antarctic climate in a complete, multi-decadal context. Additionally, such reconstructions provide a means of assessing global climate model (GCM) simulations over Antarctica. In this presentation, we employ the new extended records to evaluate GCM simulations run in support of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). The observational records used are: 1.A 50-year annual Antarctic snowfall record (Monaghan et al. 2006) 2.A ~120-year annual Antarctic near-surface temperature record based on ice core isotope records (Schneider et al. 2006) 3.A 46-year annual and seasonal near-surface temperature record based on instrumental data (Monaghan et al. in prep) 4.A ~140-year annual record of the SAM (Fogt et al., in prep) Validations of these datasets indicate that they are robust

Part 1: Comparing IPCC AR4 GCMs to observed Antarctic climate variability

Annual Antarctic near-surface temperatures in Five IPCC AR4 GCM Ensembles: 1880-present Observed NASA GISS-ER MPI-ECHAM5 MRI-CGCM2_3_2a NCAR-CCSM3_0 Grand Ensemble Canadian CGCM3_1 Conclusion: Antarctic temperature in GCMs increases at 2-3x observed Bromwich et al. (in prep)

Annual Antarctic Snowfall in Five IPCC AR4 GCM Ensembles: 1880-present Observed NASA GISS-ER MPI-ECHAM5 MRI-CGCM2_3_2a NCAR-CCSM3_0 Grand Ensemble Canadian CGCM3_1 Conclusion: Antarctic snowfall in GCMs increases similarly to observed, but it is uncertain whether the GCMs would simulate a downturn since the late 1990s, as observed Bromwich et al. (in prep)

Sensitivity of Annual Antarctic Snowfall to Antarctic temperature in IPCC AR4 GCMs Conclusion: The GCMs have approximately the same sensitivity as observed. Therefore, if GCM Antarctic temperature projections are accurate, it may be expected that snowfall projections will be reasonable. Bromwich et al. (in preparation) (%/K)

SAM Reconstruction vs. IPCC AR4 SAM, Annual Mean  Gray shaded region corresponds to SAM calculated from 18 IPCC AR4 models, red is the grand ensemble mean  Blue is a reconstruction based on station pressure observations in the Southern Ocean -see Fogt et al. poster at this meeting Fogt et al. (in preparation) Conclusion: Most models have much lower annual SAM values in the early 20 th Century, thereby producing significant long-term trends that are not in the reconstruction. Models with and without time variable ozone forcing give different trends over the last 50 years, suggesting that accurate ozone concentrations are need for accurate 20 th and 21 st Century SAM predictions.

Part 2: Why are GCM temperature trends too strong?

Annual Antarctic Temperature vs. the SAM Observed and IPCC AR4 GCMs CCC NCAMRIMPI GIS OBS Five-year running means of detrended annual Antarctic near-surface temperature anomalies (K) plotted against detrended annual SAM anomalies (units are standard deviations) for the observations (a; ) and the GCMs (b-f; ). The observations are based on the Monaghan et al. (in prep) temperature reconstruction versus the Marshall (2003) SAM. Bromwich et al. (in prep) Conclusion: The observed sensitivity of Antarctic temperatures to the SAM is strongly negative overall. In the GCMs, the sensitivity is weak.

Annual Antarctic Temperature vs. LW Radiation for the IPCC Grand Ensemble b) T vs. LW Down, Cloudy-skyc) T vs. Precipitable Water Vapor a) T vs. LW Down, All-sky Conclusion: A LW radiation feedback at the surface is mainly due to an increase in water vapor (not clouds) in the IPCC AR4 GCMs. We still are working to figure out why so much moisture is being pumped into the Antarctic Atmosphere in the GCMs Bromwich et al. (in prep)

Summary:.Antarctic near-surface temperature trends are overestimated by ~2-3x over the 20 th century by IPCC AR4 GCMs. Snowfall projections seem reasonable given the data available. The GCMs overestimate 20 th century SAM trends. GCMs that include observed stratospheric ozone during the late 20 th century do the best at capturing the SAM increase since the 1960s. The sensitivity of Antarctic snowfall to regional temperature changes is consistent with GCM estimates of ~5%/K. However, the linkage with the temperature behavior over Antarctica is complex and arises because of changes in the atmospheric circulation. Observed Antarctic annual near-surface temperature trends are strongly related to SAM trends. However, in the IPCC AR4 GCMs, the Antarctic temperature sensitivity to the SAM is weak compared to a spurious water vapor feedback that is increasing downwelling longwave radiation in the models. Resolving this issue is of first-order importance in order to provide realistic Antarctic temperature simulations for the 21 st century.

What is the point of this? Well, obviously, Antarctic snowfall is linked to Antarctic clouds. Is the treatment of precipitation for Antarctic clouds reliable enough in global models to tackle the important issues for decadal and centennial climate variability and change? The answer is uncertain.

Are These Mesoscale Modeling Issues also Relevant for GCMs? Limited observations for comparison/inspiration/verification Low aerosol concentrations Clear-sky precipitation/diamond dust Thin ice clouds Ice cloud physics less well understood than liquid cloud physics Non-spherical ice particles Are the more frequent Arctic field programs relevant for the Antarctic? How can we make use of more advanced (two-moment) cloud microphysical parameterizations? How can we make use of remote sensing? Synergy with ice core studies

Issues for Mesoscale Modeling of Antarctic Clouds Limited observations for comparison/inspiration/verification Low aerosol concentrations Clear-sky precipitation/diamond dust Thin ice clouds Ice cloud physics less well understood than liquid cloud physics Non-spherical ice particles Are the more frequent Arctic field programs relevant for the Antarctic? How can we make use of more advanced (two-moment) cloud microphysical parameterizations? How can we make use of remote sensing? Synergy with ice core studies