Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations (original) (raw)
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Journal of Climate
The near-surface climate, including the atmosphere, ocean, sea ice, and land state and fluxes, in the initial version of the Regional Arctic System Model (RASM) are presented. The sensitivity of the RASM near-surface climate to changes in atmosphere, ocean, and sea ice parameters and physics is evaluated in four simulations. The near-surface atmospheric circulation is well simulated in all four RASM simulations but biases in surface temperature are caused by biases in downward surface radiative fluxes. Errors in radiative fluxes are due to biases in simulated clouds with different versions of RASM simulating either too much or too little cloud radiative impact over open ocean regions and all versions simulating too little cloud radiative impact over land areas. Cold surface temperature biases in the central Arctic in winter are likely due to too few or too radiatively thin clouds. The precipitation simulated by RASM is sensitive to changes in evaporation that were linked to sea surf...
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Journal of Geophysical Research, 2000
Advances in regional climate modeling must be strongly based on analysis of physical processes in comparison with data. In a data-poor region such as the Arctic; this procedure may be enhanced by a community-based approach, i.e., through collaborative analysis by several research groups. To illustrate this approach, simulations were performed with two regional climate models, HIRHAM and ARCSyM, over the Arctic basin to 65øN, laterally driven at the boundaries by observational analyses. It was found that both models are able to reproduce reasonably the main features of the large-scale flow and the surface parameters in the Arctic. Distinct differences in the simulations can be attributed to specific characteristics of the boundary layer and surface parameterizations, which result in surface flux differences, and to the lateral moisture forcing, both of which affect moisture availability in the atmosphere. Further disparities are associated with the additional degrees of freedom allowed in the coupled model ARCSyM. Issues of model configuration and experimental design are discussed, including domain size, grid spacing, boundary formulations, model initialization and spin-up, and ensemble approaches. In order to reach definitive conclusions in a regional climate model intercomparison, ensemble simulations with adequate spin-up and equivalent initialization of surface fields will be required. 1998], MERCURE project, PIRCS project [Arritt et al., 1999; Takle et al., 1999]) provide examples for frameworks which eval-uate the strengths and weaknesses of RCMs and their component parameterizations through systematic, comparative simulations.
Regional climate model of the Arctic atmosphere
Journal of Geophysical Research, 1996
A regional climate model of the whole Arctic using the dynamical package of the High-Resolution Limited Area Model (HIRLAM) and the physical parameterizations of the Hamburg General Circulation Model (ECHAM3) has been applied to simulate the climate of the Arctic north of 65 øN at a 50-km horizontal resolution. The model has been forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses at the lateral boundaries and with climatological or actual observed sea surface temperatures and sea ice cover at the lower boundary. The results of simulating the Arctic climate of the troposphere and lower stratosphere for January 1991 and July 1990 have been described. In both months the model rather closely reproduces the observed monthly mean circulation. While the general spatial patterns of surface air temperature, mean sea level pressure, and geopotential are consistent with the ECMWF analyses, the model shows biases when the results are examined in detail. The largest biases appear during winter in the planetary boundary layer and at the surface. The underestimated vertical heat and humidity transport in the model indicates the necessity of improvements in the parameterizations of vertical transfer due to boundary layer processes. The tropospheric differences between model simulations and analyses decrease with increasing height. The temperature bias in the planetary boundary layer can be reduced by increasing the model sea ice thickness. The use of actual observed sea surface temperatures and sea ice cover leads only to small improvements of the model bias in comparison with climatological sea surface temperatures and sea ice cover. The validation of model computed geopotential, radiative fluxes, surface sensible and latent heat fluxes and clouds against selected station data shows deviations between model simulations and observations due to shortcomings of the model. This first validation indicates that improvements in the physical parameterization packages of radiation and in the description of sea ice thickness and sea ice fraction are necessary to reduce the model bias.
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The Arctic surface energy budget as simulated with the IPCC AR4 AOGCMs
Climate Dynamics, 2007
Ensembles of simulations of the twentiethand twentyfirst-century climate, performed with 20 coupled models for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment, provide the basis for an evaluation of the Arctic (70°-90°N) surface energy budget. While the various observational sources used for validation contain differences among themselves, some model biases and across-model differences emerge. For all energy budget components in the twentieth-century simulations (the 20C3M simulation), the across-model variance and the differences from observational estimates are largest in the marginal ice zone (Barents, Kara, Chukchi Seas). Both downward and upward longwave radiation at the surface are underestimated in winter by many models, and the ensenmble mean annual net surface energy loss by longwave radiation is 35 W/m 2 , which is less than for the NCEP and ERA40 reanalyses but in line with some of the satellite estimates. Incoming solar radiation is overestimated by the models in spring and underesti-mated in summer and autumn. The ensemble mean annual net surface energy gain by shortwave radiation is 39 W/m 2 , which is slightly less than for the observational based estimates, In the twentyfirst-century simulations driven by the SRES A2 scenario, increased concentrations of greenhouse gasses increase (average for 2080-2100 minus average for 1980-2000 averages) the annual average ensemble mean downward longwave radiation by 30.1 W/m 2 . This was partly counteracted by a 10.7 W/m 2 reduction in downward shortwave radiation. Enhanced sea ice melt and increased surface temperatures increase the annual surface upward longwave radiation by 27.1 W/m 2 and reduce the upward shortwave radiation by 13.2 W/m 2 , giving an annual net (shortwave plus longwave) surface radiation increase of 5.8 W/m 2 , with the maximum changes in summer. The increase in net surface radiation is largely offset by an increased energy loss of 4.4 W/m 2 by the turbulent fluxes.
Journal of Climate, 2007
There were two major multiyear, Arctic-wide (60°-90°N) warm anomalies (Ͼ0.7°C) in land surface air temperature (LSAT) during the twentieth century, between 1920 and 1950 and again at the end of the century after 1979. Reproducing this decadal and longer variability in coupled general circulation models (GCMs) is a critical test for understanding processes in the Arctic climate system and increasing the confidence in the Intergovernmental Panel on Climate Change (IPCC) model projections. This study evaluated 63 realizations generated by 20 coupled GCMs made available for the IPCC Fourth Assessment for their twentieth-century climate in coupled models (20C3M) and corresponding control runs (PIcntrl). Warm anomalies in the Arctic during the last two decades are reproduced by all ensemble members, with considerable variability in amplitude among models. In contrast, only eight models generated warm anomaly amplitude of at least two-thirds of the observed midcentury warm event in at least one realization, but not its timing. The durations of the midcentury warm events in all the models are decadal, while that of the observed was interdecadal. The variance of the control runs in nine models was comparable with the variance in the observations. The random timing of midcentury warm anomalies in 20C3M simulations and the similar variance of the control runs in about half of the models suggest that the observed midcentury warm period is consistent with intrinsic climate variability. Five models were considered to compare somewhat favorably to Arctic observations in both matching the variance of the observed temperature record in their control runs and representing the decadal mean temperature anomaly amplitude in their 20C3M simulations. Seven additional models could be given further consideration. Results support selecting a subset of GCMs when making predictions for future climate by using performance criteria based on comparison with retrospective data.
Climate Dynamics, 2006
Simulations of eight different regional climate models (RCMs) have been performed for the period September 1997-September 1998, which coincides with the Surface Heat Budget of the Arctic Ocean (SHEBA) project period. Each of the models employed approximately the same domain covering the western Arctic, the same horizontal resolution of 50 km, and the same boundary forcing. The models differ in their vertical resolution as well as in the treatments of dynamics and physical parameterizations. Both the common features and differences of the simulated spatiotemporal patterns of geopotential, temperature, cloud cover, and long-/ shortwave downward radiation between the individual model simulations are investigated. With this work, we quantify the scatter among the models and therefore the magnitude of disagreement and unreliability of current Arctic RCM simulations. Even with the relatively constrained experimental design we notice a considerable scatter among the different RCMs. We found the largest across-model scatter in the 2 m temperature over land, in the surface radiation fluxes, and in the cloud cover which implies a reduced confidence level for these variables.
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