Intrinsic versus Forced Variation in Coupled Climate Model Simulations over the Arctic during the Twentieth Century (original) (raw)
Related papers
Evaluation of Coupled Climate Simulations over the Arctic for IPCC AR4
There were observed warm anomalies in surface air temperature (SAT) in the Arctic between 1920-1950 and again at the end of the century. The ability to reproduce this decadal variability in the coupled GCMs is important for understanding processes in the arctic climate system and increasing the confidence in the IPCC model projections into the future. Our study evaluated 55 ensemble runs generated by 18 coupled GCMs from around the world for their 20th century simulations (20C3M), and their corresponding control simulations (PIcntrl). Warm anomalies in the Arctic during the last two decades are reproduced by most ensemble members, but with considerable variability in magnitude between models. Among the 18 models, 12 of them generated events which were typical of mid-century arctic-wide warm anomalies (60-90oN), yet with large region-to-region, season-to-season and year-to-year variability. Control runs (without external forcing) for 14 out of 18 models also produced typical arctic m...
A comparison of GCM simulations of Arctic climate
Geophysical Research Letters, 1992
As the atmosphere, ocean, and sea ice components of global climate models are made increasingly interactive, systematic errors or biases in one component can adversely affect the other model components. The fidelity of the component interactions is especially important in the polar regions, where many atmospheric General Circulation Models (GCMs) project an amplified climatic response to increasing concentrations of greenhouse gases. In comparing the Arctic performance of five atmospheric GCMs (GFDL, GISS, NCAR, OSU, and UKMO), we illustrate key differences in the fields most relevant to sea ice/ocean forcing: surface air temperature and sea level pressure (surface wind stress). While the amplitude of the seasonal cycle of simulated air temperature is generally realistic, biases of up to 5-10°C relative to observations are apparent over much of the Arctic. The simulated sea-level pressure pattern varies widely from model to model, and in some cases is incompatible with the observed wind-forcing of sea ice from the Arctic Basin to the North Atlantic via Fram Strait. The implications that these differences have for transports of salinity are significant.
Twenty-first century Arctic climate change in the CCSM3 IPCC scenario simulations
Climate Dynamics, 2006
Arctic climate change in the Twenty-first century is simulated by the Community Climate System Model version 3.0 (CCSM3). The simulations from three emission scenarios (A2, A1B and B1) are analyzed using eight (A1B and B1) or five (A2) ensemble members. The model simulates a reasonable present-day climate and historical climate trend. The model projects a decline of sea-ice extent in the range of 1.4-3.9% per decade and 4.8-22.2% per decade in winter and summer, respectively, corresponding to the range of forcings that span the scenarios. At the end of the Twenty-first century, the winter and summer Arctic mean surface air temperature increases in a range of 4-14°C (B1 and A2) and 0.7-5°C (B1 and A2) relative to the end of the Twentieth century. The Arctic becomes ice-free during summer at the end of the Twenty-first century in the A2 scenario. Similar to the observations, the Arctic Oscillation (AO) is the dominant factor in explaining the variability of the atmosphere and sea ice in the 1870-1999 historical runs. The AO shifts to the positive phase in response to greenhouse gas forcings in the Twenty-first century. But the simulated trends in both Arctic mean sea-level pressure and the AO index are smaller than what has been observed. The Twenty-first century Arctic warming mainly results from the radiative forcing of greenhouse gases. The 1st empirical orthogonal function (explains 72.2-51.7% of the total variance) of the wintertime surface air temperature during 1870-2099 is characterized by a strong warming trend and a ''polar amplification''-type of spatial pattern. The AO, which plays a secondary role, contributes to less than 10% of the total variance in both surface temperature and sea-ice concentration.
Do General Circulation Models Underestimate the Natural Variability in the Arctic Climate?
Journal of Climate, 1997
The authors examine the natural variability of the arctic climate system simulated by two very different models: the Geophysical Fluid Dynamics Laboratory (GFDL) global climate model, and an area-averaged model of the arctic atmosphere-sea ice-upper-ocean system called the polar cap climate model, the PCCM. A 1000-yr integration of the PCCM is performed in which the model is driven by a prescribed, stochastic atmospheric energy flux convergence (D), which has spectral characteristics that are identical to the spectra of the observed D. The standard deviation of the yearly mean sea ice thickness from this model is 0.85 m; the mean sea ice thickness is 3.1 m. In contrast, the standard deviation of the yearly averaged sea ice thickness in the GFDL climate model is found to be about 6% of the climatological mean thickness and only 24% of that simulated by the PCCM. A series of experiments is presented to determine the cause of these disparate results. First, after changing the treatment of sea ice and snow albedo in the (standard) PCCM model to be identical thermodynamically to that in the GFDL model, the PCCM is driven with D from the GFDL control integration to demonstrate that the PCCM model produces an arctic climate similar to that of the GFDL model. Integrations of the PCCM are then examined in which the different prescriptions of the sea ice treatment (GFDL vs standard PCCM) and D (GFDL vs observed) are permutated. The results indicate that unarguable improvements in the treatment of sea ice in the GFDL climate model should amplify significantly the natural variability in this model. The authors present calculations that indicate the variability in the sea ice thickness is extremely sensitive to the spectrum of the atmospheric energy flux convergence. Specifically, the differences between the GFDL and observed D at timescales shorter than 3 yr are shown to have a significant impact on the sea ice variability on all timescales. A conservative best estimate for the amplitude of the natural variability in the arctic sea ice volume is presented; this estimate is a significant fraction (about 25%) of the mean sea ice thickness. The results suggest that most of the global climate models that have been used to evaluate climate change may also have artificially quiescent natural variability in the Arctic.
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.
Journal of Geophysical Research, 2006
This paper describes a simple ''multibox'' model of the Arctic atmosphere-ice-ocean system. The model consists of two major modules (an Arctic module and a Greenland Sea module) and several sub-modules. The Arctic module includes a shelf box model coupled with a thermodynamic sea ice model, and an Arctic Ocean model coupled with a sea ice model and an atmospheric box model. The Greenland Sea module includes an oceanic model coupled with a sea ice model and a statistical model of surface air temperature over the Greenland Sea. The full model is forced by daily solar radiation, wind stress, river runoff, and Pacific Water inflow through Bering Strait. For validation purposes, results from model experiments reproducing seasonal variability of the major system parameters are analyzed and compared with observations and other models. The model reproduces the seasonal variability of the Arctic system reasonably well and is used to investigate decadal Arctic climate variability in part 2 of this publication (Dukhovskoy et al., 2006).
Geophysical Research Letters, 2000
Two global climate models (HadCM2 and ECHAM) forced with the same greenhouse-gas scenario (IS92a) are found to disagree in their simulated longterm trends of the intensity of the Arctic Oscillation (AO), an atmospheric circulation pattern of the Northern Hemisphere. The simulated AO trends are strongly dependent on the model and on the initial conditions of the simulations. The simulated winter temperature increase averaged over the Northern hemisphere is very similar in both models. However, the effect of the different AO trends on temperature causes clear differences in the predicted regional warming, which are reduced if the effects of the AO is linearly discounted. The uncertainty in the predictions of circulation changes has impacts on the estimation of regional temperature changes.
Observed and modeled relationships among Arctic climate variables
2003
The complex interactions among climate variables in the Arctic have important implications for potential climate change, both globally and locally. Because the Arctic is a data-sparse region and because global climate models (GCMs) often represent Arctic climate variables poorly, significant uncertainties remain in our understanding of these processes.
Global Climate Model Performance over Alaska and Greenland
Journal of Climate, 2008
The performance of a set of fifteen global climate models used in the Coupled Model Intercomparison Project is evaluated for Alaska and Greenland, and compared with the performance over broader pan-Arctic and Northern Hemisphere extratropical domains.
Intercomparison of Arctic regional climate simulations: Case studies of January and June 1990
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.