Evaluation of an ensemble of Arctic regional climate models: spatiotemporal fields during the SHEBA year (original) (raw)
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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.
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.
Climate Dynamics, 2008
Eight atmospheric regional climate models (RCMs) were run for the period September 1997 to October 1998 over the western Arctic Ocean. This period was coincident with the observational campaign of the Surface Heat Budget of the Arctic Ocean (SHEBA) project. The RCMs shared common domains, centred on the SHEBA observation camp, along with a common model horizontal resolution, but differed in their vertical structure and physical parameterizations. All RCMs used the same lateral and surface boundary conditions. Surface downwelling solar and terrestrial radiation, surface albedo, vertically integrated water vapour, liquid water path and cloud cover from each model are evaluated against the SHEBA observation data. Downwelling surface radiation, vertically integrated water vapour and liquid water path are reasonably well simulated at monthly and daily timescales in the model ensemble mean, but with considerable differences among individual models. Simulated surface albedos are relatively accurate in the winter season, but become increasingly inaccurate and variable in the melt season, thereby compromising the net surface radiation budget. Simulated cloud cover is more or less uncorrelated with observed values at the daily timescale. Even for monthly averages, many models do not reproduce the annual cycle correctly. The inter-model spread of simulated cloud-cover is very large, with no model appearing systematically superior. Analysis of the co-variability of terms controlling the surface radiation budget reveal some of the key processes requiring improved treatment in Arctic RCMs. Improvements in the parameterization of cloud amounts and surface albedo are most urgently needed to improve the overall performance of RCMs in the Arctic.
Journal of Climate, 2006
To improve simulations of the Arctic climate and to quantify climate model errors, four regional climate models [the Arctic Regional Climate System Model (ARCSYM), the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS), the High-Resolution Limited-Area Model (HIRHAM), and the Rossby Center Atmospheric Model (RCA)] have simulated the annual Surface Heat Budget of the Arctic Ocean (SHEBA) under the Arctic Regional Climate Model Intercomparison Project (ARCMIP). The same lateral boundary and ocean surface boundary conditions (i.e., ice concentration and surface temperature) drive all of the models. This study evaluated modeled surface heat fluxes and cloud fields during May 1998, a month that included the onset of the surface icemelt. In general, observations agreed with simulated surface pressure and near-surface air properties. Simulation errors due to surface fluxes and cloud effects biased the net simulated surface heat flux, which in turn affected the timing of the simulated icemelt. Modeled cloud geometry and precipitation suggest that the RCA model produced the most accurate cloud scheme, followed by the HIRHAM model. Evaluation of a relationship between cloud water paths and radiation showed that a radiative transfer scheme in ARCSYM was closely matched with the observation when liquid clouds were dominant. Clouds and radiation are of course closely linked, and an additional comparison of the radiative transfer codes for ARCSYM and COAMPS was performed for clear-sky conditions, thereby excluding cloud effects. Overall, the schemes for radiative transfer in ARCSYM and for cloud microphysics in RCA potentially have some advantages for modeling the springtime Arctic.
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...
Sensitivity of simulated regional Arctic climate to the choice of coupled model domain
Tellus Series a-Dynamic Meteorology and Oceanography, 2014
The climate over the Arctic has undergone changes in recent decades. In order to evaluate the coupled response of the Arctic system to external and internal forcing, our study focuses on the estimation of regional climate variability and its dependence on large-scale atmospheric and regional ocean circulations. A global oceanÁsea ice model with regionally high horizontal resolution is coupled to an atmospheric regional model and global terrestrial hydrology model. This way of coupling divides the global ocean model setup into two different domains: one coupled, where the ocean and the atmosphere are interacting, and one uncoupled, where the ocean model is driven by prescribed atmospheric forcing and runs in a so-called stand-alone mode. Therefore, selecting a specific area for the regional atmosphere implies that the oceanÁatmosphere system can develop 'freely' in that area, whereas for the rest of the global ocean, the circulation is driven by prescribed atmospheric forcing without any feedbacks. Five different coupled setups are chosen for ensemble simulations. The choice of the coupled domains was done to estimate the influences of the Subtropical Atlantic, Eurasian and North Pacific regions on northern North Atlantic and Arctic climate. Our simulations show that the regional coupled oceanÁatmosphere model is sensitive to the choice of the modelled area. The different model configurations reproduce differently both the mean climate and its variability. Only two out of five model setups were able to reproduce the Arctic climate as observed under recent climate conditions (ERA-40 Reanalysis). Evidence is found that the main source of uncertainty for Arctic climate variability and its predictability is the North Pacific. The prescription of North Pacific conditions in the regional model leads to significant correlation with observations, even if the whole North Atlantic is within the coupled model domain. However, the inclusion of the North Pacific area into the coupled system drastically changes the Arctic climate variability to a point where the Arctic Oscillation becomes an 'internal mode' of variability and correlations of year-to-year variability with observational data vanish. In line with previous studies, our simulations provide evidence that Arctic sea ice export is mainly due to 'internal variability' within the Arctic region. We conclude that the choice of model domains should be based on physical knowledge of the atmospheric and oceanic processes and not on 'geographic' reasons. This is particularly the case for areas like the Arctic, which has very complex feedbacks between components of the regional climate system.
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.
Boundary-Layer Meteorology, 2005
A primary climate change signal in the central Arctic is the melting of sea ice. This is dependent on the interplay between the atmosphere and the sea ice, which is critically dependent on the exchange of momentum, heat and moisture at the surface. In assessing the realism of climate change scenarios it is vital to know the quality by which these exchanges are modelled in climate simulations. Six state-of-the-art regional-climate models are run for one year in the western Arctic, on a common domain that encompasses the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment ice-drift track. Surface variables, surface fluxes and the vertical structure of the lower troposphere are evaluated using data from the SHEBA experiment. All the models are driven by the same lateral boundary conditions, sea-ice fraction and sea and sea-ice surface temperatures. Surface pressure, near-surface air temperature, specific humidity and wind speed agree well with observations, with a falling degree of accuracy in that order. Wind speeds have systematic biases in some models, by as much as a few metres per second. The surface radiation fluxes are also surprisingly accurate, given the complexity of the problem. The turbulent momentum flux is acceptable, on average, in most models, but the turbulent heat fluxes are, however, mostly unreliable. Their correlation with observed fluxes is, in principle, insignificant, and they accumulate over a year to values an order of magnitude larger than observed. Typical instantaneous errors are easily of the same order of magnitude as the observed net atmospheric heat flux. In the light of the sensitivity of the atmosphere-ice interaction to errors in these fluxes, the ice-melt in climate change scenarios must be viewed with considerable caution.
Geoscientific Model Development
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Observed and modeled relationships among Arctic climate variables
Journal of Geophysical Research: Atmospheres, 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. In addition to the traditional approach of validating individual variables with observed fields, we demonstrate that a comparison of covariances among interrelated parameters from observations and GCM output provides a tool to evaluate the realism of modeled relationships between variables. We analyze and compare a combination of conventional observations, satellite retrievals, and GCM simulations to examine some of these relationships. The three climate variables considered in this study are surface temperature, cloud cover, and downward longwave flux. Results show that the highest correlations between daily changes in pairs of varia...