Regional climate model of the Arctic atmosphere

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 101, NO. D18, PAGES 23,401-23,422, OCTOBER 27, 1996 Regional climate model of the Arctic atmosphere Klaus Dethloff, Annette Rinke and Ralph Lehmann Alfred Wegener Institute for Polar and Marine Research, Research Department, Potsdam, Germany Jens H. Christensen Danish Meteorological Institute, Copenhagen, Denmark Michael Botzet and Bennert Machenhauer Max Planck Institute for Meteorology, Hamburg, Germany Abstract. 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. 1. Introduction Global general circulation models nowadays used for climate change scenario simulations have a horizontal resolution of around 250 km, whereas significant topographic features and associated regional temperature and precipitation patterns often occur at finer scales. Running a global model at higher horizontal resolution is computationally expensive, and it may take years before global climate simulations can be performed at resolutions high enough to accurately simulate Copyright 1996 by the American Geophysical Union. Paper number 96JD /9 6/96JD the climate at regional scale. To evaluate the consequences of climate change, we need to develop climate models capable of simulating regional temperature and precipitation patterns with a high degree of fidelity in comparison with measurements. Data analyses and global circulation studies of the atmosphere indicate that the Arctic is a region of high climatic sensitivity as discussed by Baker et al. [1980] and Barry et al. [1993]. The meridional diabatic heating gradient between the tropical heat source and the Arctic heat sink determines the strength of the atmospheric circulation in the global climate system. It drives the baroclinic instability processes and cyclone activity due to the temperature gradient between polar and middle latitudes. Global circulation models indicate that the largest changes induced by increasing greenhouse gas concentrations will occur in high latitudes. The correctness of the projections of these present models is currently under discussion. 23,401

2 23,402 DETI--ILO ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHE The current deficiencies of global climate models are related to the crudeness of model topography and inadequacies the parameterizations of physical processes as discussed by Bromwich et al. [1994]. An essential step in constructing viable climate models is to develop and incorporate more realistic representations of processes that occur in the whole Deque and Piedelivre [1995]. Whereas increased horizontal resolution in GCMs can lead to a better representation of the global circulation as discussed by Boville [1991], Sperber et al. [1994], Williamson et al. [1995] and Senior [1995], the sensitivity of a regional atmospheric model to horizontal resolution has been investigated only recently by Giorgi and Arctic. Marinucci [1996]. They showed that the effect of better The treatment of many physical processes in climate models is resolution limited. Thus we have applied a regional climate representation of topography due to increased resolution may be masked by the direct sensitivity of the model model of the Arctic troposphere and stratosphere north parameterizations to the resolution itself. of 65 øn with a high horizontal resolution of 50 km to The present paper describes the high-resolution simulation describe mesoscale structures explicitly. Owing to the of the Arctic climate during July 1990 and January 1991 with nonlinear coupling between low, medium, and high wave scales, the interactions between them vary as a function of the horizontal model resolution. The additional resolution from medium to high scales may play an important role in the nonlinear processes that force the medium-scale waves as pointed out by Williamson et al. [1995]. We are using the physical parameterizations developed for the global model ECHAM3 by Roeckner et al. [1992] and are applying them for regional climate simulations with a higher horizontal resolution. This approach allows a much more climatological sea surface temperatures and sea ice extent based on work by Brankovic and van Maanen [1985] or actual sea surface temperatures and sea ice fraction derived from ECMWF. The initial conditions are obtained by interpolating the ECMWF analysed fields of the atmospheric variables to the grid of the regional model. In contrast to the work of Walsh et al. [1993] and Lynch et al. [1995] the entire Arctic is encompassed by the model domain. The monthly mean model structures have been compared with the ECMWF analyses to determine the quality and bias of the model and to diagnose accurate description of the whole Arctic orography, coastlines systematic model deficiences and shortcomings. Doing this and land-sea distribution and involves atmospheric scales task, we have to keep in mind that over Arctic regions the data from the long planetary and baroclinic patterns to mesoscale quality is very poor due to sparse observations, and this waves. The development of regional climate models has been started by Dickinson et al. [1989] who nested the National Center for Atmospheric Research (NCAR) regional model MM4 with a influences also the quality of the ECMWF analyses. Owing to the low density of observations in the Arctic the ECMWF analyses are heavily dependent on the first-guess fields in that region. In other words, the analyses over the resolution of 60 km for the western United States in a general Arctic ocean are mainly model results themselves. It is circulation model (GCM). This model was examined and verified against observations by Giorgi and Bates [1989]. therefore likely that the large simulated deviations from these fields over that region are only reflecting the differences The physical parameterizations have been further developed between the different sets of parameterization schemes. by Giorgi et al. [1993]. Now the regionalization idea has also However, the ECMWF model is internally evaluated been applied to other regions; that is, for Europe by Giorgi et al. [1990], Sass and Christensen [1995], Cress et al. [1995]; for australia by Mc Gregor and Walsh [1993]; and for the western Arctic by Walsh et al. [1993] and Lynch et al. [1995]. Confidence in the results of climatic change experiments can be placed only if the model produces a very good simulation of the present climate. Thus we started with the simulation of recent summer and winter months ( July 1990 and January 1991). Current atmospheric general circulation models of the atmosphere produce significant biases in simulated fields of continuously and modifications are added to improve the ECMWF analyses. This guarantees the best available quality in this data sparse area we are interested in. The major problems with the ECMWF model in terms of systematic biases are found in the stratosphere, where the model is warmer compared with analyses and near the surface over sea ice, where the model often is found to be colder than the analyses in particular during winter, as discussed by Morcrette [1993]. Further, the analyses have only a horizontal resolution of 3 by 3 degrees. Therefore, regional structures of the model with Arctic surface air temperature and surface winds, as discussed its resolution of 0.5 by 0.5 degrees may be more reliable than by Walsh and Crane [1992] and Chen et al. [1995]. The hope is that such biases and climate drift can be reduced by increasing the horizontal resolution and specifying the lateral boundary conditions. The use of a lateral boundary forcing specified from a GCM introduces the biases of the the analyses. A first attempt has been undertaken to validate model simulated geopotential, radiative fluxes, surface fluxes and clouds against selected station data. One aspect of the present regionalization approach which should be added here, is that by choosing the regional model large-scale structures into the regional model. For this reason, domain properly and such as to encompass the entire Arctic we apply the regional climate model to the Arctic, forced by region and allowing for it to extend southwards enough as to analyses of the European Centre for Medium Range Weather have the boundary zone over regions where the observational Forecasts (ECMWF) at the lateral boundaries. This kind of simulation excludes the contamination of an Arctic focused experiment by global model deficiences which are largest in polar regions. coverage is better, some of the problems with the boundary fields are automatically overcome. The majority of the meteorological developments occuring within the model domain are triggered by physical processes resolved over the The horizontal resolution determines largely the accuracy of the simulations and can be increased in GCMs as by Boville [1991], regionally by a one-way nesting technique as by Giorgi [1990] or by using a variable resolution model as by model domain not just prescribed by the boundary forcing itself. Therefore, this approach provides an ideal test bench for physical parameterization schemes. This situation is somewhat different from the similar experience obtained over

3 DETffLOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPH]5;RE 23,403 Europe by Sass and Christensen [1995] who argued that the time scales for boundary information to have transversed their region was on the order of a few days. In future investigations we will use this regional climate model for the Arctic as a framework for experiments with alternative Arctic process parameterization packages and different forcings at the boundaries. 2. Model description The regional climate model HIRHAM has been developed by Christensen and van Meijgaard [1992]. It combines the adiabatic package of the limited area model HIRLAM described by Kt llberg [1990], Sass and Christensen [1995] with the physical parameterization package of the global climate model ECHAM3 described by Roeckner et al. [1992]. We apply HIRHAM over the whole Arctic integration area north of 65øN with a horizontal resolution of 0.5 degrees in latitude and longitude, corresponding to grid elements of about 50 by 50 km and 19 vertical hybrid levels in the troposphere and lower stratosphere from the ground up to 10 hpa. The model has been forced at the lateral boundaries by ECMWF analyses updated every 6 hours. At the lower boundary the model can be forced in two different ways. In the first variant climatological, that is, monthly averaged sea surface temperatures and sea ice fraction have been used based on work by Brankovic and van Maanen [1985]. Sea ice arises in a grid box if the sea surface temperature is lower than -1.8 degrees Celsius. Sea ice covers the whole grid box and sea ice thickness is then set to a constant value. 3. Simulation of the present January and July Climate A description of the simulated circulation is provided by the fields of mean sea level pressure, precipitation, surface air temperature, 500 hpa and 70 hpa geopotential. We further present the simulated surface energy budgets including radiative fluxes, sensible and latent heat fluxes and cloudiness. Our main interest concerns the description of the monthly mean atmospheric structures of the Arctic troposphere and lower stratosphere. Here we present the results of simulations for January 1991 and July For each month we carried out two runs: the first run uses climatological sea surface temperatures (SST) and sea ice extent, and the second one uses actual sea ice fraction and sea surface temperature informations from ECMWF, updated daily. In a first approach it is necessary to describe the mean circulation patterns, to diagnose model deficiences and the bias of the model determined by comparison against ECMWF analyses. These results will give hints on model shortcomings and for necessary improvements in the physical parameterizations. Since measured data for the analyses in Arctic regions are available only at a rather coarse space and time resolution, this implies a likelihood of systematic errors in the ECMWF analyses. Therefore the quality of the verification of our model results is certainly dictated by the limited accuracy of the ECMWF analyses in the Arctic region, so we make a first validation step with selected rawinsonde and radiation station data. Figure 1 presents the used orographic height with a horizontal resolution of 50 km which resolves the regional topographical pattern in the Arctic region much better than the orography used in modern GCMs, for example, Bromwich et al. [1994] Mean Sea Level Pressure Figure 2a shows the simulated monthly averaged mean sea level pressure (MSLP) computed by the model for January 1991 with climatological SST and sea ice cover, and Figure 2b displays the difference map "model minus ECMWF analyses". During January, high pressure persists over the Siberian continent and low pressure persists over the North Atlantic Ocean and both are associated with the well-known Icelandic Low. The intensity and the location of the pressure centers do match the observations in detail. The center of the Icelandic Low and also the Siberian High are well reproduced. This is an expected result because of the use of the lateral forcing by ECMWF analyses. The largest differences between the model simulations and analyses occur over the central Arctic, where the MSLP simulated by the model is higher by 5 hpa than the analyses. Over the Siberian continent the model simulated High is weaker compared with analyses. There is also a difference in surface pressure over Greenland strongly associated with interpolation problems over high topography. Bromwich et al. [1994] noted large differences in the mean sea level pressure pattern over the Arctic when In the second variant, the model has been forced by ECMWF analysed sea surface temperatures and sea ice fractions, updated daily. Sea ice may cover a fraction of the grid box and sea ice thickness is again set to a constant value. The temperatures over sea ice are computed from the surface energy budget due to absorption of solar radiation, outgoing infrared radiation, sensible and latent heat fluxes and oceanic heat flux through the sea ice layer. The temperatures over land ice are computed by solving the surface energy budget of a five-layer soil model assuming the characteristics of ice. The dynamical and using the NCAR Community Climate Model physical properties of the model have been summarized in the appendix. due to the restricted topographic representation of Greenland. Figure 2b, which describes the MSLP difference between model and analyses, shows a long wave pattern with wave numbers 1 and 2 over the Arctic Ocean. This indicates that the simulated long waves are excessively strong in comparison with analyses. Whereas the analyses are based on the real oceanic forcing in the data assimilation system, climatological SST and sea ice cover have been used in the regional climate model simulations. The sensitivity experiments with actual SST and sea ice fraction described in section 3.7. show that the bias structure indicates only small improvements. The MSLP and difference map for July 1990 with climatological SST and sea ice cover are presented in Figures 3a and 3b. During July the differences between model simulation and analyses are smaller than those in winter and are in the range of 3 hpa. Over the Arctic Ocean the simulated pressure is lower by 3 hpa compared with the analyses. The underestimation of the model simulated pressure minimum over the Arctic ocean leads to an increased meridional MSLP gradient. Assuming geostrophic conditions, this causes an exessively strong zonal wind component. Consequently, in

4 ,.. 23,404 DETHLOFF ET AL.' CLIMATE MODEL OF ARCTIC ATMOSPHERE ' ', ' ' '0... 4'0... 5'0''6'0 70 8O 9O! I Above 3000 I OO.'." [ [ Below O Figure 1. Height of the orography (meters) in the Arctic integration area with the center over the North Pole. 20 ;.,.. ;:.. :;. I Above 1025 I I I lolo- lo15....[ ] E [ Below O0 110 Figure 2a. Mean sea level pressure (hectopascal) simulated by the model, January 1991, climatological SST and sea ice cover z...:...% 40 : Above 'f.,-: ::_?...,. 4.o- ' -, :'- -_: " " : o - lo... - ; '::; -4.o- -: -s.o- ': -'"'::- - ' = Below "", *",'%* " " ",-- ',,4, --,... "½F '":':"' O0 110 Figure 2b. Mean sea level pressure difference (hectopascal) "model minus analyses", January 1991, climatological SST and sea ice cover O

5 DETHLOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHERE 23, loo Above Below Figure 3a. Mean sea level pressure (hectopascal) simulated by the model, July 1990, climatological SST and sea ice cover. I Above 3.0 I I I I ': : Below -3.0 Figure 3b. Mean sea level pressure difference (hectopascal) "model minus analyses", July 1990, climatological SST and sea ice cover. comparison with data, the model simulates an increased zonal circulation during summer. Analogously, compared with data, the model shows a tendency for weaker zonal circulation during winter. be partly connected with sources of uncertainty in the ECMWF analyses for this region and partly with the prescribed climatological sea ice parameters. Compared with the analyses, the model shows excessively warm surface 3.2. Surface Air Temperature temperatures over regions covered by multiyear sea ice, and excessively cold surface temperatures over some ice edges, suggesting for future experiments the use of regionally varying sea ice thickness data and the introduction of leads. Figure 4a presents the model simulated surface air temperature for January 1991 using climatological SST and sea ice cover and Figure 4b the difference map "model minus analyses". The Figure 5a shows the surface temperature for July 1990 with climatological SST and sea ice cover and Figure 5b the difference map "model minus analyses". During summer the warm Gulf stream and the warm Gulf of Alaska are well continents are warmer than the ocean. The differences between represented. The lowestemperatures occur over Greenland, model and analyses are much smaller than in winter with the the Canadian Archipel, and Siberia. The difference map shows largest deviations up to 4 øc over the central Arctic ocean. model temperatures up to 12 øc higher compared with the ECMWF analyses in the Central Arctic Ocean. Here we would like to remember that the ECMWF analyses are based on the ECMWF model which shows a systematic temperature bias to 3.3. Precipitation and Humidity be excessively low at the surface over sea ice during winter. In the vicinity of the sea ice edge the model simulations are colder in comparison with the analyses. These differences may Figure 6a presents the total precipitation as the sum of largescale and convective precipitation simulated by the model for January 1991 and Figure 6b for July During January a

6 ,406 DETHLO ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHERE 100 ' I Above 5 I 0-5 I -5 0 I I I I ' [ L';:: i' :/ [ Below -45 Figure 4a. Temperature at the surface (øc), simulated by the model, January 1991, climatological SST and sea ice cover. 100 ' "ø 30 lo,.,.,.-- _' /i.? Above Below o oo 11o Figure 4b. Temperature difference at the surface (øc) "model minus analyses", January 1991, climatological SST and sea ice cover. loo... I Above 25 I I , loo - [ -5 0 [ [ Below o Figure 5a. Temperature at the surface (øc), simulated by thc model, July 1990, climatological SST and sea ice cover.

7 DETHLOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHERE 23,407 11o I Above 6.0 I I I I Below -6.0 Figure 5b. Temperature difference at the surface (øc)"model minus anlyses", July 1990, climatological SST and sea ice cover. loo Above : Below loo 11o Figure 6a. Total precipitation (millimeters), simulated by the model, January 1991, climatological SST and sea ice cover. 80] ' j" -.. I Above 3 ' I ' :,.:; -.-, - I ' loo ' :,_. { I '"e' O 3O 4O 5O O0 0 Figure 6b. Total precipitation (millimeters), simulated by the model, July 1990, climatological SST and sea ice cover.

8 _ 23,408 DETHLOFF AL.: CLIMATE MODEL OF ARCTIC ATMOSPHE o o loo loo lotal I... Land /... Sea Ic Ocean I ' c: Total I, I sx,c,i ß "<x...-.,..,....,,, ,,? Specific humidity area mean [g/kg] ! I I I I ' I Specific humidity area mean [g/kg] Figure 6c. Specific humidity (grams per kilogram), simulated by the model for January 1991, climatological SST and sea ice cover, monthly mean and spatial average over all grid points; land, ocean and sea ice points, excluding the lateral boundary zone. Figure 6d. Specific humidity (grams per kilogram), simulated by the model for July 1990, climatological SST and sea ice cover, monthly mean and spatial average over all grid points; land, ocean and sea ice points, excluding the lateral boundary zone. precipitation maximum occurs at the south east coast of Greenland. The precipitation in this area and in the Gulf of Alaska are connected with the large-scale precipitation in the cyclone paths. During July the total precipitation is connected with convective precipitation over Scandinavia, European Russia, and Alaska and large-scale precipitation over Sibiria and the coasts of Greenland. Qualitatively the patterns during January and July are in agreement with the precipitation climatology of Legates and Willmott [1990]. In winter the precipitation pattern reflects the transport of moisture into the Arctic basin. The moisture supply is largely dominated by advection within the storm tracks since the evaporation from the frozen sea surface is small. Only over local areas of the Gulf stream does evaporation occur. During summer, both large-scale and convective precipitation are important. Serreze et al. [1995] computed monthly mean profiles of specific humidity north of 70 øn on the basis of rawinsonde data. Figures 6c and 6d show the model simulated specific humidity profiles for January 1991 and July The model simulated specific humidity profiles for winter and summer conditions show a very good agreement with the observation of Serreze et al. [1995]. This occurs both in the vertical structure and in the quantitative values with 0.5 to 1.0 g/kg during winter and with 4 to 5 g/kg during summer in the lower troposphere. Owing to the higher temperatures during summer specific humidity is larger than in winter Geopotential Plate 1 shows the geopotential at 500 hpa during January 1991 for climatological SST and sea ice cover, and Figure 7 shows the difference map "model minus analyses". The model captures the major troughs and ridges very well. A cold Low is situated over the Canadian Arctic. In comparison with the analyses, the simulated geopotential is 30 gpm higher north of Greenland and by the same magnitude lower north of Siberia. This means that the difference between the model and the observations shows a wavenumber one pattern over the Arctic basin. The overestimated geopotential north of Greenland is also visible in the mean sea level pressure difference in Figure 2b. In order to validate the model results independently from ECMWF analyses, Plate 2 shows the values of 500 hpa geopotential on the basis of rawinsonde station data for January 1991 of Kahl et al. [1992]. There exists a good agreement between station data and model simulations. An obvious discrepance occurs at one Russian station near Wrangel Island indicating a systematic error in the data. Plate 3 and Figure 8 presenthe geopotential at 500 hpa for July 1990 with climatological SST and sea ice cover and the difference map "model minus analyses". A cold Low occurs over the central Arctic which is lower in comparison with the analyses by 50 gpm. The difference map shows a wavenumber one pattern over the Arctic ocean. The excessively low geopotential around the pole compared to the analyses corresponds to stronger model simulated meridional pressure gradients and leads to an increased zonal circulation during summer. Similar arguments explain a decreased zonal circulation during winter. The appearence of waves with zonal wavenumber one during winter in the bias fields indicates that the model overestimates the amplitude of the observed quasistationary wave systems. The reasons for the excessively overestimated wave systems are not yet clear. Stationary

9 DETHLOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHERE 23, ' ' 9O 8O 3O 20, ' lo [--'-] Above 5350 E ['---'l o L_] E oo Below Plate 1. The 500-hPa geopotential (geopotential meters), simulated by the model, January 1991, climatological SST and sea ice cover. planetary waves are generated and maintained by topographic and thermal forcing. Since there is only a small bias in the topographic forcing due to the high horizontal resolution, in future investigations the thermal forcing of the model due to consideration of regionally varying sea ice tickness and leads has to be improved. It seems also likely that the higher horizontal resolution affects the nonlinear energy transfer into the long waves and the zonal mean flow as discussed by Rind [1988]. Plate 4 shows the values of geopotential at 500 hpa on the basis of rawinsonde station data for July 1990 which indicates a qualitative agreement between station data and model simulations The simulated 70 hpa January geopotential is shown in Figure 9. A cold Low, well known as the polar vortex, with its center north of Greenland appears. The largest differences between model and analyses occur over the central Arctic and are in the range of 30 gpm. The simulated 70 hpa July 100 ' ' ' 9O O0 110 Above Below -30 Figure 7. The 500-hPa geopotential difference (geopotential meters), "model minus analyses", January 1991, climatological SST and sea ice cover.

10 , 23,410 DETI-ILOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHERE O 10 Above o5o-5 oo OO Below 4850 Plate 2. The 500-hPa geopotential (geopotential meters) from station data, January ' 3O 2O 10! O0 110 _r- Above [ o -550o [ Below 5250 Plate 3. The 500-hPa geopotential (geopotential meters), simulated by the ]nodel, July 1990, climatological SST and sea ice cover.

11 DETHLOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHERE 23, ,, '.i... ' : ':.,-. 30 ' ' I Above 20 I I 0-10 I I li : Below -50 Figure 8. The 500-hPa geopotential difference (geopotential meters), "model minus analyses", July 1990, climatological SST and sea ice cover. geopotential is presented in Figure 10. The Low during July is lower by 50 gpm in comparison with analyses. In both months there occurs a wavenumber zero pattern in the difference plots, not shown here, indicating problems in the correct reproduction of the zonally averaged circulation. As in 500 hpa the model simulated zonal circulation at 70 hpa is stronger compared with the analyses during summer. Whereas at 500 hpa the model simulated zonal circulation is weaker compared with the analyses during winter, it is stronger in 70 hpa. This indicates incorrect diabatic heating rates at this level. A similar result has been obtained by Sass and Christensen [1995] and by Morcrette [1993] who showed that in the ECMWF analyses the stratospheric temperatures are excessively warm Surface Energy Balance To estimate the model's quality independently from the ECMWF analyses, radiative fluxes, sensible and latent heat 100,...,...- : '" :. $ ''''1''''I''''I''''1''''1''''1''''i''''i... I''''1'''' Plate 4. The 500-hPa geopotential (geopotential meters) from station data, July 1990.

12 .. ß 23,412 DETHLOFF ET AL.: CL]MATE MODEL OF ARCTIC ATMOSPHERE 60 o 50, -..i:, 30 '...c ' I Above ' I ß,::-. I "i I ',: I ß ': I I 'i I Below Figure 9. The 70-hPa geopotential (geopotential meters), simulated by the model, January 1991, climatological SST and sea ice cover. fluxes, will be discussed and compared with analyses of Russian station data and drift stations on the basis of Treshnikov et al. [1985]. These data are climatological and therefore it is difficult to compare them with our simulated fluxes for special months. Nevertheless, it should be a first attempt to validate the main features of the energy balance. Figure 1 l a shows the simulated surface radiative balance for January 1991 and Figure 11b for July 1990 using climatological SST and sea ice cover. In winter the radiative balance is negative over the whole Arctic area due to the outgoing infrared radiation during polar night and is in good agreement with the data of Treshnikov et al. [1985]. During July the radiative balance is positive due to polar day conditions. Also for summer there exists a good qualitative agreement with data. The high-resolution model simulation shows many regional details depending on regional surface temperature, snow cover depth, and clouds which are not seen in the data. To validate these small-scale features, the number of observation stations is not sufficient. Figure 1 l c compares the model simulated global solar radiation at the surface with selected Russian station data for July 1990 using the data of Marshunova et al. [1996]. Figure 11d shows the geographical distribution of these stations. This comparison shows that the model overestimates the global solaradiation at the surface up to 100 W/m 2. This result indicates insufficient atmospheric absorption and the underestimation of low clouds. In future investigations the simulated radiative fluxes should be validated against actual radiative fluxes measured at stations and collected in the GEBA data set of Ohmura et al. [1989]. Figure 12a presents the sensible heat flux and Figure 12b the latent heat flux at the surface for January 1991 using climatological SST and sea ice cover with negative values indicating upward directed fluxes and positive values directed downward. Largest fluxes occur in the vicinity of ice edges from the ocean into the atmosphere which is in very good agreement with the data analysis of Treshnikov et al. [1985]. Over most of the Arctic ocean the data show downward fluxes oo 8O 6O 30 I Above I [ [ Below Figure 10. The 70-hPa geopotential (geopotential meters), simulated by the model, July climatological SST and sea ice cover. 1990,

13 DETHLO ET AL.' CLIMATE MODEL OF ARCTIC ATMOSPHERE 23,413 Above Below -90 loo 11o Figure 11a. Radiative balance at surthce (watts per square meter) for January 1991, climatological SST and sea ice cover. I Above 240 I 22O 24O I O I I I I 12o - 14o.'"' o [ 80-1 oo E L_] 4o- 6o [-]Below 40 Figure lib. Radiative balance at surface (watts per square meter) for July 1990, climatological SST and sea ice cover. from the atmosphere to sea ice in good agreement with the simulations, except an extended region north west of Greenland. On the one hand, this indicates necessary improvements in the chosen sea ice thickness and in the parameterization schemes of vertical heat transfer from the surface into the planetary boundary layer. On the other hand, we have to remember that the data quality is insufficient for a validation. Over land the sensible heat fluxes are directed upward in agreement with data. Figure 13a presents the sensible heat flux and Figure 13b presents the latent heat flux for July 1990 using climatological SST and sea ice cover. During summer the simulated sensible heat and latent heat fluxes show a more structured distribution and good qualitative agreement with the data of Treshnikov et al. [1985], although minor deviations occur. Since the atlas of Treshnikov et al. [1985] contains climatological data, these differences are not surprising. Currently, the data quality does not allow a validation of model simulated surface fluxes. Owing to data sparsity, the computation of surface fluxes is likely to remain a problem over the Arctic ocean. During events when heat fluxes are significant as it happens over leads, thin ice, or marginal seas the accuracy of near surface temperatures is important. As discussed by Brown [1994], differences in the input surface air temperatures can cause large differences in the computed surface heat fluxes. Brown [ 1994] calculated surface fluxes from ECMWF and NMC surface analyses. The differences are huge and give an idea of the accuracy of these fluxes. When better data are available, better flux calculations would be possible Clouds Figure 14a shows the total cloud cover for January 1991 and Figure 14b for July 1990 using climatological SST and sea ice cover forcing at the lower boundary. The simulated total cloud cover over the Arctic ocean during winter is larger than during

14 23,414 DETHLOFF ET AL.' CLIMATE MODEL OF ARCTIC ATMOSPHERE 4øø l O observation I simulation Figure 11c. Comparison of simulated global solar radiation (watts per square meter) selected Russian station data for July at the surface with 90- _ vrange s a y Island 80- C ø / 'N-- uostakh Island 70-..otelny d obr heniya luskin Cape 50_ X, edine 40- ani 30- Island olom y i lan Island eiss I ,,, O0 110 Figure 11d. Geographical location of the Russian radiation stations. summer and is in qualitative agreement with the total cloud amount estimations made by Barker et al. [1994] on the basis of the International Satellite Cloud Climatology Project (ISCCP) from 1984 until Unfortunately, large differences between the available cloud climatologies have been reported by Mokhov and Schlesinger [1994] and a reliable climatology of the vertical cloud distribution is not yet available. The mean cloud cover estimation of Warren et al. [1989], based on surface and ship observations, shows an increase of cloud cover from winter to summer in contradiction to the model results. The discussion of simulated radiative fluxes in the previous section indicates underestimation of low clouds during summer. This gives hints on necessary improvements in the cloud scheme and cloud parameters. A sensitivity experiment was carried out to determine the importance of moisture processes for the Arctic climate. The height dependent relative humidity threshold for condensation was decreased by 9 % which corresponds to a reduced threshold near the surface from 99 % to 90 %. The influence of these changes in the relative humidity threshold on precipitation, MSLP, and cloud cover is small and therefore not shown here. The largest changes in the accumulated precipitation fields are

15 DETHLOFF ET AL.' CLIMATE MODEL OF ARCTIC ATMOSPHERE 23, O 8O ':.: :.i ':' :"::.. :"'... ' ":'... ::,?.:..;:. -"i. ß : 20 '" ' ')':?'"" I Above 100 I 5O- 100 I 0-50 I I '" i: E E [ Below Figure 12a. Sensible heat fluxes (watts per square meter), simulated for January 1991, climatological SST and sea ice cover. 11o I Above 50 I 0-50 I I ;½ b Below -200 Figure 12b. Latent heat fluxes (watts per square meter), simulated for January 1991, climatological SST and sea ice cover. in the order of 20 mm for January 1991 and 50 mm for July The induced changes in the MSLP are in the order of 2 hpa for January and 4 hpa for July Sensitivity to Sea Surface Temperatures and Sea Ice In a sensitivity experiment we investigated the influence of actual sea ice and SST distribution on the model simulations. Figure 15a shows the difference map "model minus ECMWF analyses" of the MSLP for January 1991 using actual SST and sea ice fraction. In comparison with the climatological run in Figure 2b, the agreement over the eastern part of the Arctic Ocean has been improved. The wavenumber one pattern in the differences changed and larger differences occur over the Canadian Arctic. Figure 15b shows the influence of actual sea ice and SST on the difference map" model minus ECMWF analyses" of the MSLP for July Compared with climatological sea ice and SST in Figure 3b the wavenumber one pattern in the differences is more pronounced over the eastern part of the Arctic ocean. There was only a small improvement in the bias structure of the MSLP and other quantities not shown here for January 1991 and July 1990 due to the application of actual sea ice fraction and SST distributions. The remaining bias indicates the necessity of a better description of the interaction between the underlying surface and the atmosphere. It is well known that in leads, the vertical heat and humidity fluxes can be increased strongly. The currently incorporated model teedback between sea ice cover and the atmosphere produces the errors in the high-latitude temperature and pressure fields and has to be improved. The model simulations show overestimated quasi-stationary wave systems during winter as discussed in sections 3.1 and 3.4. The simulations do not explicitly include atmospheric perturbations associated with teleconnection patterns outside the model area, not resolved

16 ß o 23,416 DETHLOFF ET AL.' CLIMATE MODEL OF ARCTIC ATMOSPHE loo 8o '70 40 ',-,,(..,..,,,,. 2o lo v... I Above 30 / '"½:; " i Below O 1 O0 110 Figure 13a. Sensible heat fluxes (watts per square meter), simulated for July 1990, climatological SST and sea ice cover. 100 ',,... 90,,, ; 80 " 60.0, 4o 3.,... :: '! ' i Above : - 0 o ' I Below O0 110 Figure 13b. Latent heat fluxes (watts per square meter), simulatcd for July 1990, climatological SST and sea ice cover. by the model but inherent in the analyses. This also may explain some of the deviations in the high-latitude temperature and pressure fields. In another sensitivity experiment the sea ice thickness has been increased from l m to 2m which reduces the oceanic heat flux into the atmosphere and decreases the temperature bias at the surface by 50 %. This result indicates the strong influence of sea ice thickness on the surface air temperature and the thermal regime up to 700 hpa, not shown here. In comparison with the model simulation for a sea ice thickness of l m, the increased ice thickness largely reduces the bias in the planetary boundary layer. Owing to the increased ice thickness the oceanic heat supply into the atmosphere has been reduced, leading to a colder atmosphere and better agreement with the analyses in the planetary boundary layer. This sensitivity experiment shows that for more realistic simulations of the Arctic atmosphere regional varying sca ice distribution, sea ice thickness and leads have to be considered. Similar conclusions have been drawn by Lynch et al. [1995] who showed that the inclusion of sea ice dynamics has substantial impacts on the model results for winter Vertical Bias Structure The vertical structure of the temperature bias "model minus analyses" for January 1991 is shown in Figure 16a with climatological forcing and in Figure 16b with actual sea ice cover and SST. Figures 17a and 17b show the same for July These biases are spatial averages over all points: land, ocean, or sea ice points, respectively, excluding the lateral boundary zone, as in Sass and Christensen [1995]. In both months the bias structure is similar. In comparison with the analyses the atmosphere is warmer below 950 hpa in the planetary boundary layer, colder in the middle and upper troposphere and again warmer in the lower stratosphere. The

17 ..... DETHLOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHERE 23,417 90; 80.: 60 ß :....: ;.:':".... ': %...,..,.?:.%,; :'..;. ½... %...:..., :]...,::? _....., -,....::, : Above 90 8O - 9O O - 6O 40-so o- 30 o- o Below Figure 14a. Total cloud cover (percent), simulated for January 1991, climatological SST and sea ice cover lo' Above 90 8O - 9O 7O - 8O 6O - 7O 5O - 6O 4O 5O 3O - 4O lo - 20 Below 10 Figure 14b. Total cloud cover (percent), simulated. for July 1990, climatological SST and sea ice cover. differences tend to be large in the upper layers and near the surface, where they are mainly caused by the strong influence of the model physics. The differences between the biases over land, ocean, and sea ice are most pronounced in the planetary boundary layer below 950 hpa during January, where the different surface heat transfer exerts the strongest influence on the atmosphere. At higher levels the rather strong horizontal advection is likely to wipe out more easily the differences between land, ocean, and sea ice. The magnitude of the horizontal advection is largest in January and smallest in July. Hence the deficiences and shortcomings in the physical parameterizations show up more readily in the July simulation. This holds, for example, for a large bias at 200 hpa during July indicating incorrect diabatic heating rates. The influence of actual sea ice cover and SST on the model bias during winter and summer is rather small. The specific humidity bias for January in Figure 18a and for July in Figure 18b with climatological forcing is slightly positive in the planetary boundary layer and slightly negative in the lower troposphere. As far as the analysed humidity values are reliable, the model simulation underestimates the vertical transport in the planetary boundary layer. Above 800 hpa the magnitude of the bias decreases with increasing height. The largest negative humidity bias occurs over sea ice at 850 hpa during July. The difference between the humidity biases over land, ocean, and sea ice for July is larger than for the January simulation. Figure 18b indicates that the moisture supply from the ground is not sufficient during summer, which can be related to the rather crude surface parameterization scheme. The use of actual SST and sea ice cover leads only to small changes of the model humidity bias during winter and summer. 4. Conclusions Arctic atmosphere winter and summer simulations for January 1991 and July 1990 have been performed with a regional climate model at a 50-km horizontal resolution, driven by ECMWF analyses at the lateral boundaries. The approach we have taken to improve the understanding of climate processes

18 ._ 23,418 DETHLOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHE loo t':... I Above '" ' ".o ß -.o- o.o./,:...'."...,:.,!:.:/..i:.. I, r--] -2.o E ,- - I.- i---t ? ' ,, Below O0 1; 0 Figure 15a. Mean sea level pressure difference (hectopascal) ", naoi, i-,,s analyses", l,,,,-y!99!. actual SST and sea ice fraction. 1 oo 80 o 20 ' Above Below O0 Figure 15b. Mean sea level pressure difference (hectopascal) "model minus analyses", July 1990, actual SST and sea ice fraction. in the Arctic is a high-resolution limited area system approach In the lower stratosphere there exists a tendency for similar to Walsh et al. [1993] and Lynch et al. [1995]. excessively strong zonal circulation during summer and Whereas Walsh et al. [1993] and Lynch et al. [1995] applied winter. The shortcomings of the model are smaller during their model to the western Arctic, we include the Arctic domain winter in the middle and upper troposphere and during summer north of 65 o N as a whole. In both months the model rather closely reproduces the observed monthly mean circulation. While the general circulation patterns are consistent with ECMWF analyses, the model shows biases when the results are examined in detail. The largest biases appear during winter in the planetary boundary layer. The model underestimates the vertical transports in the planetary boundary layer. The differences between model simulations and analyses become smaller with increasing height in the troposphere. The model shows a tendency for excessively strong zonal circulation during summer and excessively weak zonal circulation and overestimated quasi-stationary wave systems during winter in the middle troposphere, also influenced by the underestimated vertical heat supply from the planetary boundary layer to the troposphere in the model simulations. in the lower troposphere. Larger differences occur during summer in the upper troposphere and lower stratosphere. The differences in the large-scale patterns of geopotential in the middle troposphere show wavenumber one pattern indicating the necessity of improved thermal forcing at the lower boundary. The biases of the model appear to be partly determined by the limited accuracy of the ECMWF analyses in polar regions and partly to be consequences of the used parameterization schemes which differ from those in the ECMWF analyses. The largest discrepancies between simulations and analyses occur in the uppermost layers due to the upper boundary conditions and near the surface, where they are caused by the strong influence of model physics. A potential supplement to the present verification may be based on an extended comparison with observations at

19 _ DETHLOFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHERE 23, _ ' 400- _ 5oo t Temperature bias [K] Figure 16a. Temperature bias "model minus analyses" (K), monthly mean and spatial average over all grid points; land, ocean, and sea ice points, excluding the lateral boundary zone, January 1991, climatological SST and sea ice cover. Temperature bias [K] Figure 16b. Temperature bias "model minus analyses" (K), monthly mean and spatial average over all grid points; land, ocean, and sea ice points, excluding the lateral boundary zone, January actual SST and sea ice cover. quality-controlled rawinsonde stations. The simulated geopotential distribution, radiative balance and heat fluxes have been validated against station data. There exists qualitative agreement between model results and station data, but differences at single stations may be rather large and could be improved by more advanced parameterization schemes of the vertical heat and moisture exchange in the planetary boundary layer. The experiments described here show the potential for realistic simulations of climate processes of the Arctic troposphere and lower stratosphere in a regional climate model and they highlight some of the processes that require, 0 loo loo ' 400-.C: - _ "....?'. I... s..,c. I \\ x. ',, Z t...,,..., -" ', , _.'.,,.. '--,...% I I I I I I Temperature bias [K] Figure 17a. Temperature bias "model minus analyses" (K), monthly mean and spatial average over all grid points; land, ocean, and sea ice points, excluding the boundary zone, July 1990, climatological SST and sea ice cover. Temperature bias [K] Figure 17b. Temperature bias "model minus analyses" (K), monthly mean and spatial average over all grid points' land, ocean, and sea ice points, excluding the boundary zone, July 1990, actual SST and sea ice cover.

20 _ 23,420 DETHLO ET AL.' CLIMATE MODEL OF ARCTIC ATMOSPHERE 0 i i i i I I I ' ' 400- t- _ Total I... Land [... Sea Ice[ --- Ocean,I 2OO Sea Ice --- Ocean I.e 500-.e / \ OO O ooo I Specific humidity bias [g/kg] Specific humidity bias [g/kg] Figure 18a. Humidity bias "model minus analyses" (grams per kilogram), monthly mean and spatial average over all grid points; land, ocean, and sea ice points, excluding the boundary zone, January 199!, climatological SST and sea ice cover. Figure 18b. Humidity bias "model minus analyses" (grams per kilogram), monthly mean and spatial average over all grid points; land, ocean, and sea ice points, excluding the boundary zone, July 1990, climatological SST and sea ice cover further study. The deficiences in the model simulations especially in the planetary boundary layer and at the surface during winter and in the upper troposphere during summer may be improved by the application of more advanced parameterizations of radiation, vertical heat and moisture exchange, and cloud properties. In spite of the uncertainties induced by the verifying ECMWF analyses in Arctic regions, the results indicate that the present framework for experimentation is well suited to evaluate systematically the influence of different physical parameterization schemes. This concerns also the gravity wave drag parameterization and the horizontal diffusion parameterization schemes, which contain constants which cannot be deduced from fundamental physical assumptions as described by Boer and Lazare [1988] and Stephenson [1994]. As the model resolution increases, it is necessary to tune the diffusion scheme. The influence of changed horizontal diffusion and gravity wave drag on the model bias is currently under investigation. 0E,0N, variables staggered on Arakawa C- grid. Vertical grid 19 levels in hybrid coordinates, 5 levels in the planetary boundary layer, and 5 in the lower stratosphere. Finite differences centered second-order accuracy. Time stepping leapfrog, semi-implicit, Asselin time-filtering,time step of 300 s. Lateral boundary scheme simplified Davies relaxation with boundary zone of 10 grid points. Lateral boundary values ECMWF analyses updated every 6 hours at 3 by 3 degrees horizontal resolution and 19 vertical levels. Horizontal diffusion Linear fourth-order horizontal diffusion model scheme on levels. 5. Appendix Physical Parameterizations Dynamical Properties Basic equations Prognostic variables Integration domain Horizontal grid primitive equations. horizontal wind components, temperature, specific humidity, surface pressure, liquid water content. 110 latitude points by 100 longitude points with a horizontal resolution of 0.5 by 0.5 degrees. latitude/longitude in rotated coordinates with North Pole on Orography Radiation Grid box mean from U.S. Navy data set, slightly smoothed, presented in Figure 1. two-stream approximation; four spectral intervals in the solar part; six in the terrestrial part; diurnal cycle; prescribed carbon dioxide, ozone and aerosols; computed cloud optical depth and cloud cover, Hense et al. [1982].

21 DETHI, OFF ET AL.: CLIMATE MODEL OF ARCTIC ATMOSPHE 23,421 Clouds convective cloud scheme: mass flux scheme for deep, mid level and shallow convection; stratiform cloud scheme: budget equations for water vapour and cloud water, Sundquist [1978], Tiedtke [1989], Roeckner et al. [ 1991 ]. Planetary boundary layer in the surface layer: Monin- Obukhov theory, vertical fluxes depending on surface roughness and local stability; above the surface layer: mixing length theory with coefficients depending on wind shear, thermal stability and mixing length. Above the planetary boundary layer vertical diffusion only for unstable stratification, moist Richardson number, Blackadar [1962], Louis [1982]. Gravity wave drag orographic forcing prescribed by a sub grid scale orographic variance, surface stress due to gravity waves is calculated from linear theory and dimensionality considerations, vertical structure of momentum flux calculated from a local wave Richardson number, Palmer et al. [1986], Miller et al. [1989], Laursen and Eliassen [1989]. Land-surface processes heat diffusion equation solved in a 5 layer soil model with zero heat flux at the bottom at Initial 10 m depth, hydrological budget equations include rain, snowfall, evaporation, runoff and snow melt, Sellers et al. [1986], Blondin [1989]. values of surface fields monthly mean climatology of sea surface temperature and sea ice extent based on Brankovic and van Maanen [1985], climatological soil water content and snow depths. Acknowledgments. The HIRLAM system was developed by the HIRLAM project group, a cooperative project of the national weather services in Denmark, Finland, Iceland, Ireland, the Netherlands, Norway and Sweden. Vladimir Radionov from the Arctic and Antarctic Research Institute St. Petersburg kindly provided the data of the Russian radiation stations, and we are grateful for his support. 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