The Arctic Surface Energy Budget as Simulated with the IPCC AR4 AOGCMs

Transcription

1 The Arctic Surface Energy Budget as Simulated with the IPCC AR AOGCMs ASGEIR SORTEBERG Bjerknes Centre for Climate Research, University of Bergen, Allegaten, 7 Bergen, Norway asgeir.sorteberg@bjerknes.uib.no VLADIMIR KATTSOV Voeikov Main Geophysical Observatory of Roshydromet, 7, Karbyshev str., St.Petersburg 191 Russia kattsov@main.mgo.rssi.ru JOHN E. WALSH International Arctic Research Center, 9 Koyukuk Drive, P.O. Box 77 Fairbanks, Alaska , US jwalsh@iarc.uaf.edu TATYANA PAVLOVA Voeikov Main Geophysical Observatory of Roshydromet, 7, Karbyshev str., St.Petersburg 191 Russia pavlova@main.mgo.rssi.ru Abstract Ensembles of simulations of the th - and 1 st -century climate, performed with coupled models for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment, provide the basis for an evaluation of the Arctic (7-9 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 th -century simulations (the CM 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 W/m, which is less than for the NCEP and ERA reanalyses but in line with some of the satellite estimates. Incoming solar radiation is overestimated by the models in spring and underestimated in summer and autumn. The ensemble mean annual net surface energy gain by shortwave radiation is 9 W/m, which is slightly less than for the observational based estimates, In the 1 st -century 1

2 simulations driven by the SRES A scenario, increased concentrations of greenhouse gasses increase (average for - minus average for 19- averages) the annual average ensemble mean downward longwave radiation by.1 W/m. This was partly counteracted by a.7 W/m reduction in downward shortwave radiation. Enhanced sea ice melt and increased surface temperatures increase the annual surface upward longwave radiation by 7.1 W/m and reduce the upward shortwave radiation by 1. W/m, giving an annual net (shortwave plus longwave) surface radiation increase of. W/m, with the maximum changes in summer. The increase in net surface radiation is largely offset by an increased energy loss of. W/m by the turbulent fluxes. 1. Introduction Future climate change simulations show enhanced climate sensitivity at high latitudes, where there is also the largest spread among different models (IPCC, 1; ACIA, ; Randall et al., 199). The surface energy balance is an essential element of the climate and constitutes an important part of the energy available for melting/freezing the sea ice and warming/cooling the surface. For example, Fletcher (19) argues that an advance of the onset of sea ice melt by only one week in June would result in an additional melt of.-1. m of sea ice. On one hand, due to the Arctic s low moisture content, changes in CO and other greenhouse gasses have the potential to become more important in the Arctic than at lower latitudes. On the other hand the impact of changes in infrared absorbers depends on the vertical tropospheric temperature gradient which is small in the Arctic and therefore the impact of greenhouse gas changes could be smaller. In addition to this, the complex interactions between the atmosphere, ocean and cryosphere give rise to a variety of climate feedbacks, with the ice/snow albedo-temperature feedback (Budyko, 199) being an important factor. In a simplified climate system, the strength of the ice-albedo feedback is a function of the sea ice extent (Budyko, 199). The strength of the albedotemperature feedback, however, is a complicated function of the initial extent of the sea ice and the responses of the horizontal energy and moisture transports, as well as clouds (Held and Suarrez, 197, Hartmann, 199; Vavrus, ; Björk and Söderkvist, 1; Beesley, ) to the changes in greenhouse gases. Clouds play an especially important role in arctic feedbacks because their radiative impacts are large in the solar and longwave portions of the spectrum, and these impacts depend strongly on cloud height,

3 thickness, and hydrometeor type (liquid or ice), concentration and size. The recent changes in the permafrost (Romanovsky et al., ), snow cover (Robinson, 1999), glaciers (ACIA, ; Dyurgerov and Meier, 1997), sea ice (Vinnikov et al., 1999), temperature (ACIA, ; Serreze et al., ) and precipitation (Kattsov and Walsh, ) show a consistent picture of an Arctic climate in rapid change. However, Arctic climate is highly variable and the causes of the changes are still debated (eg. Polyakov et al., ; McBean, ). Credible model simulations are important in attributing the changes to a cause. In addition, models are the main tool in developing physically plausible climate change scenarios, given prescribed scenarios of future greenhouse gasses and aerosol loadings. In the present paper, we first evaluate the models ability to simulate the different energy terms for the present climate. global coupled (atmosphere-ocean-ice) climate models are compared to five observationally-based estimates. The motivation for this evaluation is that a realistic simulation of the present Arctic climate may be a necessary (but not sufficient) condition for a successful simulation of future global climate. Secondly we assess to which extent projected changes in greenhouse gases and aerosols may affect the surface energy budget of the Arctic. Special emphasis is placed on the behavior of the modeled ensemble mean and the spread among the different models.. Models and Data.1. The coupled models This comparative evaluation of models is made feasible by using climate simulations provided by 1 modeling groups worldwide (Table 1). The simulations were systematically collected and made available by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) as part of the process leading up to the Fourth Assessment Report (AR) of the Intergovernmental Panel for Climate Change (IPCC). The models are all coupled atmosphere-ocean models including various complexities in their treatment of sea ice. A few of the models use flux corrections, but most do not (for more details on the individual models see: As the collection of data is still ongoing, we have used what was available in the evolving

4 archive in mid-. Several of the models have not provided all the components of the surface energy budget; thus, the ensemble mean estimates for the different components may not include the same number of models in all cases. Table 1 lists the individual models and their resolution. In this study we use several groups of the archived simulations. For comparison against observed estimates we use the CM simulations, which span the period starting not later than 191 and ending not earlier than These simulations are forced with observed aerosol loadings and greenhouse gas concentrations. CM simulations with some of the models include natural forcings such as solar variability and volcanic eruptions. In addition, the indirect effects of aerosols are only taken into account in a few of the models. In the sections discussing the 1 st century simulations, the projected changes in greenhouse gases are taken from the Special Report on Emission Scenarios (SRES; Nakicenovic et al., ). Changes are calculated as differences between the -99 mean for the SRES A scenario and the mean in the CM simulation. The CO level in the SREAS A scenario increases to around ppm by the late 1 st century (IPCC, 1). Simulations with some of the models include several ensemble members started from different initial conditions. In this study, the entire ensembles were used only in an analysis of the th century trends and variability in the CM simulations. Otherwise, whenever more than one simulation was available, only the first members of the ensembles were included in the analysis. The scenario simulations for the 1 st century were not available for some IPCC AR models, thus different subsets of the models are used in 1 st century estimates discussed in Section. The reference area used in this study is 7-9ºN. Area-averaged values are calculated by using the original grid of the individual models and selecting the grid squares within the chosen region and weighting the individual grid-squares by their areas. For the spatial maps of the multimodel mean and their spread, all models are interpolated into a.x.º grid using Cressmann interpolation, where the weights are reduced exponentially with distance to the point on the. grid. Only grid points km or less from the. grid

5 point are used in the interpolation. The intermodel standard deviation (STD) is used as a measure of the level of agreement between the different models. Assuming that the model estimates are Gaussian distributed, 9% of the distribution is within ± STD of the mean. Table 1. Observationally based estimates and reanalysis With the exception of the Russian measurements made from drifting ice stations during the early 19s through 1991, in situ observations of the different terms in the energy budget are rare and usually only available for a limited region during short-term intensive field campaigns. In this study we use observationally based estimates that depict spatial variability over the whole Arctic concurrently. We use five different observational databases to gain some insight into the uncertainty related to the different methods of observational analysis. Two of these databases are based on a state of the art data assimilation procedure used in numerical weather prediction and three are based on satellite estimates. The ECMWF (ERA) and the NCAR-NCEP reanalyses are both based on a threedimensional variational assimilation of observations (Simmons and Gibson, ; Uppala et al., ; Kalnay et al. 199), but with no direct assimilation of radiative fluxes. Conventional data comes from a wide selection of sources starting with 19 (the International Geophysical Year) and 19, respectively. Here, we focus on the data from the last part of the century (after 19) when TOVS satellite data and Cloud Motion Winds were used in the assimilation. The third and fourth datasets are two versions of the surface radiation budget based on the International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer, 1999): Version of the Surface Radiation Budget (SRB) and the Version 1 polar radiation fluxes (POLAR ISCCP; Key, et al. (1999)). The inputs for the SRB data (19-199) are from different satellite sources. Cloud data was taken from the DX data of the ISCCP, which provides top of atmosphere (TOA) narrowband radiances, atmospheric

6 soundings, and cloud information. ERBE measurements provided TOA broadband clearsky albedos. Atmospheric water vapor is taken from a -D data assimilation product provided by the Data Assimilation Office at NASA GSFC and were produced with the Goddard Earth Observing System model version 1 (GEOS-1). Ozone is taken from the Total Ozone Mapping Spectrometer (TOMS). The general approach was to use the ISCCP DX data supplemented by the ERBE results as input to the SRB satellite algorithms to estimate the various surface parameters. The shortwave components of the surface radiative fluxes were computed with a broadband radiative transfer model (Pinker and Laszlo, 199) and the longwave component using the Fu-Liou Model (Fu et al., 1997). The POLAR ISCCP radiation terms (19-199) were calculated by training a neural net (a special implementation of Fluxnet, cf. Key and Schweiger, 199) with a small subset of the available ISCCP-D1 data. Fluxes were generated by the Streamer radiative transfer model (Key and Schweiger, 199). When available, a more accurate set of atmospheric temperature and water vapor profiles from the TOVS Pathfinder Path-P data set were used in place of the ISCCP profiles. A more detailed description is given in Key, et al. (1999). The fifth database is the Version 1 of the Extended Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder dataset (APP-X), spans the period from 19 to 199. The Extended APP dataset is an extension of the standard clear sky products (Maslanik et al., ; Maslanik et al., 199; Meier et al., 1997) using the Cloud and Surface Parameter Retrieval (CASPR) system (Key, 1). The calculation of cloudy sky surface skin temperature was based on an empirical relationship between the clear sky surface skin temperature, wind speed, and solar zenith angle (daytime). The cloudy sky broadband surface albedo is determined using the clear sky broadband albedo (interpolated from nearby pixels) adjusted by the APP cloud optical depth and the solar zenith angle. The all-sky radiative fluxes were computed in CASPR using FluxNet (Key and Schweiger, 199). Key (1) and references therein provide more information on the algorithms and their validation. The APP-X data are available for the local solar times and. For the longwave components the two times were averaged to obtain

7 values representative of the full day. No attempt was made to calculate the full-day shortwave components.. Simulations of the th century.1 Longwave radiation The main factor that determines the annual mean and seasonal cycle of the upwelling longwave radiation (LW) terms is the surface temperature. The primary determinants of the downwelling surface LW radiation are the boundary layer humidity and temperature; its stratification; and the amount and optical properties of clouds. LW radiation transfer in high latitudes is somewhat different from the lower latitudes. Due to the small amount of water vapour the opacity of the water vapour rotation band is smaller; also, the lower temperatures shift the maximum blackbody intensity to lower frequencies and therefore towards the low-frequency rotational band of water vapor (Ellingson et al., 199). Zhang et al., 1997 showed that for clear sky the downwelling LW radiation reaching the surface comes from a very shallow layer of the atmosphere (9% of the accumulated contribution comes from the lowest - m of the atmosphere). Thus, high vertical resolution in the boundary layer may be required in order to capture both the annual mean and especially the seasonal cycle of this element, making it a challenging task for climate models. A detailed analysis of the impact of water vapor, atmospheric temperatures and stratification on the LW radiation can be found in Curry et al. (199) and Zhang et al. (1997). As the LW radiation dominates the surface radiation balance during much of the year, the quality of the simulation of this element is crucial for an accurate representation of the Arctic mean climate and its seasonal cycle. Figure 1 shows the ensemble mean down (Figure 1a) and upward (Figure 1b) components of the LW radiation. The main observed features are well represented, with the North Atlantic currents and the high stormtrack density of the Nordic Seas contributing to maxima over the Northern Nordic Seas of - and - W/m for the downward and upward fluxes, respectively, with a gradual reduction to 1- and - W/m over the central Arctic. The area of maximum 7

8 values is also the area of maximum across model spread, with grid point standard deviations (STD) of - and - W/m for the down and upward component respectively. The spread is reduced to around 1-1 W/m for both components over the central Arctic. Figure Downward Longwave Radiation Figure a gives the annual mean surface downward LW radiation averaged over 7-9 N in the different models, together with the five observationally based estimates. With the exception of the NCEP data, the observational estimates agree fairly well with a mean of. W/m, which is close to the IPCC models ensemble mean of. W/m. The IPCC across-model spread (±1 STD) in annual mean downward component is 1.1 W/m. There is no clear relationship between the individual models annual cloud cover/sea ice fraction and annual downward LW radiation, but a relationship between estimated cloud cover and summertime downward LW radiation is evident, with models having a large cloud cover having more surface LW radiation. The discrepancies between the model ensemble mean and the ensemble mean of the observational estimates are largest over the Barents Sea area with a negative bias of -1 W/m in the models (too little energy reaching the surface). This is related to the models positive Barents Sea ice bias, which impacts the atmospheric humidity and temperature profile. Models having a large Barents Sea (1-ºE and 7-ºN) annual sea ice fraction emit less downward radiation in the Barents Sea area (the correlation r = -. (p=.) with the IAP model removed). A positive bias is seen over Greenland and the North American Arctic (- W/m ). It should be noted that the North American bias is only apparent when compared to four (ERA, NCEP, APP-X and SRB) of the five observational estimates. There is no clear ensemble mean bias in the downward component over the central Arctic.

9 The seasonal cycle in the downward LW multimodel ensemble mean (Figure b) is within the observational estimates during all months. However some models tend to underestimate the Dec-Apr downward LW radiation as a surface energy source. A possible explanation for this is the insufficient vertical resolution of AOGCMs which may prevent a correct buildup of deep wintertime surface inversions (Byrkjedal et al., ). It should be noted that the strength of the seasonal cycle differs substantially among the different observational estimates, and there is a W/m difference between the NCEP and ERA estimates in mid-summer. This is of the same magnitude as in a recently conducted comparison of the downward radiative fluxes in different datasets over the SHEBA site (Liu et al., ). Liu et al. found that the ERA and AVHRRbased estimates (quite similar to the APP-X dataset used here) describe the seasonal cycle of downward LW radiation quite well, and that an ISCCP-derived estimate (using the same cloud data, but another radiative transfer code than was used for the estimates given here) overestimate the wintertime downward LW flux and underestimate the summertime flux, resulting in a seasonal cycle that is too weak. The shape of the seasonal cycle reported by Lindsay (199) for the Arctic pack ice using the NP-stations is also quite similar to the ERA estimates. These studies indicate that the seasonal cycle in the downward component may be more realistically represented by the ERA and the AVHRR based APP-X datasets. However, it should be noted that the ERA assimilates the SHEBA radiosondes and the good quality of the ERA estimates over this site may therefore lead to overconfidence in the ability of ERA to capture the entire arctic region..1. Upward Longwave Radiation Averaged over all five observational estimates, the annual mean upward LW flux averaged over 7-9 N is. W/m, which is. W/m larger than the IPCC models ensemble mean (Figure c), for which the across model spread (±1 STD) is 1.7 W/m. This spread is comparable to the spread in the LW downward component and is linked to the state of the sea ice and its impacts on the mean arctic surface temperature. A comparison of the individual models mean annual mean ice fractions and the upward LW components shows that models with a large sea ice fraction tend to have smaller 9

10 upward LW radiation (correlation r = -. (p=.), with the IAP model removed). As with the downward component, there is a negative bias (too large an energy loss from the surface) over the Barents Sea area (- W/m ), related to the positive biases in sea ice fraction in this region. The correlation between the individual model s mean Barents Sea (1-ºE and 7-ºN) annual upward LW radiation and the annual mean Barents Sea ice fraction is -. (p=.). There is a quite large spatial discrepancy among the different observational estimates. Thus, the spatial pattern of the ensemble mean model errors is not easy to evaluate. All observational estimates show a fairly similar seasonal cycle of upward LW radiation (Figure d), although the monthly values have a spread of - W/m. Several of the models underestimate the wintertime energy loss by - W/m indicating that the models have a cold surface temperature bias..1. Net Longwave Radiation As a consequence of the models biases in the upward and downward components, the ensemble mean net LW radiation is overestimated (the LW radiation heat sink is too small) compared to the reanalyses and in line with the satellite measurements. The across-model spread in the models is. W/m with a tendency for models having a large annual cloud fraction to have the smallest energy loss (a non-significant correlation of.). However, the seasonal cycle of net LW radiation is the difference between two large terms and is not well known. This is an element that historically has been measured only rarely and our knowledge is therefore to a large extent based on simulations and regional field campaigns. As seen in Figure f the observational estimates diverge and there is no consensus on the seasonal cycle. The two ISCCP-based estimates show the strongest LW energy loss in summer (- W/m ), while the ERA, NCEP and the AVHRR-based APP-X datasets indicate the largest loss in early spring (- W/m ). Most of the models indicate a seasonal cycle in the net LW radiation similar to the ERA, NCEP and APP-X data, and the models ensemble mean follows the APP-X dataset closely. There seems to be a tendency for many models to underestimate the summertime surface energy loss, and there is a clear relationship between summertime

11 cloud cover and net LW radiation: models having a large cloud cover show the smallest surface energy loss (correlation r =.9). The summertime LW energy loss is also related to the sea ice fraction which strongly influences the surface temperatures. Models having a large sea ice fraction generally have larger LW surface energy loss (correlation r = -.). It should be noted that the relationships between the downward radiation components and cloud cover should not be taken as the direct influence of the cloud cover as the correlations do not give any causal relationships. Cloud cover changes are related to changes in both heat and moisture transport which, in addition to changing the cloud cover, may change the atmospheric temperatures and water vapor content, Consequently, it is difficult to distinguish between the direct influence of cloud fraction and the influence of atmospheric water vapor and temperature, which may co-vary with the cloud cover fraction and therefore lead to too strong statistical cloud-radiation relationships. Figure. Shortwave radiation The incoming surface solar radiation is, relatively speaking, well documented in the Arctic. Comprehensive information on the seasonal cycle and spatial distribution can be found in a variety of studies in both the Russian (Marshunova 191; Mashunova and Chernigovskii, 1971; Atlas Arktiki, 19; and Krohl 199; see Przybylak, for an excellent review of these findings) and English (Fletcher, 191; Vowinckel and Orvig, 19; 197; McKay and Morris, 19; Serreze et al., 1997) literature. The annual mean and seasonal cycle are determined by the length of the day which gives zero direct-beam flux at the North Pole from the autumnal to spring equinoxes. The annual means of the downward fluxes have a latitudinal gradient, which is modified by the occurrence of topography, clouds and their optical properties such as liquid water content, number of droplets and their size. An overview of the topic is given by Curry and Ebert (199); Curry et al. (199); Curry et al. (199) and Zhang et al. (199). 11

12 The outgoing surface solar radiation is largely determined by the surface albedo and the amount of downward radiation. The spatial pattern of the annual mean downward shortwave (SW) radiation is well simulated by the ensemble mean (Figure a), with a minimum of 7-7 W/m over the northern part of the Nordic Seas due to the synoptic transport of warm humid air and subsequent cloud formation in the area. The radiation increases to around - W/m over the central Arctic and an across-model spread (±1 STD) of -1 W/m. With the exception of the lack of a more pronounced minimum in the eastern Barents Sea, the pattern closely resembles the data of Marshunova (19). The spatial pattern of the annual upward component (Figure b) show central Arctic values of - W/m and an across-model spread that is slightly smaller than in the downward component. Figure..1 Downward Shortwave Radiation Averaged over the Arctic domain (7-9 N), the mean of the four observational estimates of the annual surface downward SW radiation fluxes is 99. W/m, (Figure a) and the ensemble mean for the models (9. W/m ) is close to three of the four observational estimates, with an across-model spread (±1 STD) of 9.1 W/m. As with the LW components, there is considerable spread among the observational estimates. This is especially pronounced for the NCEP reanalysis, which has much larger values than any of the other estimates. This bias is in line with results in Liu et al. s () comparison of the downward SW fluxes over the SHEBA site which indicate the ERA reanalysis has a smaller bias than the AVHRR, NCEP and ISCCP-based estimates (the NCEP bias averaged over a year is more than W/m averaged over a year). The NCEP bias was 1

13 also noted by Serreze and Hurst () and linked to a large negative bias in the cloud cover. Because the biases in the model ensemble mean change when different observational estimates are used, it is hard to detect any regions of strong annual biases in the downward component. Compared to the ERA reanalysis, the models show an underestimation of downward SW radiation of - W/m over the central Arctic, while for the same area there is an overestimation of - W/m when compared to the ISCCPbased estimates. The spread in the models summertime maximum (June) is over W/m (Figure b). This spread does not seem to be related to the models different cloud fraction. Compared to three of the four observational estimates there is a tendency of the models to overestimate the incoming SW radiation in spring (March-May) and underestimate the radiation in summer and autumn (June-Sep). The spring overestimation in the ensemble mean has a peak in April and May ( to W/m compared to the different observational estimates) while the summer/autumn underestimation is greatest in July ( to W/m ). The models springtime downward SW radiation is related to the model s cloud fraction, with models having a larger cloud fraction giving less surface downward radiation (the MAM correlation is -.1 p=.7). The relationship between cloud cover and summertime radiation is less clear (the correlation of -. which is reduced to -. when the IAP model was removed, is not statistically significant). As linkage between the model s cloud fraction and downward SW radiation is not very strong and the seasonal cloud cover of the Arctic is not well known, it is difficult to conclude that the model s spring and autumn biases are related to biases in the seasonal cloud cover fraction. This does not exclude any possible relationships between cloud thickness etc. and downward SW radiation which cannot be rigorously investigated with the IPCC model database. It should also be noted that the June maximum in ERA, and ISCCP-based estimates given here is - W/m smaller than the estimates for the pack ice obtained using NP station data by Lindsay (199) and the Artic Ocean averages of Ebert and Curry (199). Around half of the bias can be explained by the larger area chosen here (including the cloudier Greenland and Barents Sea region). A possible explanation for the remaining bias may be the different time periods. The estimates used here are averages from the last 1

14 two decades, while the NP-station estimates are based on data from the late 19s to the beginning of the 199s. The reported increase in spring and summer cloudiness over the last decades (Wang and Key, ) may therefore contribute to some of the discrepancy and the time evolution of both the ERA and NCEP reanalyses shows large trends in the downward SW component (see section.)... Upward Shortwave Radiation For the upward SW component the biases are more apparent. Averaged over the Arctic area, the model ensemble mean overestimates the upward SW radiation compared to three of the four observational estimates (Figure c). The across-model spread (±1 STD) is 7. W/m. Much of this overestimation comes from the Barents and Greenland Sea area (1- W/m ), indicating a tendency for the models to overestimate the sea ice extent in this area (Arzel et al., ). The bias extends over the land areas of the eastern and western Arctic, implying that is associated with positive biases in the seasonal snow cover. We expect the amount of upward SW radiation to be tightly linked to the sea ice fraction through the surface, and the annual mean sea ice fraction correlates well with the annual mean upward SW radiation (., p=.9, with the IAP model excluded). There is no unanimity among the different observational estimates on the month of maximum SW radiation (Figure d). Most models show a maximum in May, which corresponds well with the ERA- reanalysis. There is a tendency among the models to overestimate the May maximum (compared to three of the observational estimates), consistent with the biases in the downward component that seem to be dependent on the cloud fraction for the individual models (see section..1). Generally the seasonal pattern in the differences between simulated and observed upward radiation follows the pattern for the downward component, but with no clear underestimation in the summer/autumn radiation, indicating that the bias in summer/autumn downward SW radiation is counteracted by positive biases in the models summertime surface albedo. This albedo bias is likely linked to the extensive sea ice extent in many models (Arzel et al., ) 1

15 .. Net Shortwave Radiation The model ensemble mean of the net SW radiation, a net energy source to the Arctic surface, is underestimated compared to all the observational estimates by 7.1 W/m compared to the mean of the observations (Figure e). The range among the different models is comparable to that of the net LW radiation, as is the across model spread (.1 W/m ). The net SW radiation underestimation is seen during all months (Figure f) with a maximum in summer of 1- W/m compared to the different observational estimates. Figure. Turbulent Fluxes Even more so than the radiative components, knowledge of turbulent fluxes is very limited, and only a few attempts have been made to produce spatial maps of these components (Khrol, 199 reproduced in Przybylak, ). According to these maps the annual sensible heat fluxes are a modest surface heat source over the Arctic Ocean covered with perennial ice. Lindsay (199) used the NP-station data to estimate the annual mean sensible heat flux over the ice pack to be a surface heat source (a downward flux) of around W/m using the NP-station data. According to Khrol (199) the sensible heat is a substantial surface energy sink over the eastern part of the Greenland Sea and the Barents Sea. With exception of the large fluxes over the East Greenland Current reported by Khrol, the ERA values are very similar. In contrast to the sensible heat flux, the ERA- estimates show the latent heat flux to be a surface energy sink over the central Arctic Ocean. This is in accordance with the estimates of Lindsay (199), who showed the annual average latent heat flux to be. W/m (positive upward) over the Arctic ice pack. 1

16 ..1 Sensible and Latent Heat Flux Compared to the ERA and NCEP estimates, most models (Figure a) show the sensible heat flux to be a larger surface energy sink in the Arctic. The model ensemble mean is an upward flux of. W/m with a ±1 STD spread of. W/m. In case of the latent heat flux, both reanalysis-derived estimates are 1 W/m (upward), and there is an underestimation of.1 W/m in the model ensemble mean. The across model spread is. W/m. When averaged over the entire year, the sum of the ensemble mean turbulent fluxes represents an energy sink of 1. W/m. Relative to the reanalyses, the IPCC model ensemble mean estimates of the turbulent fluxes represent a much weaker energy sink than in the reanalyses over the warm West Spitsbergen Current and the Barents Sea (Figure ), with underestimations of - W/m, indicating a tendency among the models to overestimate the sea ice cover in this area (Arzel et al., ). This is also the area of the largest spread among the models with an across-model spread (±1 STD) of - W/m, compared to - W/m for the central Arctic (Figure ). Figure Compared to the seasonal cycle of the LW and SW radiative fluxes, the seasonal cycles of sensible (Figure b) and latent (Figure d) heat are small. While the ERA- estimates indicate the Arctic (7-9ºN) sensible heat flux to be a small surface energy source from November to March and a small sink during the rest of the year, the NCEP estimates indicate that the sensible heat flux is a surface energy source during the whole year. Only very few of the models capture the change in sign with season as indicated in the ERA- data. Latent heat fluxes are systematically underestimated in the IPCC ensemble mean during the entire year compared to ERA and during most of the year compared to NCEP. The underestimation is related to the tendency of the models to have excessive ice cover over the Barents and Greenland Seas. Figure 1

17 .. Evolution of fluxes through the th century One of the most intriguing features of the arctic climate evolution in the th century was the warming observed in the Arctic in 19-s, with a magnitude comparable to the warming during last few decades (McBean, ). Recently, the two warming periods in the Arctic, their relative magnitudes, possible causes, and implications for credibility of the state-of-the-art projections of the future arctic (and global) climate have been widely discussed in the literature (Polyakov et al., ; Bengtsson et al., ; Johannessen et al., ; Overland et al., ; McBean, ; Wang et al., ). While the early th century arctic warming is often attributed to the unforced natural variability of the highlatitude climate, there is no consensus on the relative importance of the increasing anthropogenic forcing for the late th century arctic warming. From this point of view, multi-member ensemble simulations of the th century climate have a potential to provide a better insight into the problem. Wang et al. () analyzed the IPCC AR CM simulations of the land surface air temperature in the Arctic, and hypothesized in particular that if mid-century warm anomalies are based on intrinsic atmospheric variability, then models should not necessarily reproduce warm events in the same years as the observed warming, but they should simulate the same variability and reproduce trends associated with external forcing. Some of the IPCC AR models were only forced with observed atmospheric concentrations of green-house gases, while others included time-varying natural forcings (e.g. volcanic and solar effects). Wang et al. () found the inclusion of natural forcings to be of minor importance relative to a model s ability to reproduce the timing of the early th century arctic warming, while a robust feature of model responses to the anthropogenic green-house gas concentrations increase were positive temperature trends both over the entire th century and its last decades. One of the foci of our study, which uses essentially the same set of climate models as Wang et al. (), was the evolution of the energy balance components in the Arctic region through the entire th century. While the lack of observational data prevented us from directly establishing the validity of the simulated radiation and turbulent fluxes in 17

18 the Arctic for the entire past century, we tried to identify common features and differences in the behavior of the models in the CM experiment. Another focus was an estimation of the connections between different arctic energy budget components and the surface air temperature, whose behavior in the th century is known better from the observational record. As a first step in the evaluation of radiation/turbulent flux evolution through the th century, we used the ERA and NCEP data which span the period. (It should be strongly emphasized that the ERA and NCEP trends may be heavily influenced by changes in the observational system and as well as by the parameterization of the fluxes in the models used to produce the reanalyses. Thus the trends should only be regarded as apparent, not necessarily actual, trends). Linear trends in this dataset were compared against IPCC model mean trends over the same time period. Only the first (or the single) members of each model ensemble were used to obtain the model mean trend (Table ). A significant positive trend was found in both the downward and upward LW component in both reanalyses. (Our statements about significance are relative to the 9% significance thresholds based on a one-sided t-test). On average the trends in downward LW radiation in the IPCC models were slightly higher than in the ERA and NCEP reanalyses, while the IPCC trend in the upward LW radiation was between the ERA and NCEP trends. For models running an ensemble of simulations for the th century, a notable feature of the evolution of the radiation budget components in each CM simulation is the high similarity between LW radiation variations, especially in recent decades. A typical example is given by the two ensemble members of PCM model, for which each ensemble member shows two distinct periods of increased downward and upward LW radiation in the th century, to a certain extent consistent with surface air temperature records. However, the early th century maxima obtained in the two ensemble members have different shapes and are shifted in time relative to each other. It is noteworthy that PCM CM runs are among the IPCC AR simulations of the th century that include observationally based natural forcing. Table 1

19 The two reanalyses as well as the IPCC models show a decrease in the downward SW radiation in recent decades, with the IPCC models having weaker decrease. The decrease in the downward component is found similarly in the upward component for both the ERA and IPCC models, On the other hand the reduction in upward SW in the NCEP data is considerably stronger than in the downward component, indicating larger changes in the albedo than in ERA and the other datasets. The decrease in the downward SW radiation is consistent with the reduction reported by Wang and Key () for the period using AVHRR data. The short wave radiation components show general decreases through the th century in the model simulations. The simulated arctic SW radiation time series for the th century are negatively correlated with the surface air temperature (Figure 7), pointing to the importance of LW radiation for increasing the surface air temperature in the Arctic along with total cloudiness increase. Figure 7 An evaluation of IPCC model performance for the entire th century using all available CM runs from ensembles with each model indicate a robust positive th century trend in the net radiation budget (Figure ). With the exception of BCCR_BCM., ECHAM/MPIOM and CCSM (1 of ensemble members), all members of the CM ensembles analyzed show an increase in the net radiation balance, with the absolute maximum of. W/m per century in the single simulation from MIROC. (hi). Figure An investigation of the temperatures simulated by the subset of the models that included natural forcings shows that, while some of the ensemble members show a resemblance to the th century s double maxima of observed arctic temperature, the inclusion of the 19

20 natural forcings clearly does not ensure a pronounced mid- th century warming, let alone its timing. On the other hand, all ensemble members generally show an increase in the upward LW radiation by the end of the th century, consistent with modeled and observed temperature trends. This provides further support to the findings of Wang et al. () concerning the relative importance of the unforced variability generated by the models in the mid- and late- th century climate simulations. Specifically, the mid- th century warming is much more consistent with unforced variability.. Projections for the 1 st century.1. Longwave radiation Figure 9 shows the patterns of the simulated changes in LW radiation by the late 1 st century (the changes are calculated as the differences between the -99 mean for the SRES A scenario and the averages in the CM scenario). The largest increase in both downward and upward LW radiation (- W/m annually) is found over the Barents Sea (Figure 9a,c), with values a few W/m smaller over the central Arctic Ocean. The Barents Sea maximum in the chanhes of downward LW is seen in most of the models and is related to the warming of the atmospheric column due to the reduction of sea ice and increased cloud cover. The strength of the downward LW changes in the Arctic are highly variable among the different models, with most models showing the largest response in autumn (Figure 9b). The autumn (SON) ensemble mean of.7 W/m is a factor. larger than the summertime changes (Table ). Figure 9 Table Due to the ability of clouds and water vapor to absorb LW radiation and the high emissivity of clouds, the wintertime changes in the Arctic LW downward component is

21 related to changes in the models Arctic cloud fraction (DJF correlation =.), with the models showing a strong increase in cloud fraction having the largest increase in downward longwave radiation (Figure b). A linear regression estimate indicates a wintertime LW /C of.9 ± 1.1 Wm - /% where LW is the change in Arctic (7-9ºN) downward LW radiation and C is the Arctic cloud cover change. The uncertainty indicates the. significance level of the regression estimate. The summertime changes in the LW downward component seem less related to the cloud fraction changes (Figure a), with a non-significant relationship of LW /C =.7 ±. Wm - /%. Figure As with the downward component, the largest upward LW changes are in the Barents and Chukchi Seas (Figure 9c), but there is larger deviation among the models in the strength of the changes, with an across-model STD of -1 W/m. This is consistent with the fact that some of the models still have at least a seasonal ice cover in these regions during the late 1 st century, while others are ice-free. As with the downward component, the largest changes are in autumn (Figure 9d) with an ensemble mean of.9 W/m (Table ). The strength of the changes in the upward LW radiation among the different models is a strong function of the changes in models surface temperature and therefore of the changes in the simulated ice cover. Figure 11 displays the relationship between changes in Arctic ice cover and upward LW radiation for winter (Figure 11b) and summer (Figure 11a). The wintertime LW /ICE is -1. ±.7 Wm - /% where ICE is the change in the Arctic ice fraction and LW is the change in the Arctic average (7-9 ºN) upward LW radiation. The sensitivity of the Arctic average upward LW radiation to sea ice changes is much smaller (but still significant) in summer, when less of the Arctic Ocean is covered by sea ice (LW /ICE = -. ±.19 Wm - /%) and the temperature of sea ice and open water surfaces do not differ substantially. Figure 11 1

22 The annual mean response in the net LW radiation over the Arctic (Figure 9e) is the difference between two large terms that partly cancel. For the SRES A scenario, the mean Arctic (7-9ºN) increase in net LW energy to the surface (a decrease in LW energy lost by the surface) is. W/m (Table ). The largest increase occurs in summer (. W/m ). On the other hand there is no consensus on the sign of the wintertime changes in the net LW component, and the DJF ensemble mean change is slightly negative (Table ). This indicates that the seasonal cycle in net LW radiation is increased in the scenario simulations.. Shortwave radiation There is a reduction in downward SW radiation by -99. The spatial pattern is similar to the pattern of changes in the downward LW radiation, with a widespread reduction over the Arctic Ocean and maximum reduction over the Barents and Chukchi Sea (Figure 1a). Seasonally, the reduction follows the strength of the SW radiation with a maximum in mid summer (JJA reduction = -.9 W/m (Table )). Figure 1 Not surprisingly, the summertime changes in Arctic downward SW radiation are well correlated to the changes in the cloud fraction (r = -.) with large increases in cloud fraction giving a large reduction in the downward SW radiation (Figure 1). The regression estimate yields a summertime sensitivity (SW /C) of -.1 ± 1. Wm - /%, where SW is the change in Arctic downward SW radiation and C the change in Arctic total cloud fraction. Figure 1

23 The changes in upward SW radiation Figure 1c) depend on the changes in the downward component and the changes in surface albedo which is related to the sea ice. Thus, the spatial pattern of the changes in upward SW is much the same as for the downward component. The sensitivity of the upward SW changes to changes in sea ice fraction is strong, with a correlation of.9 between upward SW changes and sea ice fraction changes during summertime (Figure 1). The summertime sensitivity (SW /ICE) is.9 ±. Wm - /%, where ICE is the change in the Arctic ice fraction and SW is the change in the Arctic (7-9ºN) upward SW radiation. Figure 1 The across-model (±1 STD) spread in the annual SW changes is -% smaller than the spread in the LW changes (Table ), but the summertime spread of the SW changes is a factor of - larger than the spread of the LW changes. The annual mean Arctic (7-9ºN) downward SW radiation is reduced by.7 W/m. This is counteracted by a reduction in the upward component of 1. W/m due to reduced surface albedo, giving an increase in net surface SW radiation of. W/m (Table ) with a maximum is summer (7. W/m ). This is % larger than the increase in the summertime net LW radiation and is consistent with a continued reduction (melt) of sea ice in the late 1 st century. With the exception of one model, the IPCC models all give an increase in net SW radiation. The model giving a reduced net SW radiation is the model showing the strongest increase in summertime cloud cover (Figure 1).. Turbulent Fluxes The changes in latent heat fluxes are around four times larger than the changes of the sensible heat flux, and the changes are largest over the Barents and Chukchi Seas (Figure

24 1) where many of the models show a substantial retreat of sea ice during the 1 st century. The marginal ice zone is also the area of largest spread among the different models. Over the central Arctic, annual regional changes range from to. and to W/m for the sensible and latent heat fluxes, respectively. There is a large seasonal cycle in the changes, with the largest changes in autumn and winter (Figure 1b,d). Annual changes in the total turbulent fluxes averaged over 7-9 N and all models are. W/m which is considerable less than the changes in the individual downward and upward radiative terms, but nearly as large as (but opposite in sign to) the annual changes of. W/m in net (SW and LW) surface radiation. Figure 1. Summary and conclusions Due to the sparse observational network in the Arctic, a comparison of models against observations must rely heavily on data assimilation (reanalyses) and remote sensing products. In this study, we have used five different databases for model evaluation, three of them based on satellite estimates and radiative transfer models (albeit for relatively short periods) and two based on reanalysis (three-dimensional variational assimilation of observations). All five estimates have the advantage of permitting evaluations of both the spatial variability and area averages for the entire Arctic at a particular time. However, the time span of the different estimates varies and some of the differences may well be attributed to this. Comparison of the different observationally-based estimates has not been the main focus of this study, but a few main discrepancies should be noted: Averages of Arctic (7-9ºN) downward LW radiation range from to W/m. The spread in monthly values is typically - W/m and the amplitude of the seasonal cycle is not well constrained. Upward LW radiation estimates differ by about the same amount (annually from to 7 W/m ), but there is a closer agreement on the strength of the seasonal cycle.

25 Longwave radiation as an Arctic energy sink ranges from to W/m for the different observational estimates and there is no consensus on the seasonal cycle of net LW radiation. Two estimates indicate a maximum in net surface energy loss in summer, and three estimates show the loss to be highest in early spring. The NCEP reanalyses has a strong bias in downward and upward shortwave radiation relative to the other estimates Annual downward SW radiation estimates (excluding NCEP) range from 7 to 9 W/m, and the monthly spread is typically - W/m during the months April to August. The differences in upward SW radiation estimates are somewhat smaller (ranging from to 7 W/m annually, excluding NCEP). Annual mean shortwave radiation as a net surface energy source ranges from to W/m, with the largest spread during summertime (-1 W/m ). Given the different observationally-based estimates, an evaluation of state of the art coupled climate models may to some extent be influenced by our choice of observational estimates. However, several general biases appear to be robust, as do some areas where the model spread is large. Specific findings include the following: As might be expected, for all energy budget terms, the model spread is largest in the marginal ice zone of the Barents, Kara and Chukchi Seas, where sea ice varies among he models. These are also the areas where the models most strongly deviate from the observational estimates. There is a tendency among the models to underestimate the downward LW radiation during wintertime. The DJF model ensemble mean is. W/m lower than the mean of the observations. The across-model spread (±1 STD) is 1. W/m during wintertime (DJF) and reduced to 9. W/m in summer (JJA). The DJF bias may be related to an overestimation of the sea ice extent which may feed back to the lower atmosphere. As with the downward component, there is a tendency for the models to underestimate the upward LW radiation in winter (DJF ensemble mean is 11. W/m