The Arctic surface energy budget as simulated with the IPCC AR4 AOGCMs
Asgeir SortebergÆ Vladimir KattsovÆ John E. WalshÆTatyana Pavlova
Received: 6 July 2006 / Accepted: 8 December 2006 / Published online: 20 February 2007 Springer-Verlag 2007
Abstract Ensembles of simulations of the twentieth- and twentyfirst-century climate, performed with 20 coupled models for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment, provide the basis for an evaluation of the Arctic (70–90N) surface energy budget. While the various observational sources used for validation contain differences among themselves, some model biases and across-model dif- ferences emerge. For all energy budget components in the twentieth-century simulations (the 20C3M simula- tion), the across-model variance and the differences from observational estimates are largest in the mar- ginal ice zone (Barents, Kara, Chukchi Seas). Both downward and upward longwave radiation at the sur- face are underestimated in winter by many models, and the ensenmble mean annual net surface energy loss by longwave radiation is 35 W/m2, which is less than for the NCEP and ERA40 reanalyses but in line with some of the satellite estimates. Incoming solar radiation is overestimated by the models in spring and underesti-
mated in summer and autumn. The ensemble mean annual net surface energy gain by shortwave radiation is 39 W/m2, which is slightly less than for the obser- vational based estimates, In the twentyfirst-century simulations driven by the SRES A2 scenario, increased concentrations of greenhouse gasses increase (average for 2080–2100 minus average for 1980–2000 averages) the annual average ensemble mean downward long- wave radiation by 30.1 W/m2. This was partly coun- teracted by a 10.7 W/m2 reduction in downward shortwave radiation. Enhanced sea ice melt and in- creased surface temperatures increase the annual sur- face upward longwave radiation by 27.1 W/m2 and reduce the upward shortwave radiation by 13.2 W/m2, giving an annual net (shortwave plus longwave) surface radiation increase of 5.8 W/m2 , with the maximum changes in summer. The increase in net surface radia- tion is largely offset by an increased energy loss of 4.4 W/m2by the turbulent fluxes.
1 Introduction
Future climate change simulations show enhanced cli- mate sensitivity at high latitudes, where there is also the largest spread among different models (IPCC 2001;
ACIA 2005; Randall et al. 1998). 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 (1965) argues that an advance of the onset of sea ice melt by only one week in June would result in an additional melt of 0.5–1.0 m of sea ice.
A. Sorteberg (&)
Bjerknes Centre for Climate Research, University of Bergen, Allegaten 55, 5007 Bergen, Norway
e-mail: [email protected] V. KattsovT. Pavlova
Voeikov Main Geophysical Observatory of Roshydromet, 7, Karbyshev str., St. Petersburg 194021, Russia
e-mail: [email protected] J. E. Walsh
International Arctic Research Center, 930 Koyukuk Drive, P.O. Box 757340, Fairbanks, AK 99775-7340, USA e-mail: [email protected] DOI 10.1007/s00382-006-0222-9
On one hand, due to the Arctic’s low moisture content, changes in CO2and 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 atmo- sphere, ocean and cryosphere give rise to a variety of climate feedbacks, with the ice/snow albedo-tempera- ture feedback (Budyko 1969) being an important fac- tor. In a simplified climate system, the strength of the ice-albedo feedback is a function of the sea ice extent (Budyko 1969). The strength of the albedo-tempera- ture 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 Suarrez1974, Hartmann 1994; Vav- rus2003; Bjo¨rk and So¨derkvist2002; Beesley 2000) 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, thickness, and hydrometeor type (liquid or ice), concentration and size. The recent changes in the permafrost (Roma- novsky et al. 2002), snow cover (Frei and Robinson 1999), glaciers (ACIA 2005; Dyurgerov and Meier 1997), sea ice (Vinnikov et al. 1999), temperature (ACIA 2005; Serreze et al. 2000) and precipitation (Kattsov and Walsh2000) 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 (e.g. Polyakov et al. 2003; McBean 2005). 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. Twenty global coupled (atmosphere–
ocean–ice) climate models are compared to five obser- vationally 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.
2 Models and data
2.1 The coupled models
This comparative evaluation of models is made feasible by using 20 climate simulations provided by 15 mod- eling groups worldwide (Table1). The simulations were systematically collected and made available by the Program for Climate Model Diagnosis and Inter- comparison (PCMDI) as part of the process leading up to the Fourth Assessment Report (AR4) of the Inter- governmental 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:
http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php). As the collection of data is still ongoing, we have used what was available in the evolving archive in mid-2005.
Several of the models have not provided all the com- ponents 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. Table1 lists the individual models and their resolution.
In this study we use several groups of the archived simulations. For comparison against observed esti- mates we use the 20C3M simulations, which span the period starting not later than 1901 and ending not earlier than 1999. These simulations are forced with observed aerosol loadings and greenhouse gas con- centrations. 20C3M simulations with some of the models include natural forcings such as solar vari- ability 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 twenty- first-century simulations, the projected changes in greenhouse gases are taken from the Special Report on Emission Scenarios (SRES; Nakicenovic et al.
2000). Changes are calculated as differences between the 2080–2099 mean for the SRES A2 scenario and the 1980–1999 mean in the 20C3M simulation. The CO2 level in the SREAS A2 scenario increases to around 800 ppm by the late twentyfirst century (IPCC 2001).
Simulations with some of the models include several ensemble members started from different initial con- ditions. In this study, the entire ensembles were used only in an analysis of the twentieth century trends and variability in the 20C3M 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 twentyfirst century were not available for some IPCC AR4 models, thus different subsets of the models are used in twentyfirst century estimates discussed in Sect. 4.
The reference area used in this study is 70–90N.
Area-averaged values are calculated by using the ori- ginal 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 2.5·2.5grid using Cressmann interpolation, where the weights are re- duced exponentially with distance to the point on the 2.5grid. Only grid points 600 km or less from the 2.5 grid 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 dis- tributed, 95% of the distribution is within ±2STD of the mean.
2.2 Observationally based estimates and reanalysis With the exception of the Russian measurements made from drifting ice stations during the early 1950s 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 inten- sive field campaigns. In this study we use observa- tionally based estimates that depict spatial variability over the whole Arctic concurrently. We use five dif- ferent 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 (ERA40) and the NCAR-NCEP reanalyses are both based on a three-dimensional variational assimilation of observations (Simmons and Gibson 2000; Uppala et al. 2005; Kalnay et al. 1996), but with no direct assimilation of radiative fluxes.
Conventional data comes from a wide selection of sources starting with 1958 (the International Geo- physical Year) and 1948, respectively. Here, we focus on the data from the last part of the century (after 1980) 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 Schiffer1991): Version 2 of the Surface Radiation Budget (SRB) and the Version 1 polar radiation fluxes (POLAR ISCCP; Key et al.1999). The inputs for the Table 1 List of models that participate in this study
Modeling groups IPCC ID Atmospheric
resolution Bjerknes Centre for Climate Research, University of Bergen Norway BCCR-BCM2.0 T63 L31 Canadian Centre for Climate Modeling & Analysis, Canada CCCMA-CGCM3.1 T47 L31 Meteo-France/Centre National de Recherches Me´te´orologique, France CNRM-CM3 T63 L45
CSIRO Atmospheric Research, Australia CSIRO-MK3.0 T63 L18
NOAA/Geophysical Fluid Dynamics Laboratory, USA GFDL-CM2.0 2.0 ·2.5L24
NOAA/Geophysical Fluid Dynamics Laboratory, USA GFDL-CM2.1 2.0 ·2.5L24
NASA/Goddard Institute for Space Studies, USA GISS-AOM 3 ·4L12
NASA/Goddard Institute for Space Studies, USA GISS-ER 4 ·5L20
NASA/Goddard Institute for Space Studies, USA GISS-EH 4 ·5L20
LASG/Institute of Atmospheric Physics, China IAP-FGOALS1.0_g T42 L26
Institute for Numerical Mathematics, Russia INM-CM3.0 4 ·5L21
Institute Pierre Simon Laplace, France IPSL-CM4 2.5 ·3.75L19
Center for Climate System Research, National Institute for Environmental Studies, and Frontier Research Center for Global Change, Japan
MIROC3.2(HI) T106 L56
Center for Climate System Research, National Institute for Environmental Studies, and Frontier Research Center for Global Change, Japan
MIROC3.2(MED) T42 L20
Max Planck Institute for Meteorology, Germany ECHAM5/MPI-OM T63 L31
Meteorological Research Institute, Japan MRI-CGCM2.3.2A T42 L30
National Center for Atmospheric Research, USA CCSM3 T85 L26
National Center for Atmospheric Research, USA PCM1 T42 L26
Hadley Centre for Climate Prediction and Research/Met Office, UK UKMO-HADCM3 2.5 ·3.8L19 Hadley Centre for Climate Prediction and Research/Met Office, UK UKMO-HADGEM ~1.3 ·1.9L38
SRB data (1983–1995) 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 soundings, and cloud information. ERBE measurements provided TOA broadband clear-sky albedos. Atmospheric water vapor is taken from a 4-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 broad- band radiative transfer model (Pinker and Laszlo1992) and the longwave component using the Fu–Liou Model (Fu et al.1997).
The POLAR ISCCP radiation terms (1985–1993) were calculated by training a neural net (a special implementation of Fluxnet, cf. Key and Schweiger1998) with a small subset of the available ISCCP-D1 data.
Fluxes were generated by the Streamer radiative transfer model (Key and Schweiger 1998). 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 de- tailed description is given in Key et al. (1999).
The fifth database is the Version 1 of the Extended Advanced Very High Resolution Radiometer (AV- HRR) Polar Pathfinder dataset (APP-X), spans the period from 1985 to 1993. The Extended APP dataset is an extension of the standard clear sky products (Maslanik et al.2001; Maslanik et al.1998; Meier et al.
1997) using the Cloud and Surface Parameter Retrieval (CASPR) system (Key2001). The calculation of clou- dy 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 1998). Key (2001) and references therein provide more information on the algorithms and their validation. The APP-X data are available for the local solar times 1400 and 0400 hours.
For the longwave components the two times were averaged to obtain values representative of the full day. No attempt was made to calculate the full-day shortwave components.
3 Simulations of the twentieth century
3.1 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 tem- peratures shift the maximum blackbody intensity to lower frequencies and therefore towards the low-fre- quency rotational band of water vapor (Stamnes et al.
1999). Zhang et al.1997showed that for clear sky the downwelling LW radiation reaching the surface comes from a very shallow layer of the atmosphere (90% of the accumulated contribution comes from the lowest 500–1,000 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 chal- lenging 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. (1995) and Zhang et al. (1997).
As the LW radiation dominates the surface radia- tion 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. Figure1 shows the ensemble mean down (Fig.1a) and upward (Fig.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 contribut- ing to maxima over the Northern Nordic Seas of 250–
280 and 300–330 W/m2for the downward and upward fluxes, respectively, with a gradual reduction to 215–
225 and 230–250 W/m2 over the central Arctic. The area of maximum values is also the area of maximum across model spread, with grid point standard devia- tions (STD) of 20–24 and 25–30 W/m2 for the down and upward component respectively. The spread is reduced to around 14–16 W/m2 for both components over the central Arctic.
3.1.1 Downward longwave radiation
Figure 2a gives the annual mean surface downward LW radiation averaged over 70–90N 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 220.4 W/m2, which is close to the IPCC models’
ensemble mean of 220.2 W/m2. The IPCC across-model spread (±1STD) in annual mean downward component is 14.1 W/m2. 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 10–15 W/m2 in the models (too little energy reaching the surface). This is related to the models’
positive Barents Sea ice bias, which impacts the atmo- spheric humidity and temperature profile. Models hav- ing a large Barents Sea (15–65E and 70–85N) annual sea ice fraction emit less downward radiation in the Barents Sea area (the correlation r= –0.53 (p= 0.10) with the IAP model removed). A positive bias is seen over Greenland and the North American Arctic (5–
10 W/m2). It should be noted that the North American bias is only apparent when compared to four (ERA40, 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.
The seasonal cycle in the downward LW multimodel ensemble mean (Fig.2b) is within the observational estimates during all months. However, some models
tend to underestimate the December-April downward LW radiation as a surface energy source. A possible explanation for this is the insufficient vertical resolu- tion of AOGCMs which may prevent a correct buildup of deep wintertime surface inversions (Byrkjedal et al.
2006). It should be noted that the strength of the sea- sonal cycle differs substantially among the different observational estimates, and there is a 30 W/m2 dif- ference between the NCEP and ERA40 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. 2005). Liu et al. found that the ERA40 and AVHRR-based 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 (1998) for the Arctic pack ice using the NP-stations is also quite similar to the ERA40 estimates. These studies indicate that the seasonal cycle in the downward component may be more realistically represented by the ERA40 and the AVHRR based APP-X datasets.
However, it should be noted that the ERA40 assimi- lates the SHEBA radiosondes and the good quality of the ERA40 estimates over this site may therefore lead to overconfidence in the ability of ERA40 to capture the entire arctic region.
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Fig. 1 The IPCC multimodel 1981–2000 ensemble mean down- ward (a) and upward (b) annual LW radiation (color) and intermodel spread (lines). The spread is calculated as the
standard deviation among the different models. The downwelling radiation is positive down, and the upwelling is positive up.
Units: W/m2
3.1.2 Upward longwave radiation
Averaged over all five observational estimates, the annual mean upward LW flux averaged over 70–90N is 258.4 W/m2, which is 5.0 W/m2larger than the IPCC models’ ensemble mean (Fig.2c), for which the across model spread (±1STD) is 13.7 W/m2. This spread is
comparable to the spread in the LW downward com- ponent 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 upward LW radiation (correlation r= –
a) b)
c) d)
e) f)
0 50 100 150 200 250 300
m/W(2)
SURFACE LW
↓
40 RA E
EP NC
V1
−X PP A
CP SC I
V2 RB S
AN ME CC IP
.0 M2 BC R− CC B
.1 M3 GC
−C MA CC C
M3
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.0 K3
−M RO SI C
.0 M2
−C DL GF
.1 M2
−C DL GF
OM
−A SS GI
EH S− IS G
ER S− IS G
0G 1. LS OA FG P− IA
.0 M3
−C NM I
M4
−C SL IP
I) (H .2 C3 RO MI
D) ME 2( 3. OC IR M
OM I− MP 5/ AM CH E
2A 3. 2. CM CG I− MR
.0 M3 CS C
M1 PC
M3 DC HA O− KM U
M1 GE AD
−H MO UK 225
205223 230 219 220
207 201221 217 212 221 241
189 243
211 229214
236 214234 227
215
* * *
120 140 160 180 200 220 240 260 280 300
m/W2
SURFACE LW↓
AN J FEB AR
M PR A
AY M
UN J JULAUG
EP S CT
O OV N
EC D
ERA40 NCEP APP−X V1 POLAR ISCCP SRB V2 IPCC MEAN BCCR−BCM2.0 CCCma−CGCM3.1 CNRM−CM3 CSIRO−Mk3.0 GFDL−CM2.0 GFDL−CM2.1 GISS−AOM GISS−EH GISS−ER IAP−FGOALS1.0g INM−CM3.0 IPSL−CM4 MIROC3.2(HI) MIROC3.2(MED) ECHAM5/MPI−OM MRI−CGCM2.3.2a CCSM3.0 PCM1 UKMO−HadCM3 UKMO−HadGEM1
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M3
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2A 3. 2. CM CG I− MR
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M1 PC
M3 DC HA O− KM U
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D) ME 2( 3. OC IR M
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AN J FEB AR
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OV N
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ERA40 NCEP APP−X V1 POLAR ISCCP SRB V2 IPCC MEAN BCCR−BCM2.0 CCCma−CGCM3.1 CNRM−CM3 CSIRO−Mk3.0 GFDL−CM2.0 GFDL−CM2.1 GISS−AOM GISS−EH GISS−ER IAP−FGOALS1.0g INM−CM3.0 IPSL−CM4 MIROC3.2(HI) MIROC3.2(MED) ECHAM5/MPI−OM MRI−CGCM2.3.2a CCSM3.0 PCM1 UKMO−HadCM3 UKMO−HadGEM1
Fig. 2 Arctic (70–90N) annual (left) and monthly (right) surface downward (a,b), upward (c,d) and net (e,f) LW radiation from five observational estimates and the IPCC AR4 models. Model data taken as means over year 1980–1999 using the 20C3M
scenario.Dashed linesorshaded regionindicate the range of the observational estimates. Units: W/m2. The upward component is positive up and the downward and net is positive down. *:
missing data
0.68 (p= 0.02), 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 (20–25 W/m2), related to the positive biases in sea ice fraction in this region. The correlation between the individual model’s mean Barents Sea (15–65E and 70–85N) annual upward LW radiation and the annual mean Barents Sea ice fraction is –0.65 (p = 0.04). 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 (Fig.2d), al- though the monthly values have a spread of 10–20 W/
m2. Several of the models underestimate the winter- time energy loss by 10–40 W/m2 indicating that the models have a cold surface temperature bias.
3.1.3 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 5.5 W/m2with a tendency for models having a large annual cloud fraction to have the smallest energy loss (a non-significant correlation of 0.36). 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 Fig.2f the obser- vational estimates diverge and there is no consensus on the seasonal cycle. The two ISCCP-based estimates show the strongest LW energy loss in summer (35–
45 W/m2), while the ERA40, NCEP and the AVHRR- based APP-X datasets indicate the largest loss in early spring (40–65 W/m2). Most of the models indicate a seasonal cycle in the net LW radiation similar to the ERA40, 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 cloud cover and net LW radiation: models having a large cloud cover show the smallest surface energy loss (correlation r= 0.69). 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 (correlationr= –0.48).
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 rela- tionships. 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.
3.2 Shortwave radiation
The incoming surface solar radiation is, relatively speaking, well documented in the Arctic. Compre- hensive information on the seasonal cycle and spatial distribution can be found in a variety of studies in both the Russian (Mashunova1961; Mashunova and Cher- nigovskii 1971; Atlas Arktiki 1985; Krohl 1992; see Przybylak 2003 for an excellent review of these find- ings) and English (Fletcher 1961; Vowinckel and Orvig 1964; 1970; McKay and Morris 1985; Serreze et al.
1998) 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 (1992), Curry et al.
(1993,1996) and Zhang et al. (1996).
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 (Fig. 3a), with a minimum of 70–75 W/
m2over 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 80–85 W/m2 over the central Arctic and an across-model spread (±1STD) of 8–12 W/m2. With the exception of the lack of a more pronounced minimum in the eastern Barents Sea, the pattern closely resem- bles the data of Mashunova (1961).
The spatial pattern of the annual upward component (Fig. 3b) show central Arctic values of 45–55 W/m2 and an across-model spread that is slightly smaller than in the downward component.
3.2.1 Downward shortwave radiation
Averaged over the Arctic domain (70–90N), the mean of the four observational estimates of the annual sur- face downward SW radiation fluxes is 99.6 W/m2 (Fig.4a) and the ensemble mean for the models (90.5 W/m2) is close to three of the four observational estimates , with an across-model spread (±1STD) of 9.1 W/m2. As with the LW components, there is con- siderable 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 (2005) comparison of the downward SW fluxes over the SHEBA site which indicate the ERA40 reanalysis has a smaller bias than the AVHRR, NCEP and ISCCP- based estimates (the NCEP bias averaged over a year is more than 30 W/m2). The NCEP bias was also noted by Serreze and Hurst (2000) 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 ERA40 reanalysis, the models show an underestima- tion of downward SW radiation of 5–10 W/m2over the central Arctic, while for the same area there is an overestimation of 5–10 W/m2 when compared to the ISCCP-based estimates.
The spread in the models’ summertime maximum (June) is over 100 W/m2(Fig. 4b). This spread does not seem to be related to the models different cloud frac-
tion. Compared to three of the four observational estimates there is a tendency of the models to overes- timate the incoming SW radiation in spring (March–
May) and underestimate the radiation in summer and autumn (June–September). The spring overestimation in the ensemble mean has a peak in April and May (5–
30 W/m2 compared to the different observational estimates) while the summer/autumn underestimation is greatest in July (10–35 W/m2). The models’ spring- time 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 –0.41, p= 0.07). The relationship between cloud cover and summertime radiation is less clear (the correlation of –0.34 which is reduced to –0.03 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 possi- ble 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 ERA40, and ISCCP-based estimates given here is 40–
50 W/m2 smaller than the estimates for the pack ice obtained using NP-station data by Lindsay (1998) and the Artic Ocean averages of Ebert and Curry (1993).
Around half of the bias can be explained by the larger area chosen here (including the cloudier Greenland
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Fig. 3 The IPCC multimodel ensemble mean downward (a) and upward (b) annual SW radiation (color) and intermodel spread (lines). The spread is calculated as the standard deviation among
the different models. The downwelling radiation is positive down, and the upwelling is positive up. Units: W/m2
