UNCERTAINTIES IN OBSERVATIONALLY-BASED ESTIMATES OF THE ARCTIC SURFACE RADIATION BUDGET ASGEIR SORTEBERG Bjerknes Centre for Climate Research, University of Bergen, Allegaten 55, 5007 Bergen, Norway
[email protected]
1. Introduction 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. Complex interactions between the atmosphere, ocean and cryosphere give rise to a variety of climate feedbacks, with the ice/snow albedo-temperature feedback (Budyko, 1969) 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, 1969). The strength of the albedo-temperature 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, 1974, Hartmann, 1994; Vavrus, 2003; Björk and Söderkvist, 2001; 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. In the present paper, we evaluate five different observationally based estimates of the energy fluxes. The motivation for this evaluation is that these datasets are used to force, evaluate or tune numerical simulations of the Arctic. An evaluation of the IPCC AR4 coupled models against the observationally based estimates are given in Sorteberg et al. (2007).
2. Observationally based estimates 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 intensive field campaigns. In this study we use
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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 (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). Conventional data comes from a wide selection of sources starting with 1958 (the International Geophysical 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 Schiffer, 1999): 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 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 broadband radiative transfer model (Pinker and Laszlo, 1992) 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 Schweiger, 1998) 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 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 1985 to 1993. The Extended APP 2
dataset is an extension of the standard clear sky products (Maslanik et al., 2000; Maslanik et al., 1998; Meier et al., 1997) using the Cloud and Surface Parameter Retrieval (CASPR) system (Key, 2001). 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, 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. 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. 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 (LW ) 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., 1996). 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 (90% of the accumulated contribution comes from the lowest 500-1000 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. (1995) and Zhang et al. (1997). As the LW radiation dominates the surface radiation balance during much of the year, the quality of the LW radiation estimates is crucial for an accurate representation of the Arctic mean climate and its seasonal cycle.
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Figure 1a gives the monthly mean surface downward LW radiation (LW ) averaged over 70-90°N in the different observationally based estimates. With the exception of the NCEP data, the observational estimates agree fairly well (Table 1). Annual means are ranging from 203 (NCEP) to 232W/m2 (POLAR ISCCP). It should be noted that the strength of the seasonal cycle differs substantially among the different observational estimates, and there is a over 30W/m2 difference 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 assimilates 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. Averaged over all five observational estimates, the annual mean upward LW flux (LW ) averaged over 70-90°N (Table 1) is 258.9 W/m2, ranging from 247.1 (SRB V2) to 268.5 W/m2 (POLAR ISCCP). All observational estimates show a fairly similar seasonal cycle of upward LW radiation (Figure 1c), although the monthly values have a spread of 10-20 W/m2. The Net longwave radiation (Figure 1e) 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 1e 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 (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). Correlations between the monthly anomalies of the different estimates (Table 2) indicate that the reanalysis are fairly well correlated, but show some discrepancies toward the satellite based estimates. The POLAR ISCCP estimate is deviating the most from the others.
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Figure 1: Monthly 70-90ºN mean downward (a and b), upward (c and d) longwave radiation and net (e and f) longwave radiation (LW - LW ) from different observationally based estimates (a,c and e) and 5
differences between ERA40 and the other estimates (b,d and f). Means taken over: ERA,NCEP, AVHRR APP-X: 1982-1993, SRB: 1985-1993, POLAR ISCCP: 1986-1993.
ESTIMATE ERA40 NCEP SRB V2 POLAR ISCCP AVHRR APP-X
MEAN LW MEAN LW 224.1 265.9 203.5 254.8 219.1 247.1 268.5 232.0 223.3 258.4
Table 1: Annual 70-90ºN mean downward (LW ) and upward (LW ) longwave radiation from different observationally based estimates. Means taken over: ERA,NCEP, AVHRR APP-X: 1982-1993, SRB: 1985-1993, POLAR ISCCP: 1986-1993.
CORRELATION LW ESTIMATE ERA40 NCEP SRB V2 POLAR ISCCP AVHRR APP-X
ERA40
NCEP
1 0.91 0.78 0.41 0.65
1 0.80 0.37 0.53
SRB V2 1 0.57 0.46
POLAR ISCCP
1 0.30
AVHRR APP-X
1
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ERA40 NCEP SRB V2 POLAR ISCCP AVHRR APP-X
ERA40
NCEP
1 0.92 0.80 0.30 0.79
1 0.81 0.21 0.69
SRB V2 1 0.71 0.32
POLAR ISCCP
1 0.28
AVHRR APP-X
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Table 2: Correlation between monthly mean 70-90ºN downward (LW ) and upward (LW ) longwave radiation (LW and LW ) from different observationally based estimates. Correlations taken over: ERA,NCEP, AVHRR APP-X: 1982-1993, SRB: 1985-1993, POLAR ISCCP: 1986-1993.
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4. 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 1961; Mashunova and Chernigovskii, 1971; Atlas Arktiki, 1985; and Krohl 1992; see Przybylak, 2003 for an excellent review of these findings) and English (Fletcher, 1961; Vowinckel and Orvig, 1964; 1970; McKay and Morris, 1985; 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 (1992); Curry et al. (1993); Curry et al. (1996) and Zhang et al. (1996). The outgoing surface solar radiation is largely determined by the surface albedo and the amount of downward radiation. Averaged over the Arctic domain (70-90°N), the four observational estimates (Table 3) of the annual surface downward SW radiation fluxes (SW ) ranges from 86.9 (SRB V2) to 128.6 W/m2 (NCEP). Thus, 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 (Figure 2a). 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 averaged over a year). The NCEP bias was also noted by Serreze and Hurst (2000) and linked to a large negative bias in the cloud cover. 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 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 1980s and early 1990s, while the NP-station estimates are based on data from the late 1950s to the beginning of the 1990s. The reported increase in spring and summer cloudiness over the last decades (Wang and Key,
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2003) may therefore contribute to some of the discrepancy and the time evolution of both the ERA40 and NCEP reanalyses shows large trends in the downward SW component. The shortwave upward component reflects the downward biases (Figure 2c, Table 3). Thus, the estimates range from 78.5 (NCEP) to 42.9 W/m2 (POLAR ISCCP). The biases in up and downward SW counteracts eachoter giving smaller discrepancies between the net shortwave estimates (Figure 2e). However, the estimates do not agree on the month of maximum net shortwave radiation which is in June in the NCEP reanalysis and in July in ERA40 and the satellite estimates. Correlations between the monthly anomalies of the different estimates (Table 4) indicate that despite the strong NCEP biases, the reanalysis are fairly well correlated on monthly timescales, but show some discrepancies toward the satellite based estimates. Especially the POLAR ISCCP estimates.
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Figure 2: Monthly 70-90ºN mean downward (a and b), upward (c and d) shortwave radiation and net (e and f) shortwave radiation (SW - SW ) from different observationally based estimates (a,c and e) and
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differences between ERA40 and the other estimates (b,d and f). Means taken over: ERA,NCEP, SRB: 1985-1993, POLAR ISCCP: 1986-1993. ESTIMATE ERA40 NCEP SRB V2 POLAR ISCCP
MEAN SW 90.9 128.6 86.9 93.6
MEAN SW 46.9 78.5 43.6 42.9
Table 3: Annual 70-90ºN mean downward (SW ) and upward (SW ) shortwave radiation from different observationally based estimates. Means taken over: ERA,NCEP, SRB: 1985-1993, POLAR ISCCP: 19861993.
ESTIMATE
CORRELATION SW ERA40 NCEP SRB V2
ERA40 NCEP SRB V2 POLAR ISCCP
1 0.82 0.75 0.52
1 0.64 0.41
POLAR ISCCP
1 0.90
1
CORRELATION SW
ERA40 NCEP SRB V2 POLAR ISCCP
ERA40
NCEP
SRB V2
1 0.84 0.87 0.35
1 0.76 0.33
1 0.56
POLAR ISCCP
1
Table 4: Correlation between monthly mean 70-90ºN downward (SW ) and upward (SW ) shortwave radiation from different observationally based estimates. Correlations taken over: ERA,NCEP, SRB: 1985-1993, POLAR ISCCP: 1986-1993.
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6. Summary and conclusions Due to the sparse observational network in the Arctic, area averaged estimates of the different surface radiation terms must rely heavily on data assimilation (reanalyses) and remote sensing products. In this study, we have used five different observationally based estimates, three of them based on satellite estimates and radiative transfer models 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 are too short to make proper climatological means. Comparison of the different observationally-based estimates highlights the following points: • Averages of Arctic (70-90ºN) downward LW radiation range from 203 to 232 W/m2. The spread in monthly values is typically 20-30 W/m2 and the amplitude of the seasonal cycle is not well constrained. • Upward LW radiation estimates differ by about the same amount (annually from 247 to 269 W/m2), but there is a closer agreement on the strength of the seasonal cycle. • Longwave radiation as an Arctic energy sink ranges from 28 to 42 W/m2 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. • Correlation in the monthly LW anomalies among the different estimates are typically very high between the reanalysis (approximately 0.9) and much weaker between the other estimates. • The NCEP reanalysis has a strong bias in downward and upward shortwave radiation relative to the other estimates • Annual downward SW radiation estimates range from 87 to 128 W/m2, and the monthly spread is typically 20-25 W/m2 during the months April to August. • The differences in upward SW radiation estimates are somewhat smaller (ranging from 43 to 78 W/m2 annually). • Annual mean shortwave radiation as a net surface energy source ranges from 43 to 51 W/m2, with the largest spread during summertime (10-15 W/m2). • Correlation in the monthly SW anomalies among the different reanalysis estimates are
strong
(approximately 0.8), but weaker between the reanalysis and the satelite estimates.
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