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- Author or Editor: Axel J. Schweiger x
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Abstract
Radiative fluxes and cloud forcings for the ocean areas of the Arctic are computed from the monthly cloud product of the International Satellite Cloud Climatology Project (ISCCP) for 1983–90. Spatially averaged short-wave fluxes compare well with climatological values, while downwelling longwave fluxes are significantly lower. This is probably due to the fact that the ISCCP cloud amounts are underestimates. Top-of-the-atmosphere radiative fluxes are in excellent agreement with measurements from the Earth Radiation Budget Experiment (ERBE). Computed cloud forcings indicate that clouds have a warming effect at the surface and at the top of the atmosphere during winter and a cooling effect during summer. The net radiative effect of clouds is larger at the surface during winter but greater at the top of the atmosphere during summer. Overall the net radiative effect of clouds at the top of the atmosphere is one of cooling. This is in contrast to a previous result from ERBE data showing that arctic cloud forcings have a net warming effect. Sensitivities to errors in input parameters are generally greater during winter with cloud amount being the most important parameter. During summer the surface radiation balance is most sensitive to errors in the measurements of surface reflectance.
The results are encouraging, but the estimated error of 20 W m−2 in surface net radiative fluxes is too large, given that estimates of the net radiative warming effect due to a doubling of CO2 are on the order of 4 W m−2. Because it is difficult to determine the accuracy of results with existing in situ observations, it is recommended that the development of improved algorithms for the retrieval of surface radiative properties be accompanied by the simultaneous assembly of validation datasets.
Abstract
Radiative fluxes and cloud forcings for the ocean areas of the Arctic are computed from the monthly cloud product of the International Satellite Cloud Climatology Project (ISCCP) for 1983–90. Spatially averaged short-wave fluxes compare well with climatological values, while downwelling longwave fluxes are significantly lower. This is probably due to the fact that the ISCCP cloud amounts are underestimates. Top-of-the-atmosphere radiative fluxes are in excellent agreement with measurements from the Earth Radiation Budget Experiment (ERBE). Computed cloud forcings indicate that clouds have a warming effect at the surface and at the top of the atmosphere during winter and a cooling effect during summer. The net radiative effect of clouds is larger at the surface during winter but greater at the top of the atmosphere during summer. Overall the net radiative effect of clouds at the top of the atmosphere is one of cooling. This is in contrast to a previous result from ERBE data showing that arctic cloud forcings have a net warming effect. Sensitivities to errors in input parameters are generally greater during winter with cloud amount being the most important parameter. During summer the surface radiation balance is most sensitive to errors in the measurements of surface reflectance.
The results are encouraging, but the estimated error of 20 W m−2 in surface net radiative fluxes is too large, given that estimates of the net radiative warming effect due to a doubling of CO2 are on the order of 4 W m−2. Because it is difficult to determine the accuracy of results with existing in situ observations, it is recommended that the development of improved algorithms for the retrieval of surface radiative properties be accompanied by the simultaneous assembly of validation datasets.
Abstract
One surface-based and two satellite arctic cloud climatologies are compared in terms of the annual cycle and spatial patterns of total monthly cloud amounts. Additionally, amounts and spatial patterns of low, middle, and high cloud type are compared. The surface-based dataset is for the years 1951–81, while the satellite-based data are for 1979–85 and 1983–86. The satellite cloud amounts are generally 5%−35% less than the surface observations over the entire Arctic. However, regional differences may be as high as 45%. During July the surface-based cloud amounts for the central Arctic are about 40% greater than the satellite-based, but only 10% greater in the Norwegian Sea area. Surprisingly, (ISCCP) cloud climatology and surface observations agree better during winter than during summer. Possible reasons for these differences are discussed, though it is not possible to determine which cloud climatology is the “correct” one.
Abstract
One surface-based and two satellite arctic cloud climatologies are compared in terms of the annual cycle and spatial patterns of total monthly cloud amounts. Additionally, amounts and spatial patterns of low, middle, and high cloud type are compared. The surface-based dataset is for the years 1951–81, while the satellite-based data are for 1979–85 and 1983–86. The satellite cloud amounts are generally 5%−35% less than the surface observations over the entire Arctic. However, regional differences may be as high as 45%. During July the surface-based cloud amounts for the central Arctic are about 40% greater than the satellite-based, but only 10% greater in the Norwegian Sea area. Surprisingly, (ISCCP) cloud climatology and surface observations agree better during winter than during summer. Possible reasons for these differences are discussed, though it is not possible to determine which cloud climatology is the “correct” one.
Abstract
PIOMAS-20C, an Arctic sea ice reconstruction for 1901–2010, is produced by forcing the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) with ERA-20C atmospheric data. ERA-20C performance over Arctic sea ice is assessed by comparisons with measurements and data from other reanalyses. ERA-20C performs similarly with respect to the annual cycle of downwelling radiation, air temperature, and wind speed compared to reanalyses with more extensive data assimilation such as ERA-Interim and MERRA. PIOMAS-20C sea ice thickness and volume are then compared with in situ and aircraft remote sensing observations for the period of ~1950–2010. Error statistics are similar to those for PIOMAS. We compare the magnitude and patterns of sea ice variability between the first half of the twentieth century (1901–40) and the more recent period (1980–2010), both marked by sea ice decline in the Arctic. The first period contains the so-called early-twentieth-century warming (ETCW; ~1920–40) during which the Atlantic sector saw a significant decline in sea ice volume, but the Pacific sector did not. The sea ice decline over the 1979–2010 period is pan-Arctic and 6 times larger than the net decline during the 1901–40 period. Sea ice volume trends reconstructed solely from surface temperature anomalies are smaller than PIOMAS-20C, suggesting that mechanisms other than warming, such as changes in ice motion and deformation, played a significant role in determining sea ice volume trends during both periods.
Abstract
PIOMAS-20C, an Arctic sea ice reconstruction for 1901–2010, is produced by forcing the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) with ERA-20C atmospheric data. ERA-20C performance over Arctic sea ice is assessed by comparisons with measurements and data from other reanalyses. ERA-20C performs similarly with respect to the annual cycle of downwelling radiation, air temperature, and wind speed compared to reanalyses with more extensive data assimilation such as ERA-Interim and MERRA. PIOMAS-20C sea ice thickness and volume are then compared with in situ and aircraft remote sensing observations for the period of ~1950–2010. Error statistics are similar to those for PIOMAS. We compare the magnitude and patterns of sea ice variability between the first half of the twentieth century (1901–40) and the more recent period (1980–2010), both marked by sea ice decline in the Arctic. The first period contains the so-called early-twentieth-century warming (ETCW; ~1920–40) during which the Atlantic sector saw a significant decline in sea ice volume, but the Pacific sector did not. The sea ice decline over the 1979–2010 period is pan-Arctic and 6 times larger than the net decline during the 1901–40 period. Sea ice volume trends reconstructed solely from surface temperature anomalies are smaller than PIOMAS-20C, suggesting that mechanisms other than warming, such as changes in ice motion and deformation, played a significant role in determining sea ice volume trends during both periods.
Abstract
The sea ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS), that is, how much the surface albedo in sea ice regions changes as the planet warms, and the radiative sensitivity (RS), that is, how much the top-of-atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of the atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite-based RS is within the model range of RS that differs by a factor of 2 across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS, which, in conjunction with the satellite-based RS, yields an SIAF of 0.16 ± 0.04 W m−2 K−1. This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model-specific RS and IS and find a model mean feedback parameter of 0.37 W m−2 K−1, which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.
Abstract
The sea ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS), that is, how much the surface albedo in sea ice regions changes as the planet warms, and the radiative sensitivity (RS), that is, how much the top-of-atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of the atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite-based RS is within the model range of RS that differs by a factor of 2 across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS, which, in conjunction with the satellite-based RS, yields an SIAF of 0.16 ± 0.04 W m−2 K−1. This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model-specific RS and IS and find a model mean feedback parameter of 0.37 W m−2 K−1, which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.
Abstract
The connection between sea ice variability and cloud cover over the Arctic seas during autumn is investigated by analyzing the 40-yr ECMWF Re-Analysis (ERA-40) products and the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) Polar Pathfinder satellite datasets. It is found that cloud cover variability near the sea ice margins is strongly linked to sea ice variability. Sea ice retreat is linked to a decrease in low-level cloud amount and a simultaneous increase in midlevel clouds. This pattern is apparent in both data sources. Changes in cloud cover can be explained by changes in the atmospheric temperature structure and an increase in near-surface temperatures resulting from the removal of sea ice. The subsequent decrease in static stability and deepening of the atmospheric boundary layer apparently contribute to the rise in cloud level. The radiative effect of this change is relatively small, as the direct radiative effects of cloud cover changes are compensated for by changes in the temperature and humidity profiles associated with varying ice conditions.
Abstract
The connection between sea ice variability and cloud cover over the Arctic seas during autumn is investigated by analyzing the 40-yr ECMWF Re-Analysis (ERA-40) products and the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) Polar Pathfinder satellite datasets. It is found that cloud cover variability near the sea ice margins is strongly linked to sea ice variability. Sea ice retreat is linked to a decrease in low-level cloud amount and a simultaneous increase in midlevel clouds. This pattern is apparent in both data sources. Changes in cloud cover can be explained by changes in the atmospheric temperature structure and an increase in near-surface temperatures resulting from the removal of sea ice. The subsequent decrease in static stability and deepening of the atmospheric boundary layer apparently contribute to the rise in cloud level. The radiative effect of this change is relatively small, as the direct radiative effects of cloud cover changes are compensated for by changes in the temperature and humidity profiles associated with varying ice conditions.
