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Abstract
The near-surface atmosphere of the polar region is characterized by temperature inversions throughout most of the year. However, radiosonde data are sparse, and numerical weather prediction models have relatively poor vertical resolution for boundary layer studies. A method is developed for detecting and estimating the characteristics of clear-sky, low-level temperature inversions using the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. The method is based on an empirical relationship between the inversion strength, defined as the temperature difference across the inversion, or depth, defined as the altitude difference, and the difference between brightness temperatures in the 7.2-µm water vapor and 11-µm infrared window bands. Results indicate that inversion strength can be estimated unbiasedly with a root-mean-square error (rmse) of 2°–3°C and an R 2 of 0.80–0.97. Inversion depth can be estimated with an rmse of 130–250 m and an R 2 of 0.62–0.82. With MODIS, temperature inversions can be observed at a spatial resolution as high as 1 km2 and a temporal sampling of up to 14 times per day, providing an opportunity for detailed studies of the spatial distribution and temporal evolution of the high-latitude boundary layer.
Abstract
The near-surface atmosphere of the polar region is characterized by temperature inversions throughout most of the year. However, radiosonde data are sparse, and numerical weather prediction models have relatively poor vertical resolution for boundary layer studies. A method is developed for detecting and estimating the characteristics of clear-sky, low-level temperature inversions using the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. The method is based on an empirical relationship between the inversion strength, defined as the temperature difference across the inversion, or depth, defined as the altitude difference, and the difference between brightness temperatures in the 7.2-µm water vapor and 11-µm infrared window bands. Results indicate that inversion strength can be estimated unbiasedly with a root-mean-square error (rmse) of 2°–3°C and an R 2 of 0.80–0.97. Inversion depth can be estimated with an rmse of 130–250 m and an R 2 of 0.62–0.82. With MODIS, temperature inversions can be observed at a spatial resolution as high as 1 km2 and a temporal sampling of up to 14 times per day, providing an opportunity for detailed studies of the spatial distribution and temporal evolution of the high-latitude boundary layer.
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
Recent studies have shown that the Arctic climate has changed markedly over the past 20 years. Two major reanalysis products that can be used for studying recent changes unfortunately exhibit relatively large errors in the wind field over the Arctic where there are few radiosonde data available for assimilation. At least 10 numerical weather prediction centers worldwide have demonstrated that satellite-derived polar winds have a positive impact on global weather forecasts. The impact on reanalyses should be similar. Therefore, a polar wind dataset spanning more than 20 years was generated using Advanced Very High Resolution Radiometer (AVHRR) data. Comparisons with winds from radiosondes show biases in the AVHRR-derived winds of 0.1–0.8 m s−1, depending on the level. In addition, AVHRR has lower root-mean-square speed errors and speed biases than the 40-yr ECMWF reanalysis product (ERA-40) when compared with rawinsondes not assimilated into the reanalysis. Therefore, it is recommended that the historical AVHRR polar winds be assimilated into future versions of the reanalysis products. The authors also explore possible kinematic reasons for the disparities between ERA-40 and AVHRR wind fields. AVHRR and ERA-40 speed and direction differences for various kinematic flow features are investigated. Results show that, on average, AVHRR winds are faster in jet streams and ridges but are slower in troughs and jet exit regions. The results from this study could lead to a better dynamical understanding of why the reanalysis product produces a less-accurate wind vector field over regions that are void of radiosonde data.
Abstract
Recent studies have shown that the Arctic climate has changed markedly over the past 20 years. Two major reanalysis products that can be used for studying recent changes unfortunately exhibit relatively large errors in the wind field over the Arctic where there are few radiosonde data available for assimilation. At least 10 numerical weather prediction centers worldwide have demonstrated that satellite-derived polar winds have a positive impact on global weather forecasts. The impact on reanalyses should be similar. Therefore, a polar wind dataset spanning more than 20 years was generated using Advanced Very High Resolution Radiometer (AVHRR) data. Comparisons with winds from radiosondes show biases in the AVHRR-derived winds of 0.1–0.8 m s−1, depending on the level. In addition, AVHRR has lower root-mean-square speed errors and speed biases than the 40-yr ECMWF reanalysis product (ERA-40) when compared with rawinsondes not assimilated into the reanalysis. Therefore, it is recommended that the historical AVHRR polar winds be assimilated into future versions of the reanalysis products. The authors also explore possible kinematic reasons for the disparities between ERA-40 and AVHRR wind fields. AVHRR and ERA-40 speed and direction differences for various kinematic flow features are investigated. Results show that, on average, AVHRR winds are faster in jet streams and ridges but are slower in troughs and jet exit regions. The results from this study could lead to a better dynamical understanding of why the reanalysis product produces a less-accurate wind vector field over regions that are void of radiosonde data.
Abstract
An accurate determination of cloud particle phase is required for the retrieval of other cloud properties from satellite and for radiative flux calculations in climate models. The physical principles underlying phase determination using the advanced very high resolution radiometer (AVHRR) satellite sensor are described for daytime and nighttime, cold cloud and warm cloud conditions. It is demonstrated that the spectral properties of cloud particles provide necessary, but not sufficient, information for phase determination, because the relationship between the cloud and surface temperatures is also important. Algorithms based on these principles are presented and tested. Validation with lidar and aircraft data from two Arctic field experiments shows the procedures to be accurate in identifying the phase of homogeneous water and ice clouds, though optically thin, mixed-phase, and multilayer clouds are problematic.
Abstract
An accurate determination of cloud particle phase is required for the retrieval of other cloud properties from satellite and for radiative flux calculations in climate models. The physical principles underlying phase determination using the advanced very high resolution radiometer (AVHRR) satellite sensor are described for daytime and nighttime, cold cloud and warm cloud conditions. It is demonstrated that the spectral properties of cloud particles provide necessary, but not sufficient, information for phase determination, because the relationship between the cloud and surface temperatures is also important. Algorithms based on these principles are presented and tested. Validation with lidar and aircraft data from two Arctic field experiments shows the procedures to be accurate in identifying the phase of homogeneous water and ice clouds, though optically thin, mixed-phase, and multilayer clouds are problematic.
Abstract
Surface cloud radiative forcing from the newly extended Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x) dataset and surface cloud radiative forcing calculated using cloud and surface properties from the International Satellite Cloud Climatology Project (ISCCP) D-series product were used in this 9-yr (1985–93) study. On the monthly timescale, clouds were found to have a warming effect on the surface of the Antarctic continent every month of the year in both datasets. Over the ocean poleward of 58.75°S, clouds were found to have a warming effect on the surface from March through October in the ISCCP-derived dataset and from April through September in the APP-x dataset. Net surface fluxes from both datasets were validated against net surface fluxes calculated from measurements of upwelling and downwelling shortwave and longwave radiation at the Neumayer and Amundsen–Scott South Pole Stations in the Antarctic. The net all-wave surface flux from the ISCCP-derived dataset was found to be within 0.4–50 W m−2 of the net all-wave flux at the two stations on the monthly timescale. The APP-x net all-wave surface flux was found to be within 0.9–24 W m−2. Model sensitivity studies were conducted to gain insight into how the surface radiation budget in a cloudy atmosphere will change if certain cloud and surface properties were to change in association with regional and/or global climate change. The results indicate that the net cloud forcing will be most sensitive to changes in cloud amount, surface reflectance, cloud optical depth, and cloud-top pressure.
Abstract
Surface cloud radiative forcing from the newly extended Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x) dataset and surface cloud radiative forcing calculated using cloud and surface properties from the International Satellite Cloud Climatology Project (ISCCP) D-series product were used in this 9-yr (1985–93) study. On the monthly timescale, clouds were found to have a warming effect on the surface of the Antarctic continent every month of the year in both datasets. Over the ocean poleward of 58.75°S, clouds were found to have a warming effect on the surface from March through October in the ISCCP-derived dataset and from April through September in the APP-x dataset. Net surface fluxes from both datasets were validated against net surface fluxes calculated from measurements of upwelling and downwelling shortwave and longwave radiation at the Neumayer and Amundsen–Scott South Pole Stations in the Antarctic. The net all-wave surface flux from the ISCCP-derived dataset was found to be within 0.4–50 W m−2 of the net all-wave flux at the two stations on the monthly timescale. The APP-x net all-wave surface flux was found to be within 0.9–24 W m−2. Model sensitivity studies were conducted to gain insight into how the surface radiation budget in a cloudy atmosphere will change if certain cloud and surface properties were to change in association with regional and/or global climate change. The results indicate that the net cloud forcing will be most sensitive to changes in cloud amount, surface reflectance, cloud optical depth, and cloud-top pressure.
Abstract
With broad spectral coverage and high spatial and temporal resolutions, satellite sensors can provide the data needed for the analysis of spatial and temporal variations of climate parameters in data-sparse regions such as the Arctic and Antarctic. The newly available Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP) dataset was used to retrieve cloud fraction, cloud optical depth, cloud particle phase and size, cloud-top pressure and temperature, surface skin temperature, surface broadband albedo, radiative fluxes, and cloud forcing over the Arctic Ocean and surrounding landmasses for the 18-yr period from 1982 to 1999. In the Arctic, Greenland is the coldest region with the highest surface albedo, while northeastern Russia has the highest surface temperature in summer. Arctic annual mean cloud coverage is about 70%, with the largest cloudiness occurring in September and the lowest cloudiness occurring in April. On annual average, Arctic cloud visible optical depth is about 5–6. Arctic precipitable water is near 0.2 cm in winter and 1.5 cm in summer. The largest downwelling shortwave radiative flux at the surface occurs in June; the largest upwelling shortwave radiative flux occurs in May. The largest downwelling and upwelling longwave radiative fluxes as well as the net all-wave radiative flux occur in July, with the largest loss of longwave radiation from the surface in April.
Abstract
With broad spectral coverage and high spatial and temporal resolutions, satellite sensors can provide the data needed for the analysis of spatial and temporal variations of climate parameters in data-sparse regions such as the Arctic and Antarctic. The newly available Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP) dataset was used to retrieve cloud fraction, cloud optical depth, cloud particle phase and size, cloud-top pressure and temperature, surface skin temperature, surface broadband albedo, radiative fluxes, and cloud forcing over the Arctic Ocean and surrounding landmasses for the 18-yr period from 1982 to 1999. In the Arctic, Greenland is the coldest region with the highest surface albedo, while northeastern Russia has the highest surface temperature in summer. Arctic annual mean cloud coverage is about 70%, with the largest cloudiness occurring in September and the lowest cloudiness occurring in April. On annual average, Arctic cloud visible optical depth is about 5–6. Arctic precipitable water is near 0.2 cm in winter and 1.5 cm in summer. The largest downwelling shortwave radiative flux at the surface occurs in June; the largest upwelling shortwave radiative flux occurs in May. The largest downwelling and upwelling longwave radiative fluxes as well as the net all-wave radiative flux occur in July, with the largest loss of longwave radiation from the surface in April.
Abstract
Over the past 20 yr, some Arctic surface and cloud properties have changed significantly. Results of an analysis of satellite data show that the Arctic has warmed and become cloudier in spring and summer but has cooled and become less cloudy in winter. The annual rate of surface temperature change is 0.057°C for the Arctic region north of 60°N. The surface broadband albedo has decreased significantly in autumn, especially over the Arctic Ocean, indicating a later freeze-up and snowfall. The surface albedo has decreased at an annual rate of −0.15% (absolute). Cloud fraction has decreased at an annual rate of −0.6% (absolute) in winter and increased at annual rates of 0.32% and 0.16% in spring and summer, respectively. On an annual time scale, there is no trend in cloud fraction. During spring and summer, changes in sea ice albedo that result from surface warming tend to modulate the radiative effect of increasing cloud cover. On an annual time scale, the all-wave cloud forcing at the surface has decreased at an annual rate of –0.335 W m−2, indicating an increased cooling by clouds. There are large correlations between surface temperature anomalies and climate indices such as the Arctic Oscillation (AO) index for some areas, implying linkages between global climate change and Arctic climate change.
Abstract
Over the past 20 yr, some Arctic surface and cloud properties have changed significantly. Results of an analysis of satellite data show that the Arctic has warmed and become cloudier in spring and summer but has cooled and become less cloudy in winter. The annual rate of surface temperature change is 0.057°C for the Arctic region north of 60°N. The surface broadband albedo has decreased significantly in autumn, especially over the Arctic Ocean, indicating a later freeze-up and snowfall. The surface albedo has decreased at an annual rate of −0.15% (absolute). Cloud fraction has decreased at an annual rate of −0.6% (absolute) in winter and increased at annual rates of 0.32% and 0.16% in spring and summer, respectively. On an annual time scale, there is no trend in cloud fraction. During spring and summer, changes in sea ice albedo that result from surface warming tend to modulate the radiative effect of increasing cloud cover. On an annual time scale, the all-wave cloud forcing at the surface has decreased at an annual rate of –0.335 W m−2, indicating an increased cooling by clouds. There are large correlations between surface temperature anomalies and climate indices such as the Arctic Oscillation (AO) index for some areas, implying linkages between global climate change and Arctic climate change.
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
Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.
Abstract
Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.
Abstract
A method is presented to assess the influence of changes in Arctic cloud cover on the surface temperature trend, allowing for a more robust diagnosis of causes for surface warming or cooling. Seasonal trends in satellite-derived Arctic surface temperature under clear-, cloudy-, and all-sky conditions are examined for the period 1982–2004. The satellite-derived trends are in good agreement with trends in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product and surface-based weather station measurements in the Arctic. Surface temperature trends under clear and cloudy conditions have patterns similar to the all-sky trends, though the magnitude of the trends under cloudy conditions is smaller than those under clear-sky conditions, illustrating the negative feedback of clouds on the surface temperature trends. The all-sky surface temperature trend is divided into two parts: the first part is a linear combination of the surface temperature trends under clear and cloudy conditions; the second part is caused by changes in cloud cover as a function of the clear–cloudy surface temperature difference. The relative importance of these two components is different in the four seasons, with the first part more important in spring, summer, and autumn, but with both parts being equally important in winter. The contribution of biases in satellite retrievals is also evaluated.
Abstract
A method is presented to assess the influence of changes in Arctic cloud cover on the surface temperature trend, allowing for a more robust diagnosis of causes for surface warming or cooling. Seasonal trends in satellite-derived Arctic surface temperature under clear-, cloudy-, and all-sky conditions are examined for the period 1982–2004. The satellite-derived trends are in good agreement with trends in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product and surface-based weather station measurements in the Arctic. Surface temperature trends under clear and cloudy conditions have patterns similar to the all-sky trends, though the magnitude of the trends under cloudy conditions is smaller than those under clear-sky conditions, illustrating the negative feedback of clouds on the surface temperature trends. The all-sky surface temperature trend is divided into two parts: the first part is a linear combination of the surface temperature trends under clear and cloudy conditions; the second part is caused by changes in cloud cover as a function of the clear–cloudy surface temperature difference. The relative importance of these two components is different in the four seasons, with the first part more important in spring, summer, and autumn, but with both parts being equally important in winter. The contribution of biases in satellite retrievals is also evaluated.
