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modulated by its capacity to store energy. Given that oceans are 10 times more efficient at storing heat than other components of the climate system (e.g., land, ice, atmosphere; Levitus et al. 2001 ), the global net radiation at the TOA should be in phase with and of similar magnitude as the global ocean heat storage. Wong et al. (2006) showed that this is indeed the case by comparing TOA net flux anomalies from the Earth Radiation Budget Experiment (ERBE) and ocean heat content anomalies from in
modulated by its capacity to store energy. Given that oceans are 10 times more efficient at storing heat than other components of the climate system (e.g., land, ice, atmosphere; Levitus et al. 2001 ), the global net radiation at the TOA should be in phase with and of similar magnitude as the global ocean heat storage. Wong et al. (2006) showed that this is indeed the case by comparing TOA net flux anomalies from the Earth Radiation Budget Experiment (ERBE) and ocean heat content anomalies from in
the mean value. Similarly, the annual mean TOA longwave irradiance is 239 W m −2 and the difference between the maximum and minimum values is 0.3 W m −2 , which is only 0.1% of the mean value. Somewhat larger variability of TOA reflected shortwave, longwave, and net irradiance is also reported by Duvel et al. (2001) , who used Earth Radiation Budget Experiment (ERBE), Scanner for Radiation Budget (ScaRaB)-Meteor Satellite, and ScaRaB data, but it is still small compared with the mean values. In
the mean value. Similarly, the annual mean TOA longwave irradiance is 239 W m −2 and the difference between the maximum and minimum values is 0.3 W m −2 , which is only 0.1% of the mean value. Somewhat larger variability of TOA reflected shortwave, longwave, and net irradiance is also reported by Duvel et al. (2001) , who used Earth Radiation Budget Experiment (ERBE), Scanner for Radiation Budget (ScaRaB)-Meteor Satellite, and ScaRaB data, but it is still small compared with the mean values. In
1. Introduction This paper describes the methodology used to determine the unfiltered longwave radiance from the filtered radiances of the Geostationary Earth Radiation Budget (GERB)-2 instrument on the Meteosat Second Generation ( MSG )- 1 . The unfiltering of the shortwave channel is described in Clerbaux et al. (2008 , hereafter Part I ), which is presumed to be known by the reader. As previous broadband instruments like the Clouds and the Earth’s Radiant Energy System (CERES; Wielicki
1. Introduction This paper describes the methodology used to determine the unfiltered longwave radiance from the filtered radiances of the Geostationary Earth Radiation Budget (GERB)-2 instrument on the Meteosat Second Generation ( MSG )- 1 . The unfiltering of the shortwave channel is described in Clerbaux et al. (2008 , hereafter Part I ), which is presumed to be known by the reader. As previous broadband instruments like the Clouds and the Earth’s Radiant Energy System (CERES; Wielicki
1. Introduction The Geostationary Earth Radiation Budget (GERB; Harries et al. 2005 ) instruments are the first broadband (BB) radiometers designed to operate from geostationary orbit. They are part of the Meteosat Second Generation (MSG; Schmetz et al. 2002 ) satellites’ payload and have as a main objective the accurate observation of the diurnal cycle of the earth radiation budget at the top of the atmosphere (TOA). The on-ground processing of the GERB data is distributed between the United
1. Introduction The Geostationary Earth Radiation Budget (GERB; Harries et al. 2005 ) instruments are the first broadband (BB) radiometers designed to operate from geostationary orbit. They are part of the Meteosat Second Generation (MSG; Schmetz et al. 2002 ) satellites’ payload and have as a main objective the accurate observation of the diurnal cycle of the earth radiation budget at the top of the atmosphere (TOA). The on-ground processing of the GERB data is distributed between the United
absorption cross section is related to particle volume or mass. Since the addition of small ice particles more significantly affects A than the mass, the general tendency of adding small ice particles is to increase absorption ( Foot 1988 ; Stackhouse and Stephens 1991 ). However, it should be noted that the effect of neglecting small ice particles on the radiation budget depends on the sizes and amount of these particles present in natural clouds ( Arnott et al. 1994 ), and this is not well known at
absorption cross section is related to particle volume or mass. Since the addition of small ice particles more significantly affects A than the mass, the general tendency of adding small ice particles is to increase absorption ( Foot 1988 ; Stackhouse and Stephens 1991 ). However, it should be noted that the effect of neglecting small ice particles on the radiation budget depends on the sizes and amount of these particles present in natural clouds ( Arnott et al. 1994 ), and this is not well known at
1. Introduction The Amazon rain forest is an important component of the global carbon and hydrologic cycles and is a region within the tropics with potential climate change sensitivities, especially with the recent trend of deforestation. Climate change is driven by alterations to regional and global radiation budgets, and uncertainties remain in the relationships between the biosphere, radiation, clouds, and aerosols. It is therefore important to assemble a collection of observations from
1. Introduction The Amazon rain forest is an important component of the global carbon and hydrologic cycles and is a region within the tropics with potential climate change sensitivities, especially with the recent trend of deforestation. Climate change is driven by alterations to regional and global radiation budgets, and uncertainties remain in the relationships between the biosphere, radiation, clouds, and aerosols. It is therefore important to assemble a collection of observations from
al. 2014 ). More recently, Li et al. (2015) highlighted a strong sensitivity of the WAM circulation and associated precipitation to the radiation schemes used in their simulations. Given this sensitivity of the WAM circulation and precipitation to radiation budget changes, it is important to ensure that simulated radiative properties in models are realistic. Unfortunately, climate models have large cloud and hence radiation errors in this region ( Roehrig et al. 2013 ). These model errors are
al. 2014 ). More recently, Li et al. (2015) highlighted a strong sensitivity of the WAM circulation and associated precipitation to the radiation schemes used in their simulations. Given this sensitivity of the WAM circulation and precipitation to radiation budget changes, it is important to ensure that simulated radiative properties in models are realistic. Unfortunately, climate models have large cloud and hence radiation errors in this region ( Roehrig et al. 2013 ). These model errors are
1. Introduction Clouds play an important role in both energy and moisture budgets, and the impact of clouds on climate is a long-standing issue in atmospheric sciences. Data from the Earth Radiation Budget Experiment (ERBE) show that the net radiative effect of clouds on the earth’s global mean radiation budget at the top of the atmosphere (TOA) is ∼−20 W m −2 . This results from roughly a ∼50 W m −2 increase of total reflected solar radiation (TRS) and a ∼30 W m −2 decrease of outgoing
1. Introduction Clouds play an important role in both energy and moisture budgets, and the impact of clouds on climate is a long-standing issue in atmospheric sciences. Data from the Earth Radiation Budget Experiment (ERBE) show that the net radiative effect of clouds on the earth’s global mean radiation budget at the top of the atmosphere (TOA) is ∼−20 W m −2 . This results from roughly a ∼50 W m −2 increase of total reflected solar radiation (TRS) and a ∼30 W m −2 decrease of outgoing
deviation, and correlation and highlighted that there exists large model spread and a high degree of discrepancy from observations, particularly in the upper troposphere. Dolinar et al. (2014) evaluated 28 CMIP5 AMIP GCMs’ simulated clouds and the top of the atmosphere (TOA) radiation budget and concluded that the multimodel ensemble mean CF (57.6%) is, on average, underestimated by 7.6% when compared to Clouds and the Earth's Radiant Energy System–Moderate Resolution Imaging Spectroradiometer (CERES
deviation, and correlation and highlighted that there exists large model spread and a high degree of discrepancy from observations, particularly in the upper troposphere. Dolinar et al. (2014) evaluated 28 CMIP5 AMIP GCMs’ simulated clouds and the top of the atmosphere (TOA) radiation budget and concluded that the multimodel ensemble mean CF (57.6%) is, on average, underestimated by 7.6% when compared to Clouds and the Earth's Radiant Energy System–Moderate Resolution Imaging Spectroradiometer (CERES
warming amplification, while surface albedo feedback and cloud-induced longwave feedback favor it. Vavrus (2004) found that differences in cloud feedback between high and low latitudes have a substantial contribution to the polar amplification, in combination with strongly positive snow and sea ice–albedo feedbacks. In reality, changes in the surface SW radiation budget due to the ice–albedo feedback are inextricably linked to cloud effects ( Curry et al. 1996 , 1993 ; Randall et al. 1994
warming amplification, while surface albedo feedback and cloud-induced longwave feedback favor it. Vavrus (2004) found that differences in cloud feedback between high and low latitudes have a substantial contribution to the polar amplification, in combination with strongly positive snow and sea ice–albedo feedbacks. In reality, changes in the surface SW radiation budget due to the ice–albedo feedback are inextricably linked to cloud effects ( Curry et al. 1996 , 1993 ; Randall et al. 1994
