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- Author or Editor: T. S. L’Ecuyer x
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Abstract
The lack of complete knowledge concerning the complex interactions among clouds, circulation, and climate hinders our ability to simulate the Earth’s climate correctly. This study contributes to a broader understanding of the implications of cloud and precipitation biases on the representation of coupled energy and water exchanges by bringing together a suite of cloud impact parameters (CIPs). These parameters measure the coupled impact of cloud systems on regional energy balance and hydrology by simultaneously capturing the absolute strength of the cloud albedo and greenhouse effects, the relative importance of these two radiative effects, and the efficiency of precipitating clouds to radiatively heat the atmosphere and cool the surface per unit of heating through rain production. Global distribution of these CIPs is derived using satellite observations from CloudSat and used to evaluate energy and water cycle coupling in four reanalysis datasets [both versions of the Modern-Era Retrospective Analysis for Research and Applications (MERRA and MERRA-2); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Japanese 55-year Reanalysis (JRA-55)]. The results show that the reanalyses provide a more accurate representation of the three radiation-centric parameters than the radiative efficiencies. Of the four reanalyses, MERRA and ERA-Interim provide the best overall representation of the different cloud processes but can still show significant biases. JRA-55 exhibits some clear deficiencies in many parameters, while MERRA-2 seems to introduce biases that were not evident in MERRA.
Abstract
The lack of complete knowledge concerning the complex interactions among clouds, circulation, and climate hinders our ability to simulate the Earth’s climate correctly. This study contributes to a broader understanding of the implications of cloud and precipitation biases on the representation of coupled energy and water exchanges by bringing together a suite of cloud impact parameters (CIPs). These parameters measure the coupled impact of cloud systems on regional energy balance and hydrology by simultaneously capturing the absolute strength of the cloud albedo and greenhouse effects, the relative importance of these two radiative effects, and the efficiency of precipitating clouds to radiatively heat the atmosphere and cool the surface per unit of heating through rain production. Global distribution of these CIPs is derived using satellite observations from CloudSat and used to evaluate energy and water cycle coupling in four reanalysis datasets [both versions of the Modern-Era Retrospective Analysis for Research and Applications (MERRA and MERRA-2); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Japanese 55-year Reanalysis (JRA-55)]. The results show that the reanalyses provide a more accurate representation of the three radiation-centric parameters than the radiative efficiencies. Of the four reanalyses, MERRA and ERA-Interim provide the best overall representation of the different cloud processes but can still show significant biases. JRA-55 exhibits some clear deficiencies in many parameters, while MERRA-2 seems to introduce biases that were not evident in MERRA.
Abstract
In this paper, the statistical properties of tropical ice clouds (ice water content, visible extinction, effective radius, and total number concentration) derived from 3 yr of ground-based radar–lidar retrievals from the U.S. Department of Energy Atmospheric Radiation Measurement Climate Research Facility in Darwin, Australia, are compared with the same properties derived using the official CloudSat microphysical retrieval methods and from a simpler statistical method using radar reflectivity and air temperature. It is shown that the two official CloudSat microphysical products (2B-CWC-RO and 2B-CWC-RVOD) are statistically virtually identical. The comparison with the ground-based radar–lidar retrievals shows that all satellite methods produce ice water contents and extinctions in a much narrower range than the ground-based method and overestimate the mean vertical profiles of microphysical parameters below 10-km height by over a factor of 2. Better agreements are obtained above 10-km height. Ways to improve these estimates are suggested in this study. Effective radii retrievals from the standard CloudSat algorithms are characterized by a large positive bias of 8–12 μm. A sensitivity test shows that in response to such a bias the cloud longwave forcing is increased from 44.6 to 46.9 W m−2 (implying an error of about 5%), whereas the negative cloud shortwave forcing is increased from −81.6 to −82.8 W m−2. Further analysis reveals that these modest effects (although not insignificant) can be much larger for optically thick clouds. The statistical method using CloudSat reflectivities and air temperature was found to produce inaccurate mean vertical profiles and probability distribution functions of effective radius. This study also shows that the retrieval of the total number concentration needs to be improved in the official CloudSat microphysical methods prior to a quantitative use for the characterization of tropical ice clouds. Finally, the statistical relationship used to produce ice water content from extinction and air temperature obtained by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is evaluated for tropical ice clouds. It is suggested that the CALIPSO ice water content retrieval is robust for tropical ice clouds, but that the temperature dependence of the statistical relationship used should be slightly refined to better reproduce the radar–lidar retrievals.
Abstract
In this paper, the statistical properties of tropical ice clouds (ice water content, visible extinction, effective radius, and total number concentration) derived from 3 yr of ground-based radar–lidar retrievals from the U.S. Department of Energy Atmospheric Radiation Measurement Climate Research Facility in Darwin, Australia, are compared with the same properties derived using the official CloudSat microphysical retrieval methods and from a simpler statistical method using radar reflectivity and air temperature. It is shown that the two official CloudSat microphysical products (2B-CWC-RO and 2B-CWC-RVOD) are statistically virtually identical. The comparison with the ground-based radar–lidar retrievals shows that all satellite methods produce ice water contents and extinctions in a much narrower range than the ground-based method and overestimate the mean vertical profiles of microphysical parameters below 10-km height by over a factor of 2. Better agreements are obtained above 10-km height. Ways to improve these estimates are suggested in this study. Effective radii retrievals from the standard CloudSat algorithms are characterized by a large positive bias of 8–12 μm. A sensitivity test shows that in response to such a bias the cloud longwave forcing is increased from 44.6 to 46.9 W m−2 (implying an error of about 5%), whereas the negative cloud shortwave forcing is increased from −81.6 to −82.8 W m−2. Further analysis reveals that these modest effects (although not insignificant) can be much larger for optically thick clouds. The statistical method using CloudSat reflectivities and air temperature was found to produce inaccurate mean vertical profiles and probability distribution functions of effective radius. This study also shows that the retrieval of the total number concentration needs to be improved in the official CloudSat microphysical methods prior to a quantitative use for the characterization of tropical ice clouds. Finally, the statistical relationship used to produce ice water content from extinction and air temperature obtained by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is evaluated for tropical ice clouds. It is suggested that the CALIPSO ice water content retrieval is robust for tropical ice clouds, but that the temperature dependence of the statistical relationship used should be slightly refined to better reproduce the radar–lidar retrievals.
Abstract
We assess the vertical distribution of radiative heating rates (RHRs) in climate models using a multimodel experiment and A-Train satellite observations, for the first time. As RHRs rely on the representation of cloud amount and properties, we first compare the modeled vertical distribution of clouds directly against lidar–radar combined cloud observations (i.e., without simulators). On a near-global scale (50°S–50°N), two systematic differences arise: an excess of high-level clouds around 200 hPa in the tropics, and a general lack of mid- and low-level clouds compared to the observations. Then, using RHR profiles calculated with constraints from A-Train and reanalysis data, along with their associated maximum uncertainty estimates, we show that the excess clouds and ice water content in the upper troposphere result in excess infrared heating in the vicinity of and below the clouds as well as a lack of solar heating below the clouds. In the lower troposphere, the smaller cloud amount and the underestimation of cloud-top height is coincident with a shift of the infrared cooling to lower levels, substantially reducing the greenhouse effect, which is slightly compensated by an erroneous excess absorption of solar radiation. Clear-sky RHR differences between the observations and the models mitigate cloudy RHR biases in the low levels while they enhance them in the high levels. Finally, our results indicate that a better agreement between observed and modeled cloud profiles could substantially improve the RHR profiles. However, more work is needed to precisely quantify modeled cloud errors and their subsequent effect on RHRs.
Abstract
We assess the vertical distribution of radiative heating rates (RHRs) in climate models using a multimodel experiment and A-Train satellite observations, for the first time. As RHRs rely on the representation of cloud amount and properties, we first compare the modeled vertical distribution of clouds directly against lidar–radar combined cloud observations (i.e., without simulators). On a near-global scale (50°S–50°N), two systematic differences arise: an excess of high-level clouds around 200 hPa in the tropics, and a general lack of mid- and low-level clouds compared to the observations. Then, using RHR profiles calculated with constraints from A-Train and reanalysis data, along with their associated maximum uncertainty estimates, we show that the excess clouds and ice water content in the upper troposphere result in excess infrared heating in the vicinity of and below the clouds as well as a lack of solar heating below the clouds. In the lower troposphere, the smaller cloud amount and the underestimation of cloud-top height is coincident with a shift of the infrared cooling to lower levels, substantially reducing the greenhouse effect, which is slightly compensated by an erroneous excess absorption of solar radiation. Clear-sky RHR differences between the observations and the models mitigate cloudy RHR biases in the low levels while they enhance them in the high levels. Finally, our results indicate that a better agreement between observed and modeled cloud profiles could substantially improve the RHR profiles. However, more work is needed to precisely quantify modeled cloud errors and their subsequent effect on RHRs.
Abstract
Satellite meteorology is a relatively new branch of the atmospheric sciences. The field emerged in the late 1950s during the Cold War and built on the advances in rocketry after World War II. In less than 70 years, satellite observations have transformed the way scientists observe and study Earth. This paper discusses some of the key advances in our understanding of the energy and water cycles, weather forecasting, and atmospheric composition enabled by satellite observations. While progress truly has been an international achievement, in accord with a monograph observing the centennial of the American Meteorological Society, as well as limited space, the emphasis of this chapter is on the U.S. satellite effort.
Abstract
Satellite meteorology is a relatively new branch of the atmospheric sciences. The field emerged in the late 1950s during the Cold War and built on the advances in rocketry after World War II. In less than 70 years, satellite observations have transformed the way scientists observe and study Earth. This paper discusses some of the key advances in our understanding of the energy and water cycles, weather forecasting, and atmospheric composition enabled by satellite observations. While progress truly has been an international achievement, in accord with a monograph observing the centennial of the American Meteorological Society, as well as limited space, the emphasis of this chapter is on the U.S. satellite effort.
Abstract
The objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well-instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar–lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (because of limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar–Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2-km height because of instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of-atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar–lidar instruments and radiative transfer calculations are also found above 10 km (up to 0.35 K day−1 for the shortwave and 0.8 K day−1 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud–radiation interactions in large-scale models, and limitations of each set of instrumentation should be considered when interpreting model–observation differences.
Abstract
The objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well-instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar–lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (because of limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar–Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2-km height because of instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of-atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar–lidar instruments and radiative transfer calculations are also found above 10 km (up to 0.35 K day−1 for the shortwave and 0.8 K day−1 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud–radiation interactions in large-scale models, and limitations of each set of instrumentation should be considered when interpreting model–observation differences.
Abstract
This study quantifies mean annual and monthly fluxes of Earth’s water cycle over continents and ocean basins during the first decade of the millennium. To the extent possible, the flux estimates are based on satellite measurements first and data-integrating models second. A careful accounting of uncertainty in the estimates is included. It is applied within a routine that enforces multiple water and energy budget constraints simultaneously in a variational framework in order to produce objectively determined optimized flux estimates. In the majority of cases, the observed annual surface and atmospheric water budgets over the continents and oceans close with much less than 10% residual. Observed residuals and optimized uncertainty estimates are considerably larger for monthly surface and atmospheric water budget closure, often nearing or exceeding 20% in North America, Eurasia, Australia and neighboring islands, and the Arctic and South Atlantic Oceans. The residuals in South America and Africa tend to be smaller, possibly because cold land processes are negligible. Fluxes were poorly observed over the Arctic Ocean, certain seas, Antarctica, and the Australasian and Indonesian islands, leading to reliance on atmospheric analysis estimates. Many of the satellite systems that contributed data have been or will soon be lost or replaced. Models that integrate ground-based and remote observations will be critical for ameliorating gaps and discontinuities in the data records caused by these transitions. Continued development of such models is essential for maximizing the value of the observations. Next-generation observing systems are the best hope for significantly improving global water budget accounting.
Abstract
This study quantifies mean annual and monthly fluxes of Earth’s water cycle over continents and ocean basins during the first decade of the millennium. To the extent possible, the flux estimates are based on satellite measurements first and data-integrating models second. A careful accounting of uncertainty in the estimates is included. It is applied within a routine that enforces multiple water and energy budget constraints simultaneously in a variational framework in order to produce objectively determined optimized flux estimates. In the majority of cases, the observed annual surface and atmospheric water budgets over the continents and oceans close with much less than 10% residual. Observed residuals and optimized uncertainty estimates are considerably larger for monthly surface and atmospheric water budget closure, often nearing or exceeding 20% in North America, Eurasia, Australia and neighboring islands, and the Arctic and South Atlantic Oceans. The residuals in South America and Africa tend to be smaller, possibly because cold land processes are negligible. Fluxes were poorly observed over the Arctic Ocean, certain seas, Antarctica, and the Australasian and Indonesian islands, leading to reliance on atmospheric analysis estimates. Many of the satellite systems that contributed data have been or will soon be lost or replaced. Models that integrate ground-based and remote observations will be critical for ameliorating gaps and discontinuities in the data records caused by these transitions. Continued development of such models is essential for maximizing the value of the observations. Next-generation observing systems are the best hope for significantly improving global water budget accounting.
Abstract
New objectively balanced observation-based reconstructions of global and continental energy budgets and their seasonal variability are presented that span the golden decade of Earth-observing satellites at the start of the twenty-first century. In the absence of balance constraints, various combinations of modern flux datasets reveal that current estimates of net radiation into Earth’s surface exceed corresponding turbulent heat fluxes by 13–24 W m−2. The largest imbalances occur over oceanic regions where the component algorithms operate independent of closure constraints. Recent uncertainty assessments suggest that these imbalances fall within anticipated error bounds for each dataset, but the systematic nature of required adjustments across different regions confirm the existence of biases in the component fluxes. To reintroduce energy and water cycle closure information lost in the development of independent flux datasets, a variational method is introduced that explicitly accounts for the relative accuracies in all component fluxes. Applying the technique to a 10-yr record of satellite observations yields new energy budget estimates that simultaneously satisfy all energy and water cycle balance constraints. Globally, 180 W m−2 of atmospheric longwave cooling is balanced by 74 W m−2 of shortwave absorption and 106 W m−2 of latent and sensible heat release. At the surface, 106 W m−2 of downwelling radiation is balanced by turbulent heat transfer to within a residual heat flux into the oceans of 0.45 W m−2, consistent with recent observations of changes in ocean heat content. Annual mean energy budgets and their seasonal cycles for each of seven continents and nine ocean basins are also presented.
Abstract
New objectively balanced observation-based reconstructions of global and continental energy budgets and their seasonal variability are presented that span the golden decade of Earth-observing satellites at the start of the twenty-first century. In the absence of balance constraints, various combinations of modern flux datasets reveal that current estimates of net radiation into Earth’s surface exceed corresponding turbulent heat fluxes by 13–24 W m−2. The largest imbalances occur over oceanic regions where the component algorithms operate independent of closure constraints. Recent uncertainty assessments suggest that these imbalances fall within anticipated error bounds for each dataset, but the systematic nature of required adjustments across different regions confirm the existence of biases in the component fluxes. To reintroduce energy and water cycle closure information lost in the development of independent flux datasets, a variational method is introduced that explicitly accounts for the relative accuracies in all component fluxes. Applying the technique to a 10-yr record of satellite observations yields new energy budget estimates that simultaneously satisfy all energy and water cycle balance constraints. Globally, 180 W m−2 of atmospheric longwave cooling is balanced by 74 W m−2 of shortwave absorption and 106 W m−2 of latent and sensible heat release. At the surface, 106 W m−2 of downwelling radiation is balanced by turbulent heat transfer to within a residual heat flux into the oceans of 0.45 W m−2, consistent with recent observations of changes in ocean heat content. Annual mean energy budgets and their seasonal cycles for each of seven continents and nine ocean basins are also presented.
