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Norman G. Loeb
,
David A. Rutan
,
Seiji Kato
, and
Weijie Wang

Abstract

Satellite and reanalysis data are used to observe interannual variations in atmospheric diabatic heating and circulation within the ascending and descending branches of the Hadley circulation (HC) during the past 12 yr. The column-integrated divergence of dry static energy (DSE) and kinetic energy is inferred from satellite-based observations of atmospheric radiation, precipitation latent heating, and reanalysis-based surface sensible heat flux for monthly positions of the HC branches, determined from a mass weighted zonal mean meridional streamfunction analysis. Mean surface radiative fluxes inferred from satellite and surface measurements are consistent to 1 W m−2 (<1%) over land and 4 W m−2 (2%) over ocean. In the ascending branch, where precipitation latent heating dominates over radiative cooling, discrepancies in latent heating among different precipitation datasets reach 22 W m−2 (17%), compared to 3–6 W m−2 in the descending branches. Whereas direct calculations of DSE divergence from two reanalyses show opposite trends, the implied DSE divergence from the satellite observations of atmospheric diabatic heating exhibits no trend in all three HC branches and is strongly correlated (reaching 0.90) with midtropospheric vertical velocity. The implied DSE divergence from satellite observations thus provides a useful independent measure of HC circulation strength variability. The sensitivity to circulation change is 4–5 times larger for precipitation latent heating compared to atmospheric radiative cooling in the descending branches and 20 times larger in the ascending branch. The difference in sensitivity is due to cloud radiative effects, which enhance atmospheric radiative cooling in the descending branches in response to an increase in HC strength but decrease it in the ascending branch.

Full access
Seiji Kato
,
Fred G. Rose
,
David A. Rutan
, and
Thomas P. Charlock

Abstract

The zonal mean atmospheric cloud radiative effect, defined as the difference between the top-of-the-atmosphere (TOA) and surface cloud radiative effects, is estimated from 3 yr of Clouds and the Earth’s Radiant Energy System (CERES) data. The zonal mean shortwave effect is small, though it tends to be positive (warming). This indicates that clouds increase shortwave absorption in the atmosphere, especially in midlatitudes. The zonal mean atmospheric cloud radiative effect is, however, dominated by the longwave effect. The zonal mean longwave effect is positive in the tropics and decreases with latitude to negative values (cooling) in polar regions. The meridional gradient of the cloud effect between midlatitude and polar regions exists even when uncertainties in the cloud effect on the surface enthalpy flux and in the modeled irradiances are taken into account. This indicates that clouds increase the rate of generation of the mean zonal available potential energy. Because the atmospheric cooling effect in polar regions is predominately caused by low-level clouds, which tend to be stationary, it is postulated here that the meridional and vertical gradients of the cloud effect increase the rate of meridional energy transport by the dynamics of the atmosphere from the midlatitudes to the polar region, especially in fall and winter. Clouds then warm the surface in the polar regions except in the Arctic in summer. Clouds, therefore, contribute toward increasing the rate of meridional energy transport from the midlatitudes to the polar regions through the atmosphere.

Full access
Seiji Kato
,
Norman G. Loeb
,
Fred G. Rose
,
David R. Doelling
,
David A. Rutan
,
Thomas E. Caldwell
,
Lisan Yu
, and
Robert A. Weller

Abstract

The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m−2 for downward shortwave and −2.5 (7.1) W m−2 for downward longwave irradiances over ocean and −1.7 (7.8) W m−2 for downward shortwave and −1.0 (7.6) W m−2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.

Full access
Norman G. Loeb
,
Fred G. Rose
,
Seiji Kato
,
David A. Rutan
,
Wenying Su
,
Hailan Wang
,
David R. Doelling
,
William L. Smith
, and
Andrew Gettelman

Abstract

A new method of determining clear-sky radiative fluxes from satellite observations for climate model evaluation is presented. The method consists of applying adjustment factors to existing satellite clear-sky broadband radiative fluxes that make the observed and simulated clear-sky flux definitions more consistent. The adjustment factors are determined from the difference between observation-based radiative transfer model calculations of monthly mean clear-sky fluxes obtained by ignoring clouds in the atmospheric column and by weighting hourly mean clear-sky fluxes with imager-based clear-area fractions. The global mean longwave (LW) adjustment factor is −2.2 W m−2 at the top of the atmosphere and 2.7 W m−2 at the surface. The LW adjustment factors are pronounced at high latitudes during winter and in regions with high upper-tropospheric humidity and cirrus cloud cover, such as over the west tropical Pacific, and the South Pacific and intertropical convergence zones. In the shortwave (SW), global mean adjustment is 0.5 W m−2 at TOA and −1.9 W m−2 at the surface. It is most pronounced over sea ice off of Antarctica and over heavy aerosol regions, such as eastern China. However, interannual variations in the regional SW and LW adjustment factors are small compared to those in cloud radiative effect. After applying the LW adjustment factors, differences in zonal mean cloud radiative effect between observations and climate models decrease markedly between 60°S and 60°N and poleward of 65°N. The largest regional improvements occur over the west tropical Pacific and Indian Oceans. In contrast, the impact of the SW adjustment factors is much smaller.

Open access
Seiji Kato
,
Fred G. Rose
,
David A. Rutan
,
Tyler J. Thorsen
,
Norman G. Loeb
,
David R. Doelling
,
Xianglei Huang
,
William L. Smith
,
Wenying Su
, and
Seung-Hee Ham

Abstract

The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed top-of-atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3 W m−2 and decrease by 0.2 W m−2, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW + LW surface irradiance of 1.1 W m−2. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4 W m−2, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6 W m−2, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW + SW net irradiance is 8 W m−2.

Open access
Seiji Kato
,
Norman G. Loeb
,
John T. Fasullo
,
Kevin E. Trenberth
,
Peter H. Lauritzen
,
Fred G. Rose
,
David A. Rutan
, and
Masaki Satoh

Abstract

Effects of water mass imbalance and hydrometeor transport on the enthalpy flux and water phase on diabatic heating rate in computing the regional energy and water budget of the atmosphere over ocean are investigated. Equations of energy and water budget of the atmospheric column that explicitly consider the velocity of liquid and ice cloud particles, and rain and snow are formulated by separating water variables from dry air. Differences of energy budget equations formulated in this study from those used in earlier studies are that 1) diabatic heating rate depends on water phase, 2) diabatic heating due to net condensation of nonprecipitating hydrometeors is included, and 3) hydrometeors can be advected with a different velocity from the dry-air velocity. Convergence of water vapor associated with phase change and horizontal transport of hydrometeors is to increase diabatic heating in the atmospheric column where hydrometeors are formed and exported and to reduce energy where hydrometeors are imported and evaporated. The process can improve the regional energy and water mass balance when energy data products are integrated. Effects of enthalpy transport associated with water mass transport through the surface are cooling to the atmosphere and warming to the ocean when the enthalpy is averaged over the global ocean. There is no net effect to the atmosphere and ocean columns combined. While precipitation phase changes the regional diabatic heating rate up to 15 W m−2, the dependence of the global mean value on the temperature threshold of melting snow to form rain is less than 1 W m−2.

Open access