2.4 Surface Radiation Budget from CERES Edition 4.0 EBAF-Surface Data Product

Monday, 9 July 2018: 11:30 AM
Regency E/F (Hyatt Regency Vancouver)
Seiji kato, LRC, Hampton, VA; and F. G. Rose, D. A. Rutan, T. Thorsen, N. Loeb, D. R. Doelling, X. Huang, W. L. Smith Jr., W. Su, and S. H. Ham

The algorithm to produce the Clouds and the Earth’s Energy System (CERES) Ed4.0 Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed top-of-atmosphere (TOA) irradiances to match with Ed4.0 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 hPa 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 Observation (CALIPSO), and CloudSat. Remaining errors in surface, cloud and atmospheric properties are corrected in the Lagrange multiplier process. Ed4.0 global annual mean (January 2005 thorough December 2014) surface net shortwave (SW) and longwave (LW) irradiances, respectively, increases by 1.3 Wm-2 and decreases by 0.2 Wm-2 compared to EBAF Ed2.8 counterparts (the previous version). This results in an increase in net SW+LW surface irradiance by 1.1 Wm-2. The uncertainty in surface irradiances over ocean, land and polar regions at various spatial scales are estimated using surface observations. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are, respectively, 3 Wm-2 and 4 Wm-2, and the uncertainties in upward and downward longwave irradiance are respectively, 3 Wm-2 and 6 Wm-2. With an assumption of all errors being independent the uncertainty in the global annual mean surface LW+SW net irradiance is 8 Wm-2.

Therefore, the residual of surface energy balance computed with satellite data products of nearly 15Wm-2 (Kato et al. 2011; Stephens et al. 2012; Loeb et al. 2014; L’Ecuyer et al. 2015) is outside the 1s uncertainty of the net surface irradiance. In addition, the difference of the global annual mean net irradiance derived from Ed2.8 and Ed4.0 EBAF-surface is only 1.1Wm-2. Given differences in inputs and algorithm used in two different versions, the small difference suggests robustness of the global annual mean net surface irradiance.


Kato, S., F. G. Rose, S. Sun-Mack, W. F. Miller, Y. Chen, D. A. Rutan, G. L. Stephens, N. G. Loeb, P. Minnis, B. A. Wielicki, D. M. Winker, T. P. Charlock, P. W. Stackhouse, K.-M. Xu, and W. Collins, 2011: Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO, CloudSat, and MODIS derived cloud and aerosol properties, J. Geophys. Res., VOL 116, D19209, doi:10.1029/2011JD16050.

L’Ecuyer, T., and Coauthors, 2015: The observed state of the en- ergy budget in the early twenty-first century. J. Climate, 28, 8319–8346, doi:10.1175/JCLI-D-14-00556.1.

Loeb, N. G., D. A. Rutan, S. Kato, and W. Wang, 2014: Observing interannual variations in Hadley circulation atmospheric diabatic heating and circulation strength, J. Climate, 27, DOI: 10.1175/JCLI-D-13-00656.1.

Stephens, G. L., and Coauthors, 2012: An update on Earth’s energy balance in light of the latest global observations. Nat. Geosci., 5, 691–696, doi:10.1038/ngeo1580.

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