Climate Dynamics

, Volume 46, Issue 7–8, pp 2123–2144 | Cite as

Evaluation and intercomparison of clouds, precipitation, and radiation budgets in recent reanalyses using satellite-surface observations

  • Erica K. Dolinar
  • Xiquan DongEmail author
  • Baike Xi


Atmospheric reanalysis datasets offer a resource for investigating climate processes and extreme events; however, their uncertainties must first be addressed. In this study, we evaluate the five reanalyzed (20CR, CFSR, Era-Interim, JRA-25, and MERRA) cloud fraction (CF), precipitation rates (PR), and top-of-atmosphere (TOA) and surface radiation budgets using satellite observations during the period 03/2000–02/2012. Compared to the annual averaged CF of 56.7 % from CERES MODIS (CM) four of the five reanalyses underpredict CFs by 1.7–4.6 %, while 20CR overpredicts this result by 7.4 %. PR from the Tropical Rainfall Measurement Mission (TRMM) is 3.0 mm/day and the reanalyzed PRs agree with TRMM within 0.1–0.6 mm/day. The shortwave (SW) and longwave (LW) TOA cloud radiative effects (CREtoa) calculated by CERES EBAF (CE) are −48.1 and 27.3 W/m2, respectively, indicating a net cooling effect of −20.8 W/m2. Of the available reanalysis results, the CFSR and MERRA calculated net CREtoa values agree with CE within 1 W/m2, while the JRA-25 result is ~10 W/m2 more negative than the CE result, predominantly due to the underpredicted magnitude of the LW warming in the JRA-25 reanalysis. A regime metric is developed using the vertical motion field at 500 hPa over the oceans. Aptly named the “ascent” and “descent” regimes, these areas are distinguishable in their characteristic synoptic patterns and the predominant cloud-types; convective-type clouds and marine boundary layer (MBL) stratocumulus clouds. In general, clouds are overpredicted (underpredicted) in the ascent (descent) regime and the biases are often larger in the ascent regime than in the descent regime. PRs are overpredicted in both regimes; however the observed and reanalyzed PRs over the ascent regime are an order of magnitude larger than those over the descent regime, indicating different types of clouds exist in these two regimes. Based upon the Atmospheric Radiation Measurement Program ground-based and CM satellite observations, as well as reanalyzed results, the annual CFs are 15 % higher at the Azores site than at the Nauru site (70.2 vs. 55.2 %), less SW radiation (~20 %) is transmitted the surface, and less LW radiation (~60 W/m2) is emitted back to the surface. Also, the seasonal variations in both CF and surface radiation fluxes are much smaller at the Nauru site than at the Azores site. The dichotomy between the atmospheric ascent and descent regimes is a good measure for determining which parameterization scheme requires more improvement (convective vs. MBL clouds) in these five reanalyses.


Tropical Rainfall Measurement Mission Cloud Fraction Climate Forecast System Reanalysis Cloud Radiative Effect Stratocumulus Cloud 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by NOAA MAPP Grant under NA13OAR4310105, NASA CERES Project under Grant NNX14AP84G and DOE ASR Project with award number DE-SC0008486 at the University of North Dakota. Ms Erica Dolinar is supported under the NASA Earth and Space Science Fellowship (NESSF) Program. The reanalysis datasets are available at and at (for MERRA). CERES-MODIS clouds, EBAF-TOA and EBAF-Surface products are produced by the NASA CERES Team, available at The ground-based observations were obtained from the Atmospheric Radiation Measurement (ARM) Program sponsored by the U.S. Department of Energy (DOE) Office of Energy Research, Office of Health and Environmental Research, Environmental Sciences Division. The data can be downloaded from A special thanks is extended to the anonymous reviewers who have provided excellent feedback concerning the content of this manuscript.


  1. AghaKouchak A, Nasrollahi N, Habib E (2009) Accounting for uncertainties of the TRMM satellite estimates. Remote Sens 1:606–619. doi: 10.3390/rs1030606 CrossRefGoogle Scholar
  2. Arakawa A, Schubert WH (1974) Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. J Atmos Sci 31:674–701CrossRefGoogle Scholar
  3. Bacmeister JT, Suarez MJ, Robertson FR (2006) Rain re-evaporation, boundary layer–convection interactions, and Pacific rainfall patterns in an AGCM. J Atmos Sci 63:3383–3403CrossRefGoogle Scholar
  4. Barkstrom BR (1984) The earth radiation budget experiment (ERBE). Bull Am Meteorol Soc 65:1170–1185CrossRefGoogle Scholar
  5. Bloom S, Takacs L, DaSilva A, Ledvina D (1996) Data assimilation using incremental analysis updates. Mon Weather Rev 124:1256–1271CrossRefGoogle Scholar
  6. Bony S, Dufresne JL (2005) Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys Res Lett 32:L20806. doi: 10.1029/2005GL023851 CrossRefGoogle Scholar
  7. Bony S, Dufresne JL, Le Treui H, Morcrette JJ, Snior C (2004) On dynamic and thermodynamic components of cloud changes. Clim Dyn 22:71–86. doi: 10.1007/s00382-003-0369-6 CrossRefGoogle Scholar
  8. Bosilovich MG, Robertson FR, Chen J (2011) Global energy and water budgets in MERRA. J Clim 24:5721–5739CrossRefGoogle Scholar
  9. CERES EBAF_Ed2.8 data quality summary
  10. CERES EBAF-Surface_Ed2.8 data quality summary
  11. Chou M-D, Suarez MJ (1999) A solar radiation parameterization for atmospheric studies. NASA technical report series on global modeling and data assimilation, NASA/TM-1999-104606, vol 15, 40 ppGoogle Scholar
  12. Chou MD, Suarez MJ, Liang XZ, an MMH (2001) A thermal infrared radiation parameterization for atmospheric studies. NASA technical report series on global modeling and data assimilation, NASA/TM-2001-104606, vol 19, 56 ppGoogle Scholar
  13. Coakley JA, Cess RD, Yurevich FB (1983) The effect of tropospheric aerosols on the earth’s radiation budget: a parameterization for climate models. J Atmos Sci 40:116–138CrossRefGoogle Scholar
  14. Compo GP, Whitaker JS, Sardeshmukh PD (2006) Feasibility of a 100-year reanalysis using only surface pressure data. Bull Am Meteorol Soc 87:175–190CrossRefGoogle Scholar
  15. Compo GP et al (2011) The twentieth century reanalysis project. Q J R Meteorol Soc 137:1–28. doi: 10.1002/qj.776 CrossRefGoogle Scholar
  16. Dee DP, Uppala S (2009) Variational bias correction of satellite radiance in the Era-Interim reanalysis. Q J R Meteorol Soc 135:1830–1841. doi: 10.1002/qj.493 CrossRefGoogle Scholar
  17. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. doi: 10.1002/qj.828 CrossRefGoogle Scholar
  18. Doelling DR, Loeb NG, Keyes DF, Nordeen ML, Morstad D, Nguyen C, Wielicki BA, Young DF, Sun M (2013) Geostationary enhanced temporal interpolation for CERES flux products. J Atmos Ocean Technol 30:1072–1090CrossRefGoogle Scholar
  19. Dolinar EK, Dong X, Xi B, Jiang J, Su H (2015) Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations. Clim Dyn 44:2229–2247. doi: 10.1007/s00382-014-2158-9 CrossRefGoogle Scholar
  20. Dong X, Minnis P, Xi B, Sun-Mack S, Chen Y (2008) Comparison of CERES-MODIS stratus cloud properties with ground-based measurements at the DOE ARM Southern Great Plains site. J Geophys Res 113:D03204. doi: 10.1029/2007JD008438 Google Scholar
  21. Dong X, Xi B, Kennedy A, Minnis P, Wood R (2014) A 19-month record of marine aerosol–cloud–radiation properties derived from DOE arm mobile facility deployment at the azores. Part I: cloud fraction and single-layered MBL cloud properties. J Clim 27:3665–3682. doi: 10.1175/JCLI-D-13-00553.1 CrossRefGoogle Scholar
  22. Habib E, Krajewski WF (2002) Uncertainty of the TRMM ground-validation radar-rainfall products: application of the TEFLUN-B field campaign. J Appl Meteorol 41:558–572CrossRefGoogle Scholar
  23. Hou Y, Moorthi S, Compana K (2002) Parameterization of solar radiation transfer in NCEP models. Office note 441. NCEP, Washington DCGoogle Scholar
  24. Huffman GJ (1997) Estimates of root-mean-square random error for finite samples of estimated precipitation. J Appl Meteorol 36:1191–1201CrossRefGoogle Scholar
  25. Huffman GJ, Bolvin D, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeo 8:38–55. doi: 10.1175/JHM560.1 CrossRefGoogle Scholar
  26. Ichikawa H, Masunaga H, Tsushima Y, Kanzawa H (2012) Reproducibility by climate models of cloud radiative forcing associated with tropical convection. J Clim 25:1247–1262CrossRefGoogle Scholar
  27. Jakob C (1998) Cloud cover in the ECMWF reanalysis. J Clim 12:947–959CrossRefGoogle Scholar
  28. Jiang JH et al (2012) Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA ‘A-Train’ satellite observations. J Geophys Res 117:D14105. doi: 10.1029/2011JD017237 Google Scholar
  29. Joseph JH, Wiscombe WJ, Weinman JA (1976) The delta-Eddington approximation for radiative flux transfer. J Atmos Sci 33:2452–2459CrossRefGoogle Scholar
  30. Kato S, Loeb NG, Rose FG, Doelling DR, Rutan DA, Caldwell TE, Yu L, Weller RA (2013) Surface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances. J Clim 26:2719–2740. doi: 10.1175/JCLI-D-12-00436.1 CrossRefGoogle Scholar
  31. Kawai H, Inoue T (2006) A simple parameterization scheme for subtropical marine stratocumulus. SOLA 2:17–20CrossRefGoogle Scholar
  32. Kennedy AD, Dong X, Xi B, Xie S, Zhang Y, Chen J (2011) A comparison of MERRA and NARR reanalysis datasets with the DOE ARM SGP continuous forcing data. J Clim 24:4541–4557CrossRefGoogle Scholar
  33. Köhler M, Ahlgrimm M, Beljaars ACM (2011) Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model. Q J R Meteorol Soc 137:43–57CrossRefGoogle Scholar
  34. Lauer A, Hamilton K (2012) Simulating clouds with global climate models: a comparison on CMIP5 results with CMIP3 and satellite data. J Clim 26:3823–3845. doi: 10.1175/JCLI-D-12-00451.1 CrossRefGoogle Scholar
  35. Lock AP, Brown AR, Bush MR, Martin GM, Smith RNB (2000) A new boundary layer mixing scheme. Part I: scheme description and single-column model tests. Mon. Weather Rev. 138:3187–3199CrossRefGoogle Scholar
  36. Loeb NG, Wielicki BA, Doelling DR, Smith GL, Keyes DF, Kato S, Manalo-Smith N, Wong T (2009) Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J. Clim 22:748–766. doi: 10.1175/2008JCLI2637.1 CrossRefGoogle Scholar
  37. Loeb NG, Lyman JM, Johnson GC, Allan RP, Doelling DR, Wong T, Soden BJ, Stephens GL (2012) Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Nat Geosci 5:110–113. doi: 10.1038/NGEO1375 CrossRefGoogle Scholar
  38. Louis J, Tiedtke M, and Geleyn J (1982) A short history of the PBL parameterization at ECMWF. In: Proceedings ECMWF workshop on planetary boundary layer parameterization, Reading, United Kingdom, ECMWF, 59–80Google Scholar
  39. Mead J B, and Widener KB (2005) W-band ARM cloud radar, preprints In: 32nd International conference on radar meteorology, American Meteorological Society, Albuquerque, N.M., P1R.3.
  40. Minnis P, Young DF, Wielicki BA, Heck PW, Dong X, Stowe LL, RM Welch (1999) CERES cloud properties derived from multispectral VIRS data. In: Proceedings of SPIE 3867, satellite remote sensing of clouds and the atmosphere IV, 91, 8 December 1999. doi: 10.1117/12.373047
  41. Minnis P, Young DF, Wielicki BA, Sun-Mack S, Trepte QZ, Chen Y, Heck PW, and Dong X (2002) A global cloud database from VIRS and MODIS for CERES. In: Proceedings of SPIE 4891, optical remote sensing of the atmosphere and clouds III 115, 9 April 2003. doi: 10.1117/12.467317
  42. Minnis et al (2011) CERES edition-2 cloud property retrievals using TRMM VIRS and terra and aqua MODIS data. Part II: examples of average results and comparisons with other data. IEEE Trans Geosci Remote Sens 49:4401–4430CrossRefGoogle Scholar
  43. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102D:16663–16682CrossRefGoogle Scholar
  44. Moorthi S, Suarez MJ (1992) Relaxed Arakawa-Schubert: a parameterization of moist convectionfor general circulation models. Mon. Weather Rev. 120:978–1002CrossRefGoogle Scholar
  45. Moorthi S, Pan H-L, and Caplan P (2001) Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. NWS Tech. Procedures Bulletin 484, 14 pp
  46. Onogi K, Tsutsui J, Koide H, Sakamoto M, Kobayashi S, Hatsushika H, Matsumoto T, Yamazaki N, Kamahori H, Takahashi K, Kadukora S, Wada K, Kato K, Oyama R, Ose T, Mannoji N, Taira R (2007) The JRA-25 Reanalysis. J Meteorol Soc Jap 85(3):369–432CrossRefGoogle Scholar
  47. Ramanathan V, Cess RD, Harrison EF, Minnis P, Barkstorm BR, Ahmad E, Hartmann D (1989) Cloud-radiative forcing and climate: results from the earth radiation budget experiment. Science 243:57–63CrossRefGoogle Scholar
  48. Randall D, Pan D-M (1993) Implementation of the Arakawa-Schubert cumulus parameterization with prognostic closure. Meteorological monograph/the representation of cumulus convection in numerical models. J Amos Sci 46:137–144Google Scholar
  49. Rienecker MM et al (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648. doi: 10.1175/JCLI-D-11-00015.1 CrossRefGoogle Scholar
  50. Rienecker MM and co-authors (2011b) Atmospheric reanalyses-recent progress and prospects from the future. NASA technical report series on global Modeling and data assimilation vol 29:2010Google Scholar
  51. Robertson FR, Bosilovich M, Chen J, Miller T (2011) The effect of satellite observing system changes on merra water and energy fluxes. J Clim 24:5197–5217. doi: 10.1175/2011JCLI4227.1 CrossRefGoogle Scholar
  52. Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q, Van den Dool HM, Pan H-L, Moorthi S, Behringer D, Stokes D, Peña M, Lord S, White G, Ebisuzaki W, Peng P, Xie P (2006) The NCEP climate forecast system. J Clim 19:3483–3517. doi: 10.1175/JCLI3812.1 CrossRefGoogle Scholar
  53. Saha et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057. doi: 10.1175/2010BAMS3001.1 CrossRefGoogle Scholar
  54. Shi Y and Long CN (2002) Techniques and methods used to determine the best estimate of radiation fluxes at SGP central facility. In Proceedings of the twelfth atmospheric radiation measurement (ARM) science team meeting, St. Petersburg, Florida, 8–12 April 2002, p. 12 pages. U.S. Department of Energy—DOE, Washington, DCGoogle Scholar
  55. Simmons AJ, Uppala SM, Dee DP, Kobayashi S (2007) ERA-Interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter 110:25–35Google Scholar
  56. Stanfield R, Dong X, Xi B, Del Genio A, Minnis P, Doelling D, Loeb N (2015) Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part II: TOA radiation budgets and cloud radiative forcings. J Clim 28:1842–1864. doi: 10.1175/JCLI-D-14-00249.1 CrossRefGoogle Scholar
  57. Sugi M, Kuma K, Tada K, Tamiya K, Hasegawa N, Iwasaki T, Yamada S, Kitade T (1990) Description and performance of the JMA operational global spectral model (JMA GSM88). Geophys Mag 43:105–130Google Scholar
  58. Sundqvist H, Berge E, Kristjansson JE (1989) Condensation and cloud studies with a mesoscale numerical weather prediction model. Mon Weather Rev 117:1641–1657CrossRefGoogle Scholar
  59. Treadon RE, Pan HL, Wu W-S, Lin Y, Olson WS, Kuligowski RJ (2002) Global and regional moisture analyses at NCEP. In: Proceedings ECMWF/GEWEX workshop on humidity analysis, Reading, United Kingdom, ECMWF, 33–47Google Scholar
  60. Wagner TM, Graf H-F (2010) An ensemble cumulus convection parameterization with explicit cloud treatment. J Atmos Sci 67:3854–3869. doi: 10.1175/2010JAS3485.1 CrossRefGoogle Scholar
  61. Wang H, Su W (2013) Evaluating and understanding top of the atmosphere cloud radiative effects in Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) Coupled Model Intercomparison Project Phase 5 (CMIP5) models using satellite observations. J Geophys Res Atmos 118:683–699. doi: 10.1029/2012JD018619 CrossRefGoogle Scholar
  62. Whitaker JS, Hamill TM (2002) Ensemble data assimilation without perturbed observations. Mon Weather Rev 130:1913–1924CrossRefGoogle Scholar
  63. Wielicki BA, Cess RD, King MD, Randall DA, Harrison EF (1995) Mission to planet earth: role of clouds and radiation in climate. Bull Am Meteorol Soc 76:2125–2153. doi: 10.1175/1520-0477(1995)076,2125:MTPERO.2.0.CO;2 CrossRefGoogle Scholar
  64. Wild M, Folini D, Hakuba MZ, Schär C, Seneviratne SI, Kato S, Rutan D, Ammann C, Wood EF, König-Langlo G (2014) The energy balance over land and oceans: an assessment based on direct observations and CMIP5 climate models. Dyn Clin. doi: 10.1007/s00382-014-2430-z Google Scholar
  65. Wood R (2012) Review: stratocumulus clouds. Mon Weather Rev 140:2373–2423. doi: 10.1175/MWR-D-11-00121.1 CrossRefGoogle Scholar
  66. Xi B, Dong X, Minnis P, Khaiyer M (2010) A 10-year climatology of cloud cover and vertical distribution from both surface and GOES observations over DOE ARM SGP site. J Geophys Res 115:D12124. doi: 10.1029/2009JD012800 CrossRefGoogle Scholar
  67. Xi B, Dong X, Giannecchini K, Minnis P, Kato S (2014a) An overview of Arctic cloud fraction and height detected by active and passive remote sensing over the ARM NSA site. Geophys. Res. Lett. (Submitted)Google Scholar
  68. Xi B, Dong X, Minnis P, Sun-Mack S (2014b) Validation of CERES-MODIS Edition 4 Marine boundary layer cloud properties using DOE ARM AMF measurements and the Azores. JGR. doi: 10.1002/2014JD021813 Google Scholar
  69. Xie S, Cederwall RT, Zhang MH (2004) Developing long-term single-column model/cloud system-resolving model forcing using numerical weather prediction products constrained by surface and top of the atmosphere observations. J Geophys Res 109:D01104. doi: 10.1029/2003JD004045 Google Scholar
  70. Zhao QY, Carr FH (1997) A prognostic cloud scheme for operational NWP models. Mon Weather Rev 125:1931–1953CrossRefGoogle Scholar
  71. Zib BJ, Dong X, Xi B, Kennedy A (2012) Evaluation and intercomparison of cloud fraction and radiative fluxes in recent reanalyses over the Arctic using BSRN surface observations. J Clim 25:2291–2305. doi: 10.1175/JCLI-D-11-00147.1 CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Atmospheric ScienceUniversity of North DakotaGrand ForksUSA

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