Abstract
An extensive analysis of radiative flux biases in the Climate Forecast System Model Version 2 (CFSv2) is done. Annual mean and seasonal variations of biases at the surface and top of the atmosphere (TOA) are reported in the global domain. Large regional biases in shortwave (SW) and longwave (LW) radiation are observed over convectively active zones in the tropics. The relative contribution of various processes responsible for the reported biases is quantified. The poor simulation of clouds and inadequate representation of surface properties seem to be major contributors. Over certain regions, errors due to different processes add up, whereas, over other regions, errors tend to nullify each other. Surface and atmospheric variables taken as input parameters in the radiative transfer modules are compared with satellite-based observations. The maximum biases in SW and LW radiation are observed over the regions of persistent low clouds. The magnitude of the SW and LW biases at the TOA is in phase with the biases in cloud fraction by and large. However, the error in the radiative fluxes due to errors in surface radiative properties is of equal importance. The cold bias in near-surface air temperature reported in other studies may partly be attributed to an underestimation in the net SW radiation at the surface. In the present study, a plausible prescription is also provided to correct the source of the biases.
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Abhik S, Krishna RPM, Mahakur M, Ganai M, Mukhopadhyay P, Dudhia J (2017) Revised cloud processes to improve the mean and intraseasonal variability of Indian summer monsoon in climate forecast system: Part 1. J Adv Model Earth Syst. https://doi.org/10.1002/2016MS000819
Arakawa A, Wu CM (2013) A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. J Atmos Sci. https://doi.org/10.1175/JAS-D-12-0330.1
Baker NC, Taylor PC (2016) A framework for evaluating climate model performance metrics. J Clim. https://doi.org/10.1175/JCLI-D-15-0114.1
Bony S, Emanuel KA (2005) On the role of moist processes in tropical intraseasonal variability: cloud-radiation and moisture-convection feedbacks. J Atmos Sci 62(8):2770–2789. https://doi.org/10.1175/JAS3506.1
Buizza R, Miller M, Palmer TN (1999) Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Q J R Meteorol Soc 125(560):2887–2908. https://doi.org/10.1256/smsqj.56005
Clough SA, Shephard MW, Mlawer EJ, Delamere JS, Iacono MJ, Cady-Pereira K, Brown PD (2005) Atmospheric radiative transfer modeling: A summary of the AER codes. J Quant Spectrosc Radiat Transfer 91(2):233–244. https://doi.org/10.1016/j.jqsrt.2004.05.058
Costa SMS, Shine KP (2012) Outgoing longwave radiation due to directly transmitted surface emission. J Atmos Sci 69(6):1865–1871. https://doi.org/10.1175/JAS-D-11-0248.1
Dolinar EK, Dong X, Xi B, Jiang JH, Su H (2015) Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations. Clim Dyn 44(7–8):2229–2247. https://doi.org/10.1007/s00382-014-2158-9
Dolinar EK, Dong X, Xi B (2016) Evaluation and intercomparison of clouds, precipitation, and radiation budgets in recent reanalyses using satellite-surface observations. Clim Dyn 46(7–8):2123–2144. https://doi.org/10.1007/s00382-015-2693-z
Donohoe A, Battisti DS (2011) Atmospheric and surface contributions to planetary albedo. J Clim 24(16):4402–4418. https://doi.org/10.1175/2011JCLI3946.1
Feltz M, Borbas E, Knuteson R, Hulley G, Hook S (2018) The combined ASTER and MODIS emissivity over land (CAMEL) global broadband infrared emissivity product. Remote Sensing. https://doi.org/10.3390/rs10071027
Ganai M, Krishna RPM, Mukhopadhyay P, Mahakur M (2016) The impact of revised simplified arakawa-schubert scheme on the simulation of mean and diurnal variability associated with active and break phases of Indian summer monsoon using CFSv2. J Geophys Res. https://doi.org/10.1002/2016JD025393
Ganai M, Krishna RPM, Tirkey S et al (2019) The Impact of Modified fractional cloud condensate to precipitation conversion parameter in revised simplified Arakawa-Schubert Convection Parameterization Scheme on the Simulation of Indian Summer Monsoon and Its Forecast Application on an Extreme Rainfall. J Geophys Res Atmos 124:5379–5399. https://doi.org/10.1029/2019JD030278
Gleckler PJ, Taylor KE, Durack PJ, Nadeau D, Biard J, Ferraro R, Tuma M (2017) obs4MIPs data specifications (ODS) v2.1. 2017
Goswami BB, Mani NJ, Mukhopadhyay P, Waliser DE, Benedict JJ, Maloney ED, Goswami BN (2011) Monsoon intraseasonal oscillations as simulated by the superparameterized Community Atmosphere Model. J Geophys Res Atmos 116(22):1–17. https://doi.org/10.1029/2011JD015948
Goswami BB, Deshpande M, Mukhopadhyay P, Saha SK, Rao SA, Murthugudde R, Goswami BN (2014) Simulation of monsoon intraseasonal variability in NCEP CFSv2 and its role on systematic bias. Clim Dyn 43(9–10):2725–2745. https://doi.org/10.1007/s00382-014-2089-5
Hazra A, Chaudhari HS, Saha SK, Pokhrel S, Goswami BN (2017) Progress towards achieving the challenge of Indian Summer Monsoon climate simulation in a coupled ocean-atmosphere model. J Adv Model Earth Syst 9(6):2268–2290. https://doi.org/10.1002/2017MS000966
Hess M, Koepke P, Schult I (1998) Optical Properties of Aerosols and Clouds: The Software Package OPAC. Bull Am Meteor Soc. https://doi.org/10.1175/1520-0477(1998)079%3c0831:OPOAAC%3e2.0.CO;2
Hourdin F, Mauritsen T, Gettelman A, Golaz JC, Balaji V, Duan Q, Williamson D (2017) The art and science of climate model tuning. Bull Am Meteorol Soc 98(3):589–602. https://doi.org/10.1175/BAMS-D-15-00135.1
Jakob C (2010) Accelerating progress in global atmospheric model development through improved parameterizations: Challenges, opportunities, and strategies. Bull Am Meteorol Soc 91(7):869–875. https://doi.org/10.1175/2009BAMS2898.1
Jiang JH, Su H, Zhai C, Perun VS, Del Genio A, Nazarenko LS, Stephens GL (2012) Evaluation of cloud and water vapor simulations in CMIP5 climate models Using NASA “A-Train” satellite observations. J Geophys Res Atmos. https://doi.org/10.1029/2011JD017237
Kato S, Rose FG, Rutan DA, Thorsen TJ, Loeb NG, Doelling DR, Ham SH (2018) Surface irradiances of edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data product. J Clim 31(11):4501–4527. https://doi.org/10.1175/JCLI-D-17-0523.1
Khouider B, Majda AJ, Katsoulakis MA (2003) Coarse-grained stochastic models for tropical convection and climate. Proc Natl Acad Sci USA 100(21):11941–11946. https://doi.org/10.1073/pnas.1634951100
Kothe S, Dobler A, Beck A, Ahrens B (2011) The radiation budget in a regional climate model. Clim Dyn 36(5–6):1023–1036. https://doi.org/10.1007/s00382-009-0733-2
Lauer A, Hamilton K (2013) Simulating clouds with global climate models: A comparison of CMIP5 results with CMIP3 and satellite data. J Clim 26(11):3823–3845. https://doi.org/10.1175/JCLI-D-12-00451.1
Li JLF, Waliser DE, Stephens G, Lee S, L’Ecuyer T, Kato S, Ma HY (2013) Characterizing and understanding radiation budget biases in CMIP3/CMIP5 GCMs, contemporary GCM, and reanalysis. J Geophys Res Atmos 118(15):8166–8184. https://doi.org/10.1002/jgrd.50378
Li J-LF, Waliser DE, Stephens G, Lee S (2016) Characterizing and understanding cloud ice and radiation budget biases in global climate models and reanalysis. Meteorol Monogr. https://doi.org/10.1175/amsmonographs-d-15-0007.1
Lin JWB, David Neelin J (2003) Toward stochastic deep convective parameterization in general circulation models. Geophys Res Lett 30(4):1–4. https://doi.org/10.1029/2002GL016203
Lin JWB, Neelin JD (2002) Considerations for stochastic convective parameterization. J Atmos Sci 59(5):959–975. https://doi.org/10.1175/1520-0469(2002)059%3c0959:CFSCP%3e2.0.CO;2
Loeb NG, Wielicki BA, Doelling DR, Smith GL, Keyes DF, Kato S, Wong T (2009) Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J Clim 22(3):748–766. https://doi.org/10.1175/2008JCLI2637.1
Loeb NG, Doelling DR, Wang H, Su W, Nguyen C, Corbett JG, Kato S (2018) Clouds and the earth’s radiant energy system (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) edition-40 data product. J Clim 31(2):895–918. https://doi.org/10.1175/JCLI-D-17-0208.1
Majda AJ, Khouider B (2002) Stochastic and mesoscopic models for tropical convection. Proc Natl Acad Sci USA 99(3):1123–1128. https://doi.org/10.1073/pnas.032663199
Nam C, Bony S, Dufresne JL, Chepfer H (2012) The too few, too bright tropical low-cloud problem in CMIP5 models. Geophys Res Lett 39(21):1–7. https://doi.org/10.1029/2012GL053421
Olsen ET, Friedman S, Fishbein E, Granger S, Hearty T, Irion FW, Manning E (2007) AIRS/AMSU/HSB version 6 changes from version 5. Jet Propuls. Lab. Calif. Inst. Technol. PasadenaCalif. Available online: http://Disc.Sci.Gsfc.Nasa.Gov/Airs/Doc./V6_Docs/V6releasedocs-1/V6_Chang. __V5. Pdf. Accessed 4 Dec 2014
Oreopoulos L, Mlawer E, Delamere J, Shippert T, Cole J, Fomin B, Rossow WB (2012) The continual intercomparison of radiation codes: results from phase i. J GeophysRes Atmos 117(6):1–19. https://doi.org/10.1029/2011JD016821
Palmer TN, Buizza R, Doblas-Reyes F, Jung T, Leutbecher M, Shutts G, Weisheimer A (2009) Stochastic parametrization and model uncertainty. ECMWF Tech. Memo. 598: 42. Retrieved from https://www2.physics.ox.ac.uk/sites/default/files/2011-08-15/techmemo598_stochphys_2009_pdf_50419.pdf. Accessed 10 July 2020
Pan H-LH-L, Wu W-SW-S (1995) Implementing a mass flux convective parameterization package for the NMC medium-range forecast model. NMC Office Note
Pincus R, Batstone CP, Patrick Hofmann RJ, Taylor KE, Glecker PJ (2008) Evaluating the present-day simulation of clouds, precipitation, and radiation in climate models. J Geophys Res Atmos 113(14):1–10. https://doi.org/10.1029/2007JD009334
Rai A, Saha SK (2018) Evaluation of energy fluxes in the NCEP climate forecast system version 2.0 (CFSv2). Clim Dyn 50(1–2):101–114. https://doi.org/10.1007/s00382-017-3587-z
Rao SA, Goswami BN, Sahai AK, Rajagopal EN, Mukhopadhyay P, Rajeevan M, Maini P (2019) Monsoon mission: a targeted activity to improve monsoon prediction across scales. Bull Am Meteorol Soc 100(12):2509–2532. https://doi.org/10.1175/bams-d-17-0330.1
Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Tripp P, Becker E (2014) The NCEP climate forecast system version 2. J Clim 27(6):2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1
Sohn BJ, Bennartz R (2008) Contribution of water vapor to observational estimates of longwave cloud radiative forcing. J Geophys Res Atmos 113(20):1–9. https://doi.org/10.1029/2008JD010053
Srinivasan J (2001) A simple thermodynamic model for seasonal variation of monsoon rainfall. Curr Sci 80(1):73–77
Stanfield RE, Dong X, Xi B, Kennedy A, Del Genio AD, Minnis P, Jiang JH (2014) Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part I: cloud fraction and properties. J Clim 27(11):4189–4208. https://doi.org/10.1175/jcli-d-13-00558.1
Stanfield RE, Dong X, Xi B, Del Genio AD, 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 budget and CREs. J Clim 28(5):1842–1864. https://doi.org/10.1175/JCLI-D-14-00249.1
Stephens GL, O’Brien D, Webster PJ, Pilewski P, Kato S, Li JL (2015) The albedo of Earth. Rev Geophys. https://doi.org/10.1002/2014RG000449
Sujith K, Saha SK, Rai A, Pokhrel S, Chaudhari HS, Hazra A, Goswami BN (2019) Effects of a multilayer snow scheme on the global teleconnections of the Indian summer monsoon. Q J R Meteorol Soc 145(720):1102–1117. https://doi.org/10.1002/qj.3480
Teixeira J, Waliser D, Ferraro R, Gleckler P, Lee T, Potter G (2014) Satellite observations for CMIP5: The genesis of Obs4MIPs. Bull Am Meteorol Soc 95(9):1329–1334. https://doi.org/10.1175/BAMS-D-12-00204.1
Tian B, Fetzer EJ, Kahn BH, Teixeira J, Manning E, Hearty T (2013) Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology. J Geophys Res Atmos 118(1):114–134. https://doi.org/10.1029/2012JD018607
Trenberth KE, Fasullo JT, Kiehl J (2009) Earth’s global energy budget. Bull Am Meteorol Soc. https://doi.org/10.1175/2008BAMS2634.1
Van Weverberg K, Morcrette CJ, Petch J, Klein SA, Ma HY, Zhang C, Thieman MM (2018) CAUSES: attribution of surface radiation biases in NWP and climate models near the U.S. Southern Great Plains. J Geophys Res Atmos 123(7):3612–3644. https://doi.org/10.1002/2017JD027188
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(2):683–699. https://doi.org/10.1029/2012JD018619
Wielicki BA, Barkstrom BR, Harrison EF, Lee RB, Smith GL, Cooper JE (1996) Clouds and the earth’s radiant energy system (CERES): an earth observing system experiment. Bull Am Meteorol Soc. https://doi.org/10.1175/1520-0477(1996)077%3c0853:CATERE%3e2.0.CO;2
Wilber AC, Kratz DP, Gupta SK (1999) Surface emissivity maps for use in satellite retrievals of longwave radiation, NASA/TP-1999–209362. Retrieved from https://ntrs.nasa.gov/search.jsp?R=19990100634. Accessed 14 Oct 2020
Wild M (2008) Short-wave and long-wave surface radiation budgets in GCMs: a review based on the IPCC-AR4/CMIP3 models. Tellus, Ser A Dyn Meteorol Oceanogr 60(5):932–945. https://doi.org/10.1111/j.1600-0870.2008.00342.x
Wild M (2020) The global energy balance as represented in CMIP6 climate models. Clim Dyn 55(3–4):553–577. https://doi.org/10.1007/s00382-020-05282-7
Wild M, Folini D, Schär C, Loeb N, Dutton EG, König-Langlo G (2013) The global energy balance from a surface perspective. Clim Dyn 40(11–12):3107–3134. https://doi.org/10.1007/s00382-012-1569-8
Wild M, Folini D, Hakuba MZ, Schär C, Seneviratne SI, Kato S, König-Langlo G (2015) The energy balance over land and oceans: an assessment based on direct observations and CMIP5 climate models. Clim Dyn 44(11–12):3393–3429. https://doi.org/10.1007/s00382-014-2430-z
Wild M, Hakuba MZ, Folini D, Dörig-Ott P, Schär C, Kato S, Long CN (2019) The cloud-free global energy balance and inferred cloud radiative effects: an assessment based on direct observations and climate models. Clim Dyn 52(7–8):4787–4812. https://doi.org/10.1007/s00382-018-4413-y
Xiang B, Zhao M, Held IM, Golaz JC (2017) Predicting the severity of spurious “double ITCZ” problem in CMIP5 coupled models from AMIP simulations. Geophys Res Lett 44(3):1520–1527. https://doi.org/10.1002/2016GL071992
Yi B, Rapp AD, Yang P, Baum BA, King MD (2017) A comparison of Aqua MODIS ice and liquid water cloud physical and optical properties between collection 6 and collection 5.1: cloud radiative effects. J Geophys Res Atmos 122(8):4550–4564
Zhang GJ, Song X (2016) Parameterization of microphysical processes in convective clouds in global climate models. Meteorolog Monogr 56:12.1-12.18. https://doi.org/10.1175/amsmonographs-d-15-0015.1
Zhang C, Xie S, Klein SA, Ma HY, Tang S, Van Weverberg K, Petch J (2018) CAUSES: diagnosis of the summertime warm bias in CMIP5 climate models at the ARM Southern Great Plains Site. J Geophys Res Atmos 123(6):2968–2992. https://doi.org/10.1002/2017JD027200
Zhang B, Kramer RJ, Soden BJ (2019) Radiative feedbacks associated with the Madden–Julian oscillation. J Clim 32(20):7055–7065. https://doi.org/10.1175/JCLI-D-19-0144.1
Zhao Q, Carr FH (1997) A prognostic cloud scheme for operational NWP models. Mon Weather Rev 125(8):1931–1953. https://doi.org/10.1175/1520-0493(1997)125%3c1931:APCSFO%3e2.0.CO;2
Zhou L, Zhang M, Bao Q, Liu Y (2015) On the incident solar radiation in CMIP5 models. Geophys Res Lett 42(6):1930–1935. https://doi.org/10.1002/2015GL063239
Acknowledgements
Indian Institute of Tropical Meteorology (IITM), Pune is an autonomous institute, fully funded by the Ministry of Earth Sciences, Govt. of India. We are grateful to anonymous reviewers and editor for their constructive comments on the manuscript. NASA’s GES DISC and CERES team is duly acknowledged for providing AIRS, MODIS, and CERES-EBAF data. We thank the director, IITM, for his support and encouragement. The Aditya HPC computing facility at IITM is duly acknowledged.
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Kumar, S., Phani, R., Mukhopadhyay, P. et al. An assessment of radiative flux biases in the climate forecast system model CFSv2. Clim Dyn 56, 1541–1569 (2021). https://doi.org/10.1007/s00382-020-05546-2
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DOI: https://doi.org/10.1007/s00382-020-05546-2