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Evaluating cloud radiative effect from CMIP6 and two satellite datasets over the Tibetan Plateau based on CERES observation

Abstract

Based on 12 years (March 2000–February 2012) of monthly data from Clouds and the Earth’s Radiant Energy System energy balanced and filled (CERES-EBAF), this study systematically evaluates the applicability of Advanced Very High Resolution Radiometer (AVHRR) and second Along-Track Scanning Radiometer and advanced ATSR (AATSR) flux products at the top-of-the-atmosphere (TOA), and the ability of atmosphere-only simulations of the Coupled Model Intercomparison Project Phase6 (CMIP6/AMIP) model in reproducing the observed spatial–temporal patterns of TOA cloud radiative effect (CRE) over the Tibetan Plateau (TP). Results show that TOA radiative fluxes from AVHRR and AATSR can be used to analyze their spatial/temporal characteristics over TP region, especially for AVHRR, but none of them can capture the observed CRE trend since 2000. In particular, when using AATSR TOA radiative flux in clear-sky of TP, the large bias of SW flux (regional mean about 30.48 Wm−2) compared with CERES-EBAF must be taken seriously. The multimodel ensemble mean (MEM) can sufficiently reproduce the temporal changes of CREs, particularly the shortwave CRE. Regarding the geographical pattern of CREs of MEM, the annual mean deviations of longwave CRE are very small, while obvious underestimations can be found in the southeastern TP for shortwave CRE. Additionally, the spatial distribution of CREs is difficult to reproduce for many individual models due to albedo and temperature biases of surface. Our results also demonstrated that MEM still has evident difficulties to capture realistic CRE trends in TP due to poor simulations in surface and cloud properties (particularly cloud fraction).

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Data availability

The CERES_EBAF_Ed4.1 and CERES-SSF1deg products are publicly available through the NASA Langley Research Center CERES ordering tool at https://ceres.larc.nasa.gov/data/. The ESA Cloud-cci version 3 products, AVHRR-PMv3 and ATSR2-AATSRv3, for this research are included in these papers: Stengel et al. (2020) and Poulsen et al. (2020), or obtained through https://climate.esa.int/en/odp/#/project. And the CMIP6 products are obtained from https://esgf-node.llnl.gov/search/cmip6/.

References

  1. Allan RP (2011) Combining satellite data and models to estimate cloud radiative effect at the surface and in the atmosphere. Meteorol Appl 18:324–333. https://doi.org/10.1002/met.285

    Article  Google Scholar 

  2. Allan RP, Ringer MA (2003) Inconsistencies between satellite estimates of longwave cloud forcing and dynamical fields from reanalyses. Geophys Res Lett 30:1491. https://doi.org/10.1029/2003GL017019

    Article  Google Scholar 

  3. Baker MB (1997) Cloud microphysics and climate. Science 276:1072–1078. https://doi.org/10.1126/science.276.5315.1072

    Article  Google Scholar 

  4. Bender FA, Rodhe H, Charlson RJ, Ekman AM, Loeb N (2006) 22 views of the global albedo—comparison between 20 GCMs and two satellites. Tellus A: Dyn Meteorol Oceanogr 58:320–330. https://doi.org/10.1111/j.1600-0870.2006.00181.x

    Article  Google Scholar 

  5. Bibi S, Wang L, Li X, Zhou J, Chen D, Yao T (2018) Climatic and associated cryospheric, biospheric, and hydrological changes on the Tibetan Plateau: a review. Int J Climatol 381:E1–E17. https://doi.org/10.1002/joc.5411

    Article  Google Scholar 

  6. Boucher O (2013) Clouds and aerosols in climate change 2013: the physical science basis. In: Stocker TF et al (eds) Contribution of Working Group I to IPCC AR5. Cambridge Univ Press, Cambridge (Reprinted)

    Google Scholar 

  7. Cagnazzo C, Manzini E, Giorgetta MA, Forster PDF, Morcrette JJ (2007) Impact of an improved shortwave radiation scheme in the MAECHAM5 General Circulation Model. Atmos Chem Phys 7:2503–2515. https://doi.org/10.5194/acp-7-2503-2007

    Article  Google Scholar 

  8. Cesana G, Kay JE, Chepfer H, English JM, de Boer G (2012) Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP. Geophys Res Lett. https://doi.org/10.1029/2012GL053385

    Article  Google Scholar 

  9. Chen B, Chao WC, Liu X (2003) Enhanced climatic warming in the Tibetan Plateau due to doubling CO2: a model study. Clim Dyn 20:401–413. https://doi.org/10.1007/s00382-002-0282-4

    Article  Google Scholar 

  10. Chen D, Xu B, Yao T, Guo Z, Cui P, Chen F et al (2015) Assessment of past, present and future environmental changes on the Tibetan Plateau. Chin Sci Bull 60:3025–3035. https://doi.org/10.1360/N972014-01370

    Article  Google Scholar 

  11. Chen X, Liu Y, Wu G (2017) Understanding the surface temperature cold bias in CMIP5 AGCMs over the Tibetan Plateau. Adv Atmos Sci 34:1447–1460. https://doi.org/10.1007/s00376-017-6326-9

    Article  Google Scholar 

  12. Cherian R, Quaas J (2020) Trends in AOD, clouds, and cloud radiative effects in satellite data and CMIP5 and CMIP6 model simulations over aerosol source regions. Geophys Res Lett 47:e2020G-e87132G. https://doi.org/10.1029/2020GL087132

    Article  Google Scholar 

  13. Choi Y, Lindzen RS, Ho C, Kim J (2010) Space observations of cold-cloud phase change. P Natl Acad Sci USA 107:11211–11216. https://doi.org/10.1073/pnas.1006241107

    Article  Google Scholar 

  14. Doelling DR, Loeb NG, Keyes DF, Nordeen ML, Morstad D, Nguyen C et al (2013) Geostationary enhanced temporal interpolation for CERES flux products. J Atmos Ocean Tech 30:1072–1090. https://doi.org/10.1175/JTECH-D-12-00136.1

    Article  Google Scholar 

  15. Duan A, Wu G (2006) Change of cloud amount and the climate warming on the Tibetan Plateau. Geophys Res Lett 33:L22704. https://doi.org/10.1029/2006GL027946

    Article  Google Scholar 

  16. Duan A, Xiao Z (2015) Does the climate warming hiatus exist over the Tibetan Plateau? Sci Rep-Uk 5:1–9. https://doi.org/10.1038/srep13711

    Article  Google Scholar 

  17. Duan A, Wu G, Liu Y, Ma Y, Zhao P (2012) Weather and climate effects of the Tibetan Plateau. Adv Atmos Sci 29:978–992. https://doi.org/10.1007/s00376-012-1220-y

    Article  Google Scholar 

  18. Engström A, Bender FM, Charlson RJ, Wood R (2015) The nonlinear relationship between albedo and cloud fraction on near-global, monthly mean scale in observations and in the CMIP5 model ensemble. Geophys Res Lett 42:9571–9578. https://doi.org/10.1002/2015GL066275

    Article  Google Scholar 

  19. Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ et al (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016

    Article  Google Scholar 

  20. Eyring V, Cox PM, Flato GM, Gleckler PJ, Abramowitz G, Caldwell P et al (2019) Taking climate model evaluation to the next level. Nat Clim Change 9:102–110. https://doi.org/10.1038/s41558-018-0355-y

    Article  Google Scholar 

  21. Fernandez-Gonzalez S, Wang PK, Gascon E, Valero F, Sanchez JL (2016) Latent cooling and microphysics effects in deep convection. Atmos Res 180:189–199. https://doi.org/10.1016/j.atmosres.2016.05.022

    Article  Google Scholar 

  22. Fildier B, Collins WD (2015) Origins of climate model discrepancies in atmospheric shortwave absorption and global precipitation changes. Geophys Res Lett 42:8749–8757. https://doi.org/10.1002/2015GL065931

    Article  Google Scholar 

  23. Flato G, Marotzke J, Abiodun B, Braconnot P, Chou SC, Collins W et al (2013) Evaluation of climate models. Climate change 2013: the physical science basis. In: Stocker TF et al (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, pp 741–866 (Reprinted)

    Google Scholar 

  24. Forbes RM, Ahlgrimm M (2014) On the Representation of high-latitude boundary layer mixed-phase cloud in the ECMWF global model. Mon Weather Rev 142:3425–3445. https://doi.org/10.1175/MWR-D-13-00325.1

    Article  Google Scholar 

  25. Hamilton JP, Whitelaw GS, Fenech A (2001) Mean annual temperature and total annual precipitation trends at Canadian biosphere reserves. Environ Monit Assess 67:239–275. https://doi.org/10.1023/A:1006490707949

    Article  Google Scholar 

  26. Hartmann DL, Ockert-Bell ME, Michelsen ML (1992) The effect of cloud type on Earth’s energy balance: Global analysis. J Clim 5:1281–1304. https://doi.org/10.1175/1520-0442(1992)005%3c1281:TEOCTO%3e2.0.CO;2

    Article  Google Scholar 

  27. Hua S, Liu Y, Jia R, Chang S, Wu C, Zhu Q et al (2018) Role of clouds in accelerating cold-season warming during 2000–2015 over the Tibetan Plateau. Int J Climatol 38:4950–4966. https://doi.org/10.1002/joc.5709

    Article  Google Scholar 

  28. IJmker J, Stauch G, Pötsch S, Diekmann B, Wünnemann B, Lehmkuhl F (2012) Dry periods on the NE Tibetan Plateau during the late. Quat Palaeogeogr Palaeocl 346:108–119. https://doi.org/10.1016/j.palaeo.2012.06.005

    Article  Google Scholar 

  29. Jian B, Li J, Zhao Y, He Y, Wang J, Huang J (2020) Evaluation of the CMIP6 planetary albedo climatology using satellite observations. Clim Dyn 54:5145–5161. https://doi.org/10.1007/s00382-020-05277-4

    Article  Google Scholar 

  30. Jian B, Li J, Wang G, Zhao Y, Li Y, Wang J et al (2021) Evaluation of the CMIP6 marine subtropical stratocumulus cloud albedo and its controlling factors. Atmos Chem Phys 21:9809–9828. https://doi.org/10.5194/acp-21-9809-2021

    Article  Google Scholar 

  31. Jiang D, Hu D, Tian Z, Lang X (2020) Differences between CMIP6 and CMIP5 models in simulating climate over China and the East Asian Monsoon. Adv Atmos Sci 37:1102–1118. https://doi.org/10.1007/s00376-020-2034-y

    Article  Google Scholar 

  32. Kato S, Rose FG, Rutan DA, Thorsen TJ, Loeb NG, Doelling DR et al (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 31:4501–4527. https://doi.org/10.1175/JCLI-D-17-0523.1

    Article  Google Scholar 

  33. Kawamoto K, Hayasaka T (2011) Cloud and aerosol contributions to variation in shortwave surface irradiance over East Asia in July during 2001 and 2007. J Quant Spectrosc Radiat 112:329–337. https://doi.org/10.1016/j.jqsrt.2010.08.002

    Article  Google Scholar 

  34. Kendall MG (1948) Rank correlation methods. Griffin

    Google Scholar 

  35. Kiehl JT (1994) On the observed near cancellation between longwave and shortwave cloud forcing in tropical regions. J Clim 7(4):559–565 https://www.jstor.org/stable/26197877

    Article  Google Scholar 

  36. Kuang X, Jiao JJ (2016) Review on climate change on the Tibetan Plateau during the last half century. J Geophys Res Atmos 121:3979–4007. https://doi.org/10.1002/2015JD024728

    Article  Google Scholar 

  37. Letu H, Yang K, Nakajima TY, Ishimoto H, Nagao TM, Riedi J et al (2020) High-resolution retrieval of cloud microphysical properties and surface solar radiation using Himawari-8/AHI next-generation geostationary satellite. Remote Sens Environ 239:111583. https://doi.org/10.1016/j.rse.2019.111583

    Article  Google Scholar 

  38. Li Y, Zhang M (2016) Cumulus over the Tibetan Plateau in the Summer Based on CloudSat-CALIPSO Data. J Clim 29:1219–1230. https://doi.org/10.1175/JCLI-D-15-0492.1

    Article  Google Scholar 

  39. Li J, Huang J, Stamnes K, Wang T, Lv Q, Jin H (2015) A global survey of cloud overlap based on CALIPSO and CloudSat measurements. Atmos Chem Phys 15:519–536. https://doi.org/10.5194/acp-15-519-2015

    Article  Google Scholar 

  40. Li Y, Wang T, Zeng Z, Peng S, Lian X, Piao S (2016) Evaluating biases in simulated land surface albedo from CMIP5 global climate models. J Geophys Res-Atmos 121:6178–6190. https://doi.org/10.1002/2016JD024774

    Article  Google Scholar 

  41. Li J, Wu K, Li F, Chen Y, Huang Y, Feng Y (2017) Effects of latent heat in various cloud microphysics processes on autumn rainstorms with different intensities on Hainan Island, China. Atmos Res 189:47–60. https://doi.org/10.1016/j.atmosres.2017.01.010

    Article  Google Scholar 

  42. Li J, Lv Q, Jian B, Zhang M, Zhao C, Fu Q et al (2018) The impact of atmospheric stability and wind shear on vertical cloud overlap over the Tibetan Plateau. Atmos Chem Phys 18:7329–7343. https://doi.org/10.5194/acp-18-7329-2018

    Article  Google Scholar 

  43. Li J, Jian B, Zhao C, Zhao Y, Wang J, Huang J (2019) Atmospheric instability dominates the long-term variation of cloud vertical overlap over the Southern Great Plains Site. J Geophys Res-Atmos 124:9691–9701. https://doi.org/10.1029/2019JD030954

    Article  Google Scholar 

  44. Li F, Zhao K, Lu H, Wang G, Qiu J (2021a) Modes of exploitation of atmospheric water resources in the Qinghai-Tibet plateau. Int J Climatol 41:3237–3246. https://doi.org/10.1002/joc.7016

    Article  Google Scholar 

  45. Li J, Sun Z, Liu Y, You Q, Chen G, Bao Q (2021b) Top-of-atmosphere radiation budget and cloud radiative effects over the Tibetan Plateau and adjacent Monsoon Regions from CMIP6 simulations. J Geophys Res Atmos 126:e2020J–e34345J. https://doi.org/10.1029/2020JD034345

    Article  Google Scholar 

  46. Lin X, Wen J, Liu Q, You D, Wu S, Hao D et al (2020) Spatiotemporal variability of land surface albedo over the Tibet Plateau from 2001 to 2019. Remote Sens-Basel. https://doi.org/10.3390/rs12071188

    Article  Google Scholar 

  47. Liu C, Yang P, Nasiri SL, Platnick S, Meyer KG, Wang C et al (2015) A fast visible infrared imaging radiometer suite simulator for cloudy atmospheres. J Geophys Res Atmos 120:240–255

    Article  Google Scholar 

  48. Liu C, Yao B, Natraj V, Weng F, Le T, Shia R et al (2020) A spectral data compression (SDCOMP) radiative transfer model for high-spectral-resolution radiation simulations. J Atmos Sci 77:2055–2066. https://doi.org/10.1175/JAS-D-19-0238.1

    Article  Google Scholar 

  49. Loeb NG, Doelling DR, Wang H, Su W, Nguyen C, Corbett JG et al (2018) Clouds and the Earth’s Radiant Energy System (CERES) energy balanced and filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product. J Clim 31:895–918. https://doi.org/10.1175/JCLI-D-17-0208.1

    Article  Google Scholar 

  50. Ma Q, You Q, Ma Y, Cao Y, Zhang J, Niu M et al (2021) Changes in cloud amount over the Tibetan Plateau and impacts of large-scale circulation. Atmos Res 249:105332. https://doi.org/10.1016/j.atmosres.2020.105332

    Article  Google Scholar 

  51. Mann HB (1945) Nonparametric tests against trend. Econome J Economet Soc. https://doi.org/10.2307/1907187

    Article  Google Scholar 

  52. Matus AV, L’Ecuyer TS (2017) The role of cloud phase in Earth’s radiation budget. J Geophys Res Atmos 122:2559–2578. https://doi.org/10.1002/2016JD025951

    Article  Google Scholar 

  53. Min M, Wang P, Campbell JR, Zong X, Li Y (2010) Midlatitude cirrus cloud radiative forcing over China. J Geophys Res Atmos 115:D20210. https://doi.org/10.1029/2010JD014161

    Article  Google Scholar 

  54. Min M, Li J, Wang F, Liu Z, Menzel WP (2020) Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms. Remote Sens Environ 239:111616. https://doi.org/10.1016/j.rse.2019.111616

    Article  Google Scholar 

  55. Moroney C, Davies R, Muller JP (2002) Operational retrieval of cloud-top heights using MISR data. IEEE T Geosci Remote 40:1532–1540. https://doi.org/10.1109/TGRS.2002.801150

    Article  Google Scholar 

  56. Philipp D, Stengel M, Ahrens B (2020) Analyzing the Arctic Feedback Mechanism between Sea Ice and Low-Level Clouds Using 34 Years of Satellite Observations. J Clim 33:7479–7501. https://doi.org/10.1175/JCLI-D-19-0895.1

    Article  Google Scholar 

  57. Planton YY, Guilyardi E, Wittenberg AT, Lee J, Gleckler PJ, Bayr T et al (2021) Evaluating climate models with the CLIVAR 2020 ENSO metrics package. B Am Meteorol Soc 102:E193–E217. https://doi.org/10.1175/BAMS-D-19-0337.1

    Article  Google Scholar 

  58. Poulsen CA, McGarragh GR, Thomas GE, Stengel M, Christensen MW, Povey AC et al (2020) Cloud_cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties. Earth Syst Sci Data 12:2121–2135. https://doi.org/10.5194/essd-12-2121-2020

    Article  Google Scholar 

  59. PVIRv6.1 (2020) Product Validation and Intercomparison Report (PVIR)—ESA Cloud_cci, Issue 6, Revision: 1, Date of Issue:02/03/2020. https://climate.esa.int/en/odp/#/project/cloud. https://ceres.larc.nasa.gov/data/

  60. Ramanathan V, Cess RD, Harrison EF, Minnis P, Barkstrom BR, Ahmad E et al (1989) Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science 243:57–63. https://doi.org/10.1126/science.243.4887.57

    Article  Google Scholar 

  61. Rangwala I, Miller JR, Xu M (2009) Warming in the Tibetan Plateau: possible influences of the changes in surface water vapor. Geophys Res Lett 36:L06703. https://doi.org/10.1029/2009GL037245

    Article  Google Scholar 

  62. Shupe MD, Intrieri JM (2004) Cloud radiative forcing of the Arctic surface: the influence of cloud properties, surface albedo, and solar zenith angle. J Clim 17:616–628. https://doi.org/10.1175/1520-0442(2004)017%3c0616:CRFOTA%3e2.0.CO;2

    Article  Google Scholar 

  63. Stengel M, Stapelberg S, Sus O, Finkensieper S, Wuerzler B, Philipp D et al (2020) Cloud_cci advanced very high resolution radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties. Earth Syst Sci Data 12:41–60. https://doi.org/10.5194/essd-12-41-2020

    Article  Google Scholar 

  64. Stephens GL, O’Brien D, Webster PJ, Pilewski P, Kato S, Li JL (2015) The albedo of Earth. Rev Geophys 53:141–163. https://doi.org/10.1002/2014RG000449

    Article  Google Scholar 

  65. Stocker TF, Qin D, Plattner G, Tignor M, Allen SK, Boschung J et al (2013) Climate change 2013: the physical science basis. Intergovernmental panel on climate change, working group I contribution to the IPCC fifth assessment report (AR5). New York

  66. Storelvmo T, Kristjansson JE, Lohmann U (2008) Aerosol influence on mixed-phase clouds in CAM-Oslo. J Atmos Sci 65:3214–3230. https://doi.org/10.1175/2008JAS2430.1

    Article  Google Scholar 

  67. Sun W, Videen G, Kato S, Lin B, Lukashin C, Hu Y (2011) A study of subvisual clouds and their radiation effect with a synergy of CERES, MODIS, CALIPSO, and AIRS data. J Geophys Res Atmos 116:D22207. https://doi.org/10.1029/2011JD016422

    Article  Google Scholar 

  68. Tarasova TA, Fomin BA (2000) Solar radiation absorption due to water vapor: Advanced broadband parameterizations. J Appl Meteorol 39:1947–1951. https://doi.org/10.1175/1520-0450(2000)039%3c1947:SRADTW%3e2.0.CO;2

    Article  Google Scholar 

  69. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res-Atmos 106:7183–7192. https://doi.org/10.1029/2000JD900719

    Article  Google Scholar 

  70. Tian L, Zhang Y, Zhu J (2014) Decreased surface albedo driven by denser vegetation on the Tibetan Plateau. Environ Res Lett 9:104001. https://doi.org/10.1088/1748-9326/9/10/104001

    Article  Google Scholar 

  71. Turetsky MR, Abbott BW, Jones MC, Anthony KW, Olefeldt D, Schuur EAG et al (2020) Carbon release through abrupt permafrost thaw. Nat Geosci 13:138. https://doi.org/10.1038/s41561-019-0526-0

    Article  Google Scholar 

  72. Vignesh PP, Jiang JH, Pangaluru K, Su H, Smay T, Brighton N et al (2020) Assessment of CMIP6 cloud fraction and comparison with satellite observations. Earth Space Sci 7:e2019EA000975. https://doi.org/10.1029/2019EA000975

    Article  Google Scholar 

  73. Wang K, Liang S (2009) Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites. Remote Sens Environ 113:1556–1565. https://doi.org/10.1016/j.rse.2009.03.009

    Article  Google Scholar 

  74. 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. https://doi.org/10.1029/2012JD018619

    Article  Google Scholar 

  75. Wang C, Shi H, Hu H, Wang Y, Xi B (2015a) Properties of cloud and precipitation over the Tibetan Plateau. Adv Atmos Sci 32:1504–1516. https://doi.org/10.1007/s00376-015-4254-0

    Article  Google Scholar 

  76. Wang L, Lu D, He Q (2015b) The impact of surface properties on downward surface shortwave radiation over the Tibetan Plateau. Adv Atmos Sci 32:759–771. https://doi.org/10.1007/s00376-014-4131-2

    Article  Google Scholar 

  77. Wang H, Zhang H, Xie B, Jing X, He J, Liu Y (2021a) Evaluating the impacts of cloud microphysical and overlap parameters on simulated clouds in global climate models. Adv Atmos Sci. https://doi.org/10.1007/s00376-021-0369-7

    Article  Google Scholar 

  78. Wang J, Jian B, Wang G, Zhao Y, Li Y, Letu H et al (2021b) Climatology of cloud phase, cloud radiative effects and precipitation properties over the Tibetan Plateau. Remote Sens-Basel 13:363. https://doi.org/10.3390/rs13030363

    Article  Google Scholar 

  79. Wei J, Wang Z, Gu M, Luo J, Wang Y (2021) An evaluation of the Arctic clouds and surface radiative fluxes in CMIP6 models. Acta Oceanol Sin 40:85–102. https://doi.org/10.1007/s13131-021-1705-6

    Article  Google Scholar 

  80. Wielicki BA, Barkstrom BR, Harrison EF, Lee RB III, Smith GL, Cooper JE (1996) Clouds and the Earth’s Radiant Energy System (CERES): an earth observing system experiment. Bull Am Meteorol Soc 77:853–868. https://doi.org/10.1175/1520-0477(1996)077%3c0853:CATERE%3e2.0.CO;2

    Article  Google Scholar 

  81. Wild M (2008) Short-wave and long-wave surface radiation budgets in GCMs: a review based on the IPCC-AR4/CMIP3 models. Tellus A 60:932–945. https://doi.org/10.1111/j.1600-0870.2008.00342.x

    Article  Google Scholar 

  82. Wild M (2020) The global energy balance as represented in CMIP6 climate models. Clim Dyn 55:553–577. https://doi.org/10.1007/s00382-020-05282-7

    Article  Google Scholar 

  83. Wu G, Liu Y, Zhang Q, Duan A, Wang T, Wan R et al (2007) The influence of mechanical and thermal forcing by the Tibetan Plateau on Asian climate. J Hydrometeorol 8:770–789. https://doi.org/10.1175/JHM609.1

    Article  Google Scholar 

  84. Wu G, Duan A, Liu Y, Mao J, Ren R, Bao Q et al (2015) Tibetan Plateau climate dynamics: recent research progress and outlook. Natl Sci Rev 2:100–116. https://doi.org/10.1093/nsr/nwu045

    Article  Google Scholar 

  85. Xu X, Lu C, Shi X, Gao S (2008) World water tower: an atmospheric perspective. Geophys Res Lett. https://doi.org/10.1029/2008GL035867

    Article  Google Scholar 

  86. Xu Y, Shen Y, Wu Z (2013) Spatial and temporal variations of land surface temperature over the Tibetan Plateau based on harmonic analysis. Mt Res Dev 33:85–94. https://doi.org/10.1659/MRD-JOURNAL-D-12-00090.1

    Article  Google Scholar 

  87. Yan Y, Liu Y, Lu J (2016) Cloud vertical structure, precipitation, and cloud radiative effects over Tibetan Plateau and its neighboring regions. J Geophys Res Atmos 121:5864–5877. https://doi.org/10.1002/2015JD024591

    Article  Google Scholar 

  88. Yan H, Huang J, He Y, Liu Y, Wang T, Li J (2020a) Atmospheric water vapor budget and its long-term trend over the Tibetan Plateau. J Geophys Res-Atmos 125:e2020JD033297. https://doi.org/10.1029/2020JD033297

    Article  Google Scholar 

  89. Yan Y, Liu X, Liu Y, Lu J (2020b) Comparison of mixed-phase clouds over the Arctic and the Tibetan Plateau: seasonality and vertical structure of cloud radiative effects. Clim Dyn 54:4811–4822. https://doi.org/10.1007/s00382-020-05257-8

    Article  Google Scholar 

  90. Yang Y, Ren R (2017) On the contrasting decadal changes of diurnal surface temperature range between the Tibetan Plateau and southeastern China during the 1980s–2000s. Adv Atmos Sci 34:181–198. https://doi.org/10.1007/s00376-016-6077-z

    Article  Google Scholar 

  91. Yang P, Liou KN, Wyser K, Mitchell D (2000) Parameterization of the scattering and absorption properties of individual ice crystals. J Geophys Res Atmos 105:4699–4718. https://doi.org/10.1029/1999JD900755

    Article  Google Scholar 

  92. Yang P, Liou K, Bi L, Liu C, Yi B, Baum BA (2015) On the radiative properties of ice clouds: Light scattering, remote sensing, and radiation parameterization. Adv Atmos Sci 32:32–63. https://doi.org/10.1007/s00376-014-0011-z

    Article  Google Scholar 

  93. Yang Q, Liu J, Leppäranta M, Sun Q, Li R, Zhang L et al (2016) Albedo of coastal landfast sea ice in Prydz Bay, Antarctica: observations and parameterization. Adv Atmos Sci 33:535–543

    Article  Google Scholar 

  94. Yao T, Xue Y, Chen D, Chen F, Thompson L, Cui P et al (2019) Recent third pole’s rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: Multidisciplinary approach with observations, modeling, and analysis. Bull Am Meteorol Soc 100:423–444. https://doi.org/10.1175/BAMS-D-17-0057.1

    Article  Google Scholar 

  95. Ye D, Wu G (1998) The role of the heat source of the Tibetan Plateau in the general circulation. Meteorol Atmos Phys 67:181–198. https://doi.org/10.1007/BF01277509

    Article  Google Scholar 

  96. Yu RC, Wang B, Zhou TJ (2004) Climate effects of the deep continental stratus clouds generated by the Tibetan Plateau. J Clim 17:2702–2713. https://doi.org/10.1175/1520-0442(2004)017%3c2702:CEOTDC%3e2.0.CO;2

    Article  Google Scholar 

  97. Yuan T, Oreopoulos L (2013) On the global character of overlap between low and high clouds. Geophys Res Lett 40:5320–5326. https://doi.org/10.1002/grl.50871

    Article  Google Scholar 

  98. Zhang MH, Cess RD, Kwon TY, Chen MH (1994) Approaches of comparison for clear-sky radiative fluxes from general circulation models with Earth Radiation Budget Experiment data. J Geophys Res-Atmos 99:5515–5523. https://doi.org/10.1029/93JD03341

    Article  Google Scholar 

  99. Zhang XB, Harvey KD, Hogg WD, Yuzyk TR (2001) Trends in Canadian streamflow. Water Resour Res 37:987–998. https://doi.org/10.1029/2000WR900357

    Article  Google Scholar 

  100. Zhang QB, Cheng G, Yao T, Kang X, Huang J (2003) A 2,326-year tree-ring record of climate variability on the northeastern Qinghai-Tibetan Plateau. Geophys Res Lett 30:1739. https://doi.org/10.1029/2003GL017425

    Article  Google Scholar 

  101. Zhao C, Wang Y, Wang Q, Li Z, Wang Z, Liu D (2014) A new cloud and aerosol layer detection method based on micropulse lidar measurements. J Geophys Res-Atmos 119:6788–6802. https://doi.org/10.1002/2014JD021760

    Article  Google Scholar 

  102. Zhong L, Ma Y, Su Z, Salama MS (2010) Estimation of land surface temperature over the Tibetan Plateau using AVHRR and MODIS data. Adv Atmos Sci 27:1110–1118. https://doi.org/10.1007/s00376-009-9133-0

    Article  Google Scholar 

  103. Zhou T, Zhang W (2021) Anthropogenic warming of Tibetan Plateau and constrained future projection. Environ Res Lett 16:44039. https://doi.org/10.1088/1748-9326/abede8

    Article  Google Scholar 

  104. Zhu Y, Yang S (2020) Evaluation of CMIP6 for historical temperature and precipitation over the Tibetan Plateau and its comparison with CMIP5. Adv Clim Change Res 11:239–251. https://doi.org/10.1016/j.accre.2020.08.001

    Article  Google Scholar 

  105. Zhuo H, Liu Y, Jin J (2016) Improvement of land surface temperature simulation over the Tibetan Plateau and the associated impact on circulation in East Asia. Atmos Sci Lett 17:162–168. https://doi.org/10.1002/asl.638

    Article  Google Scholar 

  106. https://ceres.larc.nasa.gov/data/

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Acknowledgements

This work was jointly supported by the NSFC Major Project (42090030), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA2006010301), National Science Fund for Excellent Young Scholars (42022037), and the National Science Foundation of China (91837209). We would like to thank the CERES, ESA Cloud-cci and CMIP6 science teams for providing these excellent and accessible data products that made this study possible.

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Correspondence to Jiming Li.

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Zhao, Y., Zhao, Y., Li, J. et al. Evaluating cloud radiative effect from CMIP6 and two satellite datasets over the Tibetan Plateau based on CERES observation. Clim Dyn (2021). https://doi.org/10.1007/s00382-021-05991-7

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Keywords

  • Tibetan plateau
  • Cloud radiative effect
  • CMIP6
  • Satellite observations