Abstract
The Earth’s planetary albedo (PA) has an essential impact on the global radiation budget. Based on 14 years of monthly data from the Clouds and the Earth’s Radiant Energy System energy balanced and filled (CERES-EBAF) Ed4.1 dataset and atmosphere-only simulations of the Coupled Model Intercomparison Project Phase6 (CMIP6/AMIP), this study investigates the ability of CMIP6/AMIP model in reproducing the observed inter-month changes, annual cycle and trend of PA at near-global and regional scales. Statistical results indicate that some persistent biases in the previous models continue to exist in the CMIP6 models; however, some progresses have been made. In CMIP6/AMIP, large negative correlations for PA between the model ensemble mean and observation are addressed over the subtropical stratocumulus regions. In addition, the simulation of PA in drylands and tropical oceans remains a challenge in CMIP6 models. Over the most regions, PA biases are governed by cloud albedo forcing biases. These results demonstrate the importance of improving cloud process simulations for accurately representing the PA in models. For the annual cycles, the model ensemble mean captures the difference in amplitude between the two peak values of PA (June and December), as well as the phase of the seasonal cycle, despite PA is systematically overestimated. The differences between different terrestrial climatic regions are also examined. Results indicate that the relative biases of PA are greatest in semi-arid (2.2%) and semi-humid (2.8%) regions, whereas the minimum relative bias occurs in arid regions (0.3%) due to compensating errors.
Similar content being viewed by others
References
Bao S et al (2018) Spatiotemporal distributions of cloud parameters and the temperature response over the mongolian plateau during 2006–2015 based on MODIS data. IEEE J-Stars 12:549–558. https://doi.org/10.1109/JSTARS.2018.2857827
Bender FAM, Rodhe H, Charlson RJ, Ekman AML, Loeb N (2006) 22 views of the global albedo—comparison between 20 GCMs and two satellites. Tellus A 58:320–330. https://doi.org/10.1111/j.1600-0870.2006.00181.x
Bender FAM, Engstrom A, Karlsson J (2016) Factors controlling cloud albedo in marine subtropical stratocumulus regions in climate models and satellite observations. J Clim 29:3559–3587. https://doi.org/10.1175/JCLI-D-15-0095.1
Boucher O et al (2013) Clouds and aerosols. In: Stocker TF, Qin D, Plattnes G-K, Tignos M, Allen SK, Boshung J, Naules A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge
Chen C et al (2019) China and India lead in greening of the world through land-use management. Nat Sustain 2:122. https://doi.org/10.1038/s41893-019-0220-7
Doelling DR et al (2013) Geostationary enhanced temporal interpolation for CERES flux products. J Atmos Ocean Technol 30:1072–1090. https://doi.org/10.1175/JTECH-D-12-00136.1
Donohoe A, Battisti DS (2011) Atmospheric and surface contributions to planetary Albedo. J Clim 24:4402–4418. https://doi.org/10.1175/2011JCLI3946.1
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (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
Fan T et al (2018a) Emission or atmospheric processes? An attempt to attribute the source of large bias of aerosols in eastern China simulated by global climate models. Atmos Chem Phys 18:1395–1417. https://doi.org/10.5194/acp-18-1395-2018
Fan T et al (2018b) Quantify contribution of aerosol errors to cloud fraction biases in CMIP5 atmospheric model Intercomparison project simulations. Int J Climatol 38:3140–3156. https://doi.org/10.1002/joc.5490
Feingold G, Jiang HL, Harrington JY (2005) On smoke suppression of clouds in Amazonia. Geophys Res Lett 32:L02804. https://doi.org/10.1029/2004gl021369
Feng S, Fu Q (2013) Expansion of global drylands under a warming climate. Atmos Chem Phys 13:10081–10094. https://doi.org/10.5194/acp-13-10081-2013
Forzieri G, Alkama R, Miralles DG, Cescatti A (2017) Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science 356:1180–1184. https://doi.org/10.1126/science.aal1727
Fu Q, Feng S (2014) Responses of terrestrial aridity to global warming. J Geophys Res-Atmos 119:7863–7875. https://doi.org/10.1002/2014JD021608
Fu Q, Cribb MC, Barker HW, Krueger SK, Grossman A (2000) Cloud geometry effects on atmospheric solar absorption. J Atmos Sci 57:1156–1168. https://doi.org/10.1175/1520-0469(2000)057%3c1156:CGEOAS%3e2.0.CO;2
Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res-Atmos 113:D06104. https://doi.org/10.1029/2007JD008972
Govaerts YM, Pereira JM, Pinty B, Mota B (2002) Impact of fires on surface albedo dynamics over the African continent. J Geophys Res-Atmos 107:4629. https://doi.org/10.1029/2002jd002388
Houghton JT et al (2001) Climate change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge
Hu Y, Stamnes K (1993) An accurate parameterization of the radiative properties of water clouds suitable for use in climate models. J Clim 6:728–742. https://doi.org/10.1175/1520-0442(1993)006%3c0728:AAPOTR%3e2.0.CO;2
Hu Y, Stamnes K (2000) Climate sensitivity to cloud optical properties. Tellus B 52:81–93. https://doi.org/10.1034/j.1600-0889.2000.00993.x
Huang J, Yi Y (1991) Inversion of a nonlinear dynamical model from the observation. Sci China B 34:1246–1246
Huang J et al (2007) Summer dust aerosols detected from CALIPSO over the Tibetan Plateau. Geophys Res Lett 34:L18805. https://doi.org/10.1029/2007GL029938
Huang J et al (2008) Long-range transport and vertical structure of Asian dust from CALIPSO and surface measurements during PACDEX. J Geophys Res-Atmos 113:D23212. https://doi.org/10.1029/2008JD010620
Huang J, Guan X, Ji F (2012) Enhanced cold-season warming in semi-arid regions. Atmos Chem Phys 12:5391–5398. https://doi.org/10.5194/acp-12-5391-2012
Huang J, Wang T, Wang W, Li Z, Yan H (2014) Climate effects of dust aerosols over East Asian arid and semiarid regions. J Geophys Res-Atmos 119:11398–11416. https://doi.org/10.1002/2014JD021796
Huang J, Ji M, Xie Y, Wang S, He Y, Ran J (2016a) Global semi-arid climate change over last 60 years. Clim Dyn 46:1131–1150. https://doi.org/10.1007/s00382-015-2636-8
Huang J, Yu H, Guan X, Wang G, Guo R (2016b) Accelerated dryland expansion under climate change. Nat Clim Chang 6:166. https://doi.org/10.1038/Nclimate2837
Huang J et al (2017) Dryland climate change: Recent progress and challenges. Rev Geophys 55:719–778. https://doi.org/10.1002/2016rg000550
Ichoku C et al (2016) Biomass burning, land-cover change, and the hydrological cycle in Northern sub-Saharan Africa. Environ Res Lett 11:095005. https://doi.org/10.1088/1748-9326/11/9/095005
Jian B, Li J, Wang G, He Y, Han Y, Zhang M, Huang J (2018) The impacts of atmospheric and surface parameters on long-term variations in the planetary Albedo. J Clim 31:8705–8718. https://doi.org/10.1175/JCLI-D-17-0848.S1
Kato S 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
Koch D, Del Genio AD (2010) Black carbon semi-direct effects on cloud cover: review and synthesis. Atmos Chem Phys 10:7685–7696. https://doi.org/10.5194/acp-10-7685-2010
Koren I, Martins JV, Remer LA, Afargan H (2008) Smoke invigoration versus inhibition of clouds over the Amazon. Science 321:946–949. https://doi.org/10.1126/science.1159185
Lauer A, Hamilton K (2013) Simulating clouds with global climate models: a comparison of CMIP5 results with CMIP3 and satellite data. J Clim 26:3823–3845. https://doi.org/10.1175/JCLI-D-12-00451.1
Lauer A et al (2017) Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool. Remote Sens Environ 203:9–39. https://doi.org/10.1016/j.rse.2017.01.007
Letu H et al (2018) Ice cloud properties from Himawari-8/AHI next-generation geostationary satellite: capability of the AHI to monitor the DC cloud generation process. IEEE T Geosci Remote 57:3229–3239. https://doi.org/10.1109/TGRS.2018.2882803
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
Li Y, Wang T, Zeng ZZ, Peng SS, Lian X, Piao SL (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
Li J, Lv Q, Zhang M, Wang T, Kawamoto K, Chen S, Zhang B (2017) Effects of atmospheric dynamics and aerosols on the fraction of supercooled water clouds. Atmos Chem Phys 17:1847–1863. https://doi.org/10.5194/acp-17-1847-2017
Li J 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
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
Loeb NG, Wielicki BA, Rose FG, Doelling DR (2007) Variability in global top-of-atmosphere shortwave radiation between 2000 and 2005. Geophys Res Lett 34:L03704. https://doi.org/10.1029/2006gl028196
Loeb NG, Manalo-Smith N, Su WY, Shankar M, Thomas S (2016) CERES top-of-atmosphere earth radiation budget. Climate data record: accounting for in-orbit changes in instrument calibration. Remote Sens 18:182. https://doi.org/10.3390/rs8030182
Maidment DR (1993) Handbook of hydrology, vol 9780070. McGraw-Hill, New York
McCoy DT et al (2015) Natural aerosols explain seasonal and spatial patterns of Southern Ocean cloud albedo. Sci Adv 1:e1500157. https://doi.org/10.1126/sciadv.1500157
Middleton N, Thomas D (1997) World Atlas of desertification. Edward Arnold, London
Myhre G, Hoyle CR, Berglen TF, Johnson BT, Haywood JM (2008) Modeling of the solar radiative impact of biomass burning aerosols during the Dust and Biomass-burning. Experiment (DABEX) J Geophys Res-Atmos 113:D00C16. https://doi.org/10.1029/2008jd009857
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:L21801. https://doi.org/10.1029/2012gl053421
Pallé E, Goode P, Montanes-Rodriguez P, Koonin S (2004) Changes in Earth's reflectance over the past two decades. Science 304:1299–1301. https://doi.org/10.1126/science.1094070
Qu X, Hall A (2005) Surface contribution to planetary albedo variability in cryosphere regions. J Clim 18:5239–5252. https://doi.org/10.1175/JCLI3555.1
Rechid D, Raddatz T, Jacob D (2009) Parameterization of snow-free land surface albedo as a function of vegetation phenology based on MODIS data and applied in climate modelling. Theor Appl Climatol 95:245–255. https://doi.org/10.1007/s00704-008-0003-y
Shen M et al (2015) Evaporative cooling over the Tibetan Plateau induced by vegetation growth. Proc Natl Acad Sci USA 112:9299–9304. https://doi.org/10.1073/pnas.1504418112
Solomon S et al (2007) The physical science basis: contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change Intergovernmental Panel on Climate Change (IPCC), Climate change 2007. Cambridge University Press, Cambridge
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
Stocker TF et al (2013) Climate change 2013: the physical science basis contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge
Stouffer RJ, Eyring V, Meehl GA, Bony S, Senior C, Stevens B, Taylor KE (2017) CMIP5 scientific gaps and recommendations for CMIP6. B Am Meteorol Soc 98:95–105. https://doi.org/10.1175/bams-d-15-00013.1
Tsushima Y et al (2006) Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: a multi-model study. Clim Dyn 27:113–126. https://doi.org/10.1007/s00382-006-0127-7
Van Weverberg K et al (2018) CAUSES: attribution of surface radiation biases in NWP and climate models near the US Southern Great Plains. J Geophys Res-Atmos 123:3612–3644. https://doi.org/10.1002/2017jd027188
Vignesh PP, Jiang JH, Kishore P, Su H, Smay T, Brighton N, Velicogna I (2020) Assessment of CMIP6 cloud fraction and comparison with satellite observations. Earth Space Sci 7:e2019EA000975. https://doi.org/10.1029/2019EA000975
Voigt A, Stevens B, Bader J, Mauritsen T (2014) Compensation of hemispheric albedo asymmetries by shifts of the ITCZ and tropical clouds. J Clim 27:1029–1045. https://doi.org/10.1175/JCLI-D-13-00205.1
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
Wang Y, Zhao C (2017) Can MODIS cloud fraction fully represent the diurnal and seasonal variations at DOE ARM SGP and Manus sites? J Geophys Res-Atmos 122:329–343. https://doi.org/10.1002/2016JD025954
Wilcox EM (2010) Stratocumulus cloud thickening beneath layers of absorbing smoke aerosol. Atmos Chem Phys 10:11769–11777. https://doi.org/10.5194/acp-10-11769-2010
Xie S, Liu X, Zhao C, Zhang Y (2013) Sensitivity of CAM5-simulated Arctic clouds and radiation to ice nucleation parameterization. J Clim 26:5981–5999. https://doi.org/10.1175/JCLI-D-12-00517.1
Xu H et al (2017) Warming effect of dust aerosols modulated by overlapping clouds below. Atmos Environ 166:393–402. https://doi.org/10.1016/j.atmosenv.2017.07.036
Yang Y, Zhao C, Fan H (2020) Spatiotemporal distributions of cloud properties over China based on Himawari-8 advanced Himawari imager data. Atmos Res 240:104927. https://doi.org/10.1016/j.atmosres.2020.104927
Yao R, Wang L, Huang X, Chen X, Liu Z (2019) Increased spatial heterogeneity in vegetation greenness due to vegetation greening in mainland. China Ecol Indic 99:240–250. https://doi.org/10.1016/j.ecolind.2018.12.039
Zhao C, Klein SA, Xie S, Liu X, Boyle JS, Zhang Y (2012) Aerosol first indirect effects on non-precipitating low-level liquid cloud properties as simulated by CAM5 at ARM sites. Geophys Res Lett 39:L08806. https://doi.org/10.1029/2012gl051213
Zhao C, Xie S, Chen X, Jensen MP, Dunn M (2014) Quantifying uncertainties of cloud microphysical property retrievals with a perturbation method. J Geophys Res-Atmos 119:5375–5385. https://doi.org/10.1002/2013JD021112
Zhao C, Liu L, Wang Q, Qiu Y, Wang W, Wang Y, Fan T (2016) Toward understanding the properties of high ice clouds at the Naqu site on the Tibetan Plateau using ground-based active remote sensing measurements obtained during a short period in July 2014. J Appl Meteorol Climatol 55:2493–2507. https://doi.org/10.1016/j.atmosres.2017.02.002
Zhao C, Chen Y, Li J, Letu H, Su Y, Chen T, Wu X (2019) Fifteen-year statistical analysis of cloud characteristics over China using terra and aqua moderate resolution imaging spectroradiometer observations. Int J Climatol 39:2612–2629. https://doi.org/10.1002/joc.5975
Acknowledgements
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA2006010301), the Innovative Research Groups of the National Science Foundation of China (41521004), the National Science Foundation of China (41575015), and the Fundamental Research Funds for the Central Universities (Lzujbky-2017-64).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Jian, B., Li, J., Zhao, Y. et al. Evaluation of the CMIP6 planetary albedo climatology using satellite observations. Clim Dyn 54, 5145–5161 (2020). https://doi.org/10.1007/s00382-020-05277-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-020-05277-4