Abstract
Cloud radiative kernels were built by BCC_RAD (Beijing Climate Center radiative transfer model) radiative transfer code. Then, short-term cloud feedback and its mechanisms in East Asia (0.5°S–60.5°N, 69.5°–150.5°E) were analyzed quantitatively using the kernels combined with MODIS satellite data from July 2002 to June 2018. According to the surface and monsoon types, four subregions in East Asia—the Tibetan Plateau, northwest, temperate monsoon (TM), and subtropical monsoon (SM)—were selected. The average longwave, shortwave, and net cloud feedbacks in East Asia are −0.68 ± 1.20, 1.34 ± 1.08, and 0.66 ± 0.40 W m−2 K−1 (±2σ), respectively, among which the net feedback is dominated by the positive shortwave feedback. Positive feedback in SM is the strongest of all subregions, mainly due to the contributions of nimbostratus and stratus. In East Asia, short-term feedback in spring is primarily caused by marine stratus in SM, in summer is primarily driven by deep convective cloud in TM, in autumn is mainly caused by land nimbostratus in SM, and in winter is mainly driven by land stratus in SM. Cloud feedback in East Asia is chiefly driven by decreases in mid-level and low cloud fraction owing to the changes in relative humidity, and a decrease in low cloud optical thickness due to the changes in cloud water content.
摘 要
我们首先利用 BCC_RAD 辐射传输模式构建了云辐射内核, 结合 2002 年 7 月至 2018 年 6 月的 MODIS 卫星资料, 定量计算并分析了东亚地区 (0.5°S–60.5°N, 69.5°–150.5°E) 短期云反馈的时空变化特征; 然后根据地面特征和季风类型在东亚地区选取了青藏高原、 西北地区、 温带季风区和亚热带季风区 4 个子区进行了云反馈研究. 结果表明: 东亚地区的长波云反馈为−0.68±1.20 W m-2 K-1, 短波云反馈为 1.34±1.08 W m-2 K-1, 净云反馈为 0.66±0.40 W m-2 K-1, 其中, 云反馈主要来自短波云反馈的贡献. 在 4 个子区中, 亚热带季风区的正反馈最强烈, 主要是雨层云和层云的贡献. 春季的云反馈主要来自亚热带季风区海洋层云的贡献, 夏季的云反馈主要受到温带季风区深对流云的影响, 秋季的云反馈主要受到亚热带季风区陆地雨层云的影响, 冬季的云反馈主要来自亚热带季风区陆地层云的贡献. 东亚地区的云反馈主要是由中低云云量的减少和低云光学厚度变薄引起.
Similar content being viewed by others
References
Bender, F. A.-M., V. Ramanathan, and G. Tselioudis, 2012: Changes in extratropical storm track cloudiness 1983–2008: Observational support for a poleward shift. Climate Dyn., 38, 2037–2053, https://doi.org/10.1007/s00382-011-1065-6.
Bony, S., J. L. Dufresne, H. Le Treut, J. J. Morcrette, and C. Senior, 2004: On dynamic and thermodynamic components of cloud changes. Climate Dyn., 22, 71–86, https://doi.org/10.1007/s00382-003-0369-6.
Ceppi, P., D. T. McCoy, and D. L. Hartmann, 2016: Observational evidence for a negative shortwave cloud feedback in middle to high latitudes. Geophys. Res. Lett., 43, 1331–1339, https://doi.org/10.1002/2015GL067499.
Ceppi, P., F. Brient, M. D. Zelinka, and D. L. Hartmann, 2017: Cloud feedback mechanisms and their representation in global climate models. WIREs Climate Change, 8, e465, https://doi.org/10.1002/wcc.465.
Colman, R., and L. Hanson, 2017: On the relative strength of radiative feedbacks under climate variability and change. Climate Dyn., 49, 2115–2129, https://doi.org/10.1007/s00382-016-3441-8.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Dessler, A. E., 2010: A determination of the cloud feedback from climate variations over the past decade. Science, 330, 1523–1527, https://doi.org/10.1126/science.1192546.
Dessler, A. E., 2013: Observations of climate feedbacks over 2000–10 and comparisons to climate models. J. Climate, 26, 333–342, https://doi.org/10.1175/JCLI-D-11-00640.1.
Dessler, A. E., and N. G. Loeb, 2013: Impact of dataset choice on calculations of the short-term cloud feedback. J. Geophys. Res. Atmos., 118, 2821–2826, https://doi.org/10.1002/jgrd.50199.
Fu, Q., and K. N. Liou, 1992: On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. J. Atmos. Sci., 49, 2139–2156, https://doi.org/10.1175/1520-0469(1992)049<2139:OTCDMF>2.0.CO;2.
GISTEMP-Team, 2019: GISS Surface Temperature Analysis (GISTEMP v4), version 4. [Available online from https://data.giss.nasa.gov/gistemp/]
Held, I. M., and B. J. Soden, 2000: Water vapor feedback and global warming. Annual Review of Energy and the Environment, 25, 441–475, https://doi.org/10.1146/annurev.energy.25.1.441.
IPCC, 2014: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 580–590.
Klein, S. A., and C. Jakob, 1999: Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon. Wea. Rev., 127, 2514–2531, https://doi.org/10.1175/1500-0493(1999)127<2514:VASOFC>2.0.CO;2.
Lenssen, N. J. L., G. A. Schmidt, J. E. Hansen, M. J. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 24, 6307–6326, https://doi.org/10.1029/2018JD029522.
Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748–766, https://doi.org/10.1175/2008JCLI2637.1.
Lu, P., H. Zhang, and J. N. Li, 2011: Correlated k-distribution treatment of cloud optical properties and related radiative impact. J. Atmos. Sci., 68, 2671–2688, https://doi.org/10.1175/JAS-D-10-05001.1.
Meehl, G. A., and Coauthors, 2007: Global climate projections. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon et al., Eds., Cambridge University Press, 747–843.
Nakajima, T., M. Tsukamoto, Y. Tsushima, A. Numaguti, and T. Kimura, 2000: Modeling of the radiative process in an atmospheric general circulation model. Appl. Opt., 39, 4869–4878, https://doi.org/10.1364/AO.39.004869.
Parkinson, C. L., 2003: Aqua: An Earth-Observing Satellite mission to examine water and other climate variables. IEEE Trans. Geosci. Remote Sens., 41, 173–183, https://doi.org/10.1109/TGRS.2002.808319.
Platnick, S., M. King, and P. Hubanks, 2017: MODIS Atmosphere L3 Monthly Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center. [Available online from https://doi.org/10.5067/MODIS/MDD08_M3.006].
Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteorol. Soc., 80, 2261–2288, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.
Senior, C. A., and J. F. B. Mitchell, 1993: Carbon dioxide and climate. The impact of cloud parameterization. J. Climate, 6, 393–418, https://doi.org/10.1175/1520-0442(1993)006<0393:CDACTI>2.0.CO;2.
Sherwood, S. C., W. Ingram, Y. Tsushima, M. Satoh, M. Roberts, P. L. Vidale, and P. A. O’Gorman, 2010: Relative humidity changes in a warmer climate. J. Geophys. Res. Atmos., 115, https://doi.org/10.1029/2009JD012585.
Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled ocean -atmosphere models. J. Climate, 19, 3354–3360, https://doi.org/10.1175/JCLI3799.1.
Tsushima, Y., and Coauthors, 2006: Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: A multimodel study. Climate Dyn., 27, 113–126, https://doi.org/10.1007/s00382-006-0127-7.
Vavrus, S., D. Waliser, A. Schweiger, and J. Francis, 2009: Simulations of 20th and 21st century Arctic cloud amount in the global climate models assessed in the IPCC AR4. Climate Dyn., 33, 1099–1115, https://doi.org/10.1007/s00382-008-0475-6.
Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Climate Dyn., 41, 3339–3362, https://doi.org/10.1007/s00382-013-1725-9.
Wang, F., X. G. Xin, Z. Z. Wang, Y. J. Cheng, J. Zhang, and S. Yang, 2014: Evaluation of cloud vertical structure simulated by recent BCC_AGCM versions through comparison with CALIPSO-GOCCP data. Adv. Atmos. Sci., 31, 721–733, https://doi.org/10.1007/s00376-013-3099-7.
Webb, M., C. Senior, S. Bony, and J.-J. Morcrette, 2001: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Climate Dyn., 17, 905–922, https://doi.org/10.1007/s003820100157.
Webb, M. J., and Coauthors, 2017: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. Geoscientific Model Development, 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017.
Wei, X. D., and H. Zhang, 2011: Analysis of optical properties of nonspherical dust aerosols. Acta Optica Sinica, 31, 0501002, https://doi.org/10.3788/AOS201131.0501002. (in Chinese with English abstract)
Wetherald, R. T., and S. Manabe, 1980: Cloud cover and climate sensitivity. J. Atmos. Sci., 37, 1485–1510, https://doi.org/10.1175/1520-0469(1980)037<1485:CCACS>2.0.CO;2.
Williams, K. D., and M. J. Webb, 2009: A quantitative performance assessment of cloud regimes in climate models. Climate Dyn., 33, 141–157, https://doi.org/10.1007/s00382-008-0443-1.
Yu, R. C., Y. Q. Yu, and M. H. Zhang, 2001: Comparing cloud radiative properties between the Eastern China and the Indian monsoon region. Adv. Atmos. Sci., 18, 1090–1102, https://doi.org/10.1007/s00376-001-0025-1.
Yue, Q., B. H. Kahn, E. J. Fetzer, M. Schreier, S. Wong, X. H. Chen, and X. L. Huang, 2016: Observation-based longwave cloud radiative kernels derived from the A-Train. J. Climate, 29, 2023–2040, https://doi.org/10.1175/JCLI-D-15-0257.1.
Yue, Q., B. H. Kahn, E. J. Fetzer, S. Wong, R. Frey, and K. G. Meyer, 2017: On the response of MODIS cloud coverage to global mean surface air temperature. J. Geophys. Res. Atmos., 122, 966–979, https://doi.org/10.1002/2016JD025174.
Yue, Q., B. H. Kahn, E. J. Fetzer, S. Wong, X. L. Huang, and M. Schreier, 2019: Temporal and spatial characteristics of short-term cloud feedback on global and local interannual climate fluctuations from A-Train observations. J. Climate, 32, 1875–1893, https://doi.org/10.1175/JCLI-D-18-0335.1.
Zelinka, M. D., C. Zhou, and S. A. Klein, 2016: Insights from a refined decomposition of cloud feedbacks. Geophys. Res. Lett., 33, 9259–9269, https://doi.org/10.1002/2016GL069917.
Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012a: Computing and partitioning cloud feedbacks using cloud property histograms. Part I: Cloud radiative kernels. J. Climate, 25, 3715–3735, https://doi.org/10.1175/JCLI-D-11-00248.1.
Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012b: Computing and partitioning cloud feedbacks using cloud property histograms. Part II: Attribution to changes in cloud amount, altitude, and optical depth. J. Climate, 25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1.
Zhang, H., 2016: Atmospheric Radiative Transfer Model of BCC_RAD. China Meteorological Press, 205 pp. (in Chinese)
Zhang, H., T. Nakajima, G. Y. Shi, T. Suzuki, and R. Imasu, 2003: An optimal approach to overlapping bands with correlated k distribution method and its application to radiative calculations. J. Geophys. Res. Atmos., 108, 4641, https://doi.org/10.1029/2002JD003358.
Zhang, H., Q. Chen, and B. Xie, 2015: A new parameterization for ice cloud optical properties used in BCC-RAD and its radiative impact. Journal of Quantitative Spectroscopy and Radiative Transfer, 150, 76–86, https://doi.org/10.1016/j.jqsrt.2014.08.024.
Zhou, C., A. E. Dessler, M. D. Zelinka, P. Yang, and T. Wang, 2014: Cirrus feedback on interannual climate fluctuations. Geophys. Res. Lett., 41, 9166–9173, https://doi.org/10.1002/2014GL062095.
Zhou, C., H. Zhang, and Z. L. Wang, 2013a: Impact of different mixing ways of black carbon and non-absorbing aerosols on the optical properties. Acta Optica Sinica, 33, 0829001, https://doi.org/10.3788/AOS201333.0829001. (in Chinese with English abstract)
Zhou, C., M. D. Zelinka, A. E. Dessler, and P. Yang, 2013b: An analysis of the short-term cloud feedback using MODIS data. J. Climate, 26, 4803–4815, https://doi.org/10.1002/2014GL062095.
Zhou, C., M. D. Zelinka, A. E. Dessler, and S. A. Klein, 2015: The relationship between interannual and long-term cloud feedbacks. Geophys. Res. Lett., 42, 10 463–10 469, https://doi.org/10.1002/2015GL066698.
Zhu, T. T., Y. Huang, and H. K. Wei, 2019: Estimating climate feedbacks using a neural network. J. Geophys. Res. Atmos., 124, 3246–3258, https://doi.org/10.1029/2018JD029223.
Acknowledgements
This work was financially supported by the National Key R&D Program of China (Grant No. 2017YFA0603502) and the National Natural Science Foundation of China (Grant Nos. 91644211 and 41575002).
Author information
Authors and Affiliations
Corresponding author
Additional information
Article Highlights
• Cloud radiative kernels by BCC_RAD were built, then the short-term cloud feedback in East Asia was studied quantitatively.
• The short-term cloud feedback in East Asia is primarily driven by nimbostratus and stratus, and the feedback is strongest in SM.
• The feedback in East Asia is caused by decreases in mid-level and low cloud fraction, and the decrease in low cloud optical thickness.
Rights and permissions
About this article
Cite this article
Wang, F., Zhang, H., Chen, Q. et al. Analysis of Short-term Cloud Feedback in East Asia Using Cloud Radiative Kernels. Adv. Atmos. Sci. 37, 1007–1018 (2020). https://doi.org/10.1007/s00376-020-9281-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-020-9281-9