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A bias-corrected projection for the changes in East Asian summer monsoon rainfall under global warming

Abstract

Projecting regional rainfall changes in a warmer climate attracts ongoing attention. However, large uncertainty still exists in multi-model projection. In this study, we introduce a bias-corrected method to correct the multi-model projection of changes in East Asian summer monsoon (EASM) rainfall based on the historical and RCP8.5 runs of 25 models from phase 5 of Coupled Model Intercomparison Project. Firstly, the total rainfall changes are separated into the thermodynamic component due to increased specific humidity and the dynamic component due to circulation changes. The thermodynamic component is corrected using the observed present-day rainfall and the increase rate of specific humidity based on the wet-get-wetter mechanism. On the other hand, the dynamic component with the circulation changes is corrected based on a “spatial emergent constraint” method, which is further validated by the perfect model approach. Together, these corrections give an integrated projection for EASM rainfall changes under global warming. Such an approach can improve the signal-to-noise ratio of projection effectively, from the original 0.73 of the multimodel mean to around 1.9. The corrected projection of EASM rainfall changes shows a pronounced increase in southern China, the northwest Pacific and a belt from northern China to northeastern China, and a weak increase in other EASM regions.

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References

  • Abe M, Shiogama H, Nozawa T, Emori S (2011) Estimation of future surface temperature changes constrained using the future-present correlated modes in inter-model variability of CMIP3 multimodel simulations. J Geophys Res 116:D18104

    Google Scholar 

  • Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P-P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4(6):1147–1167

    Google Scholar 

  • Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419(6903):224–232

    Google Scholar 

  • Bracegirdle TJ, Stephenson DB (2012) Higher precision estimates of regional polar warming by ensemble regression of climate model projections. Climate Dyn 39(12):2805–2821

    Google Scholar 

  • Bracegirdle TJ, Stephenson DB (2013) On the robustness of emergent constraints used in multimodel climate change projections of arctic warming. J Climate 26(2):669–678

    Google Scholar 

  • Brown JR, Moise AF, Colman R, Zhang H (2016) Will a warmer world mean a wetter or drier Australian monsoon? J Climate 29(12):4577–4596

    Google Scholar 

  • Byrne MP, O’Gorman PA (2015) The response of precipitation minus evapotranspiration to climate warming: why the “wet-get-wetter, dry-get-drier” scaling does not hold over land. J Climate 28(20):8078–8092

    Google Scholar 

  • Byrne MP, O’Gorman PA (2013) Link between land-ocean warming contrast and surface relative humidities in simulations with coupled climate models. Geophys Res Lett 40(19):5223–5227

    Google Scholar 

  • Chen H, Sun J (2013) Projected change in East Asian summer monsoon precipitation under RCP scenario. Meteorol Atmos Phys 121(1–2):55–77

    Google Scholar 

  • Chen X, Zhou T (2015) Distinct effects of global mean warming and regional sea surface warming pattern on projected uncertainty in the South Asian summer monsoon. Geophys Res Lett 42(21):9433–9439

    Google Scholar 

  • Chou C, Neelin JD (2004) Mechanisms of global warming impacts on regional tropical precipitation. J Climate 17:2688–2701

    Google Scholar 

  • Chou C, Neelin JD, Chen C-A, Tu J-Y (2009) Evaluating the “rich-get-richer” mechanism in tropical precipitation change under global warming. J Climate 22(8):1982–2005

    Google Scholar 

  • Chou C, Chiang JCH, Lan C-W, Chung C-H, Liao Y-C, Lee C-J (2013) Increase in the range between wet and dry season precipitation. Nat Geosci 6(4):263–267

    Google Scholar 

  • Collins M, Chandler RE, Cox PM, Huthnance JM, Rougier J, Stephenson DB (2012) Quantifying future climate change. Nat Climate Change 2(6):403–409

    Google Scholar 

  • Cox PM, Pearson D, Booth BB, Friedlingstein P, Huntingford C, Jones CD, Luke CM (2013) Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494:341

    Google Scholar 

  • de Carvalho LMV (2016) The monsoons and climate change. In: de Carvalho LMV, Jones C (eds) The monsoons and climate change: observations and modeling. Springer International Publishing, Berlin

    Google Scholar 

  • Ding Y, Chan JCL (2005) The East Asian summer monsoon: an overview. Meteorol Atmos Phys 89:117–142

    Google Scholar 

  • Endo H, Kitoh A (2014) Thermodynamic and dynamic effects on regional monsoon rainfall changes in a warmer climate. Geophys Res Lett 41(5):1704–1711

    Google Scholar 

  • Gao Y, Wang H, Jiang D (2015) An intercomparison of CMIP5 and CMIP3 models for interannual variability of summer precipitation in Pan-Asian monsoon region. Int J Climatol 35(13):3770–3780

    Google Scholar 

  • Gill AE (1980) Some simple solutions for heat-induced tropical circulation. Q J R Meteorol Soc 106(449):447–462

    Google Scholar 

  • Hall A, Cox P, Huntingford C, Klein S (2019) Progressing emergent constraints on future climate change. Nat Climate Change 9(4):269–278

    Google Scholar 

  • Ham Y-G, Kug J-S, Choi J-Y, Jin F-F, Watanabe M (2018) Inverse relationship between present-day tropical precipitation and its sensitivity to greenhouse warming. Nat Climate Change 8(1):64–69

    Google Scholar 

  • Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90(8):1095–1107

    Google Scholar 

  • Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Climate Dyn 37(1–2):407–418

    Google Scholar 

  • He C, Zhou T (2015) Responses of the Western North Pacific subtropical high to global warming under RCP4.5 and RCP8.5 scenarios projected by 33 CMIP5 models: the dominance of tropical Indian Ocean–tropical Western Pacific SST gradient. J Climate 28(1):365–380

    Google Scholar 

  • Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Climate 19:5686–5699

    Google Scholar 

  • Hsu P-c, Li T, Luo J-J, Murakami H, Kitoh A, Zhao M (2012) Increase of global monsoon area and precipitation under global warming: a robust signal? Geophys Res Lett 39(6):L06701

    Google Scholar 

  • Hu Z-Z, Yang S, Wu R (2003) Long-term climate variations in China and global warming signals. J Geophys Res 108(19):4614

    Google Scholar 

  • Huang P (2014) Regional response of annual-mean tropical rainfall to global warming. Atmos Sci Lett 15(2):103–109

    Google Scholar 

  • Huang P (2017) Time-varying response of ENSO-induced tropical pacific rainfall to global warming in CMIP5 models. Part II: intermodel uncertainty. J Climate 30(2):595–608

    Google Scholar 

  • Huang P, Ying J (2015) A multimodel ensemble pattern regression method to correct the tropical Pacific SST change patterns under global warming. J Clim 28(12):4706–4723

    Google Scholar 

  • Huang P, Xie S-P, Hu K, Huang G, Huang R (2013) Patterns of the seasonal response of tropical rainfall to global warming. Nat Geosci 6(5):357–361

    Google Scholar 

  • Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP–DOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83(11):1631–1644

    Google Scholar 

  • Kent C, Chadwick R, Rowell DP (2015) Understanding uncertainties in future projections of seasonal tropical precipitation. J Climate 28(11):4390–4413

    Google Scholar 

  • Kimoto M (2005) Simulated change of the East Asian circulation under global warming scenario. Geophys Res Lett 32:L16701

    Google Scholar 

  • Kitoh A, Endo H, Krishna Kumar K, Cavalcanti IFA, Goswami P, Zhou T (2013) Monsoons in a changing world: a regional perspective in a global context. J Geophys Res Atmos 118(8):3053–3065

    Google Scholar 

  • Klein SA, Hall A (2015) Emergent constraints for cloud feedbacks. Curr Climate Change Rep 1(4):276–287

    Google Scholar 

  • Knutti R (2010) The end of model democracy? Climate Change 102(3–4):395–404

    Google Scholar 

  • Kusunoki S, Mizuta R (2013) Changes in precipitation intensity over East Asia during the 20th and 21st centuries simulated by a global atmospheric model with a 60 km grid size. J Geophys Res Atmos 118(19):11007–11016

    Google Scholar 

  • Li X, Ting M (2017) Understanding the Asian summer monsoon response to greenhouse warming: the relative roles of direct radiative forcing and sea surface temperature change. Climate Dyn 49(7–8):2863–2880

    Google Scholar 

  • Li G, Xie S-P (2014) Tropical biases in CMIP5 multimodel ensemble: the excessive Equatorial Pacific cold tongue and double ITCZ problems. J Climate 27(4):1765–1780

    Google Scholar 

  • Li G, Xie S-P, He C, Chen Z (2017) Western Pacific emergent constraint lowers projected increase in Indian summer monsoon rainfall. Nat Climate Change 7(10):708–712

    Google Scholar 

  • Long S-M, Xie S-P, Liu W (2016) Uncertainty in tropical rainfall projections: atmospheric circulation effect and the ocean coupling. J Climate 29(7):2671–2687

    Google Scholar 

  • Mike H, Zhao Z-C, Jiang T (1994) Recent and future climate change in East Asia. Int J Climatol 14:637–658

    Google Scholar 

  • Räisänen J, Ruokolainen L, Ylhäisi J (2010) Weighting of model results for improving best estimates of climate change. Climate Dyn 35(2–3):407–422

    Google Scholar 

  • Roderick ML, Sun F, Lim WH, Farquhar GD (2014) A general framework for understanding the response of the water cycle to global warming over land and ocean. Hydrol Earth Syst Sci 18(5):1575–1589

    Google Scholar 

  • Seager R, Naik N, Vecchi GA (2010) Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J Climate 23(17):4651–4668

    Google Scholar 

  • Seo KH, Ok J, Son JH, Cha DH (2013) Assessing future changes in the East Asian Summer Monsoon using CMIP5 coupled models. J Climate 26(19):7662–7675

    Google Scholar 

  • Song F, Zhou T (2014) The climatology and interannual variability of East Asian Summer Monsoon in CMIP5 coupled models: does air-sea coupling improve the simulations? J Climate 27(23):8761–8777

    Google Scholar 

  • Sperber KR, Annamalai H, Kang IS, Kitoh A, Moise A, Turner A, Wang B, Zhou T (2013) The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dyn 41(9–10):2711–2744

    Google Scholar 

  • Thomson MC, Doblas-Reyes FJ, Mason SJ, Hagedorn R, Connor SJ, Phindela T, Morse AP, Palmer TN (2006) Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439(7076):576–579

    Google Scholar 

  • Ueda H, Iwai A, Kuwako K, Hori ME (2006) Impact of anthropogenic forcing on the Asian summer monsoon as simulated by eight GCMs. Geophys Res Lett 33(6):L06703

    Google Scholar 

  • Vecchi GA, Soden BJ, Wittenberg AT, Held IM, Leetmaa A, Harrison MJ (2006) Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing. Nature 441(7089):73–76

    Google Scholar 

  • Wang B, Yim S-Y, Lee J-Y, Liu J, Ha K-J (2014) Future change of Asian-Australian monsoon under RCP 4.5 anthropogenic warming scenario. Climate Dyn 42(1–2):83–100

    Google Scholar 

  • Wu P, Christidis N, Stott P (2013) Anthropogenic impact on Earth’s hydrological cycle. Nat Climate Change 3(9):807–810

    Google Scholar 

  • Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78(11):2539–2558

    Google Scholar 

  • Xie S-P, Deser C, Vecchi GA, Ma J, Teng H, Wittenberg AT (2010) Global warming pattern formation: sea surface temperature and rainfall. J Climate 23(4):966–986

    Google Scholar 

  • Xie S-P, Deser C, Vecchi GA, Collins M, Delworth TL, Hall A, Hawkins E, Johnson NC, Cassou C, Giannini A, Watanabe M (2015) Towards predictive understanding of regional climate change. Nat Climate Change 5(10):921–930

    Google Scholar 

  • Ying J, Huang P (2016) Cloud-radiation feedback as a leading source of uncertainty in the tropical pacific SST warming pattern in CMIP5 models. J Climate 29(10):3867–3881

    Google Scholar 

  • Zheng Y, Shinoda T, Lin J-L, Kiladis GN (2011) Sea surface temperature biases under the stratus cloud deck in the Southeast Pacific Ocean in 19 IPCC AR4 coupled general circulation models. J Climate 24(15):4139–4164

    Google Scholar 

  • Zhou Z-Q, Xie S-P (2015) Effects of climatological model biases on the projection of tropical climate change. J Climate 28(24):9909–9917

    Google Scholar 

  • Zhou S, Huang G, Huang P (2018) Changes in the East Asian summer monsoon rainfall under global warming: moisture budget decompositions and the sources of uncertainty. Climate Dyn 51(4):1363–1373

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (41575088, 41722504, 41425019, 41721004 and 41661144016), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060501), the Public Science and Technology Research Funds Projects of Ocean (201505013), and the Youth Innovation Promotion Association of CAS and the Fundamental Research Funds for the Central Universities. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP5, and the climate modeling groups (listed in Table 1) for producing and making available their model output. We also thank two anonymous reviewers for their constructive suggestions.

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Appendix: Spatial emergent constraint and Ensemble pattern regression method

Appendix: Spatial emergent constraint and Ensemble pattern regression method

The spatial emergent constraint with the ensemble pattern regression method used in this study was first developed in Huang and Ying (2015), extending the original emergent constraint for regional-mean changes, to constrain the future changes with apparent spatial pattern. The main steps are introduced here.

We indicate the historical climatology of model i by \(H_{i}\) and the future climatology by \(F_{i}\). The future change \(C_{i}\) is defined as \(C_{i} = F_{i} - H_{i}\). We suppose that there is a perfect projection of future change \(C_{real}\), which is the same in all models, and the difference between the \(C_{real}\) and the MMM change \(\bar{C} = N^{ - 1} \mathop \sum \nolimits_{i = 1}^{N} C_{i}\) is the common change bias \(\bar{C}^{\prime} = \bar{C} - C_{real}\). For the individual change bias \(C_{i}^{\prime \prime }\), this is defined as \(C_{i}^{\prime \prime } = C_{i} - \bar{C}\) and the total change bias of a model i is \(C_{i}^{\prime } = \bar{C}^{\prime} + C_{i}^{\prime \prime }\). An identical decomposition method can be applied to the \(H_{i}\). The historical climatology \(H_{i}\) consists of the observed climatology \(H_{obs}\), the common historical bias \(\bar{H}^{\prime } = N^{ - 1} \mathop \sum \nolimits_{i = 1}^{N} H_{i} - H_{obs}\) and the individual historical bias \(H_{i}^{\prime \prime } = H_{i} - \bar{H}\).

Next, we need to build up the spatially correlated mode between the historical bias and the change bias. EOF analysis is a common method to decompose a signal into a time series and spatial pattern. However, here we apply an intermodel EOF analysis to the \(H_{i}^{\prime \prime }\) of all the model and get the spatially orthogonal modes \({\text{EOF}}_{j}\), \(j = 1, \ldots ,M\) and corresponding principal coefficients \({\text{PC}}_{ij}\). For a specific \(H_{i}^{\prime \prime }\), this can be represented as \(H_{i}^{\prime \prime } = \sum\nolimits_{j = 1}^{M} {{\text{EOF}}_{j} } {\text{PC}}_{ij}\). The truncation of \(M\,{\text{EOFs}}\) depends on the representation of the EOF modes for the historical bias \(H_{i}^{\prime \prime }\) and \(\bar{H}^{\prime}\), and influences the results of the correction. Multivariant linear regression analysis is performed on PCs and individual change bias \(C^{\prime\prime}\). The estimation of \(C^{\prime\prime}\) can be calculated by the regression pattern \(\hat{b}\) and PCs:

$$\hat{C}_{i}^{\prime \prime } = \sum\limits_{j = 1}^{M} {\hat{b}_{j} } {\text{PC}}_{ij} .$$
(2)

Because the PCs are linearly independent, the regression pattern \(\hat{b}\) equals the simple linear regression result of \(C^{\prime\prime}\) onto PCs mode by mode. This simplifies the procedure of regression.

The estimation of the common change bias \(\bar{C}^{\prime}\) is based on a hypothesis that the relationship between \(\bar{C}^{\prime}\) and the common historical bias \(\bar{H}^{\prime}\) is the same as the relationship between the modes of \({\text{EOF}}_{j}\) and \(\hat{b}_{j}\). First, we project the \(\bar{H}^{\prime}\) onto \({\text{EOF}}_{j}\) and represent the \(\bar{H}^{\prime}\) by the expansion coefficient \(e_{j}\):

$$\bar{H}^{\prime} = \mathop \sum \limits_{j = 1}^{M} {\text{EOF}}_{j} e_{j} .$$
(3)

We replace the PCs in Eq. (2) by the expansion coefficient \(e_{j}\) and estimate \(\bar{C}^{\prime}\):

$$\widehat{{\bar{C}^{\prime}}} = \mathop \sum \limits_{j = 1}^{M} \hat{b}_{j} e_{j} .$$
(4)

Finally, we can correct the MMM change \(\bar{C}\) as \(\bar{C}_{C} = \bar{C} - \widehat{{\bar{C}^{\prime}}}\). The individual change in model i\(C_{i}\) can also be corrected as the correction for common bias in MMM. We then just need to substitute the common historical bias \(\bar{H}^{\prime}\) in Eq. (3) with the total historical bias \(H_{i}^{'} = H_{i} - H_{obs}\):

$$H_{i}^{'} = \mathop \sum \limits_{j = 1}^{M} {\text{EOF}}_{j} e_{ij} .$$
(5)

The expansion coefficient \(e_{ij}\) for \(H_{i}^{\prime }\) can replace the \(e_{j}\) in Eq. (4):

$$\hat{C}_{i}^{\prime } = \sum\limits_{j = 1}^{M} {\hat{b}_{j} } e_{ij} .$$
(6)

The individual change in model i is corrected as \(C_{Ci} = C_{i} - \hat{C}^{\prime}_{i}\).

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Zhou, S., Huang, G. & Huang, P. A bias-corrected projection for the changes in East Asian summer monsoon rainfall under global warming. Clim Dyn 54, 1–16 (2020). https://doi.org/10.1007/s00382-019-04980-1

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Keywords

  • East Asian summer monsoon
  • Rainfall
  • Bias correction
  • CMIP5
  • Global warming