Abstract
An outstanding problem of climate models is the persistent dry bias in simulating precipitation over the south Asian summer monsoon region. Guided by observations, it is hypothesized that the dry-bias in simulating precipitation by the models is related to underestimation of high pass variance by most models. An analysis of the simulated mean and variance in precipitation by 36 coupled models show that the dry bias in simulating the mean precipitation by the models is indeed proportional to the underestimation of the variance. Models also indicate that the underestimation of the high-pass variance arise due to the underestimation of the intense rainfall events by models. Further, it is found that the higher resolution models simulate increasingly reduced dry bias by simulating high-frequency variance better through better simulation probability of intense rainfall events. The robustness of our findings over different regions and during both boreal summer and winter seasons indicates the universality of the hypothesis.
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References
Annamalai H, Sperber KR (2016) South Asian summer monsoon variability in a changing climate. In: de Carvalho LMV, Jones C (eds) The monsoons and climate change. Springer International Publishing, pp 25–46
Baker NC, Huang H-P (2014) A comparative study of precipitation and evaporation between CMIP3 and CMIP5 climate model ensembles in semiarid regions. J Clim 27:3731–3749. doi:10.1175/JCLI-D-13-00398.1
Bengtsson L, Hodges KI, Esch M (2007) Tropical cyclones in a T159 resolution global climate model: comparison with observations and re-analyses. Tellus A 59:396–416. doi:10.1111/j.1600-0870.2007.00236.x
Duchon CE (1979) Lanczos filtering in one and two dimensions. J Appl Meteorol 18:1016–1022. doi:10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2
Goswami BN (2012) South Asian Monsoon. In: Waliser DE, Lau WK-M (eds) South Asian monsoon. Springer, Berlin, pp 21–72
Goswami BN, Krishnamurthy V, Annamalai H (1999) A broad-scale circulation index for the interannual variability of the Indian summer monsoon. Q J R Meteorol Soc 125:611–633. doi:10.1002/qj.49712555412
Goswami BB, Mani NJ, Mukhopadhyay P et al (2011) Monsoon intraseasonal oscillations as simulated by the superparameterized community atmosphere model. J Geophys Res 116:D22104. doi:10.1029/2011JD015948
Houze RA (1997) Stratiform precipitation in regions of convection: a meteorological paradox? Bull Am Meteorol Soc 78:2179–2196. doi:10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2
Huffman GJ, Adler RF, Bolvin DT, Nelkin EJ (2010) The TRMM multi-satellite precipitation analysis (TMPA). In: Gebremichael M, Hossain F (eds) Satellite rainfall applications for surface hydrology. Springer, Netherlands, pp 3–22
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. doi:10.1175/JCLI-D-12-00451.1
Li G, Xie SP (2014) Tropical biases in CMIP5 multimodel ensemble: the excessive equatorial pacific cold tongue and double ITCZ problems. J Clim 27:1765–1780. doi:10.1175/JCLI-D-13-00337.1
Li J-LF, Waliser DE, Stephens G et al (2013) Characterizing and understanding radiation budget biases in CMIP3/CMIP5 GCMs, contemporary GCM, and reanalysis. J Geophys Res Atmos 118:8166–8184. doi:10.1002/jgrd.50378
Lin JL (2007) The double-ITCZ problem in IPCC AR4 coupled GCMs: ocean–atmosphere feedback analysis. J Clim 20:4497–4525. doi:10.1175/JCLI4272.1
Lin J-L, Kiladis GN, Mapes BE et al (2006) Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: convective signals. J Clim 19:2665–2690
Lin JL, Weickman KM, Kiladis GN et al (2008) Subseasonal variability associated with Asian summer monsoon simulated by 14 IPCC AR4 coupled GCMs. J Clim 21:4541–4567. doi:10.1175/2008JCLI1816.1
Ramu DA, Sabeerali CT, Chattopadhyay R et al (2016) Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: impact of atmospheric horizontal resolution. J Geophys Res Atmos 121:2205–2221. doi:10.1002/2015JD024629
Sabeerali CT, Ramu Dandi A, Dhakate A et al (2013) Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs. J Geophys Res Atmos 118:4401–4420. doi:10.1002/jgrd.50403
Shukla J, Hagedorn R, Hoskins B et al (2009) Revolution in climate prediction is both necessary and possible: a declaration at the world modelling summit for climate prediction. Bull Am Meteorol Soc 90:175–178. doi:10.1175/2008BAMS2759.1
Sperber KR, Annamalai H, Kang IS et al (2013) The Asian summer monsoon: an intercomparison of CMIP5 versus CMIP3 simulations of the late twentieth century. Clim Dyn 41:2711–2744. doi:10.1007/s00382-012-1607-6
Trenberth KE (1997) The definition of el nino. Bull Am Meteorol Soc 78:2771–2777. doi:10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2
Waliser DE, Graham NE (1993) Convective cloud systems and warm-pool sea surface temperatures: coupled interactions and self-regulation. J Geophys Res 98:12881. doi:10.1029/93JD00872
Wang B (2005) Intraseasonal variability in the atmosphere–ocean climate system. Springer, Berlin
Webster PJ, Magaña VO, Palmer TN et al (1998) Monsoons: processes, predictability, and the prospects for prediction. J Geophys Res 103:14451–14510. doi:10.1029/97JC02719
Xie S-P, Philander SGH (1994) A coupled ocean–atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus A 46:340–350. doi:10.1034/j.1600-0870.1994.t01-1-00001.x
Acknowledgements
BBG is grateful to Prof. Claude E. Duchon, University of Oklahoma, USA for the patient help provided in understanding Lanczos filter technique. BNG thanks Ministry of Earth Sciences, Govt. of India for Pisharoty Chair Professorship and IISER, Pune for facilities. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S1 in Online Resource) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
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Goswami, B.B., Goswami, B.N. A road map for improving dry-bias in simulating the South Asian monsoon precipitation by climate models. Clim Dyn 49, 2025–2034 (2017). https://doi.org/10.1007/s00382-016-3439-2
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DOI: https://doi.org/10.1007/s00382-016-3439-2