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A dominant mode in the first phase of the Asian summer monsoon rainfall: role of antecedent remote land surface temperature

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Abstract

The first/initial phase (during May to June) of the Asian summer monsoon (ASM), primarily driven by land-sea thermal gradient, varies from year to year and enormously affects people’s livelihood and the economy of this region. Moreover, the first phase, associated with the sub-seasonal variability (days to weeks), witnesses many extreme hydroclimatic events. Therefore, it is crucial to understand the sources of predictability of the initial phase of the ASM. Here we identify a dominant mode of variability in June rainfall over the entire Asian monsoon region. This mode is found to be linked with the spring (April, May) land surface temperature (LST) of the areas centred around the Western Third Pole (WTP). The Third Pole is the high elevation area centred on the Tibetan plateau. The WTP region is also home to many glaciers and steep mountains, including the second-highest peak in the world (i.e. Karakorum range). Consequently, spring LST has a strong inverse relationship with snow water equivalent (r = \(-\)0.65) over WTP, suggesting a seminal role of land surface processes in the first phase of ASM variability. The observed dominant modes and their teleconnections are also investigated in the 30-years re-forecast by five global coupled climate models participating in the “Impact of Initialized Land Surface Temperature and Snowpack on Sub-seasonal to Seasonal Prediction phase I” project (LS4P-I; Xue et al. (Geosc Model Devel 14(7):4465–4494, 2021; Bul Amer Meteor Soc 103: E2756-E2767, 2022)). While most models faithfully reproduce the observed link of June rainfall over South Asia with the remote LST, all models fail to capture the same over east Asia. In general, models show a significant bias in simulating the LST and the dominant modes of rainfall variability. Our findings may improve the understanding of the Asian summer monsoon variability and predictability, which may help improve the dynamical sub-seasonal to seasonal forecast system.

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Availability of data and materials

All data used in this study are freely available. Gridded surface temperature and precipitation data from Climatic Research Unit Time Series (CRU TS; \(0.5^\circ \times 0.5^\circ \); monthly) for 1901–2019 is used (Harris et al. 2020). The monthly indices of ENSO, PDO, AMO and IOD is obtained from the site https://psl.noaa.gov/data/climateindices/list/. Indian summer monsoon onset date over Kerala based on India Meteorological Department’s (IMD’s) subjective (1901–2005) and objective (1971–2019) criteria are taken from Preenu et al. (2017). Monthly gridded snow water equivalent (SWE) data for the period 1950–2019 from ERA-land reanalysis is utilized (Muñoz Sabater et al. 2021). Monthly geopotential height data at 200 & 500 hPa (1901–2015) are from 20th Century Reanalysis V3 data by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.html. The daily pressure level atmospheric temperature is from ERA5 reanalysis (1997–2020; Hersbach et al. 2020). CPC daily global temperature data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html is used for 1997–2020. Daily rainfall data from GPCP (Bolvin et al. 2009) for the period 1997–2020 is used.

Code availability

All code and scripts are available on request from the corresponding author.

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Acknowledgements

SKS, SK, HC and YS thank MoES, Government of India and Director IITM, for all the support to carry out this work.

Funding

IITM is funded by Ministry of Earth Sciences, Government of India. Ismaila Diallo was funded by the Center for Earth System Modeling, Analysis, and Data at The Pennsylvania State University. Energy Exascale Earth System Model (E3SM) project, funded by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER) under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory under contract no. DE-C52-07NA27344. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. DOE under contract no. DE-AC02-05CH11231.

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Authors and Affiliations

  1. Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pune, 411008, India

    Subodh Kumar Saha, Sujith Krishnakumar, Yashas Shivamurthy & Hemantkumar S. Chaudhari

  2. University of California, Los Angeles, CA, 90095, USA

    Yongkang Xue

  3. Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, State College, PA, 16802, USA

    Ismaila Diallo

  4. Climate Prediction Division, Japan Meteorological Agancy, Minato, Japan

    Tetsu Nakamura

  5. Lawrence Livermore National Laboratory, Livermore, 94550, CA, USA

    Qi Tang

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SKS and YX conceptualized the work, SK run the model, ID helped to setup experiments and managed model and observation data, YS and HC did data analysis, TN and QT provided data from their model reforecast experiments. All authors have contribute in writing the manuscript.

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Correspondence to Subodh Kumar Saha.

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Saha, S.K., Xue, Y., Krishnakumar, S. et al. A dominant mode in the first phase of the Asian summer monsoon rainfall: role of antecedent remote land surface temperature. Clim Dyn 61, 2735–2751 (2023). https://doi.org/10.1007/s00382-023-06709-7

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