Dr. Sumant Nigam,
Professor & Director

Dr. Alfredo Ruiz-Barradas,
Assoc. Res. Professor & Assoc. Director

Dept. of Atmospheric and Oceanic Science
3419 Atlantic Bldg., 4254 Stadium Drive
University of Maryland
College Park, MD 20742, USA

Landline:(301) 405-5381 (5391)
Mobile:   (202) 415-5626
Fax:        (301) 314-9482
Email:     nigam@umd.edu

The Laboratory for Experimental Hydroclimate Prediction seeks to develop subseasonal-to-seasonal forecasts of regional variations in precipitation, surface air temperature, and soil moisture using influential climate system components with large thermal inertia as predictors.

A distinctive feature of the laboratory's prediction strategy is its statistical approach, rooted in innovative spatiotemporal analysis of the observational record. The deployed strategy is complementary to the commonly pursued dynamical prediction paradigm where similar influences find forecast expression from initialized integrations of the atmospheric and oceanic general circulation models. The skill of statistical forecasts provides an important evaluative benchmark for dynamical forecasting. It is hoped that statistical forecasts generated by the laboratory will be more skillful than previous ones, upping the ante for dynamical forecasting of regional hydroclimate variations.

Upper ocean temperatures, in particular, meet the criterion of an influential climate system component with large thermal inertia but reliable long-term observations are available mostly at the surface. The influence of sea surface temperature (SST) on regional and faraway hydroclimate is efficiently mined in this prediction effort.

South Asian Summer Monsoon Rainfall Forecasts

Enabling SST Analysis: Seasonal SST anomalies in the global domain were analyzed in the 20th-century using the Extended Empirical Orthogonal Function (EEOF) technique, following Guan and Nigam (2008). The Hadley Centre Sea Ice and Sea Surface Temperature data (HadISST 1.1; Rayner et al. 2003) was analyzed. The physicality of the extracted variability modes was assessed using marine productivity and observational analog counts. The North Pacific and Bering Sea recruitment records obtained from Hare and Mantua (2000) and the International Pacific Halibut Commission were used to assess the physicality of the decadal modes in the Pacific. The optimal sampling window-width and number of rotated modes was determined from sensitivity analysis reported in Sengupta (2019) and Nigam et al. (2020). The EEOF technique yields, among others, the nonstationary Secular Trend, Pacific and Atlantic decadal variability modes, and ENSO, all without any advance filtering (and potential aliasing) of the SST record. The Indian Ocean Dipole does not emerge as an independent mode, consistent with Zhao and Nigam (2015).

Statistical Forecast of Summer Monsoon Rainfall: The summer monsoon rainfall forecast is developed from the spatiotemporal structure of the antecedent (fall 2022-spring 2023) SST anomalies. The anomalies were projected on the SST loading vectors, generating the SST principal components (PC) for winter 2022/2023. The obtained SST PCs were multiplied by their lagged seasonal rainfall regressions, generating the summer monsoon rainfall forecast; here, summer is defined as the June-September (J,J,A,S) period. The modeling strategy is similar to that deployed in drought reconstruction (Nigam et al. 2011). The final 2023 JJAS monsoon forecast (referred as version 2a), generated from regressions of the GPCC (ver.2022) precipitation, is shown in the following figure.

2023 Forecast of June-September averaged Rainfall Anomaly

University of Maryland's Experimental Monsoon Forecast
2023 June-September Seasonal Rainfall Anomalies
(GPCCv2022-based Forecast, ver. 2a; Base Period: 1961-2010. Units: mm/day)

Issued on May 24, 2023

Figure 1. SST-based experimental forecast of the 2023 Summer Monsoon (June-September) Rainfall Anomalies (i.e., departures from normal), relative to the 50-year (1961-2010) rainfall climatology. The forecast is based on regressions of the GPCCv2022's 0.25°×0.25° precipitation (Rustemeier et al. 2022) on the principal components of historical (1901-2016) sea surface temperature (SST; Rayner et al. 2003) variability (Nigam et al. 2020), and the recent principal components obtained from analysis of the OISSTv2.1's SSTs (Reynolds et al. 2007). Solid (green) contours represent above-average rainfall while dashed (brown) ones denote below-average rainfall; the zero contour is suppressed. Anomalies have been spatially smoothed twice with the GrADS's smth9 function. The contour interval and shading threshold is 0.5 mm/day. The final forecast (ver. 2a) for the summer monsoon rainfall is based on antecedent SST anomalies extending up to May 17, 2023.

Assessment of the Experimental SST-based Rainfall Forecast over SE Asia, and in particular over India, has been carried out since 2016. Links to PDF files are given below. Please, note that the model used in the forecasts made from 2016 until the 2020 Interim forecast, used the weighted JJA and September forecasts scheme (ver. 1a) instead of our current extended JJAS scheme(ver. 2a).

2022 Forecast & Verification of June-September averaged Rainfall Anomaly

2021 Forecast & Verification of June-September averaged Rainfall Anomaly

2020 Forecast & Verification of June-September averaged Rainfall Anomaly

2019 Forecast & Verification of June-September averaged Rainfall Anomaly

2018 Forecast & Verification of June-September averaged Rainfall Anomaly

2018 Forecast of June-September averaged Rainfall Anomaly (GPCC-based)

2017 Forecast & Verification of June-September averaged Rainfall Anomaly

2016 Forecast & Verification of June-September averaged Rainfall Anomaly

  • Becker, A., and co-authors, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth Syst. Sci. Data, 5, 71-99.
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, doi:10.1029/2007JD009132.
  • Guan, B., and S. Nigam, 2008: Pacific sea surface temperatures in the twentieth century: An evolution-centric analysis of variability and trend. Journal of Climate, 21, 2790-2809.
  • Hare, S. R., and N. J. Mantua, 2000: Empirical evidence for North Pacific regime shifts in 1977 and 1989. Prog. Oceanogr., 47, 103-145.
  • Nigam, S., B. Guan, and A. Ruiz-Barradas, 2011: Key role of fthe Atlantic Multidecadal Oscillation in 20th century drought and wet periods over the Great Plains. Geophysical Research Letters, 38.
  • Nigam, S., A. Sengupta, and A. Ruiz-Barradas, 2020: Atlantic-Pacific Links in Observed Multidecadal SST Variability: Is Atlantic Multidecadal Oscillation's Phase-Reversal Orchestrated by Pacific Decadal Oscillation? J. Climate, Early Online Releases.
  • Pai D.S., L. Sridhar, M. Rajeevan, O. P. Sreejith, N. S. Satbhai, and B. Mukhopadhyay, 2014: Development of a new high spatial resolution (0.25°×0.25°)Long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. MAUSAM, 65, pp1-18.
  • Rayner, N. A., and co-authors, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, doi:10.1029/2002JD002670
  • Reynolds, R. W., and co-authors, 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496.
  • Rustemeier, E., S. Hänsel, P. Finger, U. Schneider, and M. Ziese, 2022: GPCC Climatology Version 2022 at 0.25°: Monthly Land-Surface Precipitation Climatology for Every Month and the Total Year from Rain-Gauges built on GTS-based and Historical Data.
  • Schneider, U., A. Becker, P. Finger, E. Rustemeier, and M. Ziese, 2020: GPCC Full Data Monthly Product Version 2020 at 0.25°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historical Data.
  • Sengupta, A., 2019: Sea-Surface Temperature Based Statistcal Prediction of the South Asian Summer Monsoon Rainfall Distribution. Ph. D. Thesis. University of Maryland, 185pp.
  • Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu, and S. Yang, 2007: A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeor., 8, 607-626.
  • Zhao, Y., and S. Nigam, 2015: The Indian Ocean Dipole: A Monopole in SST. J. Climate, 28, 3-19.

  • Acknowledgements: Sumant Nigam, Agniv Sengupta, and Alfredo Ruiz-Barradas thank the U.S. National Science Foundation for supporting the investigation of seasonal predictability of the South Asian summer monsoon rainfall, via Grant AGS1439940. We also thank India's National Monsoon Mission for supporting this effort, especially Agniv Sengupta's doctoral research at the Universoty of Maryland.

    -- Last updated May 24, 2023