Dr. Sumant Nigam, Professor

Dr. Alfredo Ruiz-Barradas, Assoc. Professor

Dr. Agniv Sengupta, Former Graduate Student (now Postdoc at JPL/Caltech)




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.

El Nino-Southern Oscillation (ENSO) 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 ENSO Forecast: The ENSO forecast is developed from the spatiotemporal structure of the antecedent SST anomalies. The anomalies were projected on the SST loading vectors, generating the SST principal components (PC). The obtained SST PCs were multiplied by their seasonal SST regressions, generating the ENSO forecast. The strategy is similar to that deployed in drought reconstruction (Nigam et al. 2011).

2020 Forecast of Spring, Summer, Fall & Winter SST Anomalies

University of Maryland's Experimental ENSO Forecast
2020 Spring to Winter SST Anomalies
(Base Period: 1981-2010. Units: °C)

Figure 1.SST-based experimental forecast of the 2020 Sprimg, Summer, Fall and Winter SST anomalies. Anomalies are with respect to the 1981-2010 climatology. The forecast is obtained from regressions of Hadley 1°×1° SSTs on SST-based principal components of four ENSO-related modes, two Pacific Decadal variability modes, four Atlantic modes including AMO, NAO, Atlantic Nino, and a Subarctic mode, and a non-secular trend mode (Agniv 2019, Nigam et al. 2020). Anomalies have been smoothed spatially once with the GrADS's smth9 function. Orange shading represents positive SST anomalies while blue shading represents negative anomalies. The contour interval and shading threshold are 0.25°C; the zero contour is suppressed. The forecast is based on antecedent SST anomalies extending up to February 2020. The dark blue insets in each panel show the area-averaged SST anomalies in the different NIÑO regions.


References
  • 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.
  • Kaiser, H. F., 1958: Varimax criterion for analytic rotations in factor analysis. Psychometrika, 412, 23, 187-200.
  • 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.
  • 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
  • Sengupta, A., 2019: Sea-Surface Temperature Based Statistcal Prediction of the South Asian Summer Monsoon Rainfall Distribution. Ph. D. Thesis. University of Maryland, 185pp.
  • Weare, B., and J. Nasstrom, 1982: Examples of extended empirical orthogonal function analyses. Monthly Weather Review, 110, 481-485.
  • Zhao, Y., and S. Nigam, 2015: The Indian Ocean Dipole: A Monopole in SST. J. Climate, 28, 3-19.


  • Acknowledgements: Sumant Nigam, Alfredo Ruiz-Barradas and Agniv Sengupta thank the U.S. National Science Foundation 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 April 12, 2020