Unraveling the Mystery of Indian Summer Monsoon Prediction: Improved Estimate of Predictability Limit

Abstract

Large socioeconomic impact of the Indian summer monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather low potential predictability (PP) of the seasonal ISM, contributed significantly by “internal,” interannual variability was considered insurmountable. Here we show that the internal variability contributed by the ISM subseasonal (synoptic + intraseasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g., El Niño and Southern Oscillation). This provides a scientific basis for predictability of the ISM rainfall beyond the conventional estimates of PP. We establish a much higher actual limit of PP (r∼0.82) through an extensive reforecast experiment (1,920 years of simulation) by improving two major physics in a global coupled climate model, which raises a hope for a very reliable dynamical seasonal ISM forecasting in the near future.

A Road Map to IndOOS-2: Better Observations of the Rapidly Warming Indian Ocean

“…However, these seasonal circulations are poorly represented in coupled models, giving rise to strong biases in monsoon rainfall (Annamalai et al 2017). While in the past the Indian monsoon was thought to have inherently low predictability (Rajeevan et al 2012), recent work has shown that improved coupled model physics can resolve links between monsoon rainfall and various climate modes, including the IOD and ENSO (Saha et al 2019). This offers renewed motivation to collect the measurements that can initialize and validate these models.…”

Distinguishing Spread Among Ensemble Members Between Drought and Flood Indian Summer Monsoon Years in the Past 58 Years (1958–2015) Reforecasts

“…Many previous studies have used fully coupled climate ensemble forecast system to evaluate seasonal prediction skill and predictability of ISMR in the context of a deterministic seasonal forecast (i.e., ensemble mean prediction) and/or a probabilistic seasonal forecast using ensemble spread (Abhilash et al, 2015;Bauer et al, 2015;Kulkarni et al, 2012;Palmer et al, 2005;Saha et al, 2019). In contrast, relatively little attention has been paid to year-to-year variation of ensemble spread (ESP) of ISMR prediction, which may indicate the forecast uncertainty of the deterministic forecast.…”

Comment on “Unraveling the Mystery of Indian Summer Monsoon Prediction: Improved Estimate of Predictability Limit” by Saha et al.

“…In the absence of a clear physical explanation, we examine the sensitivity of the same calculation to the details of analysis. It should be noted that this study only analyzes observed rainfall and does not comment further on the modeling results of Saha et al (2019).…”

“…Following the calculation by Saha et al (2019), we use the India Meteorological Department (IMD) daily gridded rainfall dataset over India (land-only) that has a resolution of 0.25 • × 0.25 • and spans 1901• and spans -2015• and spans (Pai et al, 2014. We first make a spatial average over Central India (74.5 • to 86.5 • E, 16.5 • to 26.5 • N) and All-India (66.5 • to 100.5 • E, 6.5 • to 32.5 • N), as well All-India without Central India included.…”

“…Other than some minor differences stated next, results are the same whether this variation is included or neglected. Figure 1 shows the mean-variance correlations over Central India and All-India made by Saha et al (2019) for the time period of 1981-2010 (gray lines). It is first important to point out that this is a correlation with cumulative variance and not separate variance bands.…”