Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

The deterministic prediction of the evolution of monsoon systems has proved to be a challenging task (Charney and Shukla 1982; Webster et al. 1998; Krishnamurthy and Shukla 2012). However, there is evidence that it is possible to predict the onset dates of monsoon systems on subseasonal to seasonal timescales using large-scale monsoon indexes (Vellinga et al. 2013; Alessandri et al. 2015).

Alessandri et al. (2015) used two different indices based on large-scale features of the Indian Summer Monsoon (ISM) and found that the onset date can be predicted skillfully as much as a month in advance. Vellinga et al. (2013) investigated the forecast skill for the onset date of the monsoon in the Sahel region of West Africa and verified probabilistic skill at 2 to 3 months lead-time.

In this work we take a different approach from previous studies. Instead of trying to predict the onset (or demise) dates of a monsoon system, which is complex large-scale phenomenon, we will predict the onset and the demise dates of the rainy season associated with a particular monsoon system. Although the start and end of the rainy season can assume different interpretations, a prediction of these arbitrary dates can not only be related to the onset of the monsoon as a whole but it can also provide valuable information for decision makers. Sectors related to water management such as agriculture, management of waterborne diseases, and electric energy generation would greatly benefit from the prediction of the dates of onset or demise of monsoons. Therefore, the objective of this work is to evaluate how far in advance can model simulations perform better than using climatological values to forecast the onset and demise of the rainy season over some of the monsoonal regions.

In this work, the onset and demise dates of the rainy and dry seasons are characterized based on a method that captures a seasonal change in the precipitation regime. This method uses only precipitation data and was developed by (Bombardi and Carvalho 2009), based on the method proposed by (Liebmann and Marengo 2001). In this work we use daily precipitation from the Climate Prediction Center (CPC_UNI) from 1979 to 2014 (Xie et al. 2007; Chen et al. 2008) (and daily precipitation estimates from the Tropical Rainfall Measuring Mission  (TRMM) from 1998 to 2013 (Huffman et al. 2007), see supplementary material).  We also use simulations from three coupled models participating in the Subseasonal-to-Seasonal (S2S) project (Robertson et al. 2015).

North America (Fig. 1a), South America (Fig. 1b), West Africa (Fig. 1c), East Asia (Fig. 1f), and Northern Australia (Fig. 1g) are regions were the hindcasts outperform the climatology in predicting the onset of the rainy season by as much as a month in advance. On the other hand, the South African (Fig. 1d) and the Indian (Fig. 1e) monsoon show low forecast skill for onset date.

Fig.png

Figure 1 – Lead-time RMSE of rainy season onset date forecasts. Lead-time 0 (zero) refers to the observed onset date. Thick lines show lead times where the RMSE of hindcasts is smaller than the RMSE related to the climatology and their difference is statistically significant at 5% level according to an f-test of the squared errors. The dashed line shows the RMSE in relation to the climatology for the CMA model grid for the sole purpose of visualization.

The demise dates show subseasonal forecast skill over parts of North and South America, South Africa, East Asia, and Northern Australia. Some models also show good demise date forecast skill over the Indian monsoon region. West Africa shows low forecast skill for demise date (not shown).

References

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