Use of El Niño–Southern Oscillation related seasonal precipitation predictability in developing regions for potential societal benefit. (31st May 2019)

• Record Type: Journal Article
• Authors: Landman, Willem A.; Barnston, Anthony G.; Vogel, Coleen; Savy, Janique
• Is Part Of: International journal of climatology. Volume 39:Number 14(2019)
• Page Start: 5327
• Page End: 5337
• Publication Date: 2019-05-31
• Subjects: collaboration -- emerging economies -- ENSO -- human development -- seasonal climate modelling -- skill; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.6157
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: Some of the biggest emerging market economies include countries in South America, Asia and Africa. Broad‐scale political and developmental similarities (e.g., societally impactful developmental challenges related to climate variability) offer opportunities for comparative research resulting in potentially improved understanding of the complexities of various climate adaptation interventions including disaster risk reduction. Countries or geographical regions of the world significantly affected by climate extremes may consider collaboration on issues such as understanding and modelling of the climate system, especially when there is a common, dominant and somewhat plausible climate mode such as the El Niño–Southern Oscillation (ENSO) affecting the regions' climate variability. Better ENSO and subsequent climate predictions alone, however, are not enough to reduce the risks associated with such events. The socio‐economic and political context in which climate finds expression and in which climate forecasts have potential value also need to be understood. Here we present seasonal precipitation forecast skill over 20 geographical regions including emerging or developing regions, but also a few developed regions, in order to rank their ENSO‐related seasonal rainfall predictability in an attempt to cluster regions of similar ENSO climate predictability. We then also provide some of the broad contours to investigate the level of human "development" within these clusters in order to begin to understand some of the socio‐economic factors that configure vulnerabilities. Such profiles begin to show some areas of macro‐level vulnerability that may then provide further possible inter‐area collaborations, albeit at very gross level scales. Abstract : Median and mean correlations (Pearson and Spearman) for each of the 20 regions described in Table 2 and for each of the 3 models described in Table 1. The correlation values are obtained by comparing bias‐corrected (through 5‐year‐out cross‐validation) model hindcasts with CRU values. Southern Africa's results are marked with "13" and for the Limpopo River catchment area values (median Pearson) an "o" is used. The list shows the regions ranked in terms of highest to lowest median Pearson values according to the GFDL model.