Use of El Niño–Southern Oscillation related seasonal precipitation predictability in developing regions for potential societal benefit

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.

Season-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast

“…Since Uruguay is located in a region that is strongly influenced by ENSO (Landman et al, 2019), it provides a good opportunity to assess the use of seasonal climate forecasts (which are issued on a monthly basis) as a tool that can help to improve farmers management decisions. Our hypothesis is that seasonal precipitation forecasts can be a useful tool to optimize grain yield, since they can produce an outlook of expected precipitation during the critical period for soybean seed yield setting (Giménez, 2007) with enough time before sowing to allow taking specific agronomic management decisions.…”

Forecasts of “normal”

“…High probabilities for normal can occur only if the corresponding deterministic forecast is close to the climatological mean, and if the skill is well‐above 0.4 (see Section 2.4). This skill threshold exceeds estimates of predictability for many parts of the world (Rowell, 1998; Doblas‐Reyes et al ., 2013; Landman et al ., 2019), and suggests that for at least some forecast systems normal should have the highest probability only occasionally. Objective forecast systems appear to reflect at least some of these limitations on the probability of normal; they rarely indicate highest probability on normal.…”

Evaluation of the forecast skill of North American Multi‐Model Ensemble for monthly and seasonal precipitation forecasts over Iran

“…The El Niño–Southern Oscillation (ENSO) has a large contribution to seasonal forecast skill (Goddard and Dilley, 2005; Balmaseda and Anderson, 2009; Weisheimer et al ., 2009; Landman et al ., 2012) such that the seasonal prediction skill level is dependent on the strength of the teleconnection between ENSO and seasonal variability of precipitation (Landman et al ., 2019). The connection between the ENSO signal and January–March (JFM) precipitation in Iran is weak (Nazemosadat and Cordery, 2000; Nazemosadat and Ghasemi, 2004), but is statistically significant for October–December (OND) rainfall season (Nazemosadat and Cordery, 2000).…”