Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach

Tongtiegang Zhao,Quanxi Shao,Tongbi Tu,Yu Tian,Haoling Chen,Xiaohong Chen

doi:10.5194/hess-25-5717-2021

Abstract

Abstract. Climate teleconnections are essential for the verification of valuable precipitation forecasts generated by global climate models (GCMs). This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection by using the coefficient of determination (R2). Specifically, observed precipitation is respectively regressed against GCM forecasts, Niño3.4 and both of them, and then the intersection operation is implemented to quantify the overlapping R2 for GCM forecasts and Niño3.4. The significance of overlapping R2 and the sign of ENSO teleconnection facilitate three cases of attribution, i.e., significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection. A case study is devised for the Climate Forecast System version 2 (CFSv2) seasonal forecasts of global precipitation. For grid cells around the world, the ratio of significantly positive anomaly correlation attributable to positive (negative) ENSO teleconnection is respectively 10.8 % (11.7 %) in December–January–February (DJF), 7.1 % (7.3 %) in March–April–May (MAM), 6.3 % (7.4 %) in June–July–August (JJA) and 7.0 % (14.3 %) in September–October–November (SON). The results not only confirm the prominent contributions of ENSO teleconnection to GCM forecasts, but also present spatial plots of regions where significantly positive anomaly correlation is subject to positive ENSO teleconnection, negative ENSO teleconnection and teleconnections other than ENSO. Overall, the proposed attribution approach can serve as an effective tool to investigate the sources of predictability for GCM seasonal forecasts of global precipitation.

Highlights

Precipitation is one of the most important hydrological variables and is an integral part of the global water cycle (Huffman et al, 2007; Ushio et al, 2009; Scofield and Kuligowski, 2003; Schneider et al, 2016; Beck et al, 2019)
In December– January–February (DJF), significantly positive anomaly correlation for Climate Forecast System version 2 (CFSv2) forecasts is attributable to positive El Niño–Southern Oscillation (ENSO) teleconnection in southern North America and East Africa, and it is attributable to negative ENSO teleconnection in northern South America and southern Africa
These results conform to previous findings that regions exhibiting positive ENSO teleconnection change substantially by season (Mason and Goddard, 2001; Vashisht et al, 2021; Chen and Kumar, 2020) and that performances of Global climate models (GCMs) forecasts vary by season (Vano et al, 2014; Johnson et al, 2019; Zhao et al, 2020b)

Summary

Introduction

Precipitation is one of the most important hydrological variables and is an integral part of the global water cycle (Huffman et al, 2007; Ushio et al, 2009; Scofield and Kuligowski, 2003; Schneider et al, 2016; Beck et al, 2019). It plays a key role in driving hydrological processes at catchment, regional and continental scales (e.g., Yuan et al, 2014; Greuell et al, 2018; Robertson et al, 2013; Wu et al, 2014; Lakshmi and Satyanarayana, 2019). GCM forecasts have been increasingly incorporated into forecasting systems of streamflow, crop yield and soil water, and they are shown to create enormous socioeconomic benefits (e.g., Vano et al, 2014; Peng et al, 2018; Wang et al, 2019)

Methods

Results

Discussion

Conclusion