Seasonal Prediction of Winter Precipitation Anomalies over Central Southwest Asia: A Canonical Correlation Analysis Approach

Abstract

Central southwest Asia (CSWA; 20°–47°N, 40°–85°E) is a water-stressed region prone to significant variations in precipitation during its winter precipitation season of November–April. Wintertime precipitation is crucial for regional water resources, agriculture, and livelihood; however, in recent years droughts have been a notable feature of CSWA interannual variability. Here, the predictability of CSWA wintertime precipitation is explored based on its time-lagged relationship with the preceding months’ (September–October) sea surface temperature (SST), using a canonical correlation analysis (CCA) approach. For both periods, results indicate that for CSWA much of the seasonal predictability arises from SST variations in the Pacific related to El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO). Additional sources of skill that play a weaker predictive role include long-term SST trends, North Atlantic variability, and regional teleconnections. CCA cross-validation skill shows that the regional potential predictability has a strong dependency on the ENSO phenomenon, and the strengthening (weakening) of this relationship yields forecasts with higher (lower) predictive skill. This finding is validated by the mean cross-validated correlation skill of 0.71 and 0.38 obtained for the 1980/81–2014/15 and 1950/51–2014/15 CCA analyses, respectively. The development of cold (warm) ENSO conditions during September–October, in combination with cold (warm) PDO conditions, is associated with a northward (southward) shift of the jet stream and a strong tendency of negative (positive) winter precipitation anomalies; other sources of predictability influence the regional precipitation directly during non-ENSO years or by modulating the impact of ENSO teleconnection based on their relative strengths.

Tropical Indian Ocean Mediates ENSO Influence Over Central Southwest Asia During the Wet Season

“…The movement of WDs to the region is aided by the presence of a Subtropical Westerly Jet (SWJ), which persists throughout the wet season (Hoell et al, 2013). The interannual variability of wet season precipitation over the region is strongly linked to the remote influence of the El Niño–Southern Oscillation (ENSO), with a generally positive (negative) anomaly during its warm (cold) phase (Abid et al, 2016; Barlow et al, 2002; Hoell et al, 2015; Kamil et al, 2019; Kang et al, 2015; Mariotti, 2007; Rana et al, 2018; Yadav et al, 2010; Yu et al, 2018).…”

“…However, to our knowledge, a systematic evaluation of ENSO induced diabatic heating anomalies over the Indian Ocean, such as the TWEIO, and their role in the ENSO teleconnection to remote regions, such as CSWA, has not yet been explored. Moreover, with the exception of a few (e.g., Hoell et al, 2018), most of the studies that investigate the ENSO‐CSWA teleconnection thus far have focused on their relationship at seasonal scales (Kamil et al, 2019; Kang et al, 2015; Rana et al, 2018; Yadav et al, 2010) and a gap exists in our understanding toward its intraseasonal characteristics.…”

Identificação de Preditores Para as Chuvas do Setor Leste do Nordeste do Brasil Utilizando Análise de Correlação Canônica

“…A ACC também é usada eficazmente para descrição de dados, verificação de modelos numéricos e construção de modelos estatísticos de previsão, proporcionando o conhecimento de quais configurações tendem a ocorrer simultaneamente entre dois ou mais campos distintos e qual o grau de conexão entre eles (L'Heureux et al, 2015;Rana et al, 2018). Ela se resume em associar índices a cada um dos conjuntos de dados (X2: variável explicativa e X1: resposta), definidos como combinações lineares dos valores em cada um dos conjuntos (Função Ortogonal Empírica -FOE), de forma a maximizar a correlação entre os dois índices.…”

“…Recentemente, alguns pesquisadores usaram a análise de correlação canônica para previsões e cenários climáticos e estudos diagnósticos, como por exemplo, Rana et al (2018) para a previsão sazonal da precipitação pluvial de inverno no centro-sudoeste da Ásia, e Forootan et al (2016) para entender a variabilidade das chuvas na Austrália.…”

Improved Teleconnective Predictability of Monthly Precipitation Amounts Using Canonical Correlation Analysis

“…50% of the variability is explained by the first six factors F1-F6. The last column indicates the correlation between the two sets of canonical variates; for example, the first canonical predictor and predictand are closely correlated with approximately 0.86.The correlations between original variables and canonical variates called canonical factor loadings, allow understanding of how the canonical variates are related to the original variables(Marzban et al 2014;Rana et al 2018). In the following, these loadings are interpreted to recognize the physical mechanisms behind maximally correlated the canonical variate pairs.Fig.…”