Hotspots of Predictability: Identifying Regions of High Precipitation Predictability at Seasonal Timescales From Limited Time Series Observations

Mamalakis, Antonios; AghaKouchak, Amir; Randerson, James T.; Foufoula‐Georgiou, Efi

doi:10.1029/2021wr031302

“…Similarly, precipitation anomalies in Amazonia are found to be associated with Atlantic and Pacific SST anomalies in the tropics [23][24][25] . The patterns of SST anomalies and SSTderived indices in the Pacific are also used for predicting winter precipitation over southern regions of the United States [26][27][28][29] .Multiple lines of evidence indicate that modes of climate variability and associated teleconnections may change by the end of the 21st century 30,31 . For instance, recent studies have shown that the variability of El Niño-Southern Oscillation (ENSO)-driven precipitation is likely to be enhanced over the central-eastern Pacific owing to surface warming, even though change in the strength of ENSO-related SST variability remains uncertain [32][33][34] .…”

mentioning

confidence: 99%

“…Similarly, precipitation anomalies in Amazonia are found to be associated with Atlantic and Pacific SST anomalies in the tropics [23][24][25] . The patterns of SST anomalies and SSTderived indices in the Pacific are also used for predicting winter precipitation over southern regions of the United States [26][27][28][29] .…”

mentioning

confidence: 99%

Climate-driven changes in the predictability of seasonal precipitation

Le

¹

,

Randerson

²

,

Willett

³

et al. 2023

Self Cite

View full text Add to dashboard Cite

Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management.

show abstract

“…Accordingly, we used the 3 month average SST data of JFM (January-February-March), FMA (February-March-April), MAM (March-April-May), and AMJ (April-May-June) to predict the occurrence of CDHEs in June, July, August, and September, respectively. All the time series were linearly detrended and this is a general practice (Hari et al 2022, Mamalakis et al 2022) to avoid the influence of long-term trends. For discussion, we divided the Indian mainland into ten sub-regions (figure S5) based on previous studies (Guntu et al 2020, 2023, Guntu and Agarwal 2021).…”

Section: Datamentioning

confidence: 99%

“…However, it is important to note that one of the assumptions of the logistic regression model is the independence of predictors. Therefore, using multiple predictors can lead to overfitting of the prediction model (Mamalakis et al 2022). Overfitting results in reduced generalization performance, especially when predictors are interdependent (Meyer et al 2019).…”

Section: Identification Of Predictorsmentioning

confidence: 99%

See 1 more Smart Citation

Improving the predictability of compound dry and hot extremes through complexity science

Guntu,

Agarwal

2023

Environ. Res. Lett.

5

0

View full text Add to dashboard Cite

Compound Dry and Hot Extremes (CDHE) will have an adverse impact on socioeconomic factors during the Indian summer monsoon, and a future exacerbation is anticipated. The occurrence of CDHE is influenced by teleconnections, which play a crucial role in determining its likelihood on a seasonal scale. Despite the importance, there is a lack of studies unravelling the teleconnections of CDHE in India. Previous investigations specifically focused on the teleconnections between precipitation or temperature and climate indices. Hence, there is a need to unravel the teleconnections of CDHE. In this study, we present a framework that combines event coincidence analysis (ECA) with complexity science. ECA evaluates the synchronization between CDHE and climate indices. Subsequently, complexity science is utilized to construct a driver-CDHE network to identify the key drivers of CDHE. To evaluate the effectiveness of the proposed drivers, a logistic regression model is employed. The occurrence of CDHE exhibits distinct patterns from July to September when considering intra-seasonal variability. Our findings contribute to the identification of drivers associated with CDHE. The primary driver for Eastern, Western India and Central India is the indices in the Pacific Ocean and Atlantic Ocean, respectively, followed by the indices in the Indian Ocean. These identified drivers outperform the traditional Niño 3.4-based predictions. Overall, our results demonstrate the effectiveness of integrating ECA and complexity science to enhance the prediction of CDHE occurrences.

show abstract