Skillful Subseasonal Prediction of United States Extreme Warm Days and Standardized Precipitation Index in Boreal Summer

Abstract

Skillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States (US). We use a leading principal component of US soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP’s Climate Forecast System version 2 (CFSv2) at weeks 3-4 in the eastern US. It is found that the North Pacific SST anomalies persist several weeks and are associated with a persistent wave train pattern (WTZ500), which leads to increased occurrences of blocking and extreme temperature over the eastern US. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and decrease in latent heat flux, which may help maintain the overlying anticyclone. The clear sky conditions associated with blocking anticyclones further decrease soil moisture conditions and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, reservoir operation, and provide mitigation of impacts from extreme heat events.

Heat Waves: Physical Understanding and Scientific Challenges

“…ML methods have also been proposed as an alternative paradigm to extended dynamical predictions (e.g., Cohen et al., 2019). Several statistical and ML models have been developed for the prediction of temperature extremes at different lead times, including weather (Chattopadhyay et al., 2020), subseasonal (e.g., Miller et al., 2021; Vijverberg et al., 2020; Weirich Benet et al., 2023) and seasonal (Kämäräinen et al., 2019; Pyrina et al., 2021; R. Z. Zhang et al., 2022) time scales. They include a diversity of approaches in terms of model complexity, predictors, lags and trained data sets.…”

“…They include a diversity of approaches in terms of model complexity, predictors, lags and trained data sets. In some cases, statistical models compete with (or are superior to) operational dynamical models in predicting warm extremes 3–4 weeks in advance (e.g., López‐Gómez et al., 2022; Miller et al., 2021; Weirich Benet et al., 2023). Improvement has sometimes been achieved with relatively simple methods and few precursors, suggesting different levels of predictability or model sensitivities to the training set (e.g., similar performance of simple and complex models for small data sets).…”

Exploring the relationship between temperature forecast errors and Earth system variables

“…Forecasting systems need to capture all possible Earth system sources of subseasonal predictability to minimize these errors (Vitart and Robertson, 2018; de Andrade et al, 2019). The assimilation of climate variables, such as radiation and precipitation, and hence the representation of energy and water supply at the Earth's surface, is relevant to inform the weather forecast model to make accurate predictions of the evolution of temperature (Miller et al, 2021). This requires a global and dense observational network of respective measurements and/or regular satellite observations as a basis for an adequate data assimilation.…”

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

“…Ravuri et al, 2021;Neri et al, 2019), and geographical domains (from point to street-level, single river catchment through to global approaches). Hybrid models have been applied to predict a variety of hydrometeorological variables, including extreme heat and precipitation (Miller et al, 2021;Najafi et al, 2021;Miao et al, 2019;Ma et al, 2022), seasonal climate variables (Golian et al, 2022;Baker et al, 2020), tropical cyclones/hurricanes (Vecchi et al, 2011;Murakami et al, 2016;Kang and Elsner, 2020;Klotzbach et al, 2020), streamflow (Wood and Schaake, 2008;Mendoza et al, 2017;Rasouli et al, 2012;Duan et al, 2020), flooding (Slater and Villarini, 2018), drought (Madadgar et al, 2016;Wu et al, 2021), sea level (Khouakhi et al, 2019), and reservoir levels (Tian et al, 2021), over a range of and predictions have numerous operational and strategic applications, including water resources planning, reservoir inflow management (Tian et al, 2021;Essenfelder et al, 2020), surface water flooding (Rözer et al, 2021), flood risk mitigation, navigation (Meißner et al, 2017), and agricultural crop forecasting (Cao et al, 2022;Slater et al, 2021b). The envisaged dynamical predictors may include various model outputs such as meteorological forecasts with lead times up to 14 days; initialized climate predictions with sub-seasonal to decadal lead times; sub-seasonal runoff predictions, and/or land surface 110 2 Hybrid forecasting Hybrid forecasting encompasses approaches for pre-/post-processing hydroclimate predictions (Section 2.1), and for developing predictive models themselves, including short-term hybrid forecasts (Section 2.2), or sub-seasonal to decadal predictions (Section 2.3), and the integration of ML within parallel and coupled hybrid models (Section 2.4 and Table 3).…”