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- Author or Editor: Peter B. Gibson x
- Bulletin of the American Meteorological Society x
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Abstract
Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.
Abstract
Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.
Abstract
California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.
Abstract
California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
