Multi‐Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated With Atmospheric Rivers Over the Western U.S.

Zhang, Zhenhai; DeFlorio, Michael J.; Monache, Luca Delle; Subramanian, Aneesh; Ralph, M.; Waliser, Duane E.; Zheng, Minghua; Guan, Bin; Goodman, A.; Molod, Andrea; Kumar, Arun; Lin, Hai

doi:10.1029/2022jd037608

Cited by 2 publications

(1 citation statement)

References 80 publications

(157 reference statements)

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“…Brier skill score (BSS) is used to measure the accuracy of the algorithm using model and reference data (Zhang et al, 2023). BSS is evaluated from the mean square error values of model and reference data.…”

Section: Brier Skill Scorementioning

confidence: 99%

Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

Sabarinath,

Naga Rajesh,

Gunthe

et al. 2023

Intl Journal of Climatology

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Indian subcontinent witnessed a rise in surface air temperature (SAT) in recent decades, during the summer months of March, April and May. The monsoon core region (MCR) of India experiences a hot and humid climate, with temperatures typically highest in May and June before the onset of the monsoon. Global climate model (GCM) simulations of SAT are very much essential to understand the future climate of Indian MCR. Biases in GCMs simulations are due to insufficient knowledge of parameterizations and various assumptions that are made to simulate the complex interactions between land, ocean and atmosphere. The objective of this study is to correct the bias in the Coupled Model Intercomparison Project Phase 6 (CMIP6)–GCM simulations of SAT during March, April and May months over MCR for the historical period 1985–2014 and shared socio‐economic pathways (SSPs) SSP2‐4.5 and SSP5‐8.5 for the period 2015–2100. SAT dataset of fifth‐generation reanalysis (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is used as reference dataset to perform bias correction for the historical period. Preliminary investigation of both SAT datasets has shown that there exists considerable warm bias (1.47°C) over the MCR. Bias correction is performed using a one‐dimensional convolutional neural network (CNN‐1D) and a convolutional long short‐term memory network (CNN‐LSTM) deep learning algorithm. The performance of these algorithms is evaluated with the statistical metrics such as root‐mean‐square error (RMSE), normalized root‐mean‐square error, Nash–Sutcliffe efficiency, mean absolute error, percent bias, correlation coefficient and dynamic time warping. RMSE and percent bias were decreased to 0.35°C and 0.8% with CNN‐LSTM algorithm. The CNN‐LSTM algorithm also preserves the year‐to‐year variability of SAT. Hence, CNN‐LSTM algorithm is found to be suitable for the bias correction of GCM simulations of SAT with encouraging results.

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Section: Brier Skill Scorementioning

confidence: 99%

Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

Sabarinath,

Naga Rajesh,

Gunthe

et al. 2023

Intl Journal of Climatology

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Heresy in ENSO teleconnections: Atmospheric Rivers as disruptors of canonical seasonal precipitation anomalies in the Southwestern US

Luna-Niño,

Gershunov,

Ralph

et al. 2024

Preprint

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In spite of forecasts for anomalous dryness based on the canonical La Niña signal, Water Years 2011, 2017, and 2023 brought copious precipitation to California and the Southwestern United States (SWUS). Although El Niño—Southern Oscillation (ENSO) is the main source of seasonal precipitation predictability for the region, outstanding Atmospheric River (AR) activity produced the unexpected regional wetness in each of these heretical water years (WYs). We define heretical WYs as those that result in precipitation anomalies that oppose those expected based on ENSO alone. We assess the contribution of ARs and other storms to these WYs, finding that heretical La Niña/El Niño WYs were characterized by anomalously robust/deficient AR activity. In California, precipitation accumulation during the heretical La Niña WYs was comparable to or even exceeded that observed during the exceedingly wet WY1998 — the textbook canonical El Niño year. Our findings indicate a weaker/stronger relationship between ENSO and AR/non-AR precipitation, primarily driven by storm frequency. Although ARs can disrupt the ENSO-precipitation signal, ENSO still influences the frequency of AR precipitation in the southwestern U.S. desert, the region influenced by ARs that make landfall in Baja California, Mexico. These results highlight the complexity of ENSO's impact on precipitation in the Western US and underscore the need for a nuanced understanding of ENSO’s influence on ARs to improve seasonal precipitation prediction.

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Multi‐Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated With Atmospheric Rivers Over the Western U.S.

Abstract: Predictions at subseasonal-to-seasonal (S2S, more than 2 weeks but less than a season lead time) timescales are common challenges in both weather and climate models (

Cited by 2 publications

References 80 publications

Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

Heresy in ENSO teleconnections: Atmospheric Rivers as disruptors of canonical seasonal precipitation anomalies in the Southwestern US

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