Multi-Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated with Atmospheric Rivers over the Western U.S.

March 24, 2023

A new paper entitled “Multi-Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated with Atmospheric Rivers over the Western U.S.” was recently published in the Journal of Geophysical Research: Atmospheres and authored by CW3E researcher Zhenhai Zhang, Michael DeFlorio (CW3E), Luca Delle Monache (CW3E), Aneesh Subramanian (University of Colorado Boulder), Martin Ralph (CW3E), Duane Waliser (NASA JPL), Minghua Zheng (CW3E), Bin Guan (NASA JPL), Alexander Goodman (NASA JPL), Andrea Molod (NASA GMAO), Frederic Vitart (ECMWF), Arun Kumar (NCEP CPC), and Hai Lin (ECCC). As part of CW3E’s 2019-2024 Strategic Plan, CW3E seeks to improve atmospheric river (AR) prediction at subseasonal to seasonal (S2S; 2-week to 6-month) lead times. This work contributes to that specific goal and provides an assessment of subseasonal prediction skill of weekly water vapor transport associated with ARs over the Western U.S. in four dynamical model hindcast systems.

More skillful S2S forecasts of ARs are in high demand in the water supply management and flood control communities. This study aims to provide a multi-model S2S prediction skill assessment for the accumulated water vapor transport associated with ARs, which is closely related to wintertime extreme precipitation over the western U.S. The subseasonal prediction skill is evaluated at 1–4 weeks lead time in four dynamical model hindcast datasets from National Centers for Environmental Prediction (NCEP), European Centre for Medium‐Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), and Global Modeling and Assimilation Office (GMAO) at National Aeronautics and Space Administration (NASA). Three reanalysis datasets, including NCEP CFSR, ECMWF ERA5, and NASA MERRA2, are used to evaluate the sensitivity of the prediction skill to the choice of reference dataset.

This study shows that the AR-related water vapor transport is underestimated in ECMWF and ECCC over most of the investigated region, while its maximum has a southeastward shift in NCEP and GMAO at 3–4 weeks lead time. The root mean square error, anomaly correlation coefficient, and Brier skill score are calculated to quantify the prediction skill in both a deterministic and probabilistic sense. At week-3 lead time, the models have significant skill near the lower latitudes (< 40N) of the eastern North Pacific, extending northeastward to the California coastal area (Figure 1). Models have higher skills in forecasting no and strong AR-related water vapor transport cases than weak cases at weeks 1–4 lead time. The impacts of the Madden-Julian Oscillation (MJO) on the prediction skill are also explored. The results show that the MJO’s impact at week-3 lead time is larger than at week-1 and week-2 lead. At week-3 lead, the modulation of prediction skill by MJO mainly occurs over Central and Southern California. However, the impacts have large uncertainties across models, which might be caused by the different model performances in predicting the MJO and the relevant teleconnections.

This work is supported by the California Department of Water Resources Atmospheric River Program. It provides scientific support to the CW3E Subseasonal AR Experimental Forecasts, which are developed via a close collaboration between CW3E and NASA JPL.

Figure 1: (a)-(d): Anomaly correlation coefficients (ACCs) of the AR T-IVT for the four models with respect to ERA5 at week-1 lead time during the cool seasons of the hindcast periods. (e)-(h), (i)-(l), and (m)-(p) are the same as (a)-(d) but for week-2, week-3, and week-4 lead times, respectively. Only ACC values at 95% confidence level based on a 1000-resampling bootstrap statistical significance test are plotted.

Zhang, Z., DeFlorio, M. J., Delle Monache, L., Subramanian, A. C., Ralph, F. M., Waliser, D. E., … & Lin, H. Multi‐Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated with Atmospheric Rivers over the Western US. Journal of Geophysical Research: Atmospheres, e2022JD037608. http://doi.org/10.1029/2022JD037608