Abstract

Abstract Global warming intensifies atmospheric water vapor transport between ocean and land, which increases the likelihood of extreme precipitation and floods. However, accurate estimations of water vapor exchange between ocean and land are difficult due to the lack of available data and effective methods. This study developed a novel eight-direction-vector decomposition algorithm for calculating water vapor flux between ocean and land based on the ERA5 dataset, and the results showed that global water vapor exchange between ocean and land had significantly increased in the past 40 years, except for Antarctica. During 1980–2018, the average annual net water vapor inflow from ocean to land (Qnet) was 44.68 × 1015 kg yr−1, and Qnet increased at a rate of 1.48 × 1015 kg yr−1 decade−1. The intensified atmospheric water vapor exchange between ocean and land was directly caused by the increase of atmospheric water vapor content, which largely depended on the rising air temperature, and it was found that water vapor flux between ocean and land increased by over 8% K−1 with the increasing air temperature at the global average. This study also identified El Niño–Southern Oscillation (ENSO) as an important contributor to the global ocean–land water vapor exchange anomalies. A strong El Niño event (MEI = 1) can result in a 1.36 × 1015 kg yr−1 (3.03%) decrease in Qnet, and a strong La Niña event (MEI = −1) can increase Qnet by 1.38 × 1015 kg yr−1 (3.09%). The eight-direction-vector decomposition algorithm was effective in ocean–land water vapor flux estimations at different spatial and temporal scales, which could provide great insights into the mechanisms of extreme precipitation events. Significance Statement This study developed a novel approach on water vapor flux estimation (i.e., the eight-direction-vector decomposition algorithm) and achieved a high-temporal–spatial-resolution estimation of water vapor flux between ocean and land. It was found that water vapor flux between ocean and land was intensified by increasing air temperature at a rate of 8% K−1, and El Niño yielded an anomaly low net water vapor input from ocean to land at the global scale. The algorithm developed in this study can be used for estimating water vapor fluxes at different spatial and temporal scales, which is crucial for evaluating the role of water vapor flux on formations for extreme weather (e.g., torrential rainstorms and heat waves) and climatic extremes (e.g., droughts and floods).

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