Observed Evolution of the Tropical Atmospheric Water Cycle with Sea Surface Temperature

Abstract

Better understanding of how moisture, clouds, and precipitation covary under climate warming lacks a comprehensive observational view. This paper analyzes the tropical atmospheric water cycle’s evolution with sea surface temperature (SST), using for the first time, the synergistic dataset of instantaneous observations of the relative humidity profile from the Megha-Tropiques satellite, clouds from the CALIPSO satellite, and near-surface precipitation from the CloudSat satellite, and quantifies their rates of change with SST warming. The dataset is partitioned into three vertical velocity regimes, with cloudy grid boxes categorized by phase (ice or liquid), opacity (opaque or thin), and the presence of near-surface precipitation. Opaque cloud cover is always larger in the presence of near-surface precipitation (high ice clouds especially). Low liquid water clouds in the descending regime dominate for SSTs < 299.25 K, where the free troposphere is dry (~20%), and opaque liquid water cloud cover decreases with SST warming (−8% K−1) and thin liquid water cloud cover stays constant (~20%). High ice clouds dominate the ascending regime in which, for 299.25 < SST < 301.75 K, humidity increases with SST in the lower free troposphere and peaks around 302 K. Over the warm SST range (>301.75 K), in the ascending regime, opaque high ice cloud cover decreases with SST (−13% K−1), while thin ice cloud cover increases (+6% K−1). Over the warm SST range, total cloudiness decreases with warming in all regimes. This paper characterizes fundamental relationships between aspects of the tropical atmospheric water cycle and SST.

Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 2. Focus on Marine Low Level Clouds

“…local time (CALIPSO equatorial crossing time) to be consistent with Höjgård‐Olsen et al. (2020) that included CloudSat observations which are only available during daytime since 2011. Moreover, we reproduced the analysis with CALIPSO nighttime data (01:30a.m.)…”

“…As in Höjgård‐Olsen et al. (2020), median values are computed for each 0.25 K SST bin of minimum 100 available values, and a bootstrapping algorithm (500 random samplings with replacement) removes nonsignificant values. On this local scale, median‐based regressions are roughly similar across timescales (−2.5%–−4.5%/K).…”

Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 1. Focus on High Level Cloud Situations Over Ocean

“…As in Höjgård‐Olsen et al. (2020), median values are computed for each 0.25 K SST bin of minimum five available values. A bootstrapping algorithm (500 random samplings with replacement) then removes non‐significant median values that lie outside of the 90% confidence interval.…”

Evaluation of tropical water vapour from CMIP6 GCMs using the ESA CCI "Water Vapour" climate data records

“…Therefore, we can use the mid-tropospheric atmospheric vertical velocity at 500 hPa (noted ω500 in hPa/day) as a proxy for the vertical motions in the tropics (Bony et al, 2004). While such framework has been greatly used to study tropical clouds and their distribution (e.g., Konsta et al, 2012;Höjgård-Olsen et al, 2020), this link between vertical motion and TCWV is documented (e.g., Brogniez and Pierrehumbert, 2007) and further illustrated on Figure 1. bins of the period and this value is considered as the reference to normalize the results.…”