Links Between Sea Surface Temperature Structures, Clouds and Rainfall: Study Case of the Mediterranean Sea

Abstract

Ocean and atmosphere communicate through exchanges at the air-sea interface, a complex surface in perennial disequilibrium. At large scales, atmospheric dynamics force ocean variability (Gill, 2016) as strong winds enhance surface turbulent heat fluxes and generate upper ocean mixing, reducing sea surface temperature (SST). At finer scales, (100km)  , ocean structures influence the atmospheric dynamics (Chelton et al., 2001), impacting air temperature, frictional stress, and the marine atmospheric boundary layer (MABL) stability. The upper ocean and lower atmosphere layers interact through different processes forming a feedback loop (e.g., Strobach et al. (2020)).Ocean thermal structures affect the stability of the air-column and its thermodynamical properties through thermal and dynamical adjustments of the MABL (Small et al., 2008). The resulting small-scale processes, called thermal feedback, have important upscale and remote impacts at seasonal timescales on local surface wind (Chelton et al., 2004), basin-scale atmospheric circulation (Desbiolles et al., 2018), cloud cover, rainfall location over ocean -as shown in long-term average over the Gulf Stream (Minobe et al., 2008) or in composite analyses over Southern Ocean eddies (Frenger et al., 2013) -and climatic variability of heat/ tropical lows inland (Desbiolles et al., 2020). Thus, small-scale air-sea interactions significantly affect the water cycle.Two main mechanisms have been proposed to describe the surface wind response to small-scale variability of SST: the downward momentum mixing (hereafter DM), notably introduced by Hayes et al. (1989), and the pressure adjustment (PA), highlighted by Lindzen and Nigam (1987). The DM mechanism brings into play large eddies within the MABL, stimulated by turbulent fluctuations of momentum, temperature and moisture, that redistribute horizontal momentum in the vertical direction (Small et al., 2008). This process modifies the MABL stability over SST patterns (Businger & Shaw, 1984), entailing, as a net effect, weaker (stronger) surface winds over the cold (warm) flank of an SST gradient (Wallace et al., 1989). As a consequence, a positive correlation emerges between wind divergence and downwind SST gradient (e.g., Chelton

Introducing New Metrics for the Atmospheric Pressure Adjustment to Thermal Structures at the Ocean Surface

“…It appears that a different response is detected according to the variables considered. In particular, no relationship between the wind divergence and the SST Laplacian is detected, in agreement with previous studies such as Meroni et al (2020) and Desbiolles et al (2021). However, a significant Spearman r correlation is found between the across-wind variables, suggesting that PA is actually at play, as found from the numerical simulations presented in this work.…”

“…An example of application of these new metrics to high-resolution satellite data in the Mediterranean Sea shows that by looking at the across-wind direction, a PA-mediated wind response emerges on sub-daily time scales, which has never been observed before using the standard metrics (Desbiolles et al, 2021;Meroni et al, 2020). Future efforts devoted to characterize the spatio-temporal variability of the PA-mediated response using satellite data at high resolution from current and future missions (such as those proposed in the European Space Agency, ESA, Earth Explorer X Harmony, ESA (2020)) will allow to better characterize air-sea feedbacks and to properly parameterize them in climate models.…”

“…As the least-square estimate of the linear trend is not robust with respect to the presence of outliers, the extreme values in the binned scatter plot can control the value of the coupling coefficient, especially when instantaneous data are considered. To avoid this, the data are organized into percentile bins, so that the statistics are computed over bins with the same number of points, as in Desbiolles et al (2021). In particular, we compute the mean value and standard error of the dependent variable (y axis) conditioned to the percentile bins of the control variable (x axis).…”

“…and Gaube et al (2019) have shown that DMM controls the fast atmospheric response over the Tropical Instability Waves of the eastern Pacific cold tongue, over Southern ocean mesoscale eddies and over a sub-mesoscale filament of the Gulf Stream, respectively. Meroni et al (2020) and Desbiolles et al (2021), by looking at 25 years of satellite and reanalysis data, have highlighted the prominent role of DMM on daily scales in affecting both the surface wind response and the subsequent cloud and precipitation signature over SST fronts in the Mediterranean Sea. On the other hand, the observational work of Li and Carbone (2012) argues that PA explains convective rainfall excitation over the western Pacific tropical warm pool on daily scales, and the work by Ma et al (2020) successfully describes the fast atmospheric response to the cold wakes generated by tropical cyclones in terms of secondary circulations controlled by PA (Pasquero et al, 2021).…”

Fast Atmospheric Response to a Cold Oceanic Mesoscale Patch in the North‐Western Tropical Atlantic

“…Similar behaviors are observed over mesoscale eddies of all Western Boundary Currents (e.g., Chen et al., 2017; Liu et al., 2018; J. Ma et al., 2015; Rouault et al., 2016). In the Mediterranean Sea, the DMM modulates, on daily time scales, the surface wind speed and divergence (Meroni et al., 2020), with effects on the horizontal wind within the ABL, the cloud cover, and the rainfall probability (Desbiolles et al., 2021). In the tropical oceans, there are examples of the action of the PA mechanism over daily and weekly scales.…”