Sugar, Gravel, Fish, and Flowers: Dependence of Mesoscale Patterns of Trade-Wind Clouds on Environmental Conditions

Abstract

Trade-wind clouds exhibit a large diversity of spatial organizations at the mesoscale. Over the tropical western Atlantic, a recent study has visually identified four prominent mesoscale patterns of shallow convection, referred to as flowers, fish, gravel, and sugar. We show that these four patterns can be identified objectively from satellite observations by analyzing the spatial distribution of infrared brightness temperatures. By applying this analysis to 19 years of data, we examine relationships between cloud patterns and large-scale environmental conditions. This investigation reveals that on daily and interannual timescales, the near-surface wind speed and the strength of the lower-tropospheric stability discriminate the occurrence of the different organization patterns. These results, combined with the tight relationship between cloud patterns, low-level cloud amount, and cloud-radiative effects, suggest that the mesoscale organization of shallow clouds might change under global warming. The role of shallow convective organization in determining low-cloud feedback should thus be investigated.

Keywords: low‐cloud feedback; mesoscale organization; shallow convection; trade‐wind clouds.

Figure 1

(a) Illustration of the four prominent cloud patterns of shallow convective organization pointed out by Stevens et al. (2019) over the tropical western Atlantic near Barbados. The four satellite images (48–58° W, 10–20° N) are derived from MODIS imagery. (b) Characterization of the shallow convective organization using infrared geostationary satellite data through two metrics: a convective organization index (I ${}_{org}$) and the mean object size ( $S$). The lower and upper terciles of I ${}_{org}$ and $S$ define four classes of mesoscale organization (Quadrants A, B, C, and D). (c) Relative occurrence of the four cloud patterns defined by Stevens et al. (2019) in each quadrant of the (S, I ${}_{org}$) distribution.

Figure 2

Large‐scale environmental conditions (daily‐mean SST, $V_s$, and EIS) composited over the 2000–2019 period as a function of the mesoscale cloud patterns (FL = flowers; FI = fish; GR = gravel; SU = sugar) inferred from GridSat data. Black markers indicate the mean of the distribution, thin vertical bars the range between the 25th and 75th percentile values, and thick lines $\pm$ the standard error on the mean.

Figure 3

Scatter plot of daily‐mean values of EIS and near‐surface wind $V_s$ over 2000–2019. The mesoscale cloud patterns classified as flowers, fish, gravel, or sugar using (left) GridSat or (right) MODIS observations are indicated in colors. Also reported is the mean (EIS and $V_s$) value computed over the whole period for each cloud pattern. Thin bars indicate the 25th and 75th percentiles of the distributions, and thick bars indicate $\pm$ the standard error on the mean.

Figure 4

Interannual anomalies of (a) the organization index (I ${}_{org}$) and (b) the mean cloud object size $S$ computed from GridSat or MODIS observations over the period 2000–2019 during DJF (the correlation between GridSat and MODIS time series is 0.90 for I ${}_{org}$ and 0.78 for $S$). Interannual evolution of (c) $V_s$ and (d) EIS derived from ERA interim for the same period. Note that in (a–d), the year is defined by the January–February months of the DJF season (e.g., 2010 corresponds to December 2009–February 2010). The shading represents $\pm$1 standard deviation of daily‐mean values around the DJF mean. (e) Examples, for a few DJF seasons, of the daily cloud patterns identified from GridSat data represented as a function of the daily (EIS and $V_s$) conditions of that season (the gray lines are just visual guides).

Figure 5

(a) and (b) same as Figure 2 but for the low‐cloud amount derived from MODIS cloud products and the NET cloud‐radiative effect derived from CERES observations. (c) Same as Figure 3 but for daily‐mean values of NET CRE and low‐level cloud amount.