EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation

Abstract

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air-sea interactions and convective organization.

Keywords: Atmospheric circulation; Cloud feedback; Field campaign; Shallow convection; Trade-wind cumulus.

Fig. 1

Vertical profiles of the low-cloud fraction, and of its response to global warming, predicted by two general circulation models (MPI and IPSL) in the trade-wind cumulus regime. For each model, results are shown for two versions differing only by their representation of lower-tropospheric mixing (after Stevens et al. ; Vial et al. 2016)

Fig. 2

Envisioned flight strategy for the EUREC⁴A core measurements

Fig. 3

Large-scale sounding array envisioned for EUREC⁴A, comprised by the Barbados Cloud Observatory (BCO) and approximately four research vessels. Buoy stations are indicated by open circles. The ships will serve as advanced surface remote sensing platforms, atmosphere and ocean sounding stations, bases for fleets of autonomous vehicles, and means of laying down an array of drifters/floats or buoys

Fig. 4

Shallow cloud organization observed from MODIS on 9 February 2017 (Barbados, which is about 20 km wide, is highlighted in green). The top of cloud clusters does not exceed 3–4 km

Fig. 5

(left) Research flights performed during NARVAL2 on 19 August and the vertical profiles of large-scale mass divergence D and large-scale vertical velocity $\mathit{\omega }$ derived from the dropsondes measurements for each circle

Fig. 6

Vertical profiles of water vapour mixing ratio (left) from the NARVAL 1 flights, (middle) condensed water ($q_l$) and (right) cloud fraction from RICO. The NARVAL 1 water vapour is derived from all sondes for which surface air temperatures exceed ${25}^{\circ }\text{C}$, as measured east of Barbados in December 2013. The distribution is described by the box plots showing range (5–95%), interquartile and median. Cloud condensate profiles are for similar conditions but during RICO and adapted from vanZanten et al. (2011). Flight date is characterized by box plots (interquartile and 5–95%) and dots (flight averaged). The line is the ensemble mean of 12 large-eddy simulations. Cloud and cloud-core fraction profiles are derived from LES simulations (adapted from vanZanten et al. 2011). Ensemble (interquartile) spread among LES simulations is given by the shading, and the mean profiles from non-precipitating simulations are shown by the thin dashed line. Approximate flight level for the cloud-base legs of the ATR-42 is also indicated

Fig. 7

(Top) Lidar backscatter ratio measured on 2 June 2017 (RF07), in the South of France from an ultra-light aircraft carrying a 355-nm horizontally pointing lidar: two rectangular legs were flown within the subcloud layer and one above the base of shallow cumuli. (Bottom left): Example of an individual lidar signal (corrected from aerosol attenuation) detecting two clouds in a row. (Bottom right): Histogram of the distance between the first and last cloud detections along each individual lidar beam and of the distance between the ULA and the last detected cloud. Note that the ALiAS lidar that will be on-board the ATR-42 will be five times more powerful than the lidar used on-board the ULA

Fig. 8

Boundary-layer profiles (normalized by the maximum cloud top height and minimum cloud base height) of hourly averaged a cloud fraction, b vertical velocity and c mass flux for all, core and vertically coherent updraft samples collected at the island of Graciosa in the Azores. (Adapted from Ghate et al. 2011)

Fig. 9

Schematic representation of the subcloud layer and of the main physical processes affecting its mass budget

Fig. 10

The route of ocean eddies. Statistics of ocean mesoscale eddies derived from satellite altimetry (shown is the fraction of the time inside an anticyclonic eddy)

Fig. 11

RV SONNE (SO172) ship survey through an anticyclonic eddy (North Brazil Current Ring) west of Barbados (ship was going east to west, time axis is reversed for clarity). a Meridional current section (triangles at 0 m denote ship was stationary), b sea surface salinity (psu), and c air temperature