Some of our recent research highlights include the use of observational insights of the variability of low-level cloudiness in the trades to evaluate numerical weather prediction and climate models. Additionally, we are working towards a better characterization and understanding of the trade-wind cumuli in their role as precipitating shallow systems. We also continue our contributions to international assessments of clouds, aerosol and radiation.


1. Understanding and constraining the shallow cumulus cloud feedback


While deep convection is known to organize into cloud clusters not only in simulations but also in observations [1] and as such effecting the mean atmospheric environment, one of the most ubiquitous clouds, the trade wind cumuli, were thought to be randomly distributed. With the start of satellite-era it became however clear, that the shallow trade wind cumuli show different types of organization on the meso-scale and are far less often randomly distributed.

The organization of shallow convection in the downwind trades can be grouped into four distinct categories by visual inspection [2]. A neural network trained on these categories further revealed that these patterns occur in all major ocean basins under different meteorological conditions [3] and their top-of-atmosphere radiation budget differs owing to the differences in low-cloud fraction [4].

While the meso-scale patterns show a strong relationship with surface wind speed and lower-tropospheric stability [4], their driving mechanism and properties are currently unclear.

Pairing satellite measurements with profiling and in-situ measurements at the Barbados Cloud Observatory, that collects representative observations of the maritime trade wind atmosphere, is utilized in a current study to connect the meso-scale appearance with high-resolution measurements. By such we gain knowledge about the cloud- and atmosphere-composition, that is otherwise not possible from satellite retrievals alone [5].

Figure 1: The stratiform component of the meso-scale organization types is one of the characteristics that set the patterns apart from each other. While the cloudiness at cloud base (~700m) is rather constant among the patterns, the top-of-atmosphere radiation is altered by the stratiform cloud fraction. Shown here are the cloud fraction profiles measured by the BCO cloud radar during periods of automatically identified meso-scale organization.

Together with the observations gathered during the EUREC4A field campaign with its strong atmospheric and oceanographic component we will advance our understanding about these shallow meso-scale patterns, their interaction with the ocean and potentially their alteration with a changing climate.


[1] Schulz, H.; Stevens, B. Observing the Tropical Atmosphere in Moisture Space. J. Atmos. Sci.2018, 75 (10), 3313–3330.

[2] Stevens, B.; Bony, S.; Brogniez, H.; Hentgen, L.; Hohenegger, C.; Kiemle, C.; L’Ecuyer, T. S.; Naumann, A. K.; Schulz, H.; Siebesma, P. A.; Vial, J.; Winker, D. M.; Zuidema, P. Sugar, Gravel, Fish, and Flowers: Mesoscale Cloud Patterns in the Tradewinds. Quarterly Journal of the Royal Meteorological Society0 (ja).

[3] Bony, S.; Schulz, H.; Vial, J.; Stevens, B. Sugar, Gravel, Fish, and Flowers: Dependence of Mesoscale Patterns of Trade-Wind Clouds on Environmental Conditions. Geophysical Research Letters2020, 47 (7), e2019GL085988.

[4] Rasp, S.; Schulz, H.; Bony, S.; Stevens, B. Combining Crowd-Sourcing and Deep Learning to Explore the Meso-Scale Organization of Shallow Convection. Bull. Amer. Meteor. Soc.

[5] Schulz, H.; Ryan, E.; Stevens, B. Observing meso-scale organization in the trades: classification, characterization and evolution, in preparation


2. The influence of the large-scale environment on cloudiness


Our limited understanding of how the atmospheric state varies at short time-scales, especially in terms of the circulation, has long withheld an in-depth investigation of what controls cloudiness at scales where individual clouds or cloud systems evolve (process scale). Measurements of divergence as well as cloudiness made during field campaigns like EUREC4A and NARVAL2 provide us the opportunity to understand how clouds are influenced by the thermodynamic and dynamic controls in their immediate environment.

From the NARVAL2 case studies (George et al, 2020), we find that at the process scale, the sub-cloud layer vertical motion explains the variability in cloudiness, which cannot be explained by the conventional cloud-controlling factors - most of which are thermodynamic in nature. The vertical velocity in the sub-cloud layer also works as a predictor of the cloudiness, by its influence on the shallow convective mass flux. We also see converging air masses in the boundary layer to be moister and having a more well-mixed cloud layer than environments with a diverging regime in the boundary layer (see Figure-1). This highlights the importance of the dynamics on cloudiness, especially on shorter scales of time and space - something that is not currently considered by cloud parameterisations. 


Figure 1: For NARVAL2 case-studies, the static energy profiles (left), cloudiness profiles (centre) and the vertical motion profiles (right) are shown. The grey, horizontal lines, in all profiles are, from bottom to top, levels of LCL, maximum Brünt-Vaisala frequency squared (N$^{2}$), 700 hPa and 500 hPa. The static energy values, $\omega$ and vertical velocity are determined from dropsonde measurements, and the cloudiness profiles are obtained from the HAMP radar, WALES lidar and the GOES satellite, as per the legend. Note that the cloudiness profiles have an X-axis on the natural logarithmic scale. For convenience, we provide the arithmetic values of the tick labels on the X-axis, $e^{-2}$ = 0.1353; $e^{2}$ = 7.3891; $e^{4}$ = 54.5982. The case-studies are classified as either cloud-suppressed regimes (marked S), active convection regimes (marked A) or edge of active convection regimes (marked E).


The EUREC4A measurements can help answer our question of “what controls cloudiness at the process scale” with robust, statistical estimates. The different, well-characterised  environments sampled during the campaign reveal the variability present in the trade-wind regions. We are particularly interested in investigating the variability of the large-scale dynamics in space and time, and the effect that it has on other environmental factors. A broad range of collocated cloud observations will also help us understand how clouds are controlled by their environments. Moreover, we also look into reanalysis products from IFS (ECMWF) to investigate the biases between the estimates of divergence between reanalysis and observations, thus furthering our understanding of how the large-scale atmospheric circulation varies. 


[6] George, G., Stevens, B., Bony, S., Klingebiel, M., Vogel, R., 2020, Observed impact of large-scale vertical motion on cloudiness, Journal of Atmospheric Sciences, in preparation 


3. Aerosols in the climate system


Observations of the atmospheric composition are needed to evaluate the output of simplifying atmospheric models. In addition to observational statistics of individual variables, also observational relationships among different atmospheric properties (as those of aerosols in context of those of clouds, precipitation or wind) are important to constrain processes and also to identify the more important processes in atmospheric modeling. For climate models, whose results cover the entire globe and all seasons, satellite products are a preferred reference. However, prior to any application, quality and usefulness of satellite products need to be demonstrated.


To document strengths and limitations of satellite products and to improve retrieval model assumptions, comparisons to trusted data-samples are needed. These are provided by in-situ data from short campaigns, but mainly by ground-based monitoring via remote sensing (- preferably using the well-defined sun-properties as background). While ground-based monitoring networks (AERONET, BSRN) are well established over continents, atmospheric references over oceans remain sparse. To improve the oceanic reference data-pool, the MPI-M coordinated the sampling of atmospheric properties on German Research Vessels since 2008. In cooperation with NASA, which provides calibrated sun-photometers and maintains the associated data (illustration and access) web-site, the MPI-M organized the sampling (of aerosol column amount, aerosol column average size and column water vapor) on many oceanic transit cruises. In addition, during the last three years, the sun-photometer data were  complemented by continuous data on cloud-base altitude and cloud structure via (long-term deployments) of  ceilometers and cloud cameras of the MPI-M.   

For data applications, the sampled aerosol-statistics over oceans and continents are combined (note, that the MPI-M maintains an aerosol robot of the AERONET ground network in Hamburg since the year 2000). This combined aerosol reference data-set (for aerosol amount and aerosol absorption as function of size) serves as the basis of a global monthly climatology for tropospheric aerosol optical and (spectral dependent) radiative properties (AOD, SSA, ASY), as they are needed to derive radiative (climate) impacts (Kinne et al, 2013, Kinne 2019). With extra ancillary data on the relative vertical aerosol distribution and on the anthropogenic fraction from simulations with complex aerosol modules (of the AeroCom initiative), the aerosol impacts on climate was estimated in off-line radiative transfer simulations (Kinne, 2019). Hereby also climate impacts for individual components (e.g. mineral dust, soot) and climate impacts of water clouds modifications by anthropogenic aerosol are offered.


Figure 1. The left panel presents annual average maps of the MACv2 aerosol climatology. Global distributions are presented for present day column properties of aerosol amount (AOD), absorption (AAOD*10), anthropogenic AOD and fine-mode effective radius (REf*2) in um. The left panels illustrate present-day climate impacts by anthropogenic aerosol in W/m2. Maps for annual averages compare direct radiative effects at clear-sky conditions (Dclr) and all-sky conditions (Dall), aerosol indirect (Twomey) effects through modified clouds (IND) and the combined (direct and indirect) effect (COM). Blue colors indicate ‘cooling’ net-flux losses and (rare) red colors indicate ‘warming’ net-flux gains. Values below the labels indicate global averages.


Other applications of the aerosol climatology are comparisons to global modeling with complex aerosol modules in the framework of the AeroCom initiative (AeroCom activities and annual meeting are co-organized by the MPI-M) and both comparisons to and first guesses for retrieval assumptions in satellite remote sensing of aerosol in support of ESA’s climate initiative through the aerosol CCI+ sub-project.


[7] Kinne, S. (2019). Aerosol radiative effects with MACv2, ACP 19, 10919–10959.

[8] Kinne, S. (2019). The MACv2 Aerosol Climatology, Tellus B: Chemical and Physical Meteorology, 71, 1, 1-21.

[9] Kinne et al. (2013). MAC-v1: A new global aerosol climatology for climate studies. Journal of Advances in Modeling Earth Systems, 5, 704-740.