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. Low-level cloudiness in the trades


Within the trades, climate models diverge in their predictionof shortwave cloud radiative effects and climate sensitivity. Using observations at Barbados, our group has studied the major cloud modes in the trades. Despite large variations in the mean state that accompany the seasonal cycle in theNorth Atlantic trades, cloudiness near cloud base, or the lifting condensation level, is found to be relatively constant,being closely coupled to the surface fluxes. Most of the variability in total cloudiness on longer time scales is instead carriedby variations in cloud aloft, such as stratiform layers near the detrainment level of cumulus tops at 825 hPa (Figure 1). This is a marked contrast with many climate models, in which variations in cloudiness are mostly carried by variations in cloud near cloud base. Several climate models also show the opposite seasonality in the stratiform component. This suggests that models vary clouds in ways that are different from nature, which implies that modeled cloud feedbacks may be unrealistic. 




Figure 1. The profile of cloud fraction (CF) during the dry season from January through March (left), during the wet season from August through October (middle), and the difference in cloud fraction between the dry and the wet season (right). The Barbados cloud observations are shown in black, the ECMWF short and long integration forecasts in light and dark blue, and a subset of CMIP5 models in other colors. The model data are for a single gridbox just upstream of Barbados. The Figure illustrates the wide variety of cloud fraction profiles present in the models, and the inability of several models to capture the seasonality in the cloud fraction profile. In the observations (BCO), the reduction in cloud fraction near cloud base (950-925 hPa) in the dry season is primarily due to the upward shift of the lifting condensation level. Changes in cloud fraction between the two seasons is overall small, except for an increase in cloud fraction between 850-825hPa, which reflects the more frequent occurrence of stratiform layers near the detrainment level of cumulus tops. These layers can also be seen in the photograph taken from HALO in Figure 4 below. From Nuijens [3].


  1. Nuijens, L. I. Serikov, L. Hirsch, K. Lonitz and B. Stevens, 2014: The distribution and variability of low-level cloud in the North-Atlantic, QJRMS, DOI:10.1002/qj.2307
  2. Brueck, H. M., L. Nuijens, and B. Stevens, 2014: Mechanisms controlling seasonal and synoptic time scale variability in low-level cloudiness in the North Atlantic trades. J.Atmos. Sci, in review [Initiates file downloaddraft PDF].
  3. Nuijens, L., B. Medeiros, I. Sandu and M. Ahlgrimm, 2014: The behavior of trade-wind cloudiness in observations and models: the major cloud components and their variability, Journal of Advances in Modeling Earth Systems, submitted [Initiates file downloaddraft PDF].


2. Precipitating shallow systems

Low intensity rainfall from shallow convective systems in the trades likely has a non-negligible contribution to global oceanic precipitation (Figure 2). The exact contribution of these systems to total rainfall however is uncertain, given the difficulty of observing them from space using passive microwave sensors and the lack of ground-based measurements of rainfall over the open ocean. Using radars deployed at the Barbados Cloud Observatory, we have evaluated the ability of three well-known satellite products of global oceanic rainfall (GPCP, TMPA and HOAPS) to reproduce the climatological and day-to-day mean rainfall over the Northern Atlantic. This comparison shows that the satellite sensor's overestimation of the area covered by rain, due to their coarse resolution, outweighs their underestimation of the occurrence of light rain, leading to on average larger daily rain-rates in the trades compared to ground-based radar observations. This can give a misleading picture of the general ability of satellite sensors to capture light rain events [4].  



Figure 2. Averaged light rain (< 24 mm/day) volume contribution to total rainfall amount of HOAPS-C (1988 to 2005). Dotted/hatched areas refer to regions with rarely occurring rain. The red-dashed line marks the mean trade-wind trajectory, from Burdanowitz et al. [4].


If rainfall from shallow clouds is indeed non-negligible, it may be crucial for the energy budget of the trades. Precipitation can help set the equilibrium structure of the trade-wind layer, and helping it adjust to a changing large-scale flow. The role of precipitation and its associated heating and moistening tendency is explored in a new PhD project using idealized Large-Eddy Simulations.


Another question of interest is how the aerosol may regulate rainfall in these shallow systems. Using the Barbados cloud radar data, we have analyzed how the microphysical structure of trade-wind cumuli is altered during Saharan dust events. On dusty days, clouds that do not yet rain have a weaker increase of reflectivity (Z) with height than non-dusty days (Figure 3). This hints at a reduction of droplet sizes with an increase in number of dust particles, which would impact rain formation. However, when conditioning on ambient relative humidity, the dust dependency vanishes. This can be explained from theory and a simple entraining plume model, which shows that the Z-gradient is more sensitive to small changes in relative humidity, which impacts the liquid water content in clouds, than to large changes in droplet number concentration, which is regulated by aerosol [5].


Figure 3. The reflectivity (Z) gradient, from theory (black) and from observations (red/blue), which approximately scales with β2/N, where β is the sub-adiabaticity of clouds, and N the droplet number (aerosol) concentration. Reducing β2/N from 0.69 (non-dusty) to 0.45 (dusty) can be achieved by a ~ 50% increase in N, or instead a ~20% decrease in β, which is already achieved by a ~0.5% absolute decrease in relative humidity. From Lonitz et al. [5]


These findings also emphasize the necessity of observing the humidity structure along with cloud systems. The first set of research flights with HALO (Figure 3), carried out successfully in December 2013 and intersecting the A-Train satellite overpass on every flight leg, has provided such combined measurements. These data have already demonstrated that not just at the BCO, but also over the open ocean across the broader Atlantic, organized convective towers with stratiform outflow near their tops are emerging as one of the key cloud modes in the trades. 


Figure 4. A photograph taken during the first HALO-South campaign. Courtesy: Bjorn Stevens.


4. Burdanowitz, J., Nuijens, L., Stevens, B. and Klepp, C. (2014): Evaluating light rain from satellite- and ground-based remote sensing data over the subtropical North Atlantic. In review, J. Applied. Meteor.

5. Lonitz, K., Stevens, B., Nuijens, L., Sant, V. and Hirsch, L. (2014): Signatures of aerosols and meteorology in long-term radar observations of trade-wind cumuli. In preparation for JAS.



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.


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

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

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