constRaining the EffeCts of Aerosols on Precipitation (RECAP)


We are currently setting up the new ERC project RECAP. This five year research programme will systematically constrain aerosol effects on precipitation across scales, combining process-based with energetic approaches.

This work builds on our earlier ERC Starting grant ACCLAIM with focus on aerosols effects on convective clouds and climate.



Precipitation is of fundamental importance to mankind and ecosystems. Even relatively small shifts in precipitation amounts or frequencies can lead to changes in vegetation distribution, agricultural productivity and flood occurrence.

Aerosols, a suspension of fine solid particles or very small liquid droplets in the atmosphere, provide the necessary seeds for nucleation and precipitation in nearly all clouds on Earth. Aerosols can be natural or man-made. The most common condensation nuclei in pristine environments are sea salt, organic carbon and ammonium salts or sulphates arising from volcanic activity. Human generated aerosols, such as air pollutants and smoke, are commonly composed of sulphates and black carbon.

Polluted air contains higher concentrations of water-soluble particles, with clouds having more numerous, but smaller, droplets. These small droplets increase the reflectance of polluted clouds to incoming solar radiation and result in the cooling of the Earth’s surface. Aerosols, such as smoke and soot, can also absorb more solar radiation and cause heating of the atmosphere resulting in a decrease in cloud cover.

Previous research has modelled the effects of aerosols to infer their impact on cloud condensation, ice nucleation
and individual cloud or cloud system formation. Progress has been limited by the inherent complexities and uncertainty of microphysical interactions (between cloud droplet size, precipitation and entrainment), as well as co-variations with meteorology and spatial extent of the relationships from regional and global scales.

RECAP aims to break this deadlock by introducing a radically different approach to aerosol effects on precipitation. Globally, the energy flow through the atmosphere controls precipitation in a balance between latent heat release and radiative cooling or surface flux changes. However, local changes in dry static energy are significant in controlling cloud formation processes. The role of aerosols on these energetics constraints will be determined from state-of-the-art model simulations, satellite observations, and ground-based observing networks.

RECAP will systematically explore and deliver the first comprehensive and physically consistent assessment of the effect of aerosols on precipitation across scales, uniting energetic and process-driven approaches.

The Team


RECAP will expand on the latest developments in modelling and observations of aerosols, clouds and precipitation to characterise the physical interaction mechanisms. The key factors identified as producing significant ecological and societal impacts will be assessed in process and climate model studies. The scientific strategy will investigate:

  1. Precipitation response to aerosol perturbations ‘top-down’, from idealised scenarios to fully realistic case studies;
  2. Comprehensive ‘bottom-up’ analyses of aerosol perturbation propagation at aerosol and cloud microphysics scale to energy dynamics in individual clouds and cloud fields;
  3. Observational constraints from global satellite observations to detailed in-situ process observations made during aircraft fly-through surveys;

Fig 1_5.jpg


This project is centred on a new generation of aerosol-climate models capable of simulating aerosols, clouds, their interactions. They will also simulate interactions with climate systems from the micro to the global scale. The MPI ICON [Dipankar et al., 2015; Zangl et al., 2015] and the Met Office Unified Model [Walters et al., 2014] (UM) are both coupled to detailed aerosol microphysics schemes (ICON: HAM [Stier et al., 2005; Stier et al., 2007] and UM: UKCA [Bellouin et al., 2013]) ( Kipling et al., submitted). They include mechanistic coupling to the host model cloud microphysics schemes to allow simulations ranging from Large Eddy Simulation (LES), Cloud Resolving Modelling (CRM) to global climate model configurations.


RECAP’s transition of observational constraints to 4D, requires synthesis of a wide range of novel observations. Geostationary satellite multispectral imager data with introduce the time dimension from the Meteosat Second Generation (MSG SEVIRI), Meteosat Third Generation (MTG LI), Himawari 8/9 (AHI) and the GOES-R (ABI) satellites. High-time resolution of cloud properties and aerosol optical depth will also be captured. Vertical dimension data will be sourced from active satellite remote sensing of cloud properties via CloudSat Cloud Profiling Radar (CPR), the EarthCare Doppler CPR (updraft vertical velocities), the Tropical Rainfall Measuring Mission Precipitation Radar (PR) and microwave imager (TMI). The Global Precipitation Measurement mission will combine precipitation radar (DPR) and microwave images (GMI) and the multi-sensor merged products 3B42 and iMERG.

Aerosol observations will come from the CALIPSO CALIOP lidar and the EarthCare hyperspectral ATLID lidar. High-quality imaging radiometers on polar orbiting satellites will complement these datasets. Aerosol optical depth will be retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the ground-based AERONET sunphotometer network. Additional observational constraints on aerosol absorption, in combination with retrievals of aerosol absorption optical depth / single scattering albedo from the Ozone Monitoring Instrument (OMI) will be used. However, since satellite data will always suffer to some degree from retrieval errors extensive use of detailed in-situ observations will be used for verification. Ground based observational networks, such as IMPROVE, EMEP, ACTRIS, GAW provide detailed aerosol measurements with good spatio-temporal coverage over the USA and Europe but are limited to surface measurements. To guide 4D observational constraints data will be used from the largest consistent database of aerosol aircraft, ship and ground based measurements. This is synthesized in our collaborative Global Aerosol Synthesis and Science Project [] illustrated below as well as data from the most comprehensive Ice Nucleation DataBase, synthesized in the EU project BACCHUS [].

Fig 2.jpg

Global distribution of quality controlled aircraft and ship based aerosol measurements from 42 aircraft, 21 ships, 21 ground-based, 26 multiple-platform campaigns and 460 ground station with longer-term data highlighted in the Global Aerosol Synthesis and Science Project (GASSP).

Direct observations will be complemented by the ECMWF MACC re-analysis datasets, including aerosol and cloud properties. Energy budgets will be constrained with data from the Clouds and the Earth's Radiant Energy System (CERES) instrument flown on several satellites. Time-resolved measurements from the Geostationary Earth Radiation Budget Project (GERB) as well as higher resolution spectral-to-broadband converted datasets from spectral imagers such as MSG SEVIRI and third-generation instruments on GOES-R, Himawari 8/9 and MTG will also be used. High quality data from the Baseline Surface Radiation Network (BSRN) from the World Radiation Monitoring Center (WRMC) will serve as ground-truth. Water budgets will additionally be constrained through use of high-resolution water vapour retrievals from MODIS and VIIRS as well as MSG SEVIRI data with high temporal resolution. Additional constraints may arise from field campaigns proposed by the Aerosols, Cloud Precipitation and Climate initiative [PI on steering committee, Rosenfeld et al., 2014], the analysis of existing measurement campaigns with large scale radiosonde arrays, such as the DYNAMO Array-Averaged Analyses [Ciesielski et al., 2014] as well as the Atmospheric Radiation Measurement (ARM) programme’s Operational Objective Analysis System [Zhang et al., 2001; Tang and Zhang, 2015].