FDL Europe 2020 - Clouds and Aerosols
Aerosol effects on mesoscale cloud structures in marine boundary layer clouds
Published 7 JUL 2022
Frontier Development Lab
Frontier Development Lab (FDL) is a public-private partnership with ESA in Europe and NASA in the USA. FDL works with commercial partners to apply AI technologies to space science, to push the frontiers of research and develop new tools to help solve some of the biggest challenges that humanity faces. These range from the effects of climate change to predicting space weather, from improving disaster response, to identifying meteorites that could hold the key to the history of our universe.
FDL Europe 2020 was a research sprint hosted by the University of Oxford, that took place over a period of eight weeks in order to promote rapid learning and research outcomes in a collaborative atmosphere.
Clouds and Aerosols
How do aerosols influence the lifetime of mesoscale cloud structures in marine boundary layer clouds?
Marine boundary layer (MBL) clouds cover a vast area of the world’s oceans. In doing so, they reflect a large portion of incoming solar radiation, and so have a significant cooling impact of the planetary energy budget. In particular, the large stratocumulus cloud decks off of the Western seaboards of most of the major continents (highlighted in orange and red, below), have a strong cooling affect due to their great extent - measuring thousands of kms across - and proximity to the equator. Even a small change in the properties of these cloud decks could have significant impacts on future climate change.
Clouds are affected by human activity through their feedbacks to climate change, but also directly due to Aerosol Cloud Interactions (ACI). Aerosols (particulate pollutants suspended in the atmosphere) interact with clouds by forming condensation nuclei which cloud droplets form around. An increase in aerosols leads to an increase in cloud droplets, and hence an increase of the reflectivity of the cloud. This causes a cooling affect, which historically has offset a significant portion of global warming due to greenhouse gasses. These changes to cloud droplets may also influence the size and lifetime of clouds through their impacts on precipitation and evaporation processes. These secondary ACI are highly uncertain, but may have significant impacts on the climate.
The Clouds and Aerosols project team used observations from the EUMETSAT geo-stationary SEVIRI (Spinning Enhanced Visible and InfraRed Imager) satellite over the Southern Atlantic Ocean, combined with data from European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis, 5th Edition (ERA5) and Integrated Multi-satellitE Retrievals (IMERG) estimates. The aim was to better understand the aerosol impact on cloud structure through the application of multiple machine learning methods. They used unsupervised and self-supervised learning to observe cloud mesoscale structures or cloud types. They then applied a recurrent neural network (RNN) to isolate the causal impacts of aerosols on our observed cloud types.
The project team found that the model predictions support that an increase in aerosol concentration and droplet number density leads to an increase in the occurrence of close-cell stratocumulus, and an increase of cloud lifetime. However, they were not able to predict how these changes would effect the climate. Further work will involve predicting the radiative forcing impacts of cloud structure changes in order to better understand the climate impacts of aerosol effects on cloud structure and lifetime. You can learn more about this case study by reading the complete Technical Memorandum.
The Scan Partnership
NVIDIA is a key supporter of the Frontier Development Lab and the FDL Europe 2020 event , and Scan was asked to act as a technology partner of NVIDIA to provide access to multiple DGX-1 systems in order to facilitate much of the machine learning and deep learning development and training required. The Clouds and Aerosols project team used Google Cloud Platform (GCP) instances to prototype their models, prior to trained models then being deployed on a NVIDIA DGX-1 platform in order to accelerate time to results.
