ICON includes component models for the atmosphere (ICON-A), the ocean together with sea-ice (ICON-O), the land (ICON-L) and chemistry (ICON-ART), which can be used as separate models, or coupled together as a earth-system model with the option to include the full carbon cycle, bio-geochemical processes in the ocean and complex treatment of land processes. ICON-Seamless-ART (ICON-SmART) addresses the role of aerosols and atmospheric chemistry for the simulation of seasonal to decadal climate variability and change. This Earth system model will enhance the capabilities of the coupled composition, weather and climate modelling system ICON-ART (ICON, icosahedral nonhydrostatic model – developed by DWD, MPI-M and DKRZ with the atmospheric composition module ART, aerosols and reactive trace gases – developed by KIT) for seasonal to decadal predictions and climate projections in seamless global to regional model configurations. This will provide scientists, forecasters and policy-makers with a tool to investigate atmospheric composition in a changing climate and allows us to answer questions that have been previously out of reach.

Aerosols affect weather and climate by absorbing and scattering radiation. Such effects strongly depend on the optical properties of aerosols that are mainly controlled by their other characteristics like size distribution, morphology and chemical composition. Chemistry and aerosol microphysics constantly modify these characteristics causing a large spatial and temporal variability. Most atmospheric models cannot account for this variability as they rely on look-up table to treat aerosol optics. This simplification can lead to large errors in weather and climate models when it comes to aerosol radiative impacts.

I am developing a novel and computationally inexpensive machine learning approach for online representation of the aerosol optical properties. These properties are fully coupled with the chemical and microphysical variability of particles. I am implementing this approach in ICON-ART model.

The discovery of causal structures behind a phenomenon under investigation has been at the heart of scientific inquiry since the beginning. In the absence of interventional data, we are forced to depend only on observational data for causal analysis. With the limited ability of traditional correlation-based computational methods to discover causal relationships, Causal Machine Learning (CML) approaches (under Granger and Pearl Causality framework) are gaining popularity. I employ different XAI and CML approaches to study geophysical phenomena and identify causal linkages.

Movement of airmass, both on the planetary and local scale, can have huge impact on the chemical composition of the atmosphere. While the problem of fluid movement is generally viewed from the Eulerian perspective, Lagrangian approach is generally more useful when analysing the air-parcel movement from one region to another. Trajectory analysis has proved useful in a number of problems ranging from aircraft path analysis to identification of the sources of pollution.

I am working on the trajectories data mining using machine learning and receptor models like Potential Source Contribution Function (PSCF), Concentration Weighted Trajectories (CWT), Residence Time Weighted Concentration (RTWC) etc.