Project description
Getting a better handle on what we do not know increases clarity about climate change
The ability to predict seasonal climate changes and the occurrence of severe weather events is important for preparation, mitigation and policy making. Mathematical models are invaluable to our predictions of processes as well as their evolutions and outcomes. However, all models and their predictions have underlying uncertainties that reflect a variety of unknowns or different pathways. Quantifying the underlying uncertainties is equally important to predicting local or regional climate changes and seasonal events. The EU-funded IMPASTO project is enhancing our ability to represent the uncertainty in certain physical parameters by incorporating observation data via an inverse stochastic modelling paradigm. The outcomes will address the need to quantify the prediction uncertainty at sub-regional and local scales to achieve high-resolution regional seasonal climate ensemble modelling.
Objective
The emerging climate services in Europe and globally under the WMO GFCS critically depend on the science and application of Earth System (ES) modeling and prediction. An important aspect of the modelling and prediction challenge for the services is characterization of prediction uncertainty. Quantitative information about inherent uncertainty in the prediction is vital for risk assessments and decision support across public service and industry sectors. Over the past decades significant investments were made through the EC FPs into establishing the ES ensemble modeling and prediction capabilities that incorporate representation of sources of intrinsic uncertainty in the modeling systems and provide quantification of their impacts on seasonal to decadal climate prediction. The identified stakeholder priorities for the H2020 and beyond are in advancing of these prediction capabilities at regional level with high resolution models. This project is intended to contribute to the science of high resolution regional seasonal climate ensemble modeling, addressing the need for understanding and quantification of the prediction uncertainty at sub-regional and local scales. The research will focus on characterization of stochastic uncertainty associated with physical parameterizations in the regional ES models. The project will integrate the established ensemble modeling methodology from the foundational work with the global systems with a new approach to representing the stochastic uncertainty of physical parameterizations by means of inverse stochastic modeling using process-level observations. A long standing tradition in expert regional climate modeling at the hosting institution will be joined with the Researcher’s expertise and experience in geophysical modeling and cross-disciplinary methodology of data assimilation. The Researcher will be trained in the regional climate modeling and will apply the expertise to build a new research capacity at the hosting institution.
Fields of science
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Funding Scheme
MSCA-IF-EF-RI - RI – Reintegration panelCoordinator
11001 Belgrade
Serbia