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Inverse Modeling of PArameterized physics STochastic uncertainty using process-level Observations

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.

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Topic(s)

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MSCA-IF-EF-RI - RI – Reintegration panel

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Call for proposal

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(opens in new window) H2020-WF-2018-2020

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Coordinator

FACULTY OF PHYSICS OF THE UNIVERSITY OF BELGRADE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 140 021,76
Address
Studentski trg 12-16
11001 Belgrade
Serbia

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Region
Србија - север Београдски регион Београдска област
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 140 021,76
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