Integration of user needs, downscaling and model selection

ACRP project PREVAL ÖKS NEXTGEN

Project overview:

Funding: ACRP project, Climate and Energy Fund
Title: Processed based climate model evaluations over Austria for informing the next generation of Austrian climate scenarios (PREVAL ÖKS NXTGEN)
Keywords: Evaluation, user needs, model selection, bias adjustment, downscaling management
Project lead: University of Graz, Wegener Center
Contact: Martin Jury (martin.jury@uni-graz.at), Douglas Maraun (douglas.maraun@uni-graz.at)
Project partners: GeoSphere Austria, University of Innsbruck
Project duration: 01.12.2022-01.12.2025

Objective

The main objective of PREVAL ÖKS NEXTGEN is to guide the development of ÖKS NEXTGEN, in particular to ensure a process that (1) considers a broad set of user needs and values, (2) informs the selection of model ensembles and individual models, and (3) informs the design of a meaningful bias adjustment and statistical downscaling strategy.

Executive summary

PREVAL ÖKS NEXTGEN is one outcome of the started community process within the planed update of the Austrian Climate Scenarios (ÖKS). The main aim of the project is to guide the development of ÖKS, in particular (1) to ensure a process accounting for a broad set of user needs and values, (2) to inform the choice of model ensembles and individual models, and (3) to inform the design of a sensible bias adjustment and statistical downscaling strategy.

In particular, PREVAL ÖKS NEXTGEN has the following four objectives:

1. Establish a process that enables Austrian weather forecasters to provide climate services that are grounded in state-of-the-art scientific knowledge, relevant for user needs and accounting for user values.
2. Assess how the representation of large-scale processes in global climate model ensembles affects the representation of regional climate in Austria, and quantify the corresponding possible added value of both dynamical downscaling and the most recent generation of global climate models.
3. Assess the performance of currently available climate model ensembles at representing internal climate variability and long-term climate trends from decadal to centennial scale. Assess the potential of single-model initial-condition large ensembles in separating internal climate variability and long-term climate trends.
4. Identify suitable statistical bias adjustment and statistical downscaling strategies that account for the inherent limitations of the approaches, but at the same time optimise the usability of the generated climate model data for climate impact assessments.