LISTEN · Lost In translation: Strengthening communication skills between real world and climaTe modEls for seasonal to decadal predictioN
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2019-02-01 → 2021-05-03
- EU contribution
- €168,277
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Lost In translation: Strengthening communication skills between real world and climaTe modEls for seasonal to decadal predictioN
Near-term climate information is increasingly incorporated in the decision-making process of many socio-economic sectors. Attracting interest of society on the changing climate, as well as advising the stakeholder on how to benefit from climate information, along with learning their needs are clear priorities to mitigate climate change impacts. Parallel to these priorities, there is still work that needs to be done by the scientific community to provide the best near-term climate information, for it to be a valuable instrument for the decision process. The main tools to provide near-term climate information are Global Climate Models (GCMs). GCMs depict complex real-world processes providing their simplified representation through a set of equations and algorithms. Climate models are therefore imperfect and suffer from systematic errors. Model imperfections lead to differences between the model and observed mean state and thus complicate the initialisation task of near-term climate prediction systems. While in the long term the goal is to reduce the biases through model improvements and increased computer power, medium-term solutions are also needed. LISTEN aims at contributing to the climate prediction community effort to compensate for the model inadequacy by enhancing the transfer of observed information to the model during the forecast initialisation. This objective is achieved through the implementation of innovative initialisation techniques explicitly designed to tackle specific limitations detected in the methods currently in use and are applied to initialise decadal predictions. The study is developed in the context of a common framework in order to ensure efficient use and dissemination of the data and the findings within the scientific community. In addition, the forecast skill assessment is carried out with a special focus on the analysis of large-scale recurrent patterns of variability (weather regimes).
Data: CORDIS, © European Union
Project objective
Seasonal and decadal climate predictions are routinely carried out, and are widely used for their numerous socio-economic applications. The improvement of the forecast capabilities at these timescales is the focus of the international effort coordinated by the World Climate Research Programme. The strategy of LISTEN to contribute to this challenge is structured to have two stages. First, it aims at enhancing the transfer of observed information to the model during the initialisation of a forecast. This phase of the climate prediction process is of utmost importance, because it has been shown that a correct initialisation can improve the forecasts up to a few years ahead. However, the systematic errors of the models make this task challenging, because of the discrepancy between the observed and model mean climate. The main consequences are incorrect propagation of systems and a quick loss of the observed information. LISTEN will therefore implement innovative initialisation techniques. These are explicitly designed to tackle specific limitations detected in the methods currently in use. The new techniques will be tested at both seasonal and decadal timescales, and their performance will be compared to the standard methods.The second stage of the project consists in exploiting the data produced by the first stage for an in-depth assessment of the prediction skill, with a special focus over Europe. Large uncertainties remain in predicting events on regional scales, such as heat waves, droughts or heavy rain and snow. LISTEN will aim at a thorough assessment of the model strengths and weaknesses in predicting those events under different initialisation strategies. In particular, the sub-seasonal circulation and extreme weather events will be studied in the framework of circulation patterns, through the analysis of large-scale recurrent patterns of variability (weather regimes). The tools developed to compute these process-based metrics will be made publicly available.
Original text from CORDIS.
Participants
- CONSIGLIO NAZIONALE DELLE RICERCHE · RomaCoordinatorItaly
Links
- View on CORDIS
- DOI: 10.3030/799930
- https://www.isac.cnr.it/projects/listen-lost-translation-strengthening-communication-skills-between-real-world-and-climate
Data: CORDIS, © European Union