Adaptation-oriented Seamless Predictions of European ClimaTe

Projektinformationen

ASPECT

ID Finanzhilfevereinbarung: 101081460

DOI

10.3030/101081460

EK-Unterschriftsdatum 22 September 2022

Startdatum 1 Januar 2023

Enddatum 31 Dezember 2026

Finanziert unter

• Climate, Energy and Mobility

Gesamtkosten

€ 7 027 623,85

EU-Beitrag

€ 7 027 623,85

Koordiniert durch

BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACION
Spain

Periodic Reporting for period 1 - ASPECT (Adaptation-oriented Seamless Predictions of European ClimaTe)

Berichtszeitraum: 2023-01-01 bis 2024-06-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

The ASPECT project pushes the frontiers of knowledge by providing seamless climate predictions covering timescales that go from the next months to 30 years into the future, with the capacity to lead to a step change in the provision of climate information and services for adaptation. Applying a knowledge co-production approach where users are at the centre of the research, the project aims to embed fit-for-purpose climate information into societally important climate change adaptation decisions across various sectors, time scales, spatial scales and levels of decision-making. Thus, the project explores whether and how climate services that have been co-developed with particular users through impact case studies (i.e. in the agriculture, finance, governance, disaster response, and humanitarian sectors) can be scaled up to meet the decision-making and planning needs of a broader range of sectoral users and across all levels of governance. Eventually, the project will produce a climate information delivery system and a capable and enabled community of practice that ensures the uptake of cutting edge climate information and methods. Although the project focus will be on European climate information, it will also look into areas of wider policy interest (e.g. disaster preparedness) and regions of European relevance.
ASPECT brings together expertise in physical and social sciences relevant to climate variability and change, with meaningful user engagement that will influence the design of prediction systems and increase the relevance of results for societal transformation. Involvement of disciplines like communication and economics will help convey the novelty of project outcomes and their added value in terms that resonate with climate information users, purveyors and general audiences (FIG1, FIG2).

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

During this period, the project has identified the climate-related information needs of different users. Five case studies have been defined with the 5 end-users involved in the co-production of climate services. Moreover, 2 User Forums (that run across 5 different sessions) have been organised in 2023 and 2024, with the aim of expanding the knowledge base on user decision-making and building capacity by exposing users to new findings. The forums have engaged with an audience of >260 participants so far. Additionally, the results of a survey with 1864 answers on the needs of climate information by European organisations have been published. The information on user needs, including both project partners and users external to the project, has enabled the design of prediction systems that incorporate new improvements (i.e. larger ensembles and longer reforecasts) and the definition of new experimental protocols bridging seasonal to decadal predictions (S2D) and decadal to projections (D2P). This has allowed the extension of predictions beyond what is currently available, going up to 2-3 forecast years in the case of seasonal predictions produced twice a year (in May and November) and the extension of decadal predictions to 20-30 years ahead. The skill of current climate predictions has been assessed, together with opportunities that can arise from climate predictions going beyond the current skill assessment, which include improving the understanding of mechanisms of forecast skill, how sources of predictability can be used in dedicated sectors, and extending the skill assessment to climate extremes.Various downscaling methods have also been explored (i.e. statistical downscaling and event-based dynamical downscaling), including the method development, initial testing, and user interaction. Additionally, methodologies for temporal merging are being developed to combine seasonal, multi-annual and decadal climate predictions to provide consistent information to users.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Climate information across time and spatial scales is currently provided by different suites of initialised predictions (seasonal and decadal) and projections, following different protocols. For the first time, ASPECT will define common protocols (for S2D and D2P) and design a joint prediction system providing consistent climate information across time scales, taking into account user needs for climate information. The project will contribute to improve the prediction of high-impact and extreme events, which so far have received less attention at the decadal time scale, by evolving downscaling methodologies beyond the current state of the art (both for statistical and event-based dynamical downscaling). Downscaled information favours the uptake by decision-makers, since it better represents processes occurring at finer scales, at which decisions are made. Finally, the project will go beyond the state-of-the-art in user interaction by including not only the forecasting of hazards but also co-produced risk-informed indicators. Drawing on the growing literature and practice around the upscaling of climate services, the project also aims to ensure the applicability of outcomes beyond the pilot use cases.

Fig. 1 The organic tree of climate information

Fig. 2 Seamless climate predictions