Periodic Reporting for period 2 - CERISE (CopERnIcus climate change Service Evolution)
Berichtszeitraum: 2024-01-01 bis 2025-10-31
The aim of CERISE is to develop new and innovative coupled land-atmosphere data assimilation approaches and land initialisation techniques to pave the way for the next generations of the C3S reanalysis and seasonal prediction systems. These developments will be combined with innovative work on observation operator developments integrating Artificial Intelligence (AI) to ensure optimal data fusion fully integrated in coupled assimilation systems. CERISE will drastically enhance the exploitation of past, current, and future Earth system observations over land surfaces, including from the Copernicus Sentinels. In addition, CERISE will provide the proof of concept to demonstrate the feasibility of the integration of the developed approaches in the core C3S (operational Service), with the delivery of reanalysis prototype datasets (demonstrated in pre-operational environment), and seasonal prediction demonstrator datasets (demonstrated in relevant environment). The CERISE outputs aim at medium to long-term upgrades of the C3S systems with targeted progressive implementation in the project duration. The project will improve the quality and consistency of the C3S reanalysis systems and of the components of the seasonal prediction multi-system, directly addressing the evolving user needs for improved and more consistent C3S Earth system products.
The overarching objectives of the project are:
• Development of land-atmosphere coupled data assimilation to improve the climate consistency of the next generation of C3S Earth system reanalyses,
• Enhancement of the quality of user-relevant seasonal forecast information by improving forecast skill and process understanding over land,
• Demonstration of the proof of concept by delivering global and regional reanalysis prototype datasets and seasonal forecast demonstrator datasets showing the feasibility and the added value of the integration in the existing core service.
The overarching objectives of the project are:
• Development of land-atmosphere coupled data assimilation to improve the climate consistency of the next generation of C3S Earth system reanalyses,
• Enhancement of the quality of user-relevant seasonal forecast information by improving forecast skill and process understanding over land,
• Demonstration of the proof of concept by delivering global and regional reanalysis prototype datasets and seasonal forecast demonstrator datasets showing the feasibility and the added value of the integration in the existing core service.
In the first two years, CERISE established key methodological and infrastructure developments. WP1 advanced ensemble and unified land data assimilation and developed machine-learning observation operators. WP2 implemented outer-loop coupling infrastructure in ECMWF IFS and weakly coupled systems in HARMONIE-AROME. WP3 improved land surface initial conditions for seasonal forecast demonstrators, and WP4 delivered the first ERA6-Land and Arctic CARRA-Land-Pv1 prototypes. WP5 completed Phase 0 and most Phase 1 seasonal forecast demonstrators, with the SUNSET app deployed for preliminary analysis. WP6 developed land-surface diagnostics and evaluation tools, while WP7 established a centralised data archive and generated time-varying lake and land cover datasets, with vegetation data in preparation.
By month 34, CERISE further developed methodologies to enhance the use of surface-sensitive observations. WP1 advanced land data assimilation in both the ECMWF and HARMONIE-AROME systems through methodological, technical, and machine-learning developments that improved ensemble performance, surface–soil coupling, and use of satellite observations. WP2 advanced regional and global coupled data assimilation by testing land–atmosphere coupling strategies, skin-temperature coupling, and passive microwave coupled data assimilation. These developments demonstrated clear near-surface forecast improvements and confirming strong performance of outer-loop coupling in the IFS, reaching TRL7. WP3 completed the preparation and delivery of seasonal forecast initialisation datasets for both Phase 1 and Phase 2 demonstrators, with most Phase 2 datasets archived on MARS. In addition, monthly mean land surface data were provided to support intercomparison and further assessment.WP4 had largely progressed as planned, with most deliverables met and the milestone on coupled global reanalysis achieved ahead of schedule. Key achievements include the successful delivery of the ERA6-Land-Pv2 global reanalysis, the production of the first coupled ERA7 prototype, and substantial progress on regional reanalysis demonstrators across pan-Arctic and pan-European domains, with CARRA-Land-Pv2 completed in November. WP5 focused on producing seasonal forecast demonstrators using initial conditions from WP3 and WP4, with all Phase 1 demonstrators successfully archived. Phase 2 demonstrators progressed well toward completion, alongside continued development of collective assessment activities and forecast evaluation tools. WP6 completed the development of its analysis tools and initiated their application to key CERISE outputs. Progress was made on assessing prototype reanalyses and seasonal forecasts, preparing for results and recommendations due in M48. WP7 finalized long-term databases of land surface characteristics (lake cover, land cover, LAI) spanning 1925–2020, which were used in the ERA6-Land-Pv2 reanalysis.
By month 34, CERISE further developed methodologies to enhance the use of surface-sensitive observations. WP1 advanced land data assimilation in both the ECMWF and HARMONIE-AROME systems through methodological, technical, and machine-learning developments that improved ensemble performance, surface–soil coupling, and use of satellite observations. WP2 advanced regional and global coupled data assimilation by testing land–atmosphere coupling strategies, skin-temperature coupling, and passive microwave coupled data assimilation. These developments demonstrated clear near-surface forecast improvements and confirming strong performance of outer-loop coupling in the IFS, reaching TRL7. WP3 completed the preparation and delivery of seasonal forecast initialisation datasets for both Phase 1 and Phase 2 demonstrators, with most Phase 2 datasets archived on MARS. In addition, monthly mean land surface data were provided to support intercomparison and further assessment.WP4 had largely progressed as planned, with most deliverables met and the milestone on coupled global reanalysis achieved ahead of schedule. Key achievements include the successful delivery of the ERA6-Land-Pv2 global reanalysis, the production of the first coupled ERA7 prototype, and substantial progress on regional reanalysis demonstrators across pan-Arctic and pan-European domains, with CARRA-Land-Pv2 completed in November. WP5 focused on producing seasonal forecast demonstrators using initial conditions from WP3 and WP4, with all Phase 1 demonstrators successfully archived. Phase 2 demonstrators progressed well toward completion, alongside continued development of collective assessment activities and forecast evaluation tools. WP6 completed the development of its analysis tools and initiated their application to key CERISE outputs. Progress was made on assessing prototype reanalyses and seasonal forecasts, preparing for results and recommendations due in M48. WP7 finalized long-term databases of land surface characteristics (lake cover, land cover, LAI) spanning 1925–2020, which were used in the ERA6-Land-Pv2 reanalysis.
Major methodological and infrastructure developments were conducted in the first two years of the project, especially related to data assimilation and coupled assimilation and in observation preparation. Unified ensemble-based land data assimilation approaches were implemented in both regional and global reanalysis systems and outer coupling methodology was developed in the global reanalysis system. These developments are described in the WP1 and WP2 deliverables, a paper was published, and several other papers are in preparation. They bring global and regional reanalysis tools beyond the state-of-the art in the area of land and coupled land-atmosphere data assimilation. Novel diagnostic approaches were also developed (WP3-WP6) to assess land-atmosphere coupling and applied to multi-system seasonal prediction (WP5) and submitted for publications. Finally, time varying lake cover has been produced and time varying vegetation will be produced in the third year of the project (WP7), enabling for the first time to account for lake and vegetation interannual variability and trend in future climate reanalysis (WP4).
In the third year, CERISE has further advanced the state of the art in land and coupled data assimilation, and machine-learning approaches have been developed to enable assimilation of surface sensitive satellite data over land. Reanalysis prototypes have been produced globally and regionally, implementing innovative quasi-strong land–atmosphere–ocean coupling and extending ensemble-based methods to new variables such as soil and skin temperature. The inclusion of long-term, time-varying land surface characteristics—lakes, land cover, and vegetation—allowed the CERISE global land reanalysis to account for interannual variability and trends, offering capabilities that go beyond existing systems. Seasonal forecast demonstrators exploited these enhanced initialisation datasets. Novel diagnostic tools have been consolidated and applied across reanalysis and seasonal prediction systems, providing actionable feedback to improve both methods and products.
In the third year, CERISE has further advanced the state of the art in land and coupled data assimilation, and machine-learning approaches have been developed to enable assimilation of surface sensitive satellite data over land. Reanalysis prototypes have been produced globally and regionally, implementing innovative quasi-strong land–atmosphere–ocean coupling and extending ensemble-based methods to new variables such as soil and skin temperature. The inclusion of long-term, time-varying land surface characteristics—lakes, land cover, and vegetation—allowed the CERISE global land reanalysis to account for interannual variability and trends, offering capabilities that go beyond existing systems. Seasonal forecast demonstrators exploited these enhanced initialisation datasets. Novel diagnostic tools have been consolidated and applied across reanalysis and seasonal prediction systems, providing actionable feedback to improve both methods and products.