1. Work Package 23: Impact models for impact predictions Karina Williams, on behalf of Jemma Gornall WP23 Participants: Met Office, Predictia, WU, IC3, UNIVLEEDS, SMHI, ULUND, METEO-France, CETaqua, EDF

2. Impact models for impact predictions Use seasonal and decadal models to drive impact models, particularly focussing on variables relevant to the water, agricultural and forestry sectors. Run hindcasts and assess the skill in the predictions of these variables. Choice of variables and case studies will be informed by stakeholder needs. Wherever possible, inter-sectoral interactions will be considered. Image copyright Met Office

3. Evaluation For all sectors, predictions will be assessed using the tools commonly used for assessing ensemble prediction systems e.g. reliability diagrams, ROC scores Images copyright Met Office

4. Task 23.1: Data Flow A common format is needed to transfer data between the seasonal model and the various impacts models. Standard ancillaries Accessible through a web portal

5. Task 23.2: Model initialisation How should the impacts models be initialised? From reanalysis? From climatology? → Workshop (a deliverable for month 9) → Sensitivity experiments Image copyright Met Office

6. Task 23.3: Impacts models for the water sector Looks at the predictability of river runoff for a number of European catchments on seasonal timescales. Considers flood and drought indices that are particularly relevant to the stakeholder. Verification points will represent various climatologies, soil-types, land uses, and catchment scales. Image copyright Met Office

7. Participating models/partners run a hindcast simulation Compare and evaluate against observations (EWA, GRDC) Task 23.3: Impacts models for the water sector Starting point Pick two interesting events for one or more large basins Image copyright Met Office

8. Task 23.4: Impacts models for the agricultural sector Looks at the predictability of extremes of crop productivity in Europe and selected other parts of the globe on seasonal timescales. Models: LPJmL, GLAM, CGMS, JULES-impacts Develops statistical methods for the prediction of low-yielding years. Image copyright Met Office

9. Task 23.4: Impacts models for the agricultural sector Starting point Pick two interesting events (crop failures or anomalously high-yielding years) Participating models/partners run a hindcast simulation Compare and evaluate against observations e.g. EUSTAT, FAOSTAT, Monfreda et al 2008, Defra Economics and Statistics Cereal and Oilseed Production survey 2010 Image copyright Met Office

10. Task 23.5: Impacts models for the forestry sector Evaluate the use of seasonal forecasts with the LSHEL model to assess the impacts of different forestry management options. LSHEL is a high-resolution land-soil-hydrology- ecosystem-lake model, for climate and impact modelling, based on SURFEX - land-soil-lake-boundary layer model LPJ-GUESS - ecosystem model at Lund University HYPE - surface hydrology model at SMHI Image copyright Met Office

11. Task 23.5: Impacts models for the forestry sector The following management activities will be considered: Planning harvesting activities and conditions Optimizing the timing of reforestation activities Reducing pest damage Image copyright Met Office

12. WP23 Objectives To further develop the complex impact models able to address the users’ needs and inform the case studies and the prototypes: To develop a prototype operational workflow to use these models in S2D forecast mode; To assess and improve their predictive skill by analysing hind casts of low- and high-end impact events (hi/lo discharge, crop yields, etc); To develop optimal geographical forecasting units, as a function of model physics and stakeholder needs.

13. Summary Work Package 23 uses seasonal and decadal forecasts to drive impacts models Particularly focussed on variables relevant to the water, agricultural and forestry sectors Next steps: workshop on how to initialise the impacts models, choice of case studies Image copyright Met Office