1. Regional Re-analyses of Observations, Ensembles and Uncertainties of Climate information Per Undén Coordinator UERRA SMHI

2. Objective of UERRA To produce ensembles of European regional meteorological reanalyses of Essential Climate Variables for several decades and to estimate the associated uncertainties in the data sets.

4. Historical observations Data rescue – Fill in gaps in data from 1950 onwards – Sub-daily data Less than on monthly scales but we know where to find them – Long term climate records from early 20th C Data development – Quality controls – consistency in time – networks – Homogenisation over time – Improvements Gridded data sets improvements & uncertainties

5. EURO4M PROJECT (URV) Southern Mediterranean climatic database

6. E-OBS coverage Tx/n

7. Gridded data sets – E-OBS Sophisticated interpolation of observations to a regular grid

8. xxxx  ( wavelength ) Produce gridded data sets from observations

9. Data assimilation 06 UTC 12 UTC 18 UTC (06 UTC(12 UTC1-3 h)‏ (18 UTC time NWP model

10. Numerical Weather Prediction model improves over the years Due to a) Model changes b) Higher resolution c) More or better observations HIRLAM Reference 1995-2011 surface pressure RMS error: Different model versions

11. NWP model and analysis system remain fixed during the period Time : 1961 - 2014 Observations : As complete as possible or improving during the period Reanalysis quality remains the same or improving

12. More motivations for reanalysis Data sets are at different resolution over long periods – e.g. 40 km – 20 km, 10 km, improving rapidly with time ! Different variables over time – e.g. T2m, Td2m – Pmsl, wind at 10 m, T2m, Td2m Different time resolution – e.g. Once a day – 0, 6, 12, 18 May be some period missing !

13. UERRA Deterministic models 11 km European 3D-VAR re-analysis 50 years – Very demanding in CPU and data resource – HARMONIE 2 model physics (ALADIN/ALARO) – Vegetation cover ( cooperation MF) – Surface analysis improvements – soil (Deterministic MO at 12 km and UBO ~ 6 km) 5 km European 2D MESCAN (MF) cooperation – MESCAN developments – short runs – downstream from 11 km and 2 model physics 5 km European cloud MESAN analysis

14. EURO4M : SMHI WP2: 3D/2D reanalysis 3D: HIRLAM - ERA on the borders and as a large scale constraint - 60 vertical levels - 22 km horizontal resolution ERA Interim 2D: MESAN - HIRLAM as first guess - Surface parameters - 5 km grid

15. Downscaling temperature Vertical and horizontal interpolation considering differences in orography, fraction of land and distance. Effect of anisotropic structure function.

16. Two step downscaling

17. SMHI and Météo-France

18. Met Office

19. 19 Ensemble forecasts -> ensemble analyses forecast length temperature the weather development as a Probability Density Function (PDF) initial forecast

20. 4D-Var Hybrid MOGREPS-G 4D-Var Linear model 21Z15Z12Z9Z3Z6Z 0Z

21. Ensemble Data Assimilation (EDA) Met Office EDA – Downscaled from ERA-CLIM2, ERA-20C – 20 members at 36 km – Higher resolution control, 12 km – From 1970s, satellite era DWD / Uni Bonn Ensemble Kalman Filter EDA

22. Uncertainty estimations - To evaluate deterministic, ensemble reanalyses and downscaled reanalyses through comparison to ECV datasets, that were derived independently - To establish a consistent knowledge base on the uncertainty of reanalyses across all of Europe, by adopting a common evaluation procedure for ECVs, derived climate indicators, extremes and scales of variability that are of particular interest to users - To statistically assess the provided information over Europe by applying the common evaluation procedure to the reanalyses products, gridded datasets and satellite data

23. Examples of methods: Spread in ensembles of Reanalyses Validations against independent data sets – Precipitation and space based Validations against observation-gridded data sets – E-OBS 25-50 km and varying data density (sometimes low) Depending on interpolation method – Sub-regional high resolution other data sets Statistical modelling, space and time scales

24. Products Grid point fields of ECVs will be on : MARS and Web map service – ECMWF, KNMI ESGF services Hydrological downstream modelling – Validation of re-analyses Climate Indicators (CIBs)

25. ModelMO controlMO ensemble HARMONIE (SMHI/MF) deterministic 2 versions COSMO Ensemble (Univ. Bonn, DWD) Assimilation4D-VAR hybrid Ensemle transform filter -”-3D-VAREnsemble transform filter Resolution12 km 70 levels 36 km 70 levels 11 km 65 levels6/12 km 40 levels Ensemble202 physics versions for part of the period 10-20 members Period1978-20131961-20135 years ObservationsConventional and satellites Conventional plus large scale constraint from ERA Conventional and satellite

26. Beyond state of the art A manifold of European reanalyses At much higher resolution than before Spanning several decades (~ 30-50 years +) More observations and extended gridded sets Novel ensemble data assimilation methods Uncertainty estimates in several ways Data services much more extensive than before User interaction and user oriented products

27. Climate indicators Mean values – Temperarure – Wind – Humidity Extreme values, Precipitation, temperature Length of seasons – Growth (forest, vegetation, crops) – Snow Number of days of – Frost, heat, snow, precipitation, dry days

28. ERA-Interim (78 km) : Number of NOVEMBER ice days, mean temperature and wind speed 100 m agl (wind power rotor). Gridpoint in the middle of Västerbotten (SE)

29. Forest damage (red) due to storms and re-analyis gust winds (1958-2000)