1. Using Better Climate Prediction in the Implementation of NAPs – (Eastern) Europe Vesselin Alexandrov Arusha, 2006 Bulgarian Academy of Sciences Institute of Meteorology & Hydrology

2. UNCCD Section 1: Action programmes Section 1: Action programmes Affected country Parties shall prepare, make public and implement national action programmes (NAPs) as the central element of the strategy to combat desertification and mitigate the effects of drought Affected country Parties shall prepare, make public and implement national action programmes (NAPs) as the central element of the strategy to combat desertification and mitigate the effects of drought NAPs shall incorporate long-term strategies to combat desertification and mitigate the effects of drought and enhance national climatological, meteorological and hydrological capabilities and the means to provide for drought early warning NAPs shall incorporate long-term strategies to combat desertification and mitigate the effects of drought and enhance national climatological, meteorological and hydrological capabilities and the means to provide for drought early warning

3. UNCCD Article 10: NAPs Article 10: NAPs 3. NAPs may include, inter alia... : (a) establishment and/or strengthening, as appropriate, of early warning systems … (b) strengthening of drought preparedness and management, including drought contingency plans at the local, national, subregional and regional levels, which take into consideration seasonal to interannual climate predictions; (b) strengthening of drought preparedness and management, including drought contingency plans at the local, national, subregional and regional levels, which take into consideration seasonal to interannual climate predictions;

4. WMO DEFINITIONS OF METEOROLOGICAL FORECASTING RANGES 6. Long-range forecasting (Seasonal to Interannual Prediction (SIP)): from 30 days up to 2 years 6. Long-range forecasting (Seasonal to Interannual Prediction (SIP)): from 30 days up to 2 years 6.1. Monthly outlook 6.2. Three month outlook: Description of averaged weather parameters expressed as a departure from climate values for that 90 day period 6.3. Seasonal outlook In some countries, SIP are considered to be climate products 7. Climate forecasting: beyond 2 years 7.1. Climate variability prediction 7.2. Climate prediction: expected future climate including the effects of natural and human influences

5. Global Producers of Long Range Forecasts

6. EASTERN EUROPE

7. UNCCD Recommendations from the REPORT OF AD HOC PANEL: Recommendations from the REPORT OF AD HOC PANEL: EARLY WARNING SYSTEMS (2000) Integrate early warning results with the results of other climate prediction systems such as the WMO Climate Information and Prediction Services (CLIPS) and CLIVAR Integrate early warning results with the results of other climate prediction systems such as the WMO Climate Information and Prediction Services (CLIPS) and CLIVAR Encourage the further development and application of seasonal climate forecasting and long-range forecasting as tools for early warning systems Encourage the further development and application of seasonal climate forecasting and long-range forecasting as tools for early warning systems

8. source: Mike Harisson (www.wmo.int)

9. CLIPS Questionnaire ( CLIPS Questionnaire (Gocheva & Hechler, 2004) Is SIP currently successful in specified regions and sectors only ? Is SIP currently successful in specified regions and sectors only ? Albania, Cyprus: do not use SIP and have not any precise opinion about SIP Azerbaijan: about successfulness of SIP it is difficult to say something Latvia: it is difficult to point out any geographic region where SIP works better Bulgaria; Estonia, Slovenia, Cyprus: SIP seems successful for specific regions and sectors Croatia, Poland, Romania: successful in ENSO-related regions with some weak predictability in mid- latitudes (NAO) Armenia, Moldova, Kazakhstan: SIP is successful in wide geographical regions

10. Spatial pattern of correlation between modelled February-April snow cover and NCEP/NESDIS observations; a) shows the correlation for the GloSea model Spatial pattern of correlation between modelled February-April snow cover and NCEP/NESDIS observations; a) shows the correlation for the GloSea model (Shongwe et al., 2006)

11. Spatial pattern of correlation between modelled February-April snow cover and NCEP/NESDIS observations; b) shows the correlation for the ECMWF S2 model Spatial pattern of correlation between modelled February-April snow cover and NCEP/NESDIS observations; b) shows the correlation for the ECMWF S2 model (Shongwe et al., 2006)

12. CLIPS Questionnaire ( CLIPS Questionnaire (Gocheva & Hechler, 2004) Does your NMHSs provide official SIP? Does your NMHSs provide official SIP? Albania, Croatia, Cyprus, Estonia, Greece, Lithuania, Slovenia: No Bulgaria, Latvia, Serbia & Montenegro, Slovakia: monthly Belarus, Armenia, Azerbaijan, Poland: monthly and seasonal Romania: one-month forecasts, prognostic estimates for the next 2 months, following the forecasting month; seasonal supplement, containing the anomaly notification in the geophysical environment in past season and meteorological outlook for the next season; annual forecasting estimates bulletin elaborated at the beginning of each season and containing estimates of the temperature and precipitation anomalies for the next four seasons annual forecasting estimates bulletin elaborated at the beginning of each season and containing estimates of the temperature and precipitation anomalies for the next four seasonsRussia: operational 1-3 month SIP regional and global predictions

13. Seasonal predictions (UK Met Office and IRI) on the web page of Bulgarian weather service (info.meteo.bg)

14. CLIPS Questionnaire ( CLIPS Questionnaire (Gocheva & Hechler, 2004) Does your NMHS use SIP products from global producers? Does your NMHS use SIP products from global producers? Croatia, Cyprus, Estonia: No Armenia, Azerbaijan, Belarus, Latvia etc.: ROSHYDROMET Slovakia, Greece: ECMWF products Bulgaria: ECMWF, IRI, UK Met Office, Météo-France for monthly weather forecast involving local weather and climate archive data downscaling Lithuania: IRI, World Resource Institute and Swedish Regional Climate Modelling Programme Poland: ECMWF, IRI, DWD Romania: ECMWF, Met Office, IRI and Japan Meteorological Agency, etc.

18. CLIPS Questionnaire ( CLIPS Questionnaire (Gocheva & Hechler, 2004) Do you apply SIP in the management of agricultural production, water resources, etc.? Do you apply SIP in the management of agricultural production, water resources, etc.? Albania, Cyprus, Greece, Lithuania, Slovenia: No Russia, Croatia, Serbia & Montenegro, Slovakia: partial application in some sectors, occasionally, etc. Armenia, Belarus, Bulgaria, Kazakhstan, Latvia, Poland, Romania: relatively broad SIP application in various sectors of the economy: ( (Gocheva & Hechler, 2004)

19. CLIPS Questionnaire ( CLIPS Questionnaire (Gocheva & Hechler, 2004) Has your NMHS contracts for regular SIP provision with a specific sector for example, agriculture? Has your NMHS contracts for regular SIP provision with a specific sector for example, agriculture?50:50 Albania, Armenia, Belarus, Cyprus, Greece, Latvia, Lithuania, Slovakia, Slovenia: No Has your NMHS requests for SIP from any sectors? Has your NMHS requests for SIP from any sectors? 90% confirmed availability of users requests towards SIP products 90% confirmed availability of users requests towards SIP products

20. CLIPS Questionnaire ( CLIPS Questionnaire (Gocheva & Hechler, 2004) Is your SIP officially issued by the media? Is your SIP officially issued by the media? Do you develop the theoretical basis of your SIP activities by own research efforts? Do you develop the theoretical basis of your SIP activities by own research efforts? How do you maintain the theoretical basis of your operational SIP activities? How do you maintain the theoretical basis of your operational SIP activities? Do you apply downscaling methods for specific sectors/applications/locations? Do you apply downscaling methods for specific sectors/applications/locations? What are the predicted meteorological elements and parameters in your national SIP practice? What are the predicted meteorological elements and parameters in your national SIP practice?

21. Seasonal forecasting - numerical models

22. 150km global atmospheric GCM 12-50km RCM for relevant region Coupled GCM (300km atmosphere) A modelling system for detailed regional scenarios the PRUDENCE method the PRUDENCE method Observed SST/sea-ice SST/sea-ice change from coupled GCM

23. RegCM3 regional climate model (source: Pal, 2005)

24. Positive (left) and negative (right) NAO phases and related impacts on weather in Europe

25. NAO impact rainfall in winter temperature in winter source: H. Cullen and M. Visbek

26. Statistical forecast for the NAO index

29. CECILIA project (WP2 objectives) producing high resolution (10 km) 30- year time slices over four target areas producing high resolution (10 km) 30- year time slices over four target areas comparing model responses with coarser results from existing simulations to assess the gain of a higher resolution comparing model responses with coarser results from existing simulations to assess the gain of a higher resolution archiving daily data from the simulations in a common database archiving daily data from the simulations in a common database improving high resolution models for future scenarios improving high resolution models for future scenarios

30. ENSEMBLE climate prediction objectives run ensembles of different climate models to sample uncertainties run ensembles of different climate models to sample uncertainties measure variations in reliability between models measure variations in reliability between models produce probabilistic predictions of climate change produce probabilistic predictions of climate change link these projections to potential impacts: agriculture, health, energy, insurance, ecosystems, etc. link these projections to potential impacts: agriculture, health, energy, insurance, ecosystems, etc.

31. source: Giorgi, GRL, 2006 Regional Climate Change Index

32. ECHAM4 A2 climate change scenarios for annual air temperatures in Europe for the 2050s, relative to 1961-1990

33. ECHAM4 A2 climate change scenarios for annual precipitation in Europe for the 2050s, relative to 1961-1990

34. GCM simulated change of air temperature (X) and precipitation (Y) for summer in Greece (c) and Turkey (d) for the 2100, relative to 1961-1990 Climate Change Scenarios for the Balkan Peninsula Balkan Peninsula IPCC A2 emission scenario

35. Model climate change scenarios (in %) for winter (left) and summer (right) precipitation in Europe, 21 st century 21 st century

36. Changes in summer air temperature (in o C) simulated by the HadCM3 and PCM models for the 2080s, A2 SRES scenario

37. Changes in summer precipitation (in %) simulated by the HadCM3 and PCM models for the 2080s, A2 SRES scenario

38. Extreme events

39. Models project large increases in climate variability and extremes in Central and Eastern Europe (source: Schär et al. 2004) Summer (JJA) [ºC] [%] / T [ o C]

40. Mean Changes in summer Tmax: 2071-2100 vs 1961-1990 HIRHAM RCM (source: Beniston, 2006) 90% quantile 99% quantile +2 +4 +6 +8 +10 +12°C

41. Threshold exceedance: Tmax> 30°C: 2071-2100 vs 1961-1990, HIRHAM RCM (source: Beniston, 2006) 1 5 10 20 30 40 50 60 70 80 90 100 200 days

42. P (JAS) 99% (n=5d) Models project large increases in climate variability and extremes in Central and Eastern Europe

43. (C. Simota, 2005)

50. Better climate prediction – DMCSEE? DMCSEE : Drought Management Center for Southeastern Europe to serve as an operational centre for SEE for drought preparedness, monitoring and management; to serve as an operational centre for SEE for drought preparedness, monitoring and management; to create and coordinate a subregional network of NMHSs and other relevant institutions; to create and coordinate a subregional network of NMHSs and other relevant institutions; to coordinate and provide the guidelines to interpret and apply drought-related products; to coordinate and provide the guidelines to interpret and apply drought-related products; to prepare drought monitoring and forecast products and make them available to relevant institutions in participating countries; … to prepare drought monitoring and forecast products and make them available to relevant institutions in participating countries; …

51. Daily soil moisture anomalies estimated by ECMWF- ERA40 (left) and JRC-MARS (right) (source: JRC)

52. Soil moisture prediction: 7 days ahead 7 days ahead (source: JRC) 70 days ahead?