Prediction of climate extreme events at seasonal and decadal time scale

1. Prediction of climate extreme events at seasonal and decadal time scale Aida Pintó Biescas

2. Outline • Introduction • Methodology • Data description • Methods • Results • Future work • Near term work • Long term work 2

3. Outline • Introduction • Methodology • Data description • Methods • Results • Future work • Near term work • Long term work 3

4. Introduction Scenarios described by the Intergovernmental Panel on Climate Change, IPCC (Solomon et al., 2007), says that climate change will lead by the end of the century to changes in the frequency and intensity of extreme events such as tropical cyclones, heavy rainfall and droughts Lack of information (when, where, frequency, intensity,…) Need to forecast extreme events taking into account interannual variability and the impact of the anthropogenic climate change Analysis of the forecast quality of seasonal and decadal climate predictions Predicting evolution of extreme event frequency, duration and intensity and application climate forecast information in a climate services context Integrate climate information in decision making processes. 4

5. Introduction Thesis main objective: Provide a synthesis of the forecast quality of seasonal-to-decadal climate predictions for different types of extreme climate events, performed with both global dynamical and statistical models General objectives: • To study the sources of predictability and the forecast quality using state-of-the art forecast systems for extreme precipitation, temperature and drought events, and the frequency of North Atlantic tropical cyclones , at seasonal-to-decadal time scales. • Develop methods to communicate the results of the predictive climate forecast information, in the context of a climate service for the insurance and renewable-energy sectors. 5

6. Outline • Introduction • Methodology • Data description • Methods • Results • Future work • Near term work • Long term work 6

7. Methodology MODEL DATA: ENSEMBLES prediction system • Atmospheric data from daily seasonal hindcasts of the ENSEMBLES Stream 2 experiment. • Precipitation and temperature daily data for five models contributing to the multi-model: • ECMWF's IFS/HOPE • UK Met Office's HadGEM2 • Météo-France's ARPEGE/OPA • INGV's ECHAM5/OPA • ifM Kiel's ECHAM5/OM1 • 9 members each • Seasonal forecasts (hindcasts) starting every year since 1960 until 2005 for four different start dates (the first of February, May, August and November) 7

8. Methodology REFERENCE DATA: ECMWF Interim Re-analysis (ERA Interim) Daily Atmospheric Data Sets • Daily gridded reanalysis dataset for precipitation and near surface temperature. • Period 1979-Now (uploaded monthly) • Resolution 0.75º x 0.75º 8

9. Methodology IPCC 2001 definition of extreme event: “An extreme weather event is an event that is rare within its statistical reference distribution at a particular place. Definitions of "rare" vary, but an extreme weather event would normally be as rare or rarer than the 10th or 90th percentile.” Extreme indices: • 90th and 10th percentile for Tº and P • Days of extreme precipitation/temperature  nº of days with precipitation/temperature > 90th percentile of the seasonal climatology • Frost days (nº of days with Tº< 0ºC ) • Heavy precipitation days (nº of days with P>10mm/day) Annual , seasonal and monthly analysis for: • All globe • Single regions • Reproduction of important extreme past events 9

10. Methodology Current work: • Generating netCDF files with R for the extreme indices obtained. Variables: Precipitation and near-surface temperature. • For observations • For all the 5 systems from ENSEMBLES • Objective: Open the files with CFU_load to make the calculations, using common diagnostics tools: • Climatologies for both model and observations • Correlations between model-observations • For different systems • For different star dates and forecast times • Study of single regions and periods: • Warm season  August: Floods, droughts and heat waves • Cold season  Winter time: Floods, droughts and cold waves 10

11. Outline • Introduction • Methodology • Data description • Methods • Results • Future work • Near term work • Long term work 11

12. Results Preliminary results 12

13. Results Preliminary results ºC 13

14. Outline • Introduction • Methodology • Data description • Methods • Results • Future work • Near term work • Long term work 14

15. Future work Near term work • Verification of the results obtained so far using different verification tools • Continue working on the ENSEMBLES data, adapting and optimizing scripts to be able to deal with all the variables needed for the analysis of extreme events • Formulation of probability forecasts for extreme precipitation and temperature and assessment of their forecast quality. • Prepare the structure of an article to document the expected results and a one-page introduction to this paper with the necessary bibliography. Long term work • Preparation of promotional material that describes the basis of seasonal and decadal climate prediction and its potential applications in the insurance sector. • Submit to a scientific journal a first publication with the results obtained so far on the prediction of extreme seasonal precipitation and temperature. • Downloading new data from wind and repeat the same steps done for precipitation and temperature. • Definition of a North Atlantic tropical-cyclone frequency methodology based on large-scale parameters and formulation of probability forecasts of the North Atlantic tropical cyclone frequency. 15

16. Thanks Gràcies 16