1. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse

3. Status report on use of and need for research data in seasonal applications Andy Morse, Cyril Caminade and Anne Jones Department of Geography, University of Liverpool, Liverpool, United Kingdom A.P.Morse@liv.ac.uk

4. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Talk Themes Introduction & Background Research Examples Summary

5. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Introduction Weather and Climate Models - heading towards seamlessness e.g. ECMWF NWP deterministic, 25km few days Medium range EPS 51 members, to 10 days at 50km (15 days at 75km) Month – 51 members 75km Seasonal 7 or 13 month 41 members 125km & seasonal research Decadal scale EPS very experimental – currently 13 months & out to 10 years ‘decadal gap’ period 2010 to 2050 – key new funding focus UK and US Climate models – global and regional typically run through late 20 th century out to 2100 (100 to 300km) multiple single model runs - range of scenarios

6. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – climate model – data exploration Slide from Cyril Caminade University of Liverpool. Rainfall JAS Climatology – DEMETER 40 years but initial ENSEMBLES stream (new version seasonal models) only 10 years – 40 year data to follow Mean (1991-2001): Mean Bias (1991-2001): During JAS, the ITCZ reaches its top northward position. Maximum rainfall occurs over the Senegal coast, the Cameroon Gulf and over the Ethiopian Highs A common bias of coarse resolution GCM over Africa: Rainfall overestimation over the high mountains (Ethiopia). Underestimation over the low level mountains (Cameroon mounts).

7. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Like the mean biases, the models overestimate rainfall variability over the Senegal coast and the Cameroon coasts. Precipitation variance is overestimated over ocean (the Gulf of Guinea). (The bias is reduced in DEMETER as there are more models). Research Examples – climate model – data exploration Slide from Cyril Caminade University of Liverpool. Rainfall JAS Variance Standard Deviation (1991-2001): Standard Deviation Bias (1991-2001):

8. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – climate model – data exploration Common behaviour: Overestimation of rainfall during the rainy season (few models) Too Flat profile (not a clear peak centered in August) Climates Mean Seasonal cycle (16°W-45°E) slide Cyril Caminade

9. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – climate model – data exploration Multiple climate models – rainfall slide Cyril Caminade

10. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – climate model – data exploration Multiple climate models – temperature slide Cyril Caminade

11. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Observed yields are displayed in blue (from minimum to maximum). Mean is displayed as a red line. Simulated yields with downscaled multi model ensemble seasonal hindcasts are displayed as orange boxes. F. Tomei, G. Villani, V. Marletto vmarletto@arpa.emr.it ENSEMBLES Results obtained indicate the possibility to set up an operational wheat yield forecasting chain for northern Italy. Observed yields vs. simulated with seasonal hindcasts Research Examples – ranges of users

12. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – ranges of users Seasonal predictability of winter storminess (here: ECMWF System3) Loss potential of winter storms based on coupling ~300 yrs of s2d data with loss model www.meteoswiss.ch/web/en/research/projects/nccr_ii/prewistor.html Paul Della-Marta, Mark Liniger MeteoSwiss: Winter Storm Risk for Europe ENSEMBLES Publication submitted: Della-Marta et al: Improved estimates of the European winter wind storm climate and the risk of reinsurance loss using climate model data

13. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse ENSEMBLES Task 6.2.8 Construction of impact response surfaces & Task 6.2.9 Preliminary scenario impacts and risk assessment. Likelihood of low water levels in Lake Mälaren, Sweden (perturbed physics exp.) Research Examples – climate model – data exploration Phil Graham phil.graham@smhi.se

14. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – range of users Blue tongue climates using RCMs ENSEMBLES first look using RCM data sets for later comparison with s2d and towards seamlessness Ro relative anomaly over Northern Europe. The ECA observations are displayed in black the CTL (SRESA1B) multi model ensemble means are displayed in blue (red). The blue (orange) envelope highlights the spread Cyril Caminade and Andy Morse caminade@liv.ac.uk

15. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse 15 Malaria incidence maturation Gonotrophic cycle Larvae Adult mosquitoes ovipositioning death Dynamic mosquito population Temperature and rainfall-driven Dynamic malaria transmission Temperature-driven Research Examples – malaria modelling Liverpool Malaria Model (LMM) Dynamic, process-based model driven by daily temperature and rainfall

16. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – malaria modelling Tier-2 malaria runs - ROC Skill Scores Above Median Event DEMETER driven LMM. Areas of high interannual variability were selected and persisted forecast skill was removed from the scores. Jones, A. and Morse, A. (2007) CLIVAR Exchanges, 43 May 4-6 JAS Nov 4-6 FMA

17. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Fig. 2: (A) Differences in the annual average model prevalence (in %) and (B) in the standard deviation regarding the annual maximum of the model prevalence (in %) between the last decade of the A1B scenario (2041-2050) and the past period (1960-2000). Changes in the malaria distribution Climate Change University of Liverpool, A. Morse & A. Jones University of Cologne, V. Ermert & A. Fink University of Würzburg, H.Paeth LMM malaria scenarios (2041-2050): decreased malaria transmission due to precipitation reduction reduced model prevalence variability in N-Sahel  fewer epidemics/malaria retreat 13-16°N: increased variability in the S-Sahelian zone  more frequent epidemics in denser populated areas farther south: malaria transmission remains stable

18. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – malaria prediction plume 95 85 65 35 15 5 ERA Botswana malaria forecast for February 1989, LMM driven by DEMETER multi-model (ERA-driven model shown in red) Plot from Anne Jones unpublished Ph.D. thesis University of Liverpool

19. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse 19 DEMETER 7 models, each with 9 ensemble members ENSEMBLES Stream 2 5 models, each with 9 ensemble members Daily rainfall and daily bias-corrected temperature used to drive the malaria model and produce an ensemble malaria forecast. Botswana grid (5x5 @2.5 degrees) Consider November forecasts for 1982-2001 Forecast runs out for six months Consider ability to forecast threshold-defined events, e.g. Upper tercile malaria Validate against observed malaria (Thomson et al., 2005) – “tier-3” And against ERA-40-driven model (“tier-2”  potentially over continent) Seasonal forecast validation for Botswana

20. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse 20 Seasonal forecast skill: ROC skill scores for predictions of upper tercile malaria incidence over Botswana, November forecast months 4-6 (FMA), against published malaria index. 95% confidence intervals shown. DEMETER multi-model (7 models): 0.67 (0.41-0.93) ENSEMBLES multi-model (5 models): 0.70 (0.43-0.94) ERA-40 reference simulations:0.88 (0.70-1.00) Validation results (tier-3): upper tercile malaria

21. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse 21 DEMETER: ROC Area=0.44 ENSEMBLES: ROC Area=0.59 Solid bars indicate upper tercile years Probability forecasts of upper tercile malaria for Botswana, November forecast months 4-6 (FMA), compared to observed anomalies from published index (red). Visualisation of forecast performance: ECMWF model

22. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Weighting malaria model output Sensitivity to single model weight Single model weight w is varied between 0 and 1 in increments of 0.05. Weights for the other 6 models are each set to (1-w)/6, so that the total weight sums to 1. For each model in turn w=0 corresponds to a 6 model ensemble excluding that model and w=1 corresponds to the single model forecast Skill of LMM incidence forecast for Botswana as a function of single model weight Model missing Single model only

23. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Post-processing - ensemble interpretation 23 Bröcker and Smith, 2008 Kernel dressing (KD) Applies unit kernel function, K (e.g. Gaussian), to each ensemble member x i and then combines them: ax i +   BUT results show only marginal increase in ROC area for dressed v “counting” methods: CountingGaussian fit Standard Kernel Dressing (2 params) Affine Kernel Dressing (5 params) UKMO DEMETER Met Office model 0.69 0.51-0.84 0.67 0.49-0.83 0.70 0.53-0.86 0.68 0.50-0.84 Tier-2 skill for Botswana upper tercile malaria, Nov forecast FMA, 1960-2001

24. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Summary Experience in integrated EPS – initial promising results (DEMETER, ENSEMBLES) Need to make better use of current products and data and to understand limitations Impacts allow non-linear mapping of combined ensemble PDFs through time Impacts allow assessment of downscaling, dressing of ensembles etc. Impacts define forecast skill and potential user/societal value Impacts make link to decision makers/stakeholders Impacts allow linkage across modelling streams – semi seamless approach Need to develop seamless approaches with & for impacts Establish feedback from impacts communities of needs to climate science community

25. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Questions?

26. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse

27. 27 Event forecast Event observed YesNo YesHit (a)False alarm (b) NoMiss (c)Correct rejection (d) P=0 P=1 Performance assessment: decision-making context DEMETER multi-model malaria forecasts for upper tercile malaria, Botswana, November forecast months 4-6 (FMA), compared to observed anomalies from published index. DEMETER ERA-40 (cont) ERA-40 (discrete) Decision threshold, P

28. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse 28 Research Examples - DEMETER performance for Botswana Tier-3 upper tercile incidence Theoretical cost/loss versus potential economic value (measured relative to climatology) ERA-40 DEMETER Expensive to take action (never act) Cheap to take action (always act) 1-specificity sensitivity Event forecast Event observed YesNo YesHit (a)False alarm (b) NoMiss (c)Correct rejection (d) ROC diagram Slide Anne Jones, University of Liverpool

29. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – DEMETER driven malaria re-forecasts for Botswana Temperature Rainfall Climate 1982-2001 Malaria November forecast – DEMETER and ERA-40 Skill for above median events Nov 4-6 FMA Tier-3 Plot from Anne Jones University of Liverpool Solid circles =DEMETER median, boxes =quartiles, whiskers=range Hollow circles = ERA-40

30. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Background -scales Global model – regional impacts – local and microscale processes 1000s to 100s km kms to 100s m metre cm to mm Africa to mosquito 9 orders of magnitude Earth-Sun distance to galaxy scale

31. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Background - integrating impacts within EPS ‘end- to- end’ approaches towards seamlessness Climate forecasts – climate forecast developers – end user applications (health – human and animal, crops, water) – policy and decision makers - stakeholders (including governments) – social scientists (including economics) - general public … wide range of latitudes User driven – tailoring product, skill requirements, ‘acceptable’ uncertainty – mentioned above Climate Science – seamless approach, impact models, downscaling & bias correction, risks, feedback model development, adaptation Policy – decisions for impact reduction Technical – ensembles, data - cross cutting, model climates Training – probabilistic – use, validation & uncertainty

32. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Background – integrating impacts within EPS Approach is often as top down (climate models downwards – is health less top down??) but an end-to-end ‘loop’ is better Timely use of existing climate information – from observations, through and range of forecast products/output trough seasons to decades and beyond Feedback (lack of) from impacts groups to climate science from user impacts is highlighted as a key concern, WMO meeting Hawaii, April 2008

33. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – model integration –advanced rainfall sensitivity Pattern of wet days in addition to total amount is important LMM Monthly malaria incidence for Botswana as a function of artificially degraded rainfall resolution for 1982-1990. If data are averaged over a month (blue), peak incidence can be twice as high as with daily data. The season also tends to start early and finish later. Slide from Anne Jones, University of Liverpool

34. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – climate model – data exploration Plume Plot May 2-5 1 st June 1 st Sep Daily rainfall climatology from 1 st June to 30 September (1991-2001) over the Sahel (20W-45E, 10N-20N). ifmk is the MPI model From the Max Planck institute involved in ENSEMBLES. In blue is depicted the spread envelope of the ensemble for different interquantile ranges. The model median is highlighted in black, the NCEP reanalysis in red. A five-day low pass filter has been applied to the data. Slide from Cyril Caminade University of Liverpool.

35. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse Research Examples – climate model – data exploration Global climate models slide Cyril Caminade

36. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse 36 ROC Areas (95% confidence intervals) ERA-40 (continuous) and DEMETER-driven LMM malaria transmission anomaly forecasts for November start date, months 4-6 (FMA) against Thomson et al. Malaria index. ROC area > 0.5 indicates skill relative to climatology. Research Examples DEMETER performance for Botswana EventERA-40DEMETER Lower tercile 0.714 (0.438-0.938) 0.841 (0.627-1.0) Above the median 0.820 (0.615-0.969) 0.780 (0.544-0.949) Upper tercile0.879 (0.640-1.0) 0.670 (0.412-0.929) Simple probability forecast – count ensemble members Event threshold Slide from Anne Jones, University of Liverpool

37. WGSIP12 – Miami - January 2009 – Research Data Seasonal Applications - Andy Morse 37 e.g. February forecast period is heavily influenced by rainfall from initialisation period Tier-3 ROC AREASUpper tercile ERA-40 driven LMM incidence (Feb 2-4) 0.890 (0.714-1.0) DEMETER-driven LMM incidence (Feb 2-4) 0.923 (0.769-1.00) ERA-40 control run (persistence) 0.874 (0.635-1.00) Rainfall Incidence Forecast window/model lag Incidence Botswana malaria forecasts Slide Anne Jones, University of Liverpool