ECMWF Subseasonal-to-Seasonal Predictions for Extreme Temperatures in April 2016 Heatwave

1. Use of ECMWF Subseasonal-to-Seasonal (S2S) Predictions for Extreme Temperature Forecasts over Singapore and the surrounding regions during the April 2016 heatwave episode Ryan Kang, Wee Leng Tan, Thea Turkington, Raizan Rahmat Subseasonal and Seasonal Prediction Section FOCRAII 2019 9 May 2019

2. Outline • Climate information of mainland Southeast Asia • Case Study: Apr 2016 “Heatwave” • Decaying phase of a strong El Niño 2016 • Model data and methods (Deterministic and Probabilistic) • Case study analyses • Skill assessment • Summary and Conclusions • Future works

3. Climate information of mainland Southeast Asia (MSA) Extreme temperatures in Southeast Asia caused by El Niño and worsened by global warming(Thirumalai et al., 2017, Nature Communications) *Heatwave episodes in Singapore from 1979 onwards Surface air temperature climatology in MSA based on the entire CRU data set (1901–2014), which indicates that April is the warmest month in the region. * In Singapore, a heatwave is defined as occurring when the daily max temperature is at least 35°C on 3 consecutive days and the daily mean temperature throughout the period is at least 29°C

4. “Heatwave” over Peninsula CNA, 22 Apr 2016 Straits Times, 22 Apr 2016 Straits Times, 18 Mar 2016 • Early 2016 heatwave over Peninsula, April hottest • (Max) temperature > 37˚C for more than 3 days (Perlis and Pahang) • Schools closed in Malaysia, heat stroke conditions • El Niño to blame; week-to-week variations in conditions are of interest

5. Model data and method • Subseasonal-to-Seasonal (S2S) Predictions • ECMWF (51 ensemble members), every Mondays and Thursdays • Variable: Weekly average of daily mean temperature (T2M) • Hindcastmodel calibration with past 20 years (3 start dates, centered within the forecast window of 1-week, 11 x 3 X 20 = 660 re-forecast members) • Lead-dependent model climatology for mean-bias correction of model drifts • Deterministic and Probabilistic Products • Anomaly (Ensemble mean - hindcast mean) • Probability exceeding certain %-tile thresholds (Ranking and counting method)

6. CASE STUDY ANALYSIS

7. Case Study (Apr 2016): ERA Int Anomalies WK1 WK3 WK2 WK4 Week 1 to week 2 Also very warm for southern China

8. Case Study (Apr 2016): ERA, Zoom in … Week 1 to Week 2 - Became warmer Week 2 - Peak of the heat wave over Singapore WK1 WK2 Week 3- Some signs of receding warm conditions from the south Week 4 - Relief at western coast and further receding warm conditions over Singapore WK4 WK3

9. Case Study (week of 11Apr ‘16) - DeterministicECMWF S2S Forecasts Obs Anomalies LT1 LT2 • Warm week captured up to LT2 • More representative in central/north LT3

10. Case Study (week of 11Apr ‘16) - ProbabilisticECMWF S2S Forecasts ObsPercentile (>99%) LT1 LT2 • A probability of >50% for “Weekly average temperature Above 99% threshold” over Singapore for LT2 LT3

11. Case Study (week of 25Apr ‘16) - DeterministicECMWF S2S Forecasts Obs Anomalies LT2 LT1 • Receding pattern captured up to LT2 • Potentially useful forecast for cessation LT3 LT4

12. Case Study (week of 25Apr ‘16) - ProbabilisticECMWF S2S Forecasts Obs Percentile (>99%) LT2 LT1 • Low probability for “Weekly average temperature Above 99% threshold” over Singapore, up to LT2 LT3 LT4

13. SKILL ASSESSMENT

14. Verification data and method • Verification data • ERA Interim (Re-grid to 1.5° x 1.5° resolution, same as S2S resolution) • Verification Method • No ‘standard’ way unlike seasonal predictions (pentad, 7-day, 10-day?) • Example: • Forecast first 7-day week: 4 - 10 Apr • Lead time (LT) 1: 4 Apr; LT 2: 28 Mar, LT 3: 21 Mar, LT 4: 14 Mar • Sample size: Only 20 years (too little?) • Use target “full-month” hindcast verification (for increased robustness, more samples) • Assessment: Anomaly Correlation Coefficient (ACC) and Mean Square Skill Score (MSSS) for anomaly plots

15. Anomaly Correlation (ECMWF vs ERA Int) LT2 LT1 LT4 LT3

16. MSSS (ECMWF vs ERA Int) LT2 LT1 LT4 LT3

17. Summary and Conclusions • Warm week of 11 Apr 2016 is predicted by ECMWF S2S model up to a lead time (LT) of 2 weeks • Receding warm spatial pattern conditions, for the week of 25 Apr 2016 was also captured by the model up to a LT of 2 weeks • Relatively high skill for the Peninsula region: MSSS ranges between 0.3 to 0.7 up to a LT of 4 weeks

18. Summary and Conclusions • Demonstrates the ability of the ECMWF model to forecast week-to-week variations in temperature, including the peak and cessation of warmest temperature • Opportunity to provide products for worsening and/or improving extreme temperature conditions • Important implications in public’s preparedness against heat exhaustion between the weather (days) and seasonal (months) timescales

19. Future Works • Generate heat wave index: Probability of daily temperature exceeding 90%, 95% or 99%-tile for at least three consecutive days within a week • Hindcastverification score (ROC) for “Probability exceeding certain percentile thresholds” probabilistic products

21. Case Study (Apr 2016): ERA IntPercentile WK1 WK3 WK2 WK4

22. Initial datesfor 2016 model runs Verification e.g.: April, temperature, for each grid 28 Apr 21 Apr 25 Apr 18 Apr 14 Apr 7 Apr 11 Apr 4 Apr 6 x 20 = 120 samples Wk 1 (4-10 Apr) Wk 5 Wk 3 Wk 4 Wk 2 Wk 6 Weeks for verification ERA-Interim obs dataset ECMWF Forecast Anomaly Climo ‘96-’15 Anomaly Climo ‘96-’15 2016 Anomaly Climo ‘96-’15 1996 1996 1996 1997 1997 1997 … … … … … … … Lead Time 4 Fcsts Lead Time 1 Fcsts ... ... ... … … … 2014 2014 2014 2015 2015 2015