Ryan Kang, Wee Leng Tan, Thea Turkington, Raizan Rahmat 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

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

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 Year Episodes 1983 March 10-14, March 24-28, April 9-21 1998 March 20-29 2010 March 6-9 2016 April 17-19 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

“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

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) Hindcast model 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)

CASE STUDY ANALYSIS

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

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 WK3 WK4

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

Obs Percentile (>99%) Case Study (week of 11 Apr ‘16) - Probabilistic ECMWF S2S Forecasts Obs Percentile (>99%) LT1 LT2 A probability of >50% for “Weekly average temperature Above 99% threshold” over Singapore for LT2 LT3

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

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

SKILL ASSESSMENT

Verification data and method 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

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

MSSS (ECMWF vs ERA Int) LT1 LT2 LT3 LT4

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

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

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 Hindcast verification score (ROC) for “Probability exceeding certain percentile thresholds” probabilistic products

Case Study (Apr 2016): ERA Int Percentile WK1 WK3 WK2 WK4

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