NA15OAR4310081 Research supported NA14NES4320003 by NOAA grants: NA16OAR4310147 Motivation for such a platform: Develop early warning systems to accelerate the transition from reactive responses to emergency health situations to proactive risk assessment

Define excessive heat in a form that is compatible with constraints of weather / prediction models Health Sciences • Develop a real-time Deliver the probabilistic forecast forecast platform that information effectively i.e., in formats can be run ‘in-house’ at useful to decision makers academic institutions Co-investigation and federal agencies. Co-development Define forecast verification metrics • Test new ideas using that are meaningful to decision makers this platform to Climate Sciences i.e., value forecast metrics increase the value of the forecast system. Prioritize understanding of physical processes leading to improvements of weather/climate forecast models and forecast methodologies We must define what is important to forecast and whether it is predictable

Impacts of heat on human health: • Grow non-linearly as and increase. Therefore we need to use thermal discomfort indices that are based on models of the physiological effects of heat on the human body e.g., the , HUMIDEX, WBGT, UTCI, rather than just dry temperature. • Increase as a function of their duration. Therefore we need to consider consecutive days with high thermal discomfort rather than just weekly, monthly, or seasonally averaged temperature. • Depend on geographical location. Therefore we need to consider a definition of heat waves that varies as a function of location. This can be done by considering temperature thresholds (absolute values or quantiles). • Depend on earlier periods of colder or warmer than average weather because of acclimatization of the human body. Therefore we need to take in consideration environmental conditions before the heat wave in order to assess possible impacts on health. Due to the complexity of the problem there are over 100 different definitions of heat waves! Question 1: Is there forecast skill even for simplified definitions of heat waves? o Introduced a simplified definition: A heat wave is defined as two consecutive or more days of maximum exceeding the 90th percentile. o Tested forecast skill of this metric using reforecasts from the GEFS (legacy version), CFSv2, and ECMWF (2015 version) models and the Area Under the ROC Curve (AUC) as forecast score metric. Forecast skill for a simplified definition of excessive heat (Area Under the ROC Curve) Week~1 Week-2 Week-3 E E E C C C M M M W W W F F F

G G We have not G E E E No Week-3 GEFS F compute AUC for F F (for the moment) S Week-1 S S

C We have not C C F compute AUC for F F S S Week-1 S

GEFS CFS Multi-Model + + Ensemble Forecasting ECM ECM of Heat Events WF WF A more advanced definition of heat waves: The Excess Heat Factor (Nairn and Fawcett, 2014)

Let 푻풊 be the mean temperature of day i, then:

Significance of the Heat Event ퟏ 푬푯푰 = 푻 + 푻 + 푻 − 푻 풔풊품 ퟑ 풊 풊−ퟏ 풊−ퟐ ퟗퟓ% 푬푯푭 = 풎풂풙(ퟎ, 푬푯푰풔풊품) × 풎풂풙 ퟏ, 푬푯푰풂풄풄풍

Acclimatization factor 풊−ퟑ ퟏ ퟏ 푬푯푰 = 푻 + 푻 + 푻 − 푻 풂풄풄풍풊풎 ퟑ 풊 풊−ퟏ 풊−ퟐ ퟑퟎ 풌 풌=풊−ퟑퟐ

For oppressive heat events instead of the mean daily temperature (푻풊) we use the maximum between temperature and heat index: The wet Excess Heat Factor (wEHF). Wet Excess Heat Factor for Charleston, SC. (METAR data)

(a)

(b) (c) k = 0.469965 sigma = 3.772 Forecasting Health Impacts

The core model in this work is the CFSv2 which provides operationally subseasonal ensemble forecasts that are updated daily and has been shown to be skillful in predicting the dry EHF at subseasonal lead times by Ford et al. (2018).

Multi- Bias Correction model N (quantile mappings) Health Impact Core forecast Oriented model: modelling CFSv2 Deep Learning techniques for filtering unrealistic Multi- model 1 model solutions CFSv2: Model reforecast scores for Week-2

2-meter Temperature 2-meter Humidity

Anomaly Correlation Red is good

Area Under the ROC Curve (AUC) Red is good

Brier Score Blue is good Investigating land-surface / atmospheric boundary layer coupled processes

Land-surface / Atmosphere coupling hot spots

(from Koster et al. 2004) The experimental quasi-operational real time forecasting platform: excess-heat.org

Monitoring:

Percentile of the maximum daily EHF within the 7 day period prior to the forecast The experimental quasi-operational real time forecasting platform: excess-heat.org

Forecast:

Probability of exceedance of the 50th and 85th percentile of EHF for at least one day within a given period Forecast of the July 2018 Chicago Event (KORD)

Event

Event starts at day=15

Event starts at day=13

Event starts at day=11 Red line = EHF based on data from Event starts at day=9 ERA-Interim

Event starts at day=7 Forecast of the July 2018 Chicago Event (KORD)

Event Persistanse Event starts at day=15 of the event

Event starts at day=13

Event starts at day=11

Premature initiation of Event starts at day=9 the event

Event starts at day=7 Evaluation of Skill Score for 2018

Caveats in evaluating the core system for summer 2018 using the Brier skill score :

(1) The number of days with EHF above the 50th percentile is low. (2) These days are clustered in few heat events i.e., are not statistically independent. (3) Successive daily initialized forecasts are serially dependent. (4) Verification is against ERA-Interim, the newest ECMWF product is ERA-5. (5) Brier skill score is not a value score – decision makers should interpret it with caution.

Number of observed days with daily EHF > 50th percentile Brier Skill Score for dry EHF (against no-heat-wave forecast)

Days 3-7 Week-2 Week-3

In Northern Europe the Brier skill score remains high even for Week-3 suggesting that a drier than normal land surface allows excessive heat to persist in both observations and the model. Future Work: Climate Sciences

• Multi-model approaches is only one of the solutions for improving forecast skill. • A more dependable solution is to better understand physical processes during extreme events and improve parameterizations accordingly.

Research Mid-term Long-term

Process oriented studies of the coupled land- surface / boundary layer system

Dynamics of Use newly developed Two way coupling between the the transition downscaling and large scale to quasi- parameterizations in a dynamical downscaling model forced by large dynamical models stationary Large scale model using the newly regimes scale forecasts of the atmospheric flow developed parameterizations Future Work: Health Sciences

‘Macroscopic’ definition of excess heat ‘Microscopic’ definition of excess heat Thermal manikins (e.g., IESD-Fiala model)

풘푬푯푭 = 풎풂풙(ퟎ, 푬푯푰풔풊품) × 풎풂풙 ퟏ, 푬푯푰풂풄풄풍

Acclimatization factor 풊−ퟑ ퟏ ퟏ 푬푯푰 = 푳 + 푳 + 푳 − 푳 풂풄풄풍풊풎 ퟑ 풊 풊−ퟏ 풊−ퟐ ퟑퟎ 풌 풌=풊−ퟑퟐ

Research must be done on: (a) the length of these periods (b) Is acclimatization to wet heat different than to dry? (1950’s papers) (c) Time dependent weights in acclimatization factors Summary

• Progress on the subject of heat and health needs co-investigation and co-development between climate and health scientists. • Modelling of health impacts of excessive heat is a complex task and needs more consideration. • It is possible to forecast impact indices at Week-2 and perhaps Week-3 depending on geographical location. • Although multi-model approaches look promising real progress will come by focusing on understanding physical reasons for forecast shortcomings. • An experimental real time forecasting system that can serve as a baseline for engaging the climate and health sectors was developed and results are updated daily at: excess-heat.org • Future plans include understanding of relevant physical processes and their modelling followed by the development of one-way and two-way downscaling with very high resolution regional models.

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