1. WEATHER TYPE CLASSIFICATIONS IN HUMAN HEALTH STUDIES Jan Kyselý Institute of Atmospheric Physics, Prague Czech Republic with support and inputs from Radan Huth Institute of Atmospheric Physics, Prague Jiyoung Kim Korea Meteorological Administration, Seoul Applications in South Korea

2. HEALTH (?) WEATHER TYPE CLASSIFICATIONS IN HUMAN HEALTH (?) STUDIES MORTALITY

3. Population: 47 million (2005), Seoul 10.4 million Area: 99 000 km2

4. Air temperature, heat index and excess mortality in summer 2004, South Korea

5. Air temperature, heat index and excess mortality in summer 1994, South Korea +3400 excess deaths; excess mortality in all age groups absence of efficient heat-watch-warning system (HWWS)

6. Documented large natural disasters affecting Korean Peninsula since 1901 (Kysely and Kim 2009, Climate Research 38:105-116) ~3400 excess deaths represent net excess mortality as no mortality displacement effect appeared after the heat waves YearDeath toll EventAffected region over which death toll is given 19943384Heat wavesSouth Korea 19361104TyphoonSouth and North Korea 2006844FloodingNorth Korea 1959768Typhoon SarahSouth Korea and Japan 1972672Seoul, Kyonggi floodSouth Korea 2007610FloodingNorth Korea 1969408Gyeongsangbukdo, Gyeongsangnamdo, Gangwon flood and landslides South Korea 1987345Chungchongnamdo, Chollanamdo, Kangwon flood and landslides South Korea 1998324Massive rain, floods and landslidesSouth Korea 2002246Typhoon RusaSouth Korea

7. OUTLINE 1.Introduction to the methodology; the 1994 heat wave in Korea 2.Data & classification procedure 3.Results 3.1Identification of oppressive air masses 3.2Dependence on settings of the classification procedure 3.3Selected classifications C6 and C15 3.4Regression models for excess mortality within the oppressive AMs 4.Concluding remarks 5.The last slide

8. INTRODUCTION Heat stress: most direct effect of weather on human health in mid-latitudes (large seasonal temperature changes and high within-season variability of weather) most deadly among all atmospheric hazards main source of negative impacts of the likely future global warming on human mortality at least in the mid-latitudes Vulnerable population: the elderly (but other age groups as well!) persons with pre-existing diseases (e.g. cardiovascular and respiratory) other risk factors: low socio-economic status, living alone, lack of access to transportation, living on upper floors of multi-storey buildings, etc.

9. INTRODUCTION Summer 2003: the June-August period was the warmest summer in Europe since at least 1500 pronounced mortality response during the 2003 heat waves in western European countries estimated death toll largely exceeding 30 000  the 2003 heat waves were among the 10 deadliest natural disasters in Europe for the last 100yrs, and the worst one over the last 50yrs one of the reasons for such pronounced impacts: absence of efficient heat- watch-warning systems (HWWS)

10. summer 1994‘usual summer’ (2002) The 1994 heat wave

12. Excess mortality in individual age groups and genders during the 1994 heat waves The 1994 heat wave

13. unusually hot weather continued also in the second decade of August 1994: the total death toll of the 1994 heat waves was around 3400, a value by an order of magnitude larger than during any other summer over 1991-2005 results independent on the particular settings of the baseline mortality estimation since several modifications leave the results unchanged The 1994 heat wave

14. if a gradual warming of 0.04  C/year is assumed over the period 2001-2060, the recurrence interval of a very long spell of days with temperature exceeding a high threshold (as in the 1994 heat wave) is estimated to decrease to around 40 (10) years in the 2021-2030 (2041-2050) decade The 1994 heat wave (in a ‘climate change’ perspective)

15. “Air mass” classifications & mortality Heat-watch-warning systems (HWWS): apply methods to determine whether a day will be associated with elevated mortality risks according to weather forecast & take action when oppressive day is predicted often make use of objective classifications of weather types (‘air masses’, AMs) – take into account the entire weather situation rather than single elements identify ‘oppressive’ AMs associated with elevated mortality in a given location/area & apply regression models within the oppressive AMs in order to account for (and predict) excess mortality

16. The idea behind: human physiology responds to the whole ‘umbrella of air’ and not single weather elements (although there is little doubt that air temperature and humidity are the two most important parameters determining the thermal comfort) “Air mass” classifications & mortality

17. Two basic types of classifications: ‘Temporal Synoptic Index’ (TSI; Kalkstein, 1991; Kalkstein et al., 1996; McGregor, 1999) ‘Spatial Synoptic Classification’ (SSC; Kalkstein and Greene, 1997; Sheridan, 2002) in both methods, one AM is representative for a given day & location/region under study the classifications are based on a relatively standard set (although differing among studies) of input variables: air temperature (T), humidity variable (e.g. T-Td), total cloud amount (TCA), wind components, and atmospheric pressure TSI: location-specific AMs produced SSC: more universal, allows for a comparison between places “Air mass” classifications & mortality

18. Methodology: TSI & SSC differ in the statistical approach to the identification of AMs: TSI – PCA and cluster analysis used to define the AMs SSC – days are assigned to one of several predetermined types (seed days) (straightforward interpretation of the SSC over larger areas compensate for its drawback that the representative days must be identified manually, involving a large degree of subjectivity) Focus on TSI in this presentation (SSC “for comparison”): (i) no need to make regional- or continental-scale comparisons of the AMs and their links to human health (ii) avoid the subjectivity involved in the initial step of the SSC, i.e. the predetermination of the AMs “Air mass” classifications & mortality

20. both TSI and SSC utilized to a comparable extent in previous studies applications: impacts of weather conditions on mortality impacts on hospital admissions mostly summer, but winter also examined total mortality of all causes usually found the most useful and reliable characteristic of human health effects the geographic range: North America (US, Canada), Europe (UK, France, Italy, Greece, Czech Republic), Asia (China, Korea, Japan), Australia the development of HWWS (based on TSI – Kalkstein et al. 1996, or SSC – Sheridan and Kalkstein 2004)  the AM classifications have become a sort of ‘standard tools’ in biometeorological studies “Air mass” classifications & mortality

21. TSI: individual studies differ in many specific ‘settings’ of the classification procedure, including the set of input variables, the clustering algorithm, the number of clusters (AMs) formed the role of these settings and choices on results usually not discussed ! often no reasoning or justification of the settings ! important details of the classification which are needed for its ‘reproduction’ are missing (only 3 out of 11 studies on TSI specify the number of PCs retained; some studies do not present the clustering algorithm and/or even the number of AMs formed) “Air mass” classifications & mortality

22. Kyselý et al., International Journal of Climatology 2009

24. Main questions To what extent do results (i.e. the AMs formed and their links to human mortality) depend on the settings of the classification procedure? the selection of input meteorological variables the way the input variables are treated (averaged/pooled station data) the number of PCs retained for the cluster analysis the number of clusters (AMs) formed Which classification is most useful for a possible application in HWWS?

25. 1.Introduction 2.Data & classification procedure 3.Results 3.1Identification of oppressive air masses 3.2Dependence on settings of the classification procedure 3.3Selected classifications C6 and C15 3.4Regression models for excess mortality within the oppressive AMs 4.Concluding remarks 5.The last slide

26. Daily mortality data: 1991-2005 total (all-cause) mortality Excess daily mortality: deviations of the observed number of deaths from expected (baseline) number of deaths Expected (baseline) number of deaths takes into account long-term changes in mortality, the seasonal and the weekly cycles excess mortality examined in the whole population (all ages) and the elderly (persons aged 70+ years) several confounding factors controlled for e.g. days with very large accidents (aviation and maritime disasters, a store collapse, …) and death tolls due to severe natural disasters (typhoons and floods), resulting in more than 100 accidental or disaster-related deaths each Data & classification procedure

27. Long-term changes in mortality (  standardization) BUT also seasonal and weekly cycles…

28. Input meteorological data: air temperature (T), dew-point deficit (T-Td), zonal wind, meridional wind and total cloud amount (TCA) 10 stations representative for the area of South Korea, 4 times a day (3, 9, 15 and 21 LT) mid-May to mid-September AM classifications differ in the way the station data are taken into account: the input variables originate from  the average series for the area,  the pooled series at the 10 stations considered together Data & classification procedure

29. AM classification methodology: STEP 1 unrotated PCA to form a set of new orthogonal variables (PCs) the number of PCs retained conforms to the criteria recommended in literature (a large gap in explained variance; e.g. Richman 1986) more than one solution for the number of PCs possible in most cases, so time series of different numbers of PCs enter the cluster analysis STEP 2 cluster analysis: non-hierarchical k-means method (more useful for the identification of oppressive AMs than hierarchical average linkage clustering) the number of clusters: 6 (‘small’), 10 (‘moderate’) and 15 (‘large’) (a range around values appearing in literature is spanned; no objective method to determine the number of clusters was used: the data are not clearly structured and form rather a continuum than a set of well-defined separate states) Data & classification procedure

30. STEP 3 oppressive AMs: those associated with mean excess mortality significantly different from 0 (t-test) & the mean increase at least 3% relative to the baseline mortality (~ about 20 excess deaths in Korea) Data & classification procedure

31. Regression models for predicting excess mortality within the oppressive AM: STEP 4 to evaluate the impact of within-AM variations in meteorological elements, a stepwise multiple regression analysis performed on all days classified with the oppressive AM dependent variables: relative daily excess mortality in the whole population and the elderly (70+ years) independent variables: weather elements (T, Td, and heat index measured 4 times a day; daily averages of T, Td, heat index, TCA, and wind speed) & non- meteorological factors (day in sequence; time of season – within-season acclimation to heat; year – long-term changes in vulnerability to heat stress; the numbers of days with the oppressive AM since the beginning of summer and in previous summer – shorter-term and longer-term acclimation to oppressive weather conditions) meteorological variables lagged by 1 and 2 days (t-1, t-2) and changes over 24h periods (d/dt; cf. McGregor, 1999) also considered as possible predictors Data & classification procedure

32. 1.Introduction 2.Data & classification procedure 3.Results 3.1Identification of oppressive air masses 3.2Dependence on settings of the classification procedure 3.3Selected classifications C6 and C15 3.4Regression models for excess mortality within the oppressive AMs 4.Concluding remarks 5.The last slide

33. the whole set of input variables (T, T-Td, W and TCA): the solution as to the number of PCs unique and the same for both averaged and pooled input data (4 PCs retained) the reduced sets of input variables (either TCA or both TCA and W omitted): the number of PCs was ambiguous  two solutions considered for both averaged and pooled data IDENTIFICATION OF OPPRESSIVE AMs T, T-Td, TCA, W: 4 PCs T, T-Td, W: 3 / 4 PCs T, T-Td:2 / 3 PCs (avg), 2 / 5 PCs (pooled) the additional PCs describe mainly diurnal variations and local effects more PCs means more variance of the input variables explained, at the expense of possibly including too many details that may be irrelevant for the AM definition the relationship between AMs and mortality is better expressed when fewer PCs are retained

34. IDENTIFICATION OF OPPRESSIVE AMs all classifications with 15 clusters & majority of those with 10 clusters: identify an oppressive AM with enhanced mortality in two cases, a secondary oppressive AM appears, with a slightly less pronounced mortality increase Boxplots of relative excess mortality in individual AMs

35. IDENTIFICATION OF OPPRESSIVE AMs the mean relative excess mortality 3- 7% for the whole population, up to 9% for the elderly (70+ years) the mean mortality increases in the age group 70+ years always larger than in the whole population Boxplots of relative excess mortality in individual AMs

36. IDENTIFICATION OF OPPRESSIVE AMs most classifications identify an oppressive AM with enhanced mortality the mean relative excess mortality 3-7% for the whole population, up to 9% for the elderly (70+ years) the mean mortality increases in the age group 70+ years always larger than in the whole population Boxplots of relative excess mortality in individual AMs

37. IDENTIFICATION OF OPPRESSIVE AMs mean excess mortality is negative or close to zero in the other non- oppressive AMs  skill of the classification procedure in identifying weather conditions associated with mortality impacts around a quarter of days classified with the oppressive AM is associated with marked excess mortality of 10% and more above the baseline (more than 60 excess deaths a day!) Boxplots of relative excess mortality in individual AMs

38. IDENTIFICATION OF OPPRESSIVE AMs BUT not all days with the oppressive AM have excess mortality  further analysis into which meteorological and non-meteorological factors may account for the excess deaths is needed no ‘deficit mortality’ counterpart to the oppressive AM in any classification, i.e. an AM associated with pronounced average deficit mortality Boxplots of relative excess mortality in individual AMs

39. IDENTIFICATION OF OPPRESSIVE AMs weather conditions: the oppressive AM the warmest one, associated with large humidity, weak southern flow and below-average TCA (but not the smallest one among AMs) if there are >1 oppressive AMs they differ rather in ‘additional’ weather characteristics (mainly zonal and meridional wind) than T and Td Input variables: T, T-Td; pooled data; 2 PCs retained; classification with 6 AMs

40. IDENTIFICATION OF OPPRESSIVE AMs according to the terminology used in the context of AM classifications: the oppressive AM is ‘moist tropical’, with relatively small T-Td (5-6  C on average, which is much less than for the prevailing ‘dry tropical’ oppressive AM in central-European conditions; Kyselý and Huth, 2004)

41. DEPENDENCE ON SETTINGS OF THE CLASSIFICATION Basic characteristics of the oppressive AM (the relative frequency; the mean relative mortality increase on days with the oppressive AM; and the coverage of days with pronounced excess mortality) differ in individual classifications Two important criteria that the oppressive AM should meet: 1.separated from the rest of the sample in terms of mean excess mortality 2.covers large percentage of days with pronounced excess mortality (the most important criterion for the application into predicting elevated mortality risks – pronounced excess mortality in summer is usually heat-related)

42.  the less frequent the oppressive AM, the larger the mean excess mortality in the oppressive AM (the AM is better separated from the rest of the sample); however, this is at the expense of the coverage of days with elevated mortality the oppressive AM of the classifications with 6 clusters (if any) is therefore associated with smaller mean excess mortality, but a much higher percentage of days with large excess mortality compared to the classifications with 10 and 15 clusters DEPENDENCE ON SETTINGS OF THE CLASSIFICATION Coverage of days with large excess mortality Mean excess mortality (70+ yrs)

43. DEPENDENCE ON SETTINGS OF THE CLASSIFICATION Summary of the findings: additional variables (W, TCA) do not generally improve the results  the effects of wind and cloudiness are of secondary importance differences between classifications based on averaged and pooled data relatively little consistent, depend on the particular set of input variables the dependence of results on the number of PCs retained relatively large, particularly for pooled input data; fewer PCs give better results for all 3 classifications with T, T-Td and W based on pooled data: the number of PCs governs not only the mean relative excess mortality in the oppressive AMs but even the number of the oppressive AMs (0, 1, or 2)!!!

44. DEPENDENCE ON SETTINGS OF THE CLASSIFICATION Classifications based on (T, T-Td) with pooled input data and 2 retained PCs form the most interesting set: C15 – oppressive AM with very large mean relative excess mortality (6.7% in the whole population, 8.9% in the 70+ years) C6 – the oppressive AM has the largest coverage of days with large excess mortality

45. CLASSIFICATIONS C6 & C15 Boxplots of relative excess mortality in individual AMs C6C15

46. CLASSIFICATIONS C6 & C15 large interannual variations in the occurrence of the oppressive AM: 0 days in 1993 (for both C6 and C15) >30 (10) days in some summers for C6 (C15) maximum: 52 (25) days in summer 1994

47. CLASSIFICATIONS C6 & C15 within-season variability: maximum in late July or early August no occurrences in May and June

48. CLASSIFICATIONS C6 & C15 another specific characteristic = persistence: the average duration of a spell = 4.7 (2.7) days in C6 (C15), much longer than for any other AM record-breaking durations of the oppressive AMs: 29 (15) days in C6 (C15)

49. CLASSIFICATIONS C6 & C15 average mortality impacts in the oppressive AM depend on the day in sequence: mean excess mortality increases with the duration of a spell on the first few days  then a slight decline appears  followed by a sharp increase in the mortality response, mainly in the elderly, during late stages of prolonged occurrences of the oppressive weather (days 15-23 in C6, 9-15 in C15)

50. CLASSIFICATIONS C6 & C15 heat stress effects tend to cumulate over the first few days with the oppressive weather, a certain degree of short-term acclimation to heat develops after a week or so BUT this acclimation (which may be physiological as well as behavioural) does not play a role anymore if the oppressive weather persists for a very long time

51. CLASSIFICATIONS C6 & C15 the oppressive AM covers most days with pronounced excess mortality (daily excess mortality exceeds 200 deaths in the peak of the 1994 heat waves!) BUT some days with relatively large excess mortality in 1994, after the peak of the heat wave, are not classified with the oppressive AM in C15  a consequence of the trade-off between the coverage of days with pronounced excess mortality (better in C6) and mean mortality increase on days classified with the AM (better/larger in C15)

52. CLASSIFICATIONS C6 & C15

53. the oppressive AM covers a large portion of days with pronounced heat- related mortality in C15, and nearly all in C6 BUT not all days classified with the oppressive AMs associated with excess mortality: the links are complex and mortality is affected not only by meteorological elements but also other factors (timing within a season, timing within a spell of oppressive days, longer-term changes in the public perception of heat, etc.)

54. REGRESSION MODELS FOR EXCESS MORTALITY STEP 1: linear regression models developed using the step-wise screening and the whole available period of data (1991-2005); BIC used to control for overfitting

55. REGRESSION MODELS FOR EXCESS MORTALITY C6: excess mortality positively associated with day-time temperature (T15) and day-to-day change in night-time dew-point temperature (dTd3) non-meteorological factors also important: mortality impacts decrease with the number of days with the oppressive AM both in previous summer and since the beginning of summer in a given year for the whole population, mortality effects are found to decrease over time, too

56. REGRESSION MODELS FOR EXCESS MORTALITY C15: regression models somewhat more complex, with different predictors selected for the whole population and the elderly two non-meteorological factors are important: excess mortality in the oppressive AM increases with the day in sequence, and decreases with the time of season

57. larger percentage of explained variance in C15 than C6 (much smaller sample size, 98 vs. 343; possible overfitting in C15, i.e. the models may be too complex for given amount of data) REGRESSION MODELS FOR EXCESS MORTALITY

58. as expected, the model for the oppressive AM of the C15 classification produces a better fit BUT many days with excess mortality are not classified with the oppressive AM the model for the C6 classification performs reasonably well except for the 8-day mortality peak in 1994, magnitude of which is substantially underestimated

59. REGRESSION MODELS FOR EXCESS MORTALITY

60. 20 (50) days with the largest observed excess mortality (June-August 1991-2005): 19 (39) are associated with the oppressive AM of the C6 classification modelled relative excess mortality exceeds 5% on 16 (31) of them (note that large excess mortality may also be related to other factors than oppressive weather conditions, so a ‘perfect fit’ is not expected)

61. REGRESSION MODELS FOR EXCESS MORTALITY for possible applications in HWWS, the models may be evaluated in terms of a skill score based on the number of ‘successes’ (excess mortality observed and modelled, Y/Y), ‘false alerts’ (excess mortality modelled but not observed, N/Y) and ‘missing hits’ (excess mortality observed but not modelled, Y/N) we use the threat score (‘critical success index’) TS = n(Y/Y) / [n(Y/Y) + n(Y/N) + n(N/Y)], i.e. the number of correct forecasts of large excess mortality divided by the total number of cases when large excess mortality is observed and/or modelled

62. REGRESSION MODELS FOR EXCESS MORTALITY C6 OBS/MODY/Y [%]Y/N [%]N/Y [%]N/N [%]TSBias >=3%10.320.56.262.90.280.54 >=5%6.114.03.876.10.260.49 >=10%1.65.70.292.50.210.25 >=5/3%8.211.98.471.50.290.82 >=10/5%4.03.35.986.80.301.35 models’ performance over July-August 1991-2005 evaluated using different thresholds of excess mortality at which alerts would be issued the performance is better when the bias is partly compensated for (the last two rows) in C6, the days with modelled excess mortality exceeding by at least 5% the expected number of deaths cover 55% of days with observed excess mortality more than 10% above the baseline BUT TS is not large (0.30), particularly as the number of ‘false alerts’ exceeds that of ‘successes’

63. REGRESSION MODELS FOR EXCESS MORTALITY C6 C15 OBS/MODY/Y [%]Y/N [%]N/Y [%]N/N [%]TSBias >=3%10.320.56.262.90.280.54 >=5%6.114.03.876.10.260.49 >=10%1.65.70.292.50.210.25 >=5/3%8.211.98.471.50.290.82 >=10/5%4.03.35.986.80.301.35 OBS/MODY/Y [%]Y/N [%]N/Y [%]N/N [%]TSBias >=3%5.225.71.867.30.160.23 >=5%3.516.61.878.10.160.27 >=10%1.95.40.492.30.250.32 >=5/3%4.315.82.777.20.190.35 >=10/5%2.74.62.790.00.270.74 important finding: the skill of the model for C15 is smaller than for C6: ‘false alerts’ are reduced at the expense of enhanced number of ‘missing hits’

64. REGRESSION MODELS FOR EXCESS MORTALITY STEP 2: models developed using only 12 years of data (1991-2002) & their performance tested on independent 3-year sample (2003-2005) altogether 30 days in July-August 2003-2005 with relative excess mortality exceeding by at least 5% the expected number of deaths: the oppressive AM of the C6 (C15) classification covers 17 (10) of them, and on all of them in both C6 and C15, positive excess mortality was predicted C6: 12 out of 15 days with the largest excess mortality are classified with the oppressive AM  the classification is a useful tool for finding most stressful weather conditions

65. REGRESSION MODELS FOR EXCESS MORTALITY the reproduction of day-to-day variations in mortality not very successful in either classification C6 outperforms C15 – the oppressive AM of the C6 classification covers more days with large excess mortality for the application in a HWWS, the prediction of when excess mortality may be expected is more important than the prediction of the magnitude of the excess mortality itself

66. TS of the prediction of days with large excess mortality is also higher in C6 than C15 (for any threshold of what is considered ‘large’ excess mortality) for the threshold of relative excess mortality at least 3% above the baseline, the predicted days cover almost half of the observed days with large excess mortality, and the rate of ‘successes’ against ‘false alerts’ is around 3  TS=0.40

67. Seoul TSI (6, 10, 15 AMs) vs. SSC (bottom) TSI superior with respect to the coverage of days with large excess mortality OAMs in TSI15 ~ 55% DT & MT+ in SSC ~ 30%

69. CONCLUDING REMARKS 1/4 results strongly depend on the settings of the classification procedure general rules concerning the most appropriate methodology for the identification of oppressive AMs are difficult to be formulated the method has to be adjusted for specific goals and location

70. CONCLUDING REMARKS 2/4 for South Korea, the classifications based on only two input variables, T and T-Td, are superior in identifying conditions associated with large excess mortality results support the idea that air temperature and humidity are most important for characterizing the effects of daily weather on human health, while other weather elements may be relatively unimportant

71. CONCLUDING REMARKS 3/4 the classification with 6 AMs more useful for a possible application in a HWWS, particularly as it better covers and ‘predicts’ days with large excess mortality both meteorological and non-meteorological parameters are found to be predictors in regression models for excess mortality within the oppressive AMs of the selected classifications the models show better skills in predictions of when large excess mortality occurs

72. CONCLUDING REMARKS 4/4 further analysis may benefit from inclusion of air pollution factors (e.g. total suspended particulates, ozone) into models (BUT data in Korea available since 2001 only) more general models than the linear regression the use of PCs instead of raw meteorological parameters in order to overcome the issue of colinearity and get more stable regression equations

73. THE LAST SLIDE general usefulness of the air-mass-based synoptic approach in the analysis of relationships between weather and human health, including the application in HWWS BUT there is a large number of more or less subjective decisions that have to be made during the process of forming the AMs, and, most importantly, the outcome of the classification procedure, characteristics of the oppressive AM, and statistical models that link meteorological and non- meteorological parameters to excess mortality substantially depend on these decisions  MUCH ATTENTION NEEDED

74. TSI in detail: Kyselý J., Huth R., Kim J., 2009: Evaluating heat-related mortality in Korea by objective classifications of ‘air masses’. International Journal of Climatology, doi 10.1002/joc.1994. comparison of TSI and SSC (for Seoul): Kyselý J., Huth R., 2010: Relationships between summer air masses and mortality in Seoul: Comparison of weather-type classifications. Physics and Chemistry of the Earth, doi 10.1016/j.pce.2009.11.001.