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Heat as a human h azard: Climate studies with Public Health in mind

Heat as a human h azard: Climate studies with Public Health in mind. 5 -25-2012 Evan M. Oswald. Thesis background info. Overlying theme: climate knowledge useful to the end- user Motivation for project topics Heat a major health threat currently Future exasperation population models

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Heat as a human h azard: Climate studies with Public Health in mind

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  1. Heat as a human hazard: Climate studies with Public Health in mind 5-25-2012 Evan M. Oswald

  2. Thesis background info. • Overlying theme: climate knowledge useful to theend-user • Motivation for project topics • Heat a major health threat currently • Future exasperation • population models • climate change • Need/opportunity to better bridge a discipline gap • Project Topics • Analysis of extreme heat event trends in the continental United States • Investigation in Detroit’s urban climate • Trend evaluation of three high-resolution climate datasets

  3. Project #1: Title / Epid. Background • “Analysis of extreme heat event trends in the continental U.S.” • Epidemiological point of view • Mortality-temperature relationship • temperature metrics • daily highs • daily lows • daily mean • other metrics • duration • timing within heat season • cumulative degree-days • Vulnerable groups • Other studies • ozone • cold temperatures

  4. Project #1: Climate Background • Climate literature • Majority: trends in extreme temperatures • 90th and 10th percentile exceedance rates • regional and global focus • general modern era trends • warm (cold) nighttime lows more (less) frequent • hot (cool) daytime highs more (less) frequent • major spatial variability • Minority: trends in extreme heat events (duration req.) • weak variables (e.g. WSDI, 3-day heat event) • lack seasonality/local climate consideration • lack of focus on heat season events • rarely require both elevated min and max T • Results in United States: • major spatial variability • increase since 1960

  5. Project #1: Hypotheses • Over the continental U.S. (CONUS) since 1930 trends existed in the characteristics of extreme heat events (EHEs) • There is spatial structure in those trends over the CONUS • There is also temporal structure of those trends • Disparity exists between the trends of EHEs with different definitions (w.r.t. daily minimum, daily maximum and with respect to both variables)

  6. Project #1: Methods • Dataset: “daily data with ‘accurate’ monthly trends” • USHCN v2 monthly trends accurate + infilling • USHCN v1 daily dataset mapped onto v2 dataset • General experiment design • Three time periods: 1930-1970, 1970-2010, 1930-2010 • station number varies (541, 295, 165) • season: May 15-Sept 15th, variables: Tmin/Tmax • EHE definition • trigger: 2 consecutive dates above 92.5th percentile • terminator: EHE mean-percentile drops below 92.5th • EHE types: Tmin only, Tmax only, both Tmin and Tmax • EHE characteristics • mean duration, annual frequency, # EHE days per season • mean EHE intensity, annual sum EHE intensity • maximum duration, maximum intensity • mean EHE onset date (i.e. seasonal timing)

  7. Project #1: Results Here is shown the continental United States spatial average trends for each EHE characteristic metric (labeled on left), and all three temporal periods (labeled between metrics). The units of duration is days, onset date is calendar dates and intensity is cumulative percentile exceedences (i.e. the sum of the percentile exceedences above 92.5 over the duration of the EHE).

  8. Project #1: Spatial Structure Not shown: Spatial patterns between different EHE characteristics variables are effectively the same (top) The decadal trends at each station in total number of days per summer diagnosed as Tmin EHEs during the 1930-1970 period. (bottom) The decadal trends at each station in the mean TMIN EHE duration during the 1970-2010 period. For both figures, trend significance (alpha=0.10) is indicated by box shading. The different groupings are based on standard deviations away from the zero value.

  9. Project #1: Spatial Structure Spatial patterns seem to be a weak function of EHE type (top) The decadal trends at each station in maximum Tmax EHE intensity during the 1930-1970 period. (bottom) The decadal trends at each station in total number of days per summer diagnosed as Tmax EHE days during the 1970-2010 period. For both figures, trend significance (alpha=0.10) is indicated by box shading. The trend groups are based on standard deviations away from the zero value.

  10. Project #1: Spatial Structure Spatial patterns seem to be a weak function of EHE type (top) The decadal trends at each station in the sum Tmin&Tmax EHE intensity per summer during the 1930-1970 period. (bottom) The decadal trends at each station in the max Tmin&Tmax EHE duration per summer during the 1970-2010 period. For both figures, trend significance (alpha=0.10) is indicated by box shading. The 6 groups are based on standard deviations away from the zero value.

  11. Project #1: Spatial Structure • Decrease: • Central, • Northern Central • Increase: Western U.S. • EHE type dependant: • Eastern Central U.S. • Northeastern • Southeastern The decadal trends at each station in the mean EHE intensity per summer during the 1930-2010 period. The top figure is the Tmin type EHE, the middle figure is the Tmax type and on the bottom the Tmin&Tmax type EHE trends is displayed. For both figures, trend significance (alpha=0.10) is indicated by box shading. The 6 groups are based on standard deviations away from the zero value.

  12. Project #1: relationships between EHE type trends The Pearson’s correlation coefficients between the different EHE type trends for each EHE characteristic and time period. A black number indicates the means cannot be statistically considered equal at the α=0.05 significance level, and a red number signifies a failure to prove they are not equal at that level. btwnTmin &Tmin&Tmax btwnTmin and Tmax btwn. Tmax &Tmin&Tmax

  13. Project #1: Conclusions • Regarding the continental scale • Noteworthy increasing and decreasing EHE trends existed during the 1930-1970 and 1970-2010 periods, respectively • The sign and magnitude of the trend depends on EHE type during the 1930-2010 period • Regarding the regional scale during 1930-2010 • There exists spatial structure across the CONUS • The central U.S. is decreasing in EHE activity • The western U.S. is generally increasing in EHE activity • Regarding the station scale • On average between 25-50% of the individual stations had a significant trend • Significant trends of both signs exist within each sample • Regarding the EHE type trend relationships • No relationship between Tmax and Tmin type EHEs • Moderate relationship between Tmin, Tmax and the Tmin&Tmax type EHEs

  14. Project #2: Title / Epid. Background • “An investigation into the spatial variability of near-surface air temperatures in the Detroit, MI metropolitan region.” • Urban climate / Urban heat island theory • Landcover/landuse impacts 2-meter air temperatures • alteration of energy fluxes, storage, albedo • temporally dynamic • Different scales and layers • vertical • Horizontal • Past observational studies • Types of studies • Synoptic weather linkages • Climate/region linkages

  15. Project #2: End-User associations • Public health linkages • Urban regions highly population • Heat major killer • UHI intensification of Extreme heat events (EHEs) • Tmin and Tmax altered • Paucity of climate literature of intersection • Urban planning: building resilient cities • How our study facilitates these end-users • Tmin and Tmax focus • UHI and dangerous weather • Landcover/position + synoptic weather variables linked • How our study improves upon similar studies end-user studies • Incorporation of NWS observations at airports • Rigorous monitoring practices • Incorporation of popular climate products

  16. Project #2: Hypotheses • Summertime average daily extreme temperatures vary spatially across the urban/suburban domain • The magnitude of spatial variability can be diagnosed by the large-scale weather conditions • The spatial pattern can be diagnosed by land-cover/position attributes • The spatial variability exists during heat-oppressive weather

  17. Project #2: Observational network • Separate monitoring networks • 21-“HOBO” External Temp data loggers • Airport (ASOS/AWOS), Michigan Department of E.Q. • Looking beyond uncertainity • Bias/uncertainty quantification: Co-locating monitors • Controlled room, two nearby yards • Spatial Anomaly (range in SOSAs), ISSVT

  18. Project #2: Results Figure 1 • Mean range in SOSAs daily low 2.8°C, daily high 1.4°C • Student t-test non-zero range in SOSAs for both • More stability of structure in daily low • Bootstrapping noise to signal ratio Figure 2

  19. Project #2: Hot weather and IUSSVT Table 1 Mean range in SOSAs for days/time periods labeled as oppressive to public in Detroit. Red numbers indicate statistically significant at α=0.05 level Characterization of the mean range in SOSAs as a function of SSC airmass. Moist Tropical +/++ and dry Tropical are dangerous Figure 1 Relationship between Tmin and Tmax apparent temperature percentiles and normalized range in SOSAs

  20. Project #2: weather as a predictor Figure 1 • Predicting range in SOSAs • Daily low relationship > daily high relationship • Spatially averaged CC and WS obs. • Strongest variables • Overnight mean cloud cover (Tmin) • Afternoon wind speed (Tmax) • Confidence in utility • Cross validation • Bootstrapping Table 1 • NARR (reanalysis) relationship • Downwelling radiation instead of aft. CC • Performed similarly • Slightly weaker • Model forecasting?

  21. Project #2: location/land as a predictor • Predicting: mean spatial anomalies • Spatial variables • Dist. to city center • Dist. to sizeable water body • Percent impervious surface • 0.2km daily low • 1.95km daily high • Daily low relationship > daily high relationship • Strongest variables • Percent impervious (Tmin) • Distance to water (Tmax) • Distance to water good overall predictor • Confidence in utility • Cross validation • Bootstrapping Table 1

  22. Project #2: Conclusions • Regarding the existence of spatial variability across the urban/suburban landscape at the summertime temporal average level • Significant variability exists • Daily Tmax ~ noise; Daily Tmin ~ reflection of landscape • Day to day variability larger than spatial variability • Regarding the existence during weather dangerous with respect to heat-health • The variability does not disappear during hot weather • Dry Tropical airmass has larger spatial variability • Regarding the diagnosing the magnitude of spatial variability by weather conditions • Daily Tmin can be usefully modeled • Relationship with reanalysis data • Regarding the spatial pattern being explained by landcover/position attributes • Daily Tmin functionally modeled • Local percent imperiousness; distance to water

  23. Project #3: Title / Climate datasets • A temperature trend evaluation of three modern high-resolution climate datasets • Climate community datasets for U.S. trends • NCDC’s U.S. Historical Climate Network (USHCN) or GHCN • NASA GISS datasets (2.0°) • NCDC’s United States Climate Reference Network (USCRN) • Hadley Centre/University of East Angila’s CRU datasets (0.5°) • University of Delaware’s (UDel) datasets (0.5°) • Modern high resolution climate datasets • DAYMET (1km daily) • Maurer et al. 2002 (12km daily) • Prism; Di Luzio et al 2008 (4km monthly;daily) • WorldClim (1km- monthly)

  24. Project #3: new datasets background • Dataset need • small scale modeling over large domains • hydrological ecological modeling needs • Theory behind new datasets • station inclusion requirements decreased • infilling techniques • smart interpolation • predicting additional variables • Dataset uses • hydrological and ecological modeling • atmospheric science • GCM spatial downscaling • Dataset problems • Trends inaccurate (Non-climatic discontinuities) • Estimation/infilling problems

  25. Project #3: hypotheses • The three gridded climate datasets exhibit the same trends as a reference climate dataset • The gridded datasets reproduce the spatial structure of trends across the CONUS • The error in trends are not spatially autocorrelated • The error is not a function of the physical characteristics of the stations

  26. Project #3: project design • General methods/project design • Reference dataset: USHCN v1+v2 dataset • Comparison at USHCN locations • Summer season: May 15th – September 15th • Quantification of trends • Trends: OLS • Significance: Mann Kendall test • Two tests • Percentile exceedence trend comparison (90th; Tmin/Tmax) • EHE trend comparison • EHE definition: • 90th percentile, duration, running mean percentile • Tmin and Tmax required • Variable for comparison: • # per year • mean duration

  27. Project #3: project design • Continental averages • Variable comparison • Min 90th • Max 90th • EHEs • Trend accuracy as a function of • Resolution • Elevation • Distance to water • Spatial Structure • Maps of results • 2D correlation coefficients of gridded results • Spatial autocorrelation: statistical tests (Moran’s I test)

  28. Project #3: deliverables • Tables & figures of continental average trend bias & accuracy • Individual station biases characterization • Percent significant • Percent Negative/positive • Biases and accuracy spatial structure: maps, regionality • Trend bias & accuracy as a function of • elevation, • distance to water • Dataset

  29. Satisfaction of thesis motivation • Interface to translate knowledge • Historical climate trends • Urban climate characterization • High resolution gridded datasets • Results contribute to • heat-health hazard discussion • end users discussion • climate discussion

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