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Winter Season Climate Prediction for the UK Health Sector Glenn R McGregor, The University of Birmingham, UK email@example.com NOAA 29th Annual Climate Diagnostics and Prediction Workshop October 2004, Madison, Wisconsin
Winter Season Climate Prediction for the UK Health SectorGlenn R McGregor, The University of Birmingham, UKg.firstname.lastname@example.org NOAA 29th Annual Climate Diagnostics and Prediction Workshop October 2004, Madison, Wisconsin
Background: To date the UK health sector has not considered incorporating seasonal level climate information into the winter planning process.
Aim: To assess the utility of long-lead winter season climate forecasts as a basis for developing climate based health forecasts (HF) for the West Midlands region England.
Methodology: Daily de-trended all cause mortality (total and 65yrs+ per 100,000) and daily Tmean, Tmax and Tmin temperature data were averaged into monthly (D,J,F) and seasonal (DJF) values and then converted to standardised anomalies for the period 1974/75 – 1989/99. Daily temperature data were also used to count the number of days per month/season below a range of standardised thresholds (1.0, 0.5, 0.0, -0.5, -1.0 standard deviations). Delete-1 jack-knife regression was used to find the best fit linear (HF) models describing winter temperature - mortality relationships. The HF models were then applied to the problem of making retrospective forecasts of D.J, F and DJF mortality. The HF model input variables were constructed from 186 day (November start; 1987-1999) 15 member ensemble hindcasts of temperature for 9 grids points across the UK produced by the Met Office GloSea seasonal climate prediction system.
Results: Significant associations were found between mortality and Tmean, Tmax and Tmin and the number of ‘threshold’ days for most months, but most notably for February (Figs 1a & 1b). ROC analysis revealed that the GloSea hindcasts possessed skill for February only. Further the skill for Tmean, Tmax and Tmin was greater than that for the number of ‘threshold’ days. Only Tmean and Tmax based hindcasts of total (r2=.41) and 65yr+(r2=0.35) mortality for February were found to possess any skill; predictability based on the number of ‘ threshold’ days was low.
February Tmean vs Mortality
Days February Tmean < -1.0 sd vs mortality
Discussion and Conclusions: Clearly the extended range predictability of mortality depends on the degree to which input data for climate based HF models can be skilfully forecast. Winter mortality in the UK is related to periods of anomalously cold weather, which are associated with distinct atmospheric circulation modes or regimes such as blocking. Therefore, the challenge for health related seasonal prediction is to estimate the future occurrence of circulation regimes associated with anomalously cold conditions. Accordingly for the wider UK region, the extent to which coupled O- A prediction systems, like GloSea, can resolve the North Atlantic SST tripole pattern is critical as this, through O- A interaction, has a discernable impact on the European winter circulation regime and blocking occurrence.