as described in detail below , we first estimated the exposure - response relationship between observed daily mortality and temperature data in manhattan .
we then obtained downscaled future temperature projections from sixteen climate models and two greenhouse gas emissions scenarios for manhattan .
these two inputs were combined to estimate future mortality related to future temperatures , which were compared to temperature - related mortality in a climatalogical baseline period .
we separately accounted for cold - season and warm - season mortality , and also computed net annual changes .
historical data on daily deaths covering the 1982  1999 period for manhattan were obtained from the us national center for health statistics ( nchs ) .
daily death counts for all internal causes ( icd-9 codes 0799.9 for 19821998 and icd-10 codes a00-r99 for 1999 ) were pooled , excluding accidental causes .
we chose this definition for consistency with previous studies ; however this would tend to underestimate heat - related mortality since heat stroke is an 
daily tmax data were obtained from the national climatic data center for 1982 to 1999 at the central park station .
a statistical model ( see supplemental equation 1 ) was developed using poisson general linear regression with log daily non - accidental death counts as the outcome variable and the following predictors : a spline function of daily tmax with 3 degrees of freedom , a natural spline of time with 7 degrees of freedom per year , and a day of week indicator variable .
this approach was similar to that used to study temperature for 11 eastern us communities .
sensitivity analyses investigated potential confounding by ozone , pm10 , dewpoint temperature and influenza epidemics .
future temperature projections were developed using downscaled outputs from 16 global scale general circulation models ( gcms ) used in the intergovernmental panel on climate change fourth assessment report , in conjunction with two future greenhouse gas emissions scenarios .
the approach uses monthly bias - corrected and spatially disaggregated ( bcsd ) climate projections at 1/8 degree resolution derived from the wcrp cmip3 multi - model dataset .
the output from the land - based grid box corresponding to central park , ny was used to create change factors for each calendar month based on the difference between each 30-year future time slice and the same gcms 30-year baseline time slice .
these change factors are then applied to the daily central park weather data to create a future projection with the same statistical characteristics and sequence as the observations .
the approach described here does not explore how changes in intra - annual and inter - annual temperature variability may change , for several reasons .
additionally , the new york city weather station used in this study does not show a significant trend in the variance of either daily summer maximum temperatures or winter minimum temperatures .
further , in an analysis of daily projections for the nyc gridbox from three gcms , horton et al . showed that neither summer tmax nor winter tmin showed significant changes in variance through 2080 . by not considering sub - monthly changes in variability , we were able to use fine spatial resolution projections ( as the 1/8 degree bcsd product is monthly , not daily ) and analyze the entire 21 century ( whereas using daily data would have constrained the analysis to the 2046 - 2065 and 20812100 timeslices for which only a subset ( 9 ) of the gcms are available from the pcmdi data portal ) . note that by applying the delta method separately for each calendar month , we do capture one component of possible changes in intra - annual variance , changes in the annual temperature cycle .
this methodology yielded a set of 32 synthetic future temperature projections for daily tmax from 2010 to 2100 based on the three 30 year time slices , and for a baseline period 19701999 .
greenhouse gas emissions scenarios represent specific blends of demographic , social , economic , technological and environmental assumptions .
we selected 2 scenarios , a2 and b1 , which represent relatively high and low greenhouse gas growth projections , respectively .
the a2 scenario assumes relatively rapid population growth and limited sharing of technological change , which combine to produce high greenhouse gas levels by the end of this century , with emissions growing throughout the entire century .
the b1 scenario assumes a high level of environmental and social consciousness , which leads to sustainable development , low population growth , high economic and technological advancement , and low energy use .
projected mortality impacts were estimated using modeled daily tmax . for any day with tmax greater than 71f ,
the change in mortality was calculated relative to the minimum mortality temperature for the heat effect , i.e. 59 f . for any day with tmax less than 63f
, the change in mortality was calculated relative to the minimum mortality temperature for the cold effect , i.e. 72 f . for days with tmax from 63f to 71f
daily additional deaths were computed as 
 ( 1)mortality = y0ercpop 
 where : 
 mortality is daily temperature - related additional deathsy0 is baseline daily mortality rate ( per 100,000 population)pop is county populationerc is percentage change in mortality for a specified change in temperature , derived from the statistical analysis of observed data as described above . 
 
mortality is daily temperature - related additional deaths y0 is baseline daily mortality rate ( per 100,000 population ) pop is county population erc is percentage change in mortality for a specified change in temperature , derived from the statistical analysis of observed data as described above .
we computed temperature - related daily deaths in this way for each time period ( 1980s , 2020s , 2050s , 2080s ) , and then computed the average number of temperature - related deaths per year ( figure 2 ) .
we also computed percent changes in annual average deaths from the 1980s to future time periods ( table 1 ) .
the population of manhattan was based on data obtained from the us census 2000 survey , and was held constant throughout the projection period .
baseline mortality rates for all ages , which excluded deaths attributable to external causes , were obtained from the u.s . centers for disease control and prevention .
 global average temperatures have been rising for the past half - century , and the warming trend has accelerated in recent decades1 . 
 further warming is expected over the next few decades , with significant regional variations . 
 these warming trends will likely result in more frequent , intense and persistent periods of hot temperatures in summer , and generally higher temperatures in winter . 
 daily death counts in cities increase markedly when temperatures reach levels that are very high relative to what is normal in a given location24 . 
 relatively cold temperatures also appear to carry risk2,4 . 
 rising temperatures may result in more heat - related mortality but may also reduce cold - related mortality , and the net impact on annual mortality remains uncertain . 
 here we use 16 downscaled global climate models and two emissions scenarios to estimate current and future seasonal patterns in temperature - related mortality in manhattan , new york . 
 all 32 projections yielded warm season increases and cold season decreases in temperature - related mortality , with positive net annual temperature - related deaths in all cases . 
 monthly analyses showed that the largest percentage increases may occur in may and september . 
 these results suggest that , over a range of models and scenarios of future greenhouse gas emissions , increases in heat - related mortality could outweigh reductions in cold - related mortality , with shifting seasonal patterns . 
Methods
Supplementary Material