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					Climate Research in Support of
 Stakeholder Applications in the
     Urban Northeast U.S.

            Radley Horton
     Columbia University / NASA GISS

          April GloDecH Meeting
           LDEO, April 6, 2011
     Consortium for
   Climate Risk in the
    Urban Northeast
A NOAA Regional Integrated Sciences and Assessments
                   (RISA) Project

       Boston                   New York              Philadelphia
CCRUN conducts stakeholder-driven research that
reduces climate-related vulnerability and advances
opportunities for adaptation in the urban Northeast
                                      CCRUN Mission
CCRUN conducts stakeholder-driven research that
reduces climate-related vulnerability and advances
opportunities for adaptation in the urban Northeast

Storm damage in Westchester County, NY, March 12-15, 2010.      Striped bass fishing in Boston Harbor.
Source: James Estrin / The New York Times                    Source: Capt. Bill Smith /

• Five-year project
• Geographic scope includes the
  Boston – New York – Philadelphia
  urban corridor
• Focus on sectors (water, coasts,
  and health), vulnerable
  populations, and adaptation
• Partnership between Columbia
  University (Rosenzweig), City
  College of New York
  (Khanbilvardi), Drexel University
  (Montalto), the Stevens Institute of
  Technology (Blumberg), and the
  University of Massachusetts
  (Palmer)                               4
CCRUN Research Areas and Activities

           Water      Coasts       Health


           Opportunities • Vulnerability •
            Adaptation • Management

       CCRUN Prototype Projects:
      Water Resource Management

• Flooding and storm water management for the
  MWRA and Connecticut River Basin
• Delaware River Basin-New York City Water
  System Management
• Climate information for water harvesting and re-
  use strategies in Philadelphia
• Precipitation, drought indices at a range of
   CCRUN Prototype Projects: Health
• Heat/Air Quality                           During heat waves
                                             (tmax, tmin, specific
• Coastal Storms and                         humidity), even a small
  Intense Precipitation                      increase in
                                             temperature can mean a
  Events                                     large increase in energy
             0ºC       16ºC      32ºC
                                             …Leading to an
                                             increased risk of
                                             power outages and
                                             deteriorating air
    Daily Electric Energy Load               quality
 (gigawatt-hours) in NY State, vs.
   Daily-Average Temperature.                Indirect effects
     Solid Points=1966; Open
          Points=1997 =>                =More stress on systems
        Peak Load Issues
            CCRUN Prototype Projects:
            Coastal Zone Management
• Storm Surge Forecasting and Projections in
  the New York Metropolitan Region, with
  Extension to the Entire Urban Northeast
• Sea Level Rise Projections for the Northeast
Changes in
 Comparison of three sea level rise projection methods for the New York Metropolitan Region, for the
 2080s relative to 2000-2004 (Horton et al. 2008, GRL; Horton et al. 2009 NPCC CRI, 2009; Horton et
 al. JAMC in review).
 CCRUN Prototype Projects: Climate

• Historical analysis of multi-century trends and
• Statistically and dynamically downscaled
  projections at seasonal to multi-decadal
• Extreme events
• Sea level rise projections
• Stakeholder-driven analysis and presentation
      Downscaling Products and Techniques

•   Single GCM gridbox approach
    – Delta method applied to station data (NYC Panel on Climate Change)

•   Bias-Correction Spatial-Disaggregation http://gdo- (BCSD; Maurer, et al.
    2007, based on Wood et al. 2002, 2004, and Maurer 2007)
    – Direct use of monthly time series for impact models
    – For daily projections at station level, we use modified delta method and random

•   RCM simulations from the North American Regional Climate Change
    Assessment Program archive (NARCCAP; Mearns et al. 2009, EOS)
    – Changes in frequency and duration of key extremes
    – Changes in (intra-annual) variability more generally
    – Delta method
    Climate Hazard Information
    Process used to develop climate risk factors for New York City

•    Projection Range, based on 16 GCMs and 3 SRES scenarios
•    Time slice experiments based on single GCM gridbox, delta
     method approach
•    Key thresholds
      – Number of days below 32 °F (transportation sector)
      – Number of days above 90 °F (energy and health
      – Number of intense precipitation events (e.g., .5
         in./day; water sector)
•    Qualitative projections
             Statistical Downscaling
   Bias Correction Spatial Diaggregation (BCSD) Method--Steps

Bias correction
 • Regrid 1/8º observations and GCM outputs to 2º resolution
 • Generate monthly cumulative distribution functions at 2º, for
   (observations, the baseline GCM, and the future simulation)
 • Quantile map the baseline GCM onto the observed data (note: this
   preserves higher order statistics)
                   Statistical Downscaling
Bias Correction Spatial Disaggregation (BCSD) Method--Steps
  Spatial Downscaling
    • Rank each future month based on baseline GCM CDF, then replace with observed
      data as above
    • Generate factor field (temperature differences, precipitation ratios)
    • Interpolate these factors to 1/8º
    • Apply the downscaled factors to the observed 1/8º data (for temperature this is
      addition; for precipitation this is multiplication
                        NARCCAP Simulations

•   RCMs were run at 50km resolution for three experiments:
    – NCEP Reanalysis-driven, December 1979-November 2000:
        How well do the RCMs simulate ‘observations’ over the NE, when driven by
        ‘perfect’ boundary conditions?*
    – GCM hindcast-driven, December 1970-November 2000
        How sensitive are the RCM results to ‘biases’* in the driving GCMs?*

    – GCM future-driven, December 2041-November 2070, A2 SRES Scenario

        How does the forcing associated with greenhouse gases and other
        radiatively important agents manifest itself at more local scales?*

        *Several caveats here…
Annual Precipitation (in./day), 1990-1999

How well do the RCMs
simulate ‘observed’ mean
precipitation over the NE,
when driven by ‘perfect’
boundary conditions?
 Precipitation difference (%), Hindcast-driven minus R2-driven

How sensitive are the RCM
results to ‘biases’* in the
driving GCMs?*
       Projections--Mean Annual Changes

         SRES A2 2050s minus 1980s annual temperature (°F)
Projections--Mean Annual Changes

 SRES A2 2050s divided by 1980s annual precipitation (%)
       Projections--Mean Changes

 NYC Gridbox Mean Annual              Mean Annual
    (# of     Temperature             Precipitation
 simulations) Change (°F)             Change (%)
              2.5 to 6.1              -9 to 10 (5)
  GCM (16)
                   2.9 to 5.7         -6 to 23 (5)
  BCSD (16)
  NARCCAP          4.3 to 5.9         0 to 14 (5)
     (4)           (4.6)

SRES A2-driven 2050s divided by 1980s GCM hindcast-driven
Projections--% Change in # days with >.5 in prcp

      SRES A2-driven 2050s divided by 1980s GCM hindcast-driven
Projections--Mean Annual Changes

 SRES A2 2050s divided by 1980s annual precipitation (%)
         Projections--Interannual Variability

SRES A2 2050s divided by 1980s, standard deviation of annual temperature (°F)
GFDL/RCM3 Growing Season Changes, A2 2050s relative to 1980s

           Mean T                  % Change in
           Change (C°)             SD of Daily

Mean P Change (%)

               % Change in                         % Change
               Alpha                               in number
               parameter of                        of rainy
               Daily P                             days
                 Conclusions and Future Work
•   Conclusions
     – The range of results associated with
       downscaling technique is often smaller
       than other sources of uncertainty that
       influence decisions, such as:
         • Global climate sensitivity
         • Emergent patterns not captured by
            global climate models
         • Interannual to decadal variability
         • Climate impacts
         • Socio-economic changes
•   Next Steps                                                Neiman et al. 2008, Jour. of Hydromet.
     – Analyze pre-conditions for extreme
       precipitation events in GCMs and RCMs
     – Explore standardized statistically
       downscaled daily products (CMIP3 and
       CMIP5) such as BCSD, Bias Corrected
       Constructed Analogues (BCCA; Maurer
       and Hidalgo, 2008)
     – Develop localized downscaling tailored
       to sector-specific stakeholder needs
       Statistical Downscaling Model
       (e.g., SDSM; Wilby et al. 2002)
                                                Snow depth at Wanakena, NY based on SDSM
     - Stochastic approaches, weather           downscaling. (Tryhorn and Degaetano in
       generators                               NYState CliMAID Report, to be released in 2011)
     Flexible Adaptation Pathways

                                                                  Source: NPCC, 2010

  Key elements to achieve Flexible Adaptation Pathways are a guiding framework,
stakeholder engagement, expert knowledge providers, recurring assessment process,
Action Plans by decision-makers, and vertically/horizontally integrated projects with
                                ongoing evaluation

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