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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
       (CCRUN)
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
                                                                     2
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 / FishBoston.com

                                                                                               3
                        Overview
• 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




                     Climate

           Opportunities • Vulnerability •
            Adaptation • Management




                                             5
       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
  timescales
   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
                                             load….
                                             …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
  Region
• Sea Level Rise Projections for the Northeast
Changes in
Nor’Easters,
tropical
cyclones?
 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
  variability
• Statistically and dynamically downscaled
  projections at seasonal to multi-decadal
  timescales
• 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-
    dcp.ucllnl.org/downscaled_cmip3_projections/ (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
      sampling

•   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
         sectors)
      – 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?*
BCSD
       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)
              (4.1)
                   2.9 to 5.7         -6 to 23 (5)
  BCSD (16)
                   (4.2)
  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
  BCSD




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
                                   Temperature




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
                                                                                 25
                                ongoing evaluation

				
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