addressing climate change adaptation in regional transportation plans
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addressing climate change adaptation in regional transportation plans

addressing climate change adaptation in regional transportation plans addressing climate change adaptation in regional transportation plans

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11.07.2015 Views

Addressing Climate Change Adaptation in Regional Transportation PlansA Guide for California MPOs and RTPAsA2. Medium-high emissions resulting from continuous population growthcoupled with internationally uneven economic and technological growth.Under this scenario, emissions increase through the 21 st century and by 2100atmospheric carbon dioxide (CO 2 ) levels are approximately three-timesgreater than pre-industrial levels.B1. Lower emissions than A2, resulting from a population that peaks midcenturyand declines thereafter, with improving economic conditions andtechnological advancements leading to more efficient utilization of resources.Under this scenario, emissions peak mid-century and then decline, leading toa net atmospheric CO2 concentration approximately double that of preindustriallevels.Since the introduction of these emissions scenarios, the climate science, as well asglobal climate conditions, has rapidly evolved. Since these emissions scenarioswere introduced in 2000, actual global GHG emissions have exceeded 35 of the40 emissions scenarios considered for the SRES (Le Quéré et al., 2009). Newformulations of potential emissions scenarios are currently under developmentfor the IPCC’s 5 th assessment report (AR5). Rather than representingsocioeconomic conditions leading to different levels of GHG emissions, the newscenarios are based on alternative futures of atmospheric concentrations of GHGand aerosols referred to as Representative Concentration Pathways (RCPs). Forthe AR5, emissions scenarios informed by the RCPs will, for the first time,incorporate approaches to climate change mitigation in addition to scenariosconstructed without mitigation policy measures in place. Future analyses inclimate change projections will apply these new emissions scenarios and willreplace and update projections developed under the current scenario framework.As the science of climate change progresses and scientific understanding ofemissions pathways and climate dynamics improve, it will be important to keeppace with developments in climate projections and update planning documentsaccordingly.General Circulation Models and DownscalingAnother source of variability in projecting climate stressors is the generalcirculation model (GCM), or range of GCMs, employed. To identify the GCMsthat best suited to predicting climate phenomena in the State of California, Cayanet al. (2012) selected six models from the IPCC Fourth Assessment Report basedon data availability and on historic skill in representing climate patterns inCalifornia, including seasonal precipitation and temperature, annual variabilityof precipitation, and the El Niño/Southern Oscillation (ENSO) phenomenon.The six models selected for the assessment were:1. The NCAR Parallel Climate Model (PCM);2. The NOAA Geophysical Fluids Dynamics Laboratory (GFDL) model,Version 2.1;3. The NCAR Community Climate System Model (CCSM);10-10 Cambridge Systematics, Inc.

Addressing Climate Change Adaptation in Regional Transportation PlansA Guide for California MPOs and RTPAs4. The Max Plank Institute 5 th generation ECHAM model (ECHAM5/MPI-OM);5. The medium-resolution model from the Center for Climate System Researchof the University of Tokyo and collaborators (MIROC 3.2); and6. The French Centre National de Recherches Météorologiques (CNRM) models.Due to the spread of climate projections over the various models, data is oftenaveraged over multiple GCMs to avoid biasing towards any one model.Data for a series of climate stressors downscaled to the 12-kilometer (7.5-mile)scale has been archived and made available for public use 19 . This data has beenwidely applied for evaluating climate trends in California.Generate and Export DataOnce the process of selecting the relevant analysis years, identifying theapplicable emissions scenarios and GCMs, and selecting the appropriate climatestressors/thresholds has been completed, climate data accessed from thepreviously described archives can be used to inform the transportation assetvulnerability analysis. Geospatial data can be used to construct maps and tablesof present and estimated future climate conditions.10.4 CASE STUDY EXAMPLE: EXTREME TEMPERATURETHRESHOLDS FOR SCAG REGIONTo evaluate extreme heat day risk to transportation infrastructure in theSouthern California Association of Governments (SCAG) region over the courseof the 21 st century, the following variables have been identified:Analysis years. Present Conditions (1970 to 1999); and Future conditions(2010 to 2039, 2040 to 2069, and 2070 to 2099).Emissions Scenarios and GCMs. A2 and B1 emissions scenarios (given),Average of six evaluated GCMs by the State of California (given).Climate Stressor and Threshold. Extreme heat days/95°F or above.Geospatial temperature grids downloaded from the CMIP3 archive are used toproduce a table of values for the grid cell coincident with the City of RiversideTable 10.1 and Figure 10.4. Downscaled temperature grids are used to producemaps of estimated extreme heat days under the A2 and B1 emissions scenarios asshown in Figure 10.5 and Figure 10.6, respectively.19 The data used in this report was collected on August 9, 2011, from the CMIP3 archivehosted at: http://gdo-dcp.ucllnl.orgCambridge Systematics, Inc. 10-11

Address<strong>in</strong>g Climate Change Adaptation <strong>in</strong> Regional Transportation PlansA Guide for California MPOs and RTPAs4. The Max Plank Institute 5 th generation ECHAM model (ECHAM5/MPI-OM);5. The medium-resolution model from the Center for Climate System Researchof the University of Tokyo and collaborators (MIROC 3.2); and6. The French Centre National de Recherches Météorologiques (CNRM) models.Due to the spread of <strong>climate</strong> projections over the various models, data is oftenaveraged over multiple GCMs to avoid bias<strong>in</strong>g towards any one model.Data for a series of <strong>climate</strong> stressors downscaled to the 12-kilometer (7.5-mile)scale has been archived and made available for public use 19 . This data has beenwidely applied for evaluat<strong>in</strong>g <strong>climate</strong> trends <strong>in</strong> California.Generate and Export DataOnce the process of select<strong>in</strong>g the relevant analysis years, identify<strong>in</strong>g theapplicable emissions scenarios and GCMs, and select<strong>in</strong>g the appropriate <strong>climate</strong>stressors/thresholds has been completed, <strong>climate</strong> data accessed from thepreviously described archives can be used to <strong>in</strong>form the <strong>transportation</strong> assetvulnerability analysis. Geospatial data can be used to construct maps and tablesof present and estimated future <strong>climate</strong> conditions.10.4 CASE STUDY EXAMPLE: EXTREME TEMPERATURETHRESHOLDS FOR SCAG REGIONTo evaluate extreme heat day risk to <strong>transportation</strong> <strong>in</strong>frastructure <strong>in</strong> theSouthern California Association of Governments (SCAG) region over the courseof the 21 st century, the follow<strong>in</strong>g variables have been identified:Analysis years. Present Conditions (1970 to 1999); and Future conditions(2010 to 2039, 2040 to 2069, and 2070 to 2099).Emissions Scenarios and GCMs. A2 and B1 emissions scenarios (given),Average of six evaluated GCMs by the State of California (given).Climate Stressor and Threshold. Extreme heat days/95°F or above.Geospatial temperature grids downloaded from the CMIP3 archive are used toproduce a table of values for the grid cell co<strong>in</strong>cident with the City of RiversideTable 10.1 and Figure 10.4. Downscaled temperature grids are used to producemaps of estimated extreme heat days under the A2 and B1 emissions scenarios asshown <strong>in</strong> Figure 10.5 and Figure 10.6, respectively.19 The data used <strong>in</strong> this report was collected on August 9, 2011, from the CMIP3 archivehosted at: http://gdo-dcp.ucllnl.orgCambridge Systematics, Inc. 10-11

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