Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports (2019)

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29 3.1 Background on Available Climate Data The first step in performing an analysis of how climate events may affect airports is to under- stand how they can be measured and what specific data projections are available. The Intergov- ernmental Panel on Climate Change (IPCC) was established in 1988 through a collaboration of the United Nations and the World Meteorological Organization. Since then, the groups associated with the IPCC have produced a series of scientific assessments of the current state of knowledge regarding climate change. The IPCC’s Fourth Assessment Report (AR), known as AR4, uses data and climate projections collected during 2005–2006 from Phase 3 of the Coupled Model Intercomparison Project (CMIP3). IPCC has also produced its Fifth Assessment Report (AR5), which was released in 2015 and is based on more recent climate projections from Phase 5, known as CMIP5 (which itself was released in May 2013). More than 800 authors were involved in writing AR5. The different models considered in both reports are known as general circulation models (GCMs). These models use mathematical equations for a rotating sphere with thermodynamic variables representing energy sources such as radiation and latent heat. These equations are used to simulate the Earth’s atmo- sphere over time, and projections can be made for specific locations (IPCC 2007, IPCC 2014). C H A P T E R 3 State-of-the-Art Climate Measures About This Chapter Chapter 3 provides a brief background on available climate projections and measures. • Section 3.1 describes the different climate scenarios and models that are available for analysis and how to interpret them; technical issues related to accessing, con- verting, and using specific sea level rise and high-temperature data are provided in Appendix D. • Section 3.2 describes how existing available software can be used to screen for many other climate stressors in support of Step 1 (screening analysis). (However, note that reliable and detailed forecasts for them may not yet be available to support the kind of risk-adjusted Step 2 analysis described in Section 2.5.) About the Next Chapter Chapter 4 discusses identifying and classifying potential airport impacts from climate change.

30 Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports AR4 and AR5 both present projections under different future climate scenarios representing different assumptions about the path of GHG emissions reductions. The three AR4 scenarios are named B1, A1B, and A2, while the four AR5 scenarios are named Representative Concentra- tion Pathway (RCP) 2.6, 4.5, 6.0, and 8.5. Generally speaking, B1 and RCP2.6 both represent a low-emissions scenario; A2 and RCP8.5 represent high emissions and involve little or no suc- cessful global efforts to mitigate GHGs. The remaining scenarios are between these low- and high-emission scenarios. To provide an idea of the differences between the more recent AR5 scenarios, global average projections for mean temperature and mean sea level (MSL) rise are shown in Exhibit 3-1 for two future time periods. By comparing the two time periods, one can see that in all cases except for the RCP2.6 temperature estimates, both temperature and mean sea levels are expected to rise more the further into the future one looks. Not surprisingly, it is also the case that the likely range of outcomes becomes wider (less certain). This has important implications in the current context because it suggests that climate impacts will grow over time but with more uncertainty; therefore, decision makers may have to weigh whether to invest in climate resilience projects now or wait until the larger impacts are closer, when perhaps better, more reliable information can be obtained. It is important to note that it is up to the analyst to decide which scenario to use as the relevant climate assumption. This is extremely important, and choosing between, say, RCP4.5 and RCP8.5 can have significant effects on the projected climate values and, therefore, on the entire analysis. This is particularly true as the scenarios increasingly diverge in later years past the mid-century point. Source: IPCC 2013. Exhibit 3-1. AR5 global warming and MSL rise projections.

State-of-the-Art Climate Measures 31 For each scenario, there are up to 32 different available models under AR5/CMIP5. Consis- tent with the overall averages shown in Exhibit 3-1, variations in the projections across different models (even under the same scenario) grow the further out in time one goes. In this regard, it is important to understand that each model makes individual point predictions of various climate measures such as daily maximum temperature and precipitation. It is the variation in the projections across the different models for a given scenario and future date that essentially reveals the uncertainty in those projections. If they are all fairly close to each other, then there is less uncertainty than if they vary substantially. In theory, one could mix projections across models and scenarios when assessing climate risk; in practice, however, it is more common to select a single RCP scenario and then assess variations across models to estimate uncertainty within that scenario. For practical use, the raw climate projection data must be converted into more relevant mea- sures (called “climate stressors”) before they can be used to assess potential impacts on airports. For example, if an airport is concerned that its runway takeoffs may be affected when tempera- tures get too high, then the climate projections for daily maximum temperature could be used to compute the number of days each year when it would exceed some threshold value. Obviously, there are many different climate stressors that could be computed; which ones are relevant will vary airport by airport depending on location, infrastructure vulnerability, adaptation options, and so forth. 3.2 Climate Stressors A good place to start evaluating possible climate impacts on an airport is the spreadsheet- based toolkit called the Vulnerability Assessment Scoring Tool (VAST) developed by the U.S. Department of Transportation (U.S. DOT).11 This resource describes different climate measures such as high temperatures or storm surges that may be expected to increase due to climate change; potential data sources are also cited. The toolkit was developed to help state DOTs, metropolitan planning organizations, and other entities implement an indicator- based vulnerability screen. Exhibit 3-2 is taken from VAST and describes different measures that may be expected to vary in the future due to climate change; potential data sources are also cited. However, note that in some cases there may be limited forecast projections available. Another option would be to use the ACROS software tool from ACRP Report 147 (Dewberry et al. 2015). The tool allows the user to look up airport-specific CMIP5 climate projections (based on RCP8.5) that have already been converted into various climate stressor measures. Projections are provided for a base year (2010), and two future years (2030 and 2060). Depend- ing on availability at the time the software was developed, the estimates are based on projections from four to seven different climate models, and the range of results shown include the median, 25th percentile, and 75th percentile values. A confidence rating is also included for each mea- sure based on the robustness of the models and agreement between them. A listing of available climate stressors (called “vectors” in the ACROS software) and their associated confidence levels is reprinted from the report in Exhibit 3-3. The ACROS tool provides a valuable initial screening mechanism, allowing airports to get a quick assessment of how various localized climate measures may be expected to change over the coming 40 years. As described in Chapter 2, airports actually may be able to rely on data from ACROS to perform conventional analyses with specific scenario assumptions rather than undertake Monte Carlo analyses involving explicit treatment of climate event uncertainties and probabilities.

32 Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports Stressor Type Measure Potential Data Sources Temperature Total Number of Days per Year above/below a Threshold Temperature • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) Longest Number of Consecutive Days per Year above/below a Threshold Temperature • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) Number of Freeze-Thaw Cycles per Year • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Local university Annual Maximum or Minimum Temperature • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Regional climate projections -- National Climate Assessment or FHWA Climate Change Effects Typology • Local university Annual Mean Temperature • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Regional climate projections -- National Climate Assessment or FHWA Climate Change Effects Typology • Local university Precipitation Amount of Rain associated with 100-year 24-hour Storm • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Local university Number of Consecutive Days with Precipitation • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Local university Total Seasonal Precipitation • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Local university Total Annual Precipitation • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Local university Peak Discharge • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Local university Flow Velocity • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Local university Discharge Volume • Climate model outputs (e.g., DOT CMIP Climate Data Processing Tool) • Local university Sea Level Rise Modeled SLR Inundation Depth GIS Sea Level Rise model USGS Coastal Vulnerability Index http://pubs.usgs.gov/of/2004/1020/html/cvi.htm Storm Surge Modeled Surge Inundation Depth • ADCIRC model • STWAVE - STeady State spectral WAVE model • USGS Coastal Change Hazards: Hurricanes and Extreme Storms web viewer • NOAA Sea, Lake and Overland Surge from Hurricanes (SLOSH) model (http://www.nhc.noaa.gov/surge/slosh.php) Presence in FEMA Coastal Flood Zone https://msc.fema.gov/webapp/wcs/stores/servlet/FemaWelcomeView? storeId=10001&catalogId=10001&langId=-1 Source: U.S. DOT, https://toolkit.climate.gov/tool/vulnerability-assessment-scoring-tool-vast. Exhibit 3-2. VAST climate stressors.

State-of-the-Art Climate Measures 33 A primary objective of this handbook is to present methodologies for evaluating two specific types of climate events that may be particularly relevant for airports: • The increasing occurrence of high temperatures that may force airlines to impose weight restrictions on takeoffs or (in extreme cases) cancel flights entirely. • The impact of future sea level rise on the likelihood of flooding events causing disruption to airport operations or damage to infrastructure. The focus is on these specific forms of climate impacts not only because of their obvious relevance for airports but also because of their data availability in forms that can be reasonably incorporated into probabilistic scenarios via the Monte Carlo methodology. A detailed descrip- tion of how to access and use the latest available climate data derived from the CMIP5 projec- tions for these types of events is given in Appendix D. Source: ACRP Report 147 (Dewberry et al. 2015). Exhibit 3-3. ACROS climate vectors.