This appendix characterizes changes in temperature and precipitation in Mobile over the past century within the larger context of climate changes globally, nationally, and regionally.
Drawing from an array of scientific evidence, the National Research Council supports the conclusion that "climate change is occurring, is caused largely by human activities, and poses significant risks for - and in many cases is already affecting - a broad range of human and natural systems."1 The global average surface temperature is rising, with decades from 1970 to 2009 being progressively warmer than prior decades, with the warmest temperatures observed during 2000 to 2009.2 Observations have also shown changes in other related environmental variables such as increases in the frequency of intense rainfall, decreases in Northern Hemisphere snow cover and Arctic sea ice, warmer and more frequent hot days and nights, reduction of cold snaps, rising sea levels, and widespread ocean acidification.3
Over the past 50 years, the U.S. annual average temperature has risen over 2°F (1°C) and average precipitation has increased about 5%, though there is large variability across the continental United States.4 There is further evidence of precipitation change over the 20th century, with events such as the heaviest 1% of daily precipitation totals in the continental U.S. increasing by 20%.
Although nationally, temperatures show an increase over the 21st century, the southeastern United States has observed a cooling, termed the 'warming hole.'5 A recent study explored the reason for this by comparing changes in monthly, bimonthly, and seasonal daily minimum temperature, daily maximum temperature, and average daily precipitation using GHCN daily station data from 1950 to 2006.6 The study suggests the southeast has experienced less warming than the rest of the United States due to increased precipitation and cloudiness. During the day, low-level clouds block sunlight from reaching the surface, thereby keeping maximum surface temperatures cooler. Reducing maximum daily surface temperatures and/or increasing precipitation increases surface wetness. When the wetness at the surface evaporates, it does so at the expense of warming the air, furthering the cooling effect. The reason for the increased precipitation and/or cloudiness is not understood; hence, it is unclear how this relationship will affect future climate. Another recent study investigated how well climate models simulate this 'warming hole' in the central United States.7 Kunkel et al. found that climate models vary in the degree of accuracy in simulating this region, suggesting hydrologic responses on the regional scale are difficult to simulate. Therefore, they recommend using a multi-model ensemble mean for future projections.
To consider how climate and weather impact transportation engineering and planning decisions, a list of important environmental variables to investigate had to be identified. For example, transportation assets and operations are generally affected by short-term, extreme events (such as a 3-day period of heavy rain or a 2-hour thunderstorm) more so than overall climate characteristics (such as average annual precipitation). Therefore, it is important to understand how changing climate will affect these short-term events. This appendix summarizes the process used to select these variables, as well as the variables themselves.
First, a "wish list" of the environmental variables that currently affect transportation assets was developed. Next, the "wish list" was refined based on which of these projected variables could be developed for this study. This required a literature survey of best practices for using climate data to inform climate impact assessments. The findings of this survey were used to shape how the climate projections would be developed.
At the onset of this study, a list of all relevant environmental variables that impact transportation in Mobile, Alabama was developed collaboratively by engineers, planners, and climate scientists. This list was further refined based on the following considerations:
Table 26 summarizes the "wish list" of environmental variables considered in this study. The environmental variables range in temporal scale, from monthly, seasonal, and annual averages to specific weather events/hazards. For each environmental variable, Table 26 describes additional changes in the environment that are linked to the variable as well as the part of the transportation system that may be affected. This wish list was used as a tool to assess available sets of climate data for inclusion in this study.
Table 26 : Environmental Variables Identified as Useful in Transportation Assessments8
|Variable||Changes in Environment||Transportation Sectors Affected and Uses in Impact Assessment|
|Mean Annual and Seasonal Temperature||Vegetation growth, Soil moisture||
|Mean Monthly Temperature||Vegetation growth, Soil moisture, Evapotranspiration||
|Maximum Surface Air Temperature (probability of occurrences and change in the number of events above a specific threshold temperature(s))||Soil moisture, Evapotranspiration||
|Minimum Surface Temperature (probability of occurrences and change in the number of events below a specific threshold temperature(s))||Freeze-thaw conditions.||
|Growing Season Duration9||Vegetation growth, Runoff||
|Annual and Seasonal Total Precipitation||Vegetation growth, Soil moisture||
|Total Monthly Precipitation||Flooding, Drought, Soil moisture||
|Daily Intensity Index10||Flooding, Drought, Landslides, Mudslides, Soil moisture||
|Precipitation Extremes (probability of occurrences and change in the number of events above a specific threshold precipitation total(s))||Flooding, Landslides, Drought, Wildfire, Erosion, Wind, Lightening||See Daily Intensity Index|
|Peak Streamflow and Monthly Runoff||Flooding, Sedimentation, Erosion||
|Monthly Evapotranspiration||Vegetation growth, Soil moisture||See Peak Streamflow and Monthly Runoff|
|Total depth of Rainfall||Flooding||See Peak Streamflow and Monthly Runoff|
|Extreme Wind Velocities (probability of occurrences and change in the number of events above a specific threshold wind velocity)||Wind||
|Tropical Cyclone Projections11|
|Projections of Tropical Cyclone Frequency and Intensity||Flooding, Wind damage, Erosion||
|Wind and Precipitation of Observed storms||Storm surge||See Projections of Tropical Cyclone Frequency and Intensity|
|Storm Surge (as a function of relative sea level rise scenarios and cyclone projections, including wind velocity, fetch, wind field size, barometric pressure, etc.)||Flooding||See Projections of Tropical Cyclone Frequency and Intensity|
|Sea Level Rise|
|Relative Sea Level Rise (polygon files of spatial projections of relative sea level rise)||Flooding, Salinity of freshwater rivers and estuaries, Enhancing storm surge impacts||
Once the "wish list" was established, items on the list were identified that could be reasonably developed using available data. To refine the "wish list," publically available climate data sets were identified that would provide projections of temperature, precipitation, and wind variables. At a minimum, the data sets were required to cover the continental United States to allow for national replicability.
The review of publically available climate data sets identified which environmental variables were available and the associated modeling characteristics (e.g., emission scenario(s) and climate model(s)). Downscaled data are preferred for an impact assessment in Mobile, Alabama, because the climate of Mobile is impacted by coastal breezes that are not captured by the large climate model grid cell resolution. In addition, the IPCC AR4 illustrates that most climate models tend to underestimate observed annual temperature and precipitation in this area of the Gulf Coast, suggesting that downscaled data may be more reflective of local climate. These data sets generally downscale the projections produced by the IPCC AR4 climate models and the results can be processed and tailored for assessment purposes.
Each data set provides unique benefits for use in an impact assessment. For example, the North American Regional Climate Change Assessment Program's (NARCCAP) international program provides dynamically downscaled data of temperature, precipitation, and wind projections along with a number of other variables such as specific humidity. This data set uniquely provides daily maximum wind at the 10-meter height, which is important for many transportation assessments. However, this data set was not selected, because it only provides projections at mid-century for the moderately-high (A2) emission scenario. If suitable for other impact assessments, this data set should be used carefully as it has not been bias-corrected.
After assessing each of the available or soon-to-be available data sets, it was determined that the statistically downscaled USGS climate data was best suited for this study. This data set will provide downscaled mean daily maximum and minimum temperature and total daily precipitation across 10 climate models for lower (B1) and moderately-high (A2) emission scenarios from 1960 to 2100 at 1/8 degree spatial resolution. Though the climate data was not publically available at the start of this work, it was understood it would become available shortly. As this data was not publically available in time for use in this study, the USGS provided downscaled daily temperature and precipitation simulations specific to five observation stations in Mobile and Baldwin counties. The simulations for the lower (B1) and moderately-high (A2) emission scenarios were downscaled for 10 climate models, and simulations for the high (A1FI) emission scenario was downscaled for 4 climate models. Once the climate data set was chosen, the wish list was refined by removing the wind variable.
Note that this study does not rely on IPCC AR4 climate model projections for sea level rise because those models conservatively simulate the physical processes of ice melt, thereby underestimating potential sea level rise. Instead, sea level rise projections were estimated using a literature review of studies published after the IPCC AR4 and scenario-based modeling. As climate models cannot adequately resolve tropical storms and hurricanes, storm event projections were also characterized by a literature review and scenario-based modeling of storm surge (see Section 2.8.2 for the methodology).
Table 27: Modeling Characteristics of Publicly Available National Downscaled Data Based on the Selection Criteria
|Data set||Spatial Resolution||Temporal Resolution||Climate Models||Emission Scenarios||Time Horizon||Downscaling||Variables|
|North American Regional Climate Change Assessment Program (NARCCAP)*||50 kilometers (30 miles)||From every three hours to daily||4 atmosphere ocean general circulation models (AOGCMs)*||IPCC A2||2041- 2070 relative to 1971- 2000||Dynamic : 6 regional climate models (RCM)*****||Over 80 Variables|
|NCAR GIS Climate Change Scenarios**
||Approximately 4.5 kilometers (2.7 miles)||Monthly||NCAR's Community Climate Model||IPCC A1B, A2, B1||20 year periods from 2000 to 2099||Statistical||Mean Temperature, Total Precipitation|
|Bias Corrected and Downscaled WCRP CMIP3 climate projections (supports the Climate Wizard website)***||Approximately 12 kilometers (7 miles)||Monthly||16 CMIP3 models||IPCC A1B, A2, B1||1950 to 2099||Statistical||Surface Air Mean Temperature and Precipitation Rate|
|USGS climate data****||1/8 degree||Daily||13 GCMs||IPCC A1b, A1Fi, A2, B1||1960 to 2099||Statistical||Minimum Temperature, Maximum Temperature, Total Precipitation|
***This website (http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html ) also provides daily downscaled data for the Western states and monthly WCRP CMIP3 bias-corrected data (i.e., monthly projections provided by a number of climate models for the three emission scenarios that has not been downscaled but have been uniformly gridded and bias-corrected).
****USGS Downscaled Climate Projections by Katharine Hayhoe (Provisional). Available at http://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/thredds/dcp/conus
*****Regional models include: OURANOS/UQAM's Canadian Regional Climate Model (CRCM), UC San Diego/Scripps' Experimental Climate Prediction Center Regional Spectral Model (ECPC), Hadley Centre's Hadley Regional Model 3/Providing Regional Climates for Impact Studies (HRM3), Iowa State University's PSU/National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5), UC Santa Cruz's Regional Climate Model version 3 (RCM3), and Pacific Northwest National Laboratories' Weather Research & Forecasting model (WFRP). The drivers of the regional models include: NCAR's Community Climate System Model (CCSM), Third Generation Coupled Global Climate Model (CGCM3), Hadley Centre Coupled Model, version 3 (HadCM3), and NCEP/DOE AMIP-II Reanalysis.
1 NRC, 2010a
2 Arndt et al. 2010
3 NRC, 2010a
4 USGCRP, 2009
5 Portmann et al., 2009
7 Kunkel et al. 2006
8 Source: DOT FHWA 2010 and discussion with team of experts in various disciplines (e.g., engineers, planners, hydrologists, climatologists) across the ICF team and the DOT FHWA representatives.
9 Average number of days per time period that fall within the prescribed "growing season" for the particular location
10 As defined in the Gulf Coast Phase 1. Total precipitation over a given time period divided by the number of days with precipitation.
11 The ideal methodology would account for changes in tropical cyclonic development factors such as sea surface temperature, vertical moisture, temperature, and wind conditions.