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Climate Variability and Change in Mobile, Alabama

2. Setting the Stage for Climate Research

Task 2 included an assessment of the climate variables that have the greatest potential to impact transportation assets and operations: temperature, precipitation, streamflow, sea level rise, and storm surge. Wind was also calculated as part of the storm surge modeling, although it was not a specific focus of this study.1

2.1. Selection of Climate Variables

To identify the climate variables that are most relevant to transportation, transportation planners, transportation engineers, and climate scientists collaboratively developed a list of all relevant variables that impact the region's transportation. This list was refined based on the following considerations:

The variables range in temporal scale from monthly, seasonal, and annual averages to specific events and hazards. For more information about how environmental variables were selected, see Appendix B.2. Ultimately, it was decided that this study would focus on projections of temperature (changes in average conditions and extreme events), precipitation (changes in average conditions and extreme events), streamflow, sea level rise, and storm events (including storm surge).

An important part of this work was determining the appropriate format for communicating results for each climate variable. For example, this report goes beyond a generic exploration of projected changes in "temperature", looking instead at specific changes to both long-term gradual changes (e.g., change in average annual temperature or average monthly temperature) as well as short-term extreme events (e.g., number of days above 95°F (35°C)). The decisions on the format used to express climate information were vital in making this work relevant to the transportation community. Great care was taken to identify the climate effects that have the potential to impact transportation. The appropriate formats used for a transportation perspective may be quite different than the formats appropriate to other economic sectors, human health, or ecosystem services.

2.2. Methodology Overview

For each climate variable, this report first characterized the current (or recent historical) situation in Mobile, and then evaluated how that variable could change based on published literature, prevailing assumptions of future emissions of greenhouse gases, and a variety of modeled data. Table 5 provides an overview of the methods used in this report.

Table 5: Overview of Analytical Methods Used
Climate Variable Methods Used to Analyze Current/Historical Situation Methods Used to Develop Future Projections Methods Used to Evaluate Exposure under Potential Future Scenarios*
Temperature Historical data from 5 NOAA GHCN weather stations in the Mobile Region. The start of the data record varied by station, ranging from 1915 to 1956. Data was collected through September 2010 for all stations. Downscaled daily global climate model data for B1, A2, and A1FI emission scenarios =
Timeframes: 1980-2009 (hist.), 2010-2039 (near), 2040-2069 (mid), and 2070- 2099 (long)
To be addressed in Task 3 (vulnerability assessment)
Precipitation Historical data from 5 GHCN weather stations in the Mobile region. The start of the data record varied by station, ranging from 1912 to 1956. Data was collected through September 2010 for all stations. Downscaled daily global climate model data for B1, A2, and A1FI scenarios, 1980-2099.2
Timeframes: 1980-2009 (hist.), 2010-2039 (near), 2040-2069 (mid), and 2070- 2099 (long)
To be addressed in Task 3 (vulnerability assessment)
Streamflow Historical data from five stream gages in the Mobile region through the USGS Surface Water Database. The start of the discharge data record varied by station, ranging from 1951 to 1995. Data was through September 2010 for all stations. Modeled using USGS modified Thornwaite monthly water balance model, fed by projected temperature and precipitation.3
Timeframes: 2010-2039 (near), 2040-2069 (mid), and 2070- 2099 (long)
To be addressed in Task 3 (vulnerability assessment)
Sea Level Rise Historical data collected from two NOAA tidal gages. Dauphin Island data were available from 1966-2009. Pensacola data were from available from 1924-2009. Review of recent scientific literature GIS mapping of inundation areas, assuming 30 cm (by 2050),and 75 cm and 200 cm (by 2100) of global sea level rise, and accounting for local subsidence and uplift
Storms and Storm Surge Case study analysis; storms selected through discussion with local experts and literature review Review of recent scientific literature Use of ADCIRC and STWAVE models to simulate two historical hurricanes (Georges and Katrina) assuming different levels of intensity and sea level rise

* A review of the scientific literature helped in the selection of plausible scenarios of sea level rise, and then mapping was used to show how Mobile would be inundated under those scenarios. Similarly, the scientific literature and discussions among the research team and with local stakeholders aided in the selection of storm scenarios, and mapping was used to show the inundation of Mobile under those scenarios.

2.3. Dealing with Uncertainty

The future climate information developed for this report represents plausible projections, but not predictions. Modeling the climate system poses a number of challenges. There are three main sources of uncertainty in climate model simulations4:

The relative contribution of each uncertainty component to the climate model simulation's overall uncertainty varies with time horizon, spatial scale, and temporal scale. Most notably, scenario uncertainty is relatively minimal in the near-term but is currently the greatest contribution to total uncertainty by end-of-century.5 The model uncertainty represents a large portion of the total uncertainty throughout the time period, and is a dominant contributor by near-term and mid-century.6 Meanwhile, natural variability is a significant contributor to total uncertainty in the near-term, but becomes much less significant by end-of-century.7 The relative contribution of each uncertainty component also varies with spatial and temporal scale.8 Natural variability becomes a greater source of uncertainty at finer scales.9 This is one reason why incorporating downscaled projections expands the potential uncertainty in climate projections.10 As our understanding of global and local processes continues to improve, the level of uncertainty, particularly at finer scales, may be reduced.11

The uncertainty around each of these components should be considered when conducting vulnerability assessments, making decisions, and implementing policies. In this study, a number of uncertainties are qualitatively addressed:

In addition, this study incorporates an additional layer of uncertainty by using statistically downscaled temperature and precipitation projections. Downscaling of climate model projections allows scientists to incorporate local conditions, such as the effect of local topography or prevailing sea breezes, by tailoring larger-scale climate model results to a finer-scale analysis.12 However, using downscaled data introduces an additional degree of model uncertainty and natural variability into the projections that is not quantified here.13 Statistical downscaling further assumes that the relationship between today's observed data and modeled data remains stationary with time.14

For more information on uncertainty in climate model projections,15 see Appendix C.2.3.


1 There are other climate and weather effects that can be affected by climate change, and that may even have the potential to affect transportation, but were not included in this study because their anticipated effect on transportation is relatively low, or because of resource or technical limitations.

2 Downscaled daily global climate model data was provided by Dr. Katharine Hayhoe of Texas Tech.

3 Downscaled daily global climate model data was provided by Dr. Katharine Hayhoe of Texas Tech.

4 Hawkins and Sutton, 2009; Mote et al., 2010; Ray et al., 2008

5 IPCC, 2007; Hawkins and Sutton, 2009

6 Hawkins and Sutton, 2009

7 Hawkins and Sutton, 2009

8 Hawkins and Sutton, 2009; Mote et al., 2010; IPCC, 2010

9 Hawkins and Sutton, 2009; Mote et al., 2010

10 Hawkins and Sutton, 2009; IPCC 2010

11 Hawkins and Sutton, 2009; Ray et al., 2008

12 Ray et al., 2008

13 Hawkins and Sutton, 2009

14 Ray et al., 2008

15 IPCC 2010

Updated: 10/31/2014
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