Abstract

Increasing temperatures and changes in precipitation associated with climate change are expected to have increasing impacts on the contiguous United States in the coming decades, including military training and outdoor activities in general. Projections of daily temperature and precipitation from multiple global climate model projections are used to calculate the days with high heat and drought indices, which also indicate heat-related illness and fire risks. The heat stress index [the wet-bulb black-globe temperature (WBGT)] and drought index (Keetch–Byram drought index) are calculated from climate model projections from 1950–99 and 2070–99 and compared with those calculated from observed weather data for stations across the contiguous United States. Significant increases are projected across the southern United States for the days in the high index category above 32.2°C and high drought category. The higher humidity of the southeastern United States contributes to high WBGT as well, while the air temperatures are greatest in the Southwest. The highest WBGT categories occur for the daytime maximum; however, daily minimum WBGTs in the restricting category also are projected for more than 50 days per year in the Southeast. The high drought index is projected to increase across the Great Plains and the central and southern United States, affecting wildfire risks for military and public lands, including large agricultural regions. These projected impacts can be characterized as widespread and severe for large portions of the United States, with expected impacts to military planning, public health and safety, and natural resource management.

1. Introduction

Increasing surface air temperatures associated with anthropogenic climate change are expected to have growing impacts on the operation and soldier training at U.S. Army installations and training ranges in the coming decades. Projections from global climate models (GCMs) include annual average surface air temperature increases between 3° and 6°C for the contiguous United States through year 2100 for multiple greenhouse-gas emission scenarios (IPCC 2013). Regional precipitation change projections vary from decreasing precipitation in the southwest United States to increasing in the southeast and northern United States, but these projections vary considerably among different GCMs (IPCC 2013). These trends have the potential to affect U.S. Army installation soldier training, daily operations, and environmental resource management. The “Climate Change Roadmap” of the Department of Defense (Office of the Assistant Secretary of Defense 2014) highlights the risks of extreme temperatures, drought, and other weather events on military training, soldier health, and the resilience of the natural environment to training activities. Daytime training hours on installations and training ranges are particularly sensitive to increasing maximum daily temperature and heat index, as there are significant risks to soldier health and safety from high temperatures, humidity, and solar exposure. The current U.S. Army medical guidance (Headquarters, Department of the Army, 2003) prescribes the ratios of soldier training work rates, rest, and recommended water intake based on the wet-bulb black-globe temperature (WBGT), which combines the air temperature, humidity (via the wet-bulb temperature), and solar exposure (black-globe temperature). Dunne et al. (2013) analyzed the potential loss in labor hours due to high WBGT in the historical climate data and in climate projections of the Earth System Model with MOM, version 4 component, (ESM2M) of the Geophysical Fluid Dynamics Laboratory (GFDL). They found reductions of 80% in labor hours during the warmest months for the high-emission scenario through 2100, consistent with WBGT above 32.2°C (90°F): that is, the “black” category-5 restrictions of 10-min work and 50-min rest per hour. These projected impacts are expected to be similar across other GCMs, as the projected temperature changes are largely consistent for future scenarios.

The objective of this paper is to expand the analysis of the projected impacts for the contiguous United States to include a wider range of GCMs from CMIP5 (IPCC 2013), including bias-corrected downscaled projections, and to include the potential impacts of severe drought conditions that might also lead to increasing wildfire risks. This study is part of a U.S. Army research program to study the potential impacts of future climate change on training, installations, and natural resources. These potential impacts would also affect public, agricultural, and municipal activities in addition to military operations. For this purpose, the heat and drought indices derived from downscaled GCM projections are compared with those derived from observed climate data from both municipal and military stations across the contiguous United States.

2. Data and methods

The indices of heat stress and fire risk for the contiguous United States are calculated from two types of data: observational station data and GCM simulations of past and future climate projections. The observed station data used are taken from the Global Summary of the Day archive from the NOAA National Climatic Data Center (NCDC) from the years 1950–99 (NCDC 2016). These data include daily mean, maximum, minimum temperatures, mean dewpoint temperature, wind speed, cloud cover, and precipitation. The station data used here were recorded at municipal airports, military airfields, and other U.S. locations. Figure 1 shows the names and locations of the stations used in this study, where the military installations are shown [e.g., Fort Benning, Georgia, Fort Stewart, Georgia, Hill Air Force Base (AFB), Utah, Dugway Proving Ground, Utah, and Joint Base Lewis–McChord, Washington]. Figure 1 is divided into seven regions used in this study: Southwest (SW), Northwest (NW), southern Great Plains (SGP), northern Great Plains (NGP), Southeast (SE), Great Lakes (GL), and Northeast (NE). A second source of hourly data for a subset of stations was also taken from the NOAA Climate Reference Network stations (Diamond et al. 2013) to compare the hourly temperatures and heat indices with the daily maxima and minima.

Fig. 1.

Locations within the United States for which observed climate data and downscaled daily climate projections were analyzed. The seven regions shown with different colors are the Southwest (SW), Northwest (NW), southern Great Plains (SGP), northern Great Plains (NGP), Southeast (SE), Great Lakes (GL), and Northeast (NE).

Fig. 1.

Locations within the United States for which observed climate data and downscaled daily climate projections were analyzed. The seven regions shown with different colors are the Southwest (SW), Northwest (NW), southern Great Plains (SGP), northern Great Plains (NGP), Southeast (SE), Great Lakes (GL), and Northeast (NE).

The GCM-based climate projections used in this study are the bias-corrected constructed analogs (BCCA) of daily maximum and minimum temperatures and daily precipitation of Brekke et al. (2013) derived from GCM simulations in phase 5 of the Coupled Model Intercomparison Project (cf. WCRP 2011) archive that also supports the IPCC’s Fifth Assessment Report (IPCC 2013). The method of producing the BCCA data is described in detail in Maurer et al. (2007) and Brekke et al. (2013) and can be summarized by the following three steps. 1) Each GCM’s output is regridded to a common 2° × 2° (latitude × longitude) grid over the continental United States and 2) bias correction is done by adjusting the GCM temperatures and precipitation so the mean, variance, and quartiles of the frequency of daily maximum and minimum temperatures and precipitation match the observed historical data for 1950–99 on the 2° grid. While the bias correction eliminates the mean differences between the GCMs and the historical data over 1950–99, the individual years simulated by the GCMs do not match individual observed years. 3) A spatial disaggregation of the 2° × 2° data down to a 1/8° × 1/8° grid is done, which can also introduce small differences from the observed means on the 2° scale. The 12 GCMs whose downscaled BCCA projections are used are listed in Table 1. The downscaled GCM simulation data from 1950–99 are taken from the historical climate scenario, and projections for years 2070–99 are taken from the CMIP5 representative concentration pathway (RCP) 8.5 scenario, the high-emission scenario with an effective radiative forcing increase of 8.5 W m−2. The regional variations in temperatures and precipitation changes from the historical BCCA to future RCP 8.5 projections in Table 2 show that the GCMs vary the most in precipitation changes for the Southeast, northern Great Plains, and Great Lakes. The regional temperature changes are all positive but generally vary among the 12 GCMs by ±1.5°C.

Table 1.

List of GCMs whose downscaled BCCA projections are used in this study. Expansions for the model names may be found at http://www.ametsoc.org/pubsacronymlist.

List of GCMs whose downscaled BCCA projections are used in this study. Expansions for the model names may be found at http://www.ametsoc.org/pubsacronymlist.
List of GCMs whose downscaled BCCA projections are used in this study. Expansions for the model names may be found at http://www.ametsoc.org/pubsacronymlist.
Table 2.

Projected mean, minimum, and maximum changes in temperature (°C) and precipitation (%) between the historical BCCA (1950–99) and RCP 8.5 (2070–99) across the 12 GCMs over the seven regions.

Projected mean, minimum, and maximum changes in temperature (°C) and precipitation (%) between the historical BCCA (1950–99) and RCP 8.5 (2070–99) across the 12 GCMs over the seven regions.
Projected mean, minimum, and maximum changes in temperature (°C) and precipitation (%) between the historical BCCA (1950–99) and RCP 8.5 (2070–99) across the 12 GCMs over the seven regions.

Two climate indices that reflect potential impacts on military, public, and municipal activities and resources (among many others) are 1) the number of days with high heat index and 2) the number of days with high drought index. The heat index used here (the wet-bulb black-globe temperature) is used by the U.S. Army to regulate work/rest restrictions based on heat risks to soldiers in training, as shown in Table 3. The categories 1–5 correspond to the WBGT increasing from 25.6°C (78°F; Cat 1) to >32.2°C (>90°F; Cat 5). The other index, the Keetch–Bryam drought index (KBDI), is also used by the U.S. Army to evaluate risks of fire ignition on training ranges to ensure adequate firefighting resources are available. These two indices are both computed from daily temperature, precipitation, and other observed station data and are also computed from the climate model projection of climate scenarios.

Table 3.

Heat category table using WBGT work/rest restrictions during training as based on Headquarters, Department of the Army (2003).

Heat category table using WBGT work/rest restrictions during training as based on Headquarters, Department of the Army (2003).
Heat category table using WBGT work/rest restrictions during training as based on Headquarters, Department of the Army (2003).

The number of heat-restricted training days is computed from daily WBGT, which combines the wet-bulb temperature Twb, the ambient air temperature Tair, and the temperature measured inside a black globe in the incident sunlight Tg:

 
formula

While the WBGT can be measured directly at installations, it is more often computed from the observed air temperature Tair, the relative humidity, and estimates of black-globe temperature Tg. For this study, we calculate the daily maximum and minimum daily WBGT using the maximum and minimum observed dry-bulb temperature and daily dewpoint temperature (which usually changes little through the day) to obtain the relative humidity (RH) coinciding with both maximum and minimum temperatures. The graphical method for determining the wet-bulb temperature Twb from the ambient dry-bulb air temperature and relative humidity can be performed using a meteorological skew T diagram. For repeated calculations, a table for Twb values was created for the range of temperature and humidity present for our locations.

The black-globe temperature Tg is estimated using the formula by Dimiceli et al. (2011), with an algorithm based on measurements inside a sunlit globe with surface albedo of 0.05 and emissivity of 0.95. Their derived formulation is

 
formula
 
formula

where S is the surface solar irradiance in watts per square meter, fdirect and fdiffuse are the fractional direct and diffuse radiation, σ is the Stefan–Boltzmann constant, cosz is the cosine of the solar zenith angle z, and ε is the atmospheric emissivity (assumed = 1). The other term is

 
formula

where h = 0.1, and u is the wind speed in meters per hour. For the parameters for direct and diffuse fractions of solar irradiance that are not recorded in the NOAA daily data, we used fdirect = 0.7, fdiffuse = 0.3, and

 
formula

where Smax is the maximum solar irradiance and Z is the solar zenith angle at the site’s latitude φ at the solar declination angle δ (23.6°N on the summer equinox) at local solar noon:

 
formula

The maximum solar irradiance Smax varies widely between locations and with atmospheric conditions. The monthly mean of the maximum daily irradiance from the closest stations in the U.S. Climate Reference Network (Diamond et al. 2013) was used for Smax for the locations. The maximum daily temperatures usually occur later in the day than the maximum irradiance. The effects of this difference and the choices of fdirect and fdiffuse are described in section 3.

The KBDI, designated Q, is calculated by incrementing the index dQ using the daily maximum air temperature Tmax and annual mean precipitation Pann. The formulation follows the revised and corrected English-units equation of Alexander (1990) and Crane (1982) of the original Keetch and Byram (1968) index:

 
formula

The value of Q is kept between the minimum at 0 and the maximum at 800, which corresponds to a 0.20-m (8 in.) deficit in precipitation.

The risk of igniting fires on training ranges through live-fire training potentially increases with greater KBDI (i.e., with greater maximum daily temperatures and little or no precipitation). The KBDI increases with duration of days with no precipitation, to its maximum value of 800. For any particular training range, there are other potential factors to consider in determining the risk of igniting fires through live-fire training, such as the presence or abundance of dry vegetation and persistent wetlands that may not be at risk for ignition. Therefore, the on-site determination of fire risk by firing-range managers and restrictions on live-fire training are determined locally and are not based on a single factor such as KBDI.

3. Results and discussion

The two climate-related indices, the number of days in each of the categories of maximum and minimum WBGT and daily KBDI, are presented in this section for the observed station data for 1950–99 (OBS), for the BCCA historical-scenario simulations for 1950–99 (HIS), and for the BCCA projections for RCP 8.5 for 2070–99 (RCP). The mean annual numbers of days with the daily maximum WBGT in each category for the three datasets (OBS, HIST, and RCP) in the seven U.S. regions are shown in Fig. 2. The OBS results use the maximum daily temperature Tmax, the relative humidity coincident with the maximum air temperature and observed daily dewpoint temperature, and wind speed.

Fig. 2.

Number of days annually with daily maximum WBGT in each category for each location, using the observed station data (O), historical BCCA (H) for 1950–99, and RCP 8.5 (R) for 2070–99, averaged over all 12 GCMs for the locations in the seven U.S. regions.

Fig. 2.

Number of days annually with daily maximum WBGT in each category for each location, using the observed station data (O), historical BCCA (H) for 1950–99, and RCP 8.5 (R) for 2070–99, averaged over all 12 GCMs for the locations in the seven U.S. regions.

The HIS results (1950–99) for the WBGT categories use the daily maximum temperatures from the 12 GCMs for each location shown in the OBS data and also use the same solar radiation, daily wind speeds, and relative humidity from the OBS maximum temperature data. The RCP results (RCP 8.5, 2070–99) use the maximum daily temperatures from the 12 GCMs, and the solar input and winds, with the daily RH from the OBS data reduced by 5%, consistent with the average projected summer decrease shown in the CMIP5 GCMs (IPCC 2013). The annual average days with WBGT in each category for the stations in each region are shown in Table 4 and also shown for the contiguous United States (CONUS)-wide gridded data in Fig. 3. The high-WBGT (category 5, >32.2°C) days from the HIS BCCA data (1950–99) are consistently lower than those of the OBS data, particularly in the Southwest and Southeast. The number of days with maximum temperatures above 32.2°C in the BCCA HIST data is about 15% lower than that in the OBS station data in these two regions as a result of the BCCA downscaling. In addition, the highest-temperature days in the HIS data are not coincident with the high-humidity days in the OBS data, as an artifact of our methodology. Since the 12 GCMs produce independent temperature series, our methodology does not reconstruct the covariance of temperature and humidity present in the observed data. Rather than manipulate the BCCA data or humidity to match the OBS results of WBGT, we use the HIST results as the baseline to compare the changes in future projections of the RCP 8.5 results.

Table 4.

Number of days annually with daily maximum WBGT in each category, using the observed station data and historical BCCA for 1950–99, and RCP 8.5 for 2070–99, averaged over all 12 GCMs for the station locations in the seven U.S. regions, and the intermodel mean, minimum, and maximum difference in WBGT days between HIST and RCP 8.5.

Number of days annually with daily maximum WBGT in each category, using the observed station data and historical BCCA for 1950–99, and RCP 8.5 for 2070–99, averaged over all 12 GCMs for the station locations in the seven U.S. regions, and the intermodel mean, minimum, and maximum difference in WBGT days between HIST and RCP 8.5.
Number of days annually with daily maximum WBGT in each category, using the observed station data and historical BCCA for 1950–99, and RCP 8.5 for 2070–99, averaged over all 12 GCMs for the station locations in the seven U.S. regions, and the intermodel mean, minimum, and maximum difference in WBGT days between HIST and RCP 8.5.
Fig. 3.

Annual average number of days with the daily maximum WBGT in category 5 (>32.2°C) for (top) HIS data for 1950–99 and (middle) RCP data for 2070–99, and (bottom) difference between RCP 2070–99 and HIS 1950–99, averaged over the 12 downscaled GCMs.

Fig. 3.

Annual average number of days with the daily maximum WBGT in category 5 (>32.2°C) for (top) HIS data for 1950–99 and (middle) RCP data for 2070–99, and (bottom) difference between RCP 2070–99 and HIS 1950–99, averaged over the 12 downscaled GCMs.

The increase in days with maximum WBGT between the HIST and RCP data for 2070–99 is greatest for the category-5 days in the Southwest (+27 days), southern Great Plains (+55 days), and U.S. Southeast (+75 days), and the least change in the U.S. Northwest (+18 days). These increases reflect a potentially significant expansion of the duration of heat-affected activities across the southern United States. The WBGT days are projected to be greatest (200+ days) in the southwest corner of the United States. However, the higher mean relative humidity in the Southeast and southern Great Plains would result in the category-5 days in 2070–99 increasing by 75 to 100 days over much of these regions.

The specified decrease in relative humidity by 5% in the RCP 8.5 projections accounts for approximately 12 fewer days with high WBGT relative to keeping humidity unchanged in the Southeast, southern Great Plains, and much of the Southwest. The high number of WBGT days would increase further in all regions if relative humidity increases in the future, at a rate of approximately +2.5 days per 1% change in RH. Since the future projections of relative humidity vary significantly across GCMs and also change with surface-level conditions, the humidity impacts on WBGT will vary on regional and synoptic scales that are not represented by the GCM projections.

As noted in section 2, the daily maximum air temperatures usually occur later in the day than the maximum solar irradiance, but our calculations use both as if they were simultaneous, with a presumed effect on the calculated maximum WBGT. Figure 4 shows the hourly variables at four locations at which the maximum daily air temperature occurs between 1400 and 1700 local time (LT), while the maximum solar irradiance and associated WBGT occur at 1200 local solar noon. The wet-bulb temperature remains relatively steady through the day, consistent with the observed hourly dewpoint temperature through the day, while the relative humidity reaches its daytime minimum. In these locations, the difference in air temperature between local noon and its daily maximum is less than 25% of the daily temperature range of 10°–15°C. Since WBGT in Eq. (1) is calculated with 0.1Tair, the overestimation of maximum WBGT from this difference should be less than 0.5°C.

Fig. 4.

Hourly averages through June–August of the computed WBGT (°C; solid line), air temperature (°C; dotted–dashed), wet-bulb temperature (°C; dashed), and downwelling solar radiation (×10 W m−2; dotted) for stations in (a) Tucson, Arizona, (b) Boulder, Colorado, (c) Eglin AFB, Florida, and (d) Manhattan, Kansas, from the U.S. Climate Reference Network (Diamond et al. 2013).

Fig. 4.

Hourly averages through June–August of the computed WBGT (°C; solid line), air temperature (°C; dotted–dashed), wet-bulb temperature (°C; dashed), and downwelling solar radiation (×10 W m−2; dotted) for stations in (a) Tucson, Arizona, (b) Boulder, Colorado, (c) Eglin AFB, Florida, and (d) Manhattan, Kansas, from the U.S. Climate Reference Network (Diamond et al. 2013).

The effects of the direct and diffuse fractions of solar radiation on WBGT were also compared by calculating WBGT with fdirect = 0.7, 0.3, and 0.1 with the four Climate Reference Network stations’ hourly data shown in Fig. 4. The peak values of maximum WBGT for fdirect = 0.3 and 0.1 (not shown) were slightly less (approximately 1°C) than those for fdirect = 0.7, but the WBGT values were consistently higher through the day from 0900 to 1700 LT locally, as the diffuse radiation input is less dependent on the zenith angle. This simple comparison assumes, however, that the solar irradiance used in Eq. (3) is the same for both 70% direct and 30% direct radiation, which may not be true in general between clear and cloudy conditions. Since we are keeping Smax, fdirect, and fdiffuse constant in our analyses of WBGT projections, it is not necessary to investigate each of these variables independently.

The days with daily minimum WBGT in the heat-restriction training category 1 (25.6°–27.6°C) are shown in Fig. 5 for the CONUS-wide maps, which shows the expansion of the region with these WBGTs for 50–90 days yr−1 across the south-central and southeastern United States. For these regions, some of the training and physical activities are commonly shifted from daytime to night and early morning to avoid heat-related illness. However, the increase in category-1 minima by 30–80 days in 2070–99 suggests an increasing disruption of schedules for both military and public activities, with an associated increase in impacts, such as power demand for cooling at night as well as day.

Fig. 5.

Number of days annually with the daily minimum WBGT in category 1 (25.6°–27.6°C) for (top) HIS data for 1950–99 and (middle) RCP data for 2070–99, and (bottom) difference between RCP 2070–99 and HIS 1950–99, averaged over the 12 downscaled GCMs.

Fig. 5.

Number of days annually with the daily minimum WBGT in category 1 (25.6°–27.6°C) for (top) HIS data for 1950–99 and (middle) RCP data for 2070–99, and (bottom) difference between RCP 2070–99 and HIS 1950–99, averaged over the 12 downscaled GCMs.

The annual number of days with the KBDI in each category is shown in Fig. 6 for stations in the most affected regions (SW, SGP, SE, NGP, and GL) for the daily recorded station temperature and precipitation (OBS), the downscaled GCM data for the historical period (HIS), and the RCP 8.5 projections for 2070–99. There is greater variation in KBDI between stations across the Southwest than other regions in both observed and downscaled projections, as temperature and precipitation depend strongly on topography, and apparent in the maps of KBDI in Fig. 7. The regional mean KBDI days in each category are also shown in Table 6. The high-KBDI days for the HIST BCCA projections are higher than the OBS data for the southern regions (SW, SGP, and SE) and lower for the northern ones (GL and NGP). The BCCA downscaled data have a greater fraction of daily precipitation in the 0–5-mm range than the observed station data, as GCMs also have a tendency to produce more days with steady precipitation than observed, and this is not completely reversed by the quartile-based bias correction in the BCCA. There are also substantial variations in the KBDI days among the 12 GCMs, with the dominant pattern of decreasing precipitation in the summer season in the Southwest, Great Plains, Great Lakes, and Southeast, and that is reflected in the increasing KBDI. For these regions, decreasing in mean precipitation by 10% results in an approximate increase of 20–25 high-KBDI days. Since regions such as the Great Lakes have precipitation change projections of ±11, the range in the high-KBDI days across models is about 50 days.

Fig. 6.

As in Fig. 2, but for days with KBDI in each category for the stations with highest KBDI.

Fig. 6.

As in Fig. 2, but for days with KBDI in each category for the stations with highest KBDI.

Fig. 7.

Number of days annually with KBDI in category 4 (750–800) for (top) HIS data for 1950–99 and (middle) RCP data for 2070–99, and (bottom) difference between RCP 2070–99 and HIS 1950–99, averaged over the 12 downscaled GCMs.

Fig. 7.

Number of days annually with KBDI in category 4 (750–800) for (top) HIS data for 1950–99 and (middle) RCP data for 2070–99, and (bottom) difference between RCP 2070–99 and HIS 1950–99, averaged over the 12 downscaled GCMs.

The CONUS-wide maps of the KBDI days for the highest category 4 are shown in Fig. 7 for the HIS (1950–99) and RCP (2070–99). In the RCP projections, the duration of 50–100 high-KBDI days extends across the entire northern United States, and 100–200 days across the central to southern United States. The largest increase in KBDI is in the Great Plains corridor between Texas and Montana, where wildfires in dry prairie grass are also common. The impact of the high-KBDI conditions on Army training is less prescriptive than the high-WBGT days. The periods with fire risk designated as category 4 (black) by range managers should be restricted to live-fire training; however, the KBDI values for each category in Table 5 are not prescribed by specific regulations, and the fire-risk determination by a range manager depends on local environmental conditions. For installations in dry climates (e.g., the desert Southwest) there may be little combustible vegetation on training ranges and little or no changes in their future fire risk. The increase in drought conditions across the Great Plains and the southern and central United States may lead to greater fire risks in these regions where vegetation is abundant and make ignition by natural causes, military training, and public activities more likely. The long-term awareness and planning for sufficient fire-prevention and firefighting resources for these future conditions should be considered for regions where drought conditions are projected to increase.

Table 5.

Fire danger categories with live-fire training restrictions and firefighting requirements, with the KBDI range used in this study for each category.

Fire danger categories with live-fire training restrictions and firefighting requirements, with the KBDI range used in this study for each category.
Fire danger categories with live-fire training restrictions and firefighting requirements, with the KBDI range used in this study for each category.
Table 6.

Number of days annually with daily KBDI in each category, using the observed station data and historical BCCA for 1950–99, and RCP 8.5 for 2070–99, averaged over all 12 GCMs for the station locations in the five U.S. regions shown and the intermodel mean, minimum, and maximum difference in KBDI days between HIST and RCP 8.5.

Number of days annually with daily KBDI in each category, using the observed station data and historical BCCA for 1950–99, and RCP 8.5 for 2070–99, averaged over all 12 GCMs for the station locations in the five U.S. regions shown and the intermodel mean, minimum, and maximum difference in KBDI days between HIST and RCP 8.5.
Number of days annually with daily KBDI in each category, using the observed station data and historical BCCA for 1950–99, and RCP 8.5 for 2070–99, averaged over all 12 GCMs for the station locations in the five U.S. regions shown and the intermodel mean, minimum, and maximum difference in KBDI days between HIST and RCP 8.5.

4. Conclusions

Projected impacts of climate change on the contiguous United States have the potential to affect military, commercial, and public activities with high heat index and high drought index conditions. For the U.S. Army, this includes physical training, installations and infrastructure, and natural resources conservation. The public impacts apply also to strenuous physical activities, risks of heat stroke to people working outdoors, the energy demand for cooling, drought impacts on agriculture, and wildfire risks. The projections of WBGT show increase for all regions of the contiguous United States, with the greatest increase across the southern United States for both maximum and minimum daily WBGT. Changes in relative humidity also have a substantial impact on WBGT, which results in approximately 2.5 additional days with high WBGT for each 1% increase in RH. For the projections used, we included a summer decrease in RH of 5%, accounting for approximately 12 fewer days of high WBGT. Most northern U.S. locations would not be significantly affected by frequent high-WBGT (>32.2°C) days through 2099, though some episodes of high WBGT would be expected in the normal variability of climate. The southeastern and southern central United States would also experience over 50 days of minimum WBGT in the category-1 range, with expected impacts on activities, cooling power demand, and heat stress.

Although the drought index (KBDI) is consistently high in the southwest United States, the most significant change in drought index is in the Great Plains and central United States, in the region of greatest agricultural production (corn, soybean, and grain) in the United States. The regions with widespread prairie grasses also have the potential for increasing fire risks, since more vegetation could ignite during periods of drought. The impact on Army training ranges may be limited only to periods of the highest fire risk, when live-fire training would be prohibited.

This study illustrates that high-WBGT days could become widespread and severe for most of the southern United States by the 2070–99 period. The impacts on military training (specifically daytime training that cannot be completed at other times) necessary to maintain troop readiness would be significant for the summer season and may require changes in training rotations or seasonal relocation to less severe conditions. However, eliminating all training in high temperatures would not adequately acclimatize troops for operations in extreme environments elsewhere, so training should include the appropriate acclimatization for soldier safety as well as for optimal readiness.

The high drought index periods are projected to also become severe for much of the southern United States and the central U.S. agricultural region. Fire risks at training ranges during high-drought periods would continue to be managed by the range managers according to appropriate on-site safety factors, including whether dry vegetation is present and local weather conditions might cause wildfires to spread to adjacent areas. However, the climate projections suggest that the duration of seasonal drought conditions could extend by 50–70 days in many locations.

The likelihood of one future climate scenario versus another is not the focus of this study, which used the RCP 8.5 high-emission scenario considered in the IPCC Fifth Assessment Report (IPCC 2013). The results of lower-emission scenarios (e.g., RCP 2.6) have also been examined for these indices. While not presented here, the lower temperature projections in the alternate scenarios result in corresponding smaller, but still increasing, trends in WBGT and KBDI days. The results from the RCP 8.5 temperature projections should be considered as likely to occur if global greenhouse-gas emissions increase over time in a manner consistent with the RCP 8.5 scenario.

Acknowledgments

The authors acknowledge the support of the U.S. Army Research Program and the Strategic Environmental Research and Development Program (SERDP). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. The U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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