We developed plausible climate futures from climate projections produced for the World Climate Research Programme's Coupled Model Intercomparison Project phase 5 (CMIP5; Taylor et al. 2012), which was used for the IPCC Fifth Assessment Report (IPCC 2013). For park-level planning, broad spatial scale results from General Circulation Models (GCMs) need to be downscaled, or converted from a large grid (say, 50 x 50 km [~ 30 x 30 miles]) to a finer resolution that better represents conditions found at a specific location. We used data downscaled by the MACAv2-METDATA (Multivariate Adaptive Constructed Analogs) method (Abatzoglou and Brown 2012), a statistical downscaling5 method with a unique multivariate weighting scheme that ensures climate metrics are physically compatible (Kim et al. 2022). This results in a more physically realistic product at regional scales. This method has been shown to be preferable to direct daily interpolated bias correction in regions of complex terrain due to its use of a historical library of observations and its multivariate approach (Abatzoglou and Brown 2012). The product is available at a daily timestep and downscaled to 1/24 degree (~4 km). We downloaded surface maximum/minimum temperature, surface maximum/minimum relative humidity, and precipitation data for a grid cell that encompassed the park’s centroid (Figure 2) for moderate (RCP 4.5) and high (RCP 8.5) greenhouse gas emissions pathways. Experiences from previous engagements with parks (e.g., Schuurman et al. 2019, Runyon et al. 2021, Benjamin et al. 2021) have shown that a single site can be sufficient to evaluate changing trends across a whole park, but additional analysis may be warranted for large parks or those with complex topography. Only when we need to identify and/or model specific resource responses, each with unique climate sensitivities, is unusually fine-scale climate information warranted (see Lawrence et al. 2021 for considerations of spatial extent and resolution of climate futures). The MACA archive contains output from 20 GCMs for the contiguous United States for both RCPs. We considered all 40 (RCP x GCM) projections plausible representations of the future (Lawrence et al. 2021) that, collectively, represent a broad range of future climates. This use of multiple RCPs and GCMs is in accordance with U.S. Geological Survey (USGS) best practices for using climate models to inform decision-making (Terando et al. 2020) and Department of Interior policy (USDOI 2023).

We used 1979-2012 to characterize the historical period and the average centered around 2035-2065 (2050) to define a mid-century future planning period. We calculated average annual temperature and precipitation change of each of the 40 projections relative to the historical period, as illustrated in the figure below.

Projections of change in average annual precipitation and temperature (in 2050; 2035-2065), relative to a historical baseline (1979-2012), vary by GCM and RCP, as illustrated by this example for White Sands National Park. For each axis (temperature, precipitation), the mean of all projections is denoted by a dashed line, and the sides of the central box denote the 25th and 75th percentiles, giving a sense of ‘central tendency’. The “warm wet” and “hot dry” climate futures were selected here because they represent a pair of wetter and drier scenarios, with each having distinct implications for resources that can be explored. For each climate future, the average of all projections in those quadrants is represented by an asterisk, and the most extreme projection in the quadrant (i.e., that with the greatest change in precipitation or temperature relative to the average of all projections) is indicated by a circle.

We selected a small set of projections that characterized the broadest range of uncertainty (circled projections above). Lawrence et al. (2021) discussed tradeoffs between the two approaches for defining climate futures and where each is most appropriate. The use of single, end-member GCMs to calculate these climate metrics facilitated a more complete characterization of the potential climate risks to parks. We typically use two to four divergent climate futures that bracket the range of climate variation relevant to the resource-management decision of interest (Lawrence et al. 2021). For most resources, we focus on climate futures that represent a “best/worst case” or “most/least change” contrast—typically, climate futures that fall on the diagonally opposite quadrants shown above. “Warm wet” and “hot dry” (shown above) are one set of contrasting climate futures, where warm wet could lead to more precipitation and flood potential, and a hot dry climate future could indicate more drought or conditions conductive to fires. Depending on the resource of interest, these scenarios may have differing impacts. In other situations, the “warm dry” and “hot wet” (the other two corners of the graph above) are the more consequential climate futures (e.g., in the northeastern U.S.).

Metrics of temperature and precipitation alone fail to account for interactive aspects of climate, soils, and topography that affect water availability for plants and ecosystem processes. Water balance modeling accounts for the interaction of these factors to estimate ecological water availability through time. We used a simple water balance model (Thoma et al., 2020, Tercek et al., 2021) with site-specific parameters (location, elevation, slope, aspect, soils) and meteorological variables from climate futures to evaluate water-related implications of climate changes. The water balance model partitions precipitation into rain or snow. An adjustment factor, based on relative humidity, was used to account for observed differences in snow dynamics in arid and moist climates (Jennings et al., 2018). Rain and snow melt contribute to soil moisture (water stored in the top meter of soil). Precipitation that exceeds soil storage capacity becomes runoff. Potential evapotranspiration (PET), calculated via the Oudin method (Oudin et al., 2005), is the amount of water that could be evaporated and transpired from a short grass with available energy and unlimited water. This relatively simple method relies on data available for all U.S. parks, and it has been evaluated and used for many park studies (Thoma et al. 2020, Tercek et al. 2023). Actual evapotranspiration (AET), the loss of water from soil via evaporation and transpiration, is limited by soil moisture. Climatic water deficit is the amount of additional water vegetation would use if available, calculated as the difference between PET and AET (Stephenson 1998, Thoma et al. 2020)..

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We typically use two to four divergent climate futures that bracket the range of climate uncertainty relevant to resource-management decisions of interest (Lawrence et al. 2021). For most resources, the objective was to use those that represent contrasting projections. In most instances, the contrasting climate futures were “warm wet” and “hot dry”, where warm wet would generally mean more water availability and a hot dry climate future may mean more drought or fires. (See above, "How do we narrow down the range of alternative futures?") Depending on the resource, either could be a best- or worst-case scenario. In limited instances where increasing temperature and precipitation could lead to degradation of cultural resources, “warm dry” and “hot wet” climate futures would be presented.

While topography strongly affects local climate (Daly et al. 2008) and many parks may need explicit consideration of topographically rich terrain for specific planning processes (Lawrence et al. 2021), for these analyses, we used climate metric values from a single grid cell, located at the centroid of the park (Figure 2). Experiences from previous engagements with parks (e.g., Schuurman et al. 2019, Runyon et al. 2021, Benjamin et al. 2021) have shown that a single site can be sufficient to evaluate changing trends across a whole park and broadly understanding how climate change may impact park resources. However, at the level of identifying and modeling specific resource responses or impact thresholds, each with unique climate sensitivities, more spatially precise climate information may be warranted to address the climate concerns, balancing tradeoffs between information and complexity (see Lawrence et al. 2021 for considerations of spatial extent and resolution of climate futures).

The geographic extent of these climate future summaries was limited by availability of high-quality, downscaled climate data. We are working to acquire climate data for other regions and will produce summaries for those regions as soon as possible.

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