Divergent, plausible, and relevant climate futures for near- and long-term resource planning

Abstract

Scenario planning has emerged as a widely used planning process for resource management in situations of consequential, irreducible uncertainty. Because it explicitly incorporates uncertainty, scenario planning is regularly employed in climate change adaptation. An early and essential step in developing scenarios is identifying “climate futures”—descriptions of the physical attributes of plausible future climates that could occur at a specific place and time. Divergent climate futures that describe the broadest possible range of plausible conditions support information needs of decision makers, including understanding the spectrum of potential resource responses to climate change, developing strategies robust to that range, avoiding highly consequential surprises, and averting maladaptation. Here, we discuss three approaches for generating climate futures: a Representative Concentration Pathway (RCP)-ensemble, a quadrant-average, and an individual-projection approach. All are designed to capture relevant uncertainty, but they differ in utility for different applications, complexity, and effort required to implement. Using an application from Big Bend National Park as an example of numerous similar efforts to develop climate futures for National Park Service applications over the past decade, we compare these approaches, focusing on their ability to capture among-projection divergence during early-, mid-, and late-twenty-first century periods to align with near-, mid-, and long-term planning efforts. The quadrant-average approach and especially the individual-projection approach captured a broader range of plausible future conditions than the RCP-ensemble approach, particularly in the near term. Therefore, the individual-projection approach supports decision makers seeking to understand the broadest potential characterization of future conditions. We discuss tradeoffs associated with different climate future approaches and highlight suitable applications.

A Multi-Scale Blueprint for Building the Decision Context to Implement Climate Change Adaptation on National Wildlife Refuges in the United States

“…We plotted the change in annual average temperature and annual average precipitation in the 2041-2071 projections relative to the 1961-1990 historical normals for the 31 climate futures. Similar to Lawrence et al [50], we found groupings of climate futures were based on AOGCMs and not emission scenarios (Figure 3). We used the plot to identify the warm and the wet (MPI-ESM1-2-HR, SSP2-4.5) and the warmest and the wettest (UKESM1-0-LL, SSP5-8.5) projection.…”

The Pacific Drought Knowledge Exchange: A Co-Production Approach to Deliver Climate Resources to User Groups

“…The information within the CCVD portfolios is also valuable for use in decision support, for example, via scenario planning [37,38] whereby manager-researcher teams evaluate a range of future scenarios in order to understand risks and prioritize management actions in the face of uncertainty [39,40]. Scenario planning techniques are increasingly being used by managers as a climate change adaptation tool when uncertainty is high and when multiple competing climate models predict a set of plausible, but divergent, possible futures [41,42]. Given the high uncertainty in future climate, especially rainfall projections in Hawai'i where small land area makes predictions difficult [19,20,43], the PDKE CCVD portfolios add critical information as raw material to support important conservation and restoration planning discussions.…”

Robust projections and consequences of an expanding bimodal growing season in the western United States

“…Projected rates of warming are highly variable at regional and local scales. Thirteen CMIP 5 general circulation models (GCMs) within each RCP were selected from the MACA dataset such that they bracketed the range of projected future changes in temperature and precipitation (Lawrence et al, 2021; Rupp et al, 2013). The 13 GCMs were CanESM2, NorESM1‐M, BNU‐ESM, MRI‐CGCM3, CNRM‐CM5, CSIRO‐Mk3‐6‐0, MIROC‐ESM‐CHEM, MIROC5, GFDL‐ESM2G, CCSM4, HadGEM2‐CC365, inmcm4, and IPSL‐CM5A‐LR.…”