Climate Change and the American West

Global climate change is a topic that has garnered much attention in recent decades from both scientific and policy arenas. This article provides a synopsis of the current state of the science, and reviews the challenges of climate change in scientific, policy, and public arenas. Secondly, we provide a review of observed changes in global climate with a more detailed view of climatic changes and their subsequent impacts on terrestrial systems across the American West. We specifically highlight studies published since 2014 that provide current insights to the collection of science on climate change; its impacts on the American West; and complement national and international assessment reports

Modeling Current and Future Potential Distributions of Milkweeds and the Monarch Butterfly in Idaho

Monarch butterflies (Danaus plexippus) are widespread in North America but have experienced large rangewide declines. Causes of recent declines likely involve multiple biotic and abiotic stressors including climate change and loss and degradation of native milkweed (Asclepias spp.), monarchs' obligate larval host plant. Recent broad-scale modeling efforts suggest milkweed and monarch distributions in the eastern United States will expand northward during summer months while fine-scale modeling of western population overwintering sites in California indicate shifts inland and upward in elevation. However, species' response to climate measures varies at sub-regional scales across its range and both the impacts of climate change and potential adaptation measures may be sensitive to the spatial scale of climate data used, particularly in areas of complex topography. Here, we develop fine-scale models of monarch breeding habitat and milkweed distributions in Idaho, an area at the northern extent of the monarch breeding range in North America and important in western overwintering population recruitment. Our models accurately predict current distributions for showy milkweed (A. speciosa), swamp milkweed (A. incarnata), and monarch with AUC (area under the receiver operating characteristic curve) = 0.899, 0.981, and 0.929, respectively. Topographic, geographic, edaphic, and climatic factors all play important roles in determining milkweed and, thus, monarch distributions. In particular, our results suggest that at sub-regional and fine-scales, non-climatic factors such as soil depth, distance to water, and elevation contribute significantly. We further assess changes in potential habitat across Idaho under mid-21st century climate change scenarios and potential management implications of these changing distributions. Models project slight decreases (−1,318 km2) in potential suitable habitat for showy milkweed and significant increases (+5,830 km2) for swamp milkweed. Projected amounts of suitable habitat for monarch are likely to remain roughly stable with expansion nearly equal to contraction under a moderate scenario and slightly greater when under the more severe scenario. Protected areas encompass 8% of current suitable habitat for showy milkweed, 11% for swamp milkweed, and 9% for monarch. Our study shows that suitable habitat for monarchs and/or milkweeds will likely continue to be found in managed areas traditionally seen as priority habitats in Idaho through mid-century

Improved Bias Correction Techniques for Hydrological Simulations of Climate Change

Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM’s mean climate change signal, with differences of up to 2°C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models’ simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season’s values at once

Natural and Managed Watersheds Show Similar Responses to Recent Climate Change

Changes in climate are driving an intensification of the hydrologic cycle and leading to alterations of natural streamflow regimes. Human disturbances such as dams, land-cover change, and water diversions are thought to obscure climate signals in hydrologic systems. As a result, most studies of changing hydroclimatic conditions are limited to areas with natural streamflow. Here, we compare trends in observed streamflow from natural and human-modified watersheds in the United States and Canada for the 1981–2015 water years to evaluate whether comparable responses to climate change are present in both systems. We find that patterns and magnitudes of trends in median daily streamflow, daily streamflow variability, and daily extremes in human-modified watersheds are similar to those from nearby natural watersheds. Streamflow in both systems show negative trends throughout the southern and western United States and positive trends throughout the northeastern United States, the northern Great Plains, and southern prairies of Canada. The trends in both natural and human-modified watersheds are linked to local trends in precipitation and reference evapotranspiration, demonstrating that water management and land-cover change have not substantially altered the effects of climate change on human-modified watersheds compared with nearby natural watersheds

Lightning-Ignited Wildfires in the Western United States: Ignition Precipitation and Associated Environmental Conditions

Cloud-to-ground lightning with minimal rainfall (“dry” lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with \u3c2.5 mm of daily-accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning-ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics. “Holdover” fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm), and with cooler and wetter environmental conditions. Further, there is substantial variation in precipitation associated with promptly-detected (1.7–4.6 mm) and holdover (3.0–7.7 mm) fires across ecoprovinces. Consequently, the widely-used 2.5 mm threshold does not fully capture lightning ignition risk and incorporating ecoprovince-specific precipitation amounts would better inform WUS wildfire prediction and management

Seasonal Climate Variability and Change in the Pacific Northwest of the United States

Observed changes in climate of the U.S. Pacific Northwest since the early twentieth century were examined using four different datasets. Annual mean temperature increased by approximately 0.6°–0.8°C from 1901 to 2012, with corroborating indicators including a lengthened freeze-free season, increased temperature of the coldest night of the year, and increased growing-season potential evapotranspiration. Seasonal temperature trends over shorter time scales (<50 yr) were variable. Despite increased warming rates in most seasons over the last half century, nonsignificant cooling was observed during spring from 1980 to 2012. Observations show a long-term increase in spring precipitation; however, decreased summer and autumn precipitation and increased potential evapotranspiration have resulted in larger climatic water deficits over the past four decades. A bootstrapped multiple linear regression model was used to better resolve the temporal heterogeneity of seasonal temperature and precipitation trends and to apportion trends to internal climate variability, solar variability, volcanic aerosols, and anthropogenic forcing. The El Niño-Southern Oscillation and the Pacific-North American pattern were the primary modulators of seasonal temperature trends on multidecadal time scales: solar and volcanic forcing were nonsignificant predictors and contributed weakly to observed trends. Anthropogenic forcing was a significant predictor of, and the leading contributor to, long-term warming; natural factors alone fail to explain the observed warming. Conversely, poor model skill for seasonal precipitation suggests that other factors need to be considered to understand the sources of seasonal precipitation trends

Cheatgrass (Bromus tectorum) distribution in the intermountain Western United States and its relationship to fire frequency, seasonality, and ignitions

Cheatgrass (Bromus tectorum) is an invasive grass pervasive across the Intermountain Western US and linked to major increases in fire frequency. Despite widespread ecological impacts associated with cheatgrass, we lack a spatially extensive model of cheatgrass invasion in the Intermountain West. Here, we leverage satellite phenology predictors and thousands of field surveys of cheatgrass abundance to create regional models of cheatgrass distribution and percent cover. We compare cheatgrass presence to fire probability, fire seasonality and ignition source. Regional models of percent cover had low predictive power (34% of variance explained), but distribution models based on a threshold of 15% cover to differentiate high abundance from low abundance had an overall accuracy of 74%. Cheatgrass achieves ≥ 15% cover over 210,000 km2 (31%) of the Intermountain West. These lands were twice as likely to burn as those with low abundance, and four times more likely to burn multiple times between 2000 and 2015. Fire probability increased rapidly at low cheatgrass cover (1–5%) but remained similar at higher cover, suggesting that even small amounts of cheatgrass in an ecosystem can increase fire risk. Abundant cheatgrass was also associated with a 10 days earlier fire seasonality and interacted strongly with anthropogenic ignitions. Fire in cheatgrass was particularly associated with human activity, suggesting that increased awareness of fire danger in invaded areas could reduce risk. This study suggests that cheatgrass is much more spatially extensive and abundant than previously documented and that invasion greatly increases fire frequency, even at low percent cover

Controls on Interannual Variability in Lightning-Caused Fire Activity in the Western US

Lightning-caused wildfires account for a majority of burned area across the western United States (US), yet lightning remains among the more unpredictable spatiotemporal aspects of the fire environment and a challenge for both modeling and managing fire activity. A data synthesis of cloudto-ground lightning strikes, climate and fire data across the western US from 1992 to 2013 was conducted to better understand geographic variability in lightning-caused wildfire and the factors that influence interannual variability in lightning-caused wildfire at regional scales. Distinct geographic variability occurred in the proportion of fires and area burned attributed to lightning, with a majority of fires in the interior western US attributed to lightning. Lightning ignition efficiency was highest across the western portion of the region due to the concomitance of peak lightning frequency and annual nadir in fuel moisture in mid-to-late summer. For most regions the number of total and dry lightning strikes exhibited strong interannual correlation with the number of lightning-caused fires, yet were a poor predictor of area burned at regional scales. Commonality in climate–fire relationships for regional annual area burned by lightning- versus human-ignited fires suggests climate conditions, rather than lightning activity, are the predominant control of interannual variability in area burned by lightning-caused fire across much of the western US

Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA

Monthly temperature and precipitation data from 41 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) were compared to observations for the 20th century, with a focus on the United States Pacific Northwest (PNW) and surrounding region. A suite of statistics, or metrics, was calculated, that included correlation and variance of mean seasonal spatial patterns, amplitude of seasonal cycle, diurnal temperature range, annual- to decadal-scale variance, long-term persistence, and regional teleconnections to El Niño Southern Oscillation (ENSO). Performance, or credibility, was assessed based on the GCMs’ abilities to reproduce the observed metrics. GCMs were ranked in their credibility using two methods. The first simply treated all metrics equally. The second method considered two properties of the metrics: (1) redundancy of information (dependence) among metrics, and (2) confidence in the reliability of an individual metric for accurately ranking models. Confidence was related to how robust the estimate of the metric was to ensemble size, given that for most of the models only a small number of ensemble members (i.e., realizations of the 20th century) were available. A cursory comparison with 24 CMIP3 models revealed few differences between the two generations of models with respect to the statistics analyzed

Agro-Ecological Class Stability Decreases in Response to Climate Change Projections for the Pacific Northwest, USA

Climate change will impact bioclimatic drivers that regulate the geospatial distribution of dryland agro-ecological classes (AECs). Characterizing the geospatial relationship between present AECs and their bioclimatic controls will provide insights into potential future shifts in AECs as climate changes. The major objectives of this study are to quantify empirical relationships between bioclimatic variables and the current geospatial distribution of six dryland AECs of the inland Pacific Northwest (iPNW) of the United States; and apply bioclimatic projections from downscaled climate models to assess geospatial shifts of AECs under current production practices. Two Random Forest variable selection algorithms, VarSelRF and Boruta, were used to identify relevant bioclimatic variables. Three bioclimatic variables were identified by VarSelRF as useful for predictive Random Forest modeling of six AECs: (1) Holdridge evapotranspiration index; (2) spring precipitation (March, April, and May); and (3) precipitation of the warmest 4-month season (June, July, August, and September). Super-imposing future climate scenarios onto current agricultural production systems resulted in significant geospatial shifts in AECs. The Random Forest model projected a 58 and 63% increase in area under dynamic annual crop-fallow-transition (AC-T) and dynamic grain-fallow (GF) AECs, respectively. By contrast, a 46% decrease in area was projected for stable AC-T and dynamic annual crop (AC) AECs across all future time periods for Representative Concentration Pathway (RCP) 8.5. For the same scenarios, the stable AC and GF AECs showed the least declines in area (8 and 13%, respectively), compared to other AECs. Future spatial shifts from stable to dynamic AECs, particularly to dynamic AC-T and dynamic GF AECs would result in more use of fallow, a greater hazard for soil erosion, greater cropping system uncertainty, and potentially less cropping system flexibility. These projections are counter to cropping system goals of increasing intensification, diversification, and productivity