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Expanding the global protected area network is critical for addressing biodiversity declines and the climate crisis. However, how climate change will affect ecosystem representation within the protected area network remains unclear. Here we use spatial climate analogs to examine potential climate-driven shifts in terrestrial ecoregions and biomes under a +2 °C warming scenario and associated implications for achieving 30% area-based protection targets. We find that roughly half of land area will experience climate conditions that correspond with different ecoregions and nearly a quarter will experience climates from a different biome. Of the area projected to remain climatically stable, 46% is currently intact (low human modification). The area required to achieve protection targets in 87% of ecoregions exceeds the area that is intact, not protected, and projected to remain climatically stable within those ecoregions. Therefore, we propose that prioritization schemes will need to explicitly consider climate-driven changes in patterns of biodiversity. Preservation of biodiversity by expanding protected areas is complicated by expected climate change that could dissociate ecoregion or biome from climate conditions in over half of land area, according to analyses of spatial climate analogs

Rangeland seeding practices in the Intermountain western United States are predominantly implemented in the year immediately following wildfire for the purposes of Emergency Stabilization and Rehabilitation (ESR). This necessarily links restoration and rehabilitation outcomes to the probability of a single year providing sufficiently favorable microclimatic conditions for desirable plant establishment. Field research studies in rangeland restoration are also typically of limited duration, and published results may not represent the full spectrum of conditions likely to be experienced at a given site. We propose that location-specific and temporal weather analysis may enhance the interpretation of historical planting data, support expanded inferences from short-term field studies, and facilitate meta-analysis of diverse field studies in rangeland restoration. We describe access and use of new databases and tools that can be used to characterize and rank weather and soil-microclimatic variables and suggest some standard graphs and weather metrics to establish a longer-term perspective for the interpretation of rangeland restoration outcomes. Tools of this type may also be useful in the interpretation of a wide range of agricultural and natural resource applications that are driven by similar weather inputs, particularly in arid and semiarid systems that exhibit high annual and seasonal variability in precipitation and temperature.

Riparian vegetation along streams provides a suite of ecosystem services in rangelands and thus is the target of restoration when degraded by over-grazing, erosion, incision, or other disturbances. Assessments of restoration effectiveness depend on defensible monitoring data, which can be both expensive and difficult to collect. We present a method and case study to evaluate the effectiveness of restoration of riparian vegetation using a web-based cloud-computing and visualization tool ( ClimateEngine.org ) to access and process remote sensing and climate data. Restoration efforts on an Eastern Oregon ranch were assessed by analyzing the riparian areas of four creeks that had in-stream restoration structures constructed between 2008 and 2011. Within each study area, we retrieved spatially and temporally aggregated values of summer (June, July, August) normalized difference vegetation index (NDVI) and total precipitation for each water year (October-September) from 1984 to 2017. We established a pre-restoration (1984–2007) linear regression between total water year precipitation and summer NDVI for each study area, and then compared the post-restoration (2012–2017) data to this pre-restoration relationship. In each study area, the post-restoration NDVI-precipitation relationship was statistically distinct from the pre-restoration relationship, suggesting a change in the fundamental relationship between precipitation and NDVI resulting from stream restoration. We infer that the in-stream structures, which raised the water table in the adjacent riparian areas, provided additional water to the streamside vegetation that was not available before restoration and reduced the dependence of riparian vegetation on precipitation. This approach provides a cost-effective, quantitative method for assessing the effects of stream restoration projects on riparian vegetation.

Invasive annual weeds negatively impact ecosystem services and pose a major conservation threat on semiarid rangelands throughout the western United States. Rehabilitation of these rangelands is challenging due to interannual climate and subseasonal weather variability that impacts seed germination, seedling survival and establishment, annual weed dynamics, wildfire frequency, and soil stability. Rehabilitation and restoration outcomes could be improved by adopting a weather-centric approach that uses the full spectrum of available site-specific weather information from historical observations, seasonal climate forecasts, and climate-change projections. Climate data can be used retrospectively to interpret success or failure of past seedings by describing seasonal and longer-term patterns of environmental variability subsequent to planting. A more detailed evaluation of weather impacts on site conditions may yield more flexible adaptive-management strategies for rangeland restoration and rehabilitation, as well as provide estimates of transition probabilities between desirable and undesirable vegetation states. Skillful seasonal climate forecasts could greatly improve the cost efficiency of management treatments by limiting revegetation activities to time periods where forecasts suggest higher probabilities of successful seedling establishment. Climate-change projections are key to the application of current environmental models for development of mitigation and adaptation strategies and for management practices that require a multidecadal planning horizon. Adoption of new weather technology will require collaboration between land managers and revegetation specialists and modifications to the way we currently plan and conduct rangeland rehabilitation and restoration in the Intermountain West.

The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

The skill of two statistical downscaled seasonal temperature and precipitation forecasts from the North American Multimodel Ensemble (NMME) was evaluated across the western United States at spatial scales relevant to local decision-making. Both statistical downscaling approaches, spatial disaggregation (SD) and bias correction spatial disaggregation (BCSD), exhibited similar correlative skill measures; however, the BCSD method showed superior tercile-based skill measures since it corrects for variance deflation in NMME ensemble averages. Geographic and seasonal variations in downscaled forecast skill revealed patterns across the complex topography of the western United States not evident using coarse-scale skill assessments, particularly in regions subject to inversions and variability in orographic precipitation ratios. Similarly, differences in the skill of cool-season temperature and precipitation forecasts issued when the fall El Niño–Southern Oscillation (ENSO) signal was strong versus ENSO-neutral years were evident across topographic gradients in the northwestern United States.

Random matrix theory originated as the statistical theory of strongly interacting quantum systems, and has since been applied in an extremely broad range of applications. For example, it has been developed to accurately and efficiently model long range acoustic propagation in the ocean, including the construction of propagating acoustic time fronts. This problem can be viewed as dynamical symmetry breaking, i.e., the breaking of integrability, which has been explored in a variety of quantum mechanical contexts. The theory is expressed in terms of unitary propagation matrices that represent the scattering between acoustic modes due to sound speed fluctuations induced by the ocean's internal waves. The scattering exhibits a power-law decay as a function of the differences in mode numbers thereby generating a power-law, banded, random unitary matrix ensemble. In addition to its efficiency, the theory helps identify which information about the ocean environment can be deduced from the timefronts and how to connect features of the data to that environmental information. It also makes direct connections to methods used in other disordered wave guide contexts where the use of random matrix theory has a multi-decade history.

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.

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

Projected warming will have significant impacts on snowfall accumulation and melt, with implications for water availability and management in snow-dominated regions. Changes in snowfall extremes are confounded by projected increases in precipitation extremes. Downscaled climate projections from 20 global climate models were bias-corrected to montane Snowpack Telemetry stations across the western United States to assess mid-21st century changes in the mean and variability of annual snowfall water equivalent (SFE) and extreme snowfall events, defined by the 90th percentile of cumulative 3 day SFE amounts. Declines in annual SFE and number of snowfall days were projected for all stations. Changes in the magnitude of snowfall event quantiles were sensitive to historical winter temperature. At climatologically cooler locations, such as in the Rocky Mountains, changes in the magnitude of snowfall events mirrored changes in the distribution of precipitation events, with increases in extremes and less change in more moderate events. By contrast, declines in snowfall event magnitudes were found for all quantiles in warmer locations. Common to both warmer and colder sites was a relative increase in the magnitude of snowfall extremes compared to annual SFE and a larger fraction of annual SFE from snowfall extremes. The coefficient of variation of annual SFE increased up to 80% in warmer montane regions due to projected declines in snowfall days and the increased contribution of snowfall extremes to annual SFE. In addition to declines in mean annual SFE, more frequent low-snowfall years and less frequent high-snowfall years were projected for every station.

A majority of area burned in the Eastern United States (EUS) results from a limited number of exceptionally large wildfires. Relationships between climatic conditions and the occurrence of very large-fires (VLF) in the EUS were examined using composite and climate-niche analyses that consider atmospheric factors across inter-annual, sub-seasonal and synoptic temporal scales. While most large-fires in the EUS coincided with below normal fuel moisture and elevated fire weather, VLF preferentially occurred during a long-term drought accompanied by more acute sub-seasonal drought realized through fuel moisture stress and elevated fire-weather conditions. These results were corroborated across the EUS, with varying influences of drought, fire danger and fire weather discriminating VLF from other large fires across different geographical regions. We also show that the probability of VLF conditioned by fire occurrence increases when long-term drought, depleted fuel moisture and elevated fire weather align. This framework illustrates the compounding role of different timescales in VLF occurrence and serves as a basis for improving VLF predictions with seasonal climate forecasts and climate change scenarios.

An understanding of potential stream water quality conditions under future climate is critical for the sustainability of ecosystems and the protection of human health. Changes in wetland water balance under projected climate could alter wetland extent or cause wetland loss (e.g., via increased evapotranspiration and lower growing season flows leading to reduced riparian wetland inundation) or altered land use patterns. This study assessed the potential climate-induced changes to in-stream sediment and nutrient loads in the snowmelt-dominated Sprague River, Oregon, western US. Additionally, potential water quality impacts of combined changes in wetland water balance and wetland area under future climatic conditions were evaluated. The study utilized the Soil and Water Assessment Tool (SWAT) forced with statistical downscaling of general circulation model (GCM) data from the Coupled Model Intercomparison Project 5 (CMIP5) using the Multivariate Adaptive Constructed Analogs (MACA) method. Our findings suggest that, in the Sprague River, (1) mid-21st century nutrient and sediment loads could increase significantly during the high-flow season under warmer, wetter climate projections or could change only nominally in a warmer and somewhat drier future; (2) although water quality conditions under some future climate scenarios and no wetland loss may be similar to the past, the combined impact of climate change and wetland losses on nutrient loads could be large; (3) increases in stream total phosphorus (TP) concentration with wetland loss under future climate scenarios would be greatest at high-magnitude, low-probability flows; and (4) loss of riparian wetlands in both headwaters and lowlands could increase outlet TP loads to a similar degree, but this could be due to distinctly different mechanisms in different parts of the watershed.

Ocean acoustic propagation can be formulated as a wave guide with a weakly random medium generating multiple scattering. Twenty years ago, this was recognized as a quantum chaos problem, and yet random matrix theory, one pillar of quantum or wave chaos studies, has never been introduced into the subject. The modes of the wave guide provide a representation for the propagation, which in the parabolic approximation is unitary. Scattering induced by the ocean's internal waves leads to a power-law random banded unitary matrix ensemble for long-range deep ocean acoustic propagation. The ensemble has similarities, but differs, from those introduced for studying the Anderson metal-insulator transition. The resulting long-range propagation ensemble statistics agree well with those of full wave propagation using the parabolic equation., Comment: 5 pages, 4 figures

In a recent letter [Europhys. Lett. 97, 34002 (2012)], random matrix theory is introduced for long-range acoustic propagation in the ocean. The theory is expressed in terms of unitary propagation matrices that represent the scattering between acoustic modes due to sound speed fluctuations induced by the ocean's internal waves. The scattering exhibits a power-law decay as a function of the differences in mode numbers thereby generating a power-law, banded, random unitary matrix ensemble. This work gives a more complete account of that approach and extends the methods to the construction of an ensemble of acoustic timefronts. The result is a very efficient method for studying the statistical properties of timefronts at various propagation ranges that agrees well with propagation based on the parabolic equation. It helps identify which information about the ocean environment survives in the timefronts and how to connect features of the data to the surviving environmental information. It also makes direct connections to methods used in other disordered wave guide contexts where the use of random matrix theory has a multi-decade history., Comment: 10 figures, 12 pages

[1] 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 Nino 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.

Low frequency sound propagates in the ocean within a wave guide, formed by the confining effects of temperature, salinity, and pressure on the sound speed. Within the wave guide, sound scatters due to range dependent sound speed oscillations from internal waves. These weak perturbations in the sound speed serve to randomize the acoustic signal so that the structure formed by the time series of arrivals at the receivers (i.e., the timefront) contains only minimal average information about the propagation through the action of a generalized central limit theorem. This study characterizes this information by the parameters of a statistical ensemble model for the propagation. The propagation is described by the evolution of a unitary propagation matrix, whose elements are the complex probability amplitudes for modal transitions during the propagation. The ensemble model is constructed from a product of unitary random matrices utilizing complex Gaussian random variables with minimal information about the propa...

A major focus of long‐range acoustic experiments is the study of the properties of the ocean. In order to explore these properties, it is necessary to have an effective model of the ocean that is sufficient to understand the physics of the wave propagation. Large structures in the ocean cause refraction of the acoustic waves, while smaller structures cause diffraction. Since long‐range time fronts are mainly due to refractive effects, adding diffractive features to a model does not significantly change the long‐range wave propagation. However, such features cause important theoretical techniques based on refraction, e.g., ray methods, to be inaccurate and they also increase computational effort in the wave propagation calculations. A prescription to remove the diffractive features in an ocean sound‐speed model is outlined and the effects on the time fronts is shown. This removal eliminates the ‘‘microfolds’’ in the phase space manifolds [discussed by Simmens et al. in J. Acoust. Soc. Am. 102, 239 (1997)] ...

Acoustic wave fields propagating long ranges through the ocean are refracted by the inhomogeneities in the ocean's sound speed profile. Intuitively, for a given acoustic source frequency, the inhomogeneities become ineffective at refracting the field beyond a certain fine scale determined by the acoustic wavelength. On the other hand, ray methods are sensitive to infinitely fine features. Thus, it is possible to complicate arbitrarily the ray dynamics, and yet have the wave field propagate unchanged. This feature raises doubts about the ray/wave correspondence. Given the importance of various analyses relying on ray methods, a proper model should, at a minimum, exclude all of the fine structure that does not significantly alter the propagated wave field when the correspondence to the ray dynamics is integral. We develop a simple, efficient, smoothing technique to be applied to the inhomogeneities - a low pass filtering performed in the spatial domain - and give a characterization of its necessary extent as a function of acoustic source frequency. We indicate how the smoothing improves the ray/wave correspondence, and show that the so-called ``ray chaos'' problem remains above a very low frequency (about 15-25 Hz)., 16 pages with 10 figures incorporated into the text. Manuscript accepted to the North Pacific Acoustic Laboratory (NPAL) Special Section in the Journal of the Acoustical Society of America