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The USA and Canada have entered negotiations to modernize the Columbia River Treaty, signed in 1961. Key priorities are balancing flood risk and hydropower production, and improving aquatic ecosystem function while incorporating projected effects of climate change. In support of the US effort, Chegwidden et al. (2017) developed a large-ensemble dataset of past and future daily streamflows at 396 sites throughout the Columbia River basin (CRB) and selected other watersheds in western Washington and Oregon, using state-of-the art climate and hydrologic models. In this study, we use that dataset to present new analyses of the effects of future climate change on flooding using water year maximum daily streamflows. For each simulation, flood statistics are estimated from generalized extreme value distributions fit to simulated water year maximum daily streamflows for 50-year windows of the past (1950–1999) and future (2050–2099) periods. Our results contrast with previous findings: we find that the vast majority of locations in the CRB are estimated to experience an increase in future streamflow magnitudes. The near ubiquity of increases is all the more remarkable in that our approach explores a larger set of methodological variation than previous studies; however, like previous studies, our modeling system was not calibrated to minimize error in maximum daily streamflow and may be affected by unquantifiable errors. We show that on the Columbia and Willamette rivers increases in streamflow magnitudes are smallest downstream and grow larger moving upstream. For the Snake River, however, the pattern is reversed, with increases in streamflow magnitudes growing larger moving downstream to the confluence with the Salmon River tributary and then abruptly dropping. We decompose the variation in results attributable to variability in climate and hydrologic factors across the ensemble, finding that climate contributes more variation in larger basins, while hydrology contributes more in smaller basins. Equally important for practical applications like flood control rule curves, the seasonal timing of flooding shifts dramatically on some rivers (e.g., on the Snake, 20th-century floods occur exclusively in late spring, but by the end of the 21st century some floods occur as early as December) and not at all on others (e.g., the Willamette River).

The Fourth National Climate Assessment (NCA4) provided the most up-to-date understanding of climate change and its effects on the Earth system and on consequences for the United States, including impacts and associated risks, along with approaches to coping with these effects. It is intended to provide guidance to decision-makers in governmental sectors while, in practice, providing guidance for nongovernmental actors. Its regional and topical chapters highlight current knowledge, uncertainties, gaps in knowledge, and emerging threats. The current knowledge and gaps can help set a research agenda to inform future national, regional, and local climate assessments and thereby support better decision-making. The evolution of the assessment, including greater diversity in participation, and more grounded research in the Northwest represents a growing and deepening engagement with more diverse participants. This shift emphasizes the importance of diversity, inclusion, and a greater acknowledgment of multiple ways of knowing, including local and Indigenous knowledge. The Northwest chapter reflects the broader shift in framing from NCA3 to NCA4 to better understand how climate impacts pose risks to things of value in each sector or region. It considers climate impacts through five broad ways in which humans relate to the environment: natural resource economy; heritage and quality of life; water, transportation, and infrastructure; health and social systems; and frontline communities. We reflect on the assessment process and identify three recommendations to improve the assessment outcomes and processes: seek new ways to 1) engage diverse authors and stakeholders and 2) value and integrate epistemic plurality and different knowledge systems, and 3) when gaps are identified, promote research or data collection efforts designed to fill those gaps. Done well, the assessment can build support and knowledge to facilitate community action, leading to broader resilience.

Flash droughts are a recently recognized type of extreme event distinguished by sudden onset and rapid intensification of drought conditions with severe impacts. They unfold on subseasonal-to-seasonal timescales (weeks to months), presenting a new challenge for the surge of interest in improving subseasonal-to-seasonal prediction. Here we discuss existing prediction capability for flash droughts and what is needed to establish their predictability. We place them in the context of synoptic to centennial phenomena, consider how they could be incorporated into early warning systems and risk management, and propose two definitions. The growing awareness that flash droughts involve particular processes and severe impacts, and probably a climate change dimension, makes them a compelling frontier for research, monitoring and prediction. Flash droughts, which develop over the course of weeks, are difficult to forecast given the current state of subseasonal-to-seasonal prediction. This Perspective offers operational and research definitions, places them in the broader context of climate and suggests avenues for future research.

Changing climate conditions impact ecosystem dynamics and have local to global impacts on water and carbon cycles. Many processes in dynamic vegetation models (DVMs) are parameterized, and the unknown/unknowable parameter values introduce uncertainty that has rarely been quantified in projections of forced changes. In this study, we identify processes and parameters that introduce the largest uncertainties in the vegetation state simulated by the DVM Top‐down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) coupled to a regional climate model. We adjust parameters simultaneously in an ensemble of equilibrium vegetation simulations and use statistical emulation to explore sensitivities to, and interactions among, parameters. We find that vegetation distribution is most sensitive to parameters related to carbon allocation and competition. Using a suite of statistical emulators, we identify regions of parameter space that reduce the error in modeled forest cover by 31±9%. We then generate large initial atmospheric condition ensembles with 10 improved DVM parameterizations under preindustrial, contemporary, and future climate conditions to assess uncertainty in the forced response due to parameterization. We find that while most parameterizations agree on the direction of future vegetation transitions in the western United States, the magnitude varies considerably: for example, in the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranges from 4 to 28% across 10 DVM parameterizations under projected future climate conditions. We demonstrate that model parameterization contributes to uncertainty in vegetation transition and carbon cycle feedback under nonstationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedback.

Methodological choices can have strong effects on projections of climate change impacts on hydrology. In this study, we investigate the ways in which four different steps in the modeling chain influence the spread in projected changes of different aspects of hydrology. To form the basis of these analyses, we constructed an ensemble of 160 simulations from permutations of two Representative Concentration Pathways, 10 global climate models, two downscaling methods, and four hydrologic model implementations. The study is situated in the Pacific Northwest of North America, which has relevance to a diverse, multinational cast of stakeholders. We analyze the effects of each modeling decision on changes in gridded hydrologic variables of snow water equivalent and runoff, as well as streamflow at point locations. Results show that the choice of representative concentration pathway or global climate model is the driving contributor to the spread in annual streamflow volume and timing. On the other hand, hydrologic model implementation explains most of the spread in changes in low flows. Finally, by grouping the results by climate region the results have the potential to be generalized beyond the Pacific Northwest. Future hydrologic impact assessments can use these results to better tailor their modeling efforts.

The impacts of sea surface temperature (SST) anomalies and anthropogenic greenhouse gases on the likelihood of extreme drought occurring in the central United States in the year 2012 were investigated using large-ensemble simulations from a global atmospheric climate model. Two sets of experiments were conducted. In the first, the simulated hydroclimate of 2012 was compared to a baseline period (1986–2014) to investigate the impact of SSTs. In the second, the hydroclimate in a world with 2012-level anthropogenic forcing was compared to five “counterfactual” versions of a 2012 world under preindustrial forcing. SST anomalies in 2012 increased the simulated likelihood of an extreme summer precipitation deficit (e.g., the deficit with a 2% exceedance probability) by a factor of 5. The likelihood of an extreme summer soil moisture deficit increased by a similar amount, due in great part to a large spring soil moisture deficit carrying over into summer. An anthropogenic impact on precipitation was detectable in the simulations, doubling the likelihood of what would have been a rainfall deficit with a 2% exceedance probability under preindustrial-level forcings. Despite this reduction in rainfall, summer soil moisture during extreme drought was essentially unaffected by anthropogenic forcing because of 1) evapotranspiration declining roughly one-to-one with a decrease in precipitation due to severe water supply constraint and despite higher evaporative demand and 2) a decrease in stomatal conductance, and therefore a decrease in potential transpiration, with higher atmospheric CO2 concentrations.

Simulations from 35 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 5 provide projections of 21st century climate in the Columbia River Basin under scenarios of anthropogenic activity given by Representative Concentration Pathways (RCP4.5 and RCP8.5). The multi-model ensemble 30-year mean annual temperature increases by 2.8 °C (5.0 °C) by late 21st century under RCP4.5 (RCP8.5) over the 1979–1990 baseline, with 18% (24%) more warming in summer. By late 21st century, annual precipitation increases by 5% (8%), with an 8% (14%) winter increase and a 4% (10%) summer decrease, but because some models project changes of opposite sign, confidence in these sign changes is lower than those for temperature. Four questions about temperature and precipitation changes were addressed: (1) How and why do climate projections vary seasonally? (2) Is interannual variability in seasonal temperature and precipitation projected to change? (3) What explains the large inter-model spread in the projections? (4) Do projected changes in climate depend on model skill? Changes in precipitation and temperature vary seasonally as a result of changes in large-scale circulation and regional surface energy budget, respectively. Interannual temperature variability decreases slightly during the cool seasons and increases in summer, while interannual precipitation variability increases in all seasons. The magnitude of regional warming is linked to models’ global climate sensitivity, whereas internal variability dominates the inter-model spread of precipitation changes. Lastly, GCMs that better reproduce historical climate tend to project greater warming and larger precipitation increases, though these results depend on the evaluation method.

Computing resources donated by volunteers have generated the first superensemble of regional climate model results, in which the Hadley Centre Regional Model, version 3P (HadRM3P), and Hadley Centre Atmosphere Model, version 3P (HadAM3P), were implemented for the western United States at 25-km resolution. Over 136,000 valid and complete 1-yr runs have been generated to date: about 126,000 for 1960–2009 using observed sea surface temperatures (SSTs) and 10,000 for 2030–49 using projected SSTs from a global model simulation. Ensemble members differ in initial conditions, model physics, and (potentially, for future runs) SSTs. This unprecedented confluence of high spatial resolution and large ensemble size allows high signal-to-noise ratio and more robust estimates of uncertainty. This paper describes the experiment, compares model output with observations, shows select results for climate change simulations, and gives examples of the strength of the large ensemble size.

Our best estimates of 1 April snow water equivalent (SWE) in the Cascade Mountains of Washington State indicate a substantial (roughly 15–35%) decline from mid-century to 2006, with larger declines at low elevations and smaller declines or increases at high elevations. This range of values includes estimates from observations and hydrologic modeling, reflects a range of starting points between about 1930 and 1970 and also reflects uncertainties about sampling. The most important sampling issue springs from the fact that half the 1 April SWE in the Cascades is found below about 1240 m, altitudes at which sampling was poor before 1945. Separating the influences of temperature and precipitation on 1 April SWE in several ways, it is clear that long-term trends are dominated by trends in temperature, whereas variability in precipitation adds "noise" to the time series. Consideration of spatial and temporal patterns of change rules out natural variations like the Pacific Decadal Oscillation as the sole cause of the decline. Regional warming has clearly played a role, but it is not yet possible to quantify how much of that regional warming is related to greenhouse gas emissions.

Mountain snowpack stores a significant quantity of water in the western US, accumulating during the wet season and melting during the dry summers and supplying much of the water used for irrigated agriculture, and municipal and industrial uses. Updating our earlier work published in 2005, we find that with 14 additional years of data, over 90% of snow monitoring sites with long records across the western US now show declines, of which 33% are significant (vs. 5% expected by chance) and 2% are significant and positive (vs. 5% expected by chance). Declining trends are observed across all months, states, and climates, but are largest in spring, in the Pacific states, and in locations with mild winter climate. We corroborate and extend these observations using a gridded hydrology model, which also allows a robust estimate of total western snowpack and its decline. We find a large increase in the fraction of locations that posted decreasing trends, and averaged across the western US, the decline in average April 1 snow water equivalent since mid-century is roughly 15–30% or 25–50 km3, comparable in volume to the West’s largest man-made reservoir, Lake Mead. Mountain snowpack stores huge amounts of water in the western US, supplying much of the water used to grow crops. A team of researchers from Oregon State University and UCLA found that spring snowpack declined almost everywhere, especially in the coastal states and other locations with mild winter climate. (Skiers will be relieved that declines were smaller in winter.) Not surprisingly, the declines are mostly related to warming climate. Using a physically-based model of the hydrologic cycle, which takes daily weather as inputs and computes snow accumulation and melt, runoff, etc., the researchers computed the total snowpack in the western US. Total snowpack declined 15–30%, and the amount of that lost water is comparable in volume to the West’s largest man-made reservoir, Lake Mead. Many water managers are already planning for a future with less snow, but this research emphasizes that the future is here.

Simulations from a regional climate model (RCM) as part of a superensemble experiment were compared with observations of surface meteorological variables over the western United States. The RCM is the Hadley Centre Regional Climate Model, version 3, with improved physics parameterizations (HadRM3P) run at 25-km resolution and nested within the Hadley Centre Atmosphere Model, version 3 (HadAM3P). Overall, the means of seasonal temperature were well represented in the simulations; 95% of grid points were within 2.7°, 2.4°, and 3.6°C of observations in winter, spring, and summer, respectively. The model was too warm over most of the domain in summer except central California and southern Nevada. HadRM3P produced more extreme temperatures than observed. The overall magnitude and spatial pattern of precipitation were well characterized, though HadRM3P exaggerated the orographic enhancement along the coastal mountains, Cascade Range, and Sierra Nevada. HadRM3P produced warm/dry northwest, cool/wet southwest U.S. patterns associated with El Niño. However, there were notable differences, including the locations of the transition from warm (dry) to cool (wet) in the anomaly fields when compared with observations, though there was disagreement among observations. HadRM3P simulated the observed spatial pattern of mean annual temperature more faithfully than any of the RCM–GCM pairings in the North American Regional Climate Change Assessment Program (NARCCAP). Errors in mean annual precipitation from HadRM3P fell within the range of errors of the NARCCAP models. Last, this paper provided examples of the size of an ensemble required to detect changes at the local level and demonstrated the effect of parameter perturbation on regional precipitation.

Climate impact studies often require the selection of a small number of climate scenarios. Ideally, a subset would have simulations that both (1) appropriately represent the range of possible futures for the variable/s most important to the impact under investigation and (2) come from global climate models (GCMs) that provide plausible results for future climate in the region of interest. We demonstrate an approach to select a subset of GCMs that incorporates both concepts and provides insights into the range of climate impacts. To represent how an ecosystem process responds to projected future changes, we methodically sample, using a simple sensitivity analysis, how an ecosystem variable responds locally to projected regional temperature and precipitation changes. We illustrate our approach in the Pacific Northwest, focusing on (a) changes in streamflow magnitudes in critical seasons for water management and (b) changes in annual vegetation carbon.

Scientists and decision-makers are working together to design, conduct, apply, and share research in ways that maximize value to society. How can AGU foster such partnerships?

Water scarcity afflicts societies worldwide. Anticipating water shortages is vital because of water's indispensable role in social-ecological systems. But the challenge is daunting due to heterogeneity, feedbacks, and water's spatial-temporal sequencing throughout such systems. Regional system models with sufficient detail can help address this challenge. In our study, a detailed coupled human-natural system model of one such region identifies how climate change and socioeconomic growth will alter the availability and use of water in coming decades. Results demonstrate how water scarcity varies greatly across small distances and brief time periods, even in basins where water may be relatively abundant overall. Some of these results were unexpected and may appear counterintuitive to some observers. Key determinants of water scarcity are found to be the cost of transporting and storing water, society's institutions that circumscribe human choices, and the opportunity cost of water when alternative uses compete.

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 (

Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

Water scarcity may appear to be a simple concept, but it can be difficult to apply to complex natural-human systems. While aggregate scarcity indices are straightforward to compute, they do not adequately represent the spatial and temporal variations in water scarcity that arise from complex systems interactions. The uncertain effects of future climate change on water scarcity add to the need for clarity on the concept of water scarcity. Starting with a simple but robust definition—the marginal value of a unit of water we— highlight key aspects of water scarcity and illustrate its many biophysical and socioeconomic determinants. We make four central observations. First, water scarcity varies greatly across location, time, and a multitude of uses that are valued either directly or indirectly by society. Second, water scarcity is fundamentally a normative, anthropocentric concept and, thus, can and should be distinguished from the related, purely descriptive notion of water deficit. While such an anthropocentric perspective may seem limiting, it has the potential to encompass the vast range of interests that society has in water. Third, our ability to understand and anticipate changes in water scarcity requires distinguishing between the factors that affect the value or benefits of water from those affecting the costs of transforming water in space, time and form. Finally, this robust and rigorous definition of water scarcity will facilitate better communication and understanding for both policymakers and scientists.

Augmenting previous papers about the exceptional 2011-15 California drought, we offer new perspectives on the ‘snow drought’ that extended into Oregon in 2014 and Washington in 2015. Over 80% of measurement sites west of 115°W experienced record low snowpack in 2015, and we estimate a return period of 400-1000 years for California’s snowpack under the questionable assumption of stationarity. Hydrologic modeling supports the conclusion that 2015 was the most severe on record by a wide margin. Using a crowd-sourced superensemble of regional climate model simulations, we show that both human influence and sea surface temperature anomalies contributed strongly to the risk of snow drought in Oregon and Washington: the contribution of SST anomalies was about twice that of human influence. By contrast, SSTs and humans appear to have played a smaller role in creating California’s snow drought. In all three states, the anthropogenic effect on temperature exacerbated the snow drought.

Changes in near surface air temperature (ΔT) in response to anthropogenic greenhouse gas forcing are expected to show spatial heterogeneity because energy and moisture fluxes are modulated by features of the landscape that are also heterogeneous at these spatial scales. Detecting statistically meaningful heterogeneity requires a combination of high spatial resolution and a large number of simulations. To investigate spatial variability of projected ΔT, we generated regional, high-resolution (25-km horizontal), large ensemble (100 members per year), climate simulations of western United States (US) for the periods 1985 – 2014 and 2030 – 2059, the latter with atmospheric constituent concentrations from the Representative Concentration Pathway 4.5. Using the large ensemble, 95% confidence interval sizes for grid-cell-scale temperature responses were on the order of 0.1 °C, compared to 1 °C from a single ensemble member only. In both winter and spring, the snow-albedo feedback statistically explains roughly half of the spatial variability in 'T. Simulated decreases in albedo exceed 0.1 in places, with rates of change in T per 0.1 decrease in albedo ranging from 0.3 to 1.4 °C. In summer, ΔT pattern in the northwest US is correlated with the pattern of decreasing precipitation. In all seasons, changing lapse rates in the low-to-middle troposphere may account for up to 0.2 °C differences in warming across the western US. Near the coast, a major control of spatial variation is the differential warming between sea and land.

Contemporary climate change is expected to affect the distributions of most species, but the nature, tempo, and mechanics of contemporary range shifts are still largely speculative. Here, we use fine-scale distributional records developed over the past Century, combined with spatially comprehensive microclimatic data, to demonstrate a dramatic shift in the range of a climate-sensitive mammal and to infer the increasingly important role of climate in local extinctions of this species across a 38.2 million-ha area. Changes in the distribution of the American pika (Ochotona princeps) throughout the Great Basin ecoregion were characterized using records from 1898–2008, revealing a nearly five-fold increase in the rate of local extinction and an 11-fold increase in the rate of upslope range retraction during the last ten years, compared with during the 20th Century. Four of ten local pika extinctions have occurred since 1999, and across this ecoregion the low-elevation range boundary for this species is now moving upslope at an average rate of about 145 m per decade. The rapid, ecoregional range shift of this small, talus-dwelling species stands in remarkable contrast with the dynamics and determinants of endangerment previously observed for most terrestrial species, and to earlier extinction determinants for O. princeps in this region. Further investigation of widely distributed species will clarify rates at which biotic response to environmental change is occurring, and how factors driving that change are evolving in different portions of the earth.

In common with much of the western United States, the Pacific Northwest (defined in this paper as Washington and Oregon) has experienced an unusual number of droughts in the past decade. This paper describes three of these droughts in terms of the precipitation, temperature, and soil moisture anomalies, and discusses different drought impacts experienced in the Pacific Northwest (PNW). For the first drought, in 2001, low winter precipitation in the PNW produced very low streamflow that primarily affected farmers and hydropower generation. For the second, in 2003, low summer precipitation in Washington (WA), and low summer precipitation and a warm winter in Oregon (OR) primarily affected streamflow and forests. For the last, in 2005, a lack of snowpack due to warm temperatures during significant winter precipitation events in WA, and low winter precipitation in OR, had a variety of different agricultural and hydrologic impacts. Although the proximal causes of droughts are easily quantified, the ultimate causes are not as clear. Better precipitation observations in the PNW are required to provide timely monitoring of conditions leading to droughts to improve prediction in the future.

Climate models used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) on the whole reproduce the observed seasonal cycle and twentieth century warming trend of 0.8°C (1.5°F) in the Pacific Northwest, and point to much greater warming for the next century. These models project increases in annual temperature of, on average, 1.1°C (2.0°F) by the 2020s, 1.8°C (3.2°F) by the 2040s, and 3.0°C (5.3°F) by the 2080s, compared with the average from 1970 to 1999, averaged across all climate models. Rates of warming range from 0.1°C to 0.6°C (0.2°F to 1.0°F) per decade. Projected changes in annual precipitation, averaged over all models, are small (+1% to +2%), but some models project an enhanced seasonal cycle with changes toward wetter autumns and winters and drier summers. Changes in nearshore sea surface temperatures, though smaller than on land, are likely to substantially exceed interannual variability, but coastal upwelling changes little. Rates of twenty-first century sea level rise will depend on poorly known factors like ice sheet instability in Greenland and Antarctica, and could be as low as twentieth century values (20 cm, 8″) or as large as 1.3 m (50″).

This work compares the Weather Research and Forecasting (WRF) and Hadley Centre Regional Model (HadRM) simulations with the observed daily maximum and minimum temperature (Tmax and Tmin) and precipitation at Historical Climatology Network (HCN) stations over the U.S. Pacific Northwest for 2003–07. The WRF and HadRM runs were driven by the NCEP/Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (R-2) data. The simulated Tmax in WRF and HadRM as well as in R-2 compares well with the observations. Predominantly cold biases of Tmax are noted in WRF and HadRM in spring and summer, while in winter and fall more stations show warm biases, especially in HadRM. Large cold biases of Tmax are noted in R-2 at all times. The simulated Tmin compares reasonably well with the observations, although not as well as Tmax in both models and in the reanalysis R-2. Warm biases of Tmin prevail in both model simulations, while R-2 shows mainly cold biases. The R-2 data play a role in the model biases of Tmax, although there are also clear indications of resolution dependency. The model biases of Tmin originate mainly from the regional models. The temporal correlation between the simulated and observed daily precipitation is relatively low in both models and in the reanalysis; however, the correlation increases steadily for longer averaging times. The high-resolution models perform better than R-2, although the nested WRF domains do have the largest biases in precipitation during the winter and spring seasons.

The decrease in mountain snowpack associated with global warming is difficult to estimate in the presence of the large year-to-year natural variability in observations of snow-water equivalent (SWE). A more robust approach for inferring the impacts of global warming is to estimate the temperature sensitivity (λ) of spring snowpack and multiply it by putative past and future temperature rises observed across the Northern Hemisphere. Estimates of λ can be obtained from (i) simple geometric considerations based on the notion that as the seasonal-mean temperature rises by the amount δT, the freezing level and the entire snowpack should rise by the increment δT/Γ, where Γ is the mean lapse rate; (ii) the regression of 1 April SWE measurements upon mean winter temperatures; (iii) a hydrological model forced by daily temperature and precipitation observations; and (iv) the use of inferred accumulated snowfall derived from daily temperature and precipitation data as a proxy for SWE. All four methods yield an estimated sensitivity of 20% of spring snowpack lost per degree Celsius temperature rise. The increase of precipitation accompanying a 1°C warming can be expected to decrease the sensitivity to 16%. Considering observations of temperature rise over the Northern Hemisphere, it is estimated that spring snow-water equivalent in the Cascades portion of the Puget Sound drainage basin should have declined by 8%–16% over the past 30 yr resulting from global warming, and it can be expected to decline by another 11%–21% by 2050. These losses would be statistically undetectable from a trend analysis of the region’s snowpack over the past 30 yr.

A snowpack model sensitivity study, observed changes of snow cover in the NOAA satellite dataset, and snow cover simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset are used to provide new insights into the climate response of Northern Hemisphere (NH) snow cover. Under conditions of warming and increasing precipitation that characterizes both observed and projected climate change over much of the NH land area with seasonal snow cover, the sensitivity analysis indicated snow cover duration (SCD) was the snow cover variable exhibiting the strongest climate sensitivity, with sensitivity varying with climate regime and elevation. The highest snow cover–climate sensitivity was found in maritime climates with extensive winter snowfall—for example, the coastal mountains of western North America (NA). Analysis of trends in snow cover duration during the 1966–2007 period of NOAA data showed the largest decreases were concentrated in a zone where seasonal mean air temperatures were in the range of −5° to +5°C that extended around the midlatitudinal coastal margins of the continents. These findings were echoed by the climate models that showed earlier and more widespread decreases in SCD than annual maximum snow water equivalent (SWEmax), with the zone of earliest significant decrease located over the maritime margins of NA and western Europe. The lowest SCD–climate sensitivity was observed in continental interior climates with relatively cold and dry winters, where precipitation plays a greater role in snow cover variability. The sensitivity analysis suggested a potentially complex elevation response of SCD and SWEmax to increasing temperature and precipitation in mountain regions as a result of nonlinear interactions between the duration of the snow season and snow accumulation rates.

A physically based hydrology model is used to produce time series for the period 1916–2003 of evapotranspiration (ET), runoff, and soil moisture (SM) over the western United States from which long-term trends are evaluated. The results show that trends in ET in spring and summer are determined primarily by trends in precipitation and snowmelt that determine water availability. From April to June, ET trends are mostly positive due primarily to earlier snowmelt and earlier emergence of snow-free ground, and secondarily to increasing trends in spring precipitation. From July to September trends in ET are more strongly influenced by precipitation trends, with the exception of areas (most notably California) that receive little summer precipitation and have experienced large changes in snowmelt timing. Trends in the seasonal timing of ET are modest, but during the period 1947–2003 when temperature trends are large, they reflect a shift of ET from midsummer to early summer and late spring. As in other studies, it is found that runoff is occurring earlier in spring, a trend that is related primarily to increasing temperature, and is most apparent during 1947–2003. Trends in the annual runoff ratio, a variable critical to western water management, are determined primarily by trends in cool season precipitation, rather than changes in the timing of runoff or ET. It was found that the signature of temperature-related trends in runoff and SM is strongly keyed to mean midwinter [December–February (DJF)] temperatures. Areas with warmer winter temperatures show increasing trends in the runoff fraction as early as February, and colder areas as late as June. Trends toward earlier spring SM recharge are apparent and increasing trends in SM on 1 April are evident over much of the region. The 1 July SM trends are less affected by snowmelt changes and are controlled more by precipitation trends.

This paper reviews methods that have been used to evaluate global climate simulations and to downscale global climate scenarios for the assessment of climate impacts on hydrologic systems in the Pacific Northwest, USA. The approach described has been developed to facilitate integrated assessment research in support of regional resource management. Global climate model scenarios are evaluated and selected based on historic 20 th Century simulations. A statistical downscaling method is then applied to produce a regional data set. To facilitate the use of climate projections in hydrologic assessment, additional statistical mapping may be applied to generate synthetic station time series. Finally, results are presented from a regional climate model that indicate important differences in the regional climate response from what is captured by global models and statistical downscaling. 1. Introduction Some of the most important anticipated impacts of climate change are expressed through hydrologic processes such as streamflow, snowpack, and flooding. Modeling these impacts requires high- resolution regional data for future scenarios of temperature and precipitation. The science of climate change at global and regional scales is quite advanced and climate simulations are typically downscaled to as fine as 10-50 km grids or to station locations. While there remains significant research to be done to fully understand climate dynamics at these scales and to bolster confidence in future scenarios, the current climate modeling is adequate for many applications in hydrology. A principal challenge is linking global climate simulations to existing computational tools and institutional mechanisms within an integrated assessment. For example, under global climate change, system impact assessment is complicated by the constantly shifting underlying climate trends within large year-to-year variability (Arnell, 1996). The analysis of water resource systems and their reliability, yield, and specific event frequency, generally assumes a static state that can be described statistically using a time series of historic events and depends on using the observed record of the past to estimate the probability of future events. The observed record is assumed to be statistically stationary so that all events are equally probable and these probabilities are assumed to carry into the future. Typically, climate projections are based on transient simulations from multiple projected emissions scenarios and climate models. While this approach can generate a large number of projections based on various models and emissions scenarios, it does not correspond well to the current approach in resource management. This paper reviews methods developed by the Climate Impacts Group (CIG) at the University of Washington for integrated assessment of climate change impacts in the Pacific Northwest, United States. This research focuses on four diverse yet connected natural systems of the Pacific Northwest (fresh water, forests, salmon and coasts) and the socioeconomic and/or political systems associated with each. Hydrologic processes are central to the climate impacts in all sectors; thus, downscaling climate scenarios for hydrologic simulations forms the basis for quantitative analyses. Many of the approaches we have developed are based on empirical corrections to simulated climate data. These corrections are based on a relationship between the observed statistics of a parameter and the simulation of that parameter for equivalent climate conditions. This relationship is then used to correct the simulation of that parameter for future climate conditions. In its simplest form, that relationship could be a simple perturbation to correct a bias. In the quantile mapping, however, the full probability distribution is taken into account. For example, the temperature simulated by a given model for present-day conditions at a given location may be 5°C too cold compared with observations. For the future climate, one would add 5°C to all values simulated at that location to correct this bias. The bias may be simply a lapse-rate correction for unresolved topography or it may stem from a deficiency in the

Records of 1 April snow water equivalent (SWE) are examined here using multiple linear regression against reference time series of temperature and precipitation. This method permits 1) an examination of the separate roles of temperature and precipitation in determining the trends in SWE; 2) an estimation of the sensitivity of SWE to warming trends, and its distribution across western North America and as a function of elevation; and 3) inferences about responses of SWE to future warming. These results emphasize the sensitivity to warming of the mountains of northern California and the Cascades of Oregon and Washington. In addition, the contribution of modes of Pacific climate variability is examined and found to be responsible for about 10%–60% of the trends in SWE, depending on the period of record and climate index.

Recent studies have shown substantial declines in snow water equivalent (SWE) over much of the western United States in the last half century, as well as trends toward earlier spring snowmelt and peak spring streamflows. These trends are influenced both by interannual and decadal-scale climate variability, and also by temperature trends at longer time scales that are generally consistent with observations of global warming over the twentieth century. In this study, the linear trends in 1 April SWE over the western United States are examined, as simulated by the Variable Infiltration Capacity hydrologic model implemented at 1/8° latitude–longitude spatial resolution, and driven by a carefully quality controlled gridded daily precipitation and temperature dataset for the period 1915–2003. The long simulations of snowpack are used as surrogates for observations and are the basis for an analysis of regional trends in snowpack over the western United States and southern British Columbia, Canada. By isolating the trends due to temperature and precipitation in separate simulations, the influence of temperature and precipitation variability on the overall trends in SWE is evaluated. Downward trends in 1 April SWE over the western United States from 1916 to 2003 and 1947 to 2003, and for a time series constructed using two warm Pacific decadal oscillation (PDO) epochs concatenated together, are shown to be primarily due to widespread warming. These temperature-related trends are not well explained by decadal climate variability associated with the PDO. Trends in SWE associated with precipitation trends, however, are very different in different time periods and are apparently largely controlled by decadal variability rather than longer-term trends in climate.

In western North America, snow provides crucial storage of winter precipitation, effectively transferring water from the relatively wet winter season to the typically dry summers. Manual and telemetered measurements of spring snowpack, corroborated by a physically based hydrologic model, are examined here for climate-driven fluctuations and trends during the period of 1916–2002. Much of the mountain West has experienced declines in spring snowpack, especially since midcentury, despite increases in winter precipitation in many places. Analysis and modeling show that climatic trends are the dominant factor, not changes in land use, forest canopy, or other factors. The largest decreases have occurred where winter temperatures are mild, especially in the Cascade Mountains and northern California. In most mountain ranges, relative declines grow from minimal at ridgetop to substantial at snow line. Taken together, these results emphasize that the West's snow resources are already declining as earth's climate warms.

Climatic variability is a dominant factor affecting large wildfires in the western United States, an observation supported by palaeoecological data on charcoal in lake sediments and reconstructions from fire-scarred trees. Although current fire management focuses on fuel reductions to bring fuel loadings back to their historical ranges, at the regional scale extreme fire weather is still the dominant influence on area burned and fire severity. Current forecasting tools are limited to short-term predictions of fire weather, but increased understanding of large-scale oceanic and atmospheric patterns in the Pacific Ocean (e.g., El NiSouthern Oscillation, Pacific Decadal Oscillation) may improve our ability to predict climatic variability at seasonal to annual leads. Associations between these quasi-periodic patterns and fire occurrence, though evident in some regions, have been difficult to establish in others. Increased temperature in the future will likely extend fire seasons throughout the western United States, with more fires occurring earlier and later than is currently typical, and will increase the total area burned in some regions. If climatic change increases the amplitude and duration of extreme fire weather, we can expect significant changes in the distribution and abundance of dominant plant species in some ecosystems, which would thus affect habitat of some sensitive plant and animal species. Some species that are sensitive to fire may decline, whereas the distribution and abundance of species favored by fire may be enhanced. The effects of climatic change will partially depend on the extent to which resource management modifies vegetation structure and fuels.

The impacts of year-to-year and decade-to-decade climatic variations on some of the Pacific Northwest's key natural resources can be quantified to estimate sensitivity to regional climatic changes expected as part of anthropogenic global climatic change. Warmer, drier years, often associated with El Nino events and/or the warm phase of the Pacific Decadal Oscillation, tend to be associated with below-average snowpack, streamflow, and flood risk, below-average salmon survival, below-average forest growth, and above-average risk of forest fire. During the 20th century, the region experienced a warming of 0.8 °C. Using output from eight climate models, we project a further warming of 0.5–2.5 °C (central estimate 1.5 °C) by the 2020s, 1.5–3.2°C (2.3 °C) by the 2040s, and an increase in precipitation except in summer. The foremost impact of a warming climate will be the reduction of regional snowpack, which presently supplies water for ecosystems and human uses during the dry summers. Our understanding of past climate also illustrates the responses of human management systems to climatic stresses, and suggests that a warming of the rate projected would pose significant challenges to the management of natural resources. Resource managers and planners currently have few plans for adapting to or mitigating the ecological and economic effects of climatic change.

Climate and snow data for the United States and Canada are combined and used in this paper to describe climate trends in the ecologically- and economically-vital land areas of the estuaries of Georgia Basin and Puget Sound. The region’s annually averaged temperature increased 1.5 °C during the 20th century while precipitation was more variable. Consistent with the observed warming, significant declines in mountain snowpack (typically 30% since 1950) have been observed, especially in springtime and at lower elevations.

Johnstone and Mantua (1) claim that changes in atmospheric circulation were the primary cause of the observed warming of sea surface temperature around the northeastern Pacific margins and surface air temperature (SAT) in Northern California, Oregon, and Washington from 1901 to 2012. The results of Johnstone and Mantua’s report contradict our previous finding (2) that anthropogenic forcing was the leading contributor to century-scale warming in the northwestern United States. Johnstone and Mantua’s results (1) are potentially important; however, we suspect that their results depend on the choice of sea-level pressure (SLP) data, as their conclusions are predicated on a long-term decrease in SLP over the northeastern Pacific.

Recent and historical events illustrate the vulnerabilities of the U.S. west to extremes in precipitation that result from a range of meteorological phenomena. This vision provides an approach to mitigating impacts of such weather and water extremes that is tailored to the unique meteorological conditions and user needs of the Western U.S. in the 21st Century. It includes observations for tracking, predicting, and managing the occurrence and impacts of major storms and is informed by a range of user-requirements, workshops, scientific advances, and technological demonstrations. The vision recommends innovations and enhancements to existing monitoring networks for rain, snow, snowmelt, flood, and their hydrometeorological precursor conditions, including radars to monitor winds aloft and precipitation, soil moisture sensors, stream gages, and SNOTEL enhancements, as well as entirely new observational tools. Key limitations include monitoring the fuel for heavy precipitation, storms over the eastern Pacific, precipitation distributions, and snow and soil moisture conditions. This article presents motivation and context, and describes key components, an implementation strategy, and expected benefits. This document supports a Resolution of the Western States Water Council for addressing extreme events.

We show the signature of the tropical intraseasonal oscillation (TIO) in upper tropospheric moisture and dynamical fields, roughly between 200 and 100 hPa. Relationships among these fields are examined using lag-correlation analysis and using multivariate extended empirical orthogonal functions (MEEOFs), which maximize the shared explained variance among several fields for both spatial and temporal variations. The MEEOFs show that all of the fields respond to the TIO and that the TIO is the dominant factor influencing each of the fields on these timescales. Convection associated with the TIO moistens the upper troposphere up to about 150 hPa, as expected; the behavior at 100 hPa is more complex. Over the longitude range where the TIO is associated with convection, roughly 60o-180oE, 100-hPa temperature and water vapor tend to be reduced above convection on TIO timescales. East of 180 o, though, the temperature and water vapor variations at 100 hPa become decoupled. The water vapor variations, like those of 200-hPa velocity potential, appear to speed up at about 180oE.

This publication is partially funded by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement NA67RJ0155, Contribution Number 743.

Observations of water vapor with high tempo- ral and spatial resolution and good horizontal coverage just above the tropical tropopause have been scarce, but a pre- liminary version of such data has been developed using radi- ance measurements of the Microwave Limb Sounder. These data reveal distinct variations with periods in the ranges 10{ 25 days and 30{70 days, consistent with (respectively) slow Kelvin waves and the tropical intraseasonal oscillation.