Multiple recent weather and climate disasters have shattered assumptions about the nature of regional climate risks. This power of surprise comes not just from the unprecedented severity of the disasters’ component hazards, but from the intricate system interactions that have led to their devastating impacts. The usual tools for risk characterization are particularly challenged by events that, like these, stretch the limits of experience, observation, imagination, and/or modeling capability. Here, we draw from several recent projects to describe the development and application of ‘complex-risk’ storylines that arc from blue-sky discussion of fundamental uncertainties and event conceptualization through to hazard-impact-response cascades and potential state changes in natural and human systems. Such storylines entrain diverse types of knowledge to flexibly envision and strategize for yet-unrealized risks spanning a range of timescales and socioenvironmental conditions. We use our narrative set to identify 12 major emergent themes across physical, social, and institutional domains, and discuss how these themes can contribute crucial guidance for anticipating what the next unprecedented disaster might look like — and thus how to design basic and applied research that speaks to it. The themes also provide a framework that helps highlight historically overlooked geographies, hazard combinations, and event dynamics. We conclude by enumerating several stubborn cross-disciplinary challenges that we see complicating extreme-weather risk calculations, and discuss the potential for this storyline approach, among other techniques, to foster productive insights.

Recurving tropical cyclones (TC) in the Western North Pacific can affect the atmospheric circulation over Pacific-Western North America (PWNA) via interactions with the midlatitude circulation. Using reanalysis and tropical cyclone best-track data, we examine how recurving TCs that make extratropical transition modulate 500-hPa ridge characteristics over PWNA during the peak (June-September) and late (October-December) seasons. TCs recurving at lower latitudes are associated with more intense and poleward-centered ridges relative to TCs recurving at higher latitudes, and TCs that recurve farther west are associated with relatively westward-centered ridges. Characteristics of the prevailing midlatitude flow such as its amplitude and positioning relative to the recurving TCs also affect PWNA ridge characteristics, determine whether the flow is amplified or dampened following TC-midlatitude interactions. Our study highlights multiple factors shaping the diversity of midlatitude circulation responses to recurving TCs, which are relevant for predicting downstream ridge characteristics and their associated surface extremes.

Heatwaves are expected to both increase in frequency and duration under global warming. The probability distributions of heatwave durations are shaped by day-to-day correlations in temperature and so cannot be simply inferred from changes in the probabilities of daily temperature extremes. Here we show from statistical analysis of global historical and projected temperature data that changes in long-duration heatwaves increase nonlinearly with temperature. Specifically, from analysis informed by theory for autocorrelated fluctuations applied to European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) reanalysis and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations, we find that the nonlinearity results in acceleration of the rate increase with warming; that is, each increment of regional time-averaged warming increases the characteristic duration scale of long heatwaves more than the previous increment. We show that the curve for this acceleration can be approximately collapsed onto a single dependence across regions by normalizing by local temperature variability. Projections of future change can thus be compared to observations of recent change over part of their range, which supports the near-future-projected acceleration. We also find that the longest, most uncommon heatwaves for a given region have the greatest increase in likelihood, yielding a compounding source of nonlinear impacts. The duration of long heatwaves increases at an accelerating rate with warming such that a large increase in the risk of long-lasting heatwaves results from relatively modest warming, according to an analysis of historical and projected heatwaves.

Atmospheric ridges are a key part of the midlatitude circulation and drive surface weather and climate. We employ a feature-tracking algorithm to identify ridge objects across Pacific-Western North America and examine their characteristics and large-scale drivers relevant for seasonal predictability. Utilizing ERA5 reanalysis, we evaluate seasonal variations in their location, frequency, extent, and intensity and explore their variations associated with ENSO variations. Focusing on extreme ridges identified based on their extent and intensity, we find that their location and characteristics depend on the ENSO phases. Large and Intense ridges tend to center over the Gulf of Alaska in most seasons. La Niña is associated with a higher frequency, extent and intensity of Intense ridges during winter and more widespread Large ridges in spring and fall seasons. Extreme ridges are typically associated with hemisphere-scale circulation anomalies and differences between their circulation patterns during ENSO phases are largely due to changes in the mean flow.

Cloud-to-ground (CG) lightning is a major source of summer wildfire ignition in the western United States (WUS). However, future projections of lightning are uncertain since lightning is not directly simulated by most global climate models. To address this issue, we use convolutional neural network (CNN)-based parameterizations of daily June-September CG lightning. CNN parameterizations of daily CG lightning occurrence at each grid cell use fields of three thermodynamic variables-ratio of surface Moist Static Energy (MSE) to 500 hPa saturation MSE, 700-500 hPa lapse rate, and 500 hPa relative humidity. Applying these parameterizations to the Community Earth System Model version 2 Large Ensemble, we find widespread increases in CG lightning days across much of the region by the mid-21st century (2031-2060) under a moderate warming scenario. Projected increases are pronounced in the northern WUS where many grid cells experience 4-12 additional CG lightning days compared to 1995-2022 and are driven by increases in all three thermodynamic variables. To assess the risk of lightning-ignited wildfire (LIW) ignition, we also quantify the concurrence of CG lightning with high Fire Weather Index (FWI) days. By 2031-2060, CG lightning will coincide more frequently with high FWI, but the magnitude of increases relative to CG lightning days varies across the region. Future projections of CG lightning and LIW risk can be useful for understanding the changing risks of associated hazards, and guide wildland fire management and suppression planning.

AbstractThe Great Salt Lake reached the lowest water volume in its entire 170+ year record in 2022. To explain this record low we develop and apply a lake mass‐balance model and perform four simulations: one where all input and output variables are fixed to their mid‐20th century average resulting in an equilibrium lake volume, and three others where one of the input variables (precipitation or streamflow) or the output variable (evaporation) follows observations while the other two are fixed to their mid‐20th century average. Results show anomalously low streamflow accounting for the largest proportion of the lake volume departure from the equilibrium state by 2022, resulting in about three times the additional water loss over 1950–2022 as increasing evaporation, which played the second largest role. Precipitation changes played a minimal role. Though streamflow had a greater effect, the lake would not have reached the record low volume without increasing evaporation.

As climate-related extreme events intensify across the globe, governments, practitioners, and communities have focused on reducing vulnerability and building resilience. However, debates persist about the validity, differences, and similarities between social vulnerability and resilience indices. This study combines the Social Vulnerability Index (SoVI) (26 indicators) and the Baseline Resilience Indicators for Communities (BRIC) (52 indicators) to assess the Portland Metro region using ACS (2016–2020) and 2020 Census data. Through hotspot analysis, Pearson’s correlation, and linear regression, we identify key drivers as well as areas of convergence and divergence between the two indices. Results show a strong overlap between SoVI and BRIC, with distinct drivers across counties and census tracts. High SoVI/low BRIC hotspots were found in Clackamas and Multnomah counties. In Clackamas, vulnerability was due to limited hospital access, weak infrastructure and institutions, mobile homes, and inadequate community resources. In Multnomah, poverty, low educational attainment, and single-parent households were the primary drivers of vulnerability. While Clackamas had stronger environmental resilience, Multnomah showed higher resilience than Washington County due to better transportation, institutions, and community capital. Having a high proportion of migrant populations, institutionalized residents, and mobile homes reduced resilience in Washington County. These findings support the combined use of SoVI-BRIC indices for targeted resilience planning and equitable resource allocation for infrastructure development, environmental protection, social programs, and emergency preparedness across multiple scales.

We synthesized more than 70 articles, most peer reviewed, that addressed the heat wave that occurred across the Pacific Northwest of the United States and Canada in late June 2021, breaking hundreds of daily and all-time daily maximum temperature records across the region. A persistent, extraordinarily strong ridge of high pressure was a primary driver of the heat wave. Contributing mechanisms were moisture originating in the tropical western Pacific Ocean, high solar radiation, low pressure offshore, large-scale subsidence over land, and unusually dry soils. Climate change contributed to the heat wave's magnitude by increasing mean temperature, although it is unclear whether the trend in extreme temperature is steeper than the trend in mean temperature. Mortality, heat-induced illness, and the number of visits to emergency departments during the 2021 heat wave were anomalously high. Individual and compounded social determinants of adverse outcomes included older age, living alone, lower income, and lack of functioning air conditioning. Browning or scorch of tree leaves and needles following the heat wave was exten-sive, although the extent of long-term tree mortality is not yet clear. Following the heat wave, Oregon, Washington, and British Columbia established new regulations and programs to reduce the risk of heat-related illness in the workplace. It is not yet feasible to rigorously evaluate the effectiveness of these new initiatives. SIGNIFICANCE STATEMENT: We synthesized more than 70 publications that addressed the causes and consequences of the extreme heat wave across the Pacific Northwest of the United States and Canada in late June 2021 and the potential for similar future heat waves. Interest in the heat wave among scientists, policymakers, and the public continues to be intense. Climate change contributed to the heat's magnitude, and many publications indicated that a similarly intense heat wave was extremely unlikely prior to the Industrial Revolution. Mortality and heat-induced illness during the heat wave were anomalously high, especially among older adults. Extensive scorch of tree leaves and needles followed the heat wave. In response to the heat wave, Oregon, Washington, and British Columbia established new protective regulations.

Climate-related disasters threaten urban areas worldwide, yet gaps remain in understanding how multiple stressors interact to shape resilience. This study examines exposure, vulnerability, and resilience across three consecutive extreme events in the Portland metro area: the September 2020 wildfire-related air pollution, the February 2021 snowstorm, and the June 2021 heatwave. We used geographically weighted regression and Spearman rank correlation analysis to investigate relationships between hazard impacts, the social vulnerability index (SoVI), Baseline Resilience Indicators for Communities (BRIC), and proximity to community-based organizations (CBOs) in 416 census tracts. Our analysis reveals strong spatial correlations between wildfire-related air pollution, winter storm impacts, and extreme heat exposure. Communities already burdened by poor air quality, freezing precipitation, and urban heat island effects faced heightened cumulative risk. While this varied by neighborhood, racial minorities, migrant workers, non-US citizens, and low-income households were significantly affected. In contrast, affluent communities with lower SoVI scores and higher BRIC values exhibited greater resilience and were less exposed to these hazards. Although CBOs were concentrated in areas with high SoVI, they were insufficient in mitigating disaster impacts. This study underscores the urgent need for multi-hazard resilience planning centered on advancing environmental justice, vulnerability reduction, CBO capacity building, and investment in critical infrastructure in at-risk communities.

This study investigates how the El Niño phase (EN) of the El Niño-Southern Oscillation (ENSO) influences the Madden-Julian Oscillation (MJO) modulation of cool-season North Pacific atmospheric rivers (ARs) and associated AR-landfall North American precipitation between 1980 and 2020. EN changes the key drivers of MJO-AR connections by shifting MJO-driven convection east of 180° in MJO phases 6–8 and extending the northern Pacific subtropical jet eastward. Under these conditions, the MJO tropical-extratropical teleconnection is triggered east of 180° in phases 7–8, and a persistent cyclonic flow anomaly develops along the United States west coast. Anomalous northeastward integrated water vapor transport (IVT) within the cyclonic flow coupled with the MJO convection over the western (phase 7) and central (phase 8) Pacific increases AR frequency, shifting it to the east over regions that do not show a relationship with EN or MJO alone. Besides enhancing AR activity, EN background conditions increase the number of AR events, their lifetime, and mean intensity from MJO phases 6 through 8, as well as the number of MJO active days, AR initiations, and ARs making landfall over North America in phases 8 − 1. The positive precipitation anomalies and increased frequency of extreme precipitation events associated with landfalling North Pacific ARs related to MJO are also shifted to the east in EN, enhancing and extending rainfall over western North America in phases 6 − 1. Results provide new insight into the drivers of AR activity and associated precipitation along the west coast of North America with implications for improving subseasonal-to-seasonal predictions.

In September 2020, Western North America was impacted by a highly anomalous meteorological event. Over the Pacific Northwest, strong and dry easterly winds exceeded historically observed values for the time of year and contributed to the rapid spread of several large wildfires. Nine lives were lost and over 5000 homes and businesses were destroyed in Oregon. The smoke from the fires enveloped the region for nearly two weeks after the event. Concurrently, the same weather system brought record-breaking cold, dramatic 24-h temperature falls, and early-season snowfall to parts of the Rocky Mountains. Here we use synoptic analysis and air parcel backward trajectories to build a process-based understanding of this extreme event and to put it in a climatological context. The primary atmospheric driver was the rapid development of a highly amplified 500 hPa tropospheric wave pattern that persisted for several days. A record-breaking ridge of high pressure characterized the western side of the wave pattern with a record-breaking trough of low pressure to the east. A notable anticyclonic Rossby wave breaking event occurred as the wave train amplified. Air parcel backward trajectories show that dry air over the Pacific Northwest, which exacerbated the fire danger, originated in the mid-troposphere and descended through subsidence to the surface. At the same time, dramatic temperature falls were recorded along the east side of the Rocky Mountains, driven by strong transport of high-latitude air near the surface.

In addition to increasing in frequency, heat waves are expected to last longer under global warming. The probability distributions of heat wave durations are affected by correlations of temperature from one day to the next, and so cannot be simply extrapolated from changes in the probabilities of daily temperature values. Using analysis informed by theory for autocorrelated fluctuations, here we show that changes in long-duration events increase nonlinearly with temperature. This produces an acceleration of the rate increase with warming — each subsequent increment of regional time-average warming T increases the characteristic duration scale of long heat waves more than the previous increment. The curve for this acceleration can be approximately collapsed onto a single dependence across regions by normalizing by local temperature variability. Projections of future change can thus be compared to observations of recent change over part of their range. The near-future projected acceleration is supported by this comparison and consistent with theoretical expectations. Furthermore, the longest, most uncommon heat waves for a given region have the greatest increase in probability, yielding a compounding source of nonlinear impacts.

Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud-to-ground (CG) lightning across the WUS during June-September from the spatial patterns of seven large-scale meteorological variables from reanalysis (1995-2022). Individually trained CNN models at each 1 degrees x 1 degrees grid cell (n = 285 CNNs) show high skill at predicting CG lightning days across the WUS (median AUC = 0.8) and perform best in parts of the interior Southwest where summertime CG lightning is most common. Further, interannual correlation between observed and predicted CG lightning days is high (median r = 0.87), demonstrating that locally trained CNNs realistically capture year-to-year variation in CG lightning activity across the WUS. We then use layer-wise relevance propagation (LRP) to investigate the relevance of predictor variables to successful CG lightning prediction in each grid cell. Using maximum LRP values, our results show that two thermodynamic variables-ratio of surface moist static energy to free-tropospheric saturation moist static energy, and the 700-500 hPa lapse rate-are the most relevant CG lightning predictors for 93%-96% of CNNs depending on the LRP variant used. As lightning is not directly simulated by global climate models, these CNNs could be used to parameterize CG lightning in climate models to assess changes in future CG lightning occurrence with projected climate change. Understanding changes in CG lightning risk and consequently lightning-caused wildfire risk across the WUS could inform fire management, planning, and disaster preparedness.

The serial occurrence of atmospheric rivers (ARs) along the US West Coast can lead to prolonged and exacerbated hydrologic impacts, threatening flood‐control and water‐supply infrastructure due to soil saturation and diminished recovery time between storms. Here a statistical approach for quantifying subseasonal temporal clustering among extreme events is applied to a 41‐year (1979–2019) wintertime AR catalog across the western United States (US). Observed AR occurrence, compared against a randomly distributed AR timeseries with the same average event density, reveals temporal clustering at a greater‐than‐random rate across the western US with a distinct geographical pattern. Compared to the Pacific Northwest, significant AR clusters over the northern Coastal Range of California and Sierra Nevada are more frequent and occur over longer time periods. Clusters along the California Coastal Range typically persist for 2 weeks, are composed of 4–5 ARs per cluster, and account for over 85% of total AR occurrence. Across the northwest Coast‐Cascade Ranges, clusters account for ∼50% of total AR occurrence, typically last 8–10 days, and contain 3–4 individual AR events. Based on precipitation data from a high‐resolution dynamical downscaling of reanalysis, the fractions of total and extreme hourly precipitation attributable to AR clusters are largest along the northern California coast and in the Sierra Nevada. Interannual variability among clusters highlights their importance for determining whether a particular water year is anomalously wet or dry. The mechanisms behind this unusual clustering are unclear and require further research.

Humid‐heat extremes threaten human health and are increasing in frequency with global warming, so elucidating factors affecting their rate of change is critical. We investigate the role of wet‐bulb temperature ( T W ) frequency distribution tail shape on the rate of increase in extreme T W threshold exceedances under 2°C global warming. Results indicate that non‐Gaussian T W distribution tails are common worldwide across extensive, spatially coherent regions. More rapid increases in the number of days exceeding the historical 95th percentile are projected in locations with shorter‐than‐Gaussian warm side tails. Asymmetry in the specific humidity distribution, one component of T W , is more closely correlated with T W tail shape than temperature, suggesting that humidity climatology strongly influences the rate of future changes in T W extremes. Short non‐Gaussian T W warm tails have notable implications for dangerous humid‐heat in regions where current‐climate T W extremes approach human safety limits.

South America's climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influence of distinct atmospheric systems. While some studies have characterized the prevailing systems over South America, they often lacked the utilization of statistical techniques for homogenization. On the other hand, other research has employed multivariate statistical methods to identify homogeneous regions regarding temperature and precipitation, but their focus has been limited to specific areas, such as the south, southeast, and northeast. Surprisingly, there is a lack of work that compares various multivariate statistical techniques to determine homogeneous regions across the entirety of South America concerning temperature and precipitation. This paper aims to address this gap by comparing three such techniques: Cluster Analysis (K-means and Ward) and Self Organizing Maps, using data from different sources for temperature (ERA5, ERA5-Land, and CRU) and precipitation (ERA5, ERA5-Land, and CPC). Spatial patterns and time series were generated for each region over the period 1981-2010. The results from this analysis of spatially homogeneous regions concerning temperature and precipitation have the potential to significantly benefit climate analysis and forecasts. Moreover, they can offer valuable insights for various climatological studies, guiding decision-making processes in diverse fields that rely on climate information, such as agriculture, disaster management, and water resources planning.

During the last week of June 2021, the Pacific Northwest region of North America experienced a record-breaking heatwave of historic proportions. All-time high temperature records were shattered, often by several degrees, across many locations, with Canada setting a new national record, the state of Washington setting a new record, and the state of Oregon tying its previous record. Here we diagnose key meteorology that contributed to this heatwave. The event was associated with a highly anomalous midtropospheric ridge, with peak 500-hPa geopotential height anomalies centered over central British Columbia. This ridge developed over several days as part of a large-scale wave train. Back trajectory analysis indicates that synoptic-scale subsidence and associated adiabatic warming played a key role in enhancing the magnitude of the heat to the south of the ridge peak, while diabatic heating was dominant closer to the ridge center. Easterly/offshore flow inhibited marine cooling and contributed additional downslope warming along the western portions of the region. A notable surface thermally induced trough was evident throughout the event over western Oregon and Washington. An eastward shift of the thermal trough, following the eastward migration of the 500-hPa ridge, allowed an inland surge of cooler marine air and dramatic 24-h cooling, especially along the western periphery of the region. Large-scale horizontal warm-air advection played a minimal role. When compared with past highly amplified ridges over the region, this event was characterized by much higher 500-hPa geopotential heights, a stronger thermal trough, and stronger offshore flow.

Climate model projections of atmospheric circulation patterns, their frequency, and associated temperature and precipitation anomalies under a high-end global warming scenario are assessed over the Pacific Northwest of North America for the final three decades of the twenty-first century. Model simulations are from phase 6 of the Coupled Model Intercomparison Project (CMIP6) and circulation patterns are identified using the self-organizing maps (SOMs) approach, applied to 500-hPa geopotential height (Z500) anomalies. Overall, the range of projected circulation patterns is similar to that in the current climate, especially in winter, whereas in summer the models project a general reduction in the magni-tude of Z500 anomalies. Significant changes in pattern frequencies are also projected in summer, with an overall decrease in the frequency of patterns with large Z500 anomalies. In winter, patterns historically associated with anomalously cold weather in northern latitudes are projected to warm the most, and in summer the largest temperature increases are pro-jected over inland areas. Precipitation is found to increase across all seasons and most SOM patterns. However, some summer patterns that are associated with above-average precipitation in the current climate are projected to become signif-icantly drier by the end of the century. SIGNIFICANCE STATEMENT: This paper uses a novel method to analyze projections of large-scale atmospheric circulation over the Pacific Northwest of North America, reducing the uncertainty of changes to the circulation patterns over the region under a high-emissions scenario of global warming.

Abstract 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 <2.5 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.