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The Pacific Crest Trail is a National Scenic Trail that runs from Mexico to Canada. Use of the Pacific Crest Trail (PCT) has increased in the past 20 years. It is important to understand how an increase in use can impact the environment and sustainable practices, land management practices, and the hiker experience. Participants included 15 land managers in California and 25 thru-hikers who completed the PCT. In-depth interviews were conducted, and participants were asked questions about hiker interactions, trail angels, land manager interactions, and the permitting process. Data were transcribed verbatim and coded by three independent coders to reach a 90% inter-rater reliability. Emerging themes included: human impacts, permitting and education, and rule enforcement. Hikers also talked about the benefits of in-person education and the need for consistent communication between thru-hikers and land managers. Recommendations for sustainable practices include improvements to the permitting system, education, enforcement, and communication.

Climate change provides an opportunity for law professors to prepare students to raise informed, insightful questions about the legal and ethical challenges climate-related disasters present for individuals and businesses. Additionally, students might be prepared to answers their questions, drawing upon what they have learned in their Legal Environment of Business course. The purpose of this article is to show professors how to energize the environmental law unit in the Legal Environment of Business course using superstorms, environmental justice principles, and total participation techniques (TPTs). Instead of organizing the environmental law unit around a textbook chapter, this article suggests organizing the unit around disasters exacerbated by climate change. The article highlights superstorm Harvey and later introduces the Camp Fire, which destroyed Paradise, California. If professors can provoke students with information about climate-related disasters, they can inspire them to use the textbook as a resource to consider as they contemplate the extent to which laws and rules protect and preserve today's environment. Additionally, superstorms and their aftermath raise issues of environmental justice, a topic that is likely to spark debate and dialogue about inequality in the United States and around the world.

An after-school enrichment activity offered to sixth-grade students gave a group of 10 students an opportunity to explore the effects of the California drought in their community using an engaging scientific device: the UAV (unmanned aerial vehicle). Although this activity was specifically designed for a small after-school enrichment group, it could be modified to a whole-class setting by using grouping strategies. The students participated in three two-hour workshops entitled "Drones and Environmental Science." The Next Generation Science Standards (NGSS) emphasizes that students should learn the ideas of science through actually doing science and calls for full integration of science practices with other subjects, such as math and engineering. NGSS also clearly emphasizes that there is an urgency to train and make younger generations aware about the catastrophic consequences of human effects on the environment. The NGSS recommend an integrated approach to teaching Earth science with other STEM disciplines. With this activity, the goal was to help students understand the science behind how a UAV works, in addition to gaining an understanding for how UAVs can be used for scientific purposes. In this case, the class explored how the UAV can be used to help understand the devastating effects of the drought in California.

Interactive display systems give users flexibility to tailor their visual displays to different tasks and situations. However, in order for such flexibility to be beneficial, users need to understand how to tailor displays to different tasks (to possess "metarepresentational competence"). Recent research suggests that people may desire more complex and realistic displays than are most effective (Smallman & St. John, 2005). In Experiment 1, undergraduate students were tested on a comprehension task with geospatial displays (weather maps) that varied in the number of extraneous variables displayed. Their metacognitive judgments about the relative effectiveness of the displays were also solicited. Extraneous variables slowed response time and increased errors, but participants favored complex maps that looked more realistic about one third of the time. In Experiment 2, the eye fixations of undergraduate students were monitored as they performed the comprehension task. Complex maps that looked more realistic led to more eye fixations on both task-relevant and task-irrelevant regions of the displays. Experiment 3 compared performance of experienced meteorologists and undergraduate students on the comprehension and metacognitive tasks. Meteorologists were as likely as undergraduate students to prefer geographically complex (realistic) displays and more likely than undergraduates to opt for displays that added extraneous weather variables. However, meteorologists were also slower and less accurate with complex than with simple displays. This work highlights the importance of empirically testing principles of visual display design and suggests some limits to metarepresentational competence. (Contains 6 figures, 6 tables, and 5 footnotes.)

In January of 2007, freezing temperatures destroyed citrus and vegetable crops in California. By May 2007, more than 9,000 freeze-related unemployment applications had been fled. Through face-to-face interviews, this study documents the experience of 63 farmworkers to find out how they survived the freeze and accessed services. Findings revealed that more than half of the interviewees did not seek benefits from the governmental One-Stop centers set up to help them. Less than 10% of interviewees stated the benefits had covered their needs. Creating simple, nonbureaucratic guidelines for assisting affected individuals emerged as a need. (Contains 1 table.)

Technologies like solar, wind, and geothermal are exciting, but relatively new and untested in the context of universities, many of which are large enough to be cities unto themselves. And, like cities, universities count on a reliable, uninterruptible source of power. It is imperative, too, that precious dollars are wisely invested in the right kind of alternative energy solution for a school's location. A solar initiative may seem like the obvious undertaking for a school in a sun-soaked region, but without serious investigation, universities may find themselves making expensive and long-term decisions without proper data. That is why three forward-looking institutions--(1) the University of California-San Diego; (2) The Catholic University of America (District of Columbia); and (3) Adelphi University (New York)--are using state-of-the-art monitoring systems and intelligence gathering to pave the way toward widespread adoption of alternative energy power sources. The author discusses how the three eco-conscious universities are collecting data from solar, geothermal, and wind-power pilots to learn whether alternative energy is feasible on a larger scale. (Contains 7 online resources.)

Three experiments examined how bottom-up and top-down processes interact when people view and make inferences from complex visual displays (weather maps). Bottom-up effects of display design were investigated by manipulating the relative visual salience of task-relevant and task-irrelevant information across different maps. Top-down effects of domain knowledge were investigated by examining performance and eye fixations before and after participants learned relevant meteorological principles. Map design and knowledge interacted such that salience had no effect on performance before participants learned the meteorological principles; however, after learning, participants were more accurate if they viewed maps that made task-relevant information more visually salient. Effects of display design on task performance were somewhat dissociated from effects of display design on eye fixations. The results support a model in which eye fixations are directed primarily by top-down factors (task and domain knowledge). They suggest that good display design facilitates performance not just by guiding where viewers look in a complex display but also by facilitating processing of the visual features that represent task-relevant information at a given display location. (Contains 5 footnotes, 3 tables and 9 figures.)

Group activities are an excellent way to enhance learning. When students are actively involved in a relevant project, understanding and retention are improved. The proposed activity introduces a timely and interesting project typical of the type encountered in statistical practice. Using the computer to successfully developing an appropriate model is a valuable educational experience that builds confidence. (Contains 8 figures and 4 tables.)

Background: Autumn and winter Santa Ana Winds (SAW) are responsible for the largest and most destructive wildfires in southern California. Aims: (1) To contrast fires ignited on SAW days vs non-SAW days, (2) evaluate the predictive ability of the Canadian Fire Weather Index (CFWI) for these two fire types, and (3) determine climate and weather factors responsible for the largest wildfires. Methods: CAL FIRE (California Department of Forestry and Fire Protection) FRAP (Fire and Resource Assessment Program) fire data were coupled with hourly climate data from four stations, and with regional indices of SAW wind speed, and with seasonal drought data from the Palmer Drought Severity Index. Key results: Fires on non-SAW days were more numerous and burned more area, and were substantial from May to October. CFWI indices were tied to fire occurrence and size for both non-SAW and SAW days, and in the days following ignition. Multiple regression models for months with the greatest area burned explained up to a quarter of variation in area burned. Conclusions: The drivers of fire size differ between non-SAW and SAW fires. The best predictor of fire size for non-SAW fires was drought during the prior 5 years, followed by a current year vapour pressure deficit. For SAW fires, wind speed followed by drought were most important. Fires ignited on Santa Ana Wind days are the largest and most destructive fires but other fires are more numerous and account for greater area burned. Fire indices on days after ignition are important predictors of fire size, but drought and wind speed are most closely tied to fire size. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs), responsible for extreme weather conditions, are mid-latitude systems that can cause significant damage to coastal areas. While forecasting ARs beyond two weeks remains a challenge, past research suggests potential benefits may come from properly accounting for the changes in sea surface temperature (SST) through air–sea interactions. In this paper, we investigate the impact of ARs on SST over the North Pacific by analyzing 25 years of ocean reanalysis data using an SST budget equation. We show that in the region of strong ocean modification, ocean dynamics can offset over 100% of the anomalous SST warming that would otherwise arise from atmospheric forcing. Among all ocean processes, ageostrophic advection and vertical mixing (diffusion and entrainment) are the most important factors in modifying the SST tendency response. The SST tendency response to ARs varies spatially. For example, in coastal California, the driver of enhanced SST warming is the reduction in ageostrophic advection due to anomalous southerly winds. Moreover, there is a large region where the SST shows a warming response to ARs due to the overall reduction in the total clouds and subsequent increase in total incoming shortwave radiation. Strong air-sea interactions during atmospheric rivers often lead to modest upper ocean heat changes. The authors show that interior ocean dynamics are compensating for these air-sea exchanges. These findings can help improve subseasonal forecasts. [ABSTRACT FROM AUTHOR]

Foundation species are essential to ecosystem function, but their role as habitat providers is predicated on their spatial dominance. Worldwide, kelps, seagrasses, corals, and other marine foundation species have declined. This is true also for rockweeds, the canopy-forming analog of subtidal kelp forests in temperate rocky intertidal ecosystems. On the west coast of North America, dense beds of the rockweed Silvetia compressa occur across large biogeographic regions, benefitting numerous species by ameliorating physical stress caused by sun exposure, desiccation, heat, and wave disturbance. Like many rockweed species, Silvetia is long-lived, slow-growing, and short-dispersing -- characteristics that reduce its resilience to disturbance. Using a generalized additive mixed-effects model with explicit spatial effects, we analyzed canopy cover data from 30 sites spanning 18 years, and we tested the hypothesis that Silvetia population trends are tightly linked to atmospheric climate conditions, particularly Santa Ana wind events (SAWs): strong, hot, and dry downslope winds that originate inland and move offshore. We found that the rockweed had declined markedly, particularly at sites south of the major biogeographic break, Point Conception (PC), including the California Channel Islands and southern California mainland, and a highly significant negative effect of dewpoint depression, a measure of moisture content in the atmosphere, on Silvetia cover across all three regions in this study. Our results suggest that any increases in the frequency or intensity of SAWs are likely to lead to large declines and possible extirpation of Silvetia, as well as the important ecological services the species provides. [ABSTRACT FROM AUTHOR]

One of the primary tools used for determining the origin of a wildfire is analyzing burn patterns formed during the fire progression. These patterns, called fire pattern indicators, are interpreted and used to document the direction of fire movement at specific points, creating a directional map back to the specific area of origin. This concept was first set forth in 1978 by a U.S. governmental organization, the National Wildfire Coordinating Group (NWCG). Their recommendations are currently (2016) in the third edition, and in our study, we examine these indicators. Specifically, the objective was to perform a validation exercise where controlled burns were conducted of natural vegetation plots but augmented with 32 identical sets of staged artifacts which would provide additional opportunities for fire movement to create observable directional fire pattern indicators. Three adjacent plots were burned, each using a single point ignition, all located on level, scrubland terrain. The burns were conducted in the fall season, under low to moderate burning conditions. The research was structured as a preliminary study, since only mild terrain and weather conditions were encompassed. The actual fire movements were documented by drone videos, additional ground-based videos, and still photography. Within the three burn plots, a total of 12 data sites out of 32 data sites were selected: each one containing 7 to 12 individual artifacts. Each artifact was photographically documented post-fire, and the actual fire movement direction at that location was established. Assessment entailed the use of four experienced wildland fire investigators, with each one independently assessing the direction and type of fire spread at each artifact using the photographic site evidence. An analysis was then conducted to make a statistical comparison between the actual fire movement direction and the direction estimates provided by the experts analyzing the photographic evidence and the limited information on conditions provided. The results indicate an average error of 103°. These results indicate that additional efforts are needed to study the scientific basis of the indicators and to evolve improvements in both the indicators and in the accompanying guidance to investigators. [ABSTRACT FROM AUTHOR]

Wildfires in the southwestern United States, particularly in northern California (nCA), have grown in size and severity in the past decade. As they have grown larger, they have been associated with large emissions of absorbing aerosols in to the troposphere. Utilizing satellite observations from MODIS, CERES, AIRS, and CALIPSO, the meteorological effects of aerosols associated with fires during the wildfire season (June–October) were discerned over the nCA-NV (northern California and Nevada) region in the 2003–2022 time frame. As higher temperatures and low relative humidity RH dominate during high surface pressure ps atmospheric conditions, the effects of the aerosols on high (90th percentile) fire days compared to low fire (10th percentile) days were stratified based on whether ps was anomalously high or anomalously low (10th percentile). An increase in tropospheric temperatures was found to be concurrent with more absorbing aerosol aloft, which is associated with significant reductions in tropospheric RH during both 90th and 10th percentile ps conditions. Furthermore, high fire days under low ps conditions are associated with reduced cloud fraction CF , which is consistent with the traditionally-defined aerosol- cloud semi-direct effect. The reduced CF , in turn, is associated with reduced T OA SW radiative flux, a warmer surface, and less precipitation. These changes could create a positive feedback that could intensify fire weather, and therefore extend fire lifetime and impacts. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. Plain Language Summary: California winter weather can alternate between very wet conditions from atmospheric rivers making landfall along the Pacific coast to hot, dry, and windy conditions brought by Santa Ana winds blowing in from the Southwest interior. Atmospheric rivers are important for water resources while also causing flooding, whereas Santa Ana winds are often associated with wildfire, especially following prolonged dry periods. Preparing for these types of weather events is important for managing resources and protecting life and property, yet reliable forecasts beyond about 7–10 days remain a challenge. We have developed a new prediction system that combines information about approaching atmospheric weather patterns from weather forecast models along with historical information relating those patterns to impacts over California to predict the likelihood of impactful weather at 1–4 weeks lead time. By extending the window of opportunity to take management action, this new approach should aid in resource and emergency planning in water, land, and fire sectors as well as protecting residents through improved warning systems. Key Points: A hybrid dynamical‐statistical model is described for 1–4‐week forecasts of impactful California winter weather using circulation regimesThis hybrid framework reduces the number of forecasts produced, but the ones issued can be interpreted with higher confidenceThis new methodology provides skillful subseasonal forecasts with potential to improve early warnings for impactful weather events [ABSTRACT FROM AUTHOR]

Floods caused by atmospheric rivers and wildfires fanned by Santa Ana winds are common occurrences in California with devastating societal impacts. In this work, we show that winter weather variability in California, including the occurrence of extreme and impactful events, is linked to four atmospheric circulation regimes over the North Pacific Ocean previously named and identified as the "NP4 modes". These modes come in and out of phase with each other during the season, resulting in distinct weather patterns that recur throughout the historical record. Some phase combinations favor atmospheric river landfalls and extreme daily or multi-day precipitation, while other phase combinations favor anomalously hot weather and drying Santa Ana wind conditions over Southern California. This historical perspective of atmospheric circulation and impacts over 70 years reveals that weather patterns are changing in a way that enhances wildfire hazard in California, while the frequency of weather patterns linked to historical floods is not diminishing. These changes highlight the rising hazards of cascading weather extremes in California's present and future. [ABSTRACT FROM AUTHOR]

Annual burned area has increased in California over the past three decades as a result of rising temperatures and a greater atmospheric demand for moisture, a trend that is projected to continue throughout the 21st century as a result of climate change. Here, we implement a bias-correction and statistical downscaling technique to obtain high resolution, daily meteorological conditions for input into two fire weather indices: vapor pressure deficit (VPD) and the Canadian Fire Weather Index System (FWI). We focus our analysis on 10 ecoregions that together account for the diverse range of climates, ecosystems, topographies, and vegetation types found across the state of California. Our results provide evidence that fire weather conditions will become more extreme and extend into the spring and fall seasons in most areas of California by 2100, extending the amount of time vegetation is exposed to increased atmospheric demand for moisture, and heightening the overall risk for the ignition and spread of large wildfire. The ecoregion-level spatial scale adopted for this study increases the spatial specificity of fire weather information, as well as the resolution with which fire and land managers can implement strategies and counter-measures when addressing issues related to climate change. [ABSTRACT FROM AUTHOR]

Background: California's South Coast has experienced peak burned area in autumn. Following typically dry, warm summers, precipitation events and Santa Ana winds (SAWs) each occur with increasing frequency from autumn to winter and may affect fire outcomes. Aims: We investigate historical records to understand how these counteracting influences have affected fires. Methods: We defined autumn precipitation onset as the first 3 days when precipitation ≥8.5 mm, and assessed how onset timing and SAWs were associated with frequency of ≥100 ha fires and area burned during 1948–2018. Key results: Timing of autumn precipitation onset had negligible trend but varied considerably from year to year. A total of 90% of area burned in autumn through winter occurred from fires started before onset. Early onset autumns experienced considerably fewer fires and area burned than late onset autumns. SAWs were involved in many of the large fires before onset and nearly all of the lesser number after onset. Conclusions: Risk of large fires is reduced after autumn precipitation onset, but may resurge during SAWs, which provide high risk weather required to generate a large fire. Implications: During autumn before onset, and particularly during late onset autumns, high levels of preparation and vigilance are needed to avoid great fire impacts. Autumn precipitation and Santa Ana winds are opposing determinants of fire outcomes in California's South Coast region. Most of the area burned in autumn through early winter during 1948–2018 occurred from fires started before the onset of precipitation, defined as the first autumn event with 8.5 mm or greater precipitation. [ABSTRACT FROM AUTHOR]

Each year, wildfires ravage the western U.S. and change the lives of millions of inhabitants. Situated in southern California, coastal Santa Barbara has witnessed devastating wildfires in the past decade, with nearly all ignitions started by humans. Therefore, estimating the risk imposed by unplanned ignitions in this fire-prone region will further increase resilience toward wildfires. Currently, a fire-risk map does not exist in this region. The main objective of this study is to provide a spatial analysis of regions at high risk of fast wildfire spread, particularly in the first two hours, considering varying scenarios of ignition locations and atmospheric conditions. To achieve this goal, multiple wildfire simulations were conducted using the FARSITE fire spread model with three ignition modeling methods and three wind scenarios. The first ignition method considers ignitions randomly distributed in 500 m buffers around previously observed ignition sites. Since these ignitions are mainly clustered around roads and trails, the second method considers a 50 m buffer around this built infrastructure, with ignition points randomly sampled from within this buffer. The third method assumes a Euclidean distance decay of ignition probability around roads and trails up to 1000 m, where the probability of selection linearly decreases further from the transportation paths. The ignition modeling methods were then employed in wildfire simulations with varying wind scenarios representing the climatological wind pattern and strong, downslope wind events. A large number of modeled ignitions were located near the major-exit highway running north–south (HWY 154), resulting in more simulated wildfires burning in that region. This could impact evacuation route planning and resource allocation under climatological wind conditions. The simulated fire areas were smaller, and the wildfires did not spread far from the ignition locations. In contrast, wildfires ignited during strong, northerly winds quickly spread into the wildland–urban interface (WUI) toward suburban and urban areas. [ABSTRACT FROM AUTHOR]

The evaluation of water systems based on historical statistics is problematic when shifts in the hydrologic system occur due to a changing climate. An explicit link to thermodynamic and dynamic pathways in the climate system that may control water system performance is missing from current operational policies prescribed by regulation manuals within the US. In response, this study contributes an extended version of an existing weather regime (WR)–based stochastic weather generator (SWG) that allows (1) hourly simulation, (2) over the entire year, and (3) with a corrected representation of extremes. A range of climate scenarios is developed to demonstrate the insights that can be gained from linking the impacts of climate change to their thermodynamic and dynamic causal mechanisms, in this case for inflows to the Don Pedro Reservoir within the Tuolumne River Watershed of California. Application of the WR-SWG and water system modeling chain shows that the magnitude of flood events can be heavily influenced by antecedent hydrologic factors such as snow water equivalent (SWE) and soil moisture. Our results suggest that, under all climate change scenarios, SWE decreases as temperature increases and contributes more (sometimes up to 2.5 times more than the baseline) inflow as part of rain-on-snow events. The monthly reservoir inflows show the potential to cause extreme floods as the average rate of inflow increases by up to 80% with temperature increases, whereas SWE tends to increase by 50%, adding water to the stream during the high flow season. In addition to the temperature increase, if the water-holding capacity of the atmosphere increases with Clausius-Clapeyron scaling, reservoir inflows are projected to increase. This provides insight for risk-hedging policies: winter storm and spring snowmelt release and storage decisions that drive flood and drought risk, respectively. [ABSTRACT FROM AUTHOR]

Managed wildfires, i.e., naturally ignited wildfires that are managed for resource benefits, have the potential to reduce fuel loads, minimize the effects of future wildfires, and restore critical natural processes across many forest landscapes. In the United States, the 2009 federal wildland fire policy guidance was designed to provide greater flexibility in the use of managed wildfires, but the effects of this policy on wildfires in the western US are not yet fully understood. Our goal was to compare managed and full suppression wildfires and to also analyze the differences between managed wildfires across space (Arizona/New Mexico and California) and time (before and after 2009) using four metrics for each wildfire: (1) distance to wilderness, (2) distance to the wildland–urban interface (WUI), (3) the percentage of area burned with high severity, and (4) the number of land management agencies. Across the study area, we found that managed wildfires were significantly closer to wilderness areas, were farther from the WUI, had a lower percentage of area that was burned at high severity, and had fewer agencies involved in managing the fire compared to full suppression wildfires. In California, managed wildfires occurred closer to wilderness and had a larger percentage of high-severity burn area compared to those in the southwest US (Arizona and New Mexico). Within each region, however, there were no significant geographic differences between managed wildfires before and after the implementation of the 2009 policy guidance. Despite the greater flexibility of the 2009 policy guidance, the basic geographic properties of managed wildfires in these two regions have not changed. As the climate warms and droughts intensify, the use of managed wildfires will need to expand during favorable weather conditions in order to address the threat of large and uncharacteristic wildfires to people and ecosystems. [ABSTRACT FROM AUTHOR]

Pacific sardine (Sardinops sagax) stocks off the U.S. Pacific coast undergo boom and bust cycles modulated by decadal changes in oceanic and atmospheric conditions. During periods of low abundance, Pacific sardine may contract into nearshore waters, making it more challenging to be surveyed by research vessels that cannot operate in the shallowest habitats, 0 to 40.00 m. In response, a new transect-based design and methods were developed to compute bias and variance of biomass estimated from a collaborative aerial survey implemented in nearshore waters. From experimental aerial surveys conducted in 2010 and from 2018 to 2020, two professional aerial fish spotters underestimated school biomass by 10.5% and 12.6%, whereas most of the variability of aerial biomass was due to within-transect variance, with no significant contribution from the among-transect variance. Implementation of the new transect-based design to aerial surveys from 2020 to 2023 in southern and northern California showed that a substantial biomass of Pacific sardine occurred in nearshore waters, representing 69.9% of total biomass estimated from acoustic surveys in spring 2021, and 36.5% in summer 2021. Seasonal biomass estimates were variable within each region, affected by school dynamics during daytime in nearshore waters and the timing of aerial surveys as determined by weather and environmental conditions. Given that nearshore aerial biomass comprised a measurable proportion of total biomass estimates for Pacific sardine during the recent period of low abundance, it has been used to inform acoustic survey catchability, which is no longer assumed to be 1.0 in recent Pacific sardine stock assessments. [ABSTRACT FROM AUTHOR]

The NASA LIS/WRF‐Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi‐scale surface hydrological modeling capabilities of the WRF‐Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub‐surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF‐Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open‐loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF‐Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications. Plain Language Summary: Land surface models are useful because of their ability to resolve surface‐atmosphere feedbacks, including those with vegetation. Land surface models also have the capability to assimilate surface observations, usually measured through remote sensing techniques, into the model. Hydrologic models have the strength of resolving horizontal movements of water both on the surface and through the sub‐surface. In the present study, we combine the data‐assimilation capabilities of the NASA‐LIS land surface model with the WRF‐Hydro hydrologic model to combine the utility of both systems. We use this new system to demonstrate the impact of assimilating snow water equivalent, measured from an aircraft, on both the land surface and streamflow from the model in the Tuolumne River basin in Central California. Results show that assimilation of snow water equivalent into the coupled model corrects snow errors and improves the streamflow in both wet and dry years. We find that hydrologic processes that are now added to the land surface model impact simulated soil moisture and evapotranspiration. These findings are important because the ability for a model to better resolve streamflow, from snow assimilation, could be beneficial for water management. Key Points: Snow water equivalent data‐assimilation improves hydrologic response in the coupled LIS/WRF‐Hydro model for a case study in the Tuolumne River basinHorizontal surface routing increases soil moisture and evapotranspiration downstream near channels in LIS/WRF‐Hydro, compared to an LSM‐only simulation [ABSTRACT FROM AUTHOR]

Previous studies investigating deep decarbonization of bulk electric power systems and wholesale electricity markets have not sufficiently explored how future grid pathways could affect the grid's vulnerability to hydrometeorological uncertainty on multiple timescales. Here, we employ a grid operations model and a large synthetic weather ensemble to "stress test" a range of future grid pathways for the U.S. West Coast developed by ReEDS, a well‐known capacity planning model. Our results show that gradual changes in the underlying capacity mix from 2020 to 2050 can cause significant "re‐ranking" of weather years in terms of annual wholesale electricity prices (with "good" years becoming bad, and vice versa). Nonetheless, we find the highest and lowest ranking price years in terms of average electricity price remain mostly tied to extremes in hydropower availability (streamflow) and load (summer temperatures), with the strongest sensitivities related to drought. Seasonal dynamics seen today involving spring snowmelt and hot, dry summers remain well‐defined out to 2050. In California, future supply shortfalls in our model are concentrated in the evening and occur mostly during periods of high temperature anomalies in late summer months and in late winter; in the Pacific Northwest, supply shortfalls are much more strongly tied to negative streamflow anomalies. Under our more robust sampling of stationary hydrometeorological uncertainty, we also find that the ratio of dis‐patchable thermal (i.e., natural gas) capacity to wind and solar required to ensure grid reliability can differ significantly from values reported by ReEDS. Plain Language Summary: In this study we model how increased adoption of wind power, solar power, batteries and electric vehicles could alter the U.S. West Coast grid's exposure to weather uncertainty. Our results show that as the mix of technologies used on the grid changes from 2020 to 2050, it will cause a "re‐ranking" of weather years (with "good" years capable of becoming "bad" and vice versa). For example, years with low wind speeds generally become more concerning (marked by comparatively high prices) as installed wind power increases. Nonetheless, the highest and lowest price years remain most strongly tied to extremes in hydropower availability (streamflow) and electricity demand (summer air temperatures) even out to 2050. In California, supply shortfalls are concentrated in the evening during periods of anomalously high temperatures in late summer and late winter; in the Pacific Northwest, supply shortfalls are most strongly tied to negative streamflow anomalies. By subjecting various decarbonization scenarios to weather uncertainty, we also find that the amount of "firm" (natural gas) capacity needed to ensure reliability can differ significantly from values reported in a widely used capacity expansion model. Key Points: Changing capacity mixes cause "re‐ranking" of weather years in terms of annual priceNonetheless, the highest and lowest price years remain tied to extremes in hydropower production and loadCapacity expansion models may misrepresent firm capacity needed under weather uncertainty [ABSTRACT FROM AUTHOR]

Climate modeling studies and observations do not fully agree on the implications of anthropogenic warming for evapotranspiration (ET), a major component of the water cycle and driver of irrigation water demand. Here, we use California as a testbed to assess the ET impacts of changing atmospheric conditions induced by climate change on irrigated systems. Our analysis of irrigated agricultural and urban regions shows that warmer atmospheric temperatures have minimal implications for ET rates and irrigation water demands—about one percent change per degree Celsius warming (∼1% °C−1). By explicitly modeling irrigation, we control for the confounding effect of climate‐driven soil moisture changes and directly estimate water demand implications. Our attribution analysis of the drivers of ET response to global anthropogenic warming shows that as the atmospheric temperature and vapor pressure deficit depart from the ideal conditions for transpiration, regulation of stomata resistance by stressed vegetation almost completely offsets the expected increase in ET rates that would otherwise result from abiotic processes alone. We further show that anthropogenic warming of the atmosphere has minimal implications for mean relative humidity (<1.7% °C−1) and the surface available energy (<0.2% °C−1), which are critical drivers of ET. This study corroborates the growing evidence that plant physiological changes moderate the degree to which changes in potential ET are realized as actual ET. Plain Language Summary: Climate modeling studies and observations do not fully agree on the implications of climate change‐induced warming of the atmosphere for evapotranspiration (ET), a primary driver of irrigation water demand, despite its scientific and societal importance. Here, we use California as a testbed where we focus on vast irrigated urban and agricultural areas to assess the impacts of the warming climate on ET and irrigation water demand. Our analysis shows that warmer atmospheric temperatures have minimal implications for ET rates and irrigation water demand. We show that as the atmospheric temperature and humidity depart from the ideal conditions for transpiration, regulation of stomata resistance by stressed vegetation limits the increases in ET rates that would otherwise result from the increasing demand for moisture in the warmer atmosphere. Key Points: Increasing atmospheric temperature and vapor pressure deficit have minimal implications for evapotranspiration (ET) and irrigation water demandRegulation of stomata resistance by stressed vegetation offsets the expected increase in ET rates that would otherwise result from abiotic processes aloneAnthropogenic warming of the atmosphere has minimal implications for mean relative humidity and the surface available energy, which are critical drivers of ET [ABSTRACT FROM AUTHOR]

Determining wildland fire impacts on streamflow can be problematic as the hydrology in burned watersheds is influenced by post‐fire weather conditions. This study presents a quantile‐based analytical framework for assessing fire impacts on low and peak daily flow magnitudes, while accounting for post‐fire weather influences. This framework entails (a) the bootstrap method to compute the relative change in the post‐fire annual flow and weather statistics, (b) double mass analysis to detect if post‐fire baseflow and quick‐flow yield ratios are significantly altered, and (c) a quantile regression method to parse fire effects on flow at a specific quantile. We illustrate the applicability of this analytical framework using 44 western US streams with at least 5% of their watershed area burned. Results indicate that large, high‐severity burns in upland watersheds can raise the streamflow magnitude at the 0.05th and 0.95th quantiles for at least the five post‐fire years. Quantile regression results show that the median fire‐related increase in flow for the five post‐fire years can be up to 5,000% (Standard Error; S.E.< 2%) at the 0.05th quantile and 161% (S.E.< 10%) at the 0.95th quantile. The fire‐related increase in flow was often pronounced at the 0.05th quantile for streams in the Pacific Northwest and California regions. The difference in fire effects on flow (at both quantiles) across streams was related to post‐fire weather, pyrology, physiography, and land cover. The proposed analytical framework can be useful for detecting and quantifying fire effects on the low and peak stream flows in burned watersheds without overlapping disturbances. Key Points: Evaluating the impact of watershed burning on streamflow can be problematic due to the confounding effect of post‐fire weather influencesA quantile‐based analytical framework is presented for assessing fire effect on low & peak flow while accounting for weather influencesThis analytical framework can be useful for inferring fire effect on low & peak flows in burned watersheds without overlapping disturbances [ABSTRACT FROM AUTHOR]

With the development of space-based imaging technology, a larger and larger number of images with different modalities and resolutions are available. The optical images reflect the abundant spectral information and geometric shape of ground objects, whose qualities are degraded easily in poor atmospheric conditions. Although synthetic aperture radar (SAR) images cannot provide the spectral features of the region of interest (ROI), they can capture all-weather and all-time polarization information. In nature, optical and SAR images encapsulate lots of complementary information, which is of great significance for change detection (CD) in poor weather situations. However, due to the difference in imaging mechanisms of optical and SAR images, it is difficult to conduct their CD directly using the traditional difference or ratio algorithms. Most recent CD methods bring image translation to reduce their difference, but the results are obtained by ordinary algebraic methods and threshold segmentation with limited accuracy. Towards this end, this work proposes a deep translation based change detection network (DTCDN) for optical and SAR images. The deep translation firstly maps images from one domain (e.g., optical) to another domain (e.g., SAR) through a cyclic structure into the same feature space. With the similar characteristics after deep translation, they become comparable. Different from most previous researches, the translation results are imported to a supervised CD network that utilizes deep context features to separate the unchanged pixels and changed pixels. In the experiments, the proposed DTCDN was tested on four representative data sets from Gloucester, California, and Shuguang village. Compared with state-of-the-art methods, the effectiveness and robustness of the proposed method were confirmed. [ABSTRACT FROM AUTHOR]

Can farmers adapt to climate change by altering effective weather conditions on their fields? Existing technologies allow farmers to cool down plants by a few degrees during critical periods, reducing the damage from excess heat. With nonlinear effects of high temperatures on yields, slight cooling can bring significant gains in many crops. We call this approach "microclimate engineering" (MCE) and note that it could be useful as a climate change adaptation concept. Our case study deals with California pistachios, threatened by warming daytime temperatures in the winter. A new solution for sunlight reflection, based on products already used in other contexts and crops, could potentially help deal with this challenge. We develop a model to analyze grower choice and market outcomes with MCE for California pistachios. The expected increase in welfare for the period of 2020–2040 is assessed at 0.49–1.42 billion dollars under several scenarios. Simulation results show increases in consumer surplus and total welfare when MCE is available but decreases in aggregate grower profits. We also introduce market power to test its potential effects on the gains from MCE, finding mixed effects. [ABSTRACT FROM AUTHOR]

Stable flies (Stomoxys calcitrans) are blood-sucking insects commonly associated with cattle production systems worldwide and are known to cause severe irritation to cattle due to painful bites. Cattle react to biting stable flies with an aggregating behavior known as bunching. Bunching behavior reduces grazing or feed consumption and thus reduces cattle productivity and welfare. Cattle's fly-repelling behaviors include foot stomping, head tossing, tail switching and skin twitching. A longitudinal study was conducted in 2017 on 20 California dairies (average lactating herd size = 2,466 (SE±28.392)) during the stable fly season from April to July. The study objectives were to estimate the association between environmental factors and dairy characteristics including facility design, feed and manure management, total mixed ration (TMR) components fed to cattle, and operational pest management procedures and the outcome stable fly activity on California dairies. Stable fly activity was measured by counting stable flies on cow forelimbs (leg count) and on Alsynite traps (trap count) over the 13-week study period. Weekly leg counts were performed for cattle in lactating cow pens (31 pens from 10 study dairies) with counts made during the morning (AM) and again during the afternoon (PM). Trap counts were performed on all 20 study dairies. Data were analyzed using linear mixed models which revealed temporal variation in the average leg and trap counts with stable fly activity increasing from May to June and then decreasing to the lowest activity in July. Leg counts were higher during the afternoon compared to morning. Ambient temperatures ≤30⁰C and relative humidity (RH) measurements <50% were associated with higher leg and trap counts. Traps located at the periphery of study dairies had higher stable fly counts compared to traps located in the interior of the dairy. Cow pens with trees on the periphery had higher leg counts in comparison to pens away from trees. Specific TMR components were associated with both leg and trap counts. Dairies feeding by-products including almond hulls, wet distillers' grain, fruits, and vegetables had higher trap counts compared to dairies that did not feed these ingredients. At the pen level, pens with rations that contained straw had lower average leg counts compared to pens fed with rations that did not contain straw. A similar association was observed for pens with rations that contained wheat silage when ambient temperatures were ≤30⁰C. In contrast, pens with water added to the TMR while the RH was ≥50% had higher average leg counts compared to pens without water added to the TMR. Dairies that applied insecticides for fly control to their entire facility had lower trap counts compared to dairies that did not apply insecticides. Stable fly activity measured on California dairies using leg and trap counts varied according to the month, environmental factors, pen surroundings, trap location, TMR components, and insecticide use. [ABSTRACT FROM AUTHOR]

Objective: Determine compliance with and effectiveness of California regulations in reducing farmworkers' heat-related illness (HRI) risk and identify main factors contributing to HRI. Methods: In a cross-sectional study of Latino farmworkers, core body temperature (CBT), work rate, and environmental temperature (WBGT) were monitored over a work shift by individual ingestible thermistors, accelerometers, and weather stations, respectively. Multiple logistic modeling was used to identify risk factors for elevated CBT. Results: Although farms complied with Cal/OSHA regulations, worker training of HRI prevention and hydration replacement rates were insufficient. In modeling (AOR [95% CI]) male sex (3.74 [1.22 - 11.54]), WBGT (1.22 [1.08 - 1.38]), work rate (1.004 [1.002 - 1.006]), and increased BMI (1.11 [1.10 - 1.29]) were all independently associated with elevated CBT. Conclusion: Risk of HRI was exacerbated by work rate and environmental temperature despite farms following Cal/OSHA regulations. [ABSTRACT FROM AUTHOR]

This study demonstrates an incident detection algorithm that uses the meteorological and traffic parameters for improving the poor performance of the automatic incident detection (AID) algorithms under extreme weather conditions and for efficiently using the meteorological devices on advanced freeways. This algorithm comprises an incident detection module that is based on learning vector quantization (LVQ) and a meteorological influencing factor module. Field data are obtained from the Yuwu freeway in Chongqing, China, to verify the algorithm. Further, the performance of this algorithm is evaluated using commonly used criteria such as mean time to detection (MTTD), false alarm rate (FAR), and detection rate (DR). Initially, an experiment is conducted for selecting the algorithm architecture that yields the optimal detection performance. Additionally, a comparative experiment is performed using the California algorithm, exponential smoothing algorithm, standard normal deviation algorithm, and McMaster algorithm. The experimental results demonstrate that the algorithm proposed in this study is characterized by high DR, low FAR, and considerable suitability for applications in AID. [ABSTRACT FROM AUTHOR]

In the western United States, curly top disease (CTD) is caused by beet curly top virus (BCTV). In California, CTD causes economic loss to processing tomato production in central and southern areas but, historically, not in the north. Here, we document unusual CTD outbreaks in processing tomato fields in the northern production area in 2021 and 2022, and show that these were caused by the rare spinach curly top strain (BCTV-SpCT). These outbreaks were associated with proximity of fields to foothills and unusually hot, dry, and windy spring weather conditions, possibly by altering migrations of the beet leafhopper (BLH) vector from locations with BCTV-SpCT reservoirs. Support for this hypothesis came from the failure to observe CTD outbreaks and BLH migrations in 2023, when spring weather conditions were cool and wet. Our results show the climate-induced emergence of a rare plant virus strain to cause an economically important disease in a new crop and location. • Curly top disease (CTD) in the western US is caused by beet curly top virus (BCTV). • CTD affects processing tomato production economically in central and southern California, historically sparing the north. • Unprecedented CTD emerged in northern California in 2021 and 2022, involving a rare BCTV spinach curly top strain. • The lack of CTD in 2023 with cool, wet spring weather conditions highlight climate's role in virus emergence. [ABSTRACT FROM AUTHOR]

The spatiotemporal variations of fine particulate matter (PM 2.5) chemical composition have changed over time in the U.S. and increasing evidence indicated differential toxicity of PM 2.5 chemical composition. Thus, comprehensive explanation of PM 2.5 -related adverse health impacts in the U.S. necessitated a detailed analysis of spatiotemporal trends of PM 2.5 chemical composition. This research aims to analyze the changes in concentrations of PM 2.5 and its chemical composition in spatial and temporal scales in the conterminous U.S. The PM 2.5 mass concentration and chemical speciation data were downloaded from U.S. EPA Air Quality System (AQS) (2006–2020) to investigate the spatiotemporal changes of PM 2.5 and its chemical components. The results indicated that national annual average PM 2.5 concentration was significantly reduced from 11.38 ± 2.94 μg m−3 in 2006 to 8.20 ± 2.76 μg m−3 in 2020 with an average reduction of 0.21 μg m−3yr−1, mainly attributed to inorganic PM 2.5 reductions (i.e., ammonium (NH 4 +), nitrate (NO 3 −), and sulfate (SO 4 2−)) and the average reductions were 0.09 μg m−3yr−1, 0.02 μg m−3yr−1, and 0.06 μg m−3yr−1, respectively. The largest air quality improvements occurred in areas with the worst baseline air quality. Moreover, observed spikes in PM 2.5 in California in 2020 were due to higher concentrations of organic matter (OM) and elemental carbon (EC) caused by 2020 wildfires. Furthermore, while levels of SO 4 2−, NO 3 −, and NH 4 + almost levelled off in recent years, further air quality improvements may require targeting carbonaceous species. The heavily polluted days occurred less frequently in recent years and primary organic carbon (OC) accounted for a larger portion of OC in winter than in summer because of the relatively reduced formation rate of secondary organic aerosol (SOA). Our analysis revealed the spatial and temporal trends of various PM 2.5 chemical composition in the conterminous U.S. and provided insights into source contributions, atmospheric chemical conditions, and development of future emissions control strategies. [Display omitted] • Novel spatiotemporal trends analyses focusing on both PM 2.5 and chemical components. • Annual average PM 2.5 concentration was significantly reduced in 2006–2020 in U.S. • Larger air quality improvements occurred in areas with worse baseline air quality. • Spikes in 2020 annual average PM 2.5 in California were likely due to wildfires. • Further air quality improvements may require targeting carbonaceous species. [ABSTRACT FROM AUTHOR]

Weather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs. K‐means clustering) and the number of WRs. For different combinations of these components, we assess performance of a multi‐site WR‐SWG using 14 metrics across 13 major California river basins during the cold season. Results show that performance is best using a small number of WRs (4–5) identified using an HMM. We then juxtapose the number of WRs selected based on WR‐SWG performance against the number of regimes identified using metastability analysis of atmospheric fields. Results show strong agreement in the number of regimes between the two approaches, suggesting that the use of metastable regimes could inform WR‐SWG design. We conclude with a discussion of the potential to expand this framework for additional WR‐SWG design parameters and spatial scales. [ABSTRACT FROM AUTHOR]

Californian hydroclimate is strongly seasonal and prone to severe water shortages. Recent changes in climate trends have induced shifts in seasonality, thus exacerbating droughts, wildfires, and adverse water shortage effects on the environment and economy. Previous studies have examined the timing of the seasonal cycle shifts mainly as temperature driven earlier onset of the spring season. In this paper, we address quantitative changes in the onset, amounts, and termination of the precipitation season over the past 6 decades, as well as the large‐scale atmospheric circulation underpinning the seasonal cycle changes. We discover that the onset of the rainy season has been progressively delayed since the 1960s, and as a result the precipitation season has become shorter and sharper in California. The progressively later onset of the rainy season is shown to be related to the summer circulation pattern extending into autumn across the North Pacific, in particular, a delay in the strengthening of the Aleutian Low and later southward displacement of the North Pacific westerlies. Plain Language Summary: The rainy season over California is projected to show a distinct sharpening of the mean seasonal cycle, with winter precipitation increasing, and both autumn and spring precipitation decreasing. Our analysis of the past 6 decades of data for California suggests autumn decrease is already underway. A delayed start of the rainy season of 27 days since 1960s can exacerbate seasonal droughts and prolong the wildfire season. This delay occurs due to a number of conditions that controls precipitation: the summer circulation pattern has been extending throughout November across the North Pacific, and the wintertime strengthening of the Aleutian Low is delayed. Accordingly, the southward migration of the North Pacific jet stream as well as extratropical storm tracks are delayed, which marks the start of the California rainy season, are delayed. More work, using climate models, will be needed to provide a better understanding of atmospheric conditions across the North America and the North Pacific. However, our findings provide observational evidence for the projected rainfall change over California and inform ongoing discussion about the drying/wetting tendencies of the rainy season in California. Key Points: Rainfall data over the last 6 decades suggests a progressively delayed onset to the rainy season over CaliforniaA corresponding delay occurs in the transition from summer to wintertime circulation over the North PacificObserved autumn trends appear consistent with projected future shortening of the California rainy season [ABSTRACT FROM AUTHOR]

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Using a new modelling methodology known as "extreme event attribution," or EEA, climate scientists can now connect extreme weather to anthropogenic forcings. This paper seeks to uncover the significance of EEA for the epistemology of climate change, nature, and culture in the Anthropocene. First, we examine how EEA is emblematic of a larger turn in climate modelling, one that seeks to deploy anthropogenic climate change as an explanatory tool for an increasing number of socio‐natural phenomena. While some theorists have argued that the Anthropocene heralds the end of the nature–culture divide, we argue that EEA and similar modelling technologies seek to separate human influence from the natural variability of weather, thus establishing a new form of nature–culture divide mediated by computer simulation: a divide which we call "partitioned causality." Second, we demonstrate that partitioned causality is enabled by the relative hegemony of modelling technologies in climate change knowledge, as scientists retain substantial influence over who gets to "speak for" climate impacts. Finally, however, interviews with EEA scientists, journalists, and policymakers on the 2011–2017 California drought reveal that EEA remains a nascent scientific framework, one marked by epistemic slippage and divergent results. Thus, it serves as a powerful example of how emergent attempts to "domesticate" climate often become caught up in socio‐political conflicts around who – or what – has the power to shape discourses of climate change in the Anthropocene. This paper seeks to uncover the significance of extreme event attribution (EEA) for the epistemology of climate change, nature, and culture in the Anthropocene. We argue that, contrary to popular theories of the Anthropocene and its role in erasing the nature–culture divide, EEA and similar modelling technologies seek to separate human influence from the natural variability of weather, thus establishing a new form of nature–culture divide mediated by computer simulation – a divide we call "partitioned causality." We demonstrate that partitioned causality is enabled by the relative hegemony of modelling technologies in climate change knowledge, as scientists retain substantial influence over who gets to "speak for" climate impacts and influence. [ABSTRACT FROM AUTHOR]

Masting behavior — variable and synchronized reproduction by a population of plants — has long been recognized as correlating with weather. How and why weather conditions influence seed production is, however, poorly understood. We investigated the relationships between acorn production and both local weather and long-term climate in 10 populations across the geographic range of the valley oak (Quercus lobata Née), a California endemic that matures acorns in a single season. Acorn production was larger following a cold spring in the prior year and dry conditions in the winter and spring immediately preceding acorn maturation; similar patterns were also found, with minor differences, at all 10 individual sites. The strength of the relationships varied geographically in the case of the correlation between winter rainfall and annual acorn production, which was stronger (more negative) at wetter sites. Thus, in contrast to a recent study in Quercus petraea (Matt.) Liebl., weather had generally similar effects on acorn production throughout the range of Q. lobata. Similar to Q. petraea, however, the strength of the relationship between site-level annual acorn production and one of the weather factors affecting acorn production (winter rainfall in the case of Q. lobata) varied geographically in ways that may be related to differences among sites in the degree of pollen limitation. Understanding the mechanisms by which weather affects seed production is challenging but critical if we are to understand how climate change will affect masting behavior in the future. [ABSTRACT FROM AUTHOR]

Precipitation in California is highly variable and not well forecasted on subseasonal‐to‐seasonal (S2S) timescales. Understanding relationships between synoptic‐scale atmospheric circulation and hydrometeorological extremes could improve predictability. This work demonstrates the importance of four North Pacific circulation regimes (called the NP4 modes) in modulating precipitation, flooding, and water resources in California. Here we demonstrate how, on daily timescales, interactions between the NP4 modes drive coastal flow that can result in dry conditions from atmospheric ridging or wet conditions associated with enhanced onshore flow and atmospheric river (AR) landfalls. Seasonally, the prevalence of certain NP4 phase relationships can tip the scale toward wet or dry conditions. Relationships between El Niño Southern Oscillation (ENSO) and the NP4 are explored, and we provide insight into the poorly forecasted Western US seasonal precipitation during the "Godzilla" El Niño winter of 2016 by examining climate‐weather linkages in a historical context. Plain Language Summary: The amount of precipitation that falls over California in a given water year is highly variable, which presents challenges for water resource management. Skill of both dynamical and statistical models in forecasting precipitation is low at lead times beyond 2 weeks. Recent research has explored relationships between atmospheric circulation patterns and precipitation extremes in the western United States, with the goal of improving predictability. This work demonstrates the importance of four North Pacific circulation regimes in modulating coastal flow patterns that ultimately determines the amount and spatial distribution of precipitation over California on daily and seasonal timescales. We show how the interaction between these modes on daily timescales drives wet and dry episodes within a season. Seasonally, we show how the daily variability occurs within certain constraints established by the larger, more slowly varying climate system, including (but not limited to) El Niño‐Southern Oscillation. This work improves understanding of climate‐weather relationships, which has implications for predictability of hydrometeorological extremes over California and the western United States. Key Points: Coastal vapor transport and California precipitation are modulated by four North Pacific circulation regimes on daily to seasonal timescalesThe most damaging California floods have occurred when these modes were jointly aligned to reinforce onshore flowSeasonally, these modes are influenced by ENSO but much variability occurs within the constraints set by the larger‐scale climate system [ABSTRACT FROM AUTHOR]

In February 2017, a 5‐day sequence of atmospheric river storms in California, USA, resulted in extreme inflows to Lake Oroville, the state's second‐largest reservoir. Damage to the reservoir's spillway infrastructure necessitated evacuation of 188,000 people; subsequent infrastructure repairs cost $1 billion. We assess the atmospheric conditions, snowmelt, and runoff against major historical events. The event generated exceptional runoff volumes (second largest in a 30‐yr record) partially at odds with the event precipitation totals (ninth largest). We explain the discrepancy with observed record melt of deep antecedent snowpack, heavy rainfall extending to unusually high elevations, and high water vapor transport during the atmospheric river storms. An analysis of distributed snow water equivalent indicates that snowmelt increased water available for runoff watershed‐wide by 37% (25–52% at 90% confidence). The results highlight potential threats to public safety and infrastructure associated with a warmer and more variable climate. Plain Language Summary: In February 2017, extreme runoff into California's second‐largest reservoir, Lake Oroville, and cracks in the reservoir's spillways resulted in evacuations of thousands of people and major repair costs. We analyzed to what extent the atmospheric river storms that caused the extreme runoff were unusual in terms of precipitation, snowmelt, temperature, and moisture in the air. We found that the precipitation amounts were less unusual than the runoff amounts, suggesting that other factors were involved. We also found that snowmelt in the Sierra Nevada mountains above the reservoir was the heaviest on record at many locations, driven by unusually warm temperatures and deep preexisting snowpack before the storms began. Thus, the warm temperatures and record melt likely increased the water available for runoff by about a third during the spillways incident. Our findings are consistent with other studies that suggest that unusually warm temperatures during winter atmospheric river storms in the Western United States are associated with flood risk due to substantial rainfall and snowmelt. Other studies show that climate change is expected to increase the type of flood risk in the 2017 incident. Key Points: The atmospheric river event causing the 2017 Oroville Dam spillways incident was more exceptional for runoff than precipitation totalsHigh rain‐snow elevations, deep antecedent snowpack, and unprecedented snowmelt are shown to explain the discrepancyWe highlight the importance of considering snowmelt, rain‐snow elevations, and climate change in assessing current and future flood risk [ABSTRACT FROM AUTHOR]

Drought extent and severity have increased and are predicted to continue to increase in many parts of the world. Understanding tree vulnerability to drought at both individual and species levels is key to ongoing forest management and preparation for future transitions in community composition. The influence of subsurface hydrologic processes is particularly important in water‐limited ecosystems, and is an under‐studied aspect of tree drought vulnerability. With California's 2013–2016 extraordinary drought as a natural experiment, we studied four co‐occurring woodland tree species, blue oak (Quercus douglasii), valley oak (Quercus lobata), gray pine (Pinus sabiniana), and California juniper (Juniperus californica), examining drought vulnerability as a function of climate, lithology and hydrology using regional aerial dieback surveys and site‐scale field surveys. We found that in addition to climatic drought severity (i.e., rainfall), subsurface processes explained variation in drought vulnerability within and across species at both scales. Regionally for blue oak, severity of dieback was related to the bedrock lithology, with higher mortality on igneous and metamorphic substrates, and to regional reductions in groundwater. At the site scale, access to deep subsurface water, evidenced by stem water stable isotope composition, was related to canopy condition across all species. Along hillslope gradients, channel locations supported similar environments in terms of water stress across a wide climatic gradient, indicating that subsurface hydrology mediates species' experience of drought, and that areas associated with persistent access to subsurface hydrologic resources may provide important refugia at species' xeric range edges. Despite this persistent overall influence of the subsurface environment, individual species showed markedly different response patterns. We argue that hydrologic niche segregation can be a useful lens through which to interpret these differences in vulnerability to climatic drought and climate change. [ABSTRACT FROM AUTHOR]

Accurate forecasting of air quality demands better estimates of aerosol emissions. The accuracy of conventional bottom‐up approaches to estimate aerosol emissions depends on the degree to which various influencing parameters are estimated. The availability of satellite observations not only enhances the capability of determining various influencing parameters, but also provides alternate ways of assessing aerosol sources. The present study employs a Lagrangian approach to the Advection Diffusion Equation (ADE) to estimate the transported aerosols and hence the Aerosol Source Strength (ASS) using satellite‐measured Aerosol Optical Depth (AOD) and reanalysis wind data. This top‐down approach is based on the advection and diffusion of atmospheric aerosols considering wind circulation and atmospheric conditions rather than using indicative parameters. ASS was computed every 3 hr at a 0.25°×0.25° grid across California during July 2018. For the computation, AOD retrievals were obtained from the Geostationary Operational Environmental Satellite (GOES)‐16 with observations every 15 min. The data were resampled to the grid every 3 hr, and backward trajectories were run at every gridpoint to ascertain the initial aerosol concentration for the ADE. The final aerosol concentrations obtained from the ADE model were then compared with the observed AOD to obtain the ASS during that time period. The results are indicative of higher ASS around wildfire locations. The ASS values also show good correlation (R2=0.886) with Fire Radiative Power (FRP) obtained from Terra MODIS fire product. The method was further applied to investigate the spatial correlation of ASS with power plant density, which reveals a steady increase in ASS with power plant density (R2=0.82). [ABSTRACT FROM AUTHOR]