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Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS-wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water-related electricity use, which can be linked with a grid expansion model to support climate-resilient electricity planning. The water system model combines climatically-driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water-related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy-intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid-century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water-related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water-for-energy and energy-for-water to inform planning for grid reliability and decarbonization goals.

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 (

Anthropogenic climate change is decreasing seasonal snowpacks globally, with potentially catastrophic consequences on water resources, given the long-held reliance on snowpack in water management. In this Review, we examine the changes and trickle-down impacts of snow loss in the western United States (WUS). Across the WUS, snow water equivalent declines of ~25% are expected by 2050, with losses comparable with contemporary historical trends. There is less consensus on the time horizon of snow disappearance, but model projections combined with a new low-to-no snow definition suggest ~35–60 years before low-to-no snow becomes persistent if greenhouse gas emissions continue unabated. Diminished and more ephemeral snowpacks that melt earlier will alter groundwater and streamflow dynamics. The direction of these changes are difficult to constrain given competing factors such as higher evapotranspiration, altered vegetation composition and changes in wildfire behaviour in a warmer world. These changes undermine conventional WUS water management practices, but through proactive implementation of soft and hard adaptation strategies, there is potential to build resilience to extreme, episodic and, eventually, persistent low-to-no snow conditions. Federal investments offer a timely opportunity to address these vulnerabilities, but they require a concerted portfolio of activities that cross historically siloed physical and disciplinary boundaries.

Developing decision-relevant science for adaptation requires the identification of climatic parameters that are both actionable for practitioners as well as tractable for modelers. In many sectors, these decision-relevant climatic metrics and the approaches that enable their identification remain largely unknown. “Co-production” of science with scientists and decision-makers is one potential way to identify these metrics, but there is little research describing specific and successful co-production approaches. This paper examines the negotiations and outcomes from Project Hyperion, wherein scientists and water managers jointly developed decision-relevant climatic metrics for adaptive water management. We identify successful co-production strategies by analyzing the project's numerous back-and-forth engagements and tracing the evolution of the science during these engagements. We found that effective mediation between scientists and managers needed dedicated “boundary spanners” with significant modeling expertise. Translating practitioners' information needs into tractable climatic metrics required direct and indirect methods of eliciting knowledge. We identified four indirect methods that were particularly salient for extracting tacitly held knowledge and enabling shared learning: developing a hierarchical framework linking management issues with metrics, starting discussions from the planning challenges, collaboratively exploring the planning relevance of new scientific capabilities, and using analogies of other “good” metrics. The decision-relevant metrics we developed provide insights into advancing adaptation-relevant climate science in the water sector. The co-production strategies we identified can be used to design and implement productive scientist-decision-maker interactions. Overall, the approaches and metrics we developed can help climate science to expand in new and more use-inspired directions.

Atmospheric rivers (ARs) are critical to the hydrological cycle of the western United States with both favorable and formidable impacts to society based on their landfalling characteristics. In this study, we provide a first-of-its-kind evaluation of how landfalling ARs may respond to several stabilized warming scenarios. To do this we combine a recently developed AR detection workflow with an ensemble of uniform high-resolution (0.25°) Community Earth System Model simulations designed to facilitate detection and attribution of extreme events with global warming. These simulations include a world that might have been in the absence of anthropogenic warming (+0◦C), a world that corresponds to present day warming (+0.85◦C), and several future worlds corresponding to +1.5◦C, +2◦C and +3◦C global warming. We show that warming increases the number of water management relevant landfalling ARs from 19.1 ARs per year at +0◦C to 23.6 ARs per year at +3◦C. Additionally, this warming intensifies the amount of water transported by landfalling ARs resulting in a decrease in the fraction of ARs that are “mostly to primarily beneficial” to water resource management (i.e., 91% of ARs at +0◦C to 78% at +3◦C) and an increase in the fraction of ARs that are “mostly or primarily hazardous” to water resource management (i.e., 2% of ARs at +0◦C to 8% at +3◦C). Shifts in AR character also have important ramifications on flood damages, whereby for every +1◦C of additional warming from present conditions annual average flood damages increase by ~$1 billion. These findings highlight the pragmatic implications of climate mitigation aimed at limiting global warming to under +2◦C.

We introduce variable-resolution enabled Community Earth System Model (VR-CESM) results simulating historical and future climate conditions at 28 km over South America and 14 km over the Andes. Three 30-year simulations are performed: a historic (1985–2014), a near future (2030–2059), and an end-century (2070–2099) simulation under the RCP8.5 scenario. Historic results compare favourably to several temperature and precipitation reanalysis products, though local biases are present, particularly during austral summer. Future simulations highlight broad warming patterns (+3–6°C by end-century) and heterogeneous precipitation responses across South America that qualitatively agree with prior modelling efforts. Our results reveal that the interaction between temperature and precipitation changes produce shifts in several Köppen–Geiger climates. Notable changes include the near-elimination of the Andean Tundra or Alpine climates, a 15% decrease in Tropical Rainforests and a Tropical Savannah expansion of 20%. To provide a regionally focused analysis of projected climate change and to illustrate the benefits of variable resolution modelling, we analyse changes in the magnitude and trend in seasonal and daily temperature and precipitation in Chile. We also examined several metrics [e.g., snow water equivalent (SWE), temperatures on wet days, and days below 0°C] to evaluate potential impacts of climate change on the Chilean cryosphere between the end-of-century and historic periods, finding wide-ranging indications of cryospheric decline. These changes are interpreted through reductions in the timing (1–2.5 months earlier peak SWE) and magnitude (200–1,000 mm SWE decreases) of water stored as snow in the Andes, a 10–30% decrease in number of cool season wet days with temperatures below 1°C, and 50–200 fewer days (annually) with minimum temperatures below 0°C. Our aim in producing a high-resolution dataset of climate projections from VR-CESM is to support analyses of climate change throughout South America but especially in vulnerable montane regions and to provide additional results for comparison with previous, ongoing, and upcoming modelling efforts.

Atmospheric rivers (ARs) can be a boon and bane to water resource managers as they have the ability to replenish water reserves, but they can also generate million-to-billion-dollar flood damages. To investigate how anthropogenic climate change may influence AR characteristics in the coastal western United States by end century, we employ a suite of novel tools such as variable resolution in the Community Earth System Model (VR-CESM), the TempestExtremes AR detection algorithm, and the Ralph, Rutz, et al. (2019, https://doi.org/10.1175/BAMS-D-18-0023.1) AR category scale. We show that end-century ARs primarily shift from being “mostly or primarily beneficial” to “mostly or primarily hazardous” with a concomitant sharpening and intensification of winter season precipitation totals. Changes in precipitation totals are due to a significant increase in AR (+260%) rather than non-AR (+7%) precipitation, largely through increases in the most intense category of AR events and a decrease in the interval between landfalling ARs.

Variable-resolution global climate models (VRGCMs) are a dynamical downscaling method that can reach spatiotemporal scales needed for regional climate assessments. Over the years, several users of VRGCMs have assumed where the location and extent of the refinement domain should be based on knowledge of the prevailing storm tracks and resolution dependence of important regional climate processes (e.g., atmospheric rivers [ARs] and orographic uplift), but the effect of high-resolution domain size and extent on the simulation of downstream hydroclimatic phenomena has not been systematically evaluated. Here, we use variable resolution in the Community Earth System Model (VR-CESM) to perform such a test. To do this, three VR-CESM grids were generated that span the entire, two thirds, and one third of the North Pacific and evaluated for a 30-year climatology using Atmospheric Model Intercomparison Project protocols. Simulations are compared with reanalysis products offshore (fifth-generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis [ERA5]) and onshore (Livneh, 2015, https://doi.org/10.1038/sdata.2015.42, and Parameter-elevation Regressions on Independent Slopes Model [PRISM]) of the western United States. The westward expansion of refinement domain influenced integrated vapor transport (IVT), which was generally high biased but minimally impacted AR characteristics. Due to slight differences in landfalling AR counts in the western United States, California winter precipitation generally improved with westward expansion of the refinement domain. Western U.S. mountain snowpack and surface temperatures were insensitive to refinement domain size and were more influenced by changes in topographic resolution and/or land surface model version. Given minimal dependence of simulated western U.S. hydroclimate on refinement domain size over the North Pacific, we advise future VR-CESM studies to focus grid resolution on better resolving land surface heterogeneity.

Decision-makers today have relatively easy web-based access to climate projections from several different models and downscaled datasets. Yet, there is minimal guidance on the credibility and appropriate use of such models and projections for specific adaptation contexts. The few studies that provide recommendations on model choice are often based on evaluations of broad physical climate metrics (such as temperature averages or extremes) at regional scales, without additional examination of local-scale decision-relevant climatic metrics (such as growing degree days or chill hours) that underpin the adaptation action. While such broad regional skill may be considered necessary for the overall credibility of models, it is not clear whether it is sufficient to ensure good skill for decision applications. This paper evaluates the skill of different Global Circulation Models (GCMs) in predicting the decision-relevant metric of chill hours in Fresno, California, and examines how model selection impacts future projections. We find that good skill in predicting broader physical climate metrics in California does not guarantee skill in prediction of chill hours in Fresno. In fact, the models with good regional climatic skill were mutually exclusive of the ones with good skill for chill hours, which leads to some counterintuitive results for this unique metric. Since many decision-relevant metrics are non-linear derivations of primary physical quantities (like the chill hour metric), more such decision-relevant model evaluations are needed to provide better insights on model credibility and choice for adaptation decisions.

Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that finescale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region's climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

Electricity and water systems are inextricably linked through water demands for energy generation, and through energy demands for using, moving, and treating water and wastewater. Climate change may stress these interdependencies, together referred to as the energy-water nexus, by reducing water availability for hydropower generation and by increasing irrigation and electricity demand for groundwater pumping, among other feedbacks. Further, many climate adaptation measures to augment water supplies - such as water recycling and desalination - are energy-intensive. However, water and electricity system climate vulnerabilities and adaptations are often studied in isolation, without considering how multiple interactive risks may compound. This paper reviews the fragmented literature and develops a generalized framework for understanding these implications of climate change on the energy-water nexus. We apply this framework in a case study to quantify end-century direct climate impacts on California's water and electricity resources and estimate the magnitude of the indirect cross-sectoral feedback of electricity demand from various water adaptation strategies. Our results show that increased space cooling demand and decreased hydropower generation are the most significant direct climate change impacts on California's electricity sector by end-century. In California's water sector, climate change impacts directly on surface water availability exceed demand changes, but have considerable uncertainty, both in direction and magnitude. Additionally, we find that the energy demands of water sector climate adaptations could significantly affect California's future electricity system needs. If the worst-case water shortage occurs under climate change, water-conserving adaptation measures can provide large energy savings co-benefits, but other energy-intensive water adaptations may double the direct impacts of climate change on the state's electricity resource requirement. These results highlight the value of coordinated adaptation planning between the energy and water sectors to achieve mutually beneficial solutions for climate resilience.

One near-term expression of climate change is increased occurrence and intensity of extreme heat events. The evolution of extreme heat risk in cities depends on the interactions of large-scale climate change with regional dynamics and urban micro-climates as well as the distribution and demographic characteristics of people who live and work within these micro-climate areas. Here we use California as a testbed where we employ a suite of high-resolution (1.5 km) future regional climate simulations coupled with a satellite-driven urban canopy model and a spatially explicit population projection to investigate the interacting effects of climate change, population growth, and urban heat mitigation measures, such as cool roofs, on exposure to extreme heat events. We find that climate change and population growth reinforce with one another to drive substantial increases in future exposure to heat extremes, which are poised to become more frequent, longer, and more intense. Exposure to events analogous to historic high-mortality extreme heat waves increases by 3.5-6 folds. Widespread implementation of cool roofs can offset a substantial fraction (51%-100%) of the increased heat exposure and associated building energy demand owing to climate change in urbanized regions.

The mountains of the Western United States provide a vital natural service through the storage and release of mountain snowpack, lessening impacts of seasonal aridity and satiating summer water demand. However, climate change continues to undermine these important processes. To understand how snowpack may change in the headwaters of California's major reservoirs, the North American Coordinated Regional Climate Downscaling Experiment is analyzed to assess peak water volume, peak timing, accumulation rate, melt rate, and snow season length across both latitudinal and elevational gradients. Under a high-emissions scenario, end-of-century peak snowpack timing occurs 4 weeks earlier and peak water volume is 79.3% lower. The largest reductions are above Shasta, Oroville, and Folsom and between 0- and 2,000-m elevations. Regional climate model and global forcing data set choice is important in determining historical snowpack character, yet by end century all models show a significant and similar decline in mountain snowpack.

The California drought of 2012-2016 was a record-breaking event with extensive social, political, and economic repercussions. The impacts were widespread and exposed the difficulty in preparing for the effects of prolonged dry conditions. Although the lessons from this drought drove important changes to state law and policy, there is little doubt that climate change will only exacerbate future droughts. To understand the character of future drought, this paper examines this recent drought period retrospectively and prospectively, that is, as it occurred historically and if similar dynamical conditions to the historical period were to arise 30 years later (2042-2046) subject to the effects of climate change. Simulations were conducted using the Weather Research and Forecasting model using the pseudo global warming method. The simulated historical and future droughts are contrasted in terms of temperature, precipitation, snowpack, soil moisture, evapotranspiration, and forest health. Overall, the midcentury drought is observed to be significantly worse, with many more extreme heat days, record-low snowpack, increased soil drying, and record-high forest mortality. With these findings in mind, the data sets developed in this study provide a means to structure future drought planning around a drought scenario that is realistic and modeled after a memorable historical analog.

Changes in land and ocean carbon storage in response to elevated atmospheric carbon dioxide concentrations and associated climate change, known as the concentration-carbon and climate-carbon feedbacks, are principal controls on the response of the climate system to anthropogenic greenhouse gas emissions. Such feedbacks have typically been quantified in the context of natural ecosystems, but land management activities are also responsive to future atmospheric carbon and climate changes. Here we show that inclusion of such human-driven responses within an Earth system model shifts both the terrestrial concentration-carbon and climate-carbon feedbacks toward increased carbon storage. We introduce a conceptual framework for decomposing these changes into separate concentration-land cover, climate-land cover, and land cover-carbon effects, providing a parsimonious means to diagnose sources of variation across numerical models capable of estimating such feedbacks.

Anthropogenic climate change will continue to diminish the unique role that mountains perform as natural reservoirs and alter long-held assumptions of water management. Climate models are important tools to help constrain uncertainty and understand processes that shape this decline. To ensure that climate model estimates provide stakeholder relevant information, the formulation of multimetric model evaluation frameworks informed by stakeholder interactions are critical. In this study, we present one such multimetric framework to evaluate snowpack data sets in the California Sierra Nevada: the snow water equivalent (SWE) triangle. SWE triangle metrics help to describe snowpack characteristics associated with total water volume buildup, peak water availability, and the rate of water release. This approach highlights compensating errors that would not be reflected in conventional large-scale spatiotemporal analysis. To test our multimetric evaluation framework, we evaluate several publicly available snow products including the Sierra Nevada Snow Reanalysis, Livneh (L15), and the North American Land Data Assimilation System version 2 data sets. We then evaluate regional climate model skill within the North American Coordinated Regional Climate Downscaling Experiment. All data sets analyzed show variation across the various SWE triangle metrics, even within observationally constrained snow products. This spread was especially shown in spring season melt rates. Melt rate biases were prevalent throughout most regional climate model simulations, regardless of snow accumulation dynamics, and will need to be addressed to improve their utility for water stakeholders.

Fossil fuel combustion and land-use change are the two largest contributors to industrial-era increases in atmospheric CO2 concentration. Projections of these are thus fundamental inputs for coupled Earth system models (ESMs) used to estimate the physical and biological consequences of future climate system forcing. While historical data sets are available to inform past and current climate analyses, assessments of future climate change have relied on projections of energy and land use from energy-economic models, constrained by assumptions about future policy, land-use patterns and socio-economic development trajectories. Here we show that the climatic impacts on land ecosystems drive significant feedbacks in energy, agriculture, land use and carbon cycle projections for the twenty-first century. We find that exposure of human-appropriated land ecosystem productivity to biospheric change results in reductions of land area used for crops; increases in managed forest area and carbon stocks; decreases in global crop prices; and reduction in fossil fuel emissions for a low-mid-range forcing scenario. The feedbacks between climate-induced biospheric change and human system forcings to the climate system - demonstrated here - are handled inconsistently, or excluded altogether, in the one-way asynchronous coupling of energy-economic models to ESMs used to date.

Human land-use activities have resulted in large changes to the Earth's surface, with resulting implications for climate. In the future, land-use activities are likely to expand and intensify further to meet growing demands for food, fiber, and energy. The Land Use Model Intercomparison Project (LUMIP) aims to further advance understanding of the impacts of land-use and land-cover change (LULCC) on climate, specifically addressing the following questions. (1) What are the effects of LULCC on climate and biogeochemical cycling (past-future)? (2) What are the impacts of land management on surface fluxes of carbon, water, and energy, and are there regional land-management strategies with the promise to help mitigate climate change? In addressing these questions, LUMIP will also address a range of more detailed science questions to get at process-level attribution, uncertainty, data requirements, and other related issues in more depth and sophistication than possible in a multi-model context to date. There will be particular focus on the separation and quantification of the effects on climate from LULCC relative to all forcings, separation of biogeochemical from biogeophysical effects of land use, the unique impacts of land-cover change vs. land-management change, modulation of land-use impact on climate by land-atmosphere coupling strength, and the extent to which impacts of enhanced CO2 concentrations on plant photosynthesis are modulated by past and future land use.LUMIP involves three major sets of science activities: (1) development of an updated and expanded historical and future land-use data set, (2) an experimental protocol for specific LUMIP experiments for CMIP6, and (3) definition of metrics and diagnostic protocols that quantify model performance, and related sensitivities, with respect to LULCC. In this paper, we describe LUMIP activity (2), i.e., the LUMIP simulations that will formally be part of CMIP6. These experiments are explicitly designed to be complementary to simulations requested in the CMIP6 DECK and historical simulations and other CMIP6 MIPs including ScenarioMIP, C4MIP, LS3MIP, and DAMIP. LUMIP includes a two-phase experimental design. Phase one features idealized coupled and land-only model simulations designed to advance process-level understanding of LULCC impacts on climate, as well as to quantify model sensitivity to potential land-cover and land-use change. Phase two experiments focus on quantification of the historic impact of land use and the potential for future land management decisions to aid in mitigation of climate change. This paper documents these simulations in detail, explains their rationale, outlines plans for analysis, and describes a new subgrid land-use tile data request for selected variables (reporting model output data separately for primary and secondary land, crops, pasture, and urban land-use types). It is essential that modeling groups participating in LUMIP adhere to the experimental design as closely as possible and clearly report how the model experiments were executed.

We demonstrate the effectiveness of a new method for quantifying radiative forcing from land use and land cover change (LULCC) within an integrated assessment model, the Global Change Assessment Model (GCAM). The method relies on geographically differentiated estimates of radiative forcing from albedo change associated with major land cover transitions derived from the Community Earth System Model. We find that conversion of 1 km2 of woody vegetation (forest and shrublands) to non-woody vegetation (crops and grassland) yields between 0 and −0.71 nW/m2 of globally averaged radiative forcing determined by the vegetation characteristics, snow dynamics, and atmospheric radiation environment characteristic within each of 151 regions we consider globally. Across a set of scenarios designed to span a range of potential future LULCC, we find LULCC forcing ranging from −0.06 to −0.29 W/m2 by 2070 depending on assumptions regarding future crop yield growth and whether climate policy favors afforestation or bioenergy crops. Inclusion of this previously uncounted forcing in the policy targets driving future climate mitigation efforts leads to changes in fossil fuel emissions on the order of 1.5 PgC/yr by 2070 for a climate forcing limit of 4.5 Wm−2, corresponding to a 12–67 % change in fossil fuel emissions depending on the scenario. Scenarios with significant afforestation must compensate for albedo-induced warming through additional emissions reductions, and scenarios with significant deforestation need not mitigate as aggressively due to albedo-induced cooling. In all scenarios considered, inclusion of albedo forcing in policy targets increases forest and shrub cover globally.

The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human-Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. The iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human-Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.

Conventional chemical analyses of incineration by-products identify compounds of known toxicity but often fail to indicate the presence of other chemicals that may pose health risks. In a previous report, extracts from soot aerosols formed during incomplete combustion of trichloroethylene (TCE) and pyrolysis of plastics exhibited a dioxinlike response when subjected to a keratinocyte assay. To verify this dioxinlike effect, the complete extract, its polar and nonpolar fractions, some containing primarily halogenated aromatic hydrocarbons, were evaluated for toxicity using an embryo assay, for antiestrogenicity using primary liver cell cultures, and for the ability to transform the aryl hydrocarbon receptor into its DNA binding form using liver cytosol in a gel retardation assay. Each of these assays detect dioxinlike effects. Medaka (Oryzias latipes) embryos and primary liver cell cultures of rainbow trout (Oncorhynchus mykiss) were exposed to concentrations of extract ranging from 0.05 to 45 micrograms/l. Cardiotoxicity with pericardial, yolk sac, and adjacent peritoneal edema occurred after exposure of embryos to concentrations of 7 micrograms/l or greater. These same exposure levels were associated with abnormal embryo development and, at the higher concentrations, death. Some of the fractions were toxic but none was as toxic as the whole extract. In liver cells, total cellular protein and cellular lactate dehydrogenase activity were not altered by in vitro exposure to whole extract (0.05-25 micrograms/l). However, induction of cytochrome P4501A1 protein and ethoxyresorufin O-deethylase activity occurred. In the presence of whole extract, estradiol-dependent vitellogenin synthesis was reduced. Of the fractions, only fraction 1 (nonpolar) showed a similar trend, although vitellogenin synthesis inhibition was not significant. The soot extract and fractions bound to the Ah receptor and showed a significantly positive result in the gel retardation/DNA binding test. Chemical analyses using GC-MS with detection limits for 2,3,7,8-tetrachlorodibenzo-p-dioxin and dibenzofuran in the picomole range did not show presence of these compounds. Our results indicate that other chemicals associated with TCE combustion and not originally targeted for analysis may also pose health risks through dioxinlike mechanisms.