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High‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally. Plain Language Summary: The STAR‐ESDM tool is able to quickly and accurately generate future climate projections for weather stations and high‐resolution grids anywhere in the world. It does this by breaking down global or regional climate model output into different components, from the long‐term trend to the day‐to‐day variability, then merging projected changes with observations. When tested against projections generated by a complex and computationally expensive dynamical global model, STAR‐ESDM produced almost the same output, even for extreme temperature and precipitation values, at a fraction of the computational cost. Moreover, unlike most statistical downscaling models, this method isn't tied to any specific geographic area or predictand and/or predictor data set. It can be applied to any regional or global data set, whether generated by a climate or reanalysis model, derived from satellite observations, recorded at weather stations, and more. As climate impacts escalate, STAR‐ESDM offers a flexible and effective way to generate the high‐resolution climate projections needed to better gauge climate risk and enhance resilience anywhere in the world where reliable observational or quasi‐observational data, including reanalysis or satellites, are available. This is particularly relevant to under‐resourced regions, which are often most vulnerable to climate impacts as well as most lacking in future projections. Key Points: STAR‐ESDM is a rapid, flexible and generalizable approach to bias‐correct and downscale climate model output to any finer‐resolution data setPredictors/predictands can be derived from global or regional models, satellites, reanalysis, gridded observations and weather stationsProjected changes in temperature and precipitation mirror those of a high‐resolution global model at a fraction of the computational cost [ABSTRACT FROM AUTHOR]

Given the increases in spatial resolution and other improvements in climate modeling capabilities over the last decade since the CMIP3 simulations were completed, CMIP5 provides a unique opportunity to assess scientific understanding of climate variability and change over a range of historical and future conditions. With participation from over 20 modeling groups and more than 40 global models, CMIP5 represents the latest and most ambitious coordinated international climate model intercomparison exercise to date. Observations dating back to 1900 show that the temperatures in the twenty-first century have the largest spatial extent of record breaking and much above normal mean monthly maximum and minimum temperatures. The 20-yr return value of the annual maximum or minimum daily temperature is one measure of changes in rare temperature extremes.

Future disruptions to fire activity will threaten ecosystems and human well-being throughout the world, yet there are few fire projections at global scales and almost none from a broad range of global climate models (GCMs). Here we integrate global fire datasets and environmental covariates to build spatial statistical models of fire probability at a 0.5° resolution and examine environmental controls on fire activity. Fire models are driven by climate norms from 16 GCMs (A2 emissions scenario) to assess the magnitude and direction of change over two time periods, 2010–2039 and 2070–2099. From the ensemble results, we identify areas of consensus for increases or decreases in fire activity, as well as areas where GCMs disagree. Although certain biomes are sensitive to constraints on biomass productivity and others to atmospheric conditions promoting combustion, substantial and rapid shifts are projected for future fire activity across vast portions of the globe. In the near term, the most consistent increases in fire activity occur in biomes with already somewhat warm climates; decreases are less pronounced and concentrated primarily in a few tropical and subtropical biomes. However, models do not agree on the direction of near-term changes across more than 50% of terrestrial lands, highlighting major uncertainties in the next few decades. By the end of the century, the magnitude and the agreement in direction of change are projected to increase substantially. Most far-term model agreement on increasing fire probabilities (∼62%) occurs at mid- to high-latitudes, while agreement on decreasing probabilities (∼20%) is mainly in the tropics. Although our global models demonstrate that long-term environmental norms are very successful at capturing chronic fire probability patterns, future work is necessary to assess how much more explanatory power would be added through interannual variation in climate variables. This study provides a first examination of global disruptions to fire activity using an empirically based statistical framework and a multi-model ensemble of GCM projections, an important step toward assessing fire-related vulnerabilities to humans and the ecosystems upon which they depend.

Climate change is expected to alter the geographic distribution of wildfire, a complex abiotic process that responds to a variety of spatial and environmental gradients. How future climate change may alter global wildfire activity, however, is still largely unknown. As a first step to quantifying potential change in global wildfire, we present a multivariate quantification of environmental drivers for the observed, current distribution of vegetation fires using statistical models of the relationship between fire activity and resources to burn, climate conditions, human influence, and lightning flash rates at a coarse spatiotemporal resolution (100 km, over one decade). We then demonstrate how these statistical models can be used to project future changes in global fire patterns, highlighting regional hotspots of change in fire probabilities under future climate conditions as simulated by a global climate model. Based on current conditions, our results illustrate how the availability of resources to burn and climate conditions conducive to combustion jointly determine why some parts of the world are fire-prone and others are fire-free. In contrast to any expectation that global warming should necessarily result in more fire, we find that regional increases in fire probabilities may be counter-balanced by decreases at other locations, due to the interplay of temperature and precipitation variables. Despite this net balance, our models predict substantial invasion and retreat of fire across large portions of the globe. These changes could have important effects on terrestrial ecosystems since alteration in fire activity may occur quite rapidly, generating ever more complex environmental challenges for species dispersing and adjusting to new climate conditions. Our findings highlight the potential for widespread impacts of climate change on wildfire, suggesting severely altered fire regimes and the need for more explicit inclusion of fire in research on global vegetation-climate change dynamics and conservation planning.

Possible future climate changes in California are investigated from a varied set of climate change model simulations. These simulations, conducted by three state-of-the-art global climate models, provide trajectories from three greenhouse gas (GHG) emission scenarios. These scenarios and the resulting climate simulations are not “predictions,” but rather are a limited sample from among the many plausible pathways that may affect California’s climate. Future GHG concentrations are uncertain because they depend on future social, political, and technological pathways, and thus the IPCC has produced four “families” of emission scenarios. To explore some of these uncertainties, emissions scenarios A2 (a medium-high emissions) and B1 (low emissions) were selected from the current IPCC Fourth climate assessment, which provides several recent model simulations driven by A2 and B1 emissions. The global climate model simulations addressed here were from PCM1, the Parallel Climate Model from the National Center for Atmospheric Research (NCAR) and U.S. Department of Energy (DOE) group, and CM2.1 from the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluids Dynamics Laboratory (GFDL). As part of the scenarios assessment, a statistical technique using properties of historical weather data was employed to correct model biases and “downscale” the global-model simulation of future climates to a finer level of detail, onto a grid of approximately 7 miles (12 kilometers), which is more suitable for impact studies at the scales needed by California decision makers. In current climate-change simulations, temperatures over California warm significantly during the twenty-first century, with temperature increases from approximately +3ºF (1.5ºC) in the lower emissions scenario (B1) within the less responsive model (PCM1) to +8ºF (4.5ºC) in the higher emissions scenario (A2) within the more responsive model (CM2.1). Three of the simulations (all except the low-emission scenario run of the low-response model) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter, with virtually all derived from North Pacific winter storms. Relatively little change in overall precipitation is projected. Climate warming has a profound influence in diminishing snow accumulations, because there is more rain and less snow, and earlier snowmelt. These snow losses increase as the warming increases, so that they are most severe under climate changes projected by the more sensitive model with the higher GHG emissions.

This white paper focuses on how climate change-induced effects on weather will translate into changes in wildland fire severity and outcomes, particularly on the effectiveness of initial attack at limiting the area burned in contained fires and the number of fires that escape initial attack. Prior research has indicated that there is a potential for significant increases in the number of fires escaping initial attack, particularly in areas in which the fuel matrix is dominated by grass and brush. These results were driven primarily by predicted increases in wind speeds. Those findings, however, were derived using less sophisticated models of initial attack than currently available. The results of this study, using more sophisticated models and climate projections, indicate that subtle shifts in fire behavior of the sort that might be induced by the climate changes anticipated for the next century are of sufficient magnitude to generate an appreciable increase in the number of fires that escape initial attack, at least for areas where brush fuels dominate. Such escapes of considerable importance in wildland fire protection planning, given the high cost to society of a catastrophic escape like those experienced in recent decades in the BerkeleyOakland, Santa Barbara, San Diego, or Los Angeles areas. However, at least for the limited region in the Sierra Nevada considered in this study, it would appear that relatively modest augmentations to existing firefighting resources would be sufficient to compensate for changeinduced changes in wildland fire outcomes. Generalizing our findings with respect to wildland fire intensity and outcomes for the AmadorEl Dorado Unit to other private lands in the state will require both further analysis using the data for that CDF unit, and replication of this analysis using data for several other units and for the state’s federal forest lands. No attempt is made in this paper to extrapolate the results beyond the boundaries of the study area, in part because of necessary work that was identified with respect to further validation of this study’s modeling approach.

California’s coastal observations and global model projections indicate that California’s open coast and estuaries will experience increasing sea levels over the next century. Sea level rise has affected much of the coast of California, including the Southern California coast, the Central California open coast, and the San Francisco Bay and upper estuary. These trends, quantified from a small set of California tide gages, have ranged from 10–20 centimeters (cm) (3.9–7.9 inches) per century, quite similar to that estimated for global mean sea level. So far, there is little evidence that the rate of rise has accelerated, and the rate of rise at California tide gages has actually flattened since 1980, but projections suggest substantial sea level rise may occur over the next century. Climate change simulations project a substantial rate of global sea level rise over the next century due to thermal expansion as the oceans warm and runoff from melting land-based snow and ice accelerates. Sea level rise projected from the models increases with the amount of warming. Relative to sea levels in 2000, by the 2070–2099 period, sea level rise projections range from 11–54 cm (4.3–21 in) for simulations following the lower (B1) greenhouse gas (GHG) emissions scenario, from 14–61 cm (5.5–24 in) for the middle-upper (A2) emission scenario, and from 17–72 cm (6.7–28 in) for the highest (A1fi) scenario. In addition to relatively steady secular trends, sea levels along the California coast undergo shorter period variability above or below predicted tide levels and changes associated with long-term trends. These variations are caused by weather events and by seasonal to decadal climate fluctuations over the Pacific Ocean that in turn affect the Pacific coast. Highest coastal sea levels have occurred when winter storms and Pacific climate disturbances, such as El Niño, have coincided with high astronomical tides. This study considers a range of projected future global sea level rises in examining possible impacts at California coastal and estuarine stations. Two climate models and three scenarios considered in this scenarios study provide a set of possible future weather and short-period climate fluctuations, and a range of potential long-term sea level rise values. A range of mean sea level rise was considered in combination with weather and El Niño fluctuations extracted from two global climate models and two GHG emissions scenarios. The mean sea level rise values, determined from a survey of several climate models, range from approximately 10–80 cm (3.9–31 in) between 2000 and 2100. The middle to higher end of this range would substantially exceed the historical rate of sea level rise of 15–20 cm (5.9–7.9 in)per century observed at San Francisco and San Diego during the last 100 years. Gradual sea level rise progressively worsens the impacts of high tides and the surge and waves associated with storms. The potential for impacts of future sea level rise was assessed from the occurrence of hourly sea level extremes. The occurrence of extreme events follows a sharply escalating pattern as the magnitude of future sea level rise increases. The confluence of Low barometric pressures from storms and the presence large waves at the same time substantially increases the likelihood of high, damaging sea levels along the California coast. Similarly, astronomical tides and disturbances in sea level that are caused by weather and climate fluctuations are x transmitted into the San Francisco Bay and Delta, and on into the lower reaches of the Sacramento River. In addition to elevating Bay and Delta sea levels directly through inverse barometer and wind effects, storms may generate heavy precipitation and high fresh water runoff and cause floods in the Sacramento/San Joaquin Delta, increasing the potential for inundation of levees and other structures. There may also be increased risk of levee failure due to the hydraulics and geometry of these structures. Rising sea levels from climate change will increase the frequency and duration of extreme high water levels, causing historical coastal and San Francisco Bay/Delta structure design criteria to be exceeded.