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Changes in the essential climate extremes indices and surface variables for the end of the twenty-first century are assessed in this study based on two transient climate change simulations, with and without land-use and land-cover changes (LULCC), but identical atmospheric forcing. The two simulations are performed with the 5th generation of the Canadian Regional Climate Model (CRCM5) driven by the Canadian Earth System Model for the (2006-2100)-Representative Concentration Pathway 4.5 (RCP4.5) scenario. For the simulation with LULCC, land-cover data sets are taken from the global change assessment model (GCAM) representing the RCP4.5 scenario for the period 2006-2100. LULCC in RCP4.5 scenario suggest significant reduction in cultivated land (e.g. Canadian Prairies and Mississippi basin) due to afforestation. CRCM5 climate projections imply a general warming by the end of the twenty-first century, especially over the northern regions in winter. CRCM5 projects more warm spell-days per year over most areas of the continent, and implicitly more summer days and tropical nights at the expense of cold-spell, frost and ice days whose number is projected to decrease by up to 40% by the end of the twenty-first century with respect to the baseline period 1971-2000. Most land areas north of 45°N, in all seasons, as well as the southeastern United States in summer, exhibit increases in mean precipitation under the RCP4.5 scenario. In contrast, central parts of the continent in summer and much of Mexico in all seasons show reduced precipitation. In addition, large areas of North America exhibit changes of 10 to 40% (depending on the season and geographical location) in the number of heavy precipitation days. Results also suggest that the biogeophysical effects of LULCC on climate, assessed through differences between the two simulations, lead to warmer regional climates, especially in winter. The investigation of processes leading to this response shows high sensitivity of the results to changes in albedo as a response to LULCC. Overall, at the seasonal scale, results show that intense afforestation may contribute to an additional 25% of projected changes. [ABSTRACT FROM AUTHOR]

This study examines the dominant heatwave variability over North America (NA), extracted from an empirical orthogonal function (EOF) analysis of summertime monthly warm extreme index anomalies over 1959–2021. The principal mode features a dipole structure with a large area of anomaly over northwestern NA and an anomaly of opposite sign over the southern U.S. The corresponding principal component is associated with a large-scale atmospheric wave train extending from the North Pacific to North America (NP-NA) and a northeastward injection of moisture from the subtropical western Pacific towards western NA, which are key factors in supporting the NA heatwave variability. The NP-NA wave train can be systematically reinforced and supported by synoptic-scale eddies, and may also be forced by an anomalous convection over the tropical-subtropical western Pacific. Surface radiation heating directly contributes to surface temperature anomalies and is dominated by anomalous downwelling shortwave and longwave radiations. In association with a positive phase of the heatwave variability, the NP-NA wave train brings an anticyclonic anomaly over northern NA, leading to anomalous descent, reduced total cloud cover and below-normal precipitation and surface relative humidity over northern NA. Over northwestern NA, the anomalous subsidence causes air to warm through compression. Reduced cloud cover results in increased downward shortwave radiation that is a key contributor to surface radiation heating. In addition, increase in vertically integrated water vapour through the moisture injection from the North Pacific collocates with tropospheric warming. The atmosphere has more water vapor holding capability and acts as a greenhouse gas to absorb longwave radiation, leading to increased downward longwave radiation that is the second major contributor to surface radiation heating. Processes with circulation and surface radiation anomalies of opposite signs will likewise lead to the negative heatwave variability. [ABSTRACT FROM AUTHOR]

While climate change is expected to alter mean and extreme temperature and precipitation over the 21st century, regional changes in these fields remain difficult to project. For a given future emissions scenario, the uncertainty in future projections can be attributed to either model‐to‐model differences, which can presumably be reduced if models are improved, or internal variability, which cannot be constrained to a single outcome but can be used to quantify a range of plausible outcomes. This study examines the range of possible trends in North American mean and extreme temperature and precipitation across phase 6 of the Coupled Model Intercomparison Project models under the SSP3‐7.0 emissions scenario. While model‐to‐model differences primarily govern the spread in mean and extreme temperature trends across these model simulations, the spread in mean and extreme precipitation trends is dominated by internal variability in many regions. A storylines approach—a type of regression‐based analysis—is used to identify key dynamical drivers that explain the variance in future trends across model simulations. The dynamical drivers considered include trends in global mean surface temperature (GMST) and four common modes of climate variability. Combinations of these drivers can reinforce each other to create high‐impact storylines. For example, model simulations with large positive GMST and El Niño‐Southern Oscillation trends are associated with a large reduction in the annual maximum number of consecutive dry days in the American Southwest. However, limiting these storylines to only models with climate sensitivities consistent with observational constraints reduces the likelihood of a large reduction of consecutive dry days in this region. Plain Language Summary: As the globe warms over the 21st century as a result of increased human greenhouse gas emissions, changes in mean and extreme temperature and precipitation are expected across North America. Projecting these changes, however, is challenging due to large uncertainty in future emissions, computer model physics, and inherent uncertainty in the climate known as internal variability. The goal of this study is to investigate future mean and extreme temperature and precipitation trends and discern the type of uncertainty that best explains the variability in future projections. Importantly, variance in future projections that is the result of model physics uncertainty can be constrained with model improvement, while variance in future projections due to internal variability cannot. For example, excluding models with unreasonably high levels of future global warming would reduce the uncertainty in future regional temperature trends and trends in the length of dry periods in the American Southwest. Key Points: Global climate models produce a large range in 21st century mean and extreme temperature and precipitation trends across North AmericaCombinations of dynamical drivers can reinforce each other to create high impact 21st century temperature and precipitation trendsRemoving models with unlikely future warming can constrain the projected trends in 21st century temperature and precipitation trends [ABSTRACT FROM AUTHOR]

The Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1) contains 50‐member ensembles of bias‐adjusted near‐surface global and regional climate model variables on a 0.5° grid over North America for historical and future scenarios (1950–2100). Canadian Earth System Model Large Ensembles (CanESM2 LE) and Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) datasets are bias‐corrected using a multivariate quantile‐mapping algorithm for statistical consistency – in terms of marginal distributions and multivariate dependence structure – with two observationally constrained historical meteorological forcing datasets. For each observational dataset, bias‐adjusted variables are provided for two sets of 50‐member initial‐condition CanESM2 ensembles (historical plus RCP8.5 scenarios, 1950–2005 and 2006–2100, respectively; and historicalNAT scenario, 1950–2020, which excludes anthropogenic forcings), and one 50‐member CanRCM4 ensemble (historical plus RCP8.5). The archive includes daily minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation and incoming longwave radiation. Intended uses include hydrological and land surface impact modelling, as well as related event attribution studies. [ABSTRACT FROM AUTHOR]

Use of downscaled global climate model projections is expanding rapidly as climate change vulnerability assessments and adaptation planning become mainstream in many sectors. Many climate change impact analyses use climate model projections downscaled at very high spatial resolution (~1 km) but very low temporal resolution (20‐ to 30‐year normals). These applications have model selection priorities that are distinct from analyses at high temporal resolution. Here, we select a 13‐model ensemble and an 8‐model subset designed for robust change‐factor downscaling of monthly climate normals, and describe their attributes in North America. All models are selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6) archives. The 13‐model ensemble is representative of the distribution of equilibrium climate sensitivity, grid resolution, and transient regional climate changes in the CMIP6 generation. The 8‐model subset is consistent with the IPCC's recent assessment of the very likely range of Earth's equilibrium climate sensitivity. Our results emphasize several principles for selection and use of downscaled climate ensembles: (a) the ensemble must be observationally constrained to be meaningful; (b) analysis of multiple models is essential as the ensemble mean alone can be misleading; (c) small (<8‐member) ensembles should be region‐specific and used with caution; (d) higher grid resolution is not necessarily better; and (e) multiple simulations of each model/scenario combination are necessary to represent precipitation uncertainty. Although we have focused our documentation on North America, our model selection uses primarily global criteria and is applicable to downscaling climate normals in other continents. Downscaled projections for the selected models are available in ClimateNA (http://climatena.ca/). An accompanying web application (https://bcgov-env.shinyapps.io/cmip6-NA/) provides tools for further model selection and visualization of the ensemble. [ABSTRACT FROM AUTHOR]

We report on the characteristics of precipitation associated with three types of landfalling atmospheric rivers (ARs) over western North America in the winter season from 1980 to 2004. The ARs are classified according to three landfalling regions as southern, middle and northern types. Two main centers of precipitation are associated with the contributions by the ARs: one over Baja California linked to the southern type of the ARs, and the other over Washington State correlated with the northern and middle types of the ARs. ARs are seen to play a dominant role in the occurrences of extreme precipitation events, with a proportionately greater impact on more extreme events. Moisture flux convergence makes the dominant contribution to precipitation when ARs and extreme precipitation occur simultaneously in the studied areas. Moisture flux convergence in these cases is, in turn, dominated by the mean and transient moisture transported by the transient wind, with greater contribution from the latter, which is mainly concentrated in certain areas. The magnitude and direction of vertically integrated vapor transport (IVT) also play a role in determining the amount of precipitation received in the three regions considered. Larger IVT magnitude corresponds to more precipitation, while an IVT direction of about 220° (0° indicating east wind) is most favorable for high precipitation amount, which is especially obvious for the northern type of the ARs. [ABSTRACT FROM AUTHOR]

We evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the observed northern annular mode (NAM), North Atlantic Oscillation (NAO), Pacific–North America pattern (PNA), southern annular mode (SAM), Pacific decadal oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a common basis function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and high-top vertical coordinate); however, the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the postdominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious postdominant season amplitude errors found in many models can be addressed in an atmospheric-only framework, making it more tractable to address in the model development process. [ABSTRACT FROM AUTHOR]

Compound events have the potential to cause high socioeconomic and environmental losses. We examine the ability of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models to capture two bivariate compound events: the co‐occurrence of heavy rain and strong wind, and heat waves and meteorological drought. We evaluate the models over North America, Europe, Eurasia, and Australia using observations and reanalysis data set spanning 1980–2014. Some of the CMIP6 models capture the return periods of both bivariate compound events over North America, Europe, and Eurasia surprisingly well but perform less well over Australia. For heavy rain and strong wind, this poor performance was particularly clear in northern Australia which suggests limits in simulating tropical and extratropical cyclones, local convection, and mesoscale convective systems. We did not find higher model resolution improved performance in any region. Overall, our results show some CMIP6 models can be used to examine compound events, particularly over North America, Europe, and Eurasia. Plain Language Summary: Compound events, such as the co‐occurrence of heavy rain and strong wind or heat waves and drought, can have major economic, social, and environmental consequences. We therefore ask the question whether the new generation of climate models represented by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the occurrence of these important events. We found that some of the CMIP6 models do simulate these compound events surprisingly well over North America, Europe, and Eurasia. Unfortunately, they perform less well over Australia which is likely associated with the problem of simulating extratropical cyclones, local convection, and mesoscale convective systems. Our results suggest that some CMIP6 models can be used to examine these two compound events particularly over North America, Europe, and Eurasia. Key Points: Some CMIP6 models reproduce observed return periods of co‐occurring rain and wind extremes and co‐occurring heat waves and droughts wellCMIP6 models simulate these compound events over North America, Europe, or Eurasia with similar levels of skillCMIP6 models simulate these compound events over Australia with lower skill than the other regions analyzed [ABSTRACT FROM AUTHOR]

The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we analyze land–atmosphere interactions within four simulations performed by the WRF model from 1980 to 2012 over North America, using three different LSMs. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Our results show that the WRF simulation of the climatology of heat extremes can be 5 ∘ C warmer and 6 d longer depending on the employed LSM component, and similarly for cold extremes and heavy precipitation events. Areas showing large uncertainty in WRF-simulated extreme events are also identified in a model ensemble from three different regional climate model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. Thus, studies based on multi-model ensembles and reanalyses should include a variety of LSM configurations to account for the uncertainty arising from this model component or to test the performance of the selected LSM component before running the whole simulation. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating extreme temperature and precipitation events, which in turn will help to reduce uncertainties in climate model projections. [ABSTRACT FROM AUTHOR]

The interannual variability of wintertime North American surface temperature extremes and its generation and maintenance are analyzed in this study. The leading mode of the temperature extreme anomalies, revealed by empirical orthogonal function (EOF) analyses of December-February mean temperature extreme indices over North America, is characterized by an anomalous center of action over western-central Canada. In association with the leading mode of temperature extreme variability, the large-scale atmospheric circulation features an anomalous Pacific-North American (PNA)-like pattern from the preceding fall to winter, which has important implications for seasonal prediction of North American temperature extremes.A positive PNA pattern leads to more warm and fewer cold extremes over western-central Canada. The anomalous circulation over the PNA sector drives thermal advection that contributes to temperature anomalies over North America, as well as a Pacific decadal oscillation (PDO)-like sea surface temperature (SST) anomaly pattern in the midlatitude North Pacific. The PNA-like circulation anomaly tends to be supported by SST warming in the tropical central-eastern Pacific and a positive synoptic-scale eddy vorticity forcing feedback on the large-scale circulation over the PNA sector. The leading extreme mode--associated atmospheric circulation patterns obtained from the observational and reanalysis data, together with the anomalous SST and synoptic eddy activities, are reasonably well simulated in most CMIP5 models and in the multimodel mean. For most models considered, the simulated patterns of atmospheric circulation, SST, and synoptic eddy activities have lower spatial variances than the corresponding observational and reanalysis patterns over the PNA sector, especially over the North Pacific. [ABSTRACT FROM AUTHOR]

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of "extreme" are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes. [ABSTRACT FROM AUTHOR]

The persistence of heat waves and droughts is a key factor in determining their societal impact. Here, the local effect of atmospheric blocks on the persistence of summer hot and dry spells is quantified by comparing their climatological daily survival probability, that is, the probability to survive the next day, to their daily survival probability when they co‐occur with a block. The survival odds of hot spells are increased by more than 50% over most of the Northern Hemisphere extratropical land masses when co‐occurring with blocks. Dry spell persistence is also strongly increased by colocated blocks over western North America, Europe, and southern Russia, while it is significantly decreased over the western North Atlantic and the western North Pacific. These spatial differences in the effect of blocks on both spell types are explained by considering the spatially varying surface temperature and precipitation anomalies induced by the blocks. Plain Language Summary: The persistence of heat waves and droughts strongly affects how challenging these weather events are to societies. However, the meteorological phenomena that foster the persistence of heat waves and droughts are not yet fully understood. Here we focus on one such meteorological phenomenon, namely, stationary high‐pressure systems known as atmospheric blocks, and quantify their effect on the persistence of hot and dry spells that co‐occur with the blocks. For hot spells, this effect is positive almost everywhere blocks occur in the Northern Hemisphere, most strongly so over land. Dry spell persistence is also strongly affected by blocks, but the effect can be either positive or negative. Over land, the strongest positive effects of blocks on dry spell persistence are found over western North America, Europe, and southern Russia. There, blocks also increase the persistence of so‐called compound hot and dry spells, that is, periods with both hot and dry weather occurring concomitantly. Our results show that changes in the number and geographical distribution of blocks with climate change would significantly affect hot and dry spell persistence in a future climate. Key Points: Blocks strongly affect the persistence of hot, dry, and compound hot and dry spells occurring colocated with the blocksThis effect is generally positive for hot spell persistence, while it can take both signs for dry spell persistenceStrongest positive effect of blocks on compound hot and dry spell persistence is found over western North America and southern Europe [ABSTRACT FROM AUTHOR]

Using historical simulations of the Coupled Model Intercomparison Project-5 (CMIP5) and multiple observationally-based datasets, we employ skill metrics to analyze the fidelity of the simulated Northern Annular Mode, the North Atlantic Oscillation, the Pacific North America pattern, the Southern Annular Mode, the Pacific Decadal Oscillation, the North Pacific Oscillation, and the North Pacific Gyre Oscillation. We assess the benefits of a unified approach to evaluate these modes of variability, which we call the common basis function (CBF) approach, based on projecting model anomalies onto observed empirical orthogonal functions (EOFs). The CBF approach circumvents issues with conventional EOF analysis, eliminating, for example, corrections of arbitrarily assigned, but inconsistent, signs of the EOF's/PC's being compared. It also avoids the problem that sometimes the first observed EOF is more similar to a higher order model EOF, particularly if the simulated EOFs are not well separated. Compared to conventional EOF analysis of models, the CBF approach indicates that models compare significantly better with observations in terms of pattern correlation and root-mean-squared-error (RMSE) than heretofore suggested. In many cases, models are doing a credible job at capturing the observationally-based estimates of patterns; however, errors in simulated amplitudes can be large and more egregious than pattern errors. In the context of the broad distribution of errors in the CMIP5 ensemble, sensitivity tests demonstrate that our results are relatively insensitive to methodological considerations (CBF vs. conventional approach), observational uncertainties in pattern (as determined by using multiple datasets), and internal variability (when multiple realizations from the same model are compared). The skill metrics proposed in this study can provide a useful summary of the ability of models to reproduce the observed EOF patterns and amplitudes. Additionally, the skill metrics can be used as a tool to objectively highlight where potential model improvements might be made. We advocate more systematic and objective testing of simulated extratropical variability, especially during the non-dominant seasons of each mode, when many models are performing relatively poorly. [ABSTRACT FROM AUTHOR]

Shifts in species distributions are major fingerprint of climate change. Examining changes in species abundance structures at a continental scale enables robust evaluation of climate change influences, but few studies have conducted these evaluations due to limited data and methodological constraints. In this study, we estimate temporal changes in abundance from North American Breeding Bird Survey data at the scale of physiographic strata to examine the relative influence of different components of climatic factors and evaluate the hypothesis that shifting species distributions are multidirectional in resident bird species in North America. We quantify the direction and velocity of the abundance shifts of 57 permanent resident birds over 44 years using a centroid analysis. For species with significant abundance shifts in the centroid analysis, we conduct a more intensive correlative analysis to identify climate components most strongly associated with composite change of abundance within strata. Our analysis focus on two contrasts: the relative importance of climate extremes vs. averages, and of temperature vs. precipitation in strength of association with abundance change. Our study shows that 36 species had significant abundance shifts over the study period. The average velocity of the centroid is 5.89 km·yr−1. The shifted distance on average covers 259 km, 9% of range extent. Our results strongly suggest that the climate change fingerprint in studied avian distributions is multidirectional. Among 6 directions with significant abundance shifts, the northwestward shift was observed in the largest number of species ( n = 13). The temperature/average climate model consistently has greater predictive ability than the precipitation/extreme climate model in explaining strata-level abundance change. Our study shows heterogeneous avian responses to recent environmental changes. It highlights needs for more species-specific approaches to examine contributing factors to recent distributional changes and for comprehensive conservation planning for climate change adaptation. [ABSTRACT FROM AUTHOR]

This study performs high-spatial-resolution (12 km) Weather Research and Forecasting ( WRF) simulations over a very large domain (7200 km × 6180 km, covering much of North America) to explore changes in mean and extreme precipitation in the mid and late 21st century under Representative Concentration Pathways 4.5 ( RCP 4.5) and 8.5 ( RCP 8.5). We evaluate WRF model performance for a historical simulation and future projections, applying the Community Climate System Model version 4 ( CCSM4) as initial and boundary conditions with and without a bias correction. WRF simulations using boundary and initial conditions from both versions of CCSM4 show smaller biases versus evaluation data sets than does CCSM4 over western North America. WRF simulations also improve spatial details of precipitation over much of North America. However, driving the WRF with the bias-corrected CCSM4 does not always reduce the bias. WRF-projected changes in precipitation include decreasing intensity over the southwestern United States, increasing intensity over the eastern United Sates and most of Canada, and an increase in the number of days with heavy precipitation over much of North America. Projected precipitation changes are more evident in the late 21st century than the mid 21st century, and they are more evident under RCP 8.5 than under RCP 4.5 in the late 21st century. Uncertainties in the projected changes in precipitation due to different warming scenarios are non-negligible. Differences in summer precipitation changes between WRF and CCSM4 are significant over most of the United States. [ABSTRACT FROM AUTHOR]

We synthesized an expert review of climate change implications for hydroecological and terrestrial ecological systems in the northern coastal temperate rainforest of North America. Our synthesis is based on an analysis of projected temperature, precipitation, and snowfall stratified by eight biogeoclimatic provinces and three vegetation zones. Five IPCC CMIP5 global climate models (GCMs) and two representative concentration pathways (RCPs) are the basis for projections of mean annual temperature increasing from a current average (1961-1990) of 3.2 °C to 4.9-6.9 °C (5 GCM range; RCP4.5 scenario) or 6.4-8.7 °C (RCP8.5), mean annual precipitation increasing from 3130 mm to 3210-3400 mm (3-9 % increase) or 3320-3690 mm (6-18 % increase), and total precipitation as snow decreasing from 1200 mm to 940-720 mm (22-40 % decrease) or 720-500 mm (40-58 % decrease) by the 2080s (2071-2100; 30-year normal period). These projected changes are anticipated to result in a cascade of ecosystem-level effects including: increased frequency of flooding and rain-on-snow events; an elevated snowline and reduced snowpack; changes in the timing and magnitude of stream flow, freshwater thermal regimes, and riverine nutrient exports; shrinking alpine habitats; altitudinal and latitudinal expansion of lowland and subalpine forest types; shifts in suitable habitat boundaries for vegetation and wildlife communities; adverse effects on species with rare ecological niches or limited dispersibility; and shifts in anadromous salmon distribution and productivity. Our collaborative synthesis of potential impacts highlights the coupling of social and ecological systems that characterize the region as well as a number of major information gaps to help guide assessments of future conditions and adaptive capacity. [ABSTRACT FROM AUTHOR]

The article analyzes the cold extremes experienced in North America in the winter of 2013-14 and the unusually mild winter in Western Europe during the same period. Statistics were studied to show how this is model is consistent with global warming. Topics include extreme cold outbreaks in North America, winter mean temperatures and temperature trends showing that cold extremes are becoming less frequent due to greenhouse gas emissions.

Radiation management (RM) has been proposed as a conceivable climate engineering (CE) intervention to mitigate global warming. In this study, we used a coupled climate model (MPI-ESM) with a very idealized setup to investigate the efficacy and risks of CE at a local scale in space and time (regional radiation management, RRM) assuming that cloud modification is technically possible. RM is implemented in the climate model by the brightening of low-level clouds (solar radiation management, SRM) and thinning of cirrus (terrestrial radiation management, TRM). The region chosen is North America, and we simulated a period of 30 years. The implemented sustained RM resulted in a net local radiative forcing of −9.8 Wm − 2 and a local cooling of −0.8 K. Surface temperature (SAT) extremes (90th and 10th percentiles) show negative anomalies in the target region. However, substantial climate impacts were also simulated outside the target area, with warming in the Arctic and pronounced precipitation change in the eastern Pacific. As a variant of RRM, a targeted intervention to suppress heat waves (HW) was investigated in further simulations by implementing intermittent cloud modification locally, prior to the simulated HW situations. In most cases, the intermittent RRM results in a successful reduction of temperatures locally, with substantially smaller impacts outside the target area compared to the sustained RRM. [ABSTRACT FROM AUTHOR]