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Polarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean‐state Global Climate Model (GCM) biases: (a) overly strong lower‐tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid‐tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers. Plain Language Summary: Global Climate Models (GCMs) are known to have biases that, when dynamically downscaled, damage the credibility of the e. A longstanding way around this problem is bias correction (BC) after downscaling, but this practice rarely involves physics and can mislead climate data users into overvaluing the quality of the downscaled data. Further, post‐downscaling BC techniques can over correct the higher‐order statistics, calling into question the faithful preservation of the original simulated signals. For the first time, we apply a minimally invasive BC procedure to a group of 9 GCMs in order to define physical relationships between mean GCM biases and their dynamically downscaled hydroclimate variables across the western United States. We find that native GCMs tend to exhibit surprisingly common mean biases that, when downscaled, effectuate an overly wet, cold, and snowy climate across the region. Key Points: Bias correction of Global Climate Models (GCMs) reduces biases in downscaled mean precipitation, snow, and temperature across the western United StatesCascading cold, thermodynamically unstable, and cyclonic vorticity biases from GCMs to regional climate models drive wet biases in dynamical downscalingCMIP6‐wide GCM biases are similar suggesting that biases in dynamically downscaled precipitation and temperature can be anticipated [ABSTRACT FROM AUTHOR]

Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate. [ABSTRACT FROM AUTHOR]

Predicting future climate change over a region of complex terrain, such as the western United States (U.S.), remains challenging due to the low resolution of global climate models (GCMs). Yet climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality predictions. Here, we present an ensemble of dynamically downscaled simulations over the western U.S. from 1980-2100 at 9-km grid spacing, driven by sixteen latest-generation GCMs. This dataset is titled the Western U.S. Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography: (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven intensified local atmospheric updrafts tied to topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western U.S. climate. [ABSTRACT FROM AUTHOR]

Future projections of global meteorological drought are evaluated in the Multi‐Model Large Ensemble Archive, including an evaluation of the atmospheric moisture budget, conditioned on drought years. Drought is defined as 5‐year running‐mean annual precipitation below some threshold, for example, 10th percentile. Drought increases in frequency over the subtropics, in addition to certain tropical regions, consistent with previous studies. The moisture‐budget decomposition allows drought to be defined as mean‐flow, eddy, or feedback droughts, depending on which term in the equation contributes the largest negative interannual anomaly. In the historical climate, mean‐flow droughts constitute most droughts at low latitudes; eddy droughts are equally common at higher latitudes; feedback droughts (i.e., droughts exacerbated by land–atmosphere feedbacks) constitute almost all droughts in water‐limited subtropical/Mediterranean regions. The future drought increases are predominantly due to increases in feedback droughts in regions where these droughts are common historically but also over the Amazon. However, over most Mediterranean‐type regions mean‐flow droughts are also large contributors, resulting from dynamics. Eddy droughts also contribute to future increases along the equatorward flanks of historical eddy‐driven jets, likely reflecting poleward shifts therein. Model uncertainty is particularly large over the Amazon and Australia, a reflection of model diversity in processes associated with land‐atmosphere interaction. Based on these results, an availability of 3‐D atmospheric data from a wider swath of global climate model large ensembles could help constrain global drought projections based on the representation of drought mechanisms in the historical climate. Plain Language Summary: Around the globe, drought is projected to intensify and increase in frequency in the future. However, the mechanisms by which drought increases have not been shown from a global perspective. Here we show that drought, defined as a 5‐year period of extremely low precipitation, may fall under three categories. One is where the monthly average conditions in the atmosphere are conducive to drought; one is where there is an absence of individual storms over some region that depends on isolated events for its annual precipitation; one is where drought is made worse by a drying of the surface, which in turn further dries the atmosphere. In the future, climate models project an increase of drought mostly over regions where historically the latter category is prevalent. However, there are large disagreements between climate models over these regions, particularly over the Amazon and Australia. This reflects disagreements between the models in the interaction between the atmosphere and the Earth's surface. The results of this study help us to understand how climate models agree/disagree on drought intensification and why. Key Points: Multiple single‐model large ensembles can assess low‐frequency meteorological drought projections and model uncertainty thereinDroughts over global land increase mostly in regions where land‐atmosphere feedbacks exacerbate droughtModel uncertainty is largest in the Amazon and Australia, due to land‐atmosphere feedbacks and changes to atmospheric circulation [ABSTRACT FROM AUTHOR]

Large uncertainty exists in hydrologic sensitivity (HS), the global-mean precipitation increase per degree of warming, across global climate model (GCM) ensembles. Meanwhile, the global circulation and hence global precipitation are sensitive to variations of surface temperature under internal variability. El Niño–Southern Oscillation (ENSO) is the most dominant mode of global temperature variability and hence of precipitation variability. Here we show in phase 6 of the Coupled Model Intercomparison Project (CMIP6) that the strength of HS under ENSO is predictive of HS in the climate change context (r = 0.56). This correlation increases to 0.62 when only central Pacific ENSO events are considered, suggesting that they are a better proxy for HS under future warming than east Pacific ENSO events. GCMs with greater HS are associated with greater weakening of the Walker circulation and expansion of the Hadley circulation under ENSO. Observations of HS under ENSO suggest that it is significantly underestimated by the GCMs, with the lower bound of observational uncertainty almost double even the highest-HS GCMs. The ENSO-related transformation of the tropical circulation holds clues into how the GCMs may be improved in order to more reliably simulate future hydrological cycle intensification. [ABSTRACT FROM AUTHOR]

Flood hazard across the western United States (US) has generally shown decreasing trends in recent decades. This region's extreme streamflow is highly influenced by natural variability, which could either mask or amplify anthropogenic streamflow trends. In this study, we utilize a technique known as dynamical adjustment to assess historical (1970–2020) annual maximum 1‐day streamflow (Qx1d) from unregulated basins across the western US with and without the impact of natural variability. After removing natural variability, the fraction of basins with a positive (>5%) trend in Qx1d shifts from 25% to 53%. Basins with increasing (decreasing) Qx1d trends after dynamical adjustment exhibit weak (strong) drying, and furthermore are associated with intensifying precipitation extremes and/or large decreases in snowpack. Increasing flood hazard will likely emerge for such basins as the current phase of natural decadal variability shifts, and anthropogenic signals continue to intensify. Plain Language Summary: Historical observations of streamflow provide important insight into how the water cycle is responding to a changing climate. In particular, changes to extreme streamflow values, associated with highly impactful flooding events, are of great scientific interest. However, these events are largely controlled by inherently noisy processes like precipitation and temperature, which has limited the ability to isolate the influence of global warming on flooding trends to date. Here, we apply a method to determine how much each year's most extreme flood event has been influenced by natural variability in the climate system. In doing so, we can evaluate an "adjusted" timeseries of maximum annual floods from 1970 to 2020, which represents changes in flooding due to rising greenhouse gases. We find that increases in flood hazard are likely to occur, as the current phase of natural variability shifts, for basins with lower decreases in their historical annual water availability. These changes will be primarily due to warming‐driven snowpack loss and enhanced heavy rainfall. Lastly, basins with strong drying trends in their annual water availability will likely maintain their historically observed decreasing trends in flood hazard. Key Points: Internal atmospheric‐circulation variability can profoundly influence extreme streamflow trends across the western United StatesAnthropogenic climate change is driving decreasing trends in flood hazard across basins with strong drying trends in annual streamflowAnthropogenic flood hazard trend is positive in basins with weak drying, intensifying precipitation extremes and/or decreases in snowpack [ABSTRACT FROM AUTHOR]

The hydrologic cycle in California is strongly influenced by wet‐season (November–April) precipitation. Here, we demonstrate the existence of an influential mode of North Pacific atmospheric pressure variability that regulates wet‐season precipitation variability over both northern and southern California. This mode, named as the "California precipitation mode" (CPM), is statistically distinct from other well‐known modes of pressure variability such as the Pacific‐North American pattern. In addition to controlling wet‐season mean precipitation, positive days of the CPM coincide with up to 90% of the extreme (>99th percentile) precipitation days and 76% of detected atmospheric rivers (ARs) days, while the negative days correspond with 60% of the dry days. CMIP6 models capture the CPM remarkably well, including its statistical separation from the other well‐known modes of pressure variability. The models also reproduce the CPM's strong association with California wet‐season precipitation, giving confidence in the models' dynamics relating to regional hydrologic extremes. However, the models also exhibit biases in regional hydrologic extremes. The CPM is a useful way to understand the origins of those biases and select the more credible models for further analysis: Models with unrealistically strong gradients in the CPM pressure pattern generally oversimulate larger wet extremes and produce excessively long dry intervals in the historical period. Thus the hydrologic biases can be traced to the particular aspects of North Pacific atmospheric dynamics. Key Points: We identified a distinct mode of atmospheric variability that regulates California's mean and extreme wet‐season precipitationIn general, CMIP6 models reproduce the mode and its connections with California precipitation remarkably wellModel biases in regional hydrologic extremes can be understood in terms of biases in the magnitude of this mode [ABSTRACT FROM AUTHOR]

Days of extreme precipitation over California are evaluated in Coupled Model Intercomparison Project Phase 6 (CMIP6) models and the ERA‐Interim reanalysis. In the current climate, the model spread in composited precipitation on extreme precipitation days is closely related to the magnitude of composited integrated vapor transport (IVT) across models, a proxy for the intensity of atmospheric rivers. Most models underestimate the magnitude of IVT associated with extreme precipitation, according to ERA‐Interim. This is due mostly to the contribution of moisture, which almost all models overestimate, while the contribution of lower‐tropospheric wind speed is generally closer to the reanalyses. Moreover, most models underestimate the variance in the latitude of maxima of numerous variables among days of extreme California precipitation. That is, in the general circulation models there is a lack of diversity in the latitude of the disturbances bringing winter precipitation to California. In the future climate, most models project a decrease in the frequency of southward‐displaced disturbances among California extreme precipitation days. Hence, the greatest increases in extreme precipitation are over northern California. However, the historical underestimate of the latitudinal variance of disturbances calls into question the reliability of these projections. This bias should be especially considered for dynamical downscaling efforts over the region. Key Points: Coupled Model Intercomparison Project Phase 6 models overestimate moisture and hence moisture flux associated with extreme precipitation over California, according to reanalysesGeneral circulation models (GCMs) underestimate the variance in latitude of disturbances on California extreme precipitation daysGCMs project future California extremes to be increasingly associated with disturbances centered farther north [ABSTRACT FROM AUTHOR]

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes against local and global observations in a wide variety of settings, including snow schemes that are included in Earth System Models. The project aims at identifying crucial processes and snow characteristics that need to be improved in snow models in the context of local- and global-scale modeling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. ESM-SnowMIP is tightly linked to the Land Surface, Snow and Soil Moisture Model Intercomparison Project, which in turn is part of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). [ABSTRACT FROM AUTHOR]

Abstract: Snow albedo feedback (SAF) behaves similarly in the current and future climate contexts; thus, constraining the large intermodel variance in SAF will likely reduce uncertainty in climate projections. To better understand this intermodel spread, structural and parametric biases contributing to SAF variability are investigated. We find that structurally varying snowpack, vegetation, and albedo parameterizations drive most of the spread, while differences arising from model parameters are generally smaller. Models with the largest SAF biases exhibit clear structural or parametric errors. Additionally, despite widespread intermodel similarities, model interdependency has little impact on the strength of the relationship between SAF in the current and future climate contexts. Furthermore, many models now feature a more realistic SAF than in the prior generation, but shortcomings from two models limit the reduction in ensemble spread. Lastly, preliminary signs from ongoing model development are positive and suggest a likely reduction in SAF spread among upcoming models. [ABSTRACT FROM AUTHOR]

The Canadian Sea Ice and Snow Evolution (Can- SISE) Network is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine regions, and the rate of multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last 8 years. The multi-model consensus over the 2020-2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5-10% per decade (or 15-30% in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent reductions of up to 10% per decade (30% in total) are projected across southern Canada. [ABSTRACT FROM AUTHOR]

Over the past decade, substantial progress has been made in improving our understanding of surface albedo feedbacks, where changes in surface albedo from warming (cooling) can cause increases (decreases) in absorbed solar radiation, amplifying the initial warming (cooling). The goal of this review is to synthesize and assess recent research into the feedback caused by changing continental snow cover, or snow albedo feedback (SAF). Four main topics are evaluated: (i) the importance of SAF to the global energy budget, (ii) estimates of SAF from various data sources, (iii) factors influencing the spread in SAF, and (iv) outstanding issues related to our understanding of the physical processes that control SAF (and their uncertainties). SAF is found to exert a small influence on a global scale, with amplitude of ∼ 0.1 Wm−2 K−1, roughly 7% of the strength of water vapor feedback. However, SAF is an important driver of regional climate change over Northern Hemisphere (NH) extratropical land, where observation-based estimates show a peak feedback of around 1% decrease in surface albedo per degree of warming during spring. Viewed collectively, the current generation of climate models represent this process accurately, but several models still use outdated parameterizations of snow and surface albedo that contribute to biases that impact the simulation of SAF. This discussion serves to synthesize and evaluate previously published literature, while highlighting promising directions being taken at the forefront of research such as high resolution modeling and the use of large ensembles. [ABSTRACT FROM AUTHOR]

A highly uncertain aspect of anthropogenic climate change is the rate at which the global hydrologic cycle intensifies. The future change in global‐mean precipitation per degree warming, or hydrologic sensitivity, exhibits a threefold spread (1–3%/K) in current global climate models. In this study, we find that the intermodel spread in this value is associated with a significant portion of variability in future projections of extreme precipitation in the tropics, extending also into subtropical atmospheric river corridors. Additionally, there is a very tight intermodel relationship between changes in extreme and nonextreme precipitation, whereby models compensate for increasing extreme precipitation events by decreasing weak‐moderate events. Another factor linked to changes in precipitation extremes is model resolution, with higher resolution models showing a larger increase in heavy extremes. These results highlight ways various aspects of hydrologic cycle intensification are linked in models and shed new light on the task of constraining precipitation extremes. Plain Language Summary: The global water cycle is expected to intensify under climate change and can be generally characterized by greater rainfall and surface evaporation in the future. However, the rate at which the globally averaged precipitation increases is highly variable among different climate models. In this paper, we relate the intermodel variability in global water cycle intensification to differences in model projections of heavy precipitation in tropical and some extratropical regions. We also find that models consistently experience a trade‐off between increasing heavy and decreasing light‐moderate precipitation: Models with larger future increases in heavy precipitation exhibit greater compensating declines in light‐moderate rainfall. Differences in heavy precipitation changes are also tied to model resolution. Our study helps to provide new insight on the factors shaping projections of future precipitation extremes, which have strong implications for water resources, natural hazard risks associated with flooding, and ecosystem stability. Key Points: Models with higher global hydrologic sensitivity project larger extreme precipitation increases in tropical and some extratropical regionsModels with a larger increase in extreme precipitation exhibit compensating larger declines or smaller increases in light‐moderate eventsThe results showcase links between hydrologic cycle projections across spatial scales and offer new perspectives for constraining extremes [ABSTRACT FROM AUTHOR]