GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties.

This article introduces the Generalized Analog Regression Downscaling method Large Ensemble (GARD-LENS) dataset, comprised of daily precipitation, mean temperature, and temperature range over the Contiguous U.S., Alaska, and Hawaii at 12-km, 4-km, and 1-km resolutions, respectively.

Climate model large ensembles as test beds for applied compound event research.

Some of the most impactful climate and weather events result from compounding drivers. To robustly assess the current and future risk from such compound events, a better understanding of the associated sources of uncertainty is needed. Internal variability confounds detection and attribution of human-induced climate change and imposes irreducible limits on the accuracy of climate projections.

Observed humidity trends in dry regions contradict climate models.

Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas-driven hydroclimate change. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here, we present a concerning discrepancy between observed and model-based historical hydroclimate trends. Over the arid/semi-arid regions of the world, the predominant signal in all model simulations is an increase in atmospheric water vapor, on average, over the last four decades, in association with the increased water vapor-holding capacity of a warmer atmosphere.

Meteorological drivers of resource adequacy failures in current and high renewable Western U.S. power systems.

Power system resource adequacy (RA), or its ability to continually balance energy supply and demand, underpins human and economic health. How meteorology affects RA and RA failures, particularly with increasing penetrations of renewables, is poorly understood. We characterize large-scale circulation patterns that drive RA failures in the Western U.

Advancing research on compound weather and climate events via large ensemble model simulations.

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events - such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events - require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes.

Pervasive alterations to snow-dominated ecosystem functions under climate change.

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function.

Twenty-first century hydroclimate: A continually changing baseline, with more frequent extremes.

SignificanceTwenty-first century trends in hydroclimate are so large that future average conditions will, in most cases, fall into the range of what we would today consider extreme drought or pluvial states. Using large climate model ensembles, we remove the background trend and find that the risk of droughts and pluvials relative to that (changing) baseline is fairly similar to the 20th century risk. By continually adapting to long-term background changes, these risks could therefore perhaps be minimized.

Prolonged Siberian heat of 2020 almost impossible without human influence.

Unlabelled: Over the first half of 2020, Siberia experienced the warmest period from January to June since records began and on the 20th of June the weather station at Verkhoyansk reported 38 °C, the highest daily maximum temperature recorded north of the Arctic Circle. We present a multi-model, multi-method analysis on how anthropogenic climate change affected the probability of these events occurring using both observational datasets and a large collection of climate models, including state-of-the-art higher-resolution simulations designed for attribution and many from the latest generation of coupled ocean-atmosphere models, CMIP6. Conscious that the impacts of heatwaves can span large differences in spatial and temporal scales, we focus on two measures of the extreme Siberian heat of 2020: January to June mean temperatures over a large Siberian region and maximum daily temperatures in the vicinity of the town of Verkhoyansk.

Robust detection of forced warming in the presence of potentially large climate variability.

Climate warming is unequivocal and exceeds internal climate variability. However, estimates of the magnitude of decadal-scale variability from models and observations are uncertain, limiting determination of the fraction of warming attributable to external forcing. Here, we use statistical learning to extract a fingerprint of climate change that is robust to different model representations and magnitudes of internal variability.

Human-driven greenhouse gas and aerosol emissions cause distinct regional impacts on extreme fire weather.

Attribution studies have identified a robust anthropogenic fingerprint in increased 21 century wildfire risk. However, the risks associated with individual aspects of anthropogenic aerosol and greenhouse gases (GHG) emissions, biomass burning and land use/land cover change remain unknown. Here, we use new climate model large ensembles isolating these influences to show that GHG-driven increases in extreme fire weather conditions have been balanced by aerosol-driven cooling throughout the 20th century.

Time of Emergence and Large Ensemble Intercomparison for Ocean Biogeochemical Trends.

Anthropogenically forced changes in ocean biogeochemistry are underway and critical for the ocean carbon sink and marine habitat. Detecting such changes in ocean biogeochemistry will require quantification of the magnitude of the change (anthropogenic signal) and the natural variability inherent to the climate system (noise). Here we use Large Ensemble (LE) experiments from four Earth system models (ESMs) with multiple emissions scenarios to estimate Time of Emergence (ToE) and partition projection uncertainty for anthropogenic signals in five biogeochemically important upper-ocean variables.

Past warming trend constrains future warming in CMIP6 models.

Future global warming estimates have been similar across past assessments, but several climate models of the latest Sixth Coupled Model Intercomparison Project (CMIP6) simulate much stronger warming, apparently inconsistent with past assessments. Here, we show that projected future warming is correlated with the simulated warming trend during recent decades across CMIP5 and CMIP6 models, enabling us to constrain future warming based on consistency with the observed warming. These findings carry important policy-relevant implications: The observationally constrained CMIP6 median warming in high emissions and ambitious mitigation scenarios is over 16 and 14% lower by 2050 compared to the raw CMIP6 median, respectively, and over 14 and 8% lower by 2090, relative to 1995-2014.

Rapidly declining remarkability of temperature anomalies may obscure public perception of climate change.

The changing global climate is producing increasingly unusual weather relative to preindustrial conditions. In an absolute sense, these changing conditions constitute direct evidence of anthropogenic climate change. However, human evaluation of weather as either normal or abnormal will also be influenced by a range of factors including expectations, memory limitations, and cognitive biases.

Precipitation variability increases in a warmer climate.

Understanding changes in precipitation variability is essential for a complete explanation of the hydrologic cycle's response to warming and its impacts. While changes in mean and extreme precipitation have been studied intensively, precipitation variability has received less attention, despite its theoretical and practical importance. Here, we show that precipitation variability in most climate models increases over a majority of global land area in response to warming (66% of land has a robust increase in variability of seasonal-mean precipitation).

A model-tested North Atlantic Oscillation reconstruction for the past millennium.

The North Atlantic Oscillation (NAO) is the major source of variability in winter atmospheric circulation in the Northern Hemisphere, with large impacts on temperature, precipitation and storm tracks, and therefore also on strategic sectors such as insurance, renewable energy production, crop yields and water management. Recent developments in dynamical methods offer promise to improve seasonal NAO predictions, but assessing potential predictability on multi-annual timescales requires documentation of past low-frequency variability in the NAO. A recent bi-proxy NAO reconstruction spanning the past millennium suggested that long-lasting positive NAO conditions were established during medieval times, explaining the particularly warm conditions in Europe during this period; however, these conclusions are debated.