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. In observations, this increase in atmospheric water vapor has not happened, suggesting that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid/semi-arid regions. This discrepancy is most clear in locations that are arid/semi-arid year round, but it is also apparent in more humid regions during the most arid months of the year. It indicates a major gap in our understanding and modeling capabilities which could have severe implications for hydroclimate projections, including fire hazard, moving forward.

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. However, increases in the frequency of extremely wet and dry years are still present even after removing the trend, indicating that sustainably managing hydroclimate-driven risks in a warmer world will face increasingly difficult challenges.

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. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. Our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.

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. Here we show that experience of weather in recent years-rather than longer historical periods-determines the climatic baseline against which current weather is evaluated, potentially obscuring public recognition of anthropogenic climate change. We employ variation in decadal trends in temperature at weekly and county resolution over the continental United States, combined with discussion of the weather drawn from over 2 billion social media posts. These data indicate that the remarkability of particular temperatures changes rapidly with repeated exposure. Using sentiment analysis tools, we provide evidence for a "boiling frog" effect: The declining noteworthiness of historically extreme temperatures is not accompanied by a decline in the negative sentiment that they induce, indicating that social normalization of extreme conditions rather than adaptation is driving these results. Using climate model projections we show that, despite large increases in absolute temperature, anomalies relative to our empirically estimated shifting baseline are small and not clearly distinguishable from zero throughout the 21st century.

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. Here, we present a yearly NAO reconstruction for the past millennium, based on an initial selection of 48 annually resolved proxy records distributed around the Atlantic Ocean and built through an ensemble of multivariate regressions. We validate the approach in six past-millennium climate simulations, and show that our reconstruction outperforms the bi-proxy index. The final reconstruction shows no persistent positive NAO during the medieval period, but suggests that positive phases were dominant during the thirteenth and fourteenth centuries. The reconstruction also reveals that a positive NAO emerges two years after strong volcanic eruptions, consistent with results obtained from models and satellite observations for the Mt Pinatubo eruption in the Philippines.

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. We find ToEs are robust across ESMs for sea surface temperature and the invasion of anthropogenic carbon; emergence time scales are 20-30 yr. For the biological carbon pump, and sea surface chlorophyll and salinity, emergence time scales are longer (50+ yr), less robust across the ESMs, and more sensitive to the forcing scenario considered. We find internal variability uncertainty, and model differences in the internal variability uncertainty, can be consequential sources of uncertainty for projecting regional changes in ocean biogeochemistry over the coming decades. In combining structural, scenario, and internal variability uncertainty, this study represents the most comprehensive characterization of biogeochemical emergence time scales and uncertainty to date. Our findings delineate critical spatial and duration requirements for marine observing systems to robustly detect anthropogenic change.

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.S. at increasing wind and solar penetrations by integrating power system and synoptic meteorology methods. At up to 60% renewable penetration and across analyzed weather years, three high pressure patterns drive nearly all RA failures. The highest pressure anomaly is the dominant driver, accounting for 20-100% of risk hours and 43-100% of cumulative risk at 60% renewable penetration. The three high pressure patterns exhibit positive surface temperature anomalies, mixed surface solar radiation anomalies, and negative wind speed anomalies across our region, which collectively increase demand and decrease supply. Our characterized meteorological drivers align with meteorology during the California 2020 rolling blackouts, indicating continued vulnerability of power systems to these impactful weather patterns as renewables grow.

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. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.

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 21st 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. This compensation is projected to disappear due to future reductions in aerosol emissions, causing unprecedented increases in extreme fire weather risk in the 21st century as GHGs continue to rise. Changes to temperature and relative humidity drive the largest shifts in extreme fire weather conditions; this is particularly apparent over the Amazon, where GHGs cause a seven-fold increase by 2080. Our results allow increased understanding of the interacting roles of anthropogenic stressors in altering the regional expression of future wildfire risk.

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. We find a best estimate forced warming trend of 0.8°C over the past 40 years, slightly larger than observed. It is extremely likely that at least 85% is attributable to external forcing based on the median variability across climate models. Detection remains robust even when evaluated against models with high variability and if decadal-scale variability were doubled. This work addresses a long-standing limitation in attributing warming to external forcing and opens up opportunities even in the case of large model differences in decadal-scale variability, model structural uncertainty, and limited observational records.

Prolonged Siberian heat of 2020 almost impossible without human influence.

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. We show that human-induced climate change has dramatically increased the probability of occurrence and magnitude of extremes in both of these (with lower confidence for the probability for Verkhoyansk) and that without human influence the temperatures widely experienced in Siberia in the first half of 2020 would have been practically impossible. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10584-021-03052-w.

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. Observationally constrained CMIP6 warming is consistent with previous assessments based on CMIP5 models, and in an ambitious mitigation scenario, the likely range is consistent with reaching the Paris Agreement target.

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). Comparing recent decades to RCP8.5 projections for the end of the 21st century, we find that in the global, multi-model mean, precipitation variability increases 3-4% K-1 globally, 4-5% K-1 over land and 2-4% K-1 over ocean, and is remarkably robust on a range of timescales from daily to decadal. Precipitation variability increases by at least as much as mean precipitation and less than moisture and extreme precipitation for most models, regions, and timescales. We interpret this as being related to an increase in moisture which is partially mitigated by weakening circulation. We show that changes in observed daily variability in station data are consistent with increased variability.