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Reconstructing past climates remains a difficult task because pre‐instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi‐model ensembles produce lower error than reconstructions from single‐model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid‐ and high‐latitude ocean and sea‐ice regions. Furthermore, we find that multi‐model ensemble reconstructions outperform single‐model reconstructions that use covariance localization. We propose that multi‐model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables. Plain Language Summary: Understanding past climate variability is important for contextualizing climate change as well as for testing the ability of climate models to simulate the climate system before global warming. However, reconstructing past climate variability remains a complex task because pre‐instrumental paleoclimate proxy records, such as tree rings, corals, ice cores, and sediment cores, are geographically sparse and are not perfect recorders of climate information. Exactly how to extrapolate information from paleoclimate proxies to other locations and climate variables remains an outstanding issue. Traditionally, information about how one location varies with another location or variable (covariance) is derived from one climate model or from one instrumental data source. Here we find that reconstructions using covariance estimated from combinations of multiple climate models produce less error than reconstructions that use just one climate model. Key Points: Ensembles drawn from single climate models and combinations of multiple models are used to reconstruct surface air temperature variabilityReconstructions from multi‐model ensembles produce lower error than reconstructions from single‐model ensemblesReconstructions from multi‐model ensembles show the largest decreases in error in regions with few observations such as high‐latitude oceans [ABSTRACT FROM AUTHOR]

Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100‐year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions. Plain Language Summary: Ongoing and future global and regional warming will progress as a combination of internal climate variability and forced climate change. Understanding the magnitude and spatial patterns associated with internal climate variability is an important aspect of being able to predict when, where, and how climate change will be felt around the globe. Here, we show that the latest climate model simulations, which will be used in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 6 (AR6), simulate a large range in magnitudes of internal global mean temperature variability. Although there are large unforced global temperature trends in some models, we find that even the most variable models never generate unforced global temperature trends equal to the recently observed global warming trends forced by greenhouse gas emissions. We examine the regions associated with internal climate variability and forced climate change in climate model simulations and find that only forced simulations show a pattern of warming consistent with instrumental data. Key Points: Several CMIP6 control simulations show more interdecadal global mean temperature variability than previous model generationsEven the most variable CMIP6 models never show century‐length global mean temperature trends that exceed observed warming trendsUnlike in control simulations, observed global mean temperature variability is coherent with variability in tropical convective regions [ABSTRACT FROM AUTHOR]

Drought has severe consequences for humans and their environment, yet we have a limited understanding of the drivers of drought across the full range of time scales on which it occurs. Here, the atmosphere and ocean conditions that drive this continuum of drought variability in southwestern North America (SWNA) are studied using the latest observationally based products, paleoclimate reconstructions, and state-of-the-art Earth system model simulations of the last millennium. A novel application of the self-organizing maps (SOM) methodology allows for a visualization of the continuum of climate states coinciding with thousands of droughts of varying lengths in last millennium simulations from the Community Earth System Model (CESM), the Goddard Institute for Space Studies Model E2-R (GISS E2-R), and eight other members from phase 5 of the Coupled Model Intercomparison Project (CMIP5). It is found that most droughts are associated with a cool Pacific decadal oscillation (PDO) pattern, but persistent droughts can coincide with a variety of ocean–atmosphere states, including time periods showing a warm PDO or weak ocean–atmosphere anomalies. Many CMIP5 models simulate similar SWNA teleconnection patterns, but the SOM analysis demonstrates that models simulate different continuums of ocean–atmosphere states coinciding with droughts of different lengths, suggesting fundamental differences in their drought dynamics. These findings have important implications for our understanding and simulation of the drivers of persistent drought, and for their potential predictability. [ABSTRACT FROM AUTHOR]

Accurate assessments of future climate impacts require realistic simulation of interannual-century-scale temperature and precipitation variability. Here, well-constrained paleoclimate data and the latest generation of Earth system model data are used to evaluate the magnitude and spatial consistency of climate variance distributions across interannual to centennial frequencies. It is found that temperature variance generally increases with time scale in patterns that are spatially consistent among models, especially over the mid- and high-latitude oceans. However, precipitation is similar to white noise across much of the globe. When Earth system model variance is compared to variance generated by simple autocorrelation, it is found that tropical temperature variability in Earth system models is difficult to distinguish from variability generated by simple autocorrelation. By contrast, both forced and unforced Earth system models produce variability distinct from a simple autoregressive process over most high-latitude oceans. This new analysis of tropical paleoclimate records suggests that low-frequency variance dominates the temperature spectrum across the tropical Pacific and Indian Oceans, but in many Earth system models, interannual variance dominates the simulated central and eastern tropical Pacific temperature spectrum, regardless of forcing. Tropical Pacific model spectra are compared to spectra from the instrumental record, but the short instrumental record likely cannot provide accurate multidecadal-centennial-scale variance estimates. In the coming decades, both forced and natural patterns of decade-century-scale variability will determine climate-related risks. Underestimating low-frequency temperature and precipitation variability may significantly alter our understanding of the projections of these climate impacts. [ABSTRACT FROM AUTHOR]

Following a sustainable development pathway designed to keep warming below 2 °C will benefit human health. We quantify premature deaths attributable to fine particulate matter (PM2.5) air pollution and heat exposures for China, South Asia, and the United States using projections from multiple climate models under high-and low-emission scenarios. Projected changes in premature deaths are typically dominated by population aging, primarily reflecting increased longevity leading to greater sensitivity to environmental risks. Changes in PM2.5 exposure typically have small impacts on premature deaths under a high-emission scenario but provide substantial benefits under a low-emission scenario. PM2.5-attributable deaths increase in South Asia throughout the century under both scenarios but shift to decreases by late century in China, and US values decrease throughout the century. In contrast, heat exposure increases under both scenarios and combines with population aging to drive projected increases in deaths in all countries. Despite population aging, combined PM2.5-and heat-related deaths decrease under the low-emission scenario by ~2.4 million per year by midcentury and ~2.9 million by century's end, with ~3% and ~21% of these reductions from heat, respectively. Intermodel variations in exposure projections generally lead to uncertainties of <40% except for US and China heat impacts. Health benefits of low emissions are larger from reduced heat exposure than improved air quality by the late 2090s in the United States. In contrast, in South and East Asia, the PM2.5-related benefits are largest throughout the century, and their valuation exceeds the cost of decarbonization, especially in China, over the next 30 y. [ABSTRACT FROM AUTHOR]

Paleoclimate reconstruction relies on estimates of spatiotemporal relationships among climate quantities to interpolate between proxy data. This work quantifies how structural uncertainties in those relationships translate to uncertainties in reconstructions of past climate. We develop and apply a data assimilation uncertainty quantification approach to paleoclimate networks and observational uncertainties representative of data for the last millennium. We find that structural uncertainties arising from uncertain spatial covariance relationships typically contribute 10% of the total uncertainty in reconstructed temperature variability at small (∼200 km), continental, and hemispheric length scales, with larger errors (50% or larger) in regions where long‐range climate covariances are least certain. These structural uncertainties contribute far more to errors in uncertainty quantification, sometimes by a factor of 5 or higher. Accounting for and reducing uncertainties in climate model dynamics and resulting covariance relationships will improve paleoclimate reconstruction accuracy. Plain Language Summary: Reconstructing past climate conditions is important to characterize climate variability and extremes, with implications for understanding future variability and change. An important source of information is data from proxy sources like tree rings, ice cores, ocean sediment cores, and other sources, but those data are not available everywhere on the globe. Extracting climate information from these data requires information about how climate properties covary in space and time, and that information is inevitably flawed because we lack a complete picture of how the climate behaves. This work seeks to address how much those flaws matter, how they propagate to reconstructions of past climate, and how we can reduce them. We use different physically plausible representations of climate physics from three different coupled climate models in a series of reconstruction experiments and suggest that uncertain physics typically accounts for 10% of reconstruction errors in the particular case of reconstructing surface air temperature over the Last Millennium, with regionally higher values (50% or larger). We can reduce these errors by improving climate models and by improving how we represent uncertain physics in climate reconstructions. Key Points: Perfect model experiments show how uncertain climate covariance relationships lead to uncertainties in paleoclimate reconstructionsThese uncertainties typically account for 10% of total error in reconstructed Last Millennium air temperature, with large regional variabilityImproving climate model physics and accounting for its uncertainty will improve reconstruction accuracy [ABSTRACT FROM AUTHOR]

Societal benefits from climate change mitigation accrue via multiple pathways. We examine the US impacts of emission changes on several factors that are affected by both climate and air quality responses. Nationwide benefits through midcentury stem primarily from air quality improvements, which are realized rapidly, and include human health, labor productivity, and crop yield benefits. Benefits from reduced heat exposure become large around 2060, thereafter often dominating over those from improved air quality. Monetized benefits are in the tens of trillions of dollars for avoided deaths and tens of billions for labor productivity and crop yield increases and reduced hospital expenditures. Total monetized benefits this century are dominated by health and are much larger than in previous analyses due to improved understanding of the human health impacts of exposure to both heat and air pollution. Benefit-cost ratios are therefore much larger than in prior studies, especially those that neglected clean air benefits. Specifically, benefits from clean air exceed costs in the first decade, whereas benefits from climate alone exceed costs in the latter half of the century. Furthermore, monetized US benefits largely stem from US emissions reductions. Increased emphasis on the localized, near-term air quality-related impacts would better align policies with societal benefits and, by reducing the mismatch between perception of climate as a risk distant in space and time and the need for rapid action to mitigate long-term climate change, might help increase acceptance of mitigation policies. [ABSTRACT FROM AUTHOR]

Internal climate variability confounds estimates of the climate response to forcing but offers an opportunity to examine the dynamics controlling Earth's energy budget. This study analyzes the time-evolving impact of modes of low-frequency internal variability on global-mean surface temperature (GMST) and top-of-atmosphere (TOA) radiation in preindustrial control simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The results show that the slow modes of variability with the largest impact on decadal GMST anomalies are focused in high-latitude ocean regions, where they have a minimal impact on global TOA radiation. When these regions warm, positive shortwave cloud and sea ice–albedo feedbacks largely cancel the negative feedback of outgoing longwave radiation, resulting in a weak net radiative feedback. As a consequence of the weak net radiative feedback, less energy is required to sustain these long-lived temperature anomalies. In contrast to these weakly radiating high-latitude modes, El Niño–Southern Oscillation (ENSO) has a large impact on the global energy budget, such that it remains the dominant influence on global TOA radiation out to decadal and longer time scales, despite its primarily interannual time scale. These results show that on decadal and longer time scales, different processes control internal variability in GMST than control internal variability in global TOA radiation. The results are used to quantify the impact of low-frequency internal variability and ENSO on estimates of climate sensitivity from historical GMST and TOA-radiative-imbalance anomalies. [ABSTRACT FROM AUTHOR]

Multidecadal hydroclimate variability has been expressed as "megadroughts" (dry periods more severe and prolonged than observed over the twentieth century) and corresponding "megapluvial" wet periods in many regions around the world. The risk of such events is strongly affected by modes of coupled atmosphere-ocean variability and by external impacts on climate. Accurately assessing the mechanisms for these interactions is difficult, since it requires large ensembles of millennial simulations as well as long proxy time series. Here, the Community Earth System Model (CESM) Last Millennium Ensemble is used to examine statistical associations among megaevents, coupled climate modes, and forcing from major volcanic eruptions. El Niño-Southern Oscillation (ENSO) strongly affects hydroclimate extremes: larger ENSO amplitude reduces megadrought risk and persistence in the southwestern United States, the Sahel, monsoon Asia, and Australia, with corresponding increases in Mexico and the Amazon. The Atlantic multidecadal oscillation (AMO) also alters megadrought risk, primarily in the Caribbean and the Amazon. Volcanic influences are felt primarily through enhancing AMO amplitude, as well as alterations in the structure of both ENSO and AMO teleconnections, which lead to differing manifestations of megadrought. These results indicate that characterizing hydroclimate variability requires an improved understanding of both volcanic climate impacts and variations in ENSO/AMO teleconnections. [ABSTRACT FROM AUTHOR]