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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]

El Niño‐Southern Oscillation (ENSO) variability affects year‐to‐year changes in North American hydroclimate. Extra‐tropical teleconnections are not always consistent between El Niño events due to stochastic atmospheric variability and diverse sea surface temperature anomalies, making it difficult to quantify teleconnections using only instrumentally‐based records. Here we use two paleoclimate data assimilation (DA) products spanning the Last Millennium (LM) to compare changes in amplitudes and frequencies of diverse El Niño events during the pre‐industrial period and 20th century, and to assess the stationarity of their North American hydroclimate impacts on multi‐decadal to centennial timescales. Using several definitions for Central Pacific (CP) and Eastern Pacific (EP) El Niño, we find a marked increase in 20th century EP El Niño intensity, but no significant changes in CP or EP El Niño frequencies in response to anthropogenic forcing. The associated hydroclimate anomalies indicate (a) dry conditions across the eastern‐central and northwestern U.S. during CP El Niño and wetter conditions in the same regions during EP El Niño; (b) wet conditions over the southwestern U.S. for both El Niño types. The magnitude of regional hydroclimate teleconnections also shows large natural variability on multi‐decadal to centennial timescales. However, when the entire LM is considered, mean hydroclimate anomalies in North America during CP or EP El Niño are consistent in terms of sign (wet vs. dry). Results are sensitive to proxy data and model priors used in DA products. Inconsistencies between El Niño classification methods underscore the need for improved ENSO diversity classification when assessing precipitation teleconnections. Key Points: Last Millennium data assimilation products show intensification of Eastern Pacific (EP) El Niño events in the 20th century, but no change in Central Pacific (CP) frequencyShifts in El Niño hydroclimate teleconnections over North America are variable in magnitude, but consistent in sign over the Last MillenniumInconsistent results between CP and EP El Niño definitions suggest a need for more robust, spatially dependent classification schemes [ABSTRACT FROM AUTHOR]

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]

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]

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]

Multiyear droughts are a common occurrence in southwestern North America (SWNA), but it is unclear what causes these persistent dry periods. The ocean‐atmosphere conditions coinciding with droughts have traditionally been studied using correlation and composite methods, which suggest that cool conditions in the tropical Pacific are associated with SWNA droughts and warm conditions are associated with wet periods in SWNA. Nevertheless, the extent to which multiyear droughts are truly consistent with this paradigm remains unknown. This is, in part, because the temporal trajectory of ocean‐atmosphere conditions during these dry periods have not been sufficiently characterized. Here we examine the continuum of ocean‐atmosphere trajectories before, during, and after multiyear droughts in SWNA using observation‐based data and an ensemble of climate model simulations from the Community Earth System Model. An examination of sea surface temperature patterns at the beginning, middle, and end of SWNA droughts shows that an El Niño event tends to precede SWNA droughts, a cool tropical Pacific occurs during droughts, and central Pacific El Niño events end droughts. However, moderate El Niño events can occur in the middle of persistent droughts, so a warm tropical Pacific does not always end these dry periods. These findings are important for drought predictability and emphasize the need to improve simulations of the magnitude, life cycle, and frequency of occurrence of El Niño events. Plain Language Summary: Droughts lasting multiple years in southwestern North America (SWNA) have severe consequences for both humans and natural systems. Unusually cold tropical Pacific conditions (e.g., La Niña events) typically coincide with dry years in SWNA, and it is often assumed that persistent cold conditions in the tropical Pacific coincide with persistent droughts. Here we test this idea using climate model simulations and observation‐based data. Although a cool tropical Pacific tends to initiate SWNA droughts and a warm tropical Pacific (e.g., El Niño events) tends to terminate droughts, we show that El Niño events can occur in the middle of SWNA droughts without ending these dry periods. We also examine patterns of sea surface temperatures before, during, and after SWNA droughts. We find that El Niño events tend to precede SWNA droughts and central Pacific El Niño events often terminate droughts in both observations and model data; these El Niño events are perhaps the most consistent feature of multiyear droughts in SWNA. These findings have important implications for public perception of droughts and their causes, in addition to our scientific understanding and modeling of El Niño and its impacts. Key Points: We examine climate patterns associated with the life cycle of droughts in southwestern North America (SWNA) in observations and model dataEastern Pacific ENSO events often precede droughts, cool Pacific anomalies maintain droughts, and central Pacific ENSO events end droughtsThe state of the North Pacific and the type of El Niño event may modulate the likelihood of an El Niño event ending a drought [ABSTRACT FROM AUTHOR]

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