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The present study examines the assumptions, modeling structure, and results of DICE-2023, the revised Dynamic Integrated Model of Climate and the Economy (DICE), updated to 2023. The revision contains major changes in the treatment of risk, the carbon and climate modules, the treatment of nonindustrial greenhouse gases, discount rates, as well as updates on all the major components. Noteworthy changes are a significant reduction in the target for the optimal (cost-beneficial) temperature path, a lower cost of reaching the 2 °C target, an analysis of the impact of the Paris Accord, and a major increase in the estimated social cost of carbon. [ABSTRACT FROM AUTHOR]

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs (P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire–climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water suppliesand runoff-related risks. [ABSTRACT FROM AUTHOR]

In October 2017 unusual 106Ru detections across most of Europe prompted the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) to analyze the event in order to locate the origin and identify the magnitude of the release. This paper presents the inverse modeling techniques used during the event to achieve this goal. The method is based on a variational approach and consists of using air concentration measurements with the ldX long-range dispersion model included in the IRSN's C3X operational platform. The method made it possible to quickly identify the southern Urals as the most likely geographical origin of the release. Despite uncertainties regarding the starting date of the release, calculations show that it potentially began on 23 September, while most of the release was emitted on 26 September. Among the nuclear plants identified in the southern Urals, the Mayak complex is that from which the dispersion of the 106Ru plume is most consistent with observations. The reconstructed 106Ru source term from Mayak is ~250 TBq. In total, it was found that for 72% of the measurements simulated and observed air concentration agreed within a factor of 5. In addition, the simulated deposition of 106Ru agrees with the observed deposition. Outside the southern Urals, the simulations indicate that areas with highest deposition values are located in southern Scandinavia and southeastern Bulgaria and are explained by rainfall events occurring while the plume was passing over. [ABSTRACT FROM AUTHOR]

Rain formation is a critical factor governing the lifecycle and radiative forcing of clouds and therefore it is a key element of weather and climate. Cloud microphysics-turbulence interactions occur across a wide range of scales and are challenging to represent in atmospheric models with limited resolution. Based on past experiments and idealized numerical simulations, it has been postulated that cloud turbulence accelerates rain formation by enhancing drop collision-coalescence. We provide substantial evidence for significant impacts of turbulence on the evolution of cloud droplet size distributions and rain formation by comparing high-resolution observations of cumulus congestus clouds with state-of-the-art large-eddy simulations coupled with a Lagrangian particlebased microphysics scheme. Turbulent coalescence must be included in the model to accurately represent the observed drop size distributions, especially for drizzle drop sizes at lower heights in the cloud. Turbulence causes earlier rain formation and greater rain accumulation compared to simulations with gravitational coalescence only. The observed rain size distribution tail just above cloud base follows a power law scaling that deviates from theoretical scalings considering either a purely gravitation collision kernel or a turbulent kernel neglecting droplet inertial effects, providing additional evidence for turbulent coalescence in clouds. In contrast, large aerosols acting as cloud condensation nuclei ("giant CCN") do not significantly impact rain formation owing to their long timescale to reach equilibrium wet size relative to the lifetime of rising cumulus thermals. Overall, turbulent drop coalescence exerts a dominant influence on rain initiation in warm cumulus clouds, with limited impacts of giant CCN. [ABSTRACT FROM AUTHOR]

Effective policies for adaptation to climate change require understanding how impacts are related to exposures and vulnerability, the dimensions of the climate system that will change most and where human impacts will be most draconian, and the institutions best suited to respond. Here, we propose a simple method for more credibly pairing empirical statistical damage estimates derived from recent weather and outcome observations with projected future climate changes and proposed responses. We first analyze agricultural production and loan repayment data from Brazil to understand vulnerability to historical variation in the more predictable components of temperature and rainfall (trend and seasonality) as well as to shocks (both local and over larger spatial scales). This decomposed weather variation over the past two decades explains over 50% of the yield variation in major Brazilian crops and, critically, can be constructed in the same way for future climate projections. Combining our estimates with bias-corrected downscaled climate simulations for Brazil, we find increased variation in yields and revenues (including more bad years and worse outcomes) and higher agricultural loan default at midcentury. Results in this context point to two particularly acute dimensions of vulnerability: Intensified seasonality and local idiosyncratic shocks both contribute to worsening outcomes, along with a reduced capacity for spatially correlated ("covariate") shocks to ameliorate these effects through prices. These findings suggest that resilience strategies should focus on institutions such as water storage, financial services, and reinsurance. [ABSTRACT FROM AUTHOR]

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northwardpropagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-drivenMISOforecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10-to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction. [ABSTRACT FROM AUTHOR]

This paper shows that the most predictable components of internal variability in coupled atmosphere–ocean models are remarkably similar to the most predictable components of climate models without interactive ocean dynamics (i.e., models whose ocean is represented by a 50-m-deep slab ocean mixed layer with no interactive currents). Furthermore, a linear regression model derived solely from dynamical model output can skillfully predict observed anomalies in these components at least a year or two in advance, indicating that these model-derived components and associated linear dynamics are realistic. These results suggest that interactive ocean circulation is not essential for the existence of multiyear predictability previously identified in coupled models and observations. [ABSTRACT FROM AUTHOR]

The debate on the sign of the soil moisture-precipitation feedback remains open. On the one hand, studies using global coarse-resolution climate models have found strong positive feedback. However, such models cannot represent convection explicitly. On the other hand, studies using km-scale regional climate models and explicit convection have reported negative feedback. Yet, the large-scale circulation is prescribed in such models. This study revisits the soil moisture-precipitation feedback using global, coupled simulations conducted for 1 y with explicit convection and compares the results to coarse-resolution simulations with parameterized convection. We find significant differences in a majority of points with feedback that is weaker and dominantly negative with explicit convection. The model with explicit convection is more often in a wet regime and prefers the triggering of convection over dry soil in the presence of soil moisture heterogeneity, in contrast to the coarse-resolution model. Further analysis indicates that the feedback not only between soil moisture and evapotranspiration but also between evapotranspiration and precipitation is weaker, in better agreement with observations. Our findings suggest that coarse-resolution models may not be well suited to study aspects of climate change over land such as changes in droughts and heatwaves. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Presently, there are no global measurement constraints on the surface emissivity at wavelengths longer than 15 μm, even though this surface property in this far-IR region has a direct impact on the outgoing longwave radiation (OLR) and infrared cooling rates where the column precipitable water vapor (PWV) is less than 1 mm. Such dry conditions are common for high-altitude and high-latitude locations, with the potential for modeled climate to be impacted by uncertain surface characteristics. This paper explores the sensitivity of instantaneous OLR and cooling rates to changes in far-IR surface emissivity and how this unconstrained property impacts climate model projections. At high latitudes and altitudes, a 0.05 change in emissivity due to mineralogy and snow grain size can cause a 1.8-2.0 W m-2 difference in the instantaneous clear-sky OLR. A variety of radiative transfer techniques have been used to model the far-IR spectral emissivities of surface types defined by the International Geosphere-Biosphere Program. Incorporating these far-IR surface emissivities into the Representative Concen tration Pathway (RCP) 8.5 scenario of the Community Earth System Model leads to discernible changes in the spatial patterns of sur face temperature, OLR, and frozen surface extent. The model results differ at high latitudes by as much as 2°K, 10 W m-2, and 15%, respectively, after only 25 y of integration. Additionally, the calculated difference in far-IR emissivity between ocean and sea ice of between 0.1 and 0.2, suggests the potential for a far-IR positive feedback for polar climate change. [ABSTRACT FROM AUTHOR]

Despite the ubiquity of tropical cyclones and their impacts on forests, little is known about how tropical cyclone regimes shape the ecology and evolution of tree species. We used a simple meteorological model (HURRECON) to estimate wind fields from hurricanes in the Western North Atlantic and Eastern North Pacific tropical cyclone basins from storms occurring between 1851 and 2022. We characterize how the intensity and frequency of hurricanes differ among geographically distinct hurricane regimes and define four hurricane regimes for North America (Continental, Inland, Coastal, and Fringe). Along this coastal-to-inland gradient, we found major differences in the frequency and intensity of hurricane wind regimes. The Fringe regime experiences category 1 winds relatively frequently [return period (RP) 25 y], whereas the Inland regime experiences category 1 winds very infrequently (RP ~3,000 y). We discuss how species traits related to tree windfirmness, such as mechanical properties and crown traits, may vary along hurricane regime gradients. Quantitative characterization of forest hurricane regimes provides a critical step for understanding the evolutionary and ecological role of hurricane regimes in wind-prone forests. [ABSTRACT FROM AUTHOR]

As heatwaves become more frequent, intense, and longer-lasting due to climate change, the question of breaching thermal limits becomes pressing. A wet-bulb temperature (Tw) of 35 °C has been proposed as a theoretical upper limit on human abilities to biologically thermoregulate. But, recent--empirical--research using human subjects found a significantly lower maximum Tw at which thermoregulation is possible even with minimal metabolic activity. Projecting future exposure to this empirical critical environmental limit has not been done. Here, using this more accurate threshold and the latest coupled climate model results, we quantify exposure to dangerous, potentially lethal heat for future climates at various global warming levels. We find that humanity is more vulnerable to moist heat stress than previously proposed because of these lower thermal limits. Still, limiting warming to under 2 °C nearly eliminates exposure and risk of widespread uncompensable moist heatwaves as a sharp rise in exposure occurs at 3 °C of warming. Parts of the Middle East and the Indus River Valley experience brief exceedances with only 1.5 °C warming. More widespread, but brief, dangerous heat stress occurs in a +2 °C climate, including in eastern China and sub-Saharan Africa, while the US Midwest emerges as a moist heat stress hotspot in a +3 °C climate. In the future, moist heat extremes will lie outside the bounds of past human experience and beyond current heat mitigation strategies for billions of people. While some physiological adaptation from the thresholds described here is possible, additional behavioral, cultural, and technical adaptation will be required to maintain healthy lifestyles. [ABSTRACT FROM AUTHOR]

Ice core records from Greenland provide evidence for multiple abrupt cold–warm– cold events recurring at millennial time scales during the last glacial interval. Although climate variations resembling Dansgaard–Oeschger (DO) oscillations have been identified in climate archives across the globe, our understanding of the climate and ecosystem impacts of the Greenland warming events in lower latitudes remains incomplete. Here, we investigate the influence of DO-cold-to-warm transitions on the global atmospheric circulation pattern. We comprehensively analyze δ18O changes during DO transitions in a globally distributed dataset of speleothems and set those in context with simulations of a comprehensive high-resolution climate model featuring internal millennial-scale variations of similar magnitude. Across the globe, speleothem δ18O signals and model results indicate consistent large-scale changes in precipitation amount, moisture source, or seasonality of precipitation associated with the DO transitions, in agreement with northward shifts of the Hadley circulation. Furthermore, we identify a decreasing trend in the amplitude of DO transitions with increasing distances from the North Atlantic region. This provides quantitative observational evidence for previous suggestions of the North Atlantic region being the focal point for these archetypes of past abrupt climate changes. [ABSTRACT FROM AUTHOR]

Active chlorine in the atmosphere is poorly constrained and so is its role in the oxidation of the potent greenhouse gas methane, causing uncertainty in global methane budgets. We propose a photocatalytic mechanism for chlorine atom production that occurs when Sahara dust mixes with sea spray aerosol. The mechanism is validated by implementation in a global atmospheric model and thereby explaining the episodic, seasonal, and location-dependent 13C depletion in CO in air samples from Barbados [J.E. Mak, G. Kra, T. Sandomenico, P. Bergamaschi, J. Geophys. Res. Atmos. 108 (2003)], which remained unexplained for decades. The production of Cl can also explain the anomaly in the CO:ethane ratio found at Cape Verde [K. A. Read et al., J. Geophys. Res. Atmos. 114 (2009)], in addition to explaining the observation of elevated HOCl [M. J. Lawler et al., Atmos. Chem. Phys. 11, 7617-7628 (2011)]. Our model finds that 3.8 Tg(Cl) y-1 is produced over the North Atlantic, making it the dominant source of chlorine in the region; globally, chlorine production increases by 41%. The shift in the methane sink budget due to the increased role of Cl means that isotope-constrained top-down models fail to allocate 12 Tg y-1 (2% of total methane emissions) to 13C-depleted biological sources such as agriculture and wetlands. Since 2014, an increase in North African dust emissions has increased the 13C isotope of atmospheric CH4, thereby partially masking a much greater decline in this isotope, which has implications for the interpretation of the drivers behind the recent increase of methane in the atmosphere. [ABSTRACT FROM AUTHOR]

The western United States has experienced severe drought in recent decades, and climate models project increased drought risk in the future. This increased drying could have important implications for the region’s interconnected, hydropower-dependent electricity systems. Using power-plant level generation and emissions data from 2001 to 2021, we quantify the impacts of drought on the operation of fossil fuel plants and the associated impacts on greenhouse gas (GHG) emissions, air quality, and human health. We find that under extreme drought, electricity generation from individual fossil fuel plants can increase up to 65% relative to average conditions, mainly due to the need to substitute for reduced hydropower. Over 54% of this drought-induced generation is transboundary, with drought in one electricity region leading to net imports of electricity and thus increased pollutant emissions from power plants in other regions. These drought-induced emission increases have detectable impacts on local air quality, as measured by proximate pollution monitors. We estimate that the monetized costs of excess mortality and GHG emissions from drought-induced fossil generation are 1.2 to 2.5x the reported direct economic costs from lost hydro production and increased demand. Combining climate model estimates of future drying with stylized energy- transition scenarios suggests that these drought-induced impacts are likely to remain large even under aggressive renewables expansion, suggesting that more ambitious and targeted measures are needed to mitigate the emissions and health burden from the electricity sector during drought. [ABSTRACT FROM AUTHOR]

The response of trade cumulus clouds to warming remains a major source of uncertainty for climate sensitivity. Recent studies have highlighted the role of the cloud-convection coupling in explaining this spread in future warming estimates. Here, using observations from an instrumented site and an airborne field campaign, together with high-frequency climate model outputs, we show that i) over the course of the daily cycle, a cloud transition is observed from deeper cumuli during nighttime to shallower cumuli during daytime, ii) the cloud evolution that models predict from night to day reflects the strength of cloud sensitivity to convective mass flux and exhibits many similarities with the cloud evolution they predict under global warming, and iii) those models that simulate a realistic cloud transition over the daily cycle tend to predict weak trade cumulus feedback. Our findings thus show that the daily cycle is a particularly relevant testbed, amenable to process studies and anchored by observations, to assess and improve the model representation of cloud-convection coupling and thus make climate projections more reliable. [ABSTRACT FROM AUTHOR]

Leveraging artificial neural networks (ANNs) trained on climate model output, we use the spatial pattern of historical temperature observations to predict the time until critical global warming thresholds are reached. Although no observations are used during the training, validation, or testing, the ANNs accurately predict the timing of historical global warming from maps of historical annual temperature. The central estimate for the 1.5 °C global warming threshold is between 2033 and 2035, including a ± 1σ range of 2028 to 2039 in the Intermediate (SSP2-4.5) climate forcing scenario, consistent with previous assessments. However, our data-driven approach also suggests a substantial probability of exceeding the 2 °C threshold even in the Low (SSP1-2.6) climate forcing scenario. While there are limitations to our approach, our results suggest a higher likelihood of reaching 2 °C in the Low scenario than indicated in some previous assessments--though the possibility that 2 °C could be avoided is not ruled out. Explainable AI methods reveal that the ANNs focus on particular geographic regions to predict the time until the global threshold is reached. Our framework provides a unique, data-driven approach for quantifying the signal of climate change in historical observations and for constraining the uncertainty in climate model projections. Given the substantial existing evidence of accelerating risks to natural and human systems at 1.5 °C and 2 °C, our results provide further evidence for high-impact climate change over the next three decades. [ABSTRACT FROM AUTHOR]

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 Kdecade-1 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 Kdecade-1. Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 Kdecade-1 . The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models. [ABSTRACT FROM AUTHOR]

Orbital cyclicity is a fundamental pacemaker of Earth's climate system. The Newark-Hartford Basin (NHB) lake sediment record of eastern North America contains compelling geologic expressions of this cyclicity, reflecting variations of climatic conditions in tropical Pangea during the Late Triassic and earliest Jurassic (~233 to 199 Ma). Climate modeling enables a deeper mechanistic understanding of Earth system modulation during this unique greenhouse and supercontinent period. We link major features of the NHB record to the combined climatic effects of orbital forcing, paleogeographic changes, and atmospheric pCO2 variations. An ensemble of transient, orbitally driven climate simulations is assessed for nine time slices, three atmospheric pCO2 values, and two paleogeographic reconstructions. Climatic transitions from tropical humid to more seasonal and ultimately semiarid are associated with tectonic drift of the NHB from ~5 °N to 20 °N. The modeled orbital modulation of the precipitation-evaporation balance is most pronounced during the 220 to 200 Ma interval, whereas it is limited by weak seasonality and increasing aridity before and after this interval. Lower pCO2 at around 205 Ma contributes to drier climates and could have led to the observed damping of sediment cyclicity. Eccentricity-modulated precession dominates the orbitally driven climate response in the NHB region. High obliquity further amplifies summer precipitation through the seasonal shifts in the tropical rainfall belt. Regions with other proxy records are also assessed, providing guidance toward an integrated picture of global astronomical climate forcing in the Late Triassic and ultimately of other periods in Earth history. [ABSTRACT FROM AUTHOR]

Excessive precipitation over the southeastern tropical Pacific is a major common bias that persists through generations of global climate models. While recent studies suggest an overly warm Southern Ocean as the cause, models disagree on the quantitative importance of this remote mechanism in light of ocean circulation feedback. Here, using a multimodel experiment in which the Southern Ocean is radiatively cooled, we show a teleconnection from the Southern Ocean to the tropical Pacific that is mediated by a shortwave subtropical cloud feedback. Cooling the Southern Ocean preferentially cools the southeastern tropical Pacific, thereby shifting the eastern tropical Pacific rainbelt northward with the reduced precipitation bias. Regional cloud locking experiments confirm that the teleconnection efficiency depends on subtropical stratocumulus cloud feedback. This subtropical cloud feedback is too weak in most climate models, suggesting that teleconnections from the Southern Ocean to the tropical Pacific are stronger than widely thought. [ABSTRACT FROM AUTHOR]

Making informed future decisions about solar radiation modification (SRM; also known as solar geoengineering)--approaches such as stratospheric aerosol injection (SAI) that would cool the climate by reflecting sunlight--requires projections of the climate response and associated human and ecosystem impacts. These projections, in turn, will rely on simulations with global climate models. As with climate-change projections, these simulations need to adequately span a range of possible futures, describing different choices, such as start date and temperature target, as well as risks, such as termination or interruptions. SRMmodeling simulations to date typically consider only a single scenario, often with some unrealistic or arbitrarily chosen elements (such as starting deployment in 2020), and have often been chosen based on scientific rather than policy-relevant considerations (e.g., choosing quite substantial cooling specifically to achieve a bigger response). This limits the ability to compare risks both between SRM and non-SRM scenarios and between different SRM scenarios. To address this gap, we begin by outlining some general considerations on scenario design for SRM. We then describe a specific set of scenarios to capture a range of possible policy choices and uncertainties and present corresponding SAI simulations intended for broad community use. [ABSTRACT FROM AUTHOR]

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming:The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using stateof-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to -0.41 ± 0.28% K-1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes. [ABSTRACT FROM AUTHOR]

An anomalous strengthening in western Pacific subtropical high (WPSH) increases moisture transport from the tropics to East Asia, inducing anomalous boreal summer monsoonal rainfall, causing extreme weather events in the densely populated region. Such positive WPSH anomalies can be induced by central Pacific (CP) cold sea-surface temperature (SST) anomalies of an incipient La Niña and warm anomalies in the Indian and/or the tropical Atlantic Ocean, both promoting anticyclonic anomalies over the northwestern Pacific region. How variability of the WPSH, its extremity, and the associated mechanisms might respond to greenhouse warming remains elusive. Using outputs from 32 of the latest climate models, here we show an increase in WPSH variability translating into a 73% increase in frequency of strong WPSH events under a business-as-usual emission scenario, supported by a strong intermodel consensus. Under greenhouse warming, response of tropical atmosphere convection to CP SST anomalies increases, as does the response of the northwestern Pacific anticyclonic circulation. Thus, climate extremes such as floods in the Yangtze River Valley of East China associated with WPSH variability are likely to be more frequent and more severe. [ABSTRACT FROM AUTHOR]

Addressing and anticipating future South Asian monsoon changes under continuing global warming is of critical importance for the food security and socioeconomic well-being of one-quarter of the world’s population. However, climate model projections show discrepancies in future monsoon variability in South Asian monsoon domains, largely due to our still limited understanding of the monsoon response to warm climate change scenarios. Particularly, climate models are largely based on the assumption that higher solar insolation causes higher rainfall during similar warm climatic regimes, but this has not been verified by proxy data for different interglacial periods. Here, we compare Indian summer monsoon (ISM) variability during the Last Interglacial and Holocene using a sedimentary leaf wax δD and δ13 C record from the northern Bay of Bengal, representing the Ganges–Brahmaputra–Meghna (G-B-M) river catchment. In combination with a seawater salinity record, our results show that ISM intensity broadly follows summer insolation on orbital scales, but ISM intensity during the Last Interglacial was lower than during the Holocene despite higher summer insolation and greenhouse gas concentrations. We argue that sustained warmer sea surface temperature in the equatorial and tropical Indian Ocean during the Last Interglacial increased convective rain- fall above the ocean but dampened ISM intensity on land. Our study demonstrates that besides solar insolation, internal climatic feedbacks also play an important role for South Asian monsoon variability during warm climate states. This work can help to improve future climate model projections and highlights the importance of understanding controls of monsoonal rainfall under interglacial boundary conditions. [ABSTRACT FROM AUTHOR]

The catalytic depletion of Antarctic stratospheric ozone is linked to anthropogenic emissions of chlorine and bromine. Despite its larger ozone-depleting efficiency, the contribution of oceanemitted iodine to ozone hole chemistry has not been evaluated, due to the negligible iodine levels previously reported to reach the stratosphere. Based on the recently observed range (0.77 ± 0.1 parts per trillion by volume [pptv]) of stratospheric iodine injection, we use the Whole Atmosphere Community Climate Model to assess the role of iodine in the formation and recent past evolution of the Antarctic ozone hole. Our 1980-2015 simulations indicate that iodine can significantly impact the lower part of the Antarctic ozone hole, contributing, on average, 10% of the lower stratospheric ozone loss during spring (up to 4.2% of the total stratospheric column). We find that the inclusion of iodine advances the beginning and delays the closure stages of the ozone hole by 3 d to 5 d, increasing its area and mass deficit by 11% and 20%, respectively. Despite being present in much smaller amounts, and due to faster gas-phase photochemical reactivation, iodine can dominate (∼73%) the halogen-mediated lower stratospheric ozone loss during summer and early fall, when the heterogeneous reactivation of inorganic chlorine and bromine reservoirs is reduced. The stratospheric ozone destruction caused by 0.77 pptv of iodine over Antarctica is equivalent to that of 3.1 (4.6) pptv of biogenic very short-lived bromocarbons during spring (rest of sunlit period). The relative contribution of iodine to future stratospheric ozone loss is likely to increase as anthropogenic chlorine and bromine emissions decline following the Montreal Protocol. [ABSTRACT FROM AUTHOR]

The atmosphere of Venus remains mysterious, with many outstanding chemical connundra. These include the unexpected presence of ~10 ppm O2 in the cloud layers, an unknown composition of large particles in the lower cloud layers, and hard to explain measured vertical abundance profiles of SO2 and H2O. We propose a hypothesis for the chemistry in the clouds that largely addresses all of the above anomalies. We include ammonia (NH3), a key component that has been tentatively detected both by the Venera 8 and Pioneer Venus probes. NH3 dissolves in some of the sulfuric acid cloud droplets, effectively neutralizing the acid and trapping dissolved SO2 as ammonium sulfite salts. This trapping of SO2 in the clouds, together with the release of SO2 below the clouds as the droplets settle out to higher temperatures, explains the vertical SO2 abundance anomaly. A consequence of the presence of NH3 is that some Venus cloud droplets must be semisolid ammonium salt slurries, with a pH of ~1, which matches Earth acidophile environments, rather than concentrated sulfuric acid. The source of NH3 is unknown but could involve biological production; if so, then the most energy-efficient NH3-producing reaction also creates O2, explaining the detection of O2 in the cloud layers. Our model therefore predicts that the clouds are more habitable than previously thought, and may be inhabited. Unlike prior atmospheric models, ours does not require forced chemical constraints to match the data. Our hypothesis, guided by existing observations, can be tested by new Venus in situ measurements. [ABSTRACT FROM AUTHOR]

Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model-based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal. The "storyline" attribution framework, in which the impact of climate change on individual drivers of an extreme event is examined, aims to mitigate these concerns. Here we propose a methodology for attribution of extreme weather events using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) medium-range forecast model that successfully predicted the event. The use of a successful forecast ensures not only that the model is able to accurately represent the event in question, but also that the analysis is unequivocally an attribution of this specific event, rather than a mixture of multiple different events that share some characteristic. Since this attribution methodology is conditioned on the component of the event thatwas predictable at forecast initialization, we show how adjusting the lead time of the forecast can flexibly set the level of conditioning desired. This flexible adjustment of the conditioning allows us to synthesize between a storyline (highly conditioned) and a risk-based (relatively unconditioned) approach. We demonstrate this forecast-based methodology through a partial attribution of the direct radiative effect of increased CO2 concentrations on the exceptional European winter heatwave of February 2019. [ABSTRACT FROM AUTHOR]

Previous studies have identified a recent increase in wildfire activity in the western United States (WUS). However, the extent to which this trend is due to weather pattern changes dominated by natural variability versus anthropogenic warming has been unclear. Using an ensemble constructed flow analogue approach, we have employed observations to estimate vapor pressure deficit (VPD), the leading meteorological variable that controls wildfires, associated with different atmospheric circulation patterns. Our results show that for the period 1979 to 2020, variation in the atmospheric circulation explains, on average, only 32% of the observed VPD trend of 0.48 6 0.25 hPa/decade (95% CI) over the WUS during the warm season (May to September). The remaining 68% of the upward VPD trend is likely due to anthropogenic warming. The ensemble simulations of climate models participating in the sixth phase of the Coupled Model Intercomparison Project suggest that anthropogenic forcing explains an even larger fraction of the observed VPD trend (88%) for the same period and region. These models and observational estimates likely provide a lower and an upper bound on the true impact of anthropogenic warming on the VPD trend over the WUS. During August 2020, when the August Complex "Gigafire" occurred in the WUS, anthropogenic warming likely explains 50% of the unprecedented high VPD anomalies. [ABSTRACT FROM AUTHOR]

A proposal to combat free riding in international climate agreements is the establishment of a climate club—a coalition of countries in a structure to encourage high levels of participation. Empirical models of climate clubs in the early stages relied on the analysis of single-period coalition formation. The earlier results suggested that there were limits to the potential strength of clubs and that it would be difficult to have deep abatement strategies in the club framework. The current study extends the single-period approach to many periods and develops an approach analyzing “supportable policies” to analyze multiperiod clubs. The major element of the present study is the interaction between club effectiveness and rapid technological change. Neither alone will produce incentive-compatible policies that can attain the ambitious objectives of international climate policy. The trade sanctions without rapid technological decarbonization will be too costly to produce deep abatement; similarly, rapid technological decarbonization by itself will not induce deep abatement because of country free riding. However, the two together can achieve international climate objectives. [ABSTRACT FROM AUTHOR]

While paleoclimate records show that the Earth System is characterized by several different tipping points, their representation within Earth System models (ESMs) remains poorly constrained. This is because historical observations do not encompass variations large enough to provoke such regime changes, and paleoclimate conditions are rarely used to help develop and tune ESMs, which potentially ignores a rich source of information on abrupt climate change. A critical example is the early to mid-Holocene "greening" and subsequent rapid desertification of the Sahara, which most ESMs fail to reproduce, casting doubt on the representation of land-atmosphere coupling and monsoon dynamics. Here, we show that this greening and abrupt termination can be successfully simulated with one ESM after optimizing uncertain model components using both present-day observations and crucially mid-Holocene (6,000 y before present) reconstructions. The optimized model displays abrupt threshold behavior, which shows excellent agreement with long paleoclimate records that were not used in the original optimization. These results suggest that in order to realistically capture climate-system thresholds, ESMs first need to be conditioned with appropriate paleoclimate information. [ABSTRACT FROM AUTHOR]

The hydroxyl radical (OH) sets the oxidative capacity of the atmosphere and, thus, profoundly affects the removal rate of pollutants and reactive greenhouse gases. While observationally derived constraints exist for global annual mean present-day OH abundances and interannual variability, OH estimates for past and future periods rely primarily on global atmospheric chemistry models. These models disagree ± 30% in mean OH and in its changes from the preindustrial to late 21st century, even when forced with identical anthropogenic emissions. A simple steadystate relationship that accounts for ozone photolysis frequencies, water vapor, and the ratio of reactive nitrogen to carbon emissions explains temporal variability within most models, but not intermodel differences. Here, we show that departure from the expected relationship reflects the treatment of reactive oxidized nitrogen species (NOy) and the fraction of emitted carbon that reacts within each chemical mechanism, which remain poorly known due to a lack of observational data. Our findings imply a need for additional observational constraints on NOy partitioning and lifetime, especially in the remote free troposphere, as well as the fate of carbon-containing reaction intermediates to test models, thereby reducing uncertainties in projections of OH and, hence, lifetimes of pollutants and greenhouse gases. [ABSTRACT FROM AUTHOR]

The distribution of forest cover alters Earth surface mass and energy exchange and is controlled by physiology, which determines plant environmental limits. Ancient plant physiology, therefore, likely affected vegetation-climate feedbacks. We combine climate modeling and ecosystem-process modeling to simulate arboreal vegetation in the late Paleozoic ice age. Using GENESIS V3 global climate model simulations, varying pCO2, pO2, and ice extent for the Pennsylvanian, and fossil-derived leaf C:N, maximum stomatal conductance, and specific conductivity for several major Carboniferous plant groups, we simulated global ecosystem processes at a 2° resolution with Paleo-BGC. Based on leaf water constraints, Pangaea could have supported widespread arboreal plant growth and forest cover. However, these models do not account for the impacts of freezing on plants. According to our interpretation, freezing would have affected plants in 59% of unglaciated land during peak glacial periods and 73% during interglacials, when more high-latitude land was unglaciated. Comparing forest cover, minimum temperatures, and paleo-locations of Pennsylvanianaged plant fossils from the Paleobiology Database supports restriction of forest extent due to freezing. Many genera were limited to unglaciated landwhere temperatures remained above -4 °C. Freezeintolerance of Pennsylvanian arboreal vegetation had the potential to alter surface runoff, silicate weathering, CO2 levels, and climate forcing. As a bounding case, we assume total plant mortality at -4 °C and estimate that contracting forest cover increased net global surface runoff by up to 6.1%. Repeated freezing likely influenced freeze- and drought-tolerance evolution in lineages like the coniferophytes, which became increasingly dominant in the Permian and early Mesozoic. [ABSTRACT FROM AUTHOR]

Understanding tropical cyclone (TC) climatology is a problem of profound societal significance and deep scientific interest. The annual cycle is the biggest radiatively forced signal in TC variability, presenting a key test of our understanding and modeling of TC activity. TCs over the North Atlantic (NA) basin, which are usually called hurricanes, have a sharp peak in the annual cycle, with more than half concentrated in only 3 mo (August to October), yet existing theories of TC genesis often predict a much smoother cycle. Here we apply a framework originally developed to study TC response to climate change in which TC genesis is determined by both the number of pre-TC synoptic disturbances (TC "seeds") and the probability of TC genesis from the seeds. The combination of seeds and probability predicts a more consistent hurricane annual cycle, reproducing the compact season, as well as the abrupt increase from July to August in the NA across observations and climate models. The seeds-probability TC genesis framework also successfully captures TC annual cycles in different basins. The concise representation of the climate sensitivity of TCs from the annual cycle to climate change indicates that the framework captures the essential elements of the TC climate connection. [ABSTRACT FROM AUTHOR]

Humans have made such dramatic and permanent changes to Earth's landscapes that much of it is now substantially and irreversibly altered from its preanthropogenic state. Remote islands, until recently isolated from humans, offer insights into how these landscapes evolved in response to human-induced perturbations. However, little is known about when and how remote systems were colonized because archaeological data and historical records are scarce and incomplete. Here, we use a multiproxy approach to reconstruct the initial colonization and subsequent environmental impacts on the Azores Archipelago. Our reconstructions provide unambiguous evidence for widespread human disturbance of this archipelago starting between 700-60+50 and 850-60+60 Common Era (CE), ca. 700 y earlier than historical records suggest the onset of Portuguese settlement of the islands. Settlement proceeded in three phases, during which human pressure on the terrestrial and aquatic ecosystems grew steadily (i.e., through livestock introductions, logging, and fire), resulting in irreversible changes. Our climate models suggest that the initial colonization at the end of the early Middle Ages (500 to 900 CE) occurred in conjunction with anomalous northeasterly winds and warmer Northern Hemisphere temperatures. These climate conditions likely inhibited exploration from southern Europe and facilitated human settlers from the northeast Atlantic. These results are consistent with recent archaeological and genetic data suggesting that the Norse were most likely the earliest settlers on the islands. [ABSTRACT FROM AUTHOR]

More than two decades ago, my coauthors, Raymond Bradley and Malcolm Hughes, and I published the now iconic "hockey stick" curve. It was a simple graph, derived from large-scale networks of diverse climate proxy ("multiproxy") data such as tree rings, ice cores, corals, and lake sediments, that captured the unprecedented nature of the warming taking place today. It became a focal point in the debate over human-caused climate change and what to do about it. Yet, the apparent simplicity of the hockey stick curve betrays the dynamicism and complexity of the climate history of past centuries and how it can inform our understanding of humancaused climate change and its impacts. In this article, I discuss the lessons we can learn from studying paleoclimate records and climate model simulations of the "Common Era," the period of the past two millennia during which the "signal" of human-caused warming has risen dramatically from the background of natural variability. [ABSTRACT FROM AUTHOR]

Changes in mean climatic conditions will affect natural and societal systems profoundly under continued anthropogenic global warming. Changes in the high-frequency variability of temperature exert additional pressures, yet the effect of greenhouse forcing thereon has not been fully assessed or identified in observational data. Here, we show that the intramonthly variability of daily surface temperature changes with distinct global patterns as greenhouse gas concentrations rise. In both reanalyses of historical observations and state-of-the-art projections, variability increases at low to mid latitudes and decreases at northern mid to high latitudes with enhanced greenhouse forcing. These latitudinally polarized daily variability changes are identified from internal climate variability using a recently developed signal-to-noise-maximizing pattern-filtering technique. Analysis of a multimodel ensemble from the Coupled Model Intercomparison Project Phase 6 shows that these changes are attributable to enhanced greenhouse forcing. By the end of the century under a businessas-usual emissions scenario, daily temperature variability would continue to increase by up to a further 100% at low latitudes and decrease by 40% at northern high latitudes. Alternative scenarios demonstrate that these changes would be limited by mitigation of greenhouse gases. Moreover, global changes in daily variability exhibit strong covariation with warming across climate models, suggesting that the equilibrium climate sensitivity will also play a role in determining the extent of future variability changes. This global response of the high-frequency climate system to enhanced greenhouse forcing is likely to have strong and unequal effects on societies, economies, and ecosystems if mitigation and protection measures are not taken. [ABSTRACT FROM AUTHOR]

Global warming drives changes in Earth's cloud cover, which, in turn, may amplify or dampen climate change. This "cloud feedback" is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)--the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W·m-2·K-1 (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts. [ABSTRACT FROM AUTHOR]

The Toba eruption ~74,000 y ago was the largest volcanic eruption since the start of the Pleistocene and represents an important test case for understanding the effects of large explosive eruptions on climate and ecosystems. However, the magnitude and repercussions of climatic changes driven by the eruption are strongly debated. High-resolution paleoclimate and archaeological records from Africa find little evidence for the disruption of climate or human activity in the wake of the eruption in contrast with a controversial link with a bottleneck in human evolution and climate model simulations predicting strong volcanic cooling for up to a decade after a Toba-scale eruption. Here, we use a large ensemble of high-resolution Community Earth System Model (CESM1.3) simulations to reconcile climate model predictions with paleoclimate records, accounting for uncertainties in the magnitude of Toba sulfur emissions with high and low emission scenarios. We find a near-zero probability of annual mean surface temperature anomalies exceeding 4 °C in most of Africa in contrast with near 100% probabilities of cooling this severe in Asia and North America for the high sulfur emission case. The likelihood of strong decreases in precipitation is low in most of Africa. Therefore, even Toba sulfur release at the upper range of plausible estimates remains consistent with the muted response in Africa indicated by paleoclimate proxies. Our results provide a probabilistic view of the uneven patterns of volcanic climate disruption during a crucial interval in human evolution, with implications for understanding the range of environmental impacts from past and future supereruptions. [ABSTRACT FROM AUTHOR]

The greening of the Sahara, associated with the African Humid Period (AHP) between ca. 14,500 and 5,000 y ago, is arguably the largest climate-induced environmental change in the Holocene; it is usually explained by the strengthening and northward expansion of the African monsoon in response to orbital forcing. However, the strengthened monsoon in Early to Middle Holocene climate model simulations cannot sustain vegetation in the Sahara or account for the increased humidity in the Mediterranean region. Here, we present an 18,500-y pollen and leaf-wax δD record from Lake Tislit (32° N) in Morocco, which provides quantitative reconstruction of winter and summer precipitation in northern Africa. The record from Lake Tislit shows that the northern Sahara and the Mediterranean region were wetter in the AHP because of increased winter precipitation and were not influenced by the monsoon. The increased seasonal contrast of insolation led to an intensification and southward shift of the Mediterranean winter precipitation system in addition to the intensified summer monsoon. Therefore, a winter rainfall zone must have met and possibly overlapped the monsoonal zone in the Sahara. Using a mechanistic vegetation model in Early Holocene conditions, we show that this seasonal distribution of rainfall is more efficient than the increased monsoon alone in generating a green Sahara vegetation cover, in agreement with observed vegetation. This conceptual framework should be taken into consideration in Earth system paleoclimate simulations used to explore the mechanisms of African climatic and environmental sensitivity. [ABSTRACT FROM AUTHOR]

Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6. We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios. [ABSTRACT FROM AUTHOR]

Despite receiving just 30% of the Earth's present-day insolation, Mars had water lakes and rivers early in the planet's history, due to an unknown warming mechanism. A possible explanation for the >102-y-long lake-forming climates is warming by water ice clouds. However, this suggested cloud greenhouse explanation has proved difficult to replicate and has been argued to require unrealistically optically thick clouds at high altitudes. Here, we use a global climate model (GCM) to show that a cloud greenhouse can warm a Mars-like planet to global average annual-mean temperature (T) ~265 K, which is warm enough for low-latitude lakes, and stay warm for centuries or longer, but only if the planet has spatially patchy surface water sources. Warm, stable climates involve surface ice (and low clouds) only at locations much colder than the average surface temperature. At locations horizontally distant from these surface cold traps, clouds are found only at high altitudes, which maximizes warming. Radiatively significant clouds persist because ice particles sublimate as they fall, moistening the subcloud layer so that modest updrafts can sustain relatively large amounts of cloud. The resulting climates are arid (area-averaged surface relative humidity ~25%). In a warm, arid climate, lakes could be fed by groundwater upwelling, or by melting of ice following a cold-to-warm transition. Our results are consistent with the warm and arid climate favored by interpretation of geologic data, and support the cloud greenhouse hypothesis. [ABSTRACT FROM AUTHOR]

Plastic pollution is one of the most pressing environmental and social issues of the 21st century. Recent work has highlighted the atmosphere's role in transporting microplastics to remote locations [S. Allen et al., Nat. Geosci. 12, 339 (2019) and J. Brahney, M. Hallerud, E. Heim, M. Hahnenberger, S. Sukumaran, Science 368, 1257-1260 (2020)]. Here, we use in situ observations of microplastic deposition combined with an atmospheric transport model and optimal estimation techniques to test hypotheses of the most likely sources of atmospheric plastic. Results suggest that atmospheric microplastics in the western United States are primarily derived from secondary reemission sources including roads (84%), the ocean (11%), and agricultural soil dust (5%). Using our best estimate of plastic sources and modeled transport pathways, most continents were net importers of plastics from the marine environment, underscoring the cumulative role of legacy pollution in the atmospheric burden of plastic. This effort uses high-resolution spatial and temporal deposition data along with several hypothesized emission sources to constrain atmospheric plastic. Akin to global biogeochemical cycles, plastics now spiral around the globe with distinct atmospheric, oceanic, cryospheric, and terrestrial residence times. Though advancements have been made in the manufacture of biodegradable polymers, our data suggest that extant nonbiodegradable polymers will continue to cycle through the earth's systems. Due to limited observations and understanding of the source processes, there remain large uncertainties in the transport, deposition, and source attribution of microplastics. Thus, we prioritize future research directions for understanding the plastic cycle. [ABSTRACT FROM AUTHOR]

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel "structural" uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or "deep" uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research. [ABSTRACT FROM AUTHOR]

A long-standing discrepancy exists between general circulation models (GCMs) and satellite observations: The multimodel mean temperature of the midtroposphere (TMT) in the tropics warms at approximately twice the rate of observations. Using a large ensemble of simulations from a single climate model, we find that tropical TMT trends (1979-2018) vary widely and that a subset of realizations are within the range of satellite observations. Realizations with relatively small tropical TMT trends are accompanied by subdued sea-surface warming in the tropical central and eastern Pacific. Observed changes in sea-surface temperature have a similar pattern, implying that the observed tropical TMT trend has been reduced by multidecadal variability. We also assess the latest generation of GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 simulations with muted warming over the central and eastern Pacific also show reduced tropical tropospheric warming. We find that 13% of the model realizations have tropical TMT trends within the observed trend range. These simulations are from models with both small and large climate sensitivity values, illustrating that the magnitude of tropical tropospheric warming is not solely a function of climate sensitivity. For global averages, one-quarter of model simulations exhibit TMT trends in accord with observations. Our results indicate that even on 40-y timescales, natural climate variability is important to consider when comparing observed and simulated tropospheric warming and is sufficiently large to explain TMT trend differences between models and satellite data. [ABSTRACT FROM AUTHOR]

Central elements of the climate system are at risk for crossing critical thresholds (so-called tipping points) due to future greenhouse gas emissions, leading to an abrupt transition to a qualitatively different climate with potentially catastrophic consequences. Tipping points are often associated with bifurcations, where a previously stable system state loses stability when a system parameter is increased above a well-defined critical value. However, in some cases such transitions can occur even before a parameter threshold is crossed, given that the parameter change is fast enough. It is not known whether this is the case in high-dimensional, complex systems like a state-of-the-art climate model or the real climate system. Using a global ocean model subject to freshwater forcing, we show that a collapse of the Atlantic Meridional Overturning Circulation can indeed be induced even by small-amplitude changes in the forcing, if the rate of change is fast enough. Identifying the location of critical thresholds in climate subsystems by slowly changing system parameters has been a core focus in assessing risks of abrupt climate change. This study suggests that such thresholds might not be relevant in practice, if parameter changes are not slow. Furthermore, we show that due to the chaotic dynamics of complex systems there is no well-defined critical rate of parameter change, which severely limits the predictability of the qualitative long-term behavior. The results show that the safe operating space of elements of the Earth system with respect to future emissions might be smaller than previously thought. [ABSTRACT FROM AUTHOR]

The amplitude of the atmospheric CO2 seasonal cycle has increased by 30 to 50% in the Northern Hemisphere (NH) since the 1960s, suggesting widespread ecological changes in the northern extratropics. However, substantial uncertainty remains in the continental and regional drivers of this prominent amplitude increase. Here we present a quantitative regional attribution of CO2 seasonal amplification over the past 4 decades, using a tagged atmospheric transport model prescribed with observationally constrained fluxes. We find that seasonal flux changes in Siberian and temperate ecosystems together shape the observed amplitude increases in the NH. At the surface of northern high latitudes, enhanced seasonal carbon exchange in Siberia is the dominant contributor (followed by temperate ecosystems). Arctic-boreal North America shows much smaller changes in flux seasonality and has only localized impacts. These continental contrasts, based on an atmospheric approach, corroborate heterogeneous vegetation greening and browning trends from field and remote-sensing observations, providing independent evidence for regionally divergent ecological responses and carbon dynamics to global change drivers. Over surface midlatitudes and throughout the midtroposphere, increased seasonal carbon exchange in temperate ecosystems is the dominant contributor to CO2 amplification, albeit with considerable contributions from Siberia. Representing the mechanisms that control the high-latitude asymmetry in flux amplification found in this study should be an important goal for mechanistic land surface models moving forward. [ABSTRACT FROM AUTHOR]

Secondary organic aerosol (SOA) represents a major constituent of tropospheric fine particulate matter, with profound implications for human health and climate. However, the chemical mechanisms leading to SOA formation remain uncertain, and atmospheric models consistently underpredict the global SOA budget. Small α-dicarbonyls, such as methylglyoxal, are ubiquitous in the atmosphere because of their significant production from photooxidation of aromatic hydrocarbons from traffic and industrial sources as well as from biogenic isoprene. Current experimental and theoretical results on the roles of methylglyoxal in SOA formation are conflicting. Using quantum chemical calculations, we show cationic oligomerization of methylglyoxal in aqueous media. Initial protonation and hydration of methylglyoxal lead to formation of diols/tetrol, and subsequent protonation and dehydration of diols/tetrol yield carbenium ions, which represent the key intermediates for formation and propagation of oligomerization. On the other hand, our results reveal that the previously proposed oligomerization via hydration for methylglyoxal is kinetically and thermodynamically implausible. The carbenium ion-mediated mechanism occurs barrierlessly on weakly acidic aerosols and cloud/fog droplets and likely provides a key pathway for SOA formation from biogenic and anthropogenic emissions. [ABSTRACT FROM AUTHOR]