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Anthropogenic aerosols (AERs) affect several aspects of the climate system across the world through radiative forcing and microphysical effects. These influences are particularly strong across South Asia, where AER concentrations are highest and further projected to increase in coming decades. Using large ensemble experiments from Earth system model, we examine how AERs shape the evolution of seasonal precipitation over South Asia inlate 20th century and 21st century climate in the presence of rising greenhouse gases (GHGs) concentrations. We find that AERs strongly reduce monsoon precipitation, moderately reduce post-monsoon precipitation, and negligibly influence pre-monsoon precipitation. Consequently, AERs delay the emergence of GHG-forced increases in precipitation by ~5 decades in the monsoon season and ~1 decade in the post-monsoon season. However, GHGs are projected to outpace the influence of AERs by mid 21st century, causing a steep intensification of monsoon and post-monsoon precipitation. We further show that local AERs have the strongest influence on precipitation in the monsoon and post monsoon seasons in the near-future (2020-2049). However, the contribution from remote AERs changes is also important in shaping the monsoon precipitation changes over northwestern South Asia. Further, the influence of local AERs monsoon precipitation remains stationary throughout the 21st century, indicating the insensitivity of relationship between local AOD and precipitation to the projected warming. A better understanding of aerosol-climate interactions and associated precipitation responses in is pertinent for policymakers to address the critical aspect of regional consequences over South Asia induced by externally forced climate change.

The societies of the Greater Horn of Africa (GHA) are vulnerable to variability in two climatologically distinct rainy seasons, the March-May ‘long‘ rains and the October-December ‘short‘ rains. Recent trends in both rainy seasons, possibly related to patterns of low-frequency variability, have increased interest in future climate projections from General Circulation Models (GCMs). However, previous generations of GCMs historically have a poor record in simulating the regional hydroclimate. This study conducts a process-based evaluation of simulations of the GHA long and short rains in CMIP6, the latest generation of GCMs. Key biases in CMIP5 models remain or are worsened, including long rains that are too short and weak and short rains that are too long and strong. Model biases are driven by a complex set of related oceanic and atmospheric factors. A too strong climatological zonal sea surface temperature gradient in the Indian Ocean and convection over the GHA that is too deep in particular are connected with erroneously powerful short rains in models. Model mean state biases in the timing of the western Indian Ocean sea surface temperature seasonal cycle are associated with certain GHA rainfall timing biases; this connection is however not replicated in interannual variability within models, suggesting there may be a common driver of both biases. Ocean biases cannot explain rainfall biases on their own; simulations driven by historical SSTs (AMIP runs) often have larger biases than fully coupled runs. A path towards using biases to better understand uncertainty in projections of GHA rainfall is suggested.

Although anthropogenic climate change is expected to have caused large shifts in temperature and rainfall, the detection of human influence on global drought has been complicated by large internal variability and the brevity of observational records. Here we address these challenges using reconstructions of the Palmer drought severity index obtained with data from tree rings that span the past millennium. We show that three distinct periods are identifiable in climate models, observations and reconstructions during the twentieth century. In recent decades (1981 to present), the signal of greenhouse gas forcing is present but not yet detectable at high confidence. Observations and reconstructions differ significantly from an expected pattern of greenhouse gas forcing around mid-century (1950–1975), coinciding with a global increase in aerosol forcing. In the first half of the century (1900–1949), however, a signal of greenhouse-gas-forced change is robustly detectable. Multiple observational datasets and reconstructions using data from tree rings confirm that human activities were probably affecting the worldwide risk of droughts as early as the beginning of the twentieth century. Multiple observational datasets and reconstructions using data from tree rings confirm that human activities were probably affecting the worldwide risk of droughts as early as at the beginning of the twentieth century.

Future changes to the hydrological cycle are projected in a warming world, and any shifts in drought risk may prove extremely consequential for natural and human systems. In addition to long‐term moistening, drying, or warming trends, perturbations to the annual cycle of regional hydroclimate variables may also have substantial impacts. We analyze projected changes in several hydroclimate variables across the continental United States, along with shifts in the amplitude and phase of their annual cycles. We find that even in regions where no robust change in the annual mean is expected, coherent changes to the annual cycle are projected. In particular, we identify robust regional phase shifts toward earlier arrival of peak evaporation in the northern regions, and peak runoff and total soil moisture in the western regions. Changes in the amplitude of the annual cycle of total and surface soil moisture are also projected, and reflect changes to the annual cycle in surface water supply and demand. Whether changes become detectable above the background noise of internal variability depends strongly on the future scenario considered, and significant changes to the annual cycle are largely avoided in the lowest‐forcing scenario.

Over the last two decades, southwestern North America (SWNA) has been in the grip of one of the most severe droughts of the last 1,200 years, with one third to nearly one half of its severity attributable to climate change. We analyze how the risk of extreme soil moisture droughts in SWNA, analogous to the most severe 21‐year (≥ in magnitude to 2000–2020) and single‐year (≥ in magnitude to 2002) events of the last several decades, changes in projections from Phase 6 of the Coupled Model Intercomparison Project. By the end of the 21st century, SWNA experiences robust (R ≥ 0.80) soil moisture drying and substantial increases in extreme single‐year drought risk that scale strongly with warming, spanning an 8%–26% probability of occurrence across +2–4 K. Notably, our results show that 21‐year droughts analogous to 2000–2020 are up to 5 times more likely than extreme single‐year droughts under all levels of warming (≈50%). These high levels of 21‐year drought risk are largely invariant across scenarios because of large spring precipitation declines in half the models, shifting SWNA into a drier mean state. Despite projections of this sweeping and ostensibly inevitable increase in 21‐year drought risk, climate mitigation reduces their severity by reducing the magnitude of extreme single‐year droughts during these events. Our results emphasize both the importance of preparing SWNA for imminent increases in persistent drought events and constraining projected precipitation uncertainty to better resolve future long‐term drought risk.

We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO 2, characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6–3.9 K for our Baseline calculation and remains within 2.3–4.5 K under the robustness tests; corresponding 5–95% ranges are 2.3–4.7 K, bounded by 2.0–5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.

Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.

There is strong evidence that climate change will increase drought risk and severity, but these conclusions depend on the regions, seasons, and drought metrics being considered. We analyze changes in drought across the hydrologic cycle (precipitation, soil moisture, and runoff) in projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6). The multimodel ensemble shows robust drying in the mean state across many regions and metrics by the end of the 21st century, even following the more aggressive mitigation pathways (SSP1‐2.6 and SSP2‐4.5). Regional hotspots with strong drying include western North America, Central America, Europe and the Mediterranean, the Amazon, southern Africa, China, Southeast Asia, and Australia. Compared to SSP3‐7.0 and SSP5‐8.5, however, the severity of drying in the lower warming scenarios is substantially reduced and further precipitation declines in many regions are avoided. Along with drying in the mean state, the risk of the historically most extreme drought events also increases with warming, by 200–300% in some regions. Soil moisture and runoff drying in CMIP6 is more robust, spatially extensive, and severe than precipitation, indicating an important role for other temperature‐sensitive drought processes, including evapotranspiration and snow. Given the similarity in drought responses between CMIP5 and CMIP6, we speculate that both generations of models are subject to similar uncertainties, including vegetation processes, model representations of precipitation, and the degree to which model responses to warming are consistent with observations. These topics should be further explored to evaluate whether CMIP6 models offer reasons to have increased confidence in drought projections.

An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2-radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long-term sensitivity.

Anthropogenic climate change is predicted to cause spatial and temporal shifts in precipitation patterns. These may be apparent in changes to the annual cycle of zonal mean precipitation P. Trends in the amplitude and phase of the P annual cycle in two long-term, global satellite datasets are broadly similar. Model-derived fingerprints of externally forced changes to the amplitude and phase of the P seasonal cycle, combined with these observations, enable a formal detection and attribution analysis. Observed amplitude changes are inconsistent with model estimates of internal variability but not attributable to the model-predicted response to external forcing. This mismatch between observed and predicted amplitude changes is consistent with the sustained La Niña–like conditions that characterize the recent slowdown in the rise of the global mean temperature. However, observed changes to the annual cycle phase do not seem to be driven by this recent hiatus. These changes are consistent with model estimates of forced changes, are inconsistent (in one observational dataset) with estimates of internal variability, and may suggest the emergence of an externally forced signal.

Although anthropogenic climate change is expected to have caused large shifts in temperature and rainfall, the detection of human influence on global drought has been complicated by large internal variability and the brevity of observational records. Here we address these challenges using reconstructions of the Palmer drought severity index obtained with data from tree rings that span the past millennium. We show that three distinct periods are identifiable in climate models, observations and reconstructions during the twentieth century. In recent decades (1981 to present), the signal of greenhouse gas forcing is present but not yet detectable at high confidence. Observations and reconstructions differ significantly from an expected pattern of greenhouse gas forcing around mid-century (1950-1975), coinciding with a global increase in aerosol forcing. In the first half of the century (1900-1949), however, a signal of greenhouse-gas-forced change is robustly detectable. Multiple observational datasets and reconstructions using data from tree rings confirm that human activities were probably affecting the worldwide risk of droughts as early as the beginning of the twentieth century.

The 2011–16 California drought illustrates that drought-prone areas do not always experience relief once a favorable phase of El Niño–Southern Oscillation (ENSO) returns. In the twenty-first century, such an expectation is unrealistic in regions where global warming induces an increase in terrestrial aridity larger than the changes in aridity driven by ENSO variability. This premise is also flawed in areas where precipitation supply cannot offset the global warming–induced increase in evaporative demand. Here, atmosphere-only experiments are analyzed to identify land regions where aridity is currently sensitive to ENSO and where projected future changes in mean aridity exceed the range caused by ENSO variability. Insights into the drivers of these changes in aridity are obtained using simulations with the incremental addition of three different factors to the current climate: ocean warming, vegetation response to elevated CO2levels, and intensified CO2radiative forcing. The effect of ocean warming overwhelms the range of ENSO-driven temperature variability worldwide, increasing potential evapotranspiration (PET) in most ENSO-sensitive regions. Additionally, about 39% of the regions currently sensitive to ENSO will likely receive less precipitation in the future, independent of the ENSO phase. Consequently aridity increases in 67%–72% of the ENSO-sensitive area. When both radiative and physiological effects are considered, the area affected by arid conditions rises to 75%–79% when using PET-derived measures of aridity, but declines to 41% when an aridity indicator for total soil moisture is employed. This reduction mainly occurs because plant stomatal resistance increases under enhanced CO2concentrations, resulting in improved plant water-use efficiency, and hence reduced evapotranspiration and soil desiccation. Imposing CO2-invariant stomatal resistance may overestimate future drying in PET-derived indices.

This study clarifies the causes of intermodel differences in the global-average temperature response to doubled CO2, commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant source of intermodel spread in ECS. This new approach also reveals that covariances between cloud feedback and forcing, between lapse rate and longwave cloud feedbacks, and between albedo and shortwave cloud feedbacks play an important and previously underappreciated role in determining model differences in ECS. Defining feedbacks based on fixed relative rather than specific humidity (as suggested by Held and Shell) reduces the covariances between processes and leads to more straightforward interpretations of results.

Understanding the cloud response to external forcing is a major challenge for climate science. This crucial goal is complicated by intermodel differences in simulating present and future cloud cover and by observational uncertainty. This is the first formal detection and attribution study of cloud changes over the satellite era. Presented herein are CMIP5 model-derived fingerprints of externally forced changes to three cloud properties: the latitudes at which the zonally averaged total cloud fraction (CLT) is maximized or minimized, the zonal average CLT at these latitudes, and the height of high clouds at these latitudes. By considering simultaneous changes in all three properties, the authors define a coherent multivariate fingerprint of cloud response to external forcing and use models from phase 5 of CMIP (CMIP5) to calculate the average time to detect these changes. It is found that given perfect satellite cloud observations beginning in 1983, the models indicate that a detectable multivariate signal should have already emerged. A search is then made for signals of external forcing in two observational datasets: ISCCP and PATMOS-x. The datasets are both found to show a poleward migration of the zonal CLT pattern that is incompatible with forced CMIP5 models. Nevertheless, a detectable multivariate signal is predicted by models over the PATMOS-x time period and is indeed present in the dataset. Despite persistent observational uncertainties, these results present a strong case for continued efforts to improve these existing satellite observations, in addition to planning for new missions.

Palaeoclimate variations are an essential component in constraining future projections of climate change as a function of increasing anthropogenic greenhouse gases1. The Earth System Sensitivity (ESS) describes the multi-millennial response of Earth (in terms of global mean temperature) to a doubling of CO2 concentrations. A recent study2 used a correlation of inferred temperatures and radiative forcing from greenhouse gases over the past 800,000 years3 to estimate the ESS from present day CO2 is about 9°C, and to imply a long-term commitment of 3–7°C even if greenhouse gas levels remain at present-day concentrations. However, we demonstrate that the methodology of ref. 2 does not reliably estimate the ESS in the presence of orbital forcing of ice age cycles and therefore conclude that the inferred2 present-day committed warming is considerably overestimated. There is a Reply to this Comment by Snyder, C. W. Nature 547, http://dx.doi.org/10.1038/nature22804 (2017).

Wind power is a near-zero-emissions source of energy. Although at present wind turbines are placed on the Earth’s surface, high-altitude winds offer greater possibilities for power generation. This study uses a climate model to estimate power generation for both surface and high-altitude winds, and finds that the latter provide much more power, but at a possible climate cost. However, there are unlikely to be substantial climate effects in meeting the present global demand.

Changes in global (ocean and land) precipitation are among the most important and least well-understood consequences of climate change. Increasing greenhouse gas concentrations are thought to affect the zonal-mean distribution of precipitation through two basic mechanisms. First, increasing temperatures will lead to an intensification of the hydrological cycle (“thermodynamic” changes). Second, changes in atmospheric circulation patterns will lead to poleward displacement of the storm tracks and subtropical dry zones and to a widening of the tropical belt (“dynamic” changes). We demonstrate that both these changes are occurring simultaneously in global precipitation, that this behavior cannot be explained by internal variability alone, and that external influences are responsible for the observed precipitation changes. Whereas existing model experiments are not of sufficient length to differentiate between natural and anthropogenic forcing terms at the 95% confidence level, we present evidence that the observed trends result from human activities.

We describe a new family of thin-wall instantons, with negative tension bubble walls, that mediate tunneling between Minkowski or de Sitter vacua. Some of these instantons can have exponentially enhanced tunneling rates, and would dominate the Euclidean path integral, representing a severe non-perturbative instability in any theory which supports them. We give two constructions of these instantons in theories which are perturbatively stable, but violate the null energy condition. One construction uses a scalar field theory with higher-derivative kinetic term, and is similar to the Coleman-de Luccia positive tension instanton. The other construction employs a negative-tension boundary arising from Z_2 orbifolding: it resembles a "bubble of nothing" which nucleates and grows, consuming the surrounding de Sitter or Minkowski spacetime. We explain how the spectral flow for fluctutations about the thin-wall tunneling solutions automatically protects causality, for both positive and negative tension instantons. We comment briefly on the relation of these solutions to a Kalzua-Klein "bubble of nothing," cosmological models relying on ghost condensates, and string theory orientifolds., Comment: PDFLaTeX, 43pp v2: new appendix, references added