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Higher growing season temperatures can have dramatic impacts on agricultural productivity, farm incomes, and food security. We used observational data and output from 23 global climate models to show a high probability (>90%) that growing season temperatures in the tropics and subtropics by the end of the 21st century will exceed the most extreme seasonal temperatures recorded from 1900 to 2006. In temperate regions, the hottest seasons on record will represent the future norm in many locations. We used historical examples to illustrate the magnitude of damage to food systems caused by extreme seasonal heat and show that these short-run events could become long-term trends without sufficient investments in adaptation.

The Arctic has seen rapid sea-ice decline in the past three decades, whilst warming at about twice the global average rate. Yet the relationship between Arctic warming and sea-ice loss is not well understood. Here, we present evidence that trends in summertime atmospheric circulation may have contributed as much as 60% to the September sea-ice extent decline since 1979. A tendency towards a stronger anticyclonic circulation over Greenland and the Arctic Ocean with a barotropic structure in the troposphere increased the downwelling longwave radiation above the ice by warming and moistening the lower troposphere. Model experiments, with reanalysis data constraining atmospheric circulation, replicate the observed thermodynamic response and indicate that the near-surface changes are dominated by circulation changes rather than feedbacks from the changing sea-ice cover. Internal variability dominates the Arctic summer circulation trend and may be responsible for about 30–50% of the overall decline in September sea ice since 1979. The Arctic is warming and sea ice is declining, but how the two link is unclear. This study shows changes in summertime atmospheric circulation and internal variability may have caused up to 60% of September sea-ice decline since 1979.

Global radiative feedbacks have been found to vary in global climate model (GCM) simulations. Atmospheric GCMs (AGCMs) driven with historical patterns of sea surface temperatures (SSTs) and sea ice concentrations produce radiative feedbacks that trend toward more negative values, implying low climate sensitivity, over recent decades. Freely evolving coupled GCMs driven by increasing CO₂ produce radiative feedbacks that trend toward more positive values, implying increasing climate sensitivity, in the future. While this time variation in feedbacks has been linked to evolving SST patterns, the role of particular regions has not been quantified. Here, a Green’s function is derived from a suite of simulations within an AGCM (NCAR’s CAM4), allowing an attribution of global feedback changes to surface warming in each region. The results highlight the radiative response to surface warming in ascent regions of the western tropical Pacific as the dominant control on global radiative feedback changes. Historical warming from the 1950s to 2000s preferentially occurred in the western Pacific, yielding a strong global outgoing radiative response at the top of the atmosphere (TOA) and thus a strongly negative global feedback. Long-term warming in coupled GCMs occurs preferentially in tropical descent regions and in high latitudes, where surface warming yields small global TOA radiation change but large global surface air temperature change, and thus a less-negative global feedback. These results illuminate the importance of determining mechanisms of warm pool warming for understanding how feedbacks have varied historically and will evolve in the future.

Meeting the global food demand of roughly 10 billion people by the middle of the 21st century will become increasingly challenging as the Earth’s climate continues to warm. Earlier studies suggest that once the optimum growing temperature is exceeded, mean crop yields decline and the variability of yield increases even if interannual climate variability remains unchanged. Here, we use global datasets of maize production and climate variability combined with future temperature projections to quantify how yield variability will change in the world’s major maize-producing and -exporting countries under 2 °C and 4 °C of global warming. We find that as the global mean temperature increases, absent changes in temperature variability or breeding gains in heat tolerance, the coefficient of variation (CV) of maize yields increases almost everywhere to values much larger than present-day values. This higher CV is due both to an increase in the SD of yields and a decrease in mean yields. For the top four maize-exporting countries, which account for 87% of global maize exports, the probability that they have simultaneous production losses greater than 10% in any given year is presently virtually zero, but it increases to 7% under 2 °C warming and 86% under 4 °C warming. Our results portend rising instability in global grain trade and international grain prices, affecting especially the ∼800 million people living in extreme poverty who are most vulnerable to food price spikes. They also underscore the urgency of investments in breeding for heat tolerance.

Crop responses to climate warming suggest that yields will decrease as growing-season temperatures increase. Deutsch et al. show that this effect may be exacerbated by insect pests (see the Perspective by Riegler). Insects already consume 5 to 20% of major grain crops. The authors' models show that for the three most important grain crops—wheat, rice, and maize—yield lost to insects will increase by 10 to 25% per degree Celsius of warming, hitting hardest in the temperate zone. These findings provide an estimate of further potential climate impacts on global food supply and a benchmark for future regional and field-specific studies of crop-pest-climate interactions. Science , this issue p. 916 ; see also p. 846 Models of insect population growth and metabolism in a warming climate predict losses of major food crops to insect pests. Insect pests substantially reduce yields of three staple grains—rice, maize, and wheat—but models assessing the agricultural impacts of global warming rarely consider crop losses to insects. We use established relationships between temperature and the population growth and metabolic rates of insects to estimate how and where climate warming will augment losses of rice, maize, and wheat to insects. Global yield losses of these grains are projected to increase by 10 to 25% per degree of global mean surface warming. Crop losses will be most acute in areas where warming increases both population growth and metabolic rates of insects. These conditions are centered primarily in temperate regions, where most grain is produced.

Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.

The majority of the models that participated in phase 5 of the Coupled Model Intercomparison Project global warming experiments warm faster in the eastern equatorial Pacific Ocean than in the west. GFDL-ESM2M is an exception among the state-of-the-art global climate models in that the equatorial Pacific sea surface temperature (SST) in the west warms faster than in the east, and the Walker circulation strengthens in response to warming. This study shows that this “La Niña–like” trend simulated by GFDL-ESM2M could be a physically consistent response to warming, and that the forced response could have been detectable since the late twentieth century. Two additional models are examined: GFDL-ESM2G, which differs from GFDL-ESM2M only in the oceanic components, warms without a clear zonal SST gradient; and HadGEM2-CC exhibits a warming pattern that resembles the multimodel mean. A fundamental observed constraint between the amplitude of El Niño–Southern Oscillation (ENSO) and the mean-state zonal SST gradient is reproduced well by GFDL-ESM2M but not by the other two models, which display substantially weaker ENSO nonlinearity than is observed. Under this constraint, the weakening nonlinear ENSO amplitude in GFDL-ESM2M rectifies the mean state to be La Niña–like. GFDL-ESM2M exhibits more realistic equatorial thermal stratification than GFDL-ESM2G, which appears to be the most important difference for the ENSO nonlinearity. On longer time scales, the weaker polar amplification in GFDL-ESM2M may also explain the origin of the colder equatorial upwelling water, which could in turn weaken the ENSO amplitude.

The planetary albedo is partitioned into a component due to atmospheric reflection and a component due to surface reflection by using shortwave fluxes at the surface and top of the atmosphere in conjunction with a simple radiation model. The vast majority of the observed global average planetary albedo (88%) is due to atmospheric reflection. Surface reflection makes a relatively small contribution to planetary albedo because the atmosphere attenuates the surface contribution to planetary albedo by a factor of approximately 3. The global average planetary albedo in the ensemble average of phase 3 of the Coupled Model Intercomparison Project (CMIP3) preindustrial simulations is also primarily (87%) due to atmospheric albedo. The intermodel spread in planetary albedo is relatively large and is found to be predominantly a consequence of intermodel differences in atmospheric albedo, with surface processes playing a much smaller role despite significant intermodel differences in surface albedo. The CMIP3 models show a decrease in planetary albedo under a doubling of carbon dioxide—also primarily due to changes in atmospheric reflection (which explains more than 90% of the intermodel spread). All models show a decrease in planetary albedo due to the lowered surface albedo associated with a contraction of the cryosphere in a warmer world, but this effect is small compared to the spread in planetary albedo due to model differences in the change in clouds.

Radiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. This study investigates intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the west Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6.

Stratospheric volcanic aerosol can have major impacts on global climate. Despite a consensus among studies on an El Niño‐like response in the first or second post‐eruption year, the mechanisms that trigger a change in the state of El Niño‐Southern Oscillation (ENSO) following volcanic eruptions are still debated. Here, we shed light on the processes that govern the ENSO response to tropical volcanic eruptions through a series of sensitivity experiments with an Earth System Model where a uniform stratospheric volcanic aerosol loading is imposed over different parts of the tropics. Three tropical mechanisms are tested: the “ocean dynamical thermostat” (ODT); the cooling of the Maritime Continent; and the cooling of tropical northern Africa (NAFR). We find that the NAFR mechanism plays the largest role, while the ODT mechanism is absent in our simulations as La Niña‐like rather than El‐Niño‐like conditions develop following a uniform radiative forcing over the equatorial Pacific. Plain Language Summary Volcanic eruptions emit large quantity of sulfate aerosol up to the stratosphere. Such aerosol can alter global climate by interacting with solar radiation and in turn modifying atmospheric and ocean circulation. In particular, volcanic aerosol can alter the state of the El Niño‐Southern Oscillation (ENSO), the major mode of tropical climate variability. However, the mechanisms that trigger a change in the ENSO state following volcanic eruptions are still debated. In this study, we use an Earth System Model to revisit the main mechanisms that have been proposed to alter ENSO, causing positive temperature anomalies over the equatorial Pacific (EqPAC) Ocean. We tested three mechanisms: the “ocean dynamical thermostat” (ODT); the cooling of the Maritime Continent; and the cooling of tropical northern Africa (NAFR). Our experiments show that the NAFR mechanism plays the largest role, while the ODT mechanism is absent in our simulations as cold rather than warm develop over the EqPAC Ocean following the applied volcanic forcing. Key Points Radiative cooling by volcanic aerosol over the tropical northern Africa triggers El Niño‐like conditions via atmospheric circulation changes The “ocean thermostat mechanism” is absent in our simulations when a uniform aerosol forcing is applied over the equatorial Pacific (EqPAC) The Maritime Continent cooling mechanism is not at play when the aerosol forcing extends over the entire EqPAC