Temperature reconstructions from the North Atlantic region indicate frequent abrupt and severe climate fluctuations during the last glacial and Holocene periods. The driving forces for these events are unclear and coupled atmosphere–ocean models of global circulation have only simulated such events by inserting large amounts of fresh water into the northern North Atlantic Ocean. Here we report a drastic cooling event in a 15,000-yr simulation of global circulation with present-day climate conditions without the use of such external forcing. In our simulation, the annual average surface temperature near southern Greenland spontaneously fell 6–10 standard deviations below its mean value for a period of 30–40 yr. The event was triggered by a persistent northwesterly wind that transported large amounts of buoyant cold and fresh water into the northern North Atlantic Ocean. Oceanic convection shut down in response to this flow, concentrating the entire cooling of the northern North Atlantic by the colder atmosphere in the uppermost ocean layer. Given the similarity between our simulation and observed records of rapid cooling events, our results indicate that internal atmospheric variability alone could have generated the extreme climate disruptions in this region.

A coarse resolution coupled ocean–atmosphere simulation in which surface albedo feedback is suppressed by prescribing surface albedo, is compared to one where snow and sea ice anomalies are allowed to affect surface albedo. Canonical CO₂-doubling experiments were performed with both models to assess the impact of this feedback on equilibrium response to external forcing. It accounts for about half the high-latitude response to the forcing. Both models were also run for 1000 yr without forcing to assess the impact of surface albedo feedback on internal variability. Surprisingly little internal variability can be attributed to this feedback, except in the Northern Hemisphere continents during spring and in the sea ice zone of the Southern Hemisphere year-round. At these locations and during these seasons, it accounts for, at most, 20% of the variability. The main reason for this relatively weak signal is that horizontal damping processes dilute the impact of surface albedo feedback.

When snow albedo feedback in Northern Hemisphere continents is isolated from horizontal damping processes, it has a similar strength in the CO₂-doubling and internal variability contexts; a given temperature anomaly in these regions is associated with approximately the same change in snow depth and surface albedo whether it was externally forced or internally generated. This suggests that the presence of internal variability in the observed record is not a barrier to extracting information about snow albedo feedback's contribution to equilibrium climate sensitivity. This is demonstrated in principle in a “scenario run,” where estimates of past, present, and future changes in greenhouse gases and sulfate aerosols are imposed on the model with surface albedo feedback. This simulation contains a mix of internal variations and externally forced anomalies similar to the observed record. The snow albedo feedback to the scenario run's climate anomalies agrees very well with the snow albedo feedback in the CO₂-doubling context. Moreover, the portion of the scenario run corresponding to the present-day satellite record is long enough to capture this feedback, suggesting this record could be used to estimate snow albedo feedback's contribution to equilibrium climate sensitivity.

Past research on the climate response to orbital forcing has emphasized the glacial-interglacial variations in global ice volume, global-mean temperature, and the global hydrologic cycle. This emphasis may be inappropriate in the tropics, where the response to precessional forcing is likely to be somewhat independent of the glacial-interglacial variations, particularly in variables relating to the hydrologic cycle. To illustrate this point, we use an atmospheric general circulation model coupled to a slab ocean model, performing experiments that quantify the tropical climate’s response to (1) opposite phases of precessional forcing, and (2) Last Glacial Maximum boundary conditions. While the glacially-forced tropical temperature changes are typically more than an order of magnitude larger than those arising from precessional forcing, the hydrologic signals stemming from the two forcings are comparable in magnitude. The mechanisms behind these signals are investigated and shown to be quite distinct for the precessional and glacial forcing. Because of strong dynamical linkages in the tropics, the model results illustrate the impossibility of predicting the local hydrologic response to external forcing without understanding the response at much larger spatial scales. Examples from the paleoclimate record are presented as additional evidence for the importance of precessional signals in past variations of the tropical climate.

Milankovitch proposed that variations in the earth’s orbit cause climate variability through a local thermodynamic response to changes in insolation. This hypothesis is tested by examining variability in an atmospheric general circulation model coupled to an ocean mixed layer model subjected to the orbital forcing of the past 165 000 yr. During Northern Hemisphere summer, the model’s response conforms to Milankovitch’s hypothesis, with high (low) insolation generating warm (cold) temperatures throughout the hemisphere. However, during Northern Hemisphere winter, the climate variations stemming from orbital forcing cannot be solely understood as a local thermodynamic response to radiation anomalies. Instead, orbital forcing perturbs the atmospheric circulation in a pattern bearing a striking resemblance to the northern annular mode, the primary mode of simulated and observed unforced atmospheric variability. The hypothesized reason for this similarity is that the circulation response to orbital forcing reflects the same dynamics generating unforced variability. These circulation anomalies are in turn responsible for significant fluctuations in other climate variables: Most of the simulated orbital signatures in wintertime surface air temperature over midlatitude continents are directly traceable not to local radiative forcing, but to orbital excitation of the northern annular mode. This has paleoclimate implications: during the point of the model integration corresponding to the last interglacial (Eemian) period, the orbital excitation of this mode generates a 1°–2°C warm surface air temperature anomaly over Europe, providing an explanation for the warm anomaly of comparable magnitude implied by the paleoclimate proxy record. The results imply that interpretations of the paleoclimate record must account for changes in surface temperature driven not only by changes in insolation, but also by perturbations in atmospheric dynamics.

Climatological planetary albedo obtained from the International Satellite Cloud Climatology Project (ISCCP) D-series flux dataset is broken down into contributions from the surface and atmosphere in cryosphere regions. The atmosphere accounts for much more of climatological planetary albedo (≥75%) than the surface at all times of the year. The insignificance of the surface contribution over highly reflective cryosphere regions is attributed mostly to the damping effect of the atmosphere. The overlying atmosphere attenuates the surface’s contribution to climatological planetary albedo by reducing the number of solar photons initially reaching the surface and the number of photons initially reflected by the surface that actually reach the top of the atmosphere.

The ISCCP datasets were also used to determine the relative contributions of the surface and atmosphere to seasonal and interannual planetary albedo variability in cryosphere regions. Even damped by the atmosphere to the same degree as in the climatological case, the surface contribution dominates the variability in planetary albedo on seasonal and interannual time scales. The surface accounts for about 75% of the change in climatological planetary albedo from one season to another with similar zenith angle and more than 50% of its interannual variability at nearly all times of the year, especially during seasons with extensive snow and sea ice extent. The dominance of the surface in planetary albedo variability is because surface albedo variability associated with snow and ice fluctuations is significantly larger than atmospheric albedo variability due to cloud fluctuations. The large effect of snow and ice variations on planetary albedo variability suggests that if cloud fields do not change much in a future warmer climate, a retreat of snow cover or sea ice would lead to a significant increase in net incoming solar radiation, resulting in an enhancement of high-latitude climate sensitivity.

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

In 36 climate change simulations associated with phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), changes in marine low cloud cover (LCC) exhibit a large spread, and may be either positive or negative. Here we develop a heuristic model to understand the source of the spread. The model’s premise is that simulated LCC changes can be interpreted as a linear combination of contributions from factors shaping the clouds’ large-scale environment. We focus primarily on two factors—the strength of the inversion capping the atmospheric boundary layer (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). For a given global model, the respective contributions of EIS and SST are computed. This is done by multiplying (1) the current-climate’s sensitivity of LCC to EIS or SST variations, by (2) the climate-change signal in EIS or SST. The remaining LCC changes are then attributed to changes in greenhouse gas and aerosol concentrations, and other environmental factors. The heuristic model is remarkably skillful. Its SST term dominates, accounting for nearly two-thirds of the intermodel variance of LCC changes in CMIP3 models, and about half in CMIP5 models. Of the two factors governing the SST term (the SST increase and the sensitivity of LCC to SST perturbations), the SST sensitivity drives the spread in the SST term and hence the spread in the overall LCC changes. This sensitivity varies a great deal from model to model and is strongly linked to the types of cloud and boundary layer parameterizations used in the models. EIS and SST sensitivities are also estimated using observational cloud and meteorological data. The observed sensitivities are generally consistent with the majority of models as well as expectations from prior research. Based on the observed sensitivities and the relative magnitudes of simulated EIS and SST changes (which we argue are also physically reasonable), the heuristic model predicts LCC will decrease over the 21st-century. However, to place a strong constraint, for example on the magnitude of the LCC decrease, will require longer observational records and a careful assessment of other environmental factors producing LCC changes. Meanwhile, addressing biases in simulated EIS and SST sensitivities will clearly be an important step towards reducing intermodel spread in simulated LCC changes.

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems^1,2. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1–3 per cent per kelvin)^3,4,5. Part of the uncertainty may originate from atmosphere–radiation interactions. As the climate warms, increases in shortwave absorption from atmospheric moistening will suppress the precipitation increase. This occurs through a reduction of the latent heating increase required to maintain a balanced atmospheric energy budget^6,7. Using an ensemble of climate models, here we show that such models tend to underestimate the sensitivity of solar absorption to variations in atmospheric water vapour, leading to an underestimation in the shortwave absorption increase and an overestimation in the precipitation increase. This sensitivity also varies considerably among models due to differences in radiative transfer parameterizations, explaining a substantial portion of model spread in the precipitation response. Consequently, attaining accurate shortwave absorption responses through improvements to the radiative transfer schemes could reduce the spread in the predicted global precipitation increase per degree warming for the end of the twenty-first century by about 35 per cent, and reduce the estimated ensemble-mean increase in this quantity by almost 40 per cent.

In the current generation of climate models, the projected increase in global precipitation over the 21st century ranges from 2% to 10% under a high‐emission scenario. Some of this uncertainty can be traced to the rapid response to carbon dioxide (CO₂) forcing. We analyze an ensemble of simulations to better understand model spread in this rapid response. A substantial amount is linked to how the land surface partitions a change in latent versus sensible heat flux in response to the CO₂‐induced radiative perturbation; a larger increase in sensible heat results in a larger decrease in global precipitation. Model differences in the land surface response appear to be strongly related to the vegetation response to increased CO₂, specifically, the closure of leaf stomata. Future research should thus focus on evaluation of the vegetation physiological response, including stomatal conductance parameterizations, for the purpose of constraining the fast response of Earth's hydrologic cycle to CO₂ forcing.

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO₂) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. In addition, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.

A highly uncertain aspect of anthropogenic climate change is the rate at which the global hydrologic cycle intensifies. The future change in global‐mean precipitation per degree warming, or hydrologic sensitivity, exhibits a threefold spread (1–3%/K) in current global climate models. In this study, we find that the intermodel spread in this value is associated with a significant portion of variability in future projections of extreme precipitation in the tropics, extending also into subtropical atmospheric river corridors. Additionally, there is a very tight intermodel relationship between changes in extreme and nonextreme precipitation, whereby models compensate for increasing extreme precipitation events by decreasing weak‐moderate events. Another factor linked to changes in precipitation extremes is model resolution, with higher resolution models showing a larger increase in heavy extremes. These results highlight ways various aspects of hydrologic cycle intensification are linked in models and shed new light on the task of constraining precipitation extremes.