Arctic sea ice has decreased substantially over recent decades, a trend projected to continue. Shrinking ice reduces surface albedo, leading to greater surface solar absorption, thus amplifying warming and driving further melt. This sea-ice albedo feedback (SIAF) is a key driver of Arctic climate change and an important uncertainty source in climate model projections. Using an ensemble of models, we demonstrate an emergent relationship between future SIAF and an observable version of SIAF in the current climate’s seasonal cycle. This relationship is robust in constraining SIAF over the coming decades (Pearson’s r = 0.76), and then it degrades. The degradation occurs because some models begin producing ice-free conditions, signalling a transition to a new ice regime. The relationship is strengthened when models with unrealistically thin historical ice are excluded. Because of this tight relationship, reducing model errors in the current climate’s seasonal SIAF and ice thickness can narrow SIAF spread under climate change.

Purpose of Review

Highlight significant developments that have recently been made to enhance our understanding of how snow responds to climate forcing and the role that snow plays in the climate system.

Recent Findings

Widespread snow loss has occurred in recent decades, with the largest decreases in spring. These changes are primarily driven by temperature and precipitation, but changes in vegetation, light-absorbing impurities, and sea ice also contribute to variability. Changes in snow cover can also affect climate through the snow albedo feedback (SAF). Recently, considerable progress has been made in better understanding the processes contributing to SAF. We also highlight advances in knowledge of how snow variability is linked to large-scale atmospheric changes. Lastly, large-scale snow losses are expected to continue under climate change in all but the coldest climates. These projected changes to snow raise considerable concerns over future freshwater availability in snow-dominated watersheds.

Summary

The results discussed here demonstrate the widespread implications that changes to snow have on the climate system and anthropogenic activity at large.

Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help to quantify and to constrain the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).

Climate models have continued to be developed and improved since the AR4, and many models have been extended into Earth System models by including the representation of biogeochemical cycles important to climate change. These models allow for policy-relevant calculations such as the carbon dioxide (CO2) emissions compatible with a specified climate stabilization target. In addition, the range of climate variables and processes that have been evaluated has greatly expanded, and differences between models and observations are increasingly quantified using ‘performance metrics’. In this chapter, model evaluation covers simulation of the mean climate, of historical climate change, of variability on multiple time scales and of regional modes of variability. This evaluation is based on recent internationally coordinated model experiments, including simulations of historic and paleo climate, specialized experiments designed to provide insight into key climate processes and feedbacks and regional climate downscaling. Figure 9.44 provides an overview of model capabilities as assessed in this chapter, including improvements, or lack thereof, relative to models assessed in the AR4. The chapter concludes with an assessment of recent work connecting model performance to the detection and attribution of climate change as well as to future projections.

Sea surface temperature (SST) and salinity (SSS) time series from four ocean weather stations and data from an integration of the GFDL coupled ocean-atmosphere model are analyzed to test the applicability of local linear stochastic theory to the mixed-layer ocean. According to this theory, mixed-layer variability away from coasts and fronts can be explained as a ‘red noise’ response to the ‘white noise’ forcing by atmospheric disturbances. At one weather station, Papa (northeast Pacific), this stochastic theory can be applied to both salinity and temperature, explaining the relative redness of the SSS spectrum. Similar results hold for a model grid point adjacent to Papa, where the relationships between atmospheric energy and water fluxes and actual changes in SST and SSS are what is expected from local linear stochastic theory. At the other weather stations, this theory cannot adequately explain mixed-layer variability. Two oceanic processes must be taken into account: at Panulirus (near Bermuda), mososcale eddies enhance the observed variability at high frequencies. At Mike and India (North Atlantic), variations in SST and SSS advection, indicated by the coherence and equal persistence of SST and SSS anomalies, contribute to much of the low frequency variability in the model and observations. To achieve a global perspective, TOPEX altimeter data and model results are used to identify regions of the ocean where these mechanisms of variability are important. Where mesoscale eddies are as energetic as at Panulirus, indicated by the TOPEX global distribution of sea level variability, one would expect enhanced variability on short time scales. In regions exhibiting signatures of variability similar to Mike and India, variations in SST and SSS advection should dominate at low frequencies. According to the model, this mode of variability is found in the circumpolar ocean and the northern North Atlantic, where it is associated with the irregular oscillations of the model’s thermohaline circulation.

To understand the role of water vapor feedback in unperturbed surface temperature variability, a version of the Geophysical Fluid Dynamics Laboratory coupled ocean–atmosphere model is integrated for 1000 yr in two configurations, one with water vapor feedback and one without. For all spatial scales, the model with water vapor feedback has more low-frequency (timescale ≥ 2 yr) surface temperature variability than the one without. Thus water vapor feedback is positive in the context of the model’s unperturbed variability. In addition, water vapor feedback is more effective the longer the timescale of the surface temperature anomaly and the larger its spatial scale.

To understand the role of water vapor feedback in global warming, two 500-yr integrations were also performed in which CO₂ was doubled in both model configurations. The final surface global warming in the model with water vapor feedback is 3.38°C, while in the one without it is only 1.05°C. However, the model’s water vapor feedback has a larger impact on surface warming in response to a doubling of CO₂than it does on internally generated, low-frequency, global-mean surface temperature anomalies. Water vapor feedback’s strength therefore depends on the type of temperature anomaly it affects. The authors found that the degree to which a surface temperature anomaly penetrates into the troposphere is a critical factor in determining the effectiveness of its associated water vapor feedback. The more the anomaly penetrates, the stronger the feedback. It is also shown that the apparent impact of water vapor feedback is altered by other feedback mechanisms, such as albedo and cloud feedback. The sensitivity of the results to this fact is examined.

Finally, the authors compare the local and global-mean surface temperature time series from both unperturbed variability experiments to the observed record. The experiment without water vapor feedback does not have enough global-scale variability to reproduce the magnitude of the variability in the observed global-mean record, whether or not one removes the warming trend observed over the past century. In contrast, the amount of variability in the experiment with water vapor feedback is comparable to that of the global-mean record, provided the observed warming trend is removed. Thus, the authors are unable to simulate the observed levels of variability without water vapor feedback.

We examine 800-year time series of internally generated variability in both a coupled ocean-atmosphere model where water vapor anomalies are not allowed to interact with longwave radiation and one where they are. The ENSO-like phenomenon in the experiment without water vapor feedback is drastically suppressed both in amplitude and geographic extent relative to the experiment with water vapor feedback. Surprisingly, the reduced amplitude of ENSO-related sea surface temperature anomalies in the model without water vapor feedback cannot be attributed to greater longwave damping of sea surface temperature. (Differences between the two experiments in radiative feedback due to clouds counterbalance almost perfectly the differences in radiative feedback due to water vapor.) Rather, the interaction between water vapor anomalies and longwave radiation affects the ENSO-like phenomenon through its influence on the vertical structure of radiative heating: Because of the changes in water vapor associated with it, a given warm equatorial Pacific sea surface temperature anomaly is associated with a radiative heating profile that is much more gravitationally unstable when water vapor feedback is present. The warm sea surface temperature anomaly therefore results in more convection in the experiment with water vapor feedback. The increased convection, in turn, is related to a larger westerly wind-stress anomaly, which creates a larger decrease in upwelling of cold water, thereby enhancing the magnitude of the original warm sea surface temperature anomaly. In this manner, the interaction between water vapor anomalies and longwave radiation magnifies the air-sea interactions at the heart of the ENSO phenomenon; without this interaction, the coupling between sea surface temperature and wind stress is effectively reduced, resulting in smaller amplitude ENSO episodes with a more limited geographical extent.

Using two versions of the GFDL coupled ocean‐atmosphere model, one where water vapor anomalies are allowed to affect the longwave radiation calculation and one where they are not, we examine the role of water vapor feedback in internal precipitation variability and greenhouse‐gas‐forced intensification of the hydrologic cycle. Without external forcing, the experiment with water vapor feedback produces 44% more annual‐mean, global‐mean precipitation variability than the one without. We diagnose the reason for this difference: In both experiments, global‐mean surface temperature anomalies are associated with water vapor anomalies. However, when water vapor interacts with longwave radiation, the temperature anomalies are associated with larger anomalies in surface downward longwave radiation. This increases the temperature anomaly damping through latent heat flux, creating an evaporation anomaly. The evaporation anomaly, in turn, leads to an anomaly of nearly the same magnitude in precipitation. In the experiment without water vapor feedback, this mechanism is absent. While the interaction between longwave and water vapor has a large impact on the global hydrologic cycle internal variations, its effect decreases as spatial scales decrease, so water vapor feedback has only a very small impact on grid‐scale hydrologic variability. Water vapor feedback also affects the hydrologic cycle intensification when greenhouse gas concentrations increase. By the 5th century of global warming experiments where CO₂ is increased and then fixed at its doubled value, the global‐mean precipitation increase is nearly an order of magnitude larger when water vapor feedback is present. The cause of this difference is similar to the cause of the difference in internal precipitation variability: When water vapor feedback is present, the increase in water vapor associated with a warmer climate enhances downward longwave radiation. To maintain surface heat balance, evaporation increases, leading to a similar increase in precipitation. This effect is absent in the experiment without water vapor feedback. The large impact of water vapor feedback on hydrologic cycle intensification does not weaken as spatial scales decrease, unlike the internal variability case. Accurate representations of water vapor feedback are therefore necessary to simulate global‐scale hydrologic variability and intensification of the hydrologic cycle in global warming.

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.

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.

Differences in simulations of climate feedbacks are sources of significant divergence in climate models' temperature response to anthropogenic forcing. Snow albedo feedback is particularly critical for climate change prediction in heavily‐populated northern hemisphere land masses. Here we show its strength in current models exhibits a factor‐of‐three spread. These large intermodel variations in feedback strength in climate change are nearly perfectly correlated with comparably large intermodel variations in feedback strength in the context of the seasonal cycle. Moreover, the feedback strength in the real seasonal cycle can be measured and compared to simulated values. These mostly fall outside the range of the observed estimate, suggesting many models have an unrealistic snow albedo feedback in the seasonal cycle context. Because of the tight correlation between simulated feedback strength in the seasonal cycle and climate change, eliminating the model errors in the seasonal cycle will lead directly to a reduction in the spread of feedback strength in climate change. Though this comparison to observations may put the models in an unduly harsh light because of uncertainties in the observed estimate that are difficult to quantify, our results map out a clear strategy for targeted observation of the seasonal cycle to reduce divergence in simulations of climate sensitivity.

We developed a three‐dimensional radiative transfer model simulating solar fluxes over mountain surfaces precisely given distributions of atmospheric scatterers and absorbers. The model quantifies direct, diffuse, terrain‐reflected, and coupling (i.e., photons reflected and scattered more than once) fluxes. We applied it to a midlatitude mountainous surface to study these components' diurnal, seasonal, and geographical variability under clear skies. Domain‐averaged direct and diffuse fluxes together comprise over 96% of the flux year‐round, with diffuse fluxes' relative importance varying inversely with that of direct radiation. Direct fluxes generally account for at least 80% of the total. However, the domain‐averaged diffuse flux proportion increases to nearly 40% at high zenith angles, and approaches 100% when neighboring slopes obscure the surface from the Sun. Terrain‐reflected and coupling components each account for less than 1% throughout much of the year. However, together they comprise ∼3% when surface albedo increases during winter and are similarly nonnegligible in deep valleys all year. We also studied controls on geographical variations in flux components: The sky view factor, a conventional predictor of diffuse fluxes, is surprisingly weakly correlated with them, posing a parameterization challenge. Terrain‐reflected and coupling fluxes may be easier to parameterize given topography. Finally, we assessed shortwave errors in General Circulation Models with smoothed topography by comparing results with the mountainous surface to identical calculations for a flat surface with the same mean elevation. The differences range from 5 to 20 W/m² and arise because the atmosphere absorbs a different amount of sunshine when underlying topography is smoothed.

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 this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surface-albedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a correspondingly large spread in mean effective snow albedo. Models with large effective snow albedos have a large surface-albedo contrast between snow-covered and snow-free regions and exhibit a correspondingly large surface-albedo decrease when snow cover decreases. Models without explicit treatment of the vegetation canopy in their surface-albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed. The authors speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models.