We developed a 3D Monte Carlo photon tracing program for the transfer of radiation in inhomogeneous and irregular terrain to calculate broadband solar and thermal infrared fluxes. We selected an area of 100 × 100 km² in the Tibetan Plateau centered at Lhasa city and used the albedo and surface temperature from MODIS/Terra for this study. We showed that anomalies of surface solar fluxes with reference to a flat surface can be as large as 600 W/m², depending on time of day, mountain configuration, and albedo. Surface temperature is the dominating factor in determining anomalies of the surface infrared flux distribution relative to a flat surface with values as high as 70 W/m² at cold mountain surfaces. The average surface solar flux over regional domains of 100 × 100 km² and 50 × 50 km² comprising intense topography can deviate from the smoothed surface conventionally assumed in climate models and GCMs by 10–50 W/m².

Across vast, agriculturally intensive regions of the United States, the spread in predictions of summer temperature and soil moisture under global warming is curiously elevated in current climate models. Some models show modest warming of 2–3C° and little drying or slight moistening by the 22nd century, while at the other extreme are simulations with warming as large as 7–8C° and 20–40% reductions in soil moisture. We show this region of large spread arises from differences in simulations of snow albedo feedback. During winter and early spring, models with strong snow albedo feedback exhibit large reductions in snowpack and hence water storage. This water deficit persists in summer soil moisture, with reduced evapotranspiration yielding warmer temperatures. Comparison of simulated feedback strength to observations of the feedback from the current climate's seasonal cycle suggests the inter‐model differences are excessive. At the same time, the multi‐model mean feedback strength agrees reasonably well with the observed value. We estimate that if the next generation of models were brought into line with observations of snow albedo feedback, the unusually wide divergence in simulations of summer warming and drying over the US would shrink by roughly one third to one half.

The large spread of the response to anthropogenic forcing simulated by state-of-the-art climate models in the Arctic is investigated. A feedback analysis framework specific to the Arctic is developed to address this issue. The feedback analysis shows that a large part of the spread of Arctic climate change is explained by the longwave feedback parameter. The large spread of the negative longwave feedback parameter is in turn mainly due to variations in temperature feedback. The vertical temperature structure of the atmosphere in the Arctic, characterized by a surface inversion during wintertime, exerts a strong control on the temperature feedback and consequently on simulated Arctic climate change. Most current climate models likely overestimate the climatological strength of the inversion, leading to excessive negative longwave feedback. The authors conclude that the models’ near-equilibrium response to anthropogenic forcing is generally too small.

Climate change is expected to cause a reduction in the spatial extent of snow cover on land. Recent work suggests that this will exert a local influence on the atmosphere and the hydrology of snow‐margin areas through the snow‐albedo feedback (SAF) mechanism. A significant fraction of variability among IPCC AR4 general circulation model (GCM) predictions for future summertime climate change over these areas is related to the models' representation of springtime SAF. In this study, we demonstrate a nonlocal influence of SAF on the summertime circulation in the extratropical Northern Hemisphere. Increased land surface warming in models with stronger SAF is associated with large‐scale sea‐level pressure anomalies over the northern oceans and a poleward intensified subtropical jet. We find that up to 25–30% and, on average, 5–10% of the inter‐model spread in projections of the circulation response to climate change is linearly related to SAF strength.

The Arctic climate is changing rapidly¹. From 1979 to 2006, September sea-ice extent decreased by almost 25% or about 100,000 km² per year (ref. 2). In September 2007, Arctic sea-ice extent reached its lowest level since satellite observations began³and in September 2008, sea-ice cover was still low. This development has raised concerns that the Arctic Ocean could be ice-free in late summer in only a few decades, with important economic and geopolitical implications. Unfortunately, most current climate models underestimate significantly the observed trend in Arctic sea-ice decline⁴, leading to doubts regarding their projections for the timing of ice-free conditions. Here we analyse the simulated trends in past sea-ice cover in 18 state-of-art-climate models and find a direct relationship between the simulated evolution of September sea-ice cover over the twenty-first century and the magnitude of past trends in sea-ice cover. Using this relationship together with observed trends, we project the evolution of September sea-ice cover over the twenty-first century. We find that under a scenario with medium future greenhouse-gas emissions, the Arctic Ocean will probably be ice-free in September before the end of the twenty-first century.

Two main mechanisms can potentially explain the spread in the magnitude of global warming simulated by climate models: deep ocean heat uptake and climate feedbacks. Here, we show that deep oceanic heat uptake is a major source of spread in simulations of 21st century climate change. Models with deeper baseline polar mixed layers are associated with larger deep ocean warming and smaller global surface warming. Based on this result, we set forth an observational constraint on polar vertical oceanic mixing. This constraint suggests that many models may overestimate the efficiency of polar oceanic mixing and therefore may underestimate future surface warming. Thus to reduce climate change uncertainties at time‐scales relevant for policy‐making, improved understanding and modelling of oceanic mixing at high latitudes is crucial.

Observation based estimates of controls on snow albedo feedback (SAF) are needed to constrain the snow and albedo parameterizations in general circulation model (GCM) projections of air temperature over the Northern Hemisphere (NH) landmass. The total April‐May NH SAF, corresponding to the sum of the effect of temperature on surface albedo over snow covered surfaces (‘metamorphism’) and over surfaces transitioning from snow covered to snow free conditions (‘snow cover’), is derived with daily NH snow cover and surface albedo products using Advanced Very High Resolution Radiometer Polar Pathfinder satellite data and surface air temperature from ERA40 reanalysis data between 1982–1999. Without using snow cover information, the estimated total SAF, for land surfaces north of 30°N, of −0.93 ± 0.06%K⁻¹ was not significantly different (95% confidence) from estimates based on International Satellite Cloud Climatology Project surface albedo data. The SAF, constrained to only snow covered areas, grew to −1.06 ± 0.08%K⁻¹ with similar magnitudes for the ‘snow cover’ and ‘metamorphosis’ components. The SAF pattern was significantly correlated with the ‘snow cover’ component pattern over both North America and Eurasia but only over Eurasia for the ‘metamorphosis’ component. However, in contrast to GCM model based diagnoses of SAF, the control on the ‘snow cover’ component related to the albedo contrast of snow covered and snow free surfaces was not strongly correlated to the total SAF.

We show that intermodel variations in the anthropogenically-forced evolution of September sea ice extent (SSIE) in the Arctic stem mainly from two factors: the baseline climatological sea ice thickness (SIT) distribution, and the local climate feedback parameter. The roles of these two factors evolve over the course of the twenty-first century. The SIT distribution is the most important factor in current trends and those of coming decades, accounting for roughly half the intermodel variations in SSIE trends. Then, its role progressively decreases, so that around the middle of the twenty-first century the local climate feedback parameter becomes the dominant factor. Through this analysis, we identify the investments in improved simulation of Arctic climate necessary to reduce uncertainties both in projections of sea ice loss over the coming decades and in the ultimate fate of the ice pack.

The Antarctic Peninsula has warmed significantly since the 1950s. This pronounced and isolated warming trend is collectively captured by 29 twentieth-century climate hindcasts participating in the version 3 Coupled Model Intercomparison Project. To understand the factors driving warming trends in the hindcasts, we examine trends in Peninsula region’s atmospheric heat budget in every simulation. We find that atmospheric latent heat release increases in nearly all hindcasts. These increases are generally anthropogenic in origin, and account for about 60% of the ensemble-mean warming trend in the Peninsula. They are driven primarily by well-understood features of the anthropogenic intensification of global hydrological cycle. As sea surface temperature increases, moisture contained in atmospheric flows increases. When such flows are forced to ascend the Peninsula’s topography, enhanced local latent heat release results. The mechanism driving the warming of the Antarctic Peninsula is therefore clear in the models. Evidence for a similar mechanism operating in the real world is seen in the increasing snow accumulation rates inferred from ice cores drilled in the Peninsula. However, the relative importance of this mechanism and other processes previously identified as potentially causing the observed warming, such as the recent sea ice retreat in the Bellingshausen Sea, is difficult to assess. Thus the relevance of the simulated warming mechanism to the observed warming is unclear, in spite of its robustness in the models.

Pithan and Mauritsen argue that the 2009 results of Boé et al. are not consistent with current understanding of the lapse-rate feedback in the Arctic. They also argue that these results arise to an important extent from self-correlation issues. In this response, the authors argue that their results are not inconsistent with current understanding of lapse-rate feedback and demonstrate that the conclusions remain unchanged when all possibilities of self-correlation are excluded.

Projections of possible precipitation change in California under global warming have been subject to considerable uncertainty because California lies between the region anticipated to undergo increases in precipitation at mid-to-high latitudes and regions of anticipated decrease in the subtropics. Evaluation of the large-scale model experiments for phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests a greater degree of agreement on the sign of the winter (December–February) precipitation change than in the previous such intercomparison, indicating a greater portion of California falling within the increased precipitation zone. While the resolution of global models should not be relied on for accurate depiction of topographic rainfall distribution within California, the precipitation changes depend substantially on large-scale shifts in the storm tracks arriving at the coast. Significant precipitation increases in the region arriving at the California coast are associated with an eastward extension of the region of strong Pacific jet stream, which appears to be a robust feature of the large-scale simulated changes. This suggests that effects of this jet extension in steering storm tracks toward the California coast constitute an important factor that should be assessed for impacts on incoming storm properties for high-resolution regional model assessments.

Snow-albedo feedback (SAF) is examined in 25 climate change simulations participating in the Coupled Model Intercomparison Project version 5 (CMIP5). SAF behavior is compared to the feedback’s behavior in the previous (CMIP3) generation of global models. SAF strength exhibits a fivefold spread across CMIP5 models, ranging from 0.03 to 0.16 W m⁻² K⁻¹ (ensemble-mean = 0.08 W m⁻² K⁻¹). This accounts for much of the spread in 21st century warming of Northern Hemisphere land masses, and is very similar to the spread found in CMIP3 models. As with the CMIP3 models, there is a high degree of correspondence between the magnitudes of seasonal cycle and climate change versions of the feedback. Here we also show that their geographical footprint is similar. The ensemble-mean SAF strength is close to an observed estimate of the real climate’s seasonal cycle feedback strength. SAF strength is strongly correlated with the climatological surface albedo when the ground is covered by snow. The inter-model variation in this quantity is surprisingly large, ranging from 0.39 to 0.75. Models with large surface albedo when these regions are snow-covered will also have a large surface albedo contrast between snow-covered and snow-free regions, and therefore a correspondingly large SAF. Widely-varying treatments of vegetation masking of snow-covered surfaces are probably responsible for the spread in surface albedo where snow occurs, and the persistent spread in SAF in global climate models.

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.

We examine the tropical inversion strength, measured by the estimated inversion strength (EIS), and its response to climate change in 18 models associated with phase 5 of the coupled model intercomparison project (CMIP5). While CMIP5 models generally capture the geographic distribution of observed EIS, they systematically underestimate it off the west coasts of continents, due to a warm bias in sea surface temperature. The negative EIS bias may contribute to the low bias in tropical low-cloud cover in the same models. Idealized perturbation experiments reveal that anthropogenic forcing leads directly to EIS increases, independent of “temperature-mediated” EIS increases associated with long-term oceanic warming. This fast EIS response to anthropogenic forcing is strongly impacted by nearly instantaneous continental warming. The temperature-mediated EIS change has contributions from both uniform and non-uniform oceanic warming. The substantial EIS increases in uniform oceanic warming simulations are due to warming with height exceeding the moist adiabatic lapse rate in tropical warm pools. EIS also increases in fully-coupled ocean–atmosphere simulations where CO2CO2 concentration is instantaneously quadrupled, due to both fast and temperature-mediated changes. The temperature-mediated EIS change varies with tropical warming in a nonlinear fashion: The EIS change per degree tropical warming is much larger in the early stage of the simulations than in the late stage, due to delayed warming in the eastern parts of the subtropical oceans. Given the importance of EIS in regulating tropical low-cloud cover, this suggests that the tropical low-cloud feedback may also be nonlinear.

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.