Explore the vast range of titles available.

A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

A large spread in model estimates of the equilibrium climate sensitivity (ECS), defined as the global mean near-surface air-temperature increase following a doubling of atmospheric CO 2 concentration, leaves us greatly disadvantaged in guiding policy-making for climate change adaptation and mitigation. In this study, we show that the projected ECS in the latest generation of climate models is highly related to seasonal variations of extratropical low-cloud fraction (LCF) in historical simulations. Marked reduction of extratropical LCF from winter to summer is found in models with ECS > 4.75 K, in accordance with the significant reduction of extratropical LCF under a warming climate in these models. In contrast, a pronounced seasonal cycle of extratropical LCF, as supported by satellite observations, is largely absent in models with ECS < 3.3 K. The distinct seasonality in extratropical LCF in climate models is ascribed to their different prevailing cloud regimes governing the extratropical LCF variability. The degree of warming following a doubling of atmospheric CO 2 concentration predicted in the low equilibrium climate sensitivity models can be underestimated due to their deficiencies in depicting seasonal low-cloud variations over the extra-tropics.

The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions “How does the Earth system respond to forcing?” and “What are the origins and consequences of systematic model biases?” and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions.How well do clouds and other relevant variables simulated by models agree with observations?What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?Which models have the most credible representations of processes relevant to the simulation of clouds?How do clouds and their changes interact with other elements of the climate system?

A large parameter-perturbed ensemble (PPE) of the Met Office climate model is used to explore the relationship between radiative feedbacks and the present-day simulation of the associated physical processes. We highlight three tropical regimes (deep convection over ocean and land, and marine stratocumulus) in which the same set of processes drives the present-day simulation of clouds and their feedbacks. In each case, the amount of the dominant cloud types reduces in response to warming and the reduction is approximately proportional to the amount simulated in the present day. In deep convective regions, convective process parameters lead the spread among multiple contributing processes, with vegetation processes contributing as well for the land regions. Multiple parameters, such as boundary layer processes, drive stratocumulus regions. However, the low-thick clouds are systematically overestimated, suggesting a structural error in their process representations which would limit the efficacy of the constraint. The influence of convection is largely confined to the tropical deep convective regions in the present day but extends to mid-latitudes under warming. Because of this, contributing processes to the spread in the present-day and the response are different in the extra-tropics, making it much more difficult to establish links between the present-day and the feedback within the region. This suggests that identifying a constraint on convective processes in the tropics for the present-day simulations could constrain both the tropical feedbacks and feedbacks in the extra-tropics. A parameter representing deep-convective entrainment links the present-day tropical mean high cloud and clear-sky longwave flux to their feedbacks in our model, suggesting a potential process constraint from the observations. Understanding and improving the detailed processes controlling feedbacks is ultimately only possible in individual models. Different process-based constraints might be inferred for different models. The approach described here could usefully extended to other single model ensembles and the collective understandings could be valuable for improving model process and feedbacks more generally.

Key climate feedback due to water vapor and clouds rest largely on how relative humidity ℛ changes in a warmer climate, yet this has not been extensively analyzed in models. General circulation models (GCMs) from the CMIP3 archive and several higher‐resolution atmospheric GCMs examined here generally predict a characteristic pattern of ℛ trend with global temperature that has been reported previously in individual models, including increase around the tropopause, decrease in the tropical upper troposphere, and decrease in midlatitudes. This pattern is very similar to that previously reported for cloud cover in the same GCMs, confirming the role of ℛ in controlling changes in simulated cloud. Comparing different models, the trend in each part of the troposphere is approximately proportional to the upward and/or poleward gradient of ℛ in the present climate. While this suggests that the changes simply reflect a shift of the ℛ pattern upward with the tropopause and poleward with the zonal jets, the drying trend in the subtropics is roughly 3 times too large to be attributable to shifts of subtropical features, and the subtropical ℛ minima deepen in most models. ℛ trends are correlated with horizontal model resolution, especially outside the tropics, where they show signs of convergence and latitudinal gradients become close to available observations for GCM resolutions near T85 and higher. We argue that much of the systematic change in ℛ can be explained by the local specific humidity having been set (by condensation) in remote regions with different temperature changes, hence the gradients and trends each depend on a model's ability to resolve moisture transport. Finally, subtropical drying trends predicted from the warming alone fall well short of those observed in recent decades. While this discrepancy supports previous reports of GCMs underestimating Hadley cell expansion, our results imply that shifts alone are not a sufficient interpretation of changes.

A key contribution to the latest generation of climate projections for the UK (UKCP18) was a perturbed parameter ensemble (PPE) of global coupled models based on HadGEM3-GC3.05. Together with 13 CMIP5 simulations, this PPE provides users with a dataset that samples modelling uncertainty and is ideal for use in impacts studies. Evaluations of global mean surface temperatures for this PPE have shown twenty-first century warming rates consistently at the top end of the CMIP5 range. Here we investigate one potential contributory factor to this lack of spread: that the methodology to select plausible members from a larger, related PPE of atmosphere-only experiments preferentially ruled out those predicted to have more negative climate feedbacks (i.e. lower climate sensitivities). We confirm that this is indeed the case. We show that performance in extratropical long-wave cloud forcing played a key role in this by constraining ice cloud parameters, which in turn constrained the feedback distribution (though causal links are not established). The relatively weak relationship driving this constraint is shown to arise from stronger relationships for the long-wave and short-wave cloud feedback components, which largely cancel out due to changes in tropical high clouds. Moreover, we show that the strength of these constraints is due to a structural bias in extratropical long-wave cloud forcing across the PPE. We discuss how choices made in the methodology to pick the plausible PPE members may result in an overly strong constraint when there is a structural bias and possible improvements to this methodology for the future.

In the climate system, two types of radiative feedback are in operation. The feedback of the first kind involves the radiative damping of the vertically uniform temperature perturbation of the troposphere and Earth’s surface that approximately follows the Stefan–Boltzmann law of blackbody radiation. The second kind involves the change in the vertical lapse rate of temperature, water vapor, and clouds in the troposphere and albedo of the Earth’s surface. Using satellite observations of the annual variation of the outgoing flux of longwave radiation and that of reflected solar radiation at the top of the atmosphere, this study estimates the so-called “gain factor,” which characterizes the strength of radiative feedback of the second kind that operates on the annually varying, global-scale perturbation of temperature at the Earth’s surface. The gain factor is computed not only for all sky but also for clear sky. The gain factor of so-called “cloud radiative forcing” is then computed as the difference between the two. The gain factors thus obtained are compared with those obtained from 35 models that were used for the fourth and fifth Intergovernmental Panel on Climate Change assessment. Here, we show that the gain factors obtained from satellite observations of cloud radiative forcing are effective for identifying systematic biases of the feedback processes that control the sensitivity of simulated climate, providing useful information for validating and improving a climate model.

Future responses of cloud regimes are analyzed for five CMIP5 models forced with observed SSTs and subject to a patterned SST perturbation. Correlations between cloud properties in the control climate and changes in the warmer climate are investigated for each of a set of cloud regimes defined using a clustering methodology. The only significant (negative) correlation found is in the in-regime net cloud radiative effect for the stratocumulus regime. All models overestimate the in-regime albedo of the stratocumulus regime. Reasons for this bias and its relevance to the future response are investigated. A detailed evaluation of the models’ daily-mean contributions to the albedo from stratocumulus clouds with different cloud cover fractions reveals that all models systematically underestimate the relative occurrence of overcast cases but overestimate those of broken clouds. In the warmer climate the relative occurrence of overcast cases tends to decrease while that of broken clouds increases. This suggests a decrease in the climatological in-regime albedo with increasing temperature (a positive feedback); this is opposite to the feedback suggested by the analysis of the bulk in-regime albedo. Furthermore we find that the inter-model difference in the sign of the in-cloud albedo feedback is consistent with the difference in sign of the in-cloud liquid water path response, and there is a strong positive correlation between the in-regime liquid water path in the control climate and its response to warming. We therefore conclude that further breakdown of the in-regime properties into cloud cover and in-cloud properties is necessary to better understand the behavior of the stratocumulus regime. Since cloud water is a physical property and is independent of a model’s radiative assumptions, it could potentially provide a useful emergent constraint on cloud feedback.

An extended cloud-clustering method to assess the seasonal variation of clouds is applied to five CMIP5 models. The seasonal variation of the total cloud radiative effect (CRE) is dominated by variations in the relative frequency of occurrence of the different cloud regimes. Seasonal variations of the CRE within the individual regimes contribute much less. This is the case for both observations, models and model errors. The error in the seasonal variation of cloud regimes, and its breakdown into mean amplitude and time varying components, are quantified with a new metric. The seasonal variation of the CRE of the cloud regimes is relatively well simulated by the models in the tropics, but less well in the extra-tropics. The stratocumulus regime has the largest seasonal variation of shortwave CRE in the tropics, despite having a small magnitude in the climatological mean. Most of the models capture the temporal variation of the CRE reasonably well, with the main differences between models coming from the variation in amplitude. In the extra-tropics, most models fail to correctly represent both the amplitude and time variation of the CRE of congestus, frontal and stratocumulus regimes. The annual mean climatology of the CRE and its amplitude in the seasonal variation are both underestimated for the anvil regime in the tropics, the cirrus regime and the congestus regime in the extra-tropics. The models in this study that best capture the seasonal variation of the cloud regimes tend to have higher climate sensitivities.

We have conducted a multi-model intercomparison of cloud-water in five state-of-the-art AGCMs run for control and doubled carbon dioxide climates. The most notable feature of the differences between the control and doubled carbon dioxide climates is in the distribution of cloud-water in the mixed-phase temperature band. The difference is greatest at mid and high latitudes. We found that the amount of cloud ice in the mixed phase layer in the control climate largely determines how much the cloud-water distribution changes for the doubled carbon dioxide climate. Therefore evaluation of the cloud ice distribution by comparison with data is important for future climate sensitivity studies. Cloud ice and cloud liquid both decrease in the layer below the melting layer, but only cloud liquid increases in the mixed-phase layer. Although the decrease in cloud-water below the melting layer occurs at all latitudes, the increase in cloud liquid in the mixed-phase layer is restricted to those latitudes where there is a large amount of cloud ice in the mixed-phase layer. If the cloud ice in the mixed-phase layer is concentrated at high latitudes, doubling of carbon dioxide might shift the center of cloud water distribution poleward which could decrease solar reflection because solar insolation is less at higher latitude. The magnitude of this poleward shift of cloud water appears to be larger for the higher climate sensitivity models, and it is consistent with the associated changes in cloud albedo forcing. For the control climate there is a clear relationship between the differences in cloud-water and relative humidity between the different models, for both magnitude and distribution. On the other hand the ratio of cloud ice to cloud-water follows the threshold temperature which is determined in each model. Improved measurements of relative humidity could be used to constrain the modeled representation of cloud water. At the same time, comparative analysis in global cloud resolving model simulations is necessary for further understanding of the relationships suggested in this paper.