Longwave Cloud Feedback Research Articles

A systematic evaluation of high-cloud controlling factors

Abstract. Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback.

Comparison of Short‐Term Cloud Feedbacks at Top of the Atmosphere and the Surface in Observations and AMIP6 Models

AbstractWe compared short‐term cloud feedback, defined at the top of the atmosphere (TOA), the atmospheric column (ATM), and the surface (SFC), between observations and models participating in Atmospheric Model Intercomparison Project Phase 6 (AMIP6) for the period 2000–2014. The globally averaged net cloud feedbacks observed at TOA, ATM, and SFC are −0.06 ± 0.63, −0.17 ± 0.70, and 0.11 ± 0.81 W m−2 K−1, respectively. While most models produced TOA cloud feedbacks that agreed with the observations within uncertainty ranges, the intermodel spread at SFC and within ATM was relatively larger. This demonstrates that models are diverse in how their TOA feedback is distributed between ATM and SFC. Because short‐term cloud feedback is mainly driven by El Niño–Southern Oscillation (ENSO), the global‐mean cloud feedback was further decomposed into components from the ENSO and non‐ENSO regions. Results show that cloud feedback in these two regions tends to be inversely related. Compared to observations, almost all models overestimated the longwave cloud feedback in the ENSO region due to the overestimation of cloud amount changes for high‐topped clouds. For these models, it is the offset between deviations in ENSO and non‐ENSO regions that leads to the overall agreement of global mean with observations. Sensitivity tests show that the main conclusions still hold when alternative kernels are used in estimating cloud feedback.

Climate Change Response of Tropical Atlantic Clouds in a Kilometer‐Resolution Model

AbstractEstimates of the tropical cloud feedback of global climate models (GCMs) show a large inter‐model spread due to the small‐scale nature of convective cloud processes. Estimates from large‐eddy simulations (LES) are more consistent among themselves, but difficult to scale to the climate system. Here we consider a compromise between GCMs and LES, and study how tropical clouds over the Atlantic respond to a climate perturbation in a regional kilometer‐resolution model. We perform two 4‐year‐long simulations at 3.3 km horizontal grid spacing with the limited‐area model COSMO on a 9,000 × 7,000 km2 domain covering the tropical Atlantic: a control simulation and a climate change simulation using the pseudo‐global warming approach. In a previous publication we have demonstrated that this approach yields a credible representation of the tropical climate without the double‐ITCZ bias commonly seen in GCMs. Here we address the cloud feedback and find a reduction of ITCZ high‐cloud cover resulting in a negative longwave cloud feedback similar to the CMIP6 ensemble mean. We find a reduction of stratocumulus clouds, which we argue is primarily a thermodynamic response. More surprisingly, the shallow cumulus cloud cover over the West Atlantic increases, which we argue is due to increased stability resulting in weaker entrainment and a more humid boundary layer. The mean shortwave cloud feedback over the tropical Atlantic is positive, comparable to the CMIP6 ensemble mean. The emerging estimate of the total cloud‐radiative feedback over the Atlantic is slightly negative, consistent with CMIP6 GCMs, but it is substantially stronger than that of the driving GCM.

Global and Regional Climate Feedbacks in Response to Uniform Warming and Cooling

AbstractWe compare the radiative feedbacks in an ensemble of global climate models under uniform 4 K warming and cooling of sea surface temperatures. The global‐mean net feedback is less stabilizing under warming in all nine models. This results primarily from less stabilizing water vapor and extra‐tropical shortwave cloud feedbacks, partially offset by more stabilizing tropical cloud feedbacks. The zonal‐mean feedbacks are also robust across the ensemble. In the extra‐tropics, the less stabilizing shortwave cloud feedback under warming is associated with further poleward migration of the mean Southern Hemisphere jet in some models. However, additional experiments with an aquaplanet version of the HadGEM3 model suggest that the asymmetry of the jet shift is not driving the asymmetry in the cloud feedbacks at these latitudes. In the tropics, the stronger water vapor feedback under warming is offset by weaker shortwave cloud feedbacks. The result is that the ensemble spread in the differences between the global feedbacks under warming and cooling is mainly driven by their differences in the tropics. The spatial distribution of the feedbacks largely reflects the zonal‐mean behavior, although there is considerable model spread in the regional cloud feedbacks, particularly in the tropical shortwave cloud feedback. Comparison with CO2‐ and solar‐forced coupled experiments suggests that the global‐mean longwave cloud feedback is nearly invariant to warming and cooling, regardless of the nature of the forcing. The shortwave cloud feedback is generally more positive under warming in the coupled models, consistent with the uniform SST perturbation experiments.

Using Satellite Observations to Evaluate Model Microphysical Representation of Arctic Mixed-Phase Clouds.

Mixed‐phase clouds play an important role in determining Arctic warming, but are parametrized in models and difficult to constrain with observations. We use two satellite‐derived cloud phase metrics to investigate the vertical structure of Arctic clouds in two global climate models that use the Community Atmosphere Model version 6 (CAM6) atmospheric component. We report a model error limiting ice nucleation, produce a set of Arctic‐constrained model runs by adjusting model microphysical variables to match the cloud phase metrics, and evaluate cloud feedbacks for all simulations. Models in this small ensemble uniformly overestimate total cloud fraction in the summer, but have variable representation of cloud fraction and phase in the winter and spring. By relating modeled cloud phase metrics and changes in low‐level liquid cloud amount under warming to longwave cloud feedback, we show that mixed‐phase processes mediate the Arctic climate by modifying how wintertime and springtime clouds respond to warming.

The Top‐of‐Atmosphere, Surface and Atmospheric Cloud Radiative Kernels Based on ISCCP‐H Datasets: Method and Evaluation

AbstractThis study aims to create observation‐based cloud radiative kernel (CRK) datasets and evaluate them by direct comparison of CRK and the CRK‐derived cloud feedback datasets. Based on the International Satellite Cloud Climatology Project (ISCCP) H datasets, we calculate CRKs (called ISCCP‐FH or FH CRKs) as 2D joint function/histogram of cloud optical depth and cloud top pressure for shortwave (SW), longwave (LW), and their sum, Net, at the top of atmosphere (TOA), as well as, for the first time, at the surface (SFC) and in the atmosphere (ATM). With cloud fraction change (CFC) datasets from doubled‐CO2 simulation and short‐term observational anomalies, we derive all the TOA, SFC and ATM cloud feedback for SW, LW and Net using our CRKs.The direct comparison with modeled and observed CRKs (or cloud radiative effects), cloud feedback from previous model results and the Clouds and the Earth's Radiant Energy System products show that our CRKs and CRK‐derived cloud feedback are reasonably well validated. We estimate the uncertainty for the CRK‐derived cloud feedback and show that the CFC‐associated uncertainty contributes >98.5% of the total cloud feedback uncertainty while CRK's is very small. Our preliminary evaluation also shows that some near‐zero/small cloud feedback in the TOA‐alone feedback indeed results from the compensation of sizable cloud feedback of the SFC and ATM feedback and reveals some significant surface and atmospheric cloud feedback whose sum appears insignificant in TOA‐alone feedback. In addition, the atmospheric longwave cloud feedback seems to play a role in enhancing meridional atmospheric energy transport.

Cloud Feedbacks from CanESM2 to CanESM5.0 and their influence on climate sensitivity

Abstract. The newest iteration of the Canadian Earth System Model (CanESM5.0.3) has an effective climate sensitivity (EffCS) of 5.65 K, which is a 54 % increase relative to the model's previous version (CanESM2 – 3.67 K), and the highest sensitivity of all current models participating in the sixth phase of the coupled model inter-comparison project (CMIP6). Here, we explore the underlying causes behind CanESM5's increased EffCS via comparison of forcing and feedbacks between CanESM2 and CanESM5. We find only modest differences in radiative forcing as a response to CO2 between model versions. We find small increases in the surface albedo and longwave cloud feedback, as well as a substantial increase in the SW cloud feedback in CanESM5. Through the use of cloud area fraction output and cloud radiative kernels, we find that more positive low and non-low shortwave cloud feedbacks – particularly with regards to low clouds across the equatorial Pacific, as well as subtropical and extratropical free troposphere cloud optical depth – are the dominant contributors to CanESM5's increased climate sensitivity. Additional simulations with prescribed sea surface temperatures reveal that the spatial pattern of surface temperature change exerts controls on the magnitude and spatial distribution of low-cloud fraction response but does not fully explain the increased EffCS in CanESM5. The results from CanESM5 are consistent with increased EffCS in several other CMIP6 models, which has been primarily attributed to changes in shortwave cloud feedbacks.

Tracking Changes in Climate Sensitivity in CNRM Climate Models

AbstractThe equilibrium climate sensitivity (ECS) in the latest version of CNRM climate model, CNRM‐CM6‐1, and in its high‐resolution counterpart, CNRM‐CM6‐1‐HR, is significantly larger than in the previous version (CNRM‐CM5.1). The traceability of this climate sensitivity change is investigated using coupled ocean‐atmosphere model climate change simulations. These simulations show that the increase in ECS is the result of changes in the atmospheric component. A particular attention is paid to the method used to decompose the equilibrium temperature response difference, by using a linearized decomposition of the individual radiative agents diagnosed by a radiative kernel technique. The climate sensitivity increase is primarily due to the cloud radiative responses, with a predominant contribution of the tropical longwave response (including both feedback and forcing adjustment) and a significant contribution of the extratropical and tropical shortwave feedback changes. A series of stand‐alone atmosphere experiments is carried out to quantify the contributions of each atmospheric development to this difference between CNRM‐CM5.1 and CNRM‐CM6‐1. The change of the convection scheme appears to play an important role in driving the cloud changes, with a large effect on the tropical longwave cloud feedback change.

DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

Abstract. We present results from an ensemble of eight climate models, each of which has carried out simulations of the early Eocene climate optimum (EECO, ∼ 50 million years ago). These simulations have been carried out in the framework of the Deep-Time Model Intercomparison Project (DeepMIP; http://www.deepmip.org, last access: 10 January 2021); thus, all models have been configured with the same paleogeographic and vegetation boundary conditions. The results indicate that these non-CO2 boundary conditions contribute between 3 and 5 ∘C to Eocene warmth. Compared with results from previous studies, the DeepMIP simulations generally show a reduced spread of the global mean surface temperature response across the ensemble for a given atmospheric CO2 concentration as well as an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with the preindustrial period mostly arises from decreases in emissivity due to the elevated CO2 concentration (and associated water vapour and long-wave cloud feedbacks), whereas the reduction in the Eocene in terms of the meridional temperature gradient is primarily due to emissivity and albedo changes owing to the non-CO2 boundary conditions (i.e. the removal of the Antarctic ice sheet and changes in vegetation). Three of the models (the Community Earth System Model, CESM; the Geophysical Fluid Dynamics Laboratory, GFDL, model; and the Norwegian Earth System Model, NorESM) show results that are consistent with the proxies in terms of the global mean temperature, meridional SST gradient, and CO2, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale, the models lack skill. In particular, the modelled anomalies are substantially lower than those indicated by the proxies in the southwest Pacific; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxies or models in this region. Our aim is that the documentation of the large-scale features and model–data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability, and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols.

Fixed Anvil Temperature Feedback: Positive, Zero, or Negative?

AbstractThe fixed anvil temperature (FAT) theory describes a mechanism for how tropical anvil clouds respond to global warming and has been used to argue for a robust positive longwave cloud feedback. A constant cloud anvil temperature, due to increased anvil altitude, has been argued to lead to a “zero cloud emission change” feedback, which can be considered positive relative to the negative feedback associated with cloud anvil warming when cloud altitude is unchanged. Here, partial radiative perturbation (PRP) analysis is used to quantify the radiative feedback caused by clouds that follow the FAT theory (FAT–cloud feedback) and to set this in the context of other feedback components in two atmospheric general circulation models. The FAT–cloud feedback is positive in the PRP framework due to increasing anvil altitude, but because the cloud emission does not change, this positive feedback is cancelled by an equal and opposite component of the temperature feedback due to increasing emission from the cloud. To incorporate this cancellation, the thermal radiative damping with fixed relative humidity and anvil temperature (T-FRAT) decomposition framework is proposed for longwave feedbacks, in which temperature, fixed relative humidity, and FAT–cloud feedbacks are combined. In T-FRAT, the cloud feedback under the FAT constraint is zero, while that under the proportionately higher anvil temperature (PHAT) constraint is negative. The change in the observable cloud radiative effect with FAT–cloud response is also evaluated and shown to be negative due to so-called cloud masking effects. It is shown that “cloud masking” is a misleading term in this context, and these effects are interpreted more generally as “cloud climatology effects.”

Quantifying the Cloud Particle‐Size Feedback in an Earth System Model

AbstractPhysical process‐based two‐moment cloud microphysical parameterizations, in which effective cloud particle size evolves prognostically with climate change, have recently been incorporated into global climate models. The impacts of cloud particle‐size change on the cloud feedback, however, have never been explicitly quantified. Here we develop a partial radiative perturbation‐based method to estimate the cloud feedback associated with particle‐size changes in the Community Earth System Model. We find an increase of cloud particle size in the upper troposphere in response to an instantaneous doubling of atmospheric CO2. The associated net, shortwave, and longwave cloud feedbacks are estimated to be 0.18, 0.33, and −0.15 Wm−2 K−1, respectively. The cloud particle‐size feedback is dominated by its shortwave component with a maximum greater than 1.0 Wm−2 K−1 in the tropics and the Southern Ocean. We suggest that the cloud particle‐size feedback is an underappreciated contributor to the spread of cloud feedback and climate sensitivity among current models.

The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated

Abstract. According to climate model simulations, the changing altitude of middle and high clouds is the dominant contributor to the positive global mean longwave cloud feedback. Nevertheless, the mechanisms of this longwave cloud altitude feedback and its magnitude have not yet been verified by observations. Accurate, stable, and long-term observations of a metric-characterizing cloud vertical distribution that are related to the longwave cloud radiative effect are needed to achieve a better understanding of the mechanism of longwave cloud altitude feedback. This study shows that the direct measurement of the altitude of atmospheric lidar opacity is a good candidate for the necessary observational metric. The opacity altitude is the level at which a spaceborne lidar beam is fully attenuated when probing an opaque cloud. By combining this altitude with the direct lidar measurement of the cloud-top altitude, we derive the effective radiative temperature of opaque clouds which linearly drives (as we will show) the outgoing longwave radiation. We find that, for an opaque cloud, a cloud temperature change of 1 K modifies its cloud radiative effect by 2 W m−2. Similarly, the longwave cloud radiative effect of optically thin clouds can be derived from their top and base altitudes and an estimate of their emissivity. We show with radiative transfer simulations that these relationships hold true at single atmospheric column scale, on the scale of the Clouds and the Earth's Radiant Energy System (CERES) instantaneous footprint, and at monthly mean 2° × 2° scale. Opaque clouds cover 35 % of the ice-free ocean and contribute to 73 % of the global mean cloud radiative effect. Thin-cloud coverage is 36 % and contributes 27 % of the global mean cloud radiative effect. The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated provides a simple formulation of the cloud radiative effect in the longwave domain and so helps us to understand the longwave cloud altitude feedback mechanism.

A cloud feedback emulator (CFE, version 1.0) for an intermediate complexity model

Abstract. The dominant source of inter-model differences in comprehensive global climate models (GCMs) are cloud radiative effects on Earth's energy budget. Intermediate complexity models, while able to run more efficiently, often lack cloud feedbacks. Here, we describe and evaluate a method for applying GCM-derived shortwave and longwave cloud feedbacks from 4 × CO2 and Last Glacial Maximum experiments to the University of Victoria Earth System Climate Model. The method generally captures the spread in top-of-the-atmosphere radiative feedbacks between the original GCMs, which impacts the magnitude and spatial distribution of surface temperature changes and climate sensitivity. These results suggest that the method is suitable to incorporate multi-model cloud feedback uncertainties in ensemble simulations with a single intermediate complexity model.

Quantifying the Sources of Intermodel Spread in Equilibrium Climate Sensitivity

Abstract This study clarifies the causes of intermodel differences in the global-average temperature response to doubled CO2, commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant source of intermodel spread in ECS. This new approach also reveals that covariances between cloud feedback and forcing, between lapse rate and longwave cloud feedbacks, and between albedo and shortwave cloud feedbacks play an important and previously underappreciated role in determining model differences in ECS. Defining feedbacks based on fixed relative rather than specific humidity (as suggested by Held and Shell) reduces the covariances between processes and leads to more straightforward interpretations of results.

The impact of parametrized convection on cloud feedback.

We investigate the sensitivity of cloud feedbacks to the use of convective parametrizations by repeating the CMIP5/CFMIP-2 AMIP/AMIP + 4K uniform sea surface temperature perturbation experiments with 10 climate models which have had their convective parametrizations turned off. Previous studies have suggested that differences between parametrized convection schemes are a leading source of inter-model spread in cloud feedbacks. We find however that ‘ConvOff’ models with convection switched off have a similar overall range of cloud feedbacks compared with the standard configurations. Furthermore, applying a simple bias correction method to allow for differences in present-day global cloud radiative effects substantially reduces the differences between the cloud feedbacks with and without parametrized convection in the individual models. We conclude that, while parametrized convection influences the strength of the cloud feedbacks substantially in some models, other processes must also contribute substantially to the overall inter-model spread. The positive shortwave cloud feedbacks seen in the models in subtropical regimes associated with shallow clouds are still present in the ConvOff experiments. Inter-model spread in shortwave cloud feedback increases slightly in regimes associated with trade cumulus in the ConvOff experiments but is quite similar in the most stable subtropical regimes associated with stratocumulus clouds. Inter-model spread in longwave cloud feedbacks in strongly precipitating regions of the tropics is substantially reduced in the ConvOff experiments however, indicating a considerable local contribution from differences in the details of convective parametrizations. In both standard and ConvOff experiments, models with less mid-level cloud and less moist static energy near the top of the boundary layer tend to have more positive tropical cloud feedbacks. The role of non-convective processes in contributing to inter-model spread in cloud feedback is discussed.

Insights into low-latitude cloud feedbacks from high-resolution models.

Cloud feedbacks are a leading source of uncertainty in the climate sensitivity simulated by global climate models (GCMs). Low-latitude boundary-layer and cumulus cloud regimes are particularly problematic, because they are sustained by tight interactions between clouds and unresolved turbulent circulations. Turbulence-resolving models better simulate such cloud regimes and support the GCM consensus that they contribute to positive global cloud feedbacks. Large-eddy simulations using sub-100 m grid spacings over small computational domains elucidate marine boundary-layer cloud response to greenhouse warming. Four observationally supported mechanisms contribute: 'thermodynamic' cloudiness reduction from warming of the atmosphere-ocean column, 'radiative' cloudiness reduction from CO2- and H2O-induced increase in atmospheric emissivity aloft, 'stability-induced' cloud increase from increased lower tropospheric stratification, and 'dynamical' cloudiness increase from reduced subsidence. The cloudiness reduction mechanisms typically dominate, giving positive shortwave cloud feedback. Cloud-resolving models with horizontal grid spacings of a few kilometres illuminate how cumulonimbus cloud systems affect climate feedbacks. Limited-area simulations and superparameterized GCMs show upward shift and slight reduction of cloud cover in a warmer climate, implying positive cloud feedbacks. A global cloud-resolving model suggests tropical cirrus increases in a warmer climate, producing positive longwave cloud feedback, but results are sensitive to subgrid turbulence and ice microphysics schemes.

Impact of changes in the formulation of cloud‐related processes on model biases and climate feedbacks

AbstractTo test the impact of modeling uncertainties and biases on the simulation of cloud feedbacks, several configurations of the EC‐Earth climate model are built altering physical parameterizations. An overview of the various radiative feedbacks diagnosed from the reference EC‐Earth configuration is documented for the first time. The cloud feedback is positive and small. While the total feedback parameter is almost insensitive to model configuration, the cloud feedback, in particular its shortwave (SW) component, can vary considerably depending on the model settings. The lateral mass exchange rate of penetrative convection and the conversion rate from condensed water to precipitation are leading uncertain parameters affecting the radiative feedbacks diagnosed. Consistent with other studies, we find a strong correlation between low‐cloud model fidelity and low‐cloud response under global warming. It is shown that this relationship holds only for stratocumulus regimes and is contributed by low‐cloud cover, rather than low‐cloud optical thickness. Model configurations simulating higher stratocumulus cover, which is closer to the observations, exhibit a stronger positive SW cloud feedback. This feedback is likely underestimated in the reference EC‐Earth configuration, over the eastern basins of the tropical oceans. In addition, connections between simulated high‐cloud top altitude in present‐day climate and longwave cloud feedback are discussed.

High cloud increase in a perturbed SST experiment with a global nonhydrostatic model including explicit convective processes

AbstractResults are presented from a series of sensitivity tests in idealized global warming experiments using the global nonhydrostatic model, NICAM, in which convection at scales of 7–14 km is explicitly resolved. All have a strong positive longwave cloud feedback larger than that seen in conventional GCMs with parameterized convection. Consequently, the global mean net outgoing radiation decreases in response to increased sea surface temperatures. Large increases in high clouds with tops between 180 and 50 hPa are found, and these changes contribute the most to this longwave cloud feedback. Relative humidity and upper tropospheric temperature also increases strongly, again more so than typically seen in conventional GCMs. The magnitude of the response varies considerably between different versions of NICAM. Most of the NICAM control simulations show large overestimates in cloud fraction between 180 and 50 hPa compared to observations. The changes in cloud fraction in the upper troposphere are strongly correlated with their control values. Versions of NICAM with stronger cloud feedbacks have large positive biases in high‐top cloud amount and temperature in the free troposphere in their control simulations. The version which has the best agreement with the observations in this regard has the weakest longwave cloud feedback; however, this is still more strongly positive than that typically seen in conventional GCMs. These results demonstrate the potential for stronger high cloud fraction feedbacks in climate warming scenarios than currently predicted by conventional GCMs and highlight the potential relevance of deep convective processes.

Individual Feedback Contributions to the Seasonality of Surface Warming

Abstract Using the climate feedback response analysis method, the authors examine the individual contributions of the CO2 radiative forcing and climate feedbacks to the magnitude, spatial pattern, and seasonality of the transient surface warming response in a 1% yr−1 CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4). The CO2 forcing and water vapor feedback warm the surface everywhere throughout the year. The tropical warming is predominantly caused by the CO2 forcing and water vapor feedback, while the evaporation feedback reduces the warming. Most feedbacks exhibit noticeable seasonal variations; however, their net effect has little seasonal variation due to compensating effects, which keeps the tropical warming relatively invariant all year long. The polar warming has a pronounced seasonal cycle, with maximum warming in fall/winter and minimum warming in summer. In summer, the large cancelations between the shortwave and longwave cloud feedbacks and between the surface albedo feedback warming and the cooling from the ocean heat storage/dynamics feedback lead to a warming minimum. In polar winter, surface albedo and shortwave cloud feedbacks are nearly absent due to a lack of insolation. However, the ocean heat storage feedback relays the polar warming due to the surface albedo feedback from summer to winter, and the longwave cloud feedback warms the polar surface. Therefore, the seasonal variations in the cloud feedback, surface albedo feedback, and ocean heat storage/dynamics feedback, directly caused by the strong annual cycle of insolation, contribute primarily to the large seasonal variation of polar warming. Furthermore, the CO2 forcing and water vapor and atmospheric dynamics feedbacks add to the maximum polar warming in fall/winter.

Characterizing the Climate Feedback Pattern in the NCAR CCSM3-SOM Using Hourly Data

Abstract The climate feedback–response analysis method (CFRAM) was applied to 10-yr hourly output of the NCAR Community Climate System Model, version 3, using the slab ocean model (CCSM3-SOM), to analyze the strength and spatial distribution of climate feedbacks and to characterize their contributions to the global and regional surface temperature Ts changes in response to a doubling of CO2. The global mean bias in the sum of partial Ts changes associated with the CO2 forcing, and each feedback derived with the CFRAM analysis is about 2% of Ts change obtained directly from the CCSM3-SOM simulations. The pattern correlation between the two is 0.94, indicating that the CFRAM analysis using hourly model output is accurate and thus is appropriate for quantifying the contributions of climate feedback to the formation of global and regional warming patterns. For global mean Ts, the largest contributor to the warming is water vapor feedback, followed by the direct CO2 forcing and albedo feedback. The albedo feedback exhibits the largest spatial variation, followed by shortwave cloud feedback. In terms of pattern correlation and RMS difference with the modeled global surface warming, longwave cloud feedback contributes the most. On zonal average, albedo feedback is the largest contributor to the stronger warming in high latitudes than in the tropics. The longwave cloud feedback further amplifies the latitudinal warming contrast. Both the land–ocean warming difference and contributions of climate feedbacks to it vary with latitude. Equatorward of 50°, shortwave cloud feedback and dynamical advection are the two largest contributors. The land–ocean warming difference on the hemispheric scale is mainly attributable to longwave cloud feedback and convection.