Atmospheric radiative feedbacks associated with transient climate change and climate variability

This study examines in detail the ‘atmospheric’ radiative feedbacks operating in a coupled General Circulation Model (GCM). These feedbacks (defined as the change in top of atmosphere radiation per degree of global surface temperature change) are due to responses in water vapour, lapse rate, clouds and surface albedo. Two types of radiative feedback in particular are considered: those arising from century scale ‘transient’ warming (from a 1% per annum compounded CO₂ increase), and those operating under the model’s own unforced ‘natural’ variability. The time evolution of the transient (or ‘secular’) feedbacks is first examined. It is found that both the global strength and the latitudinal distributions of these feedbacks are established within the first two or three decades of warming, and thereafter change relatively little out to 100 years. They also closely approximate those found under equilibrium warming from a ‘mixed layer’ ocean version of the same model forced by a doubling of CO₂. These secular feedbacks are then compared with those operating under unforced (interannual) variability. For water vapour, the interannual feedback is only around two-thirds the strength of the secular feedback. The pattern reveals widespread regions of negative feedback in the interannual case, in turn resulting from patterns of circulation change and regions of decreasing as well as increasing surface temperature. Considering the vertical structure of the two, it is found that although positive net mid to upper tropospheric contributions dominate both, they are weaker (and occur lower) under interannual variability than under secular change and are more narrowly confined to the tropics. Lapse rate feedback from variability shows weak negative feedback over low latitudes combined with strong positive feedback in mid-to-high latitudes resulting in no net global feedback—in contrast to the dominant negative low to mid-latitude response seen under secular climate change. Surface albedo feedback is, however, slightly stronger under interannual variability—partly due to regions of extremely weak, or even negative, feedback over Antarctic sea ice in the transient experiment. Both long and shortwave global cloud feedbacks are essentially zero on interannual timescales, with the shortwave term also being very weak under climate change, although cloud fraction and optical property components show correlation with global temperature both under interannual variability and transient climate change. The results of this modelling study, although for a single model only, suggest that the analogues provided by interannual variability may provide some useful pointers to some aspects of climate change feedback strength, particularly for water vapour and surface albedo, but that structural differences will need to be heeded in such an analysis.     

The vertical distribution of climate forcings and feedbacks from the surface to top of atmosphere

The radiative forcings and feedbacks that determine Earth’s climate sensitivity are typically defined at the top-of-atmosphere (TOA) or tropopause, yet climate sensitivity itself refers to a change in temperature at the surface. In this paper, we describe how TOA radiative perturbations translate into surface temperature changes. It is shown using first principles that radiation changes at the TOA can be equated with the change in energy stored by the oceans and land surface. This ocean and land heat uptake in turn involves an adjustment of the surface radiative and non-radiative energy fluxes, with the latter being comprised of the turbulent exchange of latent and sensible heat between the surface and atmosphere. We employ the radiative kernel technique to decompose TOA radiative feedbacks in the IPCC Fourth Assessment Report climate models into components associated with changes in radiative heating of the atmosphere and of the surface. (We consider the equilibrium response of atmosphere-mixed layer ocean models subjected to an instantaneous doubling of atmospheric CO₂). It is shown that most feedbacks, i.e., the temperature, water vapor and cloud feedbacks, (as well as CO₂ forcing) affect primarily the turbulent energy exchange at the surface rather than the radiative energy exchange. Specifically, the temperature feedback increases the surface turbulent (radiative) energy loss by 2.87 W m^?2 K^?1 (0.60 W m^?2 K^?1) in the multimodel mean; the water vapor feedback decreases the surface turbulent energy loss by 1.07 W m^?2 K^?1 and increases the surface radiative heating by 0.89 W m^?2 K^?1; and the cloud feedback decreases both the turbulent energy loss and the radiative heating at the surface by 0.43 and 0.24 W m^?2 K^?1, respectively. Since changes to the surface turbulent energy exchange are dominated in the global mean sense by changes in surface evaporation, these results serve to highlight the fundamental importance of the global water cycle to Earth’s climate sensitivity.     

Radiative damping of annual variation in global mean surface temperature: comparison between observed and simulated feedback

The sensitivity of the global climate is essentially determined by the radiative damping of the global mean surface temperature anomaly through the outgoing radiation from the top of the atmosphere (TOA). Using the TOA fluxes of terrestrial and reflected solar radiation obtained from the Earth radiation budget experiment (ERBE), this study estimates the magnitude of the overall feedback, which modifies the radiative damping of the annual variation of the global mean surface temperature, and compare it with model simulations. Although the pattern of the annually varying anomaly is quite different from that of the global warming, the analysis conducted here may be used for assessing the systematic bias of the feedback that operates on the CO₂-induced warming of the surface temperature. In the absence of feedback effect, the outgoing terrestrial radiation at the TOA is approximately follows the Stefan-Boltzmann’s fourth power of the planetary emission temperature. However, it deviates significantly from the blackbody radiation due to various feedbacks involving water vapor and cloud cover. In addition, the reflected solar radiation is altered by the feedbacks involving sea ice, snow and cloud, thereby affecting the radiative damping of surface temperature. The analysis of ERBE reveals that the radiative damping is weakened by as much as 70% due to the overall effect of feedbacks, and is only 30% of what is expected for the blackbody with the planetary emission temperature. Similar feedback analysis is conducted for three general circulation models of the atmosphere, which was used for the study of cloud feedback in the preceding study. The sign and magnitude of the overall feedback in the three models are similar to those of the observed. However, when it is subdivided into solar and terrestrial components, they are quite different from the observation mainly due to the failure of the models to simulate individually the solar and terrestrial components of the cloud feedback. It is therefore desirable to make the similar comparison not only for the overall feedback but also for its individual components such as albedo- and cloud-feedbacks. Although the pattern of the annually-varying anomaly is quite different from that of global warming, the methodology of the comparative analysis presented here may be used for the identification of the systematic bias of the overall feedback in a model. A proposal is made for the estimation of the best guess value of climate sensitivity using the outputs from many climate models submitted to the Intergovernmental panel on Climate Change.     

气候反馈对温度空间模态的依赖性:IPCC AR6解读

依据政府间气候变化专门委员会(IPCC)第六次评估报告(AR6)第一工作组(WGI)报告第七章的内容,详细解读了气候反馈对温度空间模态的依赖性。与第五次评估报告(AR5)相比,AR6对于地表温度空间模态演变在驱动气候反馈变化中作用的理解已有了较大提升。AR6认为,在温室气体强迫下,北极在21世纪的增温幅度很可能大于全球平均水平,南极在百年时间尺度上的增温要强于热带地区;同时,在百年时间尺度上热带太平洋东部的变暖幅度大于西部,即热带太平洋东-西向海表温度梯度减弱。极地放大效应(尤其是南半球)和热带太平洋东-西向海表温度梯度随时间的变化是影响未来气候反馈如何演变的关键因素。随着地表增温空间模态的演变,气候反馈(尤其云反馈)预计将在未来几十年的时间尺度上逐渐增加,对气候变化更多是起放大作用。     

Climate sensitivity and response

Results from climate change simulations indicate a reasonably robust proportionality between global mean radiative forcing and global mean surface air temperature response. The "constant" of proportionality is a measure of the overall strength of climate feedback processes and hence of global climate sensitivity. Geographically, however, temperature response patterns are generally not proportional to, nor do they resemble, their parent forcing patterns. Temperature response patterns, nevertheless, exhibit a remarkable additivity whereby the sum of response patterns for different forcings closely resembles the response pattern for the sum of the forcings. The geographical distribution of contributions to the climate sensitivity/feedback are obtained diagnostically from simulations with the Canadian Centre for Climate Modelling and Analysis (CCCma) coupled global climate model (GCM). There is positive feedback over high-latitude oceans, over northern land areas, and over the equatorial Pacific. The remaining regions over oceans and tropical land areas exhibit negative feedback. The feedback results are decomposed into components associated with short-and longwave radiative processes and in terms of cloud-free atmosphere/surface and cloud feedbacks. While the geographic pattern of the feedbacks may generally be linked to local processes, all feedback processes display regions of both positive and negative values (except for the solar atmosphere/surface feedback associated with the retreat of ice and snow which is positive) and all vary from place to place so that there is no simple physical picture that operates everywhere. The stable geographical pattern of the feedback is a consequence of the balance between local physical processes rather than the dominance of a particular process. The feedback results indicate that, to first order, temperature response patterns are determined by the geographical pattern of local feedback processes. The feedback processes act to localize forcing changes and to generate temperature response patterns which depend firstly on the pattern of feedbacks and only secondarily on the pattern of the forcing. The geographical distribution of feedback processes can be regarded as a feature of the climate model (and by inference of the climate system) and not (or only comparatively weak) functions of forcing and climate state. An illustrative model is able to reproduce qualitatively the kinds of forcing/temperature response behavior seen in the CCCma GCM including the quasi-independence of forcing and response patterns, the additivity of temperature response patterns, and the resulting "non-constancy" of the global climate sensitivity.     

On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates

This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO₂) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO₂ radiative forcing. By considering tropospheric adjustments to CO₂ as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO₂ is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO₂), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.     

Climate feedback efficiency and synergy

Earth’s climate sensitivity to radiative forcing induced by a doubling of the atmospheric CO₂ is determined by feedback mechanisms, including changes in atmospheric water vapor, clouds and surface albedo, that act to either amplify or dampen the response. The climate system is frequently interpreted in terms of a simple energy balance model, in which it is assumed that individual feedback mechanisms are additive and act independently. Here we test these assumptions by systematically controlling, or locking, the radiative feedbacks in a state-of-the-art climate model. The method is shown to yield a near-perfect decomposition of change into partial temperature contributions pertaining to forcing and each of the feedbacks. In the studied model water vapor feedback stands for about half the temperature change, CO₂-forcing about one third, while cloud and surface albedo feedback contributions are relatively small. We find a close correspondence between forcing, feedback and partial surface temperature response for the water vapor and surface albedo feedbacks, while the cloud feedback is inefficient in inducing surface temperature change. Analysis suggests that cloud-induced warming in the upper tropical troposphere, consistent with rising convective cloud anvils in a warming climate enhances the negative lapse-rate feedback, thereby offsetting some of the warming that would otherwise be attributable to this positive cloud feedback. By subsequently combining feedback mechanisms we find a positive synergy acting between the water vapor feedback and the cloud feedback; that is, the combined cloud and water vapor feedback is greater than the sum of its parts. Negative synergies surround the surface albedo feedback, as associated cloud and water vapor changes dampen the anticipated climate change induced by retreating snow and ice. Our results highlight the importance of treating the coupling between clouds, water vapor and temperature in a deepening troposphere.     

On the vertical extent of atmospheric feedbacks

This study addresses the question: what vertical regions contribute the most to water vapor, surface temperature, lapse rate and cloud fraction feedback strengths in a general circulation model? Multi-level offline radiation perturbation calculations are used to diagnose the feedback contribution from each model level. As a first step, to locate regions of maximum radiative sensitivity to climate changes, the top of atmosphere radiative impact for each feedback is explored for each process by means of idealized parameter perturbations on top of a control (1?×?CO₂) model climate. As a second step, the actual feedbacks themselves are calculated using the changes modelled from a 2?×?CO₂ experiment. The impact of clouds on water vapor and lapse rate feedbacks is also isolated using `clear sky' calculations. Considering the idealized changes, it is found that the radiative sensitivity to water vapor changes is a maximum in the tropical lower troposphere. The sensitivity to temperature changes has both upper and lower tropospheric maxima. The sensitivity to idealized cloud changes is positive (warming) for upper level cloud increases but negative (cooling) for lower level increases, due to competing long and shortwave effects. Considering the actual feedbacks, it is found that water vapor feedback is a maximum in the tropical upper troposphere, due to the large relative increases in specific humidity which occur there. The actual lapse rate feedback changes sign with latitude and is a maximum (negative) again in the tropical upper troposphere. Cloud feedbacks reflect the general decrease in low- to mid-level low-latitude cloud, with an increase in the very highest cloud. This produces a net positive (negative) shortwave (longwave) cloud feedback. The role of clouds in the strength of the water vapor and lapse rate feedbacks is also discussed.     

The respective roles of surface temperature driven feedbacks and tropospheric adjustment to CO2 in CMIP5 transient climate simulations

An overview of radiative climate feedbacks and ocean heat uptake efficiency diagnosed from idealized transient climate change experiments of 14 CMIP5 models is presented. Feedbacks explain about two times more variance in transient climate response across the models than ocean heat uptake efficiency. Cloud feedbacks can clearly be identified as the main source of inter-model spread. Models with strong longwave feedbacks in the tropics feature substantial increases in cloud ice around the tropopause suggestive of changes in cloud-top heights. The lifting of the tropical tropopause goes together with a general weakening of the tropical circulation. Distinctive inter-model differences in cloud shortwave feedbacks occur in the subtropics including the equatorward flanks of the storm-tracks. Related cloud fraction changes are not confined to low clouds but comprise middle level clouds as well. A reduction in relative humidity through the lower and mid troposphere can be identified as being the main associated large-scale feature. Experiments with prescribed sea surface temperatures are analyzed in order to investigate whether the diagnosed feedbacks from the transient climate simulations contain a tropospheric adjustment component that is not conveyed through the surface temperature response. The strengths of the climate feedbacks computed from atmosphere-only experiments with prescribed increases in sea surface temperatures, but fixed CO₂ concentrations, are close to the ones derived from the transient experiment. Only the cloud shortwave feedback exhibits discernible differences which, however, can not unequivocally be attributed to tropospheric adjustment to CO₂. Although for some models a tropospheric adjustment component is present in the global mean shortwave cloud feedback, an analysis of spatial patterns does not lend support to the view that cloud feedbacks are dominated by their tropospheric adjustment part. Nevertheless, there is positive correlation between the strength of tropospheric adjustment processes and cloud feedbacks across different climate models.     

On atmospheric radiative feedbacks associated with climate variability and change

Using the method of radiative ‘kernels’ an analysis is made of water vapour, lapse rate and ‘Planck’ (uniform vertical temperature) long wave feedbacks in models participating in the World Climate Research Program (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3). Feedbacks are calculated at climate change timescales from the A1B scenario, and at three ‘variability’ timescales from the corresponding preindustrial experiments: seasonal, interannual and decadal. Surface temperature responses show different meridional patterns for the different timescales, which are then manifest in the structures of the individual feedbacks. Despite these differences, mean water vapour feedback strength in models is positive for all models and timescales, and of comparable global magnitude across all timescales except for seasonal, where it is much weaker. Taking into consideration the strong positive lapse rate feedback at seasonal timescales, combined water vapour/lapse rate feedback is indeed similar across all timescales. To a good approximation, global water vapour feedback is found to be well represented by the temperature response along with an assumption of unchanged relative humidity under both variability and climate change. A comparison is also made of model feedbacks with reanalysis derived feedbacks for seasonal and interannual timescales. No strong relationships between individual modelled feedbacks at different timescales are evident: i.e., strong feedbacks in models at variability timescales do not in general predict strong climate change feedback, with the possible exception of seasonal timescales. There are caveats on this (and other) findings however, from uncertainties associated with the kernel technique and from, at times, very large uncertainties in estimating variability related feedbacks from temperature regressions.     

On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO₂ doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.     

The role of ecosystem-atmosphere interactions in simulated Amazonian precipitation decrease and forest dieback under global climate warming

Summary A suite of simulations with the HadCM3LC coupled climate-carbon cycle model is used to examine the various forcings and feedbacks involved in the simulated precipitation decrease and forest dieback. Rising atmospheric CO₂ is found to contribute 20% to the precipitation reduction through the physiological forcing of stomatal closure, with 80% of the reduction being seen when stomatal closure was excluded and only radiative forcing by CO₂ was included. The forest dieback exerts two positive feedbacks on the precipitation reduction; a biogeophysical feedback through reduced forest cover suppressing local evaporative water recycling, and a biogeochemical feedback through the release of CO₂ contributing to an accelerated global warming. The precipitation reduction is enhanced by 20% by the biogeophysical feedback, and 5% by the carbon cycle feedback from the forest dieback. This analysis helps to explain why the Amazonian precipitation reduction simulated by HadCM3LC is more extreme than that simulated in other GCMs; in the fully-coupled, climate-carbon cycle simulation, approximately half of the precipitation reduction in Amazonia is attributable to a combination of physiological forcing and biogeophysical and global carbon cycle feedbacks, which are generally not included in other GCM simulations of future climate change. The analysis also demonstrates the potential contribution of regional-scale climate and ecosystem change to uncertainties in global CO₂ and climate change projections. Moreover, the importance of feedbacks suggests that a human-induced increase in forest vulnerability to climate change may have implications for regional and global scale climate sensitivity.     

Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs

The use of radiative kernels to diagnose climate feedbacks is a recent development that may be applied to existing climate change simulations. We apply the radiative kernel technique to transient simulations from a multi-thousand member perturbed physics ensemble of coupled atmosphere-ocean general circulation models, comparing distributions of model feedbacks with those taken from the CMIP-3 multi GCM ensemble. Although the range of clear sky longwave feedbacks in the perturbed physics ensemble is similar to that seen in the multi-GCM ensemble, the kernel technique underestimates the net clear-sky feedbacks (or the radiative forcing) in some perturbed models with significantly altered humidity distributions. In addition, the compensating relationship between global mean atmospheric lapse rate feedback and water vapor feedback is found to hold in the perturbed physics ensemble, but large differences in relative humidity distributions in the ensemble prevent the compensation from holding at a regional scale. Both ensembles show a similar range of response of global mean net cloud feedback, but the mean of the perturbed physics ensemble is shifted towards more positive values such that none of the perturbed models exhibit a net negative cloud feedback. The perturbed physics ensemble contains fewer models with strong negative shortwave cloud feedbacks and has stronger compensating positive longwave feedbacks. A principal component analysis used to identify dominant modes of feedback variation reveals that the perturbed physics ensemble produces very different modes of climate response to the multi-model ensemble, suggesting that one may not be used as an analog for the other in estimates of uncertainty in future response. Whereas in the multi-model ensemble, the first order variation in cloud feedbacks shows compensation between longwave and shortwave components, in the perturbed physics ensemble the shortwave feedbacks are uncompensated, possibly explaining the larger range of climate sensitivities observed in the perturbed simulations. Regression analysis suggests that the parameters governing cloud formation, convection strength and ice fall speed are the most significant in altering climate feedbacks. Perturbations of oceanic and sulfur cycle parameters have relatively little effect on the atmospheric feedbacks diagnosed by the kernel technique.     

全球气候变化对中国森林生态系统的影响

人类活动所引起的温室效应及由此造成的全球气候变化和对全球生态环境的影响正引起人们越来越多的重视.作为全球陆地生态系统一个重要组分,中国的森林生态系统对未来全球气候变化的响应更是人们关注的重点.作者系统地总结了全球气候变化对中国森林生态系统分布、生态系统生产力、森林树种以及森林土壤的影响,指出了现阶段该领域研究中存在的一些问题,并对今后需要加强的一些核心问题与研究重点作了展望.     

The dynamic greenhouse: Feedback processes that may influence future concentrations of atmospheric trace gases and climatic change

The sensitivity of the climate system to anthropogenic perturbations over the next century will be determined by a combination of feedbacks that amplify or damp the direct radiative effects of increasing concentrations of greenhouse gases. A number of important geophysical climate feedbacks, such as changes in water vapor, clouds, and sea ice albedo, are included in current climate models, but biogeochemical feedbacks such as changes in methane emissions, ocean CO₂ uptake, and vegetation albedo are generally neglected. The relative importance of a wide range of feedbacks is assessed here by estimating the gain associated with each individual process. The gain from biogeochemical feedbacks is estimated to be 0.05–0.29 compared to 0.17–0.77 for geophysical climate feedbacks. The potentially most significant biogeochemical feedbacks are probably release of methane hydrates, changes in ocean chemistry, biology, and circulation, and changes in the albedo of the global vegetation. While each of these feedbacks is modest compared to the water vapor feedback, the biogeochemical feedbacks in combination have the potential to substantially increase the climate change associated with any given initial forcing.The views expressed are the author's: They do not express official views of the U.S. Government or the Environmental Protection Agency.     

A new framework for isolating individual feedback processes in coupled general circulation climate models. Part II: Method demonstrations and comparisons

We here use a coupled atmosphere-surface single column climate model to illustrate how the CFRAM, a new climate feedback analysis framework formulated in Part I of the two-part series papers, can be applied to isolate individual contributions to the total temperature change of a climate system from the external forcing alone, and from each of individual physical and dynamical processes associated with the energy transfer with the space and within the climate system. We demonstrate that the isolation of individual feedbacks in the CFRAM is achieved without referencing to a virtual climate system as in the online feedback suppression method. We show that partial temperature changes estimated by the online feedback suppression method include the “compensating effects” of other feedbacks when the feedback under consideration is suppressed. The partial temperature changes are addable in the CFRAM but they are not in the online feedback suppression method. We also apply the CFRAM to isolate the contributions to the lapse rate feedback from individual physical and dynamical feedback processes. We show that the lapse rate feedback includes not only the partial effect of each feedback that directly contributes to energy flux perturbations at the TOA (such as water vapor feedback), but also the total effects of those feedbacks that do not contribute to energy flux perturbations at the TOA (such as evaporation and moist convection feedbacks). Because the contributions to the lapse rate feedback from various physical and dynamical processes tend to cancel one another, the net lapse rate feedback is a residual of many large terms. This leads to a large uncertainty not only in estimating the lapse rate feedback itself, but also in other feedbacks whose effects are either partially or totally lumped into the lapse rate feedback.     

Origins of differences in climate sensitivity, forcing and feedback in climate models

We diagnose climate feedback parameters and CO₂ forcing including rapid adjustment in twelve atmosphere/mixed-layer-ocean (“slab”) climate models from the CMIP3/CFMIP-1 project (the AR4 ensemble) and fifteen parameter-perturbed versions of the HadSM3 slab model (the PPE). In both ensembles, differences in climate feedbacks can account for approximately twice as much of the range in climate sensitivity as differences in CO₂ forcing. In the AR4 ensemble, cloud effects can explain the full range of climate sensitivities, and cloud feedback components contribute four times as much as cloud components of CO₂ forcing to the range. Non-cloud feedbacks are required to fully account for the high sensitivities of some models however. The largest contribution to the high sensitivity of HadGEM1 is from a high latitude clear-sky shortwave feedback, and clear-sky longwave feedbacks contribute substantially to the highest sensitivity members of the PPE. Differences in low latitude ocean regions (30°N/S) contribute more to the range than those in mid-latitude oceans (30–55°N/S), low/mid latitude land (55°N/S) or high latitude ocean/land (55–90°N/S), but contributions from these other regions are required to account fully for the higher model sensitivities, for example from land areas in IPSL CM4. Net cloud feedback components over the low latitude oceans sorted into percentile ranges of lower tropospheric stability (LTS) show largest differences among models in stable regions, mainly due to their shortwave components, most of which are positive in spite of increasing LTS. Differences in the mid-stability range are smaller, but cover a larger area, contributing a comparable amount to the range in climate sensitivity. These are strongly anti-correlated with changes in subsidence. Cloud components of CO₂ forcing also show the largest differences in stable regions, and are strongly anticorrelated with changes in estimated inversion strength (EIS). This is qualitatively consistent with what would be expected from observed relationships between EIS and low-level cloud fraction. We identify a number of cases where individual models show unusually strong forcings and feedbacks compared to other members of the ensemble. We encourage modelling groups to investigate unusual model behaviours further with sensitivity experiments. Most of the models fail to correctly reproduce the observed relationships between stability and cloud radiative effect in the subtropics, indicating that there remains considerable room for model improvements in the future.     

Climate feedbacks in a general circulation model incorporating prognostic clouds

 This study performs a comprehensive feedback analysis on the Bureau of Meteorology Research Centre General Circulation Model, quantifying all important feedbacks operating under an increase in atmospheric CO₂. The individual feedbacks are analysed in detail, using an offline radiation perturbation method, looking at long- and shortwave components, latitudinal distributions, cloud impacts, non-linearities under 2xCO₂ and 4xCO₂ warmings and at interannual variability. The water vapour feedback is divided into terms due to moisture height and amount changes. The net cloud feedback is separated into terms due to cloud amount, height, water content, water phase, physical thickness and convective cloud fraction. Globally the most important feedbacks were found to be (from strongest positive to strongest negative) those due to water vapour, clouds, surface albedo, lapse rate and surface temperature. For the longwave (LW) response the most important term of the cloud ‘optical property’ feedbacks is due to the water content. In the shortwave (SW), both water content and water phase changes are important. Cloud amount and height terms are also important for both LW and SW. Feedbacks due to physical cloud thickness and convective cloud fraction are found to be relatively small. All cloud component feedbacks (other than height) produce conflicting LW/SW feedbacks in the model. Furthermore, the optical property and cloud fraction feedbacks are also of opposite sign. The result is that the net cloud feedback is the (relatively small) product of conflicting physical processes. Non-linearities in the feedbacks are found to be relatively small for all but the surface albedo response and some cloud component contributions. The cloud impact on non-cloud feedbacks is also discussed: greatest impact is on the surface albedo, but impact on water vapour feedback is also significant. The analysis method here proves to be a␣powerful tool for detailing the contributions from different model processes (and particularly those of the clouds) to the final climate model sensitivity. Received: 15 June 2000 / Accepted: 10 January 2001     

The role of closed ecological systems in carbon cycle modelling

Acquiring a mechanistic understanding of the role of biotic feedbacks for the links between atmospheric CO₂ concentrations and temperature is essential for trustworthy climate predictions. Currently, computer-based simulations are the only available tool to estimate the global impact of biotic feedbacks on future atmospheric CO₂ and temperatures. Here we propose an alternative and complementary approach by using materially closed, energetically open analogue/physical models of the carbon cycle. We argue that there is unexplored potential in using a materially closed approach to improve our understanding of the magnitude and direction of many biotic carbon feedbacks and that recent technological advances make this feasible. We also suggest how such systems could be designed and discuss the advantages and limitations of establishing physical models of the global carbon cycle.