An atmospheric general circulation model (AGCM) is forced with patterns of observed sea surface temperature (SST) change and those output from atmosphere–ocean GCM (AOGCM) climate change simulations ...to demonstrate a strong dependence of climate feedback on the spatial structure of surface temperature change. Cloud and lapse rate feedbacks are found to vary the most, depending strongly on the pattern of tropical Pacific SST change. When warming is focused in the southeast tropical Pacific—a region of climatological subsidence and extensive marine low cloud cover—warming reduces the lower-tropospheric stability (LTS) and low cloud cover but is largely trapped under an inversion and hence has little remote effect. The net result is a relatively weak negative lapse rate feedback and a large positive cloud feedback. In contrast, when warming is weak in the southeast tropical Pacific and enhanced in the west tropical Pacific—a strong convective region—warming is efficiently transported throughout the free troposphere. The increased atmospheric stability results in a strong negative lapse rate feedback and increases the LTS in low cloud regions, resulting in a low cloud feedback of weak magnitude. These mechanisms help explain why climate feedback and sensitivity change on multidecadal time scales in AOGCM abrupt4xCO₂ simulations and are different from those seen in AGCM experiments forced with observed historical SST changes. From the physical understanding developed here, one should expect unusually negative radiative feedbacks and low effective climate sensitivities to be diagnosed from real-world variations in radiative fluxes and temperature over decades in which the eastern Pacific has lacked warming.
Experiments with CO₂ instantaneously quadrupled and then held constant are used to show that the relationship between the global-mean net heat input to the climate system and the global-mean surface ...air temperature change is nonlinear in phase 5 of the Coupled Model Intercomparison Project (CMIP5) atmosphere–ocean general circulation models (AOGCMs). The nonlinearity is shown to arise from a change in strength of climate feedbacks driven by an evolving pattern of surface warming. In 23 out of the 27 AOGCMs examined, the climate feedback parameter becomes significantly (95% confidence) less negative (i.e., the effective climate sensitivity increases) as time passes. Cloud feedback parameters show the largest changes. In the AOGCM mean, approximately 60% of the change in feedback parameter comes from the tropics (30°N–30°S). An important region involved is the tropical Pacific, where the surface warming intensifies in the east after a few decades. The dependence of climate feedbacks on an evolving pattern of surface warming is confirmed using the HadGEM2 and HadCM3 atmosphere GCMs (AGCMs). With monthly evolving sea surface temperatures and sea ice prescribed from its AOGCM counterpart, each AGCM reproduces the time-varying feedbacks, but when a fixed pattern of warming is prescribed the radiative response is linear with global temperature change or nearly so. It is also demonstrated that the regression and fixed-SST methods for evaluating effective radiative forcing are in principle different, because rapid SST adjustment when CO₂ is changed can produce a pattern of surface temperature change with zero global mean but nonzero change in net radiation at the top of the atmosphere (∼−0.5W m−2in HadCM3).
We quantify forcing and feedbacks across available CMIP5 coupled atmosphere‐ocean general circulation models (AOGCMs) by analysing simulations forced by an abrupt quadrupling of atmospheric carbon ...dioxide concentration. This is the first application of the linear forcing‐feedback regression analysis of Gregory et al. (2004) to an ensemble of AOGCMs. The range of equilibrium climate sensitivity is 2.1–4.7 K. Differences in cloud feedbacks continue to be important contributors to this range. Some models show small deviations from a linear dependence of top‐of‐atmosphere radiative fluxes on global surface temperature change. We show that this phenomenon largely arises from shortwave cloud radiative effects over the ocean and is consistent with independent estimates of forcing using fixed sea‐surface temperature methods. We suggest that future research should focus more on understanding transient climate change, including any time‐scale dependence of the forcing and/or feedback, rather than on the equilibrium response to large instantaneous forcing.
Key Points
Range of eqm climate sensitivity (2.1‐4.7K) is similar to that found in CMIP3
Differences in cloud feedbacks continue to be a large source of this uncertainty
Some models show small deviations from linear behaviour
Increases in cloud optical depth and liquid water path (LWP) are robust features of global warming model simulations in high latitudes, yielding a negative shortwave cloud feedback, but the ...mechanisms are still uncertain. Here the importance of microphysical processes for the negative optical depth feedback is assessed by perturbing temperature in the microphysics schemes of two aquaplanet models, both of which have separate prognostic equations for liquid water and ice. It is found that most of the LWP increase with warming is caused by a suppression of ice microphysical processes in mixed-phase clouds, resulting in reduced conversion efficiencies of liquid water to ice and precipitation. Perturbing the temperature-dependent phase partitioning of convective condensate also yields a small LWP increase. Together, the perturbations in large-scale microphysics and convective condensate partitioning explain more than two-thirds of the LWP response relative to a reference case with increased SSTs, and capture all of the vertical structure of the liquid water response. In support of these findings, a very robust positive relationship between monthly mean LWP and temperature in CMIP5 models and observations is shown to exist in mixed-phase cloud regions only. In models, the historical LWP sensitivity to temperature is a good predictor of the forced global warming response poleward of about 45°, although models appear to overestimate the LWP response to warming compared to observations. The results indicate that in climate models, the suppression of ice-phase microphysical processes that deplete cloud liquid water is a key driver of the LWP increase with warming and of the associated negative shortwave cloud feedback.
Using five climate model simulations of the response to an abrupt quadrupling of CO₂, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative ...adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO₂ quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. The authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m−2cloud masking of CO₂ forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m−2K−1, whereas accounting for rapid adjustments reduces by 0.14 W m−2K−1the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.
Eight atmospheric general circulation models (AGCMs) are forced with observed historical (1871–2010) monthly sea surface temperature and sea ice variations using the Atmospheric Model Intercomparison ...Project II data set. The AGCMs therefore have a similar temperature pattern and trend to that of observed historical climate change. The AGCMs simulate a spread in climate feedback similar to that seen in coupled simulations of the response to CO2 quadrupling. However, the feedbacks are robustly more stabilizing and the effective climate sensitivity (EffCS) smaller. This is due to a pattern effect, whereby the pattern of observed historical sea surface temperature change gives rise to more negative cloud and longwave clear‐sky feedbacks. Assuming the patterns of long‐term temperature change simulated by models, and the radiative response to them, are credible; this implies that existing constraints on EffCS from historical energy budget variations give values that are too low and overly constrained, particularly at the upper end. For example, the pattern effect increases the long‐term Otto et al. (2013, https://doi.org/10.1038/ngeo1836) EffCS median and 5–95% confidence interval from 1.9 K (0.9–5.0 K) to 3.2 K (1.5–8.1 K).
Plain Language Summary
Recent decades have seen cooling over the eastern tropical Pacific and Southern Oceans while temperatures rise globally. Climate models indicate that these regional features, and others, are not expected to continue into the future under sustained forcing from atmospheric carbon dioxide increases. This matters because climate sensitivity depends on the pattern of warming, so if the past has warmed differently from what we expect in the future, then climate sensitivity estimated from the historical record may not apply to the future. We investigate this with a suite of climate models and show that climate sensitivity simulated for observed historical climate change is smaller than for long‐term carbon dioxide increases. The results imply that historical energy budget changes only weakly constrain climate sensitivity.
Key Points
Climate sensitivity simulated for observed surface temperature change is smaller than for long‐term carbon dioxide increases
Observed historical energy budget constraints give climate sensitivity values that are too low and overly constrained, particularly at the upper end
Historical energy budget changes only weakly constrain climate sensitivity
Low cloud feedback in global warming projections by climate models is characterized by its positive sign, the mechanism of which is not well understood. Here we propose that the positive sign is ...primarily caused by the increase in upward longwave radiation from the sea surface. We devise numerical experiments that enable separation of the feedback into components coming from physically distinct causes. Results of these experiments with a climate model indicate that increases in upward longwave radiation from the sea surface cause warming and absolute drying in the boundary layer, leading to the positive low cloud feedback. The absolute drying results from decrease in surface evaporation, and also from decrease in inversion strength which enhances vertical mixing of drier free tropospheric air into the boundary layer. This mechanism is different from previously proposed understanding that positive low cloud feedback is caused by increases in surface evaporation or vertical moisture contrast.
Plain Language Summary
We project future climate change induced by atmospheric greenhouse gas increases by conducting numerical simulations using specialized computer codes, namely Global Climate Models. Results of such simulations are characterized by decreases in low cloud with warming at the Earth's surface, which amplifies the warming by reflecting less sunlight back to space and allowing more sunlight to be absorbed at the surface. This amplifying effect, called “positive low cloud feedback,” is important because the amount of future warming affects our living and safety. However, the mechanism of the low cloud decreases with warming is not well understood. Here we propose that the low cloud decrease is primarily caused by increase in upward longwave radiation from the sea surface. We devise numerical simulations that enable the separation of the low cloud feedback into components coming from physically distinct causes. Results of the simulations indicate that increases in upward longwave radiation from the sea surface cause warming and drying near the Earth's surface, leading to the low cloud decrease. This mechanism is different from previously proposed understanding that the low cloud decrease is due to increases in sea surface evaporation or vertical moisture contrast.
Key Points
The increase in longwave radiation from the sea surface is a leading order cause of the positive low cloud feedback in a climate model
This increase in longwave radiation leads to warming and drying in the boundary layer, which contributes to the decrease in the low cloud
This mechanism is not associated with increases in surface evaporation or vertical moisture contrast
Analysis of the available Coupled Model Intercomparison Project Phase 5 models suggests that sea surface temperature‐forced, atmosphere‐only global warming experiments (“amip4K,” “amipFuture,” and ...“aqua4K”) are a good guide to the global cloud feedbacks determined from coupled atmosphere‐ocean CO2‐forced simulations, including the intermodel spread. Differences in the total climate feedback parameter between the experiments arise primarily from differences in the clear‐sky feedbacks which can largely be anticipated from the nature of the experimental design. The effective CO2 radiative forcing is anticorrelated with the total feedback in the coupled simulations. This anticorrelation strengthens as the experimental design becomes simpler, the number of potential degrees of freedom of the system's response reduces, and the relevant physical processes can be identified. In the aquaplanet simulations the anticorrelation is primarily driven by opposing changes in the rapid cloud adjustment to CO2 and the net cloud response to increased surface warming. Establishing a physical explanation for this behavior is important future work.
Key Points
Cloud feedbacks in simplified experiments match those in full models
Feedback and forcing anticorrelated across models
Forcing‐feedback relationship driven by changes in clouds
In HadGEM2-A, AMIP experiments forced with observed sea surface temperatures respond to uniform and patterned +4 K SST perturbations with strong positive cloud feedbacks in the subtropical ...stratocumulus/trade cumulus transition regions. Over the subtropical Northeast Pacific at 137°W/26°N, the boundary layer cloud fraction reduces considerably in the AMIP +4 K patterned SST experiment. The near-surface wind speed and the air-sea temperature difference reduces, while the near-surface relative humidity increases. These changes limit the local increase in surface evaporation to just 3 W/m
2
or 0.6 %/K. Previous studies have suggested that increases in surface evaporation may be required to maintain maritime boundary layer cloud in a warmer climate. This suggests that the supply of water vapour from surface evaporation may not be increasing enough to maintain the low level cloud fraction in the warmer climate in HadGEM2-A. Sensitivity tests which force the surface evaporation to increase substantially in the +4 K patterned SST experiment result in smaller changes in boundary layer cloud and a weaker cloud feedback in HadGEM2-A, supporting this idea. Although global mean surface evaporation in climate models increases robustly with global temperature (and the resulting increase in atmospheric radiative cooling), local values may increase much less, having a significant impact on cloud feedback. These results suggest a coupling between cloud feedback and the hydrological cycle via changes in the patterns of surface evaporation. A better understanding of both the factors controlling local changes in surface evaporation and the sensitivity of clouds to such changes may be required to understand the reasons for inter-model differences in subtropical cloud feedback.
PDF
