The Greenhouse Effect, Stratospheric Ozone, Marine Productivity, and Global Hydrology: Feedbacks in the Global Climate System
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Albedo (alchemy)
Ozone Depletion
Cloud feedback
Abstract Climate models predict that East Asia (EA) will be substantially warmer than the present despite large inter-model uncertainty. This study investigated the major sources of the climate projections and the inter-model uncertainty. Particularly, we decomposed the differences in surface temperatures between the historical and RCP8.5 runs from 26 CMIP5 into partial surface temperature changes due to individual radiative and non-radiative processes through the climate feedback-response analysis method. Results show that anthropogenic greenhouse forcing and subsequent water vapor feedback processes are primarily responsible for the surface warming over EA. Relatively more rapid warming over the snow/ice-covered area and southern China is due to feedback processes associated with surface albedo and cloud, respectively. The regional warming is, however, compensated by the surface non-radiative (sensible and latent heat) cooling. The inter-model projection uncertainty is substantially large over high latitudes and the Tibetan Plateau mainly due to surface albedo feedback. Again, this large uncertainty is partly suppressed by surface non-radiative cooling. Water vapor and cloud feedbacks are the secondary important sources of the projection uncertainty. Moreover, the contributions of greenhouse forcing and atmospheric dynamics to the projection uncertainty are found to be minor.
Albedo (alchemy)
Cloud forcing
Cloud feedback
Forcing (mathematics)
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In an ensemble of general circulation models, the global mean albedo significantly decreases in response to strong CO2 forcing. In some of the models, the magnitude of this positive feedback is as large as the CO2 forcing itself. The models agree well on the surface contribution to the trend, due to retreating snow and ice cover, but display large differences when it comes to the contribution from shortwave radiative effects of clouds. The "cloud contribution" defined as the difference between clear-sky and all-sky albedo anomalies and denoted as ΔCC is correlated with equilibrium climate sensitivity in the models (correlation coefficient 0.76), indicating that in high sensitivity models the clouds to a greater extent act to enhance the negative clear-sky albedo trend, whereas in low sensitivity models the clouds rather counteract this trend. As a consequence, the total albedo trend is more negative in more sensitive models (correlation coefficient 0.73). This illustrates in a new way the importance of cloud response to global warming in determining climate sensitivity in models. The cloud contribution to the albedo trend can primarily be ascribed to changes in total cloud fraction, but changes in cloud albedo may also be of importance.
Albedo (alchemy)
Shortwave
Cloud albedo
Cloud forcing
Cloud feedback
Cloud fraction
Forcing (mathematics)
Ice-albedo feedback
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Cloud feedback
Albedo (alchemy)
Forcing (mathematics)
Cloud forcing
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Cloud feedback
Climate state
Runaway climate change
Earth system science
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Water cycle
Cloud feedback
Climate state
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The major evolution of the National Center for Atmospheric Research Community Atmosphere Model (CAM) is used to diagnose climate feedbacks, understand how climate feedbacks change with different physical parameterizations, and identify the processes and regions that determine climate sensitivity. In the evolution of CAM from version 4 to version 5, the water vapor, temperature, surface albedo, and lapse rate feedbacks are remarkably stable across changes to the physical parameterization suite. However, the climate sensitivity increases from 3.2 K in CAM4 to 4.0 K in CAM5. The difference is mostly due to (i) more positive cloud feedbacks and (ii) higher CO 2 radiative forcing in CAM5. The intermodel differences in cloud feedbacks are largest in the tropical trade cumulus regime and in the midlatitude storm tracks. The subtropical stratocumulus regions do not contribute strongly to climate feedbacks owing to their small area coverage. A “modified Cess” configuration for atmosphere-only model experiments is shown to reproduce slab ocean model results. Several parameterizations contribute to changes in tropical cloud feedbacks between CAM4 and CAM5, but the new shallow convection scheme causes the largest midlatitude feedback differences and the largest change in climate sensitivity. Simulations with greater cloud forcing in the mean state have lower climate sensitivity. This work provides a methodology for further analysis of climate sensitivity across models and a framework for targeted comparisons with observations that can help constrain climate sensitivity to radiative forcing.
Cloud forcing
Cloud feedback
Albedo (alchemy)
Shortwave
Forcing (mathematics)
Atmospheric models
Lapse rate
Parametrization (atmospheric modeling)
Middle latitudes
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Marine boundary layer clouds are a challenge for climate models. They cover much of the oceans, but they are driven by small-scale turbulent eddies only a few hundred meters across, which in turn respond to the cloud formation. Global climate models have a grid spacing that is far too large to simulate such fluid motions, so they parameterize these cloud formation processes. Different climate modeling groups have designed different parameterizations in which the clouds turn out to be differently sensitive to a warming climate. This drives uncertainties in our best guess at the sensitivity of global warming to greenhouse gas increases. If these cloud-forming eddies could be directly simulated using the well-known equations of fluid motion, they would no longer need to be parameterized, removing a major source of climate modeling uncertainty. In this project, we overcame software engineering challenges to successfully implemented ‘ultraparameterization’ (UP), the first global model that does this, and we tested how well it works. We simulated five-year periods with present-day temperatures and with a warmer climate, and we investigated how the UP-simulated clouds responded to climate – the ‘cloud feedback’ problem. We found little response of clouds at all latitudes to the imposed climate change, which is within the range of predictions of conventional global climate models. UP is a variation on superparameterization, in which small cloud-resolving models (CRMs) are embedded in each column of the global model. In UP, the CRM grid is fine enough (250 m horizontal × 20 m vertical) to explicitly capture boundary-layer turbulent eddies and associated clouds. Because only one small columnar patch is simulated within each climate model grid cell, this is a million-fold more efficient than simulating the entire globe on this same CRM grid., but achieves much of the same effect for the cloud properties. It doesn’t work perfectly. For instance, like conventional climate models, UP simulates too little subtropical stratocumulus cloud, a bias that we are continuing to work to reduce. However, because it directly simulates the turbulent cloud-forming processes, UP is inherently more plausible for simulating how clouds will change in a perturbed climate. In future, we hope to apply UP to another key climate modeling issue: cloud-aerosol interaction and the effect of human-produced aerosols on the climate change we have already experienced and that which is likely to come.
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Abstract The sensitivity of global climate with respect to forcing is generally described in terms of the global climate feedback—the global radiative response per degree of global annual mean surface temperature change. While the global climate feedback is often assumed to be constant, its value—diagnosed from global climate models—shows substantial time variation under transient warming. Here a reformulation of the global climate feedback in terms of its contributions from regional climate feedbacks is proposed, providing a clear physical insight into this behavior. Using (i) a state-of-the-art global climate model and (ii) a low-order energy balance model, it is shown that the global climate feedback is fundamentally linked to the geographic pattern of regional climate feedbacks and the geographic pattern of surface warming at any given time. Time variation of the global climate feedback arises naturally when the pattern of surface warming evolves, actuating feedbacks of different strengths in different regions. This result has substantial implications for the ability to constrain future climate changes from observations of past and present climate states. The regional climate feedbacks formulation also reveals fundamental biases in a widely used method for diagnosing climate sensitivity, feedbacks, and radiative forcing—the regression of the global top-of-atmosphere radiation flux on global surface temperature. Further, it suggests a clear mechanism for the “efficacies” of both ocean heat uptake and radiative forcing.
Climate commitment
Transient climate simulation
Forcing (mathematics)
Cloud feedback
Global temperature
Global Change
Abrupt climate change
Solar constant
Climate oscillation
Cloud forcing
Climate state
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