Search Results

You are looking at 1 - 10 of 10 items for :

  • Author or Editor: Karen M. Shell x
  • Journal of Climate x
  • Refine by Access: All Content x
Clear All Modify Search
Karen M. Shell

Abstract

Climate sensitivity is generally studied using two types of models. Atmosphere–ocean general circulation models (AOGCMs) include interactive ocean dynamics and detailed heat uptake. Atmospheric GCMs (AGCMs) with slab ocean models (SOMs) cannot fully simulate the ocean’s response to and influence on climate. However, AGCMs are computationally cheaper and thus are often used to quantify and understand climate feedbacks and sensitivity. Here, physical climate feedbacks are compared between AOGCMs and SOM-AGCMs from the Coupled Model Intercomparison Project phase 3 (CMIP3) using the radiative kernel technique. Both the global-average (positive) water vapor and (negative) lapse-rate feedbacks are consistently stronger in AOGCMs. Water vapor feedback differences result from an essentially constant relative humidity and peak in the tropics, where temperature changes are larger for AOGCMs. Differences in lapse-rate feedbacks extend to midlatitudes and correspond to a larger ratio of tropical- to global-average temperature changes. Global-average surface albedo feedbacks are similar between models types because of a near cancellation of Arctic and Antarctic differences. In AOGCMs, the northern high latitudes warm faster than the southern latitudes, resulting in interhemispheric differences in albedo, water vapor, and lapse-rate feedbacks lacking in the SOM-AGCMs. Meridional heat transport changes also depend on the model type, although there is a large intermodel spread. However, there are no consistent global or zonal differences in cloud feedbacks. Effects of the forcing scenario [Special Report on Emissions Scenarios A1B (SRESa1b) or the 1% CO2 increase per year to doubling (1%to2x) experiments] on feedbacks are model dependent and generally of lesser importance than the model type. Care should be taken when using SOM-AGCMs to understand AOGCM feedback behavior.

Full access
Isaac M. Held and Karen M. Shell

Abstract

An approach to climate change feedback analysis is described in which tropospheric relative humidity replaces specific humidity as the state variable that, along with the temperature structure, surface albedos, and clouds, controls the magnitude of the response of global mean surface temperature to a radiative forcing. Despite being simply a regrouping of terms in the feedback analysis, this alternative perspective has the benefit of removing most of the pervasive cancellation between water and lapse-rate feedbacks seen in models. As a consequence, the individual feedbacks have less scatter than in the traditional formulation. The role of cloud feedbacks in controlling climate sensitivity is also reflected more clearly in the new formulation.

Full access
Benjamin M. Sanderson and Karen M. Shell

Abstract

Radiative kernels have become a common tool for evaluating and comparing radiative feedbacks to climate change in different general circulation models. However, kernel feedback calculations are inaccurate for simulations where the atmosphere is significantly perturbed from its base state, such as for very large forcing or perturbed physics simulations. In addition, past analyses have not produced kernels relating to prognostic cloud variables because of strong nonlinearities in their relationship to radiative forcing. A new methodology is presented that allows for fast statistical optimizing of existing kernels such that accuracy is increased for significantly altered climatologies. International Satellite Cloud Climatology Project (ISCCP) simulator output is used to relate changes in cloud-type histograms to radiative fluxes. With minimal additional computation, an individual set of kernels is created for each climate experiment such that climate feedbacks can be reliably estimated even in significantly perturbed climates.

This methodology is applied to successive generations of the Community Atmosphere Model (CAM). Increased climate sensitivity in CAM5 is shown to be due to reduced negative stratus and stratocumulus feedbacks in the tropics and midlatitudes, strong positive stratus feedbacks in the southern oceans, and a strengthened positive longwave cirrus feedback. Results also suggest that CAM5 exhibits a stronger surface albedo feedback than its predecessors, a feature not apparent when using a single kernel. Optimized kernels for CAM5 suggest weaker global-mean shortwave cloud feedback than one would infer from using the original kernels and an adjusted cloud radiative forcing methodology.

Full access
Meghan M. Dalton and Karen M. Shell

Abstract

The climate sensitivity uncertainty of global climate models (GCMs) is partly due to the spread of individual feedbacks. One approach to constrain long-term climate sensitivity is to use the relatively short observational record, assuming there exists some relationship in feedbacks between short and long records. The present work tests this assumption by regressing short-term feedback metrics, characterized by the 20-yr feedback as well as interannual and intra-annual metrics, against long-term longwave water vapor, longwave atmospheric temperature, and shortwave surface albedo feedbacks calculated from 13 twentieth-century GCM simulations. Estimates of long-term feedbacks derived from reanalysis observations and statistically significant regressions are consistent with but no more constrained than earlier estimates.

For the interannual metric, natural variability contributes to the feedback uncertainty, reducing the ability to estimate the interannual behavior from one 20-yr time slice. For both the interannual and intra-annual metrics, uncertainty in the intermodel relationships between 20-yr metrics and 100-yr feedbacks also contributes to the feedback uncertainty. Because of differences in time scales of feedback processes, relationships between the 20-yr interannual metric and 100-yr water vapor and atmospheric temperature feedbacks are significant for only one feedback calculation method. The intra-annual and surface albedo relationships show more complex behavior, though positive correspondence between Northern Hemisphere surface albedo intra-annual metrics and 100-yr feedbacks is consistent with previous studies. Many relationships between 20-yr metrics and 100-yr feedbacks are sensitive to the specific GCMs included, highlighting that care should be taken when inferring long-term feedbacks from short-term observations.

Full access
Karen M. Shell and Richard C. J. Somerville

Abstract

Energy balance models have proven useful in understanding mechanisms and feedbacks in the climate system. An original global energy balance model is presented here. The model is solved numerically for equilibrium climate states defined by zonal average temperature as a function of latitude for both a surface and an atmospheric layer. The effects of radiative, latent, and sensible heating are parameterized. The model includes a variable lapse rate and parameterizations of the major dynamical mechanisms responsible for meridional heat transport: the Hadley cell, midlatitude baroclinic eddies, and ocean circulation. The model reproduces both the mean variation of temperature with latitude and the global average heat budget within the uncertainty of observations.

The utility of the model is demonstrated through examination of various climate feedbacks. One important feedback is the effect of the lapse rate on climate. When the planet warms as a result of an increase in the solar constant, the lapse rate acts as a negative feedback, effectively enhancing the longwave emission efficiency of the atmosphere. The lapse rate is also responsible for an increase in global average temperature when the meridional heat transport effectiveness is increased. The water vapor feedback enhances temperature changes, while the latent and sensible heating feedback reduces surface temperature changes.

Full access
Karen M. Shell, Jeffrey T. Kiehl, and Christine A. Shields

Abstract

Climate models differ in their responses to imposed forcings, such as increased greenhouse gas concentrations, due to different climate feedback strengths. Feedbacks in NCAR’s Community Atmospheric Model (CAM) are separated into two components: the change in climate components in response to an imposed forcing and the “radiative kernel,” the effect that climate changes have on the top-of-the-atmosphere (TOA) radiative budget. This technique’s usefulness depends on the linearity of the feedback processes. For the case of CO2 doubling, the sum of the effects of water vapor, temperature, and surface albedo changes on the TOA clear-sky flux is similar to the clear-sky flux changes directly calculated by CAM. When monthly averages are used rather than values from every time step, the global-average TOA shortwave change is underestimated by a quarter, partially as a result of intramonth correlations of surface albedo with the radiative kernel. The TOA longwave flux changes do not depend on the averaging period. The longwave zonal averages are within 10% of the model-calculated values, while the global average differs by only 2%. Cloud radiative forcing (ΔCRF) is often used as a diagnostic of cloud feedback strength. The net effect of the water vapor, temperature, and surface albedo changes on ΔCRF is −1.6 W m−2, based on the kernel technique, while the total ΔCRF from CAM is −1.3 W m−2, indicating these components contribute significantly to ΔCRF and make it more negative. Assuming linearity of the ΔCRF contributions, these results indicate that the net cloud feedback in CAM is positive.

Full access
Alexandra K. Jonko, Karen M. Shell, Benjamin M. Sanderson, and Gokhan Danabasoglu

Abstract

Climate feedbacks vary strongly among climate models and continue to represent a major source of uncertainty in estimates of the response of climate to anthropogenic forcings. One method to evaluate feedbacks in global climate models is the radiative kernel technique, which is well suited for model intercomparison studies because of its computational efficiency. However, the usefulness of this technique is predicated on the assumption of linearity between top-of-atmosphere (TOA) radiative fluxes and feedback variables, limiting its application to simulations of small climate perturbations, where nonlinearities can be neglected. This paper presents an extension of the utility of this linear technique to large forcings, using global climate model simulations forced with CO2 concentrations ranging from 2 to 8 times present-day values. Radiative kernels depend on the model’s radiative transfer algorithm and climate base state. For large warming, kernels based on the present-day climate significantly underestimate longwave TOA flux changes and somewhat overestimate shortwave TOA flux changes. These biases translate to inaccurate feedback estimates. It is shown that a combination of present-day kernels and kernels computed using a large forcing climate base state leads to significant improvement in the approximation of TOA flux changes and increased reliability of feedback estimates. While using present-day kernels results in a climate sensitivity that remains constant, using the new kernels shows that sensitivity increases significantly with each successive doubling of CO2 concentrations.

Full access
Alexandra K. Jonko, Karen M. Shell, Benjamin M. Sanderson, and Gokhan Danabasoglu

Abstract

Are equilibrium climate sensitivity and the associated radiative feedbacks a constant property of the climate system, or do they change with forcing magnitude and base climate? Using the radiative kernel technique, feedbacks and climate sensitivity are evaluated in a fully coupled general circulation model (GCM) for three successive doublings of carbon dioxide starting from present-day concentrations. Climate sensitivity increases by 23% between the first and third CO2 doublings. Increases in the positive water vapor and cloud feedbacks are partially balanced by a decrease in the positive surface albedo feedback and an increase in the negative lapse rate feedback. Feedbacks can be decomposed into a radiative flux change and a climate variable response to temperature change. The changes in water vapor and Planck feedbacks are due largely to changes in the radiative response with climate state. Higher concentrations of greenhouse gases and higher temperatures lead to more absorption and emission of longwave radiation. Changes in cloud feedbacks are dominated by the climate response to temperature change, while the lapse rate and albedo feedbacks combine elements of both. Simulations with a slab ocean model (SOM) version of the GCM are used to verify whether an SOM-GCM accurately reproduces the behavior of the fully coupled model. Although feedbacks differ in magnitude between model configurations (with differences as large as those between CO2 doublings for some feedbacks), changes in feedbacks between CO2 doublings are consistent in sign and magnitude in the SOM-GCM and the fully coupled model.

Full access
Karen M. Shell, Simon P. de Szoeke, Michael Makiyama, and Zhe Feng

Abstract

The vertical structure of radiative heating rates over the region of the tropical Indian Ocean associated with the MJO during the DYNAMO/ARM MJO Investigation Experiment is presented. The mean and variability of heating rates during active, suppressed, and disturbed phases are determined from the Pacific Northwest National Laboratory Combined Remote Sensing Retrieval (CombRet) from Gan Island, Maldives (0.69°S, 73.15°E). TOA and surface fluxes from the CombRet product are compared with collocated 3-hourly CERES SYN1deg Ed4A satellite retrievals. The fluxes are correlated in time with correlation coefficients around 0.9, yet CombRet time-mean OLR is 15 W m−2 larger. Previous work has suggested that CombRet undersamples high clouds, due to signal attenuation by low-level clouds and reduced instrument sensitivity with altitude. However, mean OLR differs between CombRet and CERES for all values of OLR, not just the lowest values corresponding to widespread high clouds. The discrepancy peaks for midrange OLR, suggestive of precipitating, towering cumulus convective clouds, rather than stratiform cirrus clouds. Low biases in the cloud-top height of thick clouds substantially contribute to the overestimate of OLR by CombRet. CombRet data are used to generate composite shortwave and longwave atmospheric heating rate profiles as a function of the local OLR. Although there is considerable variability in CombRet not directly related to OLR, the time–height structure of mean heating rate composites generated using OLR as the interpolant is broadly representative of tropical convective variability on intraseasonal time scales.

Free access
Brian J. Soden, Isaac M. Held, Robert Colman, Karen M. Shell, Jeffrey T. Kiehl, and Christine A. Shields

Abstract

The extent to which the climate will change due to an external forcing depends largely on radiative feedbacks, which act to amplify or damp the surface temperature response. There are a variety of issues that complicate the analysis of radiative feedbacks in global climate models, resulting in some confusion regarding their strengths and distributions. In this paper, the authors present a method for quantifying climate feedbacks based on “radiative kernels” that describe the differential response of the top-of-atmosphere radiative fluxes to incremental changes in the feedback variables. The use of radiative kernels enables one to decompose the feedback into one factor that depends on the radiative transfer algorithm and the unperturbed climate state and a second factor that arises from the climate response of the feedback variables. Such decomposition facilitates an understanding of the spatial characteristics of the feedbacks and the causes of intermodel differences. This technique provides a simple and accurate way to compare feedbacks across different models using a consistent methodology. Cloud feedbacks cannot be evaluated directly from a cloud radiative kernel because of strong nonlinearities, but they can be estimated from the change in cloud forcing and the difference between the full-sky and clear-sky kernels. The authors construct maps to illustrate the regional structure of the feedbacks and compare results obtained using three different model kernels to demonstrate the robustness of the methodology. The results confirm that models typically generate globally averaged cloud feedbacks that are substantially positive or near neutral, unlike the change in cloud forcing itself, which is as often negative as positive.

Full access