Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5

Pendergrass, Angeline G.; Conley, Andrew; Vitt, Francis

doi:10.5194/essd-10-317-2018

Cited by 117 publications

(153 citation statements)

References 16 publications

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“…The magnitudes of LR and albedo feedbacks are comparable, but the LR feedback is the largest contributor to AA among these feedbacks, followed by the albedo feedback and Planck feedback deviation according to their distance to the 1:1 line. This ranking is the same as the results derived from the CESM large‐ensemble simulations (purple symbols in Figure a) in Pendergrass et al () and the CMIP5 multimodel ensemble mean in Pithan and Mauritsen (). The sign of LW cloud feedback over the Arctic is positive and consistent among three cases (Figure S3g), but the sign for SW cloud feedback is uncertain.…”

Section: Resultssupporting

confidence: 78%

“…After that, the TOA flux anomalies are regressed onto the SAT anomalies to obtain the linear regression coefficient. According to the definition Δ R = Δ Q + λ Δ T s , we need to further deduct the Δ Q /Δ T s based on this regression coefficient to obtain the total feedbacks, where the Δ Q /Δ T s is calculated from relevant data in Pendergrass et al () and shown in the Table S1.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, we need to further add the radiative forcing difference under clear‐sky (Δ Q 0 ) and all‐sky (Δ Q ) conditions scaled by the surface temperature change (i.e., Δ( Q 0 − Q )/Δ T s ) to obtain the cloud feedback (adjustment). Here the Δ( Q 0 − Q )/Δ T s due to greenhouse gases and aerosols is calculated from relevant data in Pendergrass et al () and shown in Table S2.…”

Section: Methodsmentioning

confidence: 99%

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Local Radiative Feedbacks Over the Arctic Based on Observed Short‐Term Climate Variations

Zhang

Wang

et al. 2018

Geophysical Research Letters

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We compare various radiative feedbacks over the Arctic (60–90°N) estimated from short‐term climate variations occurring in reanalysis, satellite, and global climate model data sets using the combined Kernel‐Gregory approach. The lapse rate and surface albedo feedbacks are positive, and their magnitudes are comparable. Relative to the tropics (30°S–30°N), the lapse rate feedback is the largest contributor to Arctic amplification among all feedbacks, followed by surface albedo feedback and Planck feedback deviation from its global mean. Both shortwave and longwave water vapor feedbacks are positive, leading to a significant positive net water vapor feedback over the Arctic. The net cloud feedback has large uncertainties including its sign, which strongly depends on the data used for all‐sky and clear‐sky radiative fluxes at the top of the atmosphere, the time periods considered, and the methods used to estimate the cloud feedback.

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Section: Resultssupporting

confidence: 78%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Local Radiative Feedbacks Over the Arctic Based on Observed Short‐Term Climate Variations

Zhang

Wang

et al. 2018

Geophysical Research Letters

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“…They did, however, find significant differences between each year of the observational‐based water vapor kernel and the water vapor kernel developed using the National Center for Atmospheric Research (NCAR) CAM3 model (Shell et al, ), suggesting that interannual variability does not explain a significant portion of the differences between the observational and model‐derived radiative kernels. Similarly, Pendergrass et al () demonstrated that radiative kernels derived from a single year and single member of the CESM Large Ensemble (Kay et al, ) diagnose total flux changes with similar accuracy across all ensemble members, indicating that radiative kernel interannual variability is a relatively small source radiative kernel error.…”

Section: Methodsmentioning

confidence: 91%

Observation‐Based Radiative Kernels From CloudSat/CALIPSO

et al. 2019

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Radiative kernels describe the differential response of radiative fluxes to small perturbations in state variables and are widely used to quantify radiative feedbacks on the climate system. Radiative kernels have traditionally been generated using simulated data from a global climate model, typically sourced from the model's base climate. Consequently, these radiative kernels are subject to model bias from the climatological fields used to produce them. Here, we introduce the first observation‐based temperature, water vapor, and surface albedo radiative kernels, developed from CloudSat's fluxes and heating rates data set, 2B‐FLXHR‐LIDAR, which is supplemented with cloud information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). We compare the radiative kernels to a previously published set generated from the Geophysical Fluid Dynamics Laboratory (GFDL) model and find general agreement in magnitude and structure. However, several key differences illustrate the sensitivity of radiative kernels to the distribution of clouds. The radiative kernels are used to quantify top‐of‐atmosphere and surface cloud feedbacks in an ensemble of global climate models from the Climate Model Intercomparison Project Phase 5, showing that biases in the GFDL low clouds likely cause the GFDL kernel to underestimate longwave surface cloud feedback. Since the CloudSat kernels are free of model bias in the base state, they will be ideal for future analysis of radiative feedbacks and forcing in both models and observations and for evaluating biases in model‐derived radiative kernels.

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“…To quantify the radiative forcing of anthropogenic aerosols, we apply satellite estimates of DRE to derive aerosol radiative kernels (Kramer et al, ; Soden et al, ). Radiative kernels are useful tools for evaluating radiative feedbacks in response to changes in aerosols (Gettelman et al, ; Pendergrass et al, ). Until now, however, observation‐based aerosol radiative kernels have not been published.…”

Section: Methodsmentioning

confidence: 99%

New Estimates of Aerosol Direct Radiative Effects and Forcing From A‐Train Satellite Observations

Matus

L’Ecuyer

Henderson

2019

Geophysical Research Letters

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Aerosol direct radiative effects are assessed using multi‐sensor observations from the A‐Train satellite constellation. By leveraging vertical cloud and aerosol information from CloudSat and CALIPSO, this study reports new global estimates of aerosol radiative effects and the component owing to anthropogenic aerosols. We estimate that the global mean aerosol direct radiative effect is −2.40 W/m2 with an error of ± 0.6 W/m2 owing to uncertainties in aerosol type classification and optical depth retrievals. Anthropogenic direct radiative forcing is assessed using new observation‐based aerosol radiative kernels. Anthropogenic aerosols are found to account for 21% of the global radiative effect, or −0.50 ± 0.3 W/m2, mainly from sulfate pollution (−0.54 W/m2) partially offset by absorption from smoke (0.03 W/m2). Uncertainty estimates effectively rule out the possibility that anthropogenic aerosols warm the planet, although strong positive forcing is observed locally where anthropogenic aerosols reside above clouds and bright surfaces.

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Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5

Cited by 117 publications

References 16 publications

Local Radiative Feedbacks Over the Arctic Based on Observed Short‐Term Climate Variations

Local Radiative Feedbacks Over the Arctic Based on Observed Short‐Term Climate Variations

Observation‐Based Radiative Kernels From CloudSat/CALIPSO

New Estimates of Aerosol Direct Radiative Effects and Forcing From A‐Train Satellite Observations

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