Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model. Issue 2 (19th January 2018)

• Record Type: Journal Article
• Authors: Kuo, Chaincy; Feldman, Daniel R.; Huang, Xianglei; Flanner, Mark; Yang, Ping; Chen, Xiuhong
• Is Part Of: Journal of geophysical research. Volume 123:Issue 2(2018)
• Page Start: 789
• Page End: 813
• Publication Date: 2018-01-19
• Subjects: climate feedback -- longwave -- emissivity -- temporal -- radiative kernel; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027595
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Frozen and unfrozen surfaces exhibit different longwave surface emissivities with different spectral characteristics, and outgoing longwave radiation and cooling rates are reduced for unfrozen scenes relative to frozen ones. Here physically realistic modeling of spectrally resolved surface emissivity throughout the coupled model components of the Community Earth System Model (CESM) is advanced, and implications for model high‐latitude biases and feedbacks are evaluated. It is shown that despite a surface emissivity feedback amplitude that is, at most, a few percent of the surface albedo feedback amplitude, the inclusion of realistic, harmonized longwave, spectrally resolved emissivity information in CESM1.2.2 reduces wintertime Arctic surface temperature biases from −7.2 ± 0.9 K to −1.1 ± 1.2 K, relative to observations. The bias reduction is most pronounced in the Arctic Ocean, a region for which Coupled Model Intercomparison Project version 5 (CMIP5) models exhibit the largest mean wintertime cold bias, suggesting that persistent polar temperature biases can be lessened by including this physically based process across model components. The ice emissivity feedback of CESM1.2.2 is evaluated under a warming scenario with a kernel‐based approach, and it is found that emissivity radiative kernels exhibit water vapor and cloud cover dependence, thereby varying spatially and decreasing in magnitude over the course of the scenario from secular changes in atmospheric thermodynamics and cloud patterns. Accounting for the temporally varying radiative responses can yield diagnosed feedbacks that differ in sign from those obtained from conventional climatological feedback analysis methods. Plain Language Summary: Climate models have exhibited a persistent cold‐pole bias, whereby they systematically underestimate the average temperature and the amplification of climate change at high latitudes. A number of different explanations have been advanced for cold‐pole biases, which can be broadly divided into radiative and dynamic explanations. Here we explore in detail a relatively novel radiative explanation for the cold‐pole bias: the ice emissivity feedback. Similar to the difference in shortwave reflectivity of unfrozen and frozen surfaces, recent literature has shown that unfrozen surfaces are less emissive than frozen surfaces, which can induce a positive radiative feedback. We first present the highly nontrivial implementation of this feedback in a global circulation model (GCM) and show how to harmonize the disjointed representation of surface emissivity within the radiative transfer calculated by atmospheric and land components of a GCM. With this modified model, we show how this ice emissivity feedback depends on atmospheric water vapor and thus varies on time scales ranging from seasonal to centennial. We also show that the ice emissivity feedback is seasonally complementary to the well‐known ice‐albedo feedback, where the former is most influential during polar night. Finally, we show that including this feedback essentially eliminates the cold‐pole bias on the model we used. Key Points: LW spectral surface emissivity improves CESM Arctic surface temperature bias by 6.1 ± 1.9 degrees Kelvin Spectral emissivity kernels computed for 200+ period are nonlinear in time Temporally and spatially localized atmospheric dynamics show decreased climatological seasonal sea ice emissivity radiative response in Arctic