Crustal Structure Constraints From the Detection of the SsPp Phase on Mars
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Engineering topics
Publications and source records attributed to Doyeon Kim.
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The shortwave cloud radiative effect (SWCRE) is important on the Arctic surface radiation budget and the major source of inter-model spread in predictions of Arctic climate. To better understand the individual contributions of various radiative processes to changes in SWCRE, the paper presents the use of the extended APRP (Atmospheric Radiative Perturbation Potential) method. This involves adding the absorptivity for the upward beam and considering differences in reflectivity between upward and downward beams, as well as analyzing the cloud masking effect resulting from changes in surface albedo in more detail. Using data from the CMIP5 and CMIP6 climate models, the study decomposes the SWCRE over the Arctic surface and analyzes inter-model differences in quadrupled CO2 simulations. The study takes into account the fact that the response of SWCRE to Arctic warming is influenced by changes in surface albedo, cloud amount, and cloud microphysics. Results show that in the sunlight season, the reduction in surface albedo associated with sea ice loss is directly linked to strong negative SWCRE, which explains the considerable model discrepancy. Arctic clouds can hinder the positive surface albedo feedback by changing the albedo in two ways: (1) decreasing incoming shortwave radiation due to cloud reflection and (2) by decreasing the shortwave reaching the surface after being reflected by clouds. In addition, increased (decreased) cloud amount and cloud liquid water are shown to be less (more) incoming shortwave fluxes at the surface, but not dominating factors to the Arctic surface radiation budget and its inter-model variation. Overall, the extended APRP method offers a useful tool for analyzing the complex interactions between clouds and radiative process, reasonably decomposes the individual SWCRE responses at the Arctic surface, and emphasizes that considering not only the cloud amount or its properties, but also surface albedo change is critical for the prediction of SWCRE on the Arctic surface.
The Arctic has experienced rapid sea ice loss and a substantial decline in surface albedo, significantly impacting its radiation budget. Climate models from the Coupled Model Intercomparison Project (CMIP6) reproduce these changes. However, inconsistencies remain among models regarding the magnitude, spatial distribution, and seasonal patterns of Arctic surface albedo evolution. This study investigates these discrepancies by comparing model outputs with observation from the Clouds and the Earth's Radiant Energy System (CERES). We develop a decomposition method to assess the contributions of sea ice albedo, sea ice concentration, and sea ice extent to Arctic surface albedo. Over land, differences in snow cover account for the substantial inter-model spread in surface albedo, while over the ocean, sea ice albedo, concentration, and extent all contribute. Comparisons between CMIP6 and the Atmospheric Model Intercomparison Project (AMIP) simulations, which use identically prescribed sea ice concentrations, reveals considerable inter-model spread in Arctic Ocean surface albedo due to differences in sea ice albedo. Applying the decomposition method to projections shows that models predicting larger decreases in sea ice concentration and extent, especially in the Central Arctic, exhibit lower surface albedo and stronger sea ice albedo feedback. Beyond 2050, Arctic Ocean surface albedo decline is mainly influenced by sea ice extent indicating that the retreat of the ice edge is the most important process to constrain the surface albedo feedback. This study provides insights into factors contributing to the spread and changes in Arctic surface albedo and the associated sea ice albedo feedback.