Exploiting Ground‐Based Observations to Infer Arctic Surface Cloud Feedbacks

Abstract

Arctic amplification is a well-known yet poorly understood phenomenon. The most recent generation of global climate models (GCMs) participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) show enhanced warming in the Arctic relative to the previous generation of GCMs. A less negative cloud feedback is among the primary reasons for the enhanced projected warming in the Arctic — a region where low-level stratiform mixed-phase clouds are ubiquitous. Recent studies have demonstrated the sensitivity of projected climate warming to the amount of supercooled liquid water in mixed-phase clouds via the cloud-phase feedback mechanism. In this mechanism, the fraction of supercooled liquid water within mixed-phase clouds increases with surface warming thus leading to optically thicker clouds. However, as a poorly observed region due to challenges associated with large- scale satellite remote sensing, Arctic cloud feedbacks are poorly constrained and the current diagnostic tools available at our disposal preclude an accurate assessment of these feedbacks.

The intention of the proposed work is to exploit the unique and superior capabilities of the Department of Energy Atmospheric Radiation Measurement (ARM) ground-based instruments at the North Slope of Alaska site in Barrow, Alaska to observe low-level clouds from the surface perspective to test the hypothesis that an interannual longwave cloud-phase feedback from the surface perspective with a significant contribution from changes in cloud particle size can be observed. This will be achieved through three main tasks. The first task will be to determine the role of thermodynamic phase shifts in the interannual variability of cloud optical depth variations with temperature to establish time invariance in order to link it with the long-term cloud-phase feedback. The second and third tasks will collectively develop a unique and novel framework involving the derivation of 3-dimensional (3-D) joint histograms of cloud fraction and 3-D mixed-phase cloud radiative kernels to decompose the contribution of cloud microphysical processes to the Arctic cloud feedback from the surface perspective. Together, these three tasks will elucidate the contribution of changing cloud particle size and water path to the Arctic low-level cloud feedback to ultimately address the shortcomings in the representations of cloud microphysical processes in GCMs and thus place tighter observational constraints on the Arctic cloud-phase feedback.