Using Active Remote Sensing to Evaluate Cloud-Climate Feedbacks: a Review and a Look to the Future

Abstract

Uncertainty in the equilibrium climate sensitivity (ECS) of the Earth continues to be large. Aspects of the cloud feedback problem have been identified as fundamental to the uncertainty in ECS. Recent analyses have shown that changes to cloud forcing with climate change can be decomposed into contributions from changes in cloud occurrence that are proportional to globally averaged temperature change and changes associated with rapid adjustments in the system that are independent of changes to globally averaged surface temperature. Together these responses enhance warming due to (1) cloud feedback from increasing cloud altitude by upper tropospheric clouds and (2) decreases in cloud coverage by marine boundary layer clouds. We argue that active remote sensing from space can play a unique and crucial role in constraining our understanding of these separate phenomena. For 1, the feedback associated with changing tropical cirrus is predicted to emerge from the statistical noise of the climate system within the next one to two decades. However, active remote sensing will need to continue for that signal to be observed since accurate placement of these clouds in the vertical dimension is necessary. For 2, the processes associated with changes to marine boundary layer clouds have been linked to the coupling between cloud and precipitation microphysics and air motions over remote ocean basins where precipitation formation in shallow convection is modulated by changes to aerosols and thermodynamics. Exploiting the synergy in combined active and passive remote sensing is likely one of the only ways of constraining our evolving theoretical understanding of low-level cloud processes as represented in cloudresolving models and for validating global-scale models.

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

“…For those radar bins that cannot be identified as clouds or clutter from the probability estimate, we use their neighboring range gates to provide information to help make the final decision. A spatial filter with five range bins respecting to height (150 m) and five range bins concerning time (21.4 s), which is centered at each undecided classification bin, is employed here (Hu et al, 2020;Marchand et al, 2008). Following Ge et al (2017), if the number of cloud range bins in the box is less than 13, this range bin is considered to be clutter; otherwise, it will be marked as a cloud bin.…”

Using A-Train Observations to Evaluate Cloud Occurrence and Radiative Effects in the Community Atmosphere Model during the Southeast Asia Summer Monsoon

“…Moreover, the active sensors in the A-Train, CloudSat and CALIPSO (CC), have provided vertically resolved measurements of hydrometeors and aerosols (Stephens et al 2018). The combination of passive and active sensors in the A-Train provides independent information about clouds that can be used for deriving cloud properties (Mace 2010;Delanoë and Hogan 2010;Henderson et al 2013).…”

“…While the CPR provides information primarily on optically thicker hydrometeor layers, the lidar senses optically thin clouds that are often below the sensitivity of the radar. Taken together, observations from CC provide detailed and unprecedented cloud statistics (Mace et al 2009;Mace and Wrenn 2013). The lidar is particularly important for describing the full range of tropical ice clouds, given that the lower third of the cloud ice water path PDF is observed by the lidar only, and that both the lidar and radar are necessary to characterize the cirrus that are most important radiatively (Berry and Mace 2014).…”

“…Therefore, an accurate representation of clouds in general circulation models (GCMs) is a high priority (Stevens and Bony 2013). Satellite observations will continue to play a crucial role in this effort by advancing our understanding of how clouds interact with their environment (Mace and Berry 2017).…”

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

“…In the context of global warming, tropical low-level cloud amount decreases because of stronger surface turbulent fluxes and dryer planetary boundary layer, generating a positive climate feedback through a reduction in the reflection of short-wave radiation (Brient and Bony, 2012;Zhang et al, 2018); While the liquid water path of low-level clouds over midto high-latitude tends to increase due to a reduced conversion efficiencies of liquid water to ice and precipitation, which leads to a negative feedback (Ceppi et al, 2016;Terai et al, 2016). However, the magnitude of these low-level cloud feedbacks responds inconsistently in different climate models, producing a wide range of equilibrium climate sensitivity (Watanabe et al, 2018;Zelinka et al, 2020;Mace and Berry, 2017). To reduce this uncertainty, accurate long-term observations are important to characterize low-level clouds and understand their climate feedbacks (Turner et al, 2007;Garrett and Zhao, 2013;Toll et al, 2019).…”