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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2018

Insensitivity of the Cloud Response to Surface Warming Under Radical Changes to Boundary Layer Turbulence and Cloud Microphysics: Results From the Ultraparameterized CAM

We study the cloud response to a +4K surface warming in a new multi- scale climate model that uses enough interior resolution to begin explicitly resolving boundary layer turbulence (i.e. Ultra-Parameterization or UP), and the associated low clouds. UP's predictions are compared against those from standard Super-Parameterization (SP). The mean global cloud feedback turns out to be remarkably neutral across all of our simulations, despite some radical changes in both cloud microphysical parameter settings and cloud resolving model grid resolution. The overall cloud response to warming is the positive low-cloud feedbacks over land, negative feedbacks at high latitudes, and weak feedbacks over the low-latitude oceans. The most distinct effects of UP appear to be the result of tuning decisions impacting high latitude cloud feedback. UP's microphysics are tuned to optimize the model present-day, top-of-atmosphere radiation fluxes against CERES observations, by lowering the cloud ice-liquid phase-shift temperature ramp, adjusting the ice/liquid autoconversion rate, and increasing the ice fall speed. This reduces baseline high-latitude low cloud amounts, damping the phase change cloud feedback at high latitudes, leading to a slightly more positive global cloud feedback compared to SP. A sensitivity test that isolates these microphysical impacts from UP's grid resolution conrms that the microphysical settings are mostly responsible for the dierences between SP and UP cloud feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Toward low‐cloud‐permitting cloud superparameterization with explicit boundary layer turbulence

Abstract Systematic biases in the representation of boundary layer (BL) clouds are a leading source of uncertainty in climate projections. A variation on superparameterization (SP) called “ultraparameterization” (UP) is developed, in which the grid spacing of the cloud‐resolving models (CRMs) is fine enough (250 × 20 m) to explicitly capture the BL turbulence, associated clouds, and entrainment in a global climate model capable of multiyear simulations. UP is implemented within the Community Atmosphere Model using 2° resolution (∼14,000 embedded CRMs) with one‐moment microphysics. By using a small domain and mean‐state acceleration, UP is computationally feasible today and promising for exascale computers. Short‐duration global UP hindcasts are compared with SP and satellite observations of top‐of‐atmosphere radiation and cloud vertical structure. The most encouraging improvement is a deeper BL and more realistic vertical structure of subtropical stratocumulus (Sc) clouds, due to stronger vertical eddy motions that promote entrainment. Results from 90 day integrations show climatological errors that are competitive with SP, with a significant improvement in the diurnal cycle of offshore Sc liquid water. Ongoing concerns with the current UP implementation include a dim bias for near‐coastal Sc that also occurs less prominently in SP and a bright bias over tropical continental deep convection zones. Nevertheless, UP makes global eddy‐permitting simulation a feasible and interesting alternative to conventionally parameterized GCMs or SP‐GCMs with turbulence parameterizations for studying BL cloud‐climate and cloud‐aerosol feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Expanding the computational frontier of multi-scale atmospheric simulation to advance understanding of low cloud / climate feedbacks: Final Technical Report

Marine boundary layer clouds are a challenge for climate models. They cover much of the oceans, but they are driven by small-scale turbulent eddies only a few hundred meters across, which in turn respond to the cloud formation. Global climate models have a grid spacing that is far too large to simulate such fluid motions, so they parameterize these cloud formation processes. Different climate modeling groups have designed different parameterizations in which the clouds turn out to be differently sensitive to a warming climate. This drives uncertainties in our best guess at the sensitivity of global warming to greenhouse gas increases. If these cloud-forming eddies could be directly simulated using the well-known equations of fluid motion, they would no longer need to be parameterized, removing a major source of climate modeling uncertainty. In this project, we overcame software engineering challenges to successfully implemented ‘ultraparameterization’ (UP), the first global model that does this, and we tested how well it works. We simulated five-year periods with present-day temperatures and with a warmer climate, and we investigated how the UP-simulated clouds responded to climate – the ‘cloud feedback’ problem. We found little response of clouds at all latitudes to the imposed climate change, which is within the range of predictions of conventional global climate models. UP is a variation on superparameterization, in which small cloud-resolving models (CRMs) are embedded in each column of the global model. In UP, the CRM grid is fine enough (250 m horizontal × 20 m vertical) to explicitly capture boundary-layer turbulent eddies and associated clouds. Because only one small columnar patch is simulated within each climate model grid cell, this is a million-fold more efficient than simulating the entire globe on this same CRM grid., but achieves much of the same effect for the cloud properties. It doesn’t work perfectly. For instance, like conventional climate models, UP simulates too little subtropical stratocumulus cloud, a bias that we are continuing to work to reduce. However, because it directly simulates the turbulent cloud-forming processes, UP is inherently more plausible for simulating how clouds will change in a perturbed climate. In future, we hope to apply UP to another key climate modeling issue: cloud-aerosol interaction and the effect of human-produced aerosols on the climate change we have already experienced and that which is likely to come.

54 ENVIRONMENTAL SCIENCES↗