Observational Constraint on A Feedback From Supercooled Clouds Reduces Projected Warming Uncertainty

Gregory V. Cesana; Andrew S. Ackerman; Ann M. Fridlind; Israel Silber; Anthony D. Del Genio; Mark D. Zelinka; Hélène Chepfer; Théodore Khadir; Romain Roehrig

NASA NTRS · 20240004557

Observational Constraint on A Feedback From Supercooled Clouds Reduces Projected Warming Uncertainty

Gregory V. Cesana·Andrew S. Ackerman·Ann M. Fridlind·Israel Silber·Anthony D. Del Genio·Mark D. Zelinka·Hélène Chepfer·Théodore Khadir·Romain Roehrig

View original source ↗Export metadata ↓

Abstract

The increase of carbon-dioxide-doubling-induced warming (climate sensitivity) in the latest climate models is primarily attributed to a larger extratropical cloud feedback. This is thought to be partly driven by a greater ratio of supercooled liquid-phase clouds to all clouds, termed liquid phase ratio. We use an instrument simulator approach to show that this ratio has increased in the latest climate models and is overestimated rather than underestimated as previously thought. In our analysis of multiple models, a greater ratio corresponds to stronger negative cloud feedback, in contradiction with single-model-based studies. We trace this unexpected result to a cloud feedback involving a shift from supercooled to warm clouds as climate warms, which corresponds to greater cloud amount and optical depth and weakens the extratropical cloud feedback. Better constraining this ratio in climate models – and thus this supercooled cloud feedback – impacts their climate sensitivities by up to 1 ˚C and reduces inter-model spread.

Explore related subjects

Climate and Earth system modelling atmospheric science

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Gregory V. Cesana, Andrew S. Ackerman, Ann M. Fridlind, Israel Silber, Anthony D. Del Genio, Mark D. Zelinka, Hélène Chepfer, Théodore Khadir, Romain Roehrig. 2024-04-06. Observational Constraint on A Feedback From Supercooled Clouds Reduces Projected Warming Uncertainty. https://ntrs.nasa.gov/citations/20240004557

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2025

Unified 0.25-degree gridded infrastructure-critical extreme weather for the United States from 1979 to 2100

Extreme weather events can severely disrupt critical infrastructure, triggering cascading effects on power, transportation, and essential services. However, standard weather and climate datasets often lack specialized variables necessary for hazard assessments. We present a unified dataset of infrastructure-critical weather and climate variables across the United States at 0.25° resolution, covering daily or sub-daily intervals from 1979 to 2100. The dataset includes temperature, dew point, wind gusts, precipitation partitioned by rain, snow, and freezing rain or ice pellets, lightning, and wildfire metrics. Historical conditions (1979-2023) are synthesized from observations and reanalysis products, while future projections are derived from 14 CMIP6 global climate models (historical, SSP245, and SSP585 experiments). Physically based and data-driven methods are used to estimate variables not directly provided by existing models. By integrating these variables into a single unified dataset, we enable consistent, high-resolution assessments of weather-related infrastructure risks across past and future periods, supporting wide-ranging applications in energy, transportation, water resources, emergency management, and beyond.

Climate and Earth system modelling↗

NASA NTRS2024

Increasing Contribution of the Atmospheric Vertical Motion to Precipitation in a Warming Climate

Global warming already influences precipitation, with more intense precipitation in many locations. Although the ‘wet-get-wetter, dry-get-drier’ tendency in mean precipitation holds in many locations, the situations for precipitation extremes are more complex, due to changes in dynamic and thermodynamic influences on atmospheric moisture distributions. Here, we build a dynamically interactive atmospheric moisture model for the present (2006–2025) and the future climate (2081–2100), using outputs from coupled ocean-atmosphere general circulation models. We find that the dynamic process of vertical advection of moisture dominates the same-day precipitation, while the smaller impact of the thermodynamic process provides available moisture for several days. As climate warms, we find that the dynamical-induced precipitation more completely exhausts the vertically-integrated moisture and the distribution of the dynamic process’s impact on precipitation exhibits a greater spread in the warmer future. The dynamical process is primarily responsible for more extreme heavy precipitation as climate warms, at all latitudes.

Climate and Earth system modelling↗

NASA NTRS2023

Importance of Internal Variability for Climate Model Assessment

Benchmarking climate model simulations against observations of the climate is core to the process of building realistic climate models and developing accurate future projections. However, in many cases, models do not match historical observations, particularly on regional scales. If there is a mismatch between modeled and observed climate features, should we necessarily conclude that our models are deficient? Using several illustrative examples, we emphasize that internal variability can easily lead to marked differences between the basic features of the model and observed climate, even when decades of model and observed data are available. This can appear as an apparent failure of models to capture regional trends or changes in global teleconnections, or simulation of extreme events. Despite a large body of literature on the impact of internal variability on climate, this acknowledgment has not yet penetrated many model evaluation activities, particularly for regional climate. We emphasize that using a single or small ensemble of simulations to conclude that a climate model is in error can lead to premature conclusions on model fidelity. A large ensemble of multidecadal simulations is therefore needed to properly sample internal climate variability in order to robustly identify model deficiencies and convincingly demonstrate progress between generations of climate models.

Climate and Earth system modelling↗