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Dive into the research topics where Anthony M. DeAngelis is active.

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Featured researches published by Anthony M. DeAngelis.


Geophysical Research Letters | 2015

Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors

Xin Qu; Alex Hall; Stephen A. Klein; Anthony M. DeAngelis

Differences in simulations of tropical marine low-cloud cover (LCC) feedback are sources of significant spread in temperature responses of climate models to anthropogenic forcing. Here we show that in models the feedback is mainly driven by three large-scale changes—a strengthening tropical inversion, increasing surface latent heat flux, and an increasing vertical moisture gradient. Variations in the LCC response to these changes alone account for most of the spread in model-projected 21st century LCC changes. A methodology is devised to constrain the LCC response observationally using sea surface temperature (SST) as a surrogate for the latent heat flux and moisture gradient. In models where the current climates LCC sensitivities to inversion strength and SST variations are consistent with observed, LCC decreases systematically, which would increase absorption of solar radiation. These results support a positive LCC feedback. Correcting biases in the sensitivities will be an important step toward more credible simulation of cloud feedbacks.


Nature | 2015

An observational radiative constraint on hydrologic cycle intensification

Anthony M. DeAngelis; Xin Qu; Mark D. Zelinka; Alex Hall

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1–3 per cent per kelvin). Part of the uncertainty may originate from atmosphere–radiation interactions. As the climate warms, increases in shortwave absorption from atmospheric moistening will suppress the precipitation increase. This occurs through a reduction of the latent heating increase required to maintain a balanced atmospheric energy budget. Using an ensemble of climate models, here we show that such models tend to underestimate the sensitivity of solar absorption to variations in atmospheric water vapour, leading to an underestimation in the shortwave absorption increase and an overestimation in the precipitation increase. This sensitivity also varies considerably among models due to differences in radiative transfer parameterizations, explaining a substantial portion of model spread in the precipitation response. Consequently, attaining accurate shortwave absorption responses through improvements to the radiative transfer schemes could reduce the spread in the predicted global precipitation increase per degree warming for the end of the twenty-first century by about 35 per cent, and reduce the estimated ensemble-mean increase in this quantity by almost 40 per cent.


Journal of Climate | 2013

A Comparison of CMIP3 Simulations of Precipitation over North America with Observations: Daily Statistics and Circulation Features Accompanying Extreme Events

Anthony M. DeAngelis; Anthony J. Broccoli; Steven G. Decker

AbstractClimate model simulations of daily precipitation statistics from the third phase of the Coupled Model Intercomparison Project (CMIP3) were evaluated against precipitation observations from North America over the period 1979–99. The evaluation revealed that the models underestimate the intensity of heavy and extreme precipitation along the Pacific coast, southeastern United States, and southern Mexico, and these biases are robust among the models. The models also overestimate the intensity of light precipitation events over much of North America, resulting in fairly realistic mean precipitation in many places. In contrast, heavy precipitation is simulated realistically over northern and eastern Canada, as is the seasonal cycle of heavy precipitation over a majority of North America. An evaluation of the simulated atmospheric dynamics and thermodynamics associated with extreme precipitation events was also conducted using the North American Regional Reanalysis (NARR). The models were found to captur...


Journal of Climate | 2016

Effects of a Warming Climate on Daily Snowfall Events in the Northern Hemisphere

James F. Danco; Anthony M. DeAngelis; Bryan K. Raney; Anthony J. Broccoli

AbstractUsing simulations performed with 24 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of Northern Hemisphere daily snowfall events under the RCP8.5 emissions scenario are analyzed for the periods of 2021–50 and 2071–2100 and compared to the historical period of 1971–2000. The overall frequency of daily snowfall events is simulated to decrease across much of the Northern Hemisphere, except at the highest latitudes such as northern Canada, northern Siberia, and Greenland. Seasonal redistributions of daily snowfall event frequency and average daily snowfall are also projected to occur in some regions. For example, large portions of the Northern Hemisphere, including much of Canada, Tibet, northern Scandinavia, northern Siberia, and Greenland, are projected to experience increases in average daily snowfall and event frequency in midwinter. But in warmer months, the regions with increased snowfall become fewer in number and are...


Journal of Climate | 2018

Drivers of the Low-Cloud Response to Poleward Jet Shifts in the North Pacific in Observations and Models

Mark D. Zelinka; Kevin M. Grise; Stephen A. Klein; Chen Zhou; Anthony M. DeAngelis; Matthew W. Christensen

AbstractThe long-standing expectation that poleward shifts of the midlatitude jet under global warming will lead to poleward shifts of clouds and a positive radiative feedback on the climate system...


Journal of Climate | 2018

On the Emergent Constraints of Climate Sensitivity

Xin Qu; Alex Hall; Anthony M. DeAngelis; Mark D. Zelinka; Stephen A. Klein; Hui Su; Baijun Tian; Chengxing Zhai

AbstractDifferences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificia...


Nature | 2016

Corrigendum: An observational radiative constraint on hydrologic cycle intensification.

Anthony M. DeAngelis; Xin Qu; Mark D. Zelinka; Alex Hall

This corrects the article DOI: 10.1038/nature15770


Geophysical Research Letters | 2016

Importance of vegetation processes for model spread in the fast precipitation response to CO2 forcing

Anthony M. DeAngelis; Xin Qu; Alex Hall


Geophysical Research Letters | 2015

Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors: Positive Tropical Low-Cloud Feedback

Xin Qu; Alex Hall; Stephen A. Klein; Anthony M. DeAngelis


Geophysical Research Letters | 2018

On the Connection Between Global Hydrologic Sensitivity and Regional Wet Extremes

Chad W. Thackeray; Anthony M. DeAngelis; Alex Hall; Daniel L. Swain; Xin Qu

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Alex Hall

University of California

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Xin Qu

University of California

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Mark D. Zelinka

Lawrence Livermore National Laboratory

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Stephen A. Klein

Lawrence Livermore National Laboratory

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Baijun Tian

California Institute of Technology

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Chen Zhou

Lawrence Livermore National Laboratory

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Chengxing Zhai

California Institute of Technology

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Hui Su

California Institute of Technology

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