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New insight of Arctic cloud parameterization from regional climate model simulations, satellite-based and drifting station data.

ABSTRACT: Cloud observations from the CloudSat and CALIPSO satellites helped to explain the reduced total cloud cover (Ctot) in the atmospheric regional climate model HIRHAM5 with modified cloud physics. Arctic climate conditions are found to be better reproduced with (1) a more efficient Bergeron-Findeisen process and (2) more generalized subgrid-scale variability of total water content. As a result, the annual cycle of Ctot is improved over sea ice, associated with an almost 14% smaller area average than in the control simulation. The modified cloud scheme reduces the Ctot bias with respect to the satellite observations. Except for autumn, the cloud reduction over sea ice improves low-level temperature profiles compared to drifting station data. The HIRHAM5 sensitivity study highlights the need for improving accuracy of low-level (< 700m) cloud observations, as these clouds exert a strong impact on the near-surface climate.

SUBMITTER: Klaus D

PROVIDER: S-EPMC7402221 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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New insight of Arctic cloud parameterization from regional climate model simulations, satellite-based and drifting station data.

Klaus D D Dethlo K K Dorn W W Rinke A A Wu D L DL

Geophysical research letters 20160304 10

Cloud observations from the CloudSat and CALIPSO satellites helped to explain the reduced total cloud cover (Ctot) in the atmospheric regional climate model HIRHAM5 with modified cloud physics. Arctic climate conditions are found to be better reproduced with (1) a more efficient Bergeron-Findeisen process and (2) more generalized subgrid-scale variability of total water content. As a result, the annual cycle of Ctot is improved over sea ice, associated with an almost 14% smaller area average tha ...[more]

PMID: 32753770

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Project description:The high proportion of lipopolysaccharide (LPS) molecules in the outer membrane of Gram-negative bacteria makes it a highly effective barrier to small molecules, antibiotic drugs, and other antimicrobial agents. Given this vital role in protecting bacteria from potentially hostile environments, simulations of LPS bilayers and outer membrane systems represent a critical tool for understanding the mechanisms of bacterial resistance and the development of new antibiotic compounds that circumvent these defenses. The basis of these simulations is parameterizations of LPS, which have been developed for all major molecular dynamics force fields. However, these parameterizations differ in both the protonation state of LPS and how LPS membranes behave in the presence of various ion species. To address these discrepancies and understand the effects of phosphate charge on bilayer properties, simulations were performed for multiple distinct LPS chemotypes with different ion parameterizations in both protonated or deprotonated lipid A states. These simulations show that bilayer properties, such as the area per lipid and inter-lipid hydrogen bonding, are highly influenced by the choice of phosphate group charges, cation type, and ion parameterization, with protonated LPS and monovalent cations with modified nonbonded parameters providing the best match to the experiments. Additionally, alchemical free energy simulations were performed to determine theoretical pKa values for LPS and subsequently validated by 31P solid-state nuclear magnetic resonance experiments. Results from these complementary computational and experimental studies demonstrate that the protonated state dominates at physiological pH, contrary to the deprotonated form modeled by many LPS force fields. Overall, these results highlight the sensitivity of LPS simulations to phosphate charge and ion parameters while offering recommendations for how existing models should be updated for consistency between force fields as well as to best match experiments.

Dataset Information

New insight of Arctic cloud parameterization from regional climate model simulations, satellite-based and drifting station data.

Publications

New insight of Arctic cloud parameterization from regional climate model simulations, satellite-based and drifting station data.

Similar Datasets

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