Dataset Information

Assessing the Dynamic Versus Thermodynamic Origin of Climate Model Biases.

ABSTRACT: Global climate models present systematic biases, among others, a tendency to overestimate hot and dry summers in midlatitude regions. Here we investigate the origin of such biases in the Community Earth System Model. To disentangle the contribution of dynamics and thermodynamics, we perform simulations that include nudging of horizontal wind and compare them to simulations with a free atmosphere. Prescribing the observed large-scale circulation improves the modeled weather patterns as well as many related fields. However, the larger part of the temperature and precipitation biases of the free atmosphere configuration remains after nudging, in particular, for extremes. Our results suggest that thermodynamical processes, including land-atmosphere coupling and atmospheric parameterizations, drive the errors present in Community Earth System Model. Our result may apply to other climate models and highlight the importance of distinguishing thermodynamic and dynamic sources of biases in present-day global climate models.

SUBMITTER: Wehrli K

PROVIDER: S-EPMC6473591 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Assessing the Dynamic Versus Thermodynamic Origin of Climate Model Biases.

Wehrli Kathrin K Guillod Benoit P BP Hauser Mathias M Leclair Matthieu M Seneviratne Sonia I SI

Geophysical research letters 20180818 16

Global climate models present systematic biases, among others, a tendency to overestimate hot and dry summers in midlatitude regions. Here we investigate the origin of such biases in the Community Earth System Model. To disentangle the contribution of dynamics and thermodynamics, we perform simulations that include nudging of horizontal wind and compare them to simulations with a free atmosphere. Prescribing the observed large-scale circulation improves the modeled weather patterns as well as ma ...[more]

PMID: 31031449

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Project description:Quantitative interpretation and prediction of Hofmeister ion effects on protein processes, including folding and crystallization, have been elusive goals of a century of research. Here, a quantitative thermodynamic analysis, developed to treat noncoulombic interactions of solutes with biopolymer surface and recently extended to analyze the effects of Hofmeister salts on the surface tension of water, is applied to literature solubility data for small hydrocarbons and model peptides. This analysis allows us to obtain a minimum estimate of the hydration b1 (H2O A(-2)), of hydrocarbon surface and partition coefficients Kp, characterizing the distribution of salts and salt ions between this hydration water and bulk water. Assuming that Na+ and SO4(2-) ions of Na2SO4 (the salt giving the largest reduction in hydrocarbon solubility as well as the largest increase in surface tension) are fully excluded from the hydration water at hydrocarbon surface, we obtain the same b1 as for air-water surface (approximately 0.18 H2O A(-2)). Rank orders of cation and anion partition coefficients for nonpolar surface follow the Hofmeister series for protein processes, but are strongly offset for cations in the direction of exclusion (preferential hydration). By applying a coarse-grained decomposition of water accessible surface area (ASA) into nonpolar, polar amide, and other polar surface and the same hydration b1 to interpret peptide solubility increments, we determine salt partition coefficients for amide surface. These partition coefficients are separated into single-ion contributions based on the observation that both Cl- and Na+ (also K+) occupy neutral positions in the middle of the anion and cation Hofmeister series for protein folding. Independent of this assignment, we find that all cations investigated are strongly accumulated at amide surface while most anions are excluded. Cation and anion effects are independent and additive, allowing successful prediction of Hofmeister salt effects on micelle formation and other processes from structural information (ASA).

Dataset Information

Assessing the Dynamic Versus Thermodynamic Origin of Climate Model Biases.

Publications

Assessing the Dynamic Versus Thermodynamic Origin of Climate Model Biases.

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