Growing evidence suggests that the gut microbiome (GM) plays a critical role in health and disease. However, the contribution of GM to psychiatric disorders, especially anxiety, remains unclear. We used the Collaborative Cross (CC) mouse population-based model to identify anxiety associated host genetic and GM factors. Anxiety-like behavior of 445 mice across 30 CC strains was measured using the light/dark box assay and documented by video. A custom tracking system was developed to quantify seven anxiety-related phenotypes based on video. Mice were assigned to a low or high anxiety group by consensus clustering using seven anxiety-related phenotypes. Genome-wide association analysis (GWAS) identified 141 genes (264 SNPs) significantly enriched for anxiety and depression related functions. In the same CC cohort, we measured GM composition and identified five families that differ between high and low anxiety mice. Anxiety level was predicted with 79% accuracy and an AUC of 0.81. Mediation analyses revealed that the genetic contribution to anxiety was partially mediated by the GM. Our findings indicate that GM partially mediates and coordinates the effects of genetics on anxiety.

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.

A genome-wide screen for genetic alterations in radiation-induced thymic lymphomas generated from p53+/- and p53-/- mice showed frequent loss of heterozygosity (LOH) on chromosome 6. Fine mapping of these LOH regions revealed three non-overlapping regions, one of which was refined to a 0.2 Mb interval that contained only the gene encoding homeobox-interacting protein kinase 2 (Hipk2). More than 30% of radiation-induced tumors from both p53+/- and p53-/- mice showed heterozygous loss of one Hipk2 allele. Mice carrying a single inactive allele of Hipk2 in the germline were susceptible to induction of tumors by γ-radiation, but most tumors retained and expressed the wild-type allele, suggesting that Hipk2 is a haploinsufficient tumor suppressor gene for mouse lymphoma development. Heterozygous loss of both Hipk2 and p53 confers strong sensitization to radiation-induced lymphoma. We conclude that Hipk2 is a haploinsufficient lymphoma suppressor gene.

The measurement of OH reactivity, the inverse of the OH lifetime, provides a powerful tool to investigate atmospheric photochemistry. A new airborne OH reactivity instrument was designed and deployed for the first time on the NASA DC-8 aircraft during the second phase of Intercontinental Chemical Transport Experiment-B (INTEX-B) campaign, which was focused on the Asian pollution outflow over Pacific Ocean and was based in Hawaii and Alaska. The OH reactivity was measured by adding OH, generated by photolyzing water vapor with 185 nm UV light in a moveable wand, to the flow of ambient air in a flow tube and measuring the OH signal with laser induced fluorescence. As the wand was pulled back away from the OH detector, the OH signal decay was recorded; the slope of -Delta 1n(signal)/Delta time was the OH reactivity. The overall absolute uncertainty at the 2 sigma confidence levels is about 1 s(-1) at low altitudes (for decay about 6 s(-1)), and 0.7 s(-1) at high altitudes (for decay about 2 s(-1)). From the median vertical profile obtained in the second phase of INTEX-B, the measured OH reactivity (4.0 +/- 1.0 s(-1)) is higher than the OH reactivity calculated from assuming that OH was in steady state (3.3 +/- 0.8 s(-1)), and even higher than the OH reactivity that was calculated from the total measurements of all OH reactants (1.6 +/- 0.4 s(-1)). Model calculations show that the missing OH reactivity is consistent with the over-predicted OH and under-predicted HCHO in the boundary layer and lower troposphere. The over-predicted OH and under-predicted HCHO suggest that the missing OH sinks are most likely related to some highly reactive VOCs that have HCHO as an oxidation product.

Using measurements from the NASA Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) experiment, we show that methyl peroxy nitrate (CH₃O₂NO₂) is present in concentrations of ~5–15 pptv in the springtime arctic upper troposphere. We investigate the regional and global effects of CH₃O₂NO₂ by including its chemistry in the GEOS-Chem 3-D global chemical transport model. We find that at temperatures below 240 K inclusion of CH₃O₂NO₂ chemistry results in decreases of up to ~20 % in NO_x, ~20 % in N₂O₅, ~5 % in HNO₃, ~2 % in ozone, and increases in methyl hydrogen peroxide of up to ~14 %. Larger changes are observed in biomass burning plumes lofted to high altitude. Additionally, by sequestering NO_x at low temperatures, CH₃O₂NO₂ decreases the cycling of HO₂ to OH, resulting in a larger upper tropospheric HO₂ to OH ratio. These results may impact some estimates of lightning NO_x sources as well as help explain differences between models and measurements of upper tropospheric composition.

The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human-Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. The iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human-Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.

One of the objectives of the Deep Convective Clouds and Chemistry (DC3) field experiment was to determine the scavenging of soluble trace gases by thunderstorms. We present an analysis of scavenging of hydrogen peroxide (H2O2) and methyl hydrogen peroxide (CH3OOH) from six DC3 cases that occurred in Oklahoma and northeast Colorado. Estimates of H2O2 scavenging efficiencies are comparable to previous studies ranging from 79 to 97% with relative uncertainties of 5–25%. CH3OOH scavenging efficiencies ranged from 12 to 84% with relative uncertainties of 18–558%. The wide range of CH3OOH scavenging efficiencies is surprising, as previous studies suggested that CH3OOH scavenging efficiencies would be <10%. Cloud chemistry model simulations of one DC3 storm produced CH3OOH scavenging efficiencies of 26–61% depending on the ice retention factor of CH3OOH during cloud drop freezing, suggesting ice physics impacts CH3OOH scavenging. The highest CH3OOH scavenging efficiencies occurred in two severe thunderstorms, but there is no obvious correlation between the CH3OOH scavenging efficiency and the storm thermodynamic environment. We found a moderate correlation between the estimated entrainment rates and CH3OOH scavenging efficiencies. Changes in gas-phase chemistry due to lightning production of nitric oxide and aqueous-phase chemistry have little effect on CH3OOH scavenging efficiencies. To determine why CH3OOH can be substantially removed from storms, future studies should examine effects of entrainment rate, retention of CH3OOH in frozen cloud particles during drop freezing, and lightning-NOx production.

Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO₂, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO₂ which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO₂ and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.

A model intercomparison activity was inspired by the large suite of observations of atmospheric composition made during the International Polar Year (2008) in the Arctic. Nine global and two regional chemical transport models participated in this intercomparison and performed simulations for 2008 using a common emissions inventory to assess the differences in model chemistry and transport schemes. This paper summarizes the models and compares their simulations of ozone and its precursors and presents an evaluation of the simulations using a variety of surface, balloon, aircraft and satellite observations. Each type of measurement has some limitations in spatial or temporal coverage or in composition, but together they assist in quantifying the limitations of the models in the Arctic and surrounding regions. Despite using the same emissions, large differences are seen among the models. The cloud fields and photolysis rates are shown to vary greatly among the models, indicating one source of the differences in the simulated chemical species. The largest differences among models, and between models and observations, are in NOy partitioning (PAN vs. HNO3) and in oxygenated volatile organic compounds (VOCs) such as acetaldehyde and acetone. Comparisons to surface site measurements of ethane and propane indicate that the emissions of these species are significantly underestimated. Satellite observations of NO2 from the OMI (Ozone Monitoring Instrument) have been used to evaluate the models over source regions, indicating anthropogenic emissions are underestimated in East Asia, but fire emissions are generally overestimated. The emission factors for wildfires in Canada are evaluated using the correlations of VOCs to CO in the model output in comparison to enhancement factors derived from aircraft observations, showing reasonable agreement for methanol and acetaldehyde but underestimate ethanol, propane and acetone, while overestimating ethane emission factors.