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Nitrous oxide (N2O) is a potent long-lived greenhouse gas (GHG) and the strongest current emissions of global anthropogenic stratospheric ozone depletion weighted by its ozone depletion potential. In California, N2O is the third largest contributor to the state's anthropogenic GHG emission inventory, though no study has quantified its statewide annual emissions through top-down inverse modeling. Here we present the first annual (2013–2014) statewide top-down estimates of anthropogenic N2O emissions. Utilizing continuous N2O observations from six sites across California in a hierarchical Bayesian inversion, we estimate that annual anthropogenic emissions are 1.5–2.5 times (at 95% confidence) the state inventory (41 Gg N2O in 2014). Without mitigation, this estimate represents 4–7% of total GHG emissions assuming that other reported GHG emissions are reasonably correct. This suggests that control of N2O could be an important component in meeting California's emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO2 equivalent) by 2030 and 2050, respectively. Our seasonality analysis suggests that emissions are similar across seasons within posterior uncertainties. Future work is needed to provide source attribution for subregions and further characterization of seasonal variability.

Land-atmosphere interactions are important to climate prediction, but the underlying effects of surface forcing of the atmosphere are not well understood. In the U.S. Southern Great Plains, grassland/pasture and winter wheat are the dominant land covers but have distinct growing periods that may differently influence land-atmosphere coupling during spring and summer. Variables that influence surface flux partitioning can change seasonally, depending on the state of local vegetation. Here we use surface observations from multiple sites in the U.S. Department of Energy Atmospheric Radiation Measurement Southern Great Plains Climate Research Facility and statistical modeling at a paired grassland/agricultural site within this facility to quantify land cover influence on surface energy balance and variables controlling evaporative fraction (latent heat flux normalized by the sum of sensible and latent heat fluxes). We demonstrate that the radiative balance and evaporative fraction are closely related to green leaf area at both winter wheat and grassland/pasture sites and that the early summer harvest of winter wheat abruptly shifts the relationship between evaporative fraction and surface state variables. Prior to harvest, evaporative fraction of winter wheat is strongly influenced by leaf area and soil-atmosphere temperature differences. After harvest, variations in soil moisture have a stronger effect on evaporative fraction. This is in contrast with grassland/pasture sites, where variation in green leaf area has a large influence on evaporative fraction throughout spring and summer, and changes in soil-atmosphere temperature difference and soil moisture are of relatively minor importance.

Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land-atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35% during the spring growing season. The model accurately simulated the interannual variation in yield at the USARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

Biases in land-atmosphere coupling in climate models can contribute to climate prediction biases, but land models are rarely evaluated in the context of this coupling. We tested land-atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single-column version of the National Center for Atmospheric Research Community Earth System Model (CESM1.2.2) and an off-line Community Land Model (CLM4.5). The correlation between leaf area index (LAI) and surface evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM4.5. To estimate the impacts of these errors on climate prediction, we modified CLM4.5 by prescribing observed LAI, increasing soil resistance to evaporation, increasing minimum stomatal conductance, and increasing leaf reflectance. The modifications improved the predicted soil moisture-evaporative fraction (EF) and LAI-EF correlations in off-line CLM4.5 and reduced the root-mean-square error in summer 2 m air temperature and precipitation in the coupled model. The modifications had the largest effect on prediction during a drought in summer 2006, when a warm bias in daytime 2 m air temperature was reduced from +6°C to a smaller cold bias of -1.3°C, and a corresponding dry bias in precipitation was reduced from -111 mm to -23 mm. The role of vegetation in droughts and heat waves is underpredicted in CESM1.2.2, and improvements in land surface models can improve prediction of climate extremes.

We present an analysis of methane (CH4) emissions using atmospheric observations from 36 thirteen sites in California during June 2013 – May 2014. A hierarchical Bayesian inversion 37 method is used to estimate CH4 emissions for spatial regions (0.3° pixels for major regions) by 38 comparing measured CH4 mixing ratios with transport model (WRF-STILT) predictions based 39 on seasonally varying California-specific CH4 prior emission models. The transport model is 40 assessed using a combination of meteorological and carbon monoxide (CO) measurements 41 coupled with the gridded California Air Resources Board (CARB) carbon monoxide (CO) 42 emission inventory. Hierarchical Bayesian inversion suggests that state annual anthropogenic 43 CH4 emissions are 2.42 ± 0.49 Tg CH4/yr (at 95% confidence, including transport bias 44 uncertainty), higher (1.2 - 1.8 times) than the CARB current inventory (1.64 Tg CH4/yr in 2013). 45 We note that the estimated CH4 emissions drop to 1.0 - 1.6 times the CARB inventory if we 46 correct for the 10% median CH4 emissions assuming the bias in CO analysis is applicable to 47 CH4. The CH4 emissions from the Central Valley and urban regions (San Francisco Bay and 48 South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, 49 respectively. This study suggests that the livestock sector is likely the major contributor to the 50 state total CH4 emissions, in agreement with CARB’s inventory. Attribution to source sectors for 51 sub-regions of California using additional trace gas species would further improve the 52 quantification of California’s CH4 emissions and mitigation efforts towards the California Global 53 Warming Solutions Act of 2006 (AB-32).

In the presently reported study, we estimated length--weight relation (LWRs) for seven species of freshwater fishes from Central America. Samples were collected using seines from 60 sites across Nicaragua, Costa Rica, and Panama during field expeditions conducted between 1997 and 2012. The fishes were preserved and transported to the lab, where their total weight (W) was measured (to nearest 0.0001 g) and standard lengths were taken (to nearest 0.01 mm). Data were collected from four livebearers (Poeciliidae), Alfaro cultratus (Regan, 1908), Phallichthys amates (Miller, 1907), Poecilia gillii (Kner, 1863), and Priapichthys annectens (Regan, 1907); the cichlids (Cichlidae), Parachromis dovii (Günther, 1864) and Parachromis managuensis (Günther, 1867); and a silverside (Atherinopsidae), Atherinella hubbsi (Bussing, 1979). Estimates of parameter b ranged from 2.936 (A. hubbsi) to 3.696 (P. gillii), while estimates of parameter a ranged from 1.7 x 10-6 (P. gillii) to 1.9 x 10-5 (P. managuensis). Parameter b estimates were greater than three, consistent with allometric growth, with the exception of P. annectens, P. managuensis, and A. hubbsi, for which t-tests failed to reject the null hypothesis of isometric growth. Our results provide the first LWR information for five (71%) of these species and may prove useful for data imputation or estimating the biomass of poeciliid, cichlid, and atheriniform fishes in Central American rivers in the future. [ABSTRACT FROM AUTHOR]

A lack of optimal gene combinations, as well as low levels of genetic diversity, is often associated with the formation of species range margins. Conservation efforts rely on predictive modelling using abiotic variables and assessments of genetic diversity to determine target species and populations for controlled breeding, germplasm conservation and assisted migration. Biotic factors such as interspecific competition and hybridization, however, are largely ignored, despite their prevalence across diverse taxa and their role as key evolutionary forces. Hybridization between species with well‐developed barriers to reproductive isolation often results in the production of offspring with lower fitness. Generation of novel allelic combinations through hybridization, however, can also generate positive fitness consequences. Despite this possibility, hybridization‐mediated introgression is often considered a threat to biodiversity as it can blur species boundaries. The contribution of hybridization towards increasing genetic diversity of populations at range margins has only recently gathered attention in conservation studies. We assessed the extent to which hybridization contributes towards range dynamics by tracking spatio‐temporal changes in the central location of a hybrid zone between two recently diverged species of pines: Pinus strobiformis and P. flexilis. By comparing geographic cline centre estimates for global admixture coefficient with morphological traits associated with reproductive output, we demonstrate a northward shift in the hybrid zone. Using a combination of spatially explicit, individual‐based simulations and linkage disequilibrium variance partitioning, we note a significant contribution of adaptive introgression towards this northward movement, despite the potential for differences in regional population size to aid hybrid zone movement. Overall, our study demonstrates that hybridization between recently diverged species can increase genetic diversity and generate novel allelic combinations. These novel combinations may allow range margin populations to track favourable climatic conditions or facilitate adaptive evolution to ongoing and future climate change. [ABSTRACT FROM AUTHOR]

Abstract: Interactions between extrinsic factors, such as disruptive selection and intrinsic factors, such as genetic incompatibilities among loci, often contribute to the maintenance of species boundaries. The relative roles of these factors in the establishment of reproductive isolation can be examined using species pairs characterized by gene flow throughout their divergence history. We investigated the process of speciation and the maintenance of species boundaries between Pinus strobiformis and Pinus flexilis. Utilizing ecological niche modelling, demographic modelling and genomic cline analyses, we illustrated a divergence history with continuous gene flow. Our results supported an abundance of advanced generation hybrids and a lack of loci exhibiting steep transition in allele frequency across the hybrid zone. Additionally, we found evidence for climate‐associated variation in the hybrid index and niche divergence between parental species and the hybrid zone. These results are consistent with extrinsic factors, such as climate, being an important isolating mechanism. A build‐up of intrinsic incompatibilities and of coadapted gene complexes is also apparent, although these appear to be in the earliest stages of development. This supports previous work in coniferous species demonstrating the importance of extrinsic factors in facilitating speciation. Overall, our findings lend support to the hypothesis that varying strength and direction of selection pressures across the long lifespans of conifers, in combination with their other life history traits, delays the evolution of strong intrinsic incompatibilities. [ABSTRACT FROM AUTHOR]

Utility of perennial bioenergy crops (e.g., switchgrass and miscanthus) offers unique opportunities to transition toward a more sustainable energy pathway due to their reduced carbon footprint, averted competition with food crops, and ability to grow on abandoned and degraded farmlands. Studies that have examined biogeophysical impacts of these crops noted a positive feedback between near-surface cooling and enhanced evapotranspiration ( ET), but also potential unintended consequences of soil moisture and groundwater depletion. To better understand hydrometeorological effects of perennial bioenergy crop expansion, this study conducted high-resolution (2-km grid spacing) simulations with a state-of-the-art atmospheric model (Weather Research and Forecasting system) dynamically coupled to a land surface model. We applied the modeling system over the Southern Plains of the United States during a normal precipitation year (2007) and a drought year (2011). By focusing the deployment of bioenergy cropping systems on marginal and abandoned farmland areas (to reduce the potential conflict with food systems), the research presented here is the first realistic examination of hydrometeorological impacts associated with perennial bioenergy crop expansion. Our results illustrate that the deployment of perennial bioenergy crops leads to widespread cooling (1-2 °C) that is largely driven by an enhanced reflection of shortwave radiation and, secondarily, due to an enhanced ET. Bioenergy crop deployment was shown to reduce the impacts of drought through simultaneous moistening and cooling of the near-surface environment. However, simulated impacts on near-surface cooling and ET were reduced during the drought relative to a normal precipitation year, revealing differential effects based on background environmental conditions. This study serves as a key step toward the assessment of hydroclimatic sustainability associated with perennial bioenergy crop expansion under diverse hydrometeorological conditions by highlighting the driving mechanisms and processes associated with this energy pathway. [ABSTRACT FROM AUTHOR]

Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land-atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35% during the spring growing season. The model accurately simulated the interannual variation in yield at the USARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35%. [ABSTRACT FROM AUTHOR]