418 research outputs found

    Climatically driven loss of calcium in steppe soil as a sink for atmospheric carbon

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    During the last several thousand years the semi‐arid, cold climate of the Russian steppe formed highly fertile soils rich in organic carbon and calcium (classified as Chernozems in the Russian system). Analysis of archived soil samples collected in Kemannaya Steppe Preserve in 1920, 1947, 1970, and fresh samples collected in 1998 indicated that the native steppe Chernozems, however, lost 17–28 kg m−2 of calcium in the form of carbonates in 1970–1998. Here we demonstrate that the loss of calcium was caused by fundamental shift in the steppe hydrologic balance. Previously unleached soils where precipitation was less than potential evapotranspiration are now being leached due to increased precipitation and, possibly, due to decreased actual evapotranspiration. Because this region receives low levels of acidic deposition, the dissolution of carbonates involves the consumption of atmospheric CO2. Our estimates indicate that this climatically driven terrestrial sink of atmospheric CO2 is ∼2.1–7.4 g C m−2 a−1. In addition to the net sink of atmospheric carbon, leaching of pedogenic carbonates significantly amplified seasonal amplitude of CO2 exchange between atmosphere and steppe soil

    Portable Flux Tower Deployments Field Campaign Report

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    Contents Acronyms and Abbreviations...................................................................... iii 1.0 Summary ....................................................... 1 2.0 Results ........................................... 1 3.0 Publications and References ................................................. 2 4.0 Lessons Learned ....................................................................

    Representing winter wheat in the Community Land Model (version 4.5)

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    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 US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %

    Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM

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    Terrestrial net CH<sub>4</sub> surface fluxes often represent the difference between much larger gross production and consumption fluxes and depend on multiple physical, biological, and chemical mechanisms that are poorly understood and represented in regional- and global-scale biogeochemical models. To characterize uncertainties, study feedbacks between CH<sub>4</sub> fluxes and climate, and to guide future model development and experimentation, we developed and tested a new CH<sub>4</sub> biogeochemistry model (CLM4Me) integrated in the land component (Community Land Model; CLM4) of the Community Earth System Model (CESM1). CLM4Me includes representations of CH<sub>4</sub> production, oxidation, aerenchyma transport, ebullition, aqueous and gaseous diffusion, and fractional inundation. As with most global models, CLM4 lacks important features for predicting current and future CH<sub>4</sub> fluxes, including: vertical representation of soil organic matter, accurate subgrid scale hydrology, realistic representation of inundated system vegetation, anaerobic decomposition, thermokarst dynamics, and aqueous chemistry. We compared the seasonality and magnitude of predicted CH<sub>4</sub> emissions to observations from 18 sites and three global atmospheric inversions. Simulated net CH<sub>4</sub> emissions using our baseline parameter set were 270, 160, 50, and 70 Tg CH<sub>4</sub> yr<sup>−1</sup> globally, in the tropics, in the temperate zone, and north of 45° N, respectively; these values are within the range of previous estimates. We then used the model to characterize the sensitivity of regional and global CH<sub>4</sub> emission estimates to uncertainties in model parameterizations. Of the parameters we tested, the temperature sensitivity of CH<sub>4</sub> production, oxidation parameters, and aerenchyma properties had the largest impacts on net CH<sub>4</sub> emissions, up to a factor of 4 and 10 at the regional and gridcell scales, respectively. In spite of these uncertainties, we were able to demonstrate that emissions from dissolved CH<sub>4</sub> in the transpiration stream are small (<1 Tg CH<sub>4</sub> yr<sup>−1</sup>) and that uncertainty in CH<sub>4</sub> emissions from anoxic microsite production is significant. In a 21st century scenario, we found that predicted declines in high-latitude inundation may limit increases in high-latitude CH<sub>4</sub> emissions. Due to the high level of remaining uncertainty, we outline observations and experiments that would facilitate improvement of regional and global CH<sub>4</sub> biogeochemical models

    A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands

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    Wetlands are the largest global natural methane (CH4/ source, and emissions between 50 and 70° N latitude contribute 10-30% to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with siteto regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May-September) CarbonTracker Alaskan regional-level CH4 predictions and sitelevel observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October-April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, coldseason CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in highlatitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements
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