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Improving bottom-up and top-down estimates of carbon fluxes in the Midwestern USA.

机译：改善美国中西部自下而上和自上而下的碳通量估算。

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摘要

Carbon dioxide (CO2) is the leading contributor to global warming and climate change. The increases in fossil fuel emissions, deforestation, and changes of land use have resulted in increased CO2 levels in the atmosphere from 280 ppm in 1765 to 390 ppm in 2010. Carbon mitigation policies for managing the biosphere to increase net CO2 uptake are dependent upon accurate knowledge of the biosphere fluxes. However, Northern Hemisphere bottom-up and top-down biosphere flux estimates show significant discrepancies, especially in North America. In this study, we design an analysis framework that integrates observations with models with the goal of reducing some of the key uncertainties in estimating CO2 fluxes and concentrations in the Midwest, USA.;In this research, the biosphere model, WRF-VPRM model (Ahmadov et al., 2007) is used to simulate CO2 biosphere fluxes and atmospheric CO2 concentrations in the Midwest, USA at high spatial resolution. Reducing uncertainties in the predictions is accomplished by improving the model transport configurations (i.e. the WRF planetary boundary layer (PBL) scheme, the number of vertical layers and the horizontal resolution), utilizing a more detailed land cover map, optimizing VPRM photosynthesis and respiratory parameters for major crops (i.e. corn and soybean) against flux towers, and integrating CO2 tall tower observations and model through a top-down data assimilation method to improve the VPRM model parameters and in turn improving the flux and concentration estimates.;The WRF-VPRM model configuration with the YonSei University PBL scheme produced the most accurate CO2 concentration predictions at the WBI tower at all three tower levels with the maximum error reduction of 17.1%. Increasing the number of vertical layers improved the CO2 estimates during nighttime and early morning, especially at 30 m, where the error was reduced by a maximum of ∼ 20%. The differences in the monthly average net fluxes over the State of Iowa between the high resolution WRF-VPRM model and coarse resolution Carbon Tracker were significant, 71%, 18%, and 62% in June, July, and August, respectively.;The fluxes calculated by the VPRM model are primarily dependent on 4 model parameters, half saturation value of photosynthesis (PAR0), light use efficiency (gamma) and respiration parameters (alpha and beta These parameters are specific to vegetation types, regions, and time period. The default settings do not distinguish between corn and soybean, which are major crops in the Midwest and have significant different photosynthesis rates. When corn and soybean are explicitly included in the model, the flux estimate changed by 31.3% at 12 pm and 24.5% at 12 am.;Two different methods were used to optimize for the VPRM model parameters which are optimization against Ameriflux NEE and using a top-down variational method. The simulation using optimized parameters from the variational method reduced the error during the daytime from 11.6 ppm to 7.8 ppm. The average fluxes optimized using the variational method changed by 17% and 38.6% at 12 pm and 12 am, respectively. The more accurate VPRM parameters lead to the more accurate biosphere fluxes, which will ease the evaluation of benefits of different carbon mitigation policies.
机译：二氧化碳（CO2）是导致全球变暖和气候变化的主要因素。化石燃料排放量的增加，森林砍伐以及土地利用的变化，导致大气中的二氧化碳水平从1765年的280 ppm增加到2010年的390 ppm。管理生物圈以增加净二氧化碳吸收量的减碳政策取决于准确的关于生物圈通量的知识。但是，北半球自上而下和自上而下的生物圈通量估计值显示出显着差异，尤其是在北美。在本研究中，我们设计了一个分析框架，该模型将观察结果与模型集成在一起，目的是减少估计美国中西部CO2通量和浓度的一些关键不确定性。在本研究中，生物圈模型WRF-VPRM模型（ Ahmadov等人（2007年）用于以高空间分辨率模拟美国中西部的二氧化碳生物圈通量和大气中的二氧化碳浓度。通过改进模型的运输配置（即WRF行星边界层（PBL）方案，垂直层数和水平分辨率），使用更详细的土地覆盖图，优化VPRM光合作用和呼吸参数，可以减少预测中的不确定性。针对主要农作物（即玉米和大豆）抵御通量塔，并通过自上而下的数据同化方法整合高二氧化碳塔观测和模型，以改善VPRM模型参数，进而改善通量和浓度估算值。延世大学PBL方案的模型配置在WBI塔上的所有三个塔级上都产生了最准确的CO2浓度预测，最大误差减少了17.1％。垂直层数的增加改善了夜间和清晨（尤其是在30 m处）的CO2估计值，其中最大减少了约20％的误差。高分辨率WRF-VPRM模型和粗分辨率Carbon Tracker在爱荷华州的月平均净通量之间的差异显着，分别在6月，7月和8月分别为71％，18％和62％。 VPRM模型计算的通量主要取决于4个模型参数，光合作用的半饱和值（PAR0），光利用效率（γ）和呼吸参数（α和β），这些参数特定于植被类型，区域和时间段。默认设置不能区分玉米和大豆，因为玉米和大豆是中西部地区的主要农作物，光合作用的速率明显不同，当模型中明确包含玉米和大豆时，通量估计值在中午12点时变化了31.3％，在12点时变化了24.5％。上午12点；使用两种不同的方法对VPRM模型参数进行优化，即针对Ameriflux NEE进行优化并使用自顶向下的变分方法。变分法将白天的误差从11.6 ppm降低到7.8 ppm。使用变分法优化的平均通量在下午12点和上午12点分别变化了17％和38.6％。 VPRM参数越精确，生物圈通量就越精确，这将简化对不同碳减排政策的收益的评估。

著录项

  • 作者

    Jamroensan, Aditsuda.;

  • 作者单位

    The University of Iowa.;

  • 授予单位 The University of Iowa.;
  • 学科 Engineering Environmental.;Climate Change.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 140 p.
  • 总页数 140
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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