Generating Regional Emissions Estimates with a Novel Hierarchy of Observations and Upscaled Simulation Experiments (GREENHOUSE)

通过新颖的观测层次结构和大规模模拟实验生成区域排放估算（GREENHOUSE）

• 批准号: NE/K002619/1
• 负责人: Mathew Williams
• 金额: $ 151.63万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FK002619%2F1
• 关键词: Generating; Regional; Emissions; Estimates; Novel

The UK is committed to quantifying and managing its emissions of greenhouse gases (GHG, i.e. CO2, CH4, N2O) to reduce the threat of dangerous climate change. Sinks and sources of GHGs vary in space and time across the UK because of the landscape's mosaic of managed and semi-natural ecosystems, and the varying temporal sensitivities of each GHG's emissions to meteorology and management. Understanding spatio-temporal patterns of biogenic GHG emissions will lead to improvements in flux estimates, allow creation of inventories with greater sensitivity to management and climate, and advance the modelling of feedbacks between climate, land use and GHG emissions. Addressing Deliverable C of the NERC Greenhouse Gas Emissions and Feedbacks Research Programme, we will use extensive existing UK field data on GHG emissions, supplemented with targeted new measurements at a range of scales, to build accurate GHG inventories and improve the capabilities of two land surface models (LSMs) to estimate GHG emissions.Our measurements will underpin state-of-the-art temporal and spatial upscaling frameworks. The temporal framework will evaluate diurnal, seasonal and inter-annual variation in emissions of CO2, CH4 and N2O over dominant UK land-covers, resolving management interventions such as ploughing, fertilizing and harvesting, and the effects of weather and climate variability. The spatial framework will evaluate landscape heterogeneity at patch (m), field (ha) and landscape (km2) scales, in two campaigns combining chambers, tower and airborne flux measurements in arable croplands of eastern England, and grazing and forest landscapes of northern Britain.For modelling, we will update two LSMs - JULES and CTESSEL- so that each generates estimates of CO2, CH4 and N2O fluxes from managed landscapes. The models will be updated to include the capabilities to represent changes in land use over time, to represent changes in land management over time (crop sowing, fertilizing, harvesting, ploughing etc), and the capacity to simulate forest rotations. With these changes in place, we will determine parameterisations for dominant UK land-covers and management interventions, using our spatio-temporal data. The work is organized in five science work-packages (WP). WP1: Data assembly and preliminary analysis. We will create a database of GHG flux data and ancillary data for major UK landcovers/landuses in order to calibrate and evaluate the LSMs' capabilities, and generate spatial databases of environmental and management drivers for the models.WP2. GHG measurement at multiple scales. We will deploy advanced technology to generate new information on spatial GHG processes from simultaneous measurement from chamber (<1 m) to landscape (40 km) length scales, and on temporal flux variation from minutes to years.WP3. Earth observation (EO) to support upscaling. EO data will provide: i) driving data for LSM upscaling, from flux tower to aircraft campaign scales; and ii) spatial data for testing LSM outputs at these larger scales.WP4 Upscaling GHG processes. Firstly, the two LSMs will be updated to allow the impacts of management activities on GHG emissions to be simulated, with calibration against an array of temporal flux data. Then, we will use the LSMs to model the fluxes of GHGs at larger spatial scales, based on a rigorous understanding of how the nonlinearity of responses and the non-Gaussian distribution of environmental input variables interact, for each GHG, using all available field data at finer scales. WP5 Application at the regional scale. The LSMs will upscale GHG emissions for both campaign regions (E. England, N. Britain) using 1-km2 resolution simulations with a focus on the airborne campaign periods of 4 weeks. We will determine how regional upscaling error can be reduced with intensive spatial soil and land management data.

英国致力于量化和管理其温室气体(温室气体，即二氧化碳、甲烷、N2O)的排放，以减少危险的气候变化的威胁。在整个英国，温室气体的汇和源在空间和时间上各不相同，这是因为这里的景观由管理的和半自然的生态系统组成，而且每种温室气体的排放对气象和管理的时间敏感性各不相同。了解生物温室气体排放的时空模式将有助于改进通量估计，允许建立对管理和气候更敏感的清单，并推进气候、土地利用和温室气体排放之间的反馈建模。针对NERC温室气体排放和反馈研究计划的可交付成果C，我们将使用广泛的英国现有温室气体排放现场数据，并辅以一系列有针对性的新测量，以建立准确的温室气体清单，并提高两个陆地表面模型(LSM)估计温室气体排放的能力。我们的测量将支持最先进的时间和空间提升框架。时间框架将评估英国主要土地覆盖上二氧化碳、甲烷和N2O排放的日变化、季节变化和年际变化，解决耕作、施肥和收获等管理干预措施，以及天气和气候变化的影响。空间框架将在斑块(M)、田间(Ha)和景观(Km2)尺度上评估景观异质性，在两个活动中结合小室、塔式和空气中英格兰东部农田的通量测量，以及英国北部的放牧和森林景观。为了建模，我们将更新两个LSM-Jules和CTESSEL-以便每个LSM都从管理的景观中生成二氧化碳、CH4和N2O通量的估计。这些模型将进行更新，以包括表示土地利用随时间变化的能力、表示土地管理随时间变化(作物播种、施肥、收获、耕作等)的能力，以及模拟森林轮换的能力。随着这些变化的到位，我们将使用我们的时空数据来确定主要英国土地覆盖和管理干预的参数。这项工作分为五个科学工作包(WP)。WP1：数据汇编和初步分析。我们将创建一个温室气体通量数据和英国主要土地覆盖/土地利用的辅助数据的数据库，以校准和评估LSM的能力，并为这些模型生成环境和管理驱动因素的空间数据库。多尺度的温室气体测量。我们将采用先进技术，从小室(1米)到地貌(40公里)长度尺度的同步测量，以及从几分钟到几年的时间通量变化，生成关于温室气体空间过程的新信息。地球观测(EO)，以支持升级。EO数据将提供：i)从通量塔到飞机战役尺度的LSM提升的驱动数据；以及ii)在这些更大尺度上测试LSM产出的空间数据。首先，将更新两个LSM，以模拟管理活动对温室气体排放的影响，并根据一系列时间通量数据进行校准。然后，我们将使用LSM在更大的空间尺度上模拟温室气体的通量，基于对响应的非线性和环境输入变量的非高斯分布如何相互作用的严格理解，对于每个温室气体，使用更精细尺度上的所有可用现场数据。WP5在区域范围内的应用。LSM将使用1平方公里的分辨率模拟来提升这两个活动地区(英格兰东部和英国北部)的温室气体排放量，重点放在4周的空中活动周期上。我们将确定如何通过密集的空间土壤和土地管理数据来减少区域上尺度误差。

项目成果

Lagged effects regulate the inter-annual variability of the tropical carbon balance

• DOI: 10.5194/bg-17-6393-2020
• 发表时间: 2020-12-17
• 期刊: BIOGEOSCIENCES
• 影响因子: 4.9
• 作者: Bloom, A. Anthony;Bowman, Kevin W.;Schimel, David S.
• 通讯作者: Schimel, David S.

Incorporating dead material in ecosystem assessments and projections

• DOI: 10.1038/s41558-022-01565-5
• 发表时间: 2022-12
• 期刊: Nature Climate Change
• 影响因子: 30.7
• 作者: K. Barnhill;J. Roberts;I. Myers-Smith;M. Williams;K. Dexter;C. Ryan;U. Wolfram;S. Hennige

Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

• DOI: 10.5194/bg-11-3547-2014
• 发表时间: 2014-01-01
• 期刊: BIOGEOSCIENCES
• 影响因子: 4.9
• 作者: Ciais, P.;Dolman, A. J.;Zehner, C.
• 通讯作者: Zehner, C.

Atmospheric CO 2 inversion reveals the Amazon as a minor carbon source caused by fire emissions, with forest uptake offsetting about half of these emissions

大气 CO 2 反演显示亚马逊地区是火灾排放造成的次要碳源，森林吸收抵消了大约一半的排放量

• DOI: 10.5194/acp-23-9685-2023
• 发表时间: 2023
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: Basso L

Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil-plant-atmosphere continuum

• DOI: 10.5194/gmd-7-2193-2014
• 发表时间: 2014-01-01
• 期刊: GEOSCIENTIFIC MODEL DEVELOPMENT
• 影响因子: 5.1
• 作者: Bonan, G. B.;Williams, M.;Oleson, K. W.
• 通讯作者: Oleson, K. W.