ALBERT

Description: Accurately quantifying the spatiotemporal distribution of the biological component of CO2 surface-atmosphere exchange is necessary to improve top-down constraints on China's anthropogenic CO2 emissions. We provide hourly fluxes of CO2 as Net Ecosystem Exchange (NEE; µmol CO2m−2s−1) on a 0.25°×0.25° grid by adapting the Vegetation, Photosynthesis, and Respiration Model (VPRM) to the eastern half of China for the time period from 2005–2009; the minimal empirical parameterization of the VPRM-CHINA makes it well-suited for inverse modeling approaches. This study diverges from previous VPRM applications in that it is applied at large scale to China's ecosystems for the first time, incorporating a novel processing framework not previously applied to existing VPRM versions. In addition, the VPRM-CHINA model prescribes methods for addressing dual-cropping regions that have two separate growing season modes applied to the same model gridcell. We evaluate the VPRM-CHINA performance during the growing season and compare to other biospheric models. We calibrate the VPRM-CHINA with ChinaFlux and FluxNet data and scale up regionally using Weather Research and Forecasting (WRF) Model v3.6.1 meteorology and MODIS surface reflectances. When combined with an anthropogenic emissions model in a Lagrangian particle transport framework, we compare the ability of VPRM-CHINA relative to an ensemble mean of global hourly flux models (NASA CMS) to reproduce observations made at a site in Northern China. The measurements are heavily influenced by the Northern China administrative region. Modeled hourly timeseries using vegetation fluxes prescribed by VPRM-CHINA exhibit low bias relative to measurements during the May-September growing season. Compared to NASA CMS subset over the study region, VPRM-CHINA agrees significantly better with measurements. NASA CMS consistently underestimates regional uptake in the growing season. We find that during the peak growing season, when the heavily cropped North China Plain significantly influences measurements, VPRM-CHINA models an CO2 uptake signal comparable in magnitude to the modeled anthropogenic signal. In addition to demonstrating efficacy as a low-bias prior for top-down CO2 inventory optimization studies using ground-based measurements, high spatiotemporal resolution models such as the VPRM are critical for interpreting retrievals from global CO2 remote sensing platforms such as OCO-2 and OCO-3 (planned). Depending on the satellite time-of-day and season of crossover, efforts to interpret the relative contribution of the vegetation and anthropogenic components to the measured signal are critical in key emitting regions such as Northern China – where the magnitude of the vegetation CO2 signal is shown to be equivalent to the anthropogenic signal.

Description: Accurately quantifying the spatiotemporal distribution of the biological component of CO2 surface–atmosphere exchange is necessary to improve top-down constraints on China's anthropogenic CO2 emissions. We provide hourly fluxes of CO2 as net ecosystem exchange (NEE; µmol CO2 m−2 s−1) on a 0.25∘×0.25∘ grid by adapting the Vegetation, Photosynthesis, and Respiration Model (VPRM) to the eastern half of China for the time period from 2005 to 2009; the minimal empirical parameterization of the VPRM-CHINA makes it well suited for inverse modeling approaches. This study diverges from previous VPRM applications in that it is applied at a large scale to China's ecosystems for the first time, incorporating a novel processing framework not previously applied to existing VPRM versions. In addition, the VPRM-CHINA model prescribes methods for addressing dual-cropping regions that have two separate growing-season modes applied to the same model grid cell. We evaluate the VPRM-CHINA performance during the growing season and compare to other biospheric models. We calibrate the VPRM-CHINA with ChinaFlux and FluxNet data and scale up regionally using Weather Research and Forecasting (WRF) Model v3.6.1 meteorology and MODIS surface reflectances. When combined with an anthropogenic emissions model in a Lagrangian particle transport framework, we compare the ability of VPRM-CHINA relative to an ensemble mean of global hourly flux models (NASA CMS – Carbon Monitoring System) to reproduce observations made at a site in northern China. The measurements are heavily influenced by the northern China administrative region. Modeled hourly time series using vegetation fluxes prescribed by VPRM-CHINA exhibit low bias relative to measurements during the May–September growing season. Compared to NASA CMS subset over the study region, VPRM-CHINA agrees significantly better with measurements. NASA CMS consistently underestimates regional uptake in the growing season. We find that during the peak growing season, when the heavily cropped North China Plain significantly influences measurements, VPRM-CHINA models a CO2 uptake signal comparable in magnitude to the modeled anthropogenic signal. In addition to demonstrating efficacy as a low-bias prior for top-down CO2 inventory optimization studies using ground-based measurements, high spatiotemporal resolution models such as the VPRM are critical for interpreting retrievals from global CO2 remote-sensing platforms such as OCO-2 and OCO-3 (planned). Depending on the satellite time of day and season of crossover, efforts to interpret the relative contribution of the vegetation and anthropogenic components to the measured signal are critical in key emitting regions such as northern China – where the magnitude of the vegetation CO2 signal is shown to be equivalent to the anthropogenic signal.

Description: China has pledged reduction of carbon dioxide emissions per unit GDP by 60–65 % relative to 2005 levels, and to peak carbon emissions overall by 2030. However, disagreement among available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of control measures. In this study, we demonstrate an approach based on a long time series of surface CO2 observations to evaluate regional CO2 emissions rates in northern China estimated by three anthropogenic CO2 inventories – two of which are subsets from global inventories, and one of which is China-specific. Comparison of CO2 observations to CO2 predicted from accounting for global background concentration and atmospheric mixing of emissions suggests potential biases in the inventories. The period analyzed focuses on the key commitment period for the Paris accords (2005) and the Beijing Olympics (2008). Model-observation mismatch in concentration units is translated to mass units and is displayed against the original inventories in the measurement influence region, largely corresponding to northern China. Owing to limitations from having a single site, addressing the significant uncertainty stemming from transport error and error in spatial allocation of the emissions remains a challenge. Our analysis uses observations to support and justify increased use and development of China-specific inventories in tracking China's progress as a whole towards reducing emissions. Here we are restricted to a single measurement site; effectively evaluating and constraining inventories at relevant spatial scales requires multiple stations of high-temporal resolution observations. At this stage and with observational data limitations, we emphasize that this work is intended to be a comparison of a subset of anthropogenic CO2 emissions rates from inventories that were readily available at the time this research began. For this study's analysis time period, there was not enough spatially distinct observational data to conduct an optimization of the inventories. Rather, our analysis provides an important quantification of model-observation mismatch. In the northern China evaluation region, emission rates from the China-specific inventory produce the lowest model-observation mismatch at all timescales from daily to annual. Additionally, we note that averaged over the study time period, the unscaled China-specific inventory has substantially larger annual emissions for China as a whole (20 % higher) and the northern China evaluation region (30 %) than the unscaled global inventories. Our results lend support the rates and geographic distribution in the China-specific inventory. However, exploring this discrepancy for China as a whole requires a denser observational network in future efforts to measure and verify CO2 emissions for China both regionally and nationally. This study provides a baseline analysis for a small but import region within China, as well a guide for determining optimal locations for future ground-based measurement sites.

Description: China has pledged reduction of carbon dioxide (CO2) emissions per unit of gross domestic product (GDP) by 60 %–65 % relative to 2005 levels, and to peak carbon emissions overall by 2030. However, the lack of observational data and disagreement among the many available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of control measures. To demonstrate the value of atmospheric observations for constraining CO2 inventories we track the ability of CO2 concentrations predicted from three different CO2 inventories to match a unique multi-year continuous record of atmospheric CO2. Our analysis time window includes the key commitment period for the Paris Agreement (2005) and the Beijing Olympics (2008). One inventory is China-specific and two are spatial subsets of global inventories. The inventories differ in spatial resolution, basis in national or subnational statistics, and reliance on global or China-specific emission factors. We use a unique set of historical atmospheric observations from 2005 to 2009 to evaluate the three CO2 emissions inventories within China's heavily industrialized and populated northern region accounting for ∼33 %–41 % of national emissions. Each anthropogenic inventory is combined with estimates of biogenic CO2 within a high-resolution atmospheric transport framework to model the time series of CO2 observations. To convert the model–observation mismatch from mixing ratio to mass emission rates we distribute it over a region encompassing 90 % of the total surface influence in seasonal (annual) averaged back-trajectory footprints (L_0.90 region). The L_0.90 region roughly corresponds to northern China. Except for the peak growing season, where assessment of anthropogenic emissions is entangled with the strong vegetation signal, we find the China-specific inventory based on subnational data and domestic field studies agrees significantly better with observations than the global inventories at all timescales. Averaged over the study time period, the unscaled China-specific inventory reports substantially larger annual emissions for northern China (30 %) and China as a whole (20 %) than the two unscaled global inventories. Our results, exploiting a robust time series of continuous observations, lend support to the rates and geographic distribution in the China-specific inventory Though even long-term observations at a single site reveal differences among inventories, exploring inventory discrepancy over all of China requires a denser observational network in future efforts to measure and verify CO2 emissions for China both regionally and nationally. We find that carbon intensity in the northern China region has decreased by 47 % from 2005 to 2009, from approximately 4 kg of CO2 per USD (note that all references to USD in this paper refer to USD adjusted for purchasing power parity, PPP) in 2005 to about 2 kg of CO2 per USD in 2009 (Fig. 9c). However, the corresponding 18 % increase in absolute emissions over the same time period affirms a critical point that carbon intensity targets in emerging economies can be at odds with making real climate progress. Our results provide an important quantification of model–observation mismatch, supporting the increased use and development of China-specific inventories in tracking China's progress as a whole towards reducing emissions. We emphasize that this work presents a methodology for extending the analysis to other inventories and is intended to be a comparison of a subset of anthropogenic CO2 emissions rates from inventories that were readily available at the time this research began. For this study's analysis time period, there was not enough spatially distinct observational data to conduct an optimization of the inventories. The primary intent of the comparisons presented here is not to judge specific inventories, but to demonstrate that even a single site with a long record of high-time-resolution observations can identify major differences among inventories that manifest as biases in the model–data comparison. This study provides a baseline analysis for evaluating emissions from a small but important region within China, as well a guide for determining optimal locations for future ground-based measurement sites.

Description: China has pledged reduction of carbon dioxide emissions per unit GDP by 60–65% relative to 2005 levels, and to peak carbon emissions overall by 2030. However, disagreement among available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of regional control measures. In this study, we evaluate three anthropogenic CO2 inventories by tracking the fidelity of predicted concentrations of CO2 in the atmosphere to observations, focusing on the key commitment period for the Paris accords (2005) and the Beijing Olympics (2008). One inventory is China-specific and two are spatial subsets of global inventories. The inventories differ in spatial resolution, basis in national or subnational statistics, and reliance on global or China-specific emission factors. We use a unique set of historical atmospheric observations from 2005–2009 to evaluate the three CO2 emissions inventories within China's heavily industrialized and populated Northern region accounting for ~33–41% of national emissions. Each anthropogenic inventory is combined with estimates of biogenic CO2 within a high-resolution atmospheric transport framework to model the time series of CO2 observations. Model-observation mismatch in concentration units is translated to mass units and used to optimize the original inventories in the measurement influence region, largely corresponding to Northern China. Except for the peak growing season, where assessment of anthropogenic emissions is entangled with the strong vegetation signal, we find the China-specific inventory based on subnational data and domestic field-studies agrees significantly better with observations than the global inventories at all timescales. On average, over the study time period, the China-specific inventory has substantially larger (20%) emissions for all China than the global inventories. Our analysis uses observations to support and justify increased development of China-specific inventories in tracking China's progress towards reducing emissions. Here we are restricted to a single measurement site; effectively optimizing inventories at relevant spatial scales requires multiple high temporal resolution observations. We emphasize the need for a denser observational network in future efforts to measure and verify CO2 emissions for China both regionally and as a whole.