Contributions of economic growth, terrestrial sinks, and atmospheric transport to the increasing atmospheric CO2 concentrations over the Korean Peninsula

Background Understanding a carbon budget from a national perspective is essential for establishing effective plans to reduce atmospheric CO2 growth. The national characteristics of carbon budgets are reflected in atmospheric CO2 variations; however, separating regional influences on atmospheric signals is challenging owing to atmospheric CO2 transport. Therefore, in this study, we examined the characteristics of atmospheric CO2 variations over South and North Korea during 2000–2016 and unveiled the causes of their regional differences in the increasing rate of atmospheric CO2 concentrations by utilizing atmospheric transport modeling. Results The atmospheric CO2 concentration in South Korea is rising by 2.32 ppm year− 1, which is more than the globally-averaged increase rate of 2.05 ppm year− 1. Atmospheric transport modeling indicates that the increase in domestic fossil energy supply to support manufacturing export-led economic growth leads to an increase of 0.12 ppm year− 1 in atmospheric CO2 in South Korea. Although enhancements of terrestrial carbon uptake estimated from both inverse modeling and process-based models have decreased atmospheric CO2 by up to 0.02 ppm year− 1, this decrease is insufficient to offset anthropogenic CO2 increases. Meanwhile, atmospheric CO2 in North Korea is also increasing by 2.23 ppm year− 1, despite a decrease in national CO2 emissions close to carbon neutrality. The great increases estimated in both South Korea and North Korea are associated with changes in atmospheric transport, including increasing emitted and transported CO2 from China, which have increased the national atmospheric CO2 concentrations by 2.23 ppm year− 1 and 2.27 ppm year− 1, respectively. Conclusions This study discovered that economic activity is the determinant of regional differences in increasing atmospheric CO2 in the Korea Peninsula. However, from a global perspective, changes in transported CO2 are a major driver of rising atmospheric CO2 over this region, yielding an increase rate higher than the global mean value. Our findings suggest that accurately separating the contributions of atmospheric transport and regional sources to the increasing atmospheric CO2 concentrations is important for developing effective strategies to achieve carbon neutrality at the national level.

Carbonaceous Matter in the Atmosphere and Glaciers of the Himalayas and the Tibetan Plateau: An Investigative Review

<p>Carbonaceous matter, including organic carbon (OC) and black carbon (BC), is an important climate forcing agent and contributes to glacier retreat in the Himalayas and the Tibetan Plateau (HTP). The HTP - the so-called “Third Pole” – contains the most extensive glacial area outside of the polar regions. Considerable research on carbonaceous matter in the HTP has been conducted, although this research has been challenging due to the complex terrain and strong spatiotemporal heterogeneity of carbonaceous matter in the HTP. A comprehensive investigation of published atmospheric and snow data for HTP carbonaceous matter concentration, deposition and light absorption is presented, including how these factors vary with time and other parameters. Carbonaceous matter concentrations in the atmosphere and glaciers of the HTP are found to be low. Analysis of water-insoluable organic carbon and BC from snowpits reveals that concentrations of OC and BC in the atmosphere and glacier samples in arid regions of the HTP may be overestimated due to contributions from inorganic carbon in mineral dust. Due to the remote nature of the HTP, carbonaceous matter found in the HTP has generally been transported from outside the HTP (e.g., South Asia), although local HTP emissions may also be important at some sites. This study provides essential data and a synthesis of current thinking for studies on atmospheric transport modeling and radiative forcing of carbonaceous matter in the HTP.</p>

On tuning of atmospheric inverse methods: Comparison on ETEX and Chernobyl datasets using FLEXPART v8.1 and v10.3

Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem where the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties e.g. in measurements or atmospheric transport modeling, hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information, the prior source term, also known as the first guess, and regularization parameters for shape of the source term. While the first guess is based on information independent of the measurements, such as physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term as well as regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3.

Kriging-based Mapping of Space-borne CO2 Measurements by Combining Emission Inventory and Atmospheric Transport Modeling

<p>Atmospheric CO<sub>2</sub> measurement has proven its appositeness for different applications in carbon cycle science. Many satellites are currently measuring the atmospheric CO<sub>2</sub> concentration worldwide, for example, NASA’s Orbiting Carbon Observatory-2 (OCO-2), Exploratory Satellite for Atmospheric CO<sub>2</sub> (TanSat), Japanese Greenhouse gases Observing SATellite (GOSAT), and Environmental Satellite (ENVISAT). The OCO-2 measures the column-averaged CO<sub>2</sub> dry air mole fractions (XCO<sub>2</sub>) in the atmosphere as contiguous parallelogram footprints, each having area up to about 3 km<sup>2</sup>. The problem associated with this measurement is its narrow swath of approximately 10.6 km width which results in limited spatial coverage.</p><p>A number of research works have been reported to spatially map the available XCO<sub>2</sub> samples on a regional scale or globally in different temporal scale and spatial resolution. Kriging, a family of geostatistical interpolation method, has been a popular choice for this mapping. In our recent research, we have shown that the univariate kriging methods are not able to produce a pragmatic surface of XCO<sub>2</sub> and require the incorporation of more covariates. We have studied the OCO-2’s XCO<sub>2 </sub>observations and mapped them on a regional scale including multiple covariates, such as Open-source Data Inventory for Anthropogenic CO2 (ODIAC) and the Emissions Database for Global Atmospheric Research (EDGAR) emission estimates and land use and land cover (LULC) information. It is observed that the inclusion of these covariates is able to produce more accurate mapping compared to their baseline alternatives.</p><p>However, the CO<sub>2</sub> concentration is usually highly influenced by the transportation of the emission particles through the wind. A larger temporal measurement window may ignore its effect by assuming that the wind direction is constantly changing. However, for regional mapping of space-borne XCO<sub>2</sub> in a time instance, it is essential to model. This work has developed a novel multivariate kriging-based framework to map OCO-2’s XCO<sub>2 </sub>measurements including Stochastic Time-Inverted Lagrangian Transport-(STILT)-based atmospheric transport modeling. This model could be coupled with the biospheric flux models and emission estimates to map their local scale distributions.</p><p>In this framework, every unmeasured location that is required to be estimated is considered as the receptor point in the STILT simulation. The emission particles are tracked backward in time from each of these receptor points to simulate possible routes from their upstream locations. A footprint map is then generated which is regarded as the influence of other points to the receptor point in the whole study region. The footprint map, being combined with the emission estimates, will produce a prior CO<sub>2 </sub>concentration map. This STILT-generated prior concentration map is inserted into the multivariate kriging framework. The output map, i.e., the interpolated XCO<sub>2</sub> surface is more pragmatic to include the influence of atmospheric transport for the prediction of XCO<sub>2</sub>. The accuracy of the framework is proven by comparing the estimated data with ground-based measurements. This work is one of the initial attempts to generate a Level-3 XCO<sub>2</sub> surface on a local scale by combining STILT with a multivariate kriging method.</p>

Characterizing biospheric carbon balance using CO₂ observations from the OCO-2 satellite

NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite launched in summer of 2014. Its observations could allow scientists to constrain CO2 fluxes across regions or continents that were previously difficult to monitor. This study explores an initial step toward that goal; we evaluate the extent to which current OCO-2 observations can detect patterns in biospheric CO2 fluxes and constrain monthly CO2 budgets. Our goal is to guide top-down, inverse modeling studies and identify areas for future improvement. We find that uncertainties and biases in the individual OCO-2 observations are comparable to the atmospheric signal from biospheric fluxes, particularly during Northern Hemisphere winter when biospheric fluxes are small. A series of top-down experiments indicate how these errors affect our ability to constrain monthly biospheric CO2 budgets. We are able to constrain budgets for between two and four global regions using OCO-2 observations, depending on the month, and we can constrain CO2 budgets at the regional level (i.e., smaller than seven global biomes) in only a handful of cases (16 % of all regions and months). The potential of the OCO-2 observations, however, is greater than these results might imply. A set of synthetic data experiments suggests that retrieval errors have a salient effect. Advances in retrieval algorithms and to a lesser extent atmospheric transport modeling will improve the results. In the interim, top-down studies that use current satellite observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions.

Lead pollution recorded in Greenland ice indicates European emissions tracked plagues, wars, and imperial expansion during antiquity

Lead pollution in Arctic ice reflects midlatitude emissions from ancient lead–silver mining and smelting. The few reported measurements have been extrapolated to infer the performance of ancient economies, including comparisons of economic productivity and growth during the Roman Republican and Imperial periods. These studies were based on sparse sampling and inaccurate dating, limiting understanding of trends and specific linkages. Here we show, using a precisely dated record of estimated lead emissions between 1100 BCE and 800 CE derived from subannually resolved measurements in Greenland ice and detailed atmospheric transport modeling, that annual European lead emissions closely varied with historical events, including imperial expansion, wars, and major plagues. Emissions rose coeval with Phoenician expansion, accelerated during expanded Carthaginian and Roman mining primarily in the Iberian Peninsula, and reached a maximum under the Roman Empire. Emissions fluctuated synchronously with wars and political instability particularly during the Roman Republic, and plunged coincident with two major plagues in the second and third centuries, remaining low for >500 years. Bullion in silver coinage declined in parallel, reflecting the importance of lead–silver mining in ancient economies. Our results indicate sustained economic growth during the first two centuries of the Roman Empire, terminated by the second-century Antonine plague.

Characterizing biospheric carbon balance using CO₂ observations from the OCO-2 satellite

NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite launched in summer of 2014. Its observations could allow scientists to constrain CO2 fluxes across regions or continents that were previously difficult to monitor. This study explores an initial step toward that goal; we evaluate the extent to which current OCO-2 observations can detect patterns in biospheric CO2 fluxes and constrain monthly CO2 budgets. Our goal is to guide top-down, inverse modeling studies and identify areas for future improvement. We find that uncertainties and biases in the individual OCO-2 observations are comparable to the atmospheric signal from biospheric fluxes, particularly during northern hemisphere winter when biospheric fluxes are small. A series of top-down experiments indicate how these errors affect our ability to constrain monthly biospheric CO2 budgets. We are able to constrain budgets for between two and four global regions using OCO-2 observations, depending on the month, and we can constrain CO2 budgets at the regional level (i.e., smaller than seven global biomes) in only a handful of cases (16 % of all regions and months). The potential of the OCO-2 observations, however, is greater than these results might imply. A set of synthetic data experiments suggests that observation or retrieval errors have a salient effect. Advances in retrieval algorithms and to a lesser extent atmospheric transport modeling will improve the results. In the interim, top-down studies that use current satellite observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions.

Temporal characteristics of atmospheric ammonia and nitrogen dioxide over China based on emission data, satellite observations and atmospheric transport modeling since 1980

China is experiencing intense air pollution caused in large part by anthropogenic emissions of reactive nitrogen (Nr). Atmospheric ammonia (NH3) and nitrogen dioxide (NO2) are the most important precursors for Nr compounds (including N2O5, HNO3, HONO and particulate NO3− and NH4+) in the atmosphere. Understanding the changes in NH3 and NO2 has important implications for the regulation of anthropogenic Nr emissions and is a requirement for assessing the consequence of environmental impacts. We conducted the temporal trend analysis of atmospheric NH3 and NO2 on a national scale since 1980 based on emission data (during 1980–2010), satellite observation (for NH3 since 2008 and for NO2 since 2005) and atmospheric chemistry transport modeling (during 2008–2015).Based on the emission data, during 1980–2010, significant continuous increasing trends in both NH3 and NOx were observed in REAS (Regional Emission inventory in Asia, for NH3 0.17 and for NOx 0.16 kg N ha−1 yr−2) and EDGAR (Emissions Database for Global Atmospheric Research, for NH3 0.24 and for NOx 0.17 kg N ha−1 yr−2) over China. Based on the satellite data and atmospheric chemistry transport model (CTM) MOZART-4 (Model for Ozone and Related chemical Tracers, version 4), the NO2 columns over China increased significantly from 2005 to 2011 and then decreased significantly from 2011 to 2015; the satellite-retrieved NH3 columns from 2008 to 2014 increased at a rate of 2.37 % yr−1. The decrease in NO2 columns since 2011 may result from more stringent strategies taken to control NOx emissions during the 12th Five Year Plan, while no control policy has focused on NH3 emissions. Our findings provided an overall insight into the temporal trends of both NO2 and NH3 since 1980 based on emission data, satellite observations and atmospheric transport modeling. These findings can provide a scientific background for policy makers that are attempting to control atmospheric pollution in China. Moreover, the multiple datasets used in this study have implications for estimating long-term Nr deposition datasets to assess its impact on soil, forest, water and greenhouse balance.