. To tackle the problem of severe air pollution, China has implemented active
clean air policies in recent years. As a consequence, the emissions of major
air pollutants have decreased and the air quality has substantially improved.
Here, we quantified China's anthropogenic emission trends from 2010 to 2017
and identified the major driving forces of these trends by using a
combination of bottom-up emission inventory and index decomposition analysis
(IDA) approaches. The relative change rates of China's anthropogenic
emissions during 2010–2017 are estimated as follows: −62  % for
 SO2 , −17  % for NOx , +11  % for nonmethane
volatile organic compounds (NMVOCs), +1  % for NH3 , −27  %
for CO, −38  % for PM 10 , −35  % for PM 2.5 , −27  %
for BC, −35  % for OC, and +16  % for CO2 . The IDA results
suggest that emission control measures are the main drivers of this
reduction, in which the pollution controls on power plants and industries are
the most effective mitigation measures. The emission reduction rates markedly
accelerated after the year 2013, confirming the effectiveness of China's
Clean Air Action that was implemented since 2013. We estimated that during
2013–2017, China's anthropogenic emissions decreased by 59 % for
 SO2 , 21 % for NOx , 23 % for CO, 36 % for
PM 10 , 33 % for PM 2.5 , 28 % for BC, and 32 % for OC.
NMVOC emissions increased and NH3 emissions remained stable during
2010–2017, representing the absence of effective mitigation measures for
NMVOCs and NH3 in current policies. The relative contributions of
different sectors to emissions have significantly changed after several
years' implementation of clean air policies, indicating that it is paramount
to introduce new policies to enable further emission reductions in the
future.

https://acp.copernicus.org/articles/18/14095/2018/acp-18-14095-2018.pdf

Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations).

For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30∘ N) compared to mid-latitudes (∼ 30 %, 30–60∘ N) and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters.

Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning.

/pdf/the-global-methane-budget-2000-2017-mbxchwmkf5.pdf

The global methane budget 2000–2017

An overview of the Community Earth System Model Version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1° horizontal resolution configuration of the coupled model with both the “low-top” (40 km, with limited chemistry) and “high-top” (130 km, with comprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial science and infrastructure improvements and new capabilities since its previous major release, CESM1, resulting in improved historical simulations in comparison to CESM1 and available observations. These include major reductions in low-latitude precipitation and shortwave cloud forcing biases; better representation of the Madden-Julian Oscillation; better El Nino-Southern Oscillation-related teleconnections; and a global land carbon accumulation trend that agrees well with observationally based estimates. Most tropospheric and surface features of the low- and high-top simulations are very similar to each other, so these improvements are present in both configurations. CESM2 has an equilibrium climate sensitivity of 5.1–5.3 °C, larger than in CESM1, primarily due to a combination of relatively small changes to cloud microphysics and boundary layer parameters. In contrast, CESM2's transient climate response of 1.9–2.0 °C is comparable to that of CESM1. The model outputs from these and many other simulations are available to the research community, and they represent CESM2's contributions to the Coupled Model Intercomparison Project Phase 6.

The Community Earth System Model Version 2 (CESM2)

Contributing Authors: Katherine Calvin (USA), Joana Correia de Oliveira de Portugal Pereira (UK/Portugal), Oreane Edelenbosch (Netherlands/Italy), Johannes Emmerling (Italy/Germany), Sabine Fuss (Germany), Thomas Gasser (Austria/France), Nathan Gillett (Canada), Chenmin He (China), Edgar Hertwich (USA/Austria), Lena Höglund-Isaksson (Austria/Sweden), Daniel Huppmann (Austria), Gunnar Luderer (Germany), Anil Markandya (Spain/UK), David L. McCollum (USA/Austria), Malte Meinshausen (Australia/Germany), Richard Millar (UK), Alexander Popp (Germany), Pallav Purohit (Austria/India), Keywan Riahi (Austria), Aurélien Ribes (France), Harry Saunders (Canada/USA), Christina Schädel (USA/Switzerland), Chris Smith (UK), Pete Smith (UK), Evelina Trutnevyte (Switzerland/Lithuania), Yang Xiu (China), Wenji Zhou (Austria/China), Kirsten Zickfeld (Canada/Germany)

Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO2: 60% with 95% CI 48 to 72%), and fine particulate matter (PM2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O3: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NOx chemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO2 exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing "business as usual" air pollutant emissions from economic activities. Explore trends here: https://nina.earthengine.app/view/lockdown-pollution.

COVID-19 lockdowns cause global air pollution declines.

. We present a new data set of annual historical (1750–2014) anthropogenic chemically reactive gases (CO, CH4, NH3, NOx, SO2, NMVOCs), carbonaceous aerosols (black carbon – BC, and organic carbon – OC), and CO2 developed with the Community Emissions Data System (CEDS). We improve upon existing inventories with a more consistent and reproducible methodology applied to all emission species, updated emission factors, and recent estimates through 2014. The data system relies on existing energy consumption data sets and regional and country-specific inventories to produce trends over recent decades. All emission species are consistently estimated using the same activity data over all time periods. Emissions are provided on an annual basis at the level of country and sector and gridded with monthly seasonality. These estimates are comparable to, but generally slightly higher than, existing global inventories. Emissions over the most recent years are more uncertain, particularly in low- and middle-income regions where country-specific emission inventories are less available. Future work will involve refining and updating these emission estimates, estimating emissions' uncertainty, and publication of the system as open-source software.

/pdf/historical-1750-2014-anthropogenic-emissions-of-reactive-4l2fje0yoy.pdf

Tyler Pitkanen

Papers

Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)

input4MIPs.CMIP6.CMIP.PNNL-JGCRI.CEDS-2017-08-30

CEDS v_2019_12_23 Emission Data