Eri Saikawa

The terrestrial biosphere as a net source of greenhouse gases to the atmosphere.

The terrestrial biosphere can release or absorb the greenhouse gases, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), and therefore has an important role in regulating atmospheric composition and climate. Anthropogenic activities such as land-use change, agriculture and waste management have altered terrestrial biogenic greenhouse gas fluxes, and the resulting increases in methane and nitrous oxide emissions in particular can contribute to climate change. The terrestrial biogenic fluxes of individual greenhouse gases have been studied extensively, but the net biogenic greenhouse gas balance resulting from anthropogenic activities and its effect on the climate system remains uncertain. Here we use bottom-up (inventory, statistical extrapolation of local flux measurements, and process-based modelling) and top-down (atmospheric inversions) approaches to quantify the global net biogenic greenhouse gas balance between 1981 and 2010 resulting from anthropogenic activities and its effect on the climate system. We find that the cumulative warming capacity of concurrent biogenic methane and nitrous oxide emissions is a factor of about two larger than the cooling effect resulting from the global land carbon dioxide uptake from 2001 to 2010. This results in a net positive cumulative impact of the three greenhouse gases on the planetary energy budget, with a best estimate (in petagrams of CO2 equivalent per year) of 3.9 ± 3.8 (top down) and 5.4 ± 4.8 (bottom up) based on the GWP100 metric (global warming potential on a 100-year time horizon). Our findings suggest that a reduction in agricultural methane and nitrous oxide emissions, particularly in Southern Asia, may help mitigate climate change.

Policy Diffusion of Emission Standards: Is There a Race to the Top?

In a dramatic example of policy diffusion, the past three decades have witnessed the spread of automobile emission standards throughout the world. Contrary to fears that global competition would produce a race to the bottom, there appears to be a race to the top, not only among rich countries but also among poor ones. Using econometric analysis of the adoption of automobile emission standards over the past twenty years for 129 countries, the author argues that this global diffusion results from countries’ efforts to stay competitive in the international market. Due to the pressure from importing countries that have adopted stringent emission standards, even developing countries have rapidly moved to adopt rich country standards. The evidence shows that adoption of automobile emission standards correlates with an increase in the total value of automobile exports. Under some conditions, economic incentives in a global market can be a complement to environmental protection.

Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): Emissions of particulate matter from wood and dung cooking fires, garbage and crop residue burning, brick kilns, and other sources

The Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) characterized widespread and under-sampled combustion sources common to South Asia, including brick kilns, garbage burning, diesel and gasoline generators, diesel groundwater pumps, idling motorcycles, traditional and modern cooking stoves and fires, crop residue burning, and heating fire. Fuel-based emission factors (EFs; with units of pollutant mass emitted per kilogram of fuel combusted) were determined for fine particulate matter (PM2.5), organic carbon (OC), elemental carbon (EC), inorganic ions, trace metals, and organic species. For the forced-draft zigzag brick kiln, EFPM2.5 ranged from 12 to 19 g kg−1 with major contributions from OC (7 %), sulfate expected to be in the form of sulfuric acid (31.9 %), and other chemicals not measured (e.g., particle-bound water). For the clamp kiln, EFPM2.5 ranged from 8 to 13 g kg −1, with major contributions from OC (63.2 %), sulfate (23.4 %), and ammonium (16 %). Our brick kiln EFPM2.5 values may exceed those previously reported, partly because we sampled emissions at ambient temperature after emission from the stack or kiln allowing some particle-phase OC and sulfate to form from gaseous precursors. The combustion of mixed household garbage under dry conditions had an EFPM2.5 of 7.4± 1.2 g kg−1, whereas damp conditions generated the highest EFPM2.5 of all combustion sources in this study, reaching up to 125± 23 g kg−1. Garbage burning emissions contained triphenylbenzene and relatively high concentrations of heavy metals (Cu, Pb, Sb), making these useful markers of this source. A variety of cooking stoves and fires fueled with dung, hardwood, twigs, and/or other biofuels were studied. The use of dung for cooking and heating produced higher EFPM2.5 than other biofuel sources and consistently emitted more PM2.5 and OC than burning hardwood and/or twigs; this trend was consistent across traditional mud stoves, chimney stoves, and three-stone cooking fires. The comparisons of different cooking stoves and cooking fires revealed the highest PM emissions from threestone cooking fires (7.6–73 g kg−1), followed by traditional mud stoves (5.3–19.7 g kg−1), mud stoves with a chimney for exhaust (3.0–6.8 g kg−1), rocket stoves (1.5–7.2 g kg−1), induced-draft stoves (1.2–5.7 g kg−1), and the bhuse chulo stove (3.2 g kg−1), while biogas had no detectable PM emisPublished by Copernicus Publications on behalf of the European Geosciences Union. 2260 T. Jayarathne et al.: Emissions of particulate matter sions. Idling motorcycle emissions were evaluated before and after routine servicing at a local shop, which decreased EFPM2.5 from 8.8± 1.3 to 0.71± 0.45 g kg −1 when averaged across five motorcycles. Organic species analysis indicated that this reduction in PM2.5 was largely due to a decrease in emission of motor oil, probably from the crankcase. The EF and chemical emissions profiles developed in this study may be used for source apportionment and to update regional emission inventories.

The global N2O Model Intercomparison Project (NMIP): Objectives, Simulation Protocol and Expected Products

Hanqin Tian, Jia Yang, CHaoqun Lu, RongTing Xu, Josep g. CanadeLL, RobeRT b. JaCkson, aLmuT aRneTH, Jinfeng CHang, guangsHeng CHen, pHiLippe Ciais, sTefan geRbeR, akiHiko iTo, YuanYuan Huang, foRTunaT Joos, sebasTian LieneRT, paLmiRa messina, sTefan oLin, sHufen pan, CHangHui peng, eRi saikawa, Rona L. THompson, niCoLas VuiCHaRd, wiLfRied winiwaRTeR, sönke ZaeHLe, bowen ZHang, keRou ZHang, and qiuan ZHuCapsulesThe N2O Model Inter-Comparison Project (NMIP) aims at understanding and quantifying the budgets of global and regional terrestrial N2O fluxes, environmental controls and uncertainties associated with input data, model structure and parameters.

Effectiveness of state climate and energy policies in reducing power-sector CO 2 emissions

States have historically been the primary drivers of climate change policy in the US, particularly with regard to emissions from power plants. States have implemented policies designed either to directly curb greenhouse gas (GHG) emissions from power plants, or to encourage energy efficiency and renewable energy growth. With the federal government withdrawing from the global climate agreement, understanding which state-level policies have successfully mitigated power-plant emissions is urgent. Past research has assessed policy effectiveness using data for periods before the adoption of many policies. We assess 17 policies using the latest data on state-level power-sector CO2 emissions. We find that policies with mandatory compliance are reducing power-plant emissions, while voluntary policies are not. Electric decoupling, mandatory GHG registry/reporting and public benefit funds are associated with the largest reduction in emissions. Mandatory GHG registry/reporting and public benefit funds are also associated with a large reduction in emissions intensity.State policies play a key role in mitigation of power-sector emissions. Analysis of 17 policies in the US shows that mandatory compliance is reducing emissions, with the largest reductions related to greenhouse gas reporting and public benefit funds.

The global N2O model intercomparison project

Hanqin Tian, Jia Yang, CHaoqun Lu, RongTing Xu, Josep g. CanadeLL, RobeRT b. JaCkson, aLmuT aRneTH, Jinfeng CHang, guangsHeng CHen, pHiLippe Ciais, sTefan geRbeR, akiHiko iTo, YuanYuan Huang, foRTunaT Joos, sebasTian LieneRT, paLmiRa messina, sTefan oLin, sHufen pan, CHangHui peng, eRi saikawa, Rona L. THompson, niCoLas VuiCHaRd, wiLfRied winiwaRTeR, sönke ZaeHLe, bowen ZHang, keRou ZHang, and qiuan ZHuCapsulesThe N2O Model Inter-Comparison Project (NMIP) aims at understanding and quantifying the budgets of global and regional terrestrial N2O fluxes, environmental controls and uncertainties associated with input data, model structure and parameters.

Nitrous Oxide Emissions Estimated With the CarbonTracker‐Lagrange North American Regional Inversion Framework

1Institute of Arctic and Alpine Research (INSTAAR), University of Colorado, Boulder, CO 80309; 303-492-7924, E-mail: Cynthia.Nevison@colorado.edu 2NOAA Earth System Research Laboratory, Global Monitoring Division (GMD), Boulder, CO 80305 3Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO 80309 4Emory University, Department of Environmental Sciences, Atlanta, GA 30322 5Harvard University, Cambridge, MA 02138 6Stanford University, Stanford, CA 94305

The Global N₂O Model Intercomparison Project

Hanqin Tian, Jia Yang, CHaoqun Lu, RongTing Xu, Josep g. CanadeLL, RobeRT b. JaCkson, aLmuT aRneTH, Jinfeng CHang, guangsHeng CHen, pHiLippe Ciais, sTefan geRbeR, akiHiko iTo, YuanYuan Huang, foRTunaT Joos, sebasTian LieneRT, paLmiRa messina, sTefan oLin, sHufen pan, CHangHui peng, eRi saikawa, Rona L. THompson, niCoLas VuiCHaRd, wiLfRied winiwaRTeR, sönke ZaeHLe, bowen ZHang, keRou ZHang, and qiuan ZHuCapsulesThe N2O Model Inter-Comparison Project (NMIP) aims at understanding and quantifying the budgets of global and regional terrestrial N2O fluxes, environmental controls and uncertainties associated with input data, model structure and parameters.