Warning signs for stabilizing global CO2 emissions

Carbon dioxide (CO2) emissions from fossil fuels and industry comprise ~90% of all CO2 emissions from human activities. For the last three years, such emissions were stable, despite continuing growth in the global economy. Many positive trends contributed to this unique hiatus, including reduced coal use in China and elsewhere, continuing gains in energy efficiency, and a boom in low-carbon renewables such as wind and solar. However, the temporary hiatus appears to have ended in 2017. For 2017, we project emissions growth of 2.0% (range: 0.8%−3.0%) from 2016 levels (leap-year adjusted), reaching a record 36.8 ± 2 Gt CO2. Economic projections suggest further emissions growth in 2018 is likely. Time is running out on our ability to keep global average temperature increases below 2 °C and, even more immediately, anything close to 1.5 °C

Five decades of northern land carbon uptake revealed by the interhemispheric CO2 gradient

The global land and ocean carbon sinks have increased proportionally with increasing carbon dioxide emissions during the past decades 1 . It is thought that Northern Hemisphere lands make a dominant contribution to the global land carbon sink 2–7 ; however, the long-term trend of the northern land sink remains uncertain. Here, using measurements of the interhemispheric gradient of atmospheric carbon dioxide from 1958 to 2016, we show that the northern land sink remained stable between the 1960s and the late 1980s, then increased by 0.5 ± 0.4 petagrams of carbon per year during the 1990s and by 0.6 ± 0.5 petagrams of carbon per year during the 2000s. The increase of the northern land sink in the 1990s accounts for 65% of the increase in the global land carbon flux during that period. The subsequent increase in the 2000s is larger than the increase in the global land carbon flux, suggesting a coincident decrease of carbon uptake in the Southern Hemisphere. Comparison of our findings with the simulations of an ensemble of terrestrial carbon models 5,8 over the same period suggests that the decadal change in the northern land sink between the 1960s and the 1990s can be explained by a combination of increasing concentrations of atmospheric carbon dioxide, climate variability and changes in land cover. However, the increase during the 2000s is underestimated by all models, which suggests the need for improved consideration of changes in drivers such as nitrogen deposition, diffuse light and land-use change. Overall, our findings underscore the importance of Northern Hemispheric land as a carbon sink

China CO2 emission accounts 1997–2015

China is the world’s top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China’s CO2 emissions is the first step in implementing reduction policies. However, no annual, officially published emissions data exist for China. The current emissions estimated by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions estimates is approximately equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO2 emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement production). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions reduction policy-making in China

Five decades of northern land carbon uptake revealed by the interhemispheric CO2 gradient

The global land and ocean carbon sinks have increased proportionally with increasing carbon dioxide emissions during the past decades 1 . It is thought that Northern Hemisphere lands make a dominant contribution to the global land carbon sink 2–7 ; however, the long-term trend of the northern land sink remains uncertain. Here, using measurements of the interhemispheric gradient of atmospheric carbon dioxide from 1958 to 2016, we show that the northern land sink remained stable between the 1960s and the late 1980s, then increased by 0.5 ± 0.4 petagrams of carbon per year during the 1990s and by 0.6 ± 0.5 petagrams of carbon per year during the 2000s. The increase of the northern land sink in the 1990s accounts for 65% of the increase in the global land carbon flux during that period. The subsequent increase in the 2000s is larger than the increase in the global land carbon flux, suggesting a coincident decrease of carbon uptake in the Southern Hemisphere. Comparison of our findings with the simulations of an ensemble of terrestrial carbon models 5,8 over the same period suggests that the decadal change in the northern land sink between the 1960s and the 1990s can be explained by a combination of increasing concentrations of atmospheric carbon dioxide, climate variability and changes in land cover. However, the increase during the 2000s is underestimated by all models, which suggests the need for improved consideration of changes in drivers such as nitrogen deposition, diffuse light and land-use change. Overall, our findings underscore the importance of Northern Hemispheric land as a carbon sink

Evaluating Oceanic Uptake of Atmospheric CCl4: A Combined Analysis of Model Simulations and Observations

We provide new estimates of the air‐sea flux of CCl4 using simulations from a global ocean biogeochemistry model (NEMO‐PlankTOM) in combination with depth‐resolved CCl4 observations from global oceanic databases. Estimates of global oceanic CCl4 uptake are derived from a range of model analyses, including prescribed parameterizations using reported values on hydrolysis and degradation, and analyses optimized using the global observational databases. We evaluate the sensitivity of our results to uncertainties in air‐sea gas exchange parameterization, estimation period, and circulation processes. Our best constrained estimate of ocean CCl4 uptake for the period 1996–2000 is 20.1 Gg/year (range 16.6–22.7), corresponding to estimates of the partial atmospheric lifetime with respect to ocean uptake of 124 (110–150) years. This new oceanic lifetime implies higher emissions of CCl4 than currently estimated and therefore a larger missing atmospheric source of CCl4

Comment on Qian et al. 2008: La Niña and El Niño composites of atmospheric CO2 change

It is well known that interannual extremes in the rate of change of atmospheric CO2 are strongly influenced by the occurrence of El Niño-Southern Oscillation (ENSO) events. Qian et al. presented ENSO composites of atmospheric CO2 changes. We show that their composites do not reflect the atmospheric changes that are most relevant to understanding the role of ENSO on atmospheric CO2 variability. We present here composites of atmospheric CO2 change that differ markedly from those of Qian et al., and reveal previously unreported asymmetries between the effects on the global carbon system of El Niño and La Niña events. The calendar-year timing differs; La Niña changes in atmospheric CO2 typically occur primarily over September–May, while El Niño changes occur primarily over December–August. And the net concentration change is quite different; La Niña changes are about half the size of El Niño changes. These results illustrate new aspects of the ENSO/global carbon budget interaction and provide useful global-scale benchmarks for the evaluation of Earth System Model studies of the carbon system

Role of zooplankton dynamics for Southern Ocean phytoplankton biomass and global biogeochemical cycles

Global ocean biogeochemistry models currently employed in climate change projections use highly simplified representations of pelagic food webs. These food webs do not necessarily include critical pathways by which ecosystems interact with ocean biogeochemistry and climate. Here we present a global biogeochemical model which incorporates ecosystem dynamics based on the representation of ten plankton functional types (PFTs); six types of phytoplankton, three types of zooplankton, and heterotrophic bacteria. We improved the representation of zooplankton dynamics in our model through (a) the explicit inclusion of large, slow-growing zooplankton, and (b) the introduction of trophic cascades among the three zooplankton types. We use the model to quantitatively assess the relative roles of iron vs. grazing in determining phytoplankton biomass in the Southern Ocean High Nutrient Low Chlorophyll (HNLC) region during summer. When model simulations do not represent crustacean macrozooplankton grazing, they systematically overestimate Southern Ocean chlorophyll biomass during the summer, even when there was no iron deposition from dust. When model simulations included the developments of the zooplankton component, the simulation of phytoplankton biomass improved and the high chlorophyll summer bias in the Southern Ocean HNLC region largely disappeared. Our model results suggest that the observed low phytoplankton biomass in the Southern Ocean during summer is primarily explained by the dynamics of the Southern Ocean zooplankton community rather than iron limitation. This result has implications for the representation of global biogeochemical cycles in models as zooplankton faecal pellets sink rapidly and partly control the carbon export to the intermediate and deep ocean

Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning

Understanding the relationship between surface marine ecosystems and the export of carbon to depth by sinking organic particles is key to representing the effect of ecosystem dynamics and diversity, and their evolution under multiple stressors, on the carbon cycle and climate in models. Recent observational technologies have greatly increased the amount of data available, both for the abundance of diverse plankton groups and for the concentration and properties of particulate organic carbon in the ocean interior. Here we use synthetic model data to test the potential of using machine learning (ML) to reproduce concentrations of particulate organic carbon within the ocean interior based on surface ecosystem and environmental data. We test two machine learning methods that differ in their approaches to data-fitting, the random forest and XGBoost methods. The synthetic data are sampled from the PlankTOM12 global biogeochemical model using the time and coordinates of existing observations. We test 27 different combinations of possible drivers to reconstruct small (POCS) and large (POCL) particulate organic carbon concentrations. We show that ML can successfully be used to reproduce modelled particulate organic carbon over most of the ocean based on ecosystem and modelled environmental drivers. XGBoost showed better results compared to random forest thanks to its gradient boosting trees' architecture. The inclusion of plankton functional types (PFTs) in driver sets improved the accuracy of the model reconstruction by 58 % on average for POCS and by 22 % for POCL. Results were less robust over the equatorial Pacific and some parts of the high latitudes. For POCS reconstruction, the most important drivers were the depth level, temperature, microzooplankton and PO4, while for POCL it was the depth level, temperature, mixed-layer depth, microzooplankton, phaeocystis, PO4 and chlorophyll a averaged over the mixed-layer depth. These results suggest that it will be possible to identify linkages between surface environmental and ecosystem structure and particulate organic carbon distribution within the ocean interior using real observations and to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.</p

Online Monitoring of the Osiris Reactor with the Nucifer Neutrino Detector

Originally designed as a new nuclear reactor monitoring device, the Nucifer detector has successfully detected its first neutrinos. We provide the second shortest baseline measurement of the reactor neutrino flux. The detection of electron antineutrinos emitted in the decay chains of the fission products, combined with reactor core simulations, provides an new tool to assess both the thermal power and the fissile content of the whole nuclear core and could be used by the Inter- national Agency for Atomic Energy (IAEA) to enhance the Safeguards of civil nuclear reactors. Deployed at only 7.2m away from the compact Osiris research reactor core (70MW) operating at the Saclay research centre of the French Alternative Energies and Atomic Energy Commission (CEA), the experiment also exhibits a well-suited configuration to search for a new short baseline oscillation. We report the first results of the Nucifer experiment, describing the performances of the 0.85m3 detector remotely operating at a shallow depth equivalent to 12m of water and under intense background radiation conditions. Based on 145 (106) days of data with reactor ON (OFF), leading to the detection of an estimated 40760 electron antineutrinos, the mean number of detected antineutrinos is 281 +- 7(stat) +- 18(syst) electron antineutrinos/day, in agreement with the prediction 277(23) electron antineutrinos/day. Due the the large background no conclusive results on the existence of light sterile neutrinos could be derived, however. As a first societal application we quantify how antineutrinos could be used for the Plutonium Management and Disposition Agreement.Comment: 22 pages, 16 figures - Version

Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements

It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO2) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO2 from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO2 emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO2 based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer "atmospheric potential oxygen"(APO). We detect and quantify ffCO2 reductions during 2020-2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near-real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO2 emissions reductions from three independent lower-frequency U.K. estimates