Hybrid multi-layer Deep CNN/Aggregator feature for image classification

Deep Convolutional Neural Networks (DCNN) have established a remarkable performance benchmark in the field of image classification, displacing classical approaches based on hand-tailored aggregations of local descriptors. Yet DCNNs impose high computational burdens both at training and at testing time, and training them requires collecting and annotating large amounts of training data. Supervised adaptation methods have been proposed in the literature that partially re-learn a transferred DCNN structure from a new target dataset. Yet these require expensive bounding-box annotations and are still computationally expensive to learn. In this paper, we address these shortcomings of DCNN adaptation schemes by proposing a hybrid approach that combines conventional, unsupervised aggregators such as Bag-of-Words (BoW), with the DCNN pipeline by treating the output of intermediate layers as densely extracted local descriptors. We test a variant of our approach that uses only intermediate DCNN layers on the standard PASCAL VOC 2007 dataset and show performance significantly higher than the standard BoW model and comparable to Fisher vector aggregation but with a feature that is 150 times smaller. A second variant of our approach that includes the fully connected DCNN layers significantly outperforms Fisher vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC 2007, yet at only a small fraction of the training and testing cost.Comment: Accepted in ICASSP 2015 conference, 5 pages including reference, 4 figures and 2 table

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

Acknowledgements. This work was mainly funded by the EU FP7 CARBONES project (contracts FP7-SPACE-2009-1-242316), with also a small contribution from GEOCARBON project (ENV.2011.4.1.1-1-283080). This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program; DE-FG02-04ER63917 and DE-FG02-04ER63911), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California-Berkeley, University of Virginia. Philippe Ciais acknowledges support from the European Research Council through Synergy grant ERC-2013-SyG-610028 “IMBALANCE-P”. The authors wish to thank M. Jung for providing access to the GPP MTE data, which were downloaded from the GEOCARBON data portal (https://www.bgc-jena.mpg.de/geodb/projects/Data.php). The authors are also grateful to computing support and resources provided at LSCE and to the overall ORCHIDEE project that coordinate the development of the code (http://labex.ipsl.fr/orchidee/index.php/about-the-team).Peer reviewedPublisher PD

COVID-19 causes record decline in global CO2 emissions

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures

Rubber plantation ageing controls soil biodiversity after land conversion from cassava

The rapid expansion of perennial crops is a major threat to biodiversity in Southeast Asia. The biodiversity losses related to the conversion of forest lands to oil palm or rubber plantations (RP) are well documented by recent studies. However, the impact of the conversion from intensively managed annual crops to perennial crops on soil biodiversity has not yet been addressed. This study aims at assessing the impact on soil biodiversity of a) the shortterm effect of land use conversion from cassava crop to RP, and b) the long-term effect of RP ageing. Soil biodiversity (bacterial, fungal and macrofaunal), microbial activities and pedoclimatic characteristics were measured over a chronosequence of 1–25 years old of RP compared to cassava fields, the former crop, in Thailand. The conversion from cassava to young RP (1–3 yr) had a significant effect on microbial biomass and activities and fungal composition, but did not impact the bacterial and macrofaunal diversity. This effect of land use conversion could be explained by the change in land management due to the cultivation of pineapple in the inter-row of the young RP. Canopy closure appeared to be the main driver of soil biota shifts, as most of the biotic parameters, composition, abundance and activities were significantly modified after 7 years of RP. The changes of composition in older rubber plantations originated from the dominance of Trichoderma (fungi), Firmicutes (bacteria), and earthworms. Old rubber plantations (23–25 yr) harboured the highest microbial and macrofaunal biomass; however, they were also characterised by a significant decrease in bacterial richness. The change in pedoclimatic conditions across the rubber chronosequence, i.e. increase in soil moisture, litter and organic carbon content, was a stronger driver of soil biota evolution than land use conversion. The macrofaunal composition was more resistant to land use conversion than the bacterial composition, whereas the microbial biomass was sensitive to land use conversion, but showed resilience after 20 years. However, bacterial, fungal and macrofaunal diversity, macrofaunal and microbial biomass and microbial activities were all sensitive to RP ageing

Age of air as a diagnostic for transport timescales in global models

This paper presents the first results of an age-of-air (AoA) inter-comparison of six global transport models. Following a protocol, three global circulation models and three chemistry transport models simulated five tracers with boundary conditions that grow linearly in time. This allows for an evaluation of the AoA and transport times associated with inter-hemispheric transport, vertical mixing in the troposphere, transport to and in the stratosphere, and transport of air masses between land and ocean. Since AoA is not a directly measurable quantity in the atmosphere, simulations of ²²²Rn and SF₆ were also performed. We focus this first analysis on averages over the period 2000–2010, taken from longer simulations covering the period 1988–2014. We find that two models, NIES and TOMCAT, show substantially slower vertical mixing in the troposphere compared to other models (LMDZ, TM5, EMAC, and ACTM). However, while the TOMCAT model, as used here, has slow transport between the hemispheres and between the atmosphere over land and ocean, the NIES model shows efficient horizontal mixing and a smaller latitudinal gradient in SF₆ compared to the other models and observations. We find consistent differences between models concerning vertical mixing of the troposphere, expressed as AoA differences and modelled ²²²Rn gradients between 950 and 500hPa. All models agree, however, on an interesting asymmetry in inter-hemispheric mixing, with faster transport from the Northern Hemisphere surface to the Southern Hemisphere than vice versa. This is attributed to a rectifier effect caused by a stronger seasonal cycle in boundary layer venting over Northern Hemispheric land masses, and possibly to a related asymmetric position of the intertropical convergence zone. The calculated AoA in the mid–upper stratosphere varies considerably among the models (4–7 years). Finally, we find that the inter-model differences are generally larger than differences in AoA that result from using the same model with a different resolution or convective parameterisation. Taken together, the AoA model inter-comparison provides a useful addition to traditional approaches to evaluate transport timescales. Results highlight that inter-model differences associated with resolved transport (advection, reanalysis data, nudging) and parameterised transport (convection, boundary layer mixing) are still large and require further analysis. For this purpose, all model output and analysis software are available

Contrasting effects of CO₂ fertilization, land-use change and warming on seasonal amplitude of Northern Hemisphere CO₂ exchange

Continuous atmospheric CO₂ monitoring data indicate an increase in the amplitude of seasonal CO₂-cycle exchange (SCA_(NBP)) in northern high latitudes. The major drivers of enhanced SCA_(NBP) remain unclear and intensely debated, with land-use change, CO₂ fertilization and warming being identified as likely contributors. We integrated CO₂-flux data from two atmospheric inversions (consistent with atmospheric records) and from 11 state-of-the-art land-surface models (LSMs) to evaluate the relative importance of individual contributors to trends and drivers of the SCA_(NBP) of CO₂ fluxes for 1980–2015. The LSMs generally reproduce the latitudinal increase in SCA_(NBP) trends within the inversions range. Inversions and LSMs attribute SCA_(NBP) increase to boreal Asia and Europe due to enhanced vegetation productivity (in LSMs) and point to contrasting effects of CO₂ fertilization (positive) and warming (negative) on SCA_(NBP). Our results do not support land-use change as a key contributor to the increase in SCA_(NBP). The sensitivity of simulated microbial respiration to temperature in LSMs explained biases in SCA_(NBP) trends, which suggests that SCA_(NBP) could help to constrain model turnover times

Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959-2018

Quantification of CO2 fluxes at the Earth’s surface is required to evaluate the causes and drivers of observed increases in atmospheric CO2 concentrations. Atmospheric inversion models disaggregate observed variations in atmospheric CO2 concentration to variability in CO2 emissions and sinks. They require prior constraints fossil CO2 emissions. Here we describe GCP-GridFED (version 2019.1), a gridded fossil emissions dataset that is consistent with the national CO2 emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2019.1 provides monthly fossil CO2 emissions estimates for the period 1959-2018 at a spatial resolution of 0.1°. Estimates are provided separately for oil, coal and natural gas, for mixed international bunker fuels, and for the calcination of limestone during cement production. GCP-GridFED also includes gridded estimates of O2 uptake based on oxidative ratios for oil, coal and natural gas. It will be updated annually and made available for atmospheric inversions contributing to GCP global carbon budget assessments, thus aligning the prior constraints on top-down fossil CO2 emissions with the bottom-up estimates compiled by the GCP