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Abstract Wetlands play a critical role in mitigating carbon emission. However, little is known about soil carbon emission and their environmental controls from inland floodplain wetlands. This study aimed to determine the effects of hydrologic and edaphic controllers (water table depth [ WTD ], soil temperature [ Ts ], and soil water content [ SWC ]) on soil C emission in Dongting Lake wetland, China. One‐year emissions were measured in Carex meadow and mudflat using static chambers during 2013 to 2014, including nonflooded season ( NFs ) and flooded season ( Fs ). The results showed that soil C emission in the Carex meadow and mudflat was 307.8 and 264.3 g C·m −2 ·year −1 , respectively, and 50–66% of soil C were emitted during NFs. Compared with NFs, CO 2 emission was significantly decreased by 57% but CH 4 emission was significantly increased by 38 times during Fs in the Carex meadow. Stepwise regression combined with structural equation model analysis showed that CO 2 and CH 4 flux were mainly influenced by Ts during NFs, and they were controlled by water temperature ( Tw ) during Fs. During NFs, CO 2 flux increased with increasing Ts and SWC but decreased significantly when SWC was over 66% and 52% in the Carex meadow and mudflat, respectively. CH 4 flux showed an emission pulse at SWC and Ts of 65% and 17.2 °C, respectively. These results indicate that flooding significantly inhibited soil CO 2 emission but stimulated CH 4 emission. The continuous decrease of flooding days caused by anthropogenic disturbances may induce soil C loss in Dongting Lake wetlands. , Key Points Soil C emission in Dongting Lake floodplain was 264.3–307.8 g C.m ‐2 .year ‐1 Flooding significantly inhibited soil CO 2 emission but stimulated CH 4 emission The decline of flooding days in Dongting Lake wetlands can potentially increase soil C loss

Methane accounts for 20% of the global warming caused by greenhouse gases, and wastewater is a major anthropogenic source of methane. Based on the Intergovernmental Panel on Climate Change greenhouse gas inventory guidelines and current research findings, we calculated the amount of methane emissions from 2000 to 2014 that originated from wastewater from different provinces in China. Methane emissions from wastewater increased from 1349.01 to 3430.03 Gg from 2000 to 2014, and the mean annual increase was 167.69 Gg. The methane emissions from industrial wastewater treated by wastewater treatment plants ( E It ) accounted for the highest proportion of emissions. We also estimated the future trend of industrial wastewater methane emissions using the artificial neural network model. A comparison of the emissions for the years 2020, 2010, and 2000 showed an increasing trend in methane emissions in China and a spatial transition of industrial wastewater emissions from eastern and southern regions to central and southwestern regions and from coastal regions to inland regions. These changes were caused by changes in economics, demographics, and relevant policies. , Key Points Methane emission from wastewater from 2000 to 2014 was calculated to increase from 1349.01 Gg to 3430.03 Gg. Methane emission from wastewater from 2015 to 2020 was estimated to increase from 3875.30 Gg to 5212.75 Gg. A spatial transition of methane emission from wastewater was found and discussed in the present study.

Abstract Univariate quantile mapping (QM), a technique often used to statistically postprocess climate simulations, may generate physical inconsistency. This issue is investigated here by classifying physical inconsistency into two types. Type I refers to the attribution of an impossible value to a single variable, and type II refers to the breaking of a fixed intervariable relationship. Here QM is applied to relative humidity (RH) and its parent variables, namely, temperature, pressure, and specific humidity. Twelve sites representing various climate types across North America are investigated. Time series from an ensemble of ten 3-hourly simulations are postprocessed, with the CFSR reanalysis used as the reference product. For type I, results indicate that direct postprocessing of RH generates supersaturation values (>100%) at relatively small frequencies of occurrence. Generated supersaturation amplitudes exceed observed values in fog and clouds. Supersaturation values are generally more frequent and higher when RH is deduced from postprocessed parent variables. For type II, results show that univariate QM practically always breaks the intervariable thermodynamic relationship. Heuristic proxies are designed for comparing the initial bias with physical inconsistency of type II, and results suggest that QM generates a problem that is arguably lesser than the one it is intended to solve. When physical inconsistency is avoided by capping one humidity variable at its saturation level and deducing the other, statistical equivalence with the reference product remains much improved relative to the initial situation. A recommendation for climate services is to postprocess RH and deduce specific humidity rather than the opposite.

This study presents a critical analysis regarding the assumption of carbon neutrality in life cycle assessment (LCA) models that assess climate change impacts of bioenergy usage. We identified a complex of problems in the carbon neutrality assumption, especially regarding bioenergy derived from forest residues. In this study, we summarized several issues related to carbon neutral assumptions, with particular emphasis on possible carbon accounting errors at the product level. We analyzed errors in estimating emissions in the supply chain, direct and indirect emissions due to forest residue extraction, biogenic CO 2 emission from biomass combustion for energy, and other effects related to forest residue extraction. Various modeling approaches are discussed in detail. We concluded that there is a need to correct accounting errors when estimating climate change impacts and proposed possible remedies. To accurately assess climate change impacts of bioenergy use, greater efforts are required to improve forest carbon cycle modeling, especially to identify and correct pitfalls associated with LCA accounting, forest residue extraction effects on forest fire risk and biodiversity. Uncertainties in accounting carbon emissions in LCA are also highlighted, and associated risks are discussed.

Machine learning, an important branch of artificial intelligence, is increasingly being applied in sciences such as forest ecology. Here, we review and discuss three commonly used methods of machine learning (ML) including decision-tree learning, artificial neural network, and support vector machine and their applications in four different aspects of forest ecology over the last decade. These applications include: (i) species distribution models, (ii) carbon cycles, (iii) hazard assessment and prediction, and (iv) other applications in forest management. Although ML approaches are useful for classification, modeling, and prediction in forest ecology research, further expansion of ML technologies is limited by the lack of suitable data and the relatively “higher threshold” of applications. However, the combined use of multiple algorithms and improved communication and cooperation between ecological researchers and ML developers still present major challenges and tasks for the betterment of future ecological research. We suggest that future applications of ML in ecology will become an increasingly attractive tool for ecologists in the face of “big data” and that ecologists will gain access to more types of data such as sound and video in the near future, possibly opening new avenues of research in forest ecology.

ABSTRACT High‐resolution reanalyses offer the potential to improve our understanding of midlatitude cyclones, particularly smaller‐scale systems and those with complex structures. However, previous studies have demonstrated large variations in the frequency and characteristics of Australian midlatitude cyclones between reanalyses when using their native resolution. In this paper we use satellite observations of winds and rainfall in order to evaluate the ability of the ERA‐Interim, JRA55, MERRA and CFSR reanalyses to reproduce Australian east coast cyclones. The MERRA reanalysis produces a large number of erroneous small‐scale lows without cyclonic wind patterns using a simple pressure‐difference‐based cyclone identification and tracking method. Consequently, we recommend the ERA‐Interim reanalysis when using such methods, or applying more complex tracking methods that are able to compensate for these issues.

Abstract Nitrous oxide (N 2 O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and spatiotemporal patterns of N 2 O fluxes and the key drivers of N 2 O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N 2 O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N 2 O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N 2 O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N 2 O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N 2 O dynamics; and 3) provide a benchmarking estimate of N 2 O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N 2 O estimation derived from the NMIP is a key component of the global N 2 O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.