Enhanced carbon uptake and reduced methane emissions in a newly restored wetland

Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 125(1), (2020): e2019JG005222, doi:10.1029/2019JG005222.Wetlands play an important role in reducing global warming potential in response to global climate change. Unfortunately, due to the effects of human disturbance and natural erosion, wetlands are facing global extinction. It is essential to implement engineering measures to restore damaged wetlands. However, the carbon sink capacity of restored wetlands is unclear. We examined the seasonal change of greenhouse gas emissions in both restored wetland and natural wetland and then evaluated the carbon sequestration capacity of the restored wetland. We found that (1) the carbon sink capacity of the restored wetland showed clear daily and seasonal change, which was affected by light intensity, air temperature, and vegetation growth, and (2) the annual daytime (8–18 hr) sustained‐flux global warming potential was −11.23 ± 4.34 kg CO2 m−2 y−1, representing a much larger carbon sink than natural wetland (−5.04 ± 3.73 kg CO2 m−2 y−1) from April to December. In addition, the results showed that appropriate tidal flow management may help to reduce CH4 emission in wetland restoration. Thus, we proposed that the restored coastal wetland, via effective engineering measures, reliably acted as a large net carbon sink and has the potential to help mitigate climate change.We would like to thank Yangtze Delta Estuarine Wetland Ecosystem Ministry of Education & Shanghai Observation and Research Station for providing sites during our research. This research was supported by the National Key Research and Development Program of China (Grant 2017YFC0506002), the National Natural Science Foundation of China Overseas and Hong Kong‐Macao Scholars Collaborative Research Fund (Grant 31728003), the China Postdoctoral Science Foundation (Grant 2018M640362), the Shanghai University Distinguished Professor (Oriental Scholars) Program (Grant JZ2016006), the Open Fund of Shanghai Key Lab for Urban Ecological Processes and Eco‐Restoration (Grant SHUES2018B06), and the Scientific Projects of Shanghai Municipal Oceanic Bureau (Grant 2018‐03). The complete data set is available at https://data.4tu.nl/repository/uuid:536b2614‐c4ca‐43d2‐84dd‐6180fd859544

A practical approach for uncertainty quantification of high-frequency soil respiration using Forced Diffusion chambers

This paper examines the sources of uncertainty for the Forced Diffusion (FD) chamber soil respiration (Rs) measurement technique and demonstrates a protocol for uncertainty quantification that could be appropriate with any soil flux technique. Here we sought to quantify and compare the three primary sources of uncertainty in Rs: (1) instrumentation error; (2) scaling error, which stems from the spatial variability of Rs; and (3) random error, which arises from stochastic or unpredictable variation in environmental drivers and was quantified from repeated observations under a narrow temperature, moisture, and time range. In laboratory studies, we found that FD instrumentation error remained constant as Rs increased. In field studies from five North American ecosystems, we found that as Rs increased from winter to peak growing season, random error increased linearly with average flux by about 40% of average Rs. Random error not only scales with soil flux but scales in a consistent way (same slope) across ecosystems. Scaling error, measured at one site, similarly increased linearly with average Rs, by about 50% of average Rs. Our findings are consistent with previous findings for both soil fluxes and eddy covariance fluxes across other northern temperate ecosystems that showed random error scales linearly with flux magnitude with a slope of ~0.2. Although the mechanistic basis for this scaling of random error is unknown, it is suggestive of a broadly applicable rule for predicting flux random error. Also consistent with previous studies, we found the random error of FD follows a Laplace (double‐exponential) rather than a normal (Gaussian) distribution.Keywords: variability, soil respiration, kurtosis, random error, uncertainty, measurement erro

Data Assimilation for Atmospheric CO2: Towards Improved Estimates of CO2 Concentrations and Fluxes.

The lack of a process-level understanding of the carbon cycle is a major contributor to our uncertainty in understanding future changes in the carbon cycle and its interplay with the climate system. Recent initiatives to reduce this uncertainty, including increases in data density and the estimation of emissions and uptake (a.k.a. fluxes) at fine spatiotemporal scales, presents computational challenges that call for numerically-efficient schemes. Often based on data assimilation (DA) approaches, these schemes are common within the numerical weather prediction community. The goal of this research is to identify fundamental gaps in our knowledge regarding the precision and accuracy of DA for CO2 applications, and develop suitable methods to fill these gaps. First, a new tool for characterizing background error statistics based on predictions from carbon flux and atmospheric transport models is shown to yield improved estimates of CO2 concentration fields within an operational DA system at the European Centre for Medium-Range Weather Forecasts (ECMWF). Second, the impact of numerical approximations within existing DA approaches is explored using a simplified flux estimation problem. It is found that a complex interplay between the underlying numerical approximations and the observational characteristics regulates the performance of the DA methods. Third, a novel and versatile DA method called the geostatistical ensemble square root filter (GEnSRF) is developed to leverage the information content of atmospheric CO2 observations. The ability of GEnSRF to match the performance of a more traditional inverse modeling approach is confirmed using a series of synthetic data experiments over North America. Fourth, GEnSRF is used to assimilate high-density satellite observations from the recently launched GOSAT satellite, and deliver global data-driven estimates of fine-scale CO2 fluxes. Diagnostics tools are used to evaluate the benefit of satellite observations in constraining global surface fluxes, relative to a traditional surface monitoring network. Overall, this research has developed, applied, and evaluated a novel set of tools with unique capabilities that increase the credibility of DA methods for atmospheric CO2 applications. Such advancements are necessary if we are to accurately understand the critical controls over the atmospheric CO2 growth, and improve our understanding of carbon-climate feedbacks.PHDEnvironmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/96172/1/abhishch_1.pd