Low blank sampling method for measurement of the nitrogen isotopic composition of atmospheric NOx.

The nitrogen isotopic composition of nitrogen oxide (NOx) is useful for estimating its sources and sinks. Several methods have been developed to convert atmospheric nitric oxide (NO) and/or nitrogen dioxide (NO2) to nitrites and/or nitrates for collection. However, the collection efficiency and blanks are poorly evaluated for many collection methods. Here, we present a method for collecting ambient NOx (NO and NO2 simultaneously) with over 90% efficiency collection of NOx and low blank (approximately 0.5 µM) using a 3 wt% hydrogen peroxide (H2O2) and 0.5 M sodium hydride (NaOH) solution. The 1σ uncertainty of the nitrogen isotopic composition was ± 1.2 ‰. The advantages of this method include its portability, simplicity, and the ability to collect the required amount of sample to analyze the nitrogen isotopic composition of ambient NOx in a short period of time. Using this method, we observed the nitrogen isotopic compositions of NOx at the Tsukuba and Yoyogi sites in Japan. The averaged δ15N(NOx) value and standard deviation (1σ) in the Yoyogi site was (-2.7 ± 1.8) ‰ and in the Tsukuba site was (-1.7 ± 0.9) ‰ during the sampling period. The main NOx source appears to be the vehicle exhaust in the two sites.

Area-ratio Fraunhofer line depth (aFLD) method approach to estimate solar-induced chlorophyll fluorescence in low spectral resolution spectra in a cool-temperate deciduous broadleaf forest.

Solar-induced chlorophyll fluorescence (SIF) emissions were estimated by the "area-ratio Fraunhofer line depth (aFLD) method", a new retrieval methodology in spectra from a low spectral resolution (SR) spectroradiometer (MS-700: full width half maximum (FWHM) of 10 nm and spectral sampling interval of 3.3 nm), assisted with a scaling to reference SIF detected from high SR spectrum. The sparse pixels of a spectrum of low SR misses detecting the minimum of the O2A absorption band around at 760 nm, which makes the SIF detection by conventional FLD methods lose accuracy considerably. To overcome this, the aFLD method uses the definite integral of spectra over a wide interval between 750 and 780 nm. The integration of the spectrum is insusceptible to the change in shape of the depression curve, leading to higher accuracy of the aFLD method. Daily SIF, calculated by the aFLD method using the spectra obtained with MS-700, was scaled to reference daily SIF calculated by the spectral fitting method using the spectra obtained from August to December 2019 with an ultrafine SR spectroradiometer (QE Pro, FWHM = 0.24 nm). As a result, SIF calculated from MS-700 spectra by aFLD method was strongly correlated with the reference SIF from QE Pro spectra (r2 = 0.81) and was successfully scaled. Then, the scaled 11-year SIF from MS-700 at a deciduous broadleaf forest showed the correlation with GPP at multiple time steps: daily, monthly, and yearly, consistently during 2008-2018. The comparison of aFLD-derived SIF with the global Orbiting Carbon Observatory-2 (OCO-2) SIF data set (GOSIF) showed high correlation on monthly values during 2008-2017 (r2 = 0.85). The combining approach of the aFLD method with a scaling to reference SIF successfully detected long-term canopy SIF emissions, which has great potential to provide essential information on ecosystem-level photosynthesis.

Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: A modeling study.

Seasonality is a key feature of the biosphere and the seasonal dynamics of soil carbon (C) emissions represent a fundamental mechanism regulating the terrestrial-climate interaction. We applied a microbial explicit model-CLM-Microbe-to evaluate the impacts of microbial seasonality on soil C cycling in terrestrial ecosystems. The CLM-Microbe model was validated in simulating belowground respiratory fluxes, that is, microbial respiration, root respiration, and soil respiration at the site level. On average, the CLM-Microbe model explained 72% (n = 19, p < 0.0001), 65% (n = 19, p < 0.0001), and 71% (n = 18, p < 0.0001) of the variation in microbial respiration, root respiration, and soil respiration, respectively. We then compared the model simulations of soil respiratory fluxes and soil organic C content in top 1 m between the CLM-Microbe model with (CLM-Microbe) and without (CLM-Microbe_wos) seasonal dynamics of soil microbial biomass in natural biomes. Removing soil microbial seasonality reduced model performance in simulating microbial respiration and soil respiration, but led to slight differences in simulating root respiration. Compared with the CLM-Microbe, the CLM-Microbe_wos underestimated the annual flux of microbial respiration by 0.6%-32% and annual flux of soil respiration by 0.4%-29% in natural biomes. Correspondingly, the CLM-Microbe_wos estimated higher soil organic C content in top 1 m (0.2%-7%) except for the sites in Arctic and boreal regions. Our findings suggest that soil microbial seasonality enhances soil respiratory C emissions, leading to a decline in SOC storage. An explicit representation of soil microbial seasonality represents a critical improvement for projecting soil C decomposition and reducing the uncertainties in global C cycle projection under the changing climate.

Inversely estimating the vertical profile of the soil CO2 production rate in a deciduous broadleaf forest using a particle filtering method.

Carbon dioxide (CO2) efflux from the soil surface, which is a major source of CO2 from terrestrial ecosystems, represents the total CO2 production at all soil depths. Although many studies have estimated the vertical profile of the CO2 production rate, one of the difficulties in estimating the vertical profile is measuring diffusion coefficients of CO2 at all soil depths in a nondestructive manner. In this study, we estimated the temporal variation in the vertical profile of the CO2 production rate using a data assimilation method, the particle filtering method, in which the diffusion coefficients of CO2 were simultaneously estimated. The CO2 concentrations at several soil depths and CO2 efflux from the soil surface (only during the snow-free period) were measured at two points in a broadleaf forest in Japan, and the data were assimilated into a simple model including a diffusion equation. We found that there were large variations in the pattern of the vertical profile of the CO2 production rate between experiment sites: the peak CO2 production rate was at soil depths around 10 cm during the snow-free period at one site, but the peak was at the soil surface at the other site. Using this method to estimate the CO2 production rate during snow-cover periods allowed us to estimate CO2 efflux during that period as well. We estimated that the CO2 efflux during the snow-cover period (about half the year) accounted for around 13% of the annual CO2 efflux at this site. Although the method proposed in this study does not ensure the validity of the estimated diffusion coefficients and CO2 production rates, the method enables us to more closely approach the "actual" values by decreasing the variance of the posterior distribution of the values.

Effects of seasonal and interannual variations in leaf photosynthesis and canopy leaf area index on gross primary production of a cool-temperate deciduous broadleaf forest in Takayama, Japan.

Revealing the seasonal and interannual variations in forest canopy photosynthesis is a critical issue in understanding the ecological mechanisms underlying the dynamics of carbon dioxide exchange between the atmosphere and deciduous forests. This study examined the effects of temporal variations of canopy leaf area index (LAI) and leaf photosynthetic capacity [the maximum velocity of carboxylation (V (cmax))] on gross primary production (GPP) of a cool-temperate deciduous broadleaf forest for 5 years in Takayama AsiaFlux site, central Japan. We made two estimations to examine the effects of canopy properties on GPP; one is to incorporate the in situ observation of V (cmax) and LAI throughout the growing season, and another considers seasonality of LAI but constantly high V (cmax). The simulations indicated that variation in V (cmax) and LAI, especially in the leaf expansion period, had remarkable effects on GPP, and if V (cmax) was assumed constant GPP will be overestimated by 15%. Monthly examination of air temperature, radiation, LAI and GPP suggested that spring temperature could affect canopy phenology, and also that GPP in summer was determined mainly by incoming radiation. However, the consequences among these factors responsible for interannual changes of GPP are not straightforward since leaf expansion and senescence patterns and summer meteorological conditions influence GPP independently. This simulation based on in situ ecophysiological research suggests the importance of intensive consideration and understanding of the phenology of leaf photosynthetic capacity and LAI to analyze and predict carbon fixation in forest ecosystems.