Biosphere-atmosphere Interactions Research Articles

Functional convergence of biosphere–atmosphere interactions in response to meteorological conditions

Abstract. Understanding the dependencies of the terrestrial carbon and water cycle with meteorological conditions is a prerequisite to anticipate their behaviour under climate change conditions. However, terrestrial ecosystems and the atmosphere interact via a multitude of variables across temporal and spatial scales. Additionally these interactions might differ among vegetation types or climatic regions. Today, novel algorithms aim to disentangle the causal structure behind such interactions from empirical data. The estimated causal structures can be interpreted as networks, where nodes represent relevant meteorological variables or land-surface fluxes and the links represent the dependencies among them (possibly including time lags and link strength). Here we derived causal networks for different seasons at 119 eddy covariance flux tower observations in the FLUXNET network. We show that the networks of biosphere–atmosphere interactions are strongly shaped by meteorological conditions. For example, we find that temperate and high-latitude ecosystems during peak productivity exhibit biosphere–atmosphere interaction networks very similar to tropical forests. In times of anomalous conditions like droughts though, both ecosystems behave more like typical Mediterranean ecosystems during their dry season. Our results demonstrate that ecosystems from different climate zones or vegetation types have similar biosphere–atmosphere interactions if their meteorological conditions are similar. We anticipate our analysis to foster the use of network approaches, as they allow for a more comprehensive understanding of the state of ecosystem functioning. Long-term or even irreversible changes in network structure are rare and thus can be indicators of fundamental functional ecosystem shifts.

Unified Four-Stream Radiative Transfer Theory in the Optical-Thermal Domain with Consideration of Fluorescence for Multi-Layer Vegetation Canopies

Vegetation radiative transfer models (RTMs) are important tools to understand biosphere-atmosphere interactions. The four-stream theory has been successfully applied to solve the radiative transfer problems in homogeneous canopies for both incident solar radiation, thermal and fluorescence emission since 1984. In this note, we describe the development of a unified radiative transfer theory for optical scattering, thermal and fluorescence emission in multi-layer vegetation canopy, and provide a detailed mathematical derivation for the fluxes inside and leaving the canopy. This theory can be used to develop vegetation models for remote sensing applications and plant physiological processes, such as photosynthesis and transpiration. It can also be used to solve the radiative transfer problems in soil-water, soil-water-atmosphere, or soil-vegetation-atmosphere ensembles, besides the soil-vegetation system presented in the note.

Identifying and addressing knowledge gaps for improving greenhouse gas emissions estimates from tropical peat forest fires

Tropical peatlands are areas of high carbon density that are important in biosphere-atmosphere interactions. Drainage and burning of tropical peatlands releases about 5% of global greenhouse gas (GHG) emissions, yet there is great uncertainty in these estimates. Our comprehensive literature review of parameters required to calculate GHG emissions from burnt peat forests, following the international guidelines, revealed many gaps in knowledge of carbon pools and few recent supporting studies. To improve future estimates of the total ecosystem carbon balance and peatfire emissions this study aimed to account for all carbon pools: aboveground, deadwood, pyrogenic carbon (PyC) and peat of single and repeatedly burnt peat forests. A further aim was to identify the minimum sampling intensity required to detect with 80% power significant differences in these carbon pools among long unburnt, recently burnt and repeatedly burnt peat swamp forests.About 90 Mg C ha−1 remains aboveground as deadwood after a single fire and half of this remains after a second fire. One fire produces 4.5 ± 0.6 Mg C ha−1 of PyC, with a second fire increasing this to 7.1 ± 0.8 Mg C ha−1. For peat swamp forests these aboveground carbon pools are rarely accounted in estimates of emissions following multiple fires, while PyC has not been included in the total peat carbon mass balance. Peat bulk density and peat carbon content change with fire frequency, yet these parameters often remain constant in the published emission estimates following a single and multiple fires. Our power analysis indicated that as few as 12 plots are required to detect meaningful differences between fire treatments for the major carbon pools. Further field studies directed at improving the parameters for calculating carbon balance of disturbed peat forest ecosystems are required to better constrain peatfire GHG emission estimates.

Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data

High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).

Reframing tropical savannization: linking changes in canopy structure to energy balance alterations that impact climate

AbstractTropical ecosystems are undergoing unprecedented rates of degradation from deforestation, fire, and drought disturbances. The collective effects of these disturbances threaten to shift large portions of tropical ecosystems such as Amazon forests into savanna‐like structure via tree loss, functional changes, and the emergence of fire (savannization). Changes from forest states to a more open savanna‐like structure can affect local microclimates, surface energy fluxes, and biosphere–atmosphere interactions. A predominant type of ecosystem state change is the loss of tree cover and structural complexity in disturbed forest. Although important advances have been made contrasting energy fluxes between historically distinct old‐growth forest and savanna systems, the emergence of secondary forests and savanna‐like ecosystems necessitates a reframing to consider gradients of tree structure that span forest to savanna‐like states at multiple scales. In this Innovative Viewpoint, we draw from the literature on forest–grassland continua to develop a framework to assess the consequences of tropical forest degradation on surface energy fluxes and canopy structure. We illustrate this framework for forest sites with contrasting canopy structure that ranges from simple, open, and savanna‐like to complex and closed, representative of tropical wet forest, within two climatically distinct regions in the Amazon. Using a recently developed rapid field assessment approach, we quantify differences in cover, leaf area vertical profiles, surface roughness, albedo, and energy balance partitioning between adjacent sites and compare canopy structure with adjacent old‐growth forest; more structurally simple forests displayed lower net radiation. To address forest–atmosphere feedback, we also consider the effects of canopy structure change on susceptibility to additional future disturbance. We illustrate a converse transition—recovery in structure following disturbance—measuring forest canopy structure 10 yr after the imposition of a 5‐yr drought in the ground‐breaking Seca Floresta experiment. Our approach strategically enables rapid characterization of surface properties relevant to vegetation models following degradation, and advances links between surface properties and canopy structure variables, increasingly available from remote sensing. Concluding, we hypothesize that understanding surface energy balance and microclimate change across degraded tropical forest states not only reveals critical atmospheric forcing, but also critical local‐scale feedbacks from forest sensitivity to additional climate‐linked disturbance.

Measuring Biosphere-Atmosphere Exchange of Short-Lived Climate Forcers and Their Precursors.

Exchange of reactive trace gases over the biosphere is a key source of reactive organic carbon to the atmosphere and thus influences the formation of both ozone (O3) and secondary organic aerosol (SOA). Both O3 and aerosol particles are short-lived climate forcers and impact the radiative balance of the planet, and their sources and sinks are chemically complex. However, the biosphere also acts as a deposition sink for organic and inorganic compounds, including O3, aerosols, and their precursors. Wet and dry deposition provides a key lever on the lifetime of trace gases and particles in the atmosphere and thus on their potential to influence the radiative balance of the planet. The fluxes of reactive trace gases and particles are part of an atmospheric biogeochemical cycle that includes feedbacks through drought and other climate components.Recent advances in measurement techniques have enabled new field observations of trace gas and particle fluxes. Our method development has focused on the leaf, branch, and forest level, although satellite measurements coupled to modeling also provide promising new approaches to constraining trace gas fluxes. Leaf chamber measurements of volatile organic compound (VOC) emissions highlight leaf-to-leaf and plant-to-plant variability in both photosynthesis and emissions of individual VOCs, in addition to differences in emissions across different isomers of monoterpenes. Isomers obviously have different chemical properties (e.g., reaction rates with OH radicals, SOA yield) and thus hold different potentials as precursors for short-lived climate forcers.The biosphere acts as both sources and sinks of the oxidation products of monoterpenes and other biogenic VOCs. Developments in chemical ionization mass spectrometry have recently enabled measurements of volatile organic acids, which demonstrate a strong temperature-dependent ecosystem source, as well as a source from in-canopy chemistry. In-canopy chemistry also influences particle fluxes, although deposition should dominate their net exchange. Our field observations of chemically resolved particle fluxes demonstrate the simultaneous, competing processes driving forest exchange. To separate out these competing processes, we use black carbon as an inert tracer for particle deposition. Our recent measurements demonstrate the importance of wet deposition in controlling particle lifetime in the atmosphere. Overall, new measurement techniques have enabled both field and laboratory observations to improve our understanding of biosphere-atmosphere interactions and their influence on climate processes.

Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0

Abstract. Biosphere–atmosphere interactions strongly influence the chemical composition of the atmosphere. Simulating these interactions at a detailed process-based level has traditionally been computationally intensive and resource prohibitive, commonly due to complexities in calculating radiation and light at the leaf level within plant canopies. Here we describe a surrogate canopy physics model based on the MEGAN3 detailed canopy model parameterized using a statistical learning technique. This surrogate canopy model is specifically designed to rapidly calculate leaf-level temperature and photosynthetically active radiative (PAR) for use in large-scale chemical transport models (CTMs). Our surrogate model can reproduce the dominant spatiotemporal variability of the more detailed MEGAN3 canopy model to within 10 % across the globe. Implementation of this surrogate model into the GEOS-Chem CTM leads to small local changes in ozone dry deposition velocities of less than 5 % and larger local changes in isoprene emissions of up to ∼40 %, though annual global isoprene emissions remain largely consistent (within 5 %). These changes to surface–atmosphere exchange lead to small changes in surface ozone concentrations of ±1 ppbv, modestly reducing the northern hemispheric ozone bias, which is common to many CTMs, here from 8 to 7 ppbv. The use of this computationally efficient surrogate canopy model drives emissions of isoprene and concentrations of surface ozone closer to observationally constrained values. Additionally, this surrogate model allows for the further development and implementation of leaf-level emission factors in the calculation of biogenic emissions in the GEOS-Chem CTM. Though not the focus of this work, this ultimately enables a complete implementation of the MEGAN3 emissions framework within GEOS-Chem, which produces 570 Tg yr−1 of isoprene for 2012.

Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration

Transpiration, the dominant component of terrestrial evapotranspiration (ET), directly connects the water, energy and carbon cycles and is typically restricted by soil and atmospheric (for example, the vapour pressure deficit (VPD)) moisture stresses through plant hydraulic processes. These sources of stress are likely to diverge under climate change, with a globally enhanced VPD but more variable and uncertain changes in soil moisture. Here, using a model–data fusion approach, we demonstrate that the common empirical approach used in most Earth system models to evaluate the ET response to soil moisture and VPD, which neglects plant hydraulics, underestimates ET sensitivity to VPD and compensates by overestimating the sensitivity to soil moisture stress. A hydraulic model that describes water transport through the plant better captures ET under high VPD conditions for wide-ranging soil moisture states. These findings highlight the central role of plant hydraulics in regulating the increasing importance of atmospheric moisture stress on biosphere–atmosphere interactions under elevated temperatures. Evapotranspiration links productivity with water cycling between land and atmosphere. A model including plant hydraulics better describes the response of evapotranspiration to stress from vapour pressure deficit and soil moisture under rising temperatures than approaches common in Earth system models.

Pyruvic acid in the boreal forest: gas-phase mixing ratios and impact on radical chemistry

Abstract. Pyruvic acid (CH3C(O)C(O)OH, 2-oxopropanoic acid) is an organic acid of biogenic origin that plays a crucial role in plant metabolism, is present in tropospheric air in both gas-phase and aerosol-phase, and is implicated in the formation of secondary organic aerosols (SOAs). Up to now, only a few field studies have reported mixing ratios of gas-phase pyruvic acid, and its tropospheric sources and sinks are poorly constrained. We present the first measurements of gas-phase pyruvic acid in the boreal forest as part of the IBAIRN (Influence of Biosphere–Atmosphere Interactions on the Reactive Nitrogen budget) field campaign in Hyytiälä, Finland, in September 2016. The mean pyruvic acid mixing ratio during IBAIRN was 96 pptv, with a maximum value of 327 pptv. From our measurements we estimated the overall pyruvic acid source strength and quantified the contributions of isoprene oxidation and direct emissions from vegetation in this monoterpene-dominated forested environment. Further, we discuss the relevance of gas-phase pyruvic acid for atmospheric chemistry by investigating the impact of its photolysis on acetaldehyde and peroxy radical production rates. Our results show that, based on our present understanding of its photochemistry, pyruvic acid is an important source of acetaldehyde in the boreal environment, exceeding ethane and propane oxidation by factors of ∼10 and ∼20.

Adaptation of hydroxymethylbutenyl diphosphate reductase enables volatile isoprenoid production.

Volatile isoprenoids produced by plants are emitted in vast quantities into the atmosphere, with substantial effects on global carbon cycling. Yet, the molecular mechanisms regulating the balance between volatile and non-volatile isoprenoid production remain unknown. Isoprenoids are synthesised via sequential condensation of isopentenyl pyrophosphate (IPP) to dimethylallyl pyrophosphate (DMAPP), with volatile isoprenoids containing fewer isopentenyl subunits. The DMAPP:IPP ratio could affect the balance between volatile and non-volatile isoprenoids, but the plastidic DMAPP:IPP ratio is generally believed to be similar across different species. Here we demonstrate that the ratio of DMAPP:IPP produced by hydroxymethylbutenyl diphosphate reductase (HDR/IspH), the final step of the plastidic isoprenoid production pathway, is not fixed. Instead, this ratio varies greatly across HDRs from phylogenetically distinct plants, correlating with isoprenoid production patterns. Our findings suggest that adaptation of HDR plays a previously unrecognised role in determining in vivo carbon availability for isoprenoid emissions, directly shaping global biosphere-atmosphere interactions.

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Abstract. The dynamics of biochemical processes in terrestrial ecosystems are tightly coupled to local meteorological conditions. Understanding these interactions is an essential prerequisite for predicting, e.g. the response of the terrestrial carbon cycle to climate change. However, many empirical studies in this field rely on correlative approaches and only very few studies apply causal discovery methods. Here we explore the potential for a recently proposed causal graph discovery algorithm to reconstruct the causal dependency structure underlying biosphere–atmosphere interactions. Using artificial time series with known dependencies that mimic real-world biosphere–atmosphere interactions we address the influence of non-stationarities, i.e. periodicity and heteroscedasticity, on the estimation of causal networks. We then investigate the interpretability of the method in two case studies. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem. Secondly, we explore global Normalised Difference Vegetation Index time series (GIMMS 3g), along with gridded climate data to study large-scale climatic drivers of vegetation greenness. We compare the retrieved causal graphs to simple cross-correlation-based approaches to test whether causal graphs are considerably more informative. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. For example, we find a complete decoupling of the net ecosystem exchange from meteorological variability during summer in the Mediterranean ecosystem. However, cautious interpretations are needed, as the violation of the method's assumptions due to non-stationarities increases the likelihood to detect false links. Overall, estimating directed biosphere–atmosphere networks helps unravel complex multidirectional process interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful sets of relations, which can be powerful insights for the evaluation of terrestrial ecosystem models.

Modelling the effect of the 2018 summer heatwave and drought on isoprene emissions in a UK woodland.

Projected future climatic extremes such as heatwaves and droughts are expected to have major impacts on emissions and concentrations of biogenic volatile organic compounds (bVOCs) with potential implications for air quality, climate and human health. While the effects of changing temperature and photosynthetically active radiation (PAR) on the synthesis and emission of isoprene, the most abundant of these bVOCs, are well known, the role of other environmental factors such as soil moisture stress are not fully understood and are therefore poorly represented in land surface models. As part of the Wytham Isoprene iDirac Oak Tree Measurements campaign, continuous measurements of isoprene mixing ratio were made throughout the summer of 2018 in Wytham Woods, a mixed deciduous woodland in southern England. During this time, the United Kingdom experienced a prolonged heatwave and drought, and isoprene mixing ratios were observed to increase by more than 400% at Wytham Woods under these conditions. We applied the state-of-the-art FORest Canopy-Atmosphere Transfer canopy exchange model to investigate the processes leading to these elevated concentrations. We found that although current isoprene emissions algorithms reproduced observed mixing ratios in the canopy before and after the heatwave, the model underestimated observations by ~40% during the heatwave-drought period implying that models may substantially underestimate the release of isoprene to the atmosphere in future cases of mild or moderate drought. Stress-induced emissions of isoprene based on leaf temperature and soil water content (SWC) were incorporated into current emissions algorithms leading to significant improvements in model output. A combination of SWC, leaf temperature and rewetting emission bursts provided the best model-measurement fit with a 50% improvement compared to the baseline model. Our results highlight the need for more long-term ecosystem-scale observations to enable improved model representation of atmosphere-biosphere interactions in a changing global climate.

Evaluation of Hybrid Polarimetric Decomposition Techniques for Forest Biomass Estimation

Forest plays an important role in carbon sequestration and biosphere-atmosphere interaction. Knowledge of forest biomass content helps in assessing its sustainability and thus mitigating climate change. The advancement in remote sensing technology provides the capability of estimating biomass at a large scale. Hybrid polarimetry has gained significant attention among other radar missions due to its fundamental advantages. In this article, the potential of hybrid polarimetric SAR is evaluated for the efficient forest aboveground biomass (AGB) estimation for Barkot Forest, Uttarakhand, India. Forest biomass is calculated by means of the extended water cloud model. Scattering parameters are derived using two widely used hybrid polarimetric decomposition techniques, m−δ and m–χ decompositions. Potential insight into the efficacy of these decomposition techniques toward biomass estimation is brought forth. The modeled AGB estimates were compared with fully polarimetric data-based estimated AGB. The estimation based on the m–χ and m−δ decomposition resulted in biomass estimation with an accuracy of 75.8% and 73.4%, respectively.

Mudança dos elementos meteorológicos em função da degradação da floresta urbana

O estudo das interações biosfera-atmosfera permite descrever e caracterizar os principais processos de transferência de energia e massa, relacionados às particularidades da vegetação. Assim, as interações que ocorrem em uma floresta podem sofrer impactos significativos em virtude de mudança do clima e mudanças no uso do solo. Deste modo, o objetivo desta pesquisa visa entender o papel da floresta urbana da Tijuca na distribuição dos elementos meteorológicos mediante a sua degradação. Para esse fim, foram realizadas medições em área de floresta natural (-22º95’50’’S, -43º28’55’’W, 420 m) e degradada (-22º96’58’’S, -43º27’83’’W, 355 m), no Parque Nacional da Tijuca, localizado no município do Rio de Janeiro-RJ, com a utilização de estações meteorológicas automáticas entre os dias 18/07 a 09/08/2017, com intervalos de medição de 30 min, com medição da variabilidade da temperatura do ar e da umidade relativa do ar. Os dados foram submetidos ao coeficiente de correlação de Pearson, e aos testes de hipóteses teste t, teste F e teste Kolmogorov-Smirnov. Os resultados mostraram diferenças de temperatura média maior do que 1 °C para a área degradada, mas em uma escala diário pode ser maior do que 7°C. A diferença da umidade relativa média foi próxima de 10%, sendo menor em floresta degradada. No entanto, em alguns período diário a umidade do ar foi 30% menor em área degradada. Os testes de hipóteses aplicados mostram diferença estatísticas significativas, indicaram que há efeito significativo nos elementos meteorológicos analisados em função da degradação da floresta.

Is the biosphere-atmosphere exchange of total reactive nitrogen above forest driven by the same factors as carbon dioxide? An analysis using artificial neural networks

Phase and amplitude of ecosystem-atmosphere fluxes of reactive nitrogen compounds are poorly understood due to a lack of suitable observation methods. Understanding the biophysical controls of the surface nitrogen exchange is essential for the parameterization of process-based and chemical transport models that can be used for the determination of regional or national nitrogen budgets. In this study, we investigated similarities in time series of net total reactive nitrogen (ΣNr) and carbon dioxide (CO2) fluxes above forest with regard to their variability and driving factors. We found corresponding shapes of the mean diurnal summertime patterns of ΣNr and CO2. While ecosystem respiration leads to a net CO2 release at night, ΣNr was on average deposited throughout the entire observation period. Using artificial neural network analysis, global radiation (Rg) was identified to be the main control for both ΣNr and CO2. While the concentration of ΣNr substantially improved the coefficient of determination for ΣNr fluxes when used as a secondary driver, only minor improvements of 2–3% were found for CO2 fluxes when using for example temperature or vapour pressure deficit (VPD) as secondary driver. Considering two dominant drivers, 41 and 66% of the variability in ΣNr and CO2 fluxes, respectively, could be explained. Further data stratification for ΣNr revealed that higher concentrations, higher temperature, and higher VPD as well as dry leaf surfaces tend to favour higher deposition of ΣNr, whereas lower concentrations, lower temperature, and lower VPD as well as wet leaf surfaces mainly correspond to situations when less ΣNr was deposited or even emitted. Our results support the understanding of biosphere-atmosphere interactions, their driving factors, and establish a link between ΣNr and CO2 exchange, which may be beneficial for future developments in state-of-the-art exchange modelling.

Direct Impacts of Climate Change and Indirect Impacts of Non-Climate Change on Land Surface Phenology Variation across Northern China

Land surface phenology (LSP) is a sensitive indicator of climate change. Understanding the variation in LSP under various impacts can improve our knowledge on ecosystem dynamics and biosphere-atmosphere interactions. Over recent decades, LSP derived from remote sensing data and climate change-related variation of LSP have been widely reported at the regional and global scales. However, the smoothing methods of the vegetation index (i.e., NDVI) are diverse, and discrepancies among methods may result in different results. Additionally, LSP is affected by climate change and non-climate change simultaneously. However, few studies have focused on the isolated impacts of climate change and the impacts of non-climate change on LSP variation. In this study, four methods were applied to reconstruct the MODIS enhanced vegetation index (EVI) dataset to choose the best smoothing result to estimate LSP. Subsequently, the variation in the start of season (SOS) and end of season (EOS) under isolated impacts of climate change were analyzed. Furthermore, the indirect effects of isolated impacts of non-climate change were conducted based on the differences between the combined impact (the impacts of both climate change and non-climate change) and isolated impacts of climate change. Our results indicated that the Savitzky-Golay method is the best method of the four for smoothing EVI in Northern China. Additionally, SOS displayed an advanced trend under the impacts of both climate change and non-climate change (hereafter called the combined impact), isolated impacts of climate change, and isolated impacts of non-climate change, with mean values of −0.26, −0.07, and −0.17 days per year, respectively. Moreover, the trend of SOS continued after 2000, but the magnitudes of changes in SOS after 2000 were lower than those that were estimated over the last two decades of the twentieth century (previous studies). EOS showed a delayed trend under the combined impact and isolated impacts of non-climate change, with mean values of 0.41 and 0.43 days per year, respectively. However, EOS advanced with a mean value of −0.16 days per year under the isolated impacts of climate change. Furthermore, the absolute mean values of SOS and EOS trends under the isolated impacts of non-climate change were larger than that of the isolated impacts of climate change, indicating that the effect of non-climate change on LSP variation was larger than that of climate change. With regard to the relative contribution of climatic factors to the variation in SOS and EOS, the proportion of solar radiation was the largest for both SOS and EOS, followed by precipitation and temperature.

Continuous observation of vegetation canopy dynamics using an integrated low-cost, near-surface remote sensing system

Continuous monitoring of vegetation indices (VIs) the fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) through satellite remote sensing has advanced our understanding of biosphere–atmosphere interactions. Substantial efforts have been put into monitoring individual variables in the field, but options to concurrently monitor VIs, fPAR, and LAI in-situ have been lacking. In this paper, we present the Smart Surface Sensing System (4S), which automatically collects, transfers and processes VIs, fPAR and LAI data streams. The 4S consists of a microcomputer, controller and camera, a multi-spectral spectrometer built in with a light-emitting diode (LED) and an internet connection. Lab testing and field observations in a rice paddy site that experiences wet summer monsoon seasons confirmed the linear response of 4S to light intensities in the blue, green, red and near-infrared spectral channels, with wide ranging temperatures and humidity having only a minor impact on 4S throughout the growing season. Applied over an entire rice growing season (day of year [DOY] 120 - 248), VIs and fPAR from 4S were linearly related to corresponding VIs from a reference spectrometer (R2 = 0.98; NDVI, R2 = 0.96; EVI) and the LAI-2200 instrument (R2 = 0.76), respectively. Integration of gap fraction-based LAI from LED sensors and a green index from the micro-camera allowed tracking of the seasonality of green LAI. The continuous and diverse nature of 4S observations highlights its potential for evaluating satellite remote sensing products. We believe that 4S will be useful for the expansion of ecological sensing networks across multiple spatial and temporal scales.

Influence of winter precipitation on spring phenology in boreal forests.

Understanding the variations in spring vegetation phenology associated with preseason climate conditions can significantly improve our knowledge on ecosystem dynamics and biosphere-atmosphere interactions. Recent studies have shown that wet winters can delay the start date of the vegetation growing season (SOS) in the high latitudes. However, associated underlying mechanisms remain unclear due to the lack of observation sites as well as complex interactions between various climate and ecosystem variables. In this study, the impact of winter precipitation on year-to-year variations of the SOS in boreal forests from 1982 to 2005 was investigated. Two experiments were performed using the Community Land Model version 4.5. In the control experiment, observed precipitation was used; in the sensitivity experiment, precipitation in the year 1982 was repeated throughout the period. The SOS in the control experiment shows high temporal correlations with the SOS estimated from the satellite-retrieved leaf area index, indicating that the land model is capable of simulating realistic response of vegetation to interannual climate variability. The effects of winter precipitation on the SOS are examined by comparing the two model experiments for wet- and dry winters. After wet winters, the SOS was delayed by 2.7days over 70.1% of the boreal forests than after dry winters; this accounts for 42.5% of the interannual variation in the SOS. The SOS delay is related to the decrease in the growing degree-days (GDD) based on soil temperatures, suggesting that the effects of heat exposure on vegetation growth is strongly modulated by winter precipitation. The GDD decrease is related to both the increase in snowmelt heat flux and reduced absorption of solar radiation, which are proportional to the amount of winter precipitation and the ratio of short plants to tall trees, respectively. Our results provide a physical basis for the winter precipitation-SOS relationship, suggesting that an increase in winter precipitation can alleviate strong advancing trends in spring vegetation growth in conjunction with global warming even for temperature-limited ecosystems.

Basic and extensible post-processing of eddy covariance flux data with REddyProc

Abstract. With the eddy covariance (EC) technique, net fluxes of carbon dioxide (CO2) and other trace gases as well as water and energy fluxes can be measured at the ecosystem level. These flux measurements are a main source for understanding biosphere–atmosphere interactions and feedbacks through cross-site analysis, model–data integration, and upscaling. The raw fluxes measured with the EC technique require extensive and laborious data processing. While there are standard tools1 available in an open-source environment for processing high-frequency (10 or 20 Hz) data into half-hourly quality-checked fluxes, there is a need for more usable and extensible tools for the subsequent post-processing steps. We tackled this need by developing the REddyProc package in the cross-platform language R that provides standard CO2-focused post-processing routines for reading (half-)hourly data from different formats, estimating the u* threshold, as well as gap-filling, flux-partitioning, and visualizing the results. In addition to basic processing, the functions are extensible and allow easier integration in extended analysis than current tools. New features include cross-year processing and a better treatment of uncertainties. A comparison of REddyProc routines with other state-of-the-art tools resulted in no significant differences in monthly and annual fluxes across sites. Lower uncertainty estimates of both u* and resulting gap-filled fluxes by 50 % with the presented tool were achieved by an improved treatment of seasons during the bootstrap analysis. Higher estimates of uncertainty in daytime partitioning (about twice as high) resulted from a better accounting for the uncertainty in estimates of temperature sensitivity of respiration. The provided routines can be easily installed, configured, and used. Hence, the eddy covariance community will benefit from the REddyProc package, allowing easier integration of standard post-processing with extended analysis. 1http://fluxnet.fluxdata.org/2017/10/10/toolbox-a-rolling-list-of-softwarepackages-for-flux-related-data-processing/, last access: 17 August 2018

A comparison of the biogenic volatile organic compound emissions from the fine roots of 15 tree species in Japan and Taiwan

ABSTRACTBiogenic volatile organic compounds (BVOCs) play important roles in biosphere‒atmosphere interactions and communications among organisms, although little is known about the characteristics of BVOCs in the pedosphere and particularly emissions from fine roots. We evaluated the effects of cutting the fine roots of two tree species on BVOC emissions during sampling and compared the BVOC and CO2 emission rates in the roots of 15 tree species in forests in Japan and Taiwan. The BVOC emission rate in Pinus densiflora roots increased as the number of cross-sections increased. When the cross-section of a P. densiflora root was outside the sample bag, the BVOC emission rate was not affected by application of a cutting treatment. The mean BVOC emission rates in the fine roots of each of 15 tree species ranged from 0 to 0.38 nmol g−1s−1 for monoterpenes and 0 to 0.10 nmol g−1s−1 for a sesquiterpene (longifolene). The monoterpene emission rates in fine roots were very high for some gymnosperm and ectomycorrhizal species (Pinus densiflora 0.38 ± 0.31 nmol g−1s−1 and Abies firma 0.19 ± 0.08 nmol g−1s−1). The longifolene emission rates in fine roots were highest for one gymnosperm and arbuscular mycorrhizal species (Chamaecyparis pisifera 0.10 ± 0.10 nmol g−1s−1). The proportional carbon ratio of BVOCs to CO2 in emissions from fine roots ranged from 0 to 67% among the 15 tree species. Distinct patterns in the exact BVOC compounds and their emission rates indicated species-specific carbon availability in fine roots.