Uncertainties in the role of land vegetation in the carbon cycle

Estimating divergent forest carbon stocks and sinks via a knife set approach

Forest carbon stocks and sinks (CSSs) have been widely estimated using climate classification tables and linear regression (LR) models with common independent variables (IVs) such as the average diameter at breast height (DBH) of stems and root shoot ratio. However, this approach is relatively ineffective when the explanatory power of IVs is lower than that of unobservable variables. Various environmental and anthropogenic factors affect target variables that cause the correlation between them to be chaotic. Here, we designed a knife set (KS) approach combining LR models and the wandering through random forests (WTF) algorithm and applied it in a specific case of Phyllostachys edulis (Carrière) J. Houz. (P. edulis) forests, which have an irregular relationship between their belowground carbon (BGC) stocks and average DBH. We then validated the KS approach performed by cluster computing to estimate the aboveground carbon (AGC) and BGC stocks and the total net primary production (TNPP). The estimated CSSs were compared to the benchmark of the methodology that applied Tier 1 in the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories via 10-fold cross validation, and the KS approach significantly increased precision and accuracy of estimations. Our approach provides general insights to accurately estimate forest CSSs relying on evidence-based field data, even if some target variables are divergent in specific forest types. We also pointed out the reason why current fancy models containing machine learning (ML) or deep learning algorithms are not effective in predicting the target variables of certain chaotic systems is perhaps that the total explanatory power of observable variables is less than that of the total unobservable variables. Quantifying unobservable variables into observable variables is a linchpin of future works related to chaotic system estimation.

Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019

As China is the world's largest CO₂ emitter, it is important to understand the spatio-temporal variation of atmospheric CO₂ to reduce carbon emissions. Satellite remote sensing for carbon monitoring has been widely used and studied because of its long-term and large-scale characteristics. However, the satellite data results are very sparse with significant gaps due to narrow swath and other factors on CO₂ retrieval. The simple interpolation methods ignore the influential factors of CO₂ and loss the spatial resolution, which leads to the inability to quantify the spatio-temporal variation well. This study developed a machine learning method that considers carbon emissions, vegetation, and meteorology. Using the column-averaged dry-air mole fraction of CO₂ (XCO₂) data of SCIAMACHY, GOSAT, and OCO-2, we derived monthly-scale contiguous XCO₂ data across China from 2003 to 2019 with 0.25° resolution. The results showed a good agreement with the satellite measurements, with the bias and standard deviation of 0.11 and 1.38 ppmv for the validation dataset, respectively. Moreover, the results were consistent with the model simulation and in-situ sites, indicating the ability to reflect long-term spatio-temporal variation with a finer texture. We analyzed the spatial distribution, seasonal variation, and long-term trends of XCO₂ in China, revealing that the machine learning method has comparable performance to model simulations. The results showed that XCO₂ is dominated by anthropogenic emissions spatially and has a clear seasonal cycle, with a larger amplitude the further north. The long-term trend shows the XCO₂ increased by an average rate of 2.17 ppmv per year from 2003 to 2019 in China, which is consistent with the global. The method and data can further study the carbon cycle and climate change.

Pyrolytic Characteristics and Kinetics of Phragmites australis

The pyrolytic kinetics of Phragmites australis was investigated using thermogravimetric analysis (TGA) method with linear temperature programming process under an inert atmosphere. Kinetic expressions for the degradation rate in devolatilization and combustion steps have been obtained for P. australis with Dollimore method. The values of apparent activation energy, the most probable mechanism functions, and the corresponding preexponential factor were determined. The results show that the model agrees well with the experimental data and provide useful information for the design of pyrolytic processing system using P. australis as feedstock to produce biofuel.

Ecophysiological modelling of leaf level photosynthetic performance for three Mediterranean species with different growth forms

A leaf gross photosynthesis (A) model for three Mediterranean species from different functional groups (the evergreen sclerophyll Arbutus unedo L., the deciduous tree Quercus frainetto Ten. and the semi-deciduous shrub Phlomis fruticosa L.) has been developed through a 3-year seasonal study of gas exchange and plant ecophysiological parameters. The model estimates photosynthesis through four easily measured ecophysiological parameters (chlorophyll content, leaf mass per area (LMA), pre-dawn water potential, leaf temperature) and two meteorological parameters (PAR and average temperature of the 33-day period before measurement (T33)), with a coefficient of determination r2=0.88 (P<0.001). The enhanced accuracy of the presented model may be ascribed to the incorporation of (i) water potential effects on A and (ii) temperature effects on A not only in the short term, but also in the long term (acclimation). Water potential and temperature effects may be considered especially important for species of Mediterranean ecosystems, where strong seasonal variation of these parameters often have a major role in plant growth and survival.

Global warming and carbon dioxide through sciences

Increased atmospheric CO(2)-concentration is widely being considered as the main driving factor that causes the phenomenon of global warming. This paper attempts to shed more light on the role of atmospheric CO(2) in relation to temperature-increase and, more generally, in relation to Earth's life through the geological aeons, based on a review-assessment of existing related studies. It is pointed out that there has been a debate on the accuracy of temperature reconstructions as well as on the exact impact that CO(2) has on global warming. Moreover, using three independent sets of data (collected from ice-cores and chemistry) we perform a specific regression analysis which concludes that forecasts about the correlation between CO(2)-concentration and temperature rely heavily on the choice of data used, and one cannot be positive that indeed such a correlation exists (for chemistry data) or even, if existing (for ice-cores data), whether it leads to a "severe" or a "gentle" global warming. A very recent development on the greenhouse phenomenon is a validated adiabatic model, based on laws of physics, forecasting a maximum temperature-increase of 0.01-0.03 degrees C for a value doubling the present concentration of atmospheric CO(2). Through a further review of related studies and facts from disciplines like biology and geology, where CO(2)-change is viewed from a different perspective, it is suggested that CO(2)-change is not necessarily always a negative factor for the environment. In fact it is shown that CO(2)-increase has stimulated the growth of plants, while the CO(2)-change history has altered the physiology of plants. Moreover, data from palaeoclimatology show that the CO(2)-content in the atmosphere is at a minimum in this geological aeon. Finally it is stressed that the understanding of the functioning of Earth's complex climate system (especially for water, solar radiation and so forth) is still poor and, hence, scientific knowledge is not at a level to give definite and precise answers for the causes of global warming.

Long series relationships between global interannual CO2 increment and climate: evidence for stability and change in role of the tropical and boreal-temperate zones

Interannual variability in global CO2 increment (averaged from the Mauna Loa and South Pole Stations) shows certain strong spatial relationships to both tropical and temperate temperatures. There is a fairly strong positive year-round correlation between tropical mean annual temperatures (leading by 4 months) and annual CO2 throughout the time series since 1960, agreeing with the generally held view that the tropics play a major role in determining inter-annual variability in CO2 increment, with a major CO2 pulse following a warm year in the tropics. This 'almost no lag' climatic response is very strong during winter and relatively stable in time. However, the correlation with tropical temperature appears to have weakened in the first years of the 1990s in correspondence of the Pinatubo eruption and the positive phase of the AO/NAO. A secondary concurrent temperature signal is linked to summer variations of north temperate belt. Northern summer temperatures in the region 30-60 degrees N-and especially in the land area corresponding to the central east USA-have become relatively more closely correlated with CO2 increment. This trend has become increasingly stronger in recent years, suggesting an increasing role for growing season processes in the northern midlatitudes in affecting global CO2 increment. Once non-lagged annual tropical temperature variations are accounted for, terrestrial ecosystems, especially the temperate-boreal biomes, also show a coherent large scale lagged response. This involves an inverse response to annual temperature of preceding years centered at around 2 years before. This lagged response is most likely linked to internal biogeochemical cycles, in particular N cycling. During the study period north boreal ecosystems show a strengthening of the lagged correlation with temperature in recent years, while the lagged correlation with areas of tropical ecosystems has weakened. Residuals from a multiple correlations based on these climatic signals are directly correlated with SO, confirming an additional important role of upwelling in interannual variability of CO2 increment. Cooler summers following the Pinatubo eruption and the possible influence of the North Atlantic Oscillation (NAO/AO) are discussed as factors responsible for the shift in the relative importance of different regions over time during the series of data.