Substrate-Controlled Succession of Marine Bacterioplankton Populations Induced by a Phytoplankton Bloom

FIO-ESM (First Institute of Oceanography-Earth System Model) developed by the First Institute of Oceanography of the Ministry of Natural Resources, is an earth system model with surface gravity wave models and composed of a physical climate model and a global carbon cycle model. The Earth system model has developed from FIO-ESM v1.0, to FIO-ESM v2.0, which has been improved in both its physical climate model and the global carbon cycle model. The marine carbon cycle model of FIO-ESM v2.0 global carbon cycle model has been upgraded from the nutrient-driven model of v1.0 to the NPZD (Nutrient Phytoplankton Zooplankton Detritus) type ocean ecological carbon cycle model, and the terrestrial carbon cycle model has been upgraded from the simple light energy utilization model of v1.0 to the carbon-nitrogen coupling model considering carbon-nitrogen interaction. The atmospheric carbon cycle model is still the CO2 transport processes, with the anthropogenic carbon emissions from the fossil fuel and land use change. In terms of effects of physical process parameterization schemes on the global carbon cycle, the FIO-ESM v2.0 global carbon cycle considers not only the role of non-breaking wave induced mixing on biogeochemical variables, but also the effects of SST diurnal cycle on air-sea CO2 flux. Primary analysis shows that FIO-ESM v2.0 can simulate the global carbon cycle fairly well after considering more complex carbon cycle processes.

Response function models are often used to represent the behaviour of complex, high order global carbon cycle (GCC) and climate models in applications which require short model run times. Although apparently black-box, these response function models need not necessarily be entirely opaque, but instead may also convey useful insights into the properties of the parent model or process. By exploiting a transfer function (TF) framework to analyse the Lenton GCC model, this paper attempts to demonstrate that response function representations of GCC models can sometimes also provide structural information on the parent model from which they are identified and calibrated. We take a fifth-order TF identified from the impulse response of the Lenton model atmospheric burden, and decompose this to show how it can be re-expresses in a generic five-box form in sympathy with the structure of the parent model.

The growth rate of the atmospheric CO 2 concentration exhibits interannual anomalous variations of 1–2 ppmV yr −1 which reflect the response of the global carbon fluxes to large scale climate fluctuations. The climate sensitivity of global carbon cycle models can be explored by the simulation of these variations. Here we test the climate sensitivity of the global terrestrial carbon cycle model SILVAN 2.3 using this approach. The model has a horizontal resolution of 0.5°, a 6-day time step and considers potential vegetation only. Important features are a model-generated water balance and physiological approaches to determine net primary productivity (NPP) and phenology. In the three sensitivity experiments SILVAN 2.3 was forced in addition to the monthly climatologies by: (A) observed temperature anomalies 1854–1993, (B) observed precipitation anomalies 1900–1993, and (C) observed anomalous temperature and precipitation as well as the atmospheric CO 2 concentration increase 1765–1993. Simulated and observed anomalous CO 2 fluxes into the atmosphere 1958–1993 are well correlated. The largest fraction of the modelled anomalous CO 2 fluxes results from the temperature sensitivity of the physiological NPP model; the effect of the precipitation variations is relatively small. The simulated heterotrophic respiration is more sensitive to precipitation than to temperature. We discuss the extent to which the model response results additively from the anomalous CO 2 fluxes generated by the temperature or precipitation anomalies only.

Global Carbon Cycle and Organic Matter Accumulation in the Earth Crust The natural process of organic carbon accumulation in sediments is considered in the light of recently suggested model of global redox carbon cycle. It is inferred that in geologic time the process was uneven and some periods were favorable for the increased burial organic carbon rate. Organic carbon accumulation occurred in parallel with oxygen growth in the atmosphere. According to the model, the accumulation process is a cyclic one.

Compartmental models have been widely used in modelling systems in pharmaco-kinetics, engineering, biomedicine and ecology since 1943 and turn out to be very good approximations for many different real-life systems. Sensitivity analysis (SA) is commonly employed at a preliminary stage of model development process to increase the confidence in the model and its predictions by providing an understanding of how the model response variables respond to changes in the inputs, data used to calibrate it and model structures. This paper concerns the application of some SA techniques to a linear, deterministic, time-invariant compartmental model of global carbon cycle (GCC). The same approach is also illustrated with a more complex GCC model which has some nonlinear components. By focusing on these two structurally different models for estimating the atmospheric CO2 content in the year 2100, sensitivity of model predictions to uncertainty attached to the model input factors is studied. The application/modification of SA techniques to compartmental models with steady-state constraint is explored using the 8-compartment model, and computational methods developed to maintain the initial steady-state condition are presented. In order to adjust the values of model input factors to achieve an acceptable match between observed and predicted model conditions, windowing analysis is used.

Projecting future concentrations of atmospheric CO2 with global carbon cycle models: The importance of simulating historical changes

Conservation and sustainable development of forests are mitigation mechanisms against climate change due to the forest carbon sink capacity. Therefore, biomass estimation allows to assess forest productivity and control carbon budgets. In Ecuador, biomass and carbon sequestration studies are scarce. Thus, we estimated and forecasted changes in biomass of Ecuadorian forests through the Mathematical Spatial Model of Global Carbon Cycle and the Normalized Differential Vegetation Index. The mathematical model describes the processes of growth and decay of vegetation in terms of carbon exchange between the atmosphere, plants and soil under anthropogenic impacts. The vegetation map and the biomass of 2017 (4,86 Gt) were developed with remote sensing methodology in ENVI 5.3 and ArcGIS 10.3 programs. The observed biomass decrease between 2000 and 2010 was due to the high deforestation rate. Thanks to conservation and reforestation policies and the compensatory effect between the atmosphere and forests, a biomass increase is expected until 2060. According to the vegetation map, Amazon region has a better plant vigor, followed by Andean and Coast regions, where scattered vegetation predominates. This information is useful for planning environmental practices such as forest conservation and reforestation in order to increase carbon storage.

Assessment of the status of ecosystems experiencing anthropogenic impact is based on the ability of plant communities in such ecosystems to withstand these disturbances or to mitigate their effects fast enough. The aim of this paper is to analyze the impact of global climate change on carbon balance of plant communities of South Asia. To achieve this objective, the spatial model of global carbon cycle developed by the Computing Center of the Russian Academy of Sciences (RAS CC) was used to calculate the impact of industrial CO2 emissions as well as the main causes of carbon losses in the investigated region (deforestation and soil erosion) on the dynamics of carbon accumulation in the humus and phytomass of forest ecosystems. India was selected as a model country to assess and compare the compensatory functions of plant communities. On the basis of the spatial mathematical model of the global carbon cycle in the biosphere, changes in CO emissions as a result of burning fossil fuels, deforestation, and soil erosion associated with improper land use in South Asia were estimated. The impact of deforestation and soil erosion on climate change in South Asia is forecast up to the year 2060. A comparison of regulatory functions of different types of plant communities was performed for the study area. The calculations data revealed some regularities occurring in the ecosystems of South Asia under the impact of CO2 emissions, deforestation, and soil erosion due to improper land use. Mathematical modelling has shown the dependence of growth of humus and phytomass of vegetation on the amount of CO2 in the atmosphere. Quantitative forecast of the dynamics of the ecosystem characteristics of plant communities depending on the growing region has been performed.

The global carbon cycle controls the climate change in the Earth's environment on a geological timescale and is mainly associated with greenhouse effects produced by atmospheric carbon dioxide (CO2) and methane (CH4). This paper reviews the relationship between the global carbon cycle and presumed climate events during the Cenozoic. The global carbon cycle is primarily regulated by the balance between weathering and metamorphism-volcanism. Moreover, the organic carbon subcycle involving oxidative weathering and burial is of secondary importance. The balance of these geochemical processes results in variations of atmospheric CO2. The past climate on a geological time scale is reconstructed by several geochemical and paleontological methods or proxies. For example, sea-surface and deep-water temperature are deduced from oxygen isotope ratio and Mg/Ca ratio of foraminiferal tests. Terrestrial atmospheric temperature is estimated from leaf fossil and paleovegetation. Atmospheric CO2 level is calculated from carbon isotope ratios of phytoplankton and soil carbonate, stomatal density of leaf fossil, boron isotope ratio of foraminiferal test, Ce anomaly, and global carbon cycle modeling. It is important to consider their advantages and disadvantages in order to evaluate the paleoclimate adequately. Next, we discuss climate change based on these proxies. As a general trend, the Cenozoic climate change is characterized by a transition from ice-free to ice-covered conditions across the Eocene/Oligocene boundary. The Earth's surface environment was significantly warmed from the Paleocene to the Eocene by high levels of atmospheric CO2. Thereafter, it gradually cooled towards the present, which is possibly attributed to changes in ocean currents and other marine environments accompanying continental drift. This trend has been punctuated by several short-term climate events. The Paleocene-Eocene Thermal Maximum (PETM) was a remarkable warming event at the Paleocene/Eocene boundary, possibly attributed to the release of methane from hydrates into the atmosphere. Rapid cooling occurred at the Eocene/Oligocene boundary to form extensive continental ice sheets including the Antarctica, which seems to have been caused by atmospheric CO2 and change of oceanographic circulation and marine environment. After a moderate period from the late Oligocene to the early Miocene, there was a transient but significant warming in the middle Miocene. Since then, the Earth's environment has gradually cooled towards the present accompanied by the evolution of glaciations and marine environmental changes but a causal link between cooling and global carbon cycle has recently been pointed out. Although the carbon cycle including atmospheric CO2 and CH4 cannot explain all of the global climate changes in Cenozoic, it has undoubtedly played a dominant role on the Earth's climate.

Gradients in the carbon isotopic composition of Ordovician shallow water carbonates: A potential pitfall in estimates of ancient CO2 and O2

<scp>ASLO</scp> 2020 Award Winners

Accurate and reliable information about land cover and land use is essential to carbon cycle and climate change modeling. While historical regional-to-global scale land cover and land use data products had been produced by AVHRR and MSS/TM, this task has been advanced by sensors such as MODIS and ETM since the latter 1990s. While the accuracies and reliabilities of these data products have been improved, there have been reports from the modeling community that additional work is needed to reduce errors so that the uncertainties associated with the global carbon cycle and climate change modeling can be addressed. Remotely sensed data collected in different wavelength regions, at different viewing geometries, usually provide complementary information. Their combination has the potential to enhance remote sensing capabilities in discriminating important land cover components. In this paper, we studied multi-angle data fusion, and optical-SAR data fusion for land cover classification at regional spatial scale in the temperate forests of the eastern United States. Data from EOS-MISR, Landsat-ETM+ and RadarSat-SAR were used. The results showed significantly improved land cover classification accuracy when using the data fusion approach. These results may benefit future land cover products for global change research.

Climate change during Cenozoic inferred from global carbon cycle model including igneous and hydrothermal activities

BackgroundVariation in microbial metabolism poses one of the greatest current uncertainties in models of global carbon cycling, and is particularly poorly understood in soils. Biological Stoichiometry theory describes biochemical mechanisms linking metabolic rates with variation in the elemental composition of cells and organisms, and has been widely observed in animals, plants, and plankton. However, this theory has not been widely tested in microbes, which are considered to have fixed ratios of major elements in soils.Methodology/Principal FindingsTo determine whether Biological Stoichiometry underlies patterns of soil microbial metabolism, we compiled published data on microbial biomass carbon (C), nitrogen (N), and phosphorus (P) pools in soils spanning the global range of climate, vegetation, and land use types. We compared element ratios in microbial biomass pools to the metabolic quotient qCO2 (respiration per unit biomass), where soil C mineralization was simultaneously measured in controlled incubations. Although microbial C, N, and P stoichiometry appeared to follow somewhat constrained allometric relationships at the global scale, we found significant variation in the C∶N∶P ratios of soil microbes across land use and habitat types, and size-dependent scaling of microbial C∶N and C∶P (but not N∶P) ratios. Microbial stoichiometry and metabolic quotients were also weakly correlated as suggested by Biological Stoichiometry theory. Importantly, we found that while soil microbial biomass appeared constrained by soil N availability, microbial metabolic rates (qCO2) were most strongly associated with inorganic P availability.Conclusions/SignificanceOur findings appear consistent with the model of cellular metabolism described by Biological Stoichiometry theory, where biomass is limited by N needed to build proteins, but rates of protein synthesis are limited by the high P demands of ribosomes. Incorporation of these physiological processes may improve models of carbon cycling and understanding of the effects of nutrient availability on soil C turnover across terrestrial and wetland habitats.

This document is a plan for an intermodel comparison of atmospheric CO{sub 2} projections that includes uncertainty analysis of the global carbon cycle models used to make those projections. The plan includes a procedure for the documentation, support, and archiving of global carbon cycle models within the Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL). The best'' global carbon cycle model is not one of the objectives. Rather, the principal goals are to develop a picture of where global carbon cycle modeling stands, at present, in the projection of future atmospheric CO{sub 2} concentrations and to acquire information that can be used to determine research needs for CO{sub 2} modeling. The plan involves three phases: model implementation --- the acquisition and computer implementation of the global carbon cycle models; model analysis --- sensitivity and uncertainty analysis of each model; and synthesis --- an intermodel comparison of the projections of future atmospheric CO{sub 2} concentrations and characterization of across-model patterns in the atmospheric projections and associated uncertainties. 40 refs., 7 figs., 1 tab.