INTERROGATING THE INTERPLAY OF ATMOSPHERIC PCO2 AND PO2, CLIMATE, AND LATE PALEOZOIC TROPICAL TERRESTRIAL ECOSYSTEMS USING MODEL-DATA INTEGRATION

2020 ◽  
Author(s):  
Isabel P. Montañez ◽  
◽  
Joseph White ◽  
Jon D. Richey ◽  
Jonathan P. Wilson
Keyword(s):  
Data Integration ◽  
Terrestrial Ecosystems ◽  
Late Paleozoic ◽  
Model Data ◽  
Atmospheric Pco2

scholarly journals Influence of temporally varying weatherability on CO<sub>2</sub>–climate coupling and ecosystem change in the late Paleozoic

2020 ◽  
Author(s):  
Jon D. Richey ◽  
Isabel P. Montañez ◽  
Yves Goddéris ◽  
Cindy V. Looy ◽  
Neil P. Griffis ◽  
...  
Keyword(s):  
Steady State ◽  
Ice Age ◽  
Secular Change ◽  
Ecosystem Change ◽  
Late Paleozoic ◽  
Relative Role ◽  
C Cycle ◽  
Ecosystem Perturbation ◽  
Atmospheric Pco2

Abstract. Earth's penultimate icehouse, the Late Paleozoic Ice Age (LPIA), was a time of dynamic glaciation and repeated ecosystem perturbation, under conditions of substantial variability in atmospheric pCO2 and O2. Improved constraints on the evolution of atmospheric pCO2 and O2 : CO2 during the LPIA and its subsequent demise to permanent greenhouse conditions is crucial for better understanding the nature of linkages between atmospheric composition, climate, and ecosystem perturbation during this time. We present a new and age-recalibrated pCO2 reconstruction for a 40-Myr interval (~313 to 273 Ma) of the late Paleozoic that (1) confirms a previously hypothesized strong CO2-glaciation linkage, (2) documents synchroneity between major pCO2 and O2 : CO2 changes and compositional turnovers in terrestrial and marine ecosystems, (3) lends support for a modeled progressive decrease in the CO2 threshold for initiation of continental ice sheets during the LPIA, and (4) indicates a likely role of CO2 and O2 : CO2 thresholds in floral ecologic turnovers. Modeling of the relative role of CO2 sinks and sources, active during the LPIA and its demise, on steady-state pCO2 using an intermediate complexity climate-C cycle model (GEOCLIM) and comparison to the new multi-proxy CO2 record provides new insight into the relative influences of the uplift of the Central Pangaean Mountains, intensifying aridification, and increasing mafic rock to-granite rock ratio of outcropping rocks on the global efficiency of CO2 consumption and secular change in steady-state pCO2 through the late Paleozoic.

The Modernization of Landscapes during the Late Paleozoic-Early Mesozoic

2000 ◽  
Vol 6 ◽  
pp. 79-114 ◽  
Author(s):  
Hans Kerp
Keyword(s):  
Food Chain ◽  
Soil Formation ◽  
Terrestrial Ecosystems ◽  
Land Plants ◽  
Late Paleozoic ◽  
Primary Role ◽  
Direct Impact ◽  
Carbon Cycles ◽  
Weathering Processes ◽  
Early Mesozoic

Since their first appearance in the Middle-Late Silurian, land plants have played an increasingly important role in shaping terrestrial ecosystems and landscapes. It is difficult to overestimate their role because they form the framework for terrestrial ecosystems, provide habitats for terrestrial animals, form an important part of the food chain, affect weathering processes and have a direct impact on soil formation, and, last but not least, play a primary role in the oxygen/carbon cycles.

Understanding and quantifying carbon cycling in managed grasslands through model-data fusion

2020 ◽  
Author(s):  
Vasileios Myrgiotis ◽  
Rob Clement ◽  
Stephanie K. Jones ◽  
Ben Keane ◽  
Mark Lee ◽  
...  
Keyword(s):  
Data Fusion ◽  
Leaf Area ◽  
Primary Productivity ◽  
Process Model ◽  
Production Systems ◽  
Terrestrial Ecosystems ◽  
Model Data ◽  
Area Index ◽  
C Dynamics ◽  
Allocation Patterns

&lt;p&gt;Managed grasslands are extensive terrestrial ecosystems that provide a range of services. In addition to supporting the world&amp;#8217;s various livestock production systems they contain climatically significant amounts of carbon (C). Understanding and quantifying the C dynamics of managed grasslands is complicated yet crucial.This presentation describes a process-model of C dynamics in managed grasslands (DALEC-Grass). DALEC-Grass is a model of intermediate complexity, which calculates primary productivity, dynamicallyallocates C to biomass tissues and describes the impacts of grazing/harvesting activities. The model is integrated into a Bayesian model-data fusion framework (CARDAMOM). CARDAMOM uses observations of ecosystem functioning (e.g. leaf area, biomass, C fluxes) to optimise the model&amp;#8217;s parameters while respecting a set of biogeochemical and physiological rules. The model evaluation results presented demonstrate the model&amp;#8217;s skill in predicting primary productivity and C allocation patterns in UK grasslands using both ground and satellite based leaf area index (LAI) time series as observational constraints.&lt;/p&gt;

scholarly journals Socio-hydrologic data assimilation: Analyzing human-flood interactions by model-data integration

2020 ◽  
Author(s):  
Yohei Sawada ◽  
Risa Hanazaki
Keyword(s):  
Data Assimilation ◽  
Data Integration ◽  
Flood Risk ◽  
Particle Filtering ◽  
Risk Model ◽  
Observation System ◽  
Observation Data ◽  
Sequential Data ◽  
Model Data ◽  
Sequential Data Assimilation

Abstract. In socio-hydrology, human-water interactions are simulated by mathematical models. Although the integration of these socio-hydrologic models and observation data is necessary to improve the understanding of the human-water interactions, the methodological development of the model-data integration in socio-hydrology is in its infancy. Here we propose to apply sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle filtering for a widely adopted flood risk model and performed an idealized observation system simulation experiment to demonstrate the potential of the sequential data assimilation in socio-hydrology. In this experiment, the flood risk model's parameters, the input forcing data, and empirical social data were assumed to be somewhat imperfect. We tested if data assimilation can contribute to accurately reconstructing the historical human-flood interactions by integrating these imperfect models and imperfect and sparsely distributed data. Our results highlight that it is important to sequentially constrain both state variables and parameters when the input forcing is uncertain. Our proposed method can accurately estimate the model's unknown parameters even if the true model parameter temporally varies. The small amount of empirical data can significantly improve the simulation skill of the flood risk model. Therefore, sequential data assimilation is useful to reconstruct historical socio-hydrological processes by the synergistic effect of models and data.

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