Modelling the Terrestrial Carbon Cycle - Drivers, Benchmarks, and Model-Data Fusion
Publikation: Bog/antologi/afhandling/rapport › Ph.d.-afhandling › Forskning
The terrestrial ecosystem sequesters about one-third of anthropogenic emissions each year, thereby providinga critical ecosystem service that slows the rate of increase of atmospheric carbon dioxide and helps mitigateclimate change. Observed atmospheric carbon dioxide concentrations exhibit a large inter-annual variabilitywhich is considered to be caused primarily by the response of the terrestrial ecosystem to climate change andanthropogenic activity. A better understanding of the functioning of the terrestrial ecosystem is thereforerequired to improve our ability to predict the global carbon cycle and climate change.Ecosystem models integrate and apply knowledge of ecological processes (e.g. photosynthesis, respiration,allocation, and other plant physiological and microbial processes) to simulate net primary production, biomassaccumulation, litterfall and soil carbon amongst others, in terrestrial ecosystems worldwide. These models arewidely applied to explore, analyze and further our understanding of the complex interactions among biomes aswell as the flows of carbon, nutrients and water through ecosystems over time in response to climate changeand disturbances. Ecosystem models also allow the projection of the evolution of the carbon cycle underdifferent scenarios of future possible carbon dioxide concentrations. However, current studies havedemonstrated large uncertainties in predictions of past and present terrestrial carbon dynamics which limits ourconfidence in projections of future changes. These uncertainties, originating from model structure, parametersand data that drives the model, greatly limits our ability to accurately assess the performance of ecosystemmodels as well as our understanding of the response of ecosystems to environmental changes.This thesis aims to analyze these caveats by disentangling the causes of uncertainties in modeling terrestrialcarbon dynamics to inform future model improvement. A state-of-the-art ecosystem model LPJ-GUESS isemployed as the model platform for this study. Climate data induced uncertainty in model-based estimations ofterrestrial primary productivity are analyzed and quantified for different ecosystems. Also, different climatevariables are identified as the main contributors to total climate induced uncertainty in different regions. Inaddition, this thesis assesses the suitability of contemporary climate datasets with respect to a given researchpurpose and study area, and quantifies the effect of land use and land cover changes on the terrestrial carbonsink. Moreover, a matrix approach, which reorganizes the carbon balance equations of the ecosystem modelsinto one matrix equation while preserving dynamically modeled carbon cycle processes and mechanisms, isapplied to identify which ecological processes contribute most strongly to model-data disagreement in term ofterrestrial carbon storage and flux.Identifying and reducing uncertainty in estimations of the terrestrial carbon cycle via a modeling approachenables us better understand, quantify, and forecast the effects of climate change and anthropogenic activityon the terrestrial ecosystem, but is also of increasing relevance in the context of climate change mitigationpolicies.
|Forlag||Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen|
|Status||Udgivet - 2018|
Note vedr. afhandling
DOCTORAL DISSERTATION by due permission of the Faculty of Science, Lund University, Sweden