Modelling the terrestrial carbon cycle – drivers, benchmarks, and model-data fusion
(2018)- Abstract
- The terrestrial ecosystem sequesters about one-third of anthropogenic emissions each year, thereby providing a critical ecosystem service that slows the rate of increase of atmospheric carbon dioxide and helps mitigate climate change. Observed atmospheric carbon dioxide concentrations exhibit a large inter-annual variability which is considered to be caused primarily by the response of the terrestrial ecosystem to climate change and anthropogenic activity. A better understanding of the functioning of the terrestrial ecosystem is therefore required 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,... (More) - The terrestrial ecosystem sequesters about one-third of anthropogenic emissions each year, thereby providing a critical ecosystem service that slows the rate of increase of atmospheric carbon dioxide and helps mitigate climate change. Observed atmospheric carbon dioxide concentrations exhibit a large inter-annual variability which is considered to be caused primarily by the response of the terrestrial ecosystem to climate change and anthropogenic activity. A better understanding of the functioning of the terrestrial ecosystem is therefore required 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, biomass accumulation, litterfall and soil carbon amongst others, in terrestrial ecosystems worldwide. These models are widely applied to explore, analyze and further our understanding of the complex interactions among biomes as well as the flows of carbon, nutrients and water through ecosystems over time in response to climate change and disturbances. Ecosystem models also allow the projection of the evolution of the carbon cycle under different scenarios of future possible carbon dioxide concentrations. However, current studies have demonstrated large uncertainties in predictions of past and present terrestrial carbon dynamics which limits our confidence in projections of future changes. These uncertainties, originating from model structure, parameters and data that drives the model, greatly limits our ability to accurately assess the performance of ecosystem models 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 terrestrial carbon dynamics to inform future model improvement. A state-of-the-art ecosystem model LPJ-GUESS is employed as the model platform for this study. Climate data induced uncertainty in model-based estimations of terrestrial primary productivity are analyzed and quantified for different ecosystems. Also, different climate variables are identified as the main contributors to total climate induced uncertainty in different regions. In addition, this thesis assesses the suitability of contemporary climate datasets with respect to a given research purpose and study area, and quantifies the effect of land use and land cover changes on the terrestrial carbon sink. Moreover, a matrix approach, which reorganizes the carbon balance equations of the ecosystem models into one matrix equation while preserving dynamically modeled carbon cycle processes and mechanisms, is applied to identify which ecological processes contribute most strongly to model-data disagreement in term of terrestrial carbon storage and flux.
Identifying and reducing uncertainty in estimations of the terrestrial carbon cycle via a modeling approach enables us better understand, quantify, and forecast the effects of climate change and anthropogenic activity on the terrestrial ecosystem, but is also of increasing relevance in the context of climate change mitigation policies. (Less) - Abstract (Swedish)
- Det terrestra ekosystemet absorberar cirka en tredjedel av de antropogena utsläppen varje år, vilket är en avgörande ekosystemtjänst som minskar ökningstakten av atmosfärisk koldioxid och bidrar till att mildra klimatförändringen. Observerade koncentrationer av atmosfäriskt koldioxid uppvisar en stor årlig variabilitet som främst anses vara orsakad av det terrestra ekosystemets respons på klimatförändringar och antropogen aktivitet. En bättre förståelse för det terrestra ekosystemets funktion ger därför inblick i den globala koldioxidcykeln och klimatförändringen.
Ekosystemmodeller tillämpar kunskap om ekologiska processer (e.g. fotosyntes, respiration, kolallokering och andra växtfysiologiska och mikrobiella processer) för att... (More) - Det terrestra ekosystemet absorberar cirka en tredjedel av de antropogena utsläppen varje år, vilket är en avgörande ekosystemtjänst som minskar ökningstakten av atmosfärisk koldioxid och bidrar till att mildra klimatförändringen. Observerade koncentrationer av atmosfäriskt koldioxid uppvisar en stor årlig variabilitet som främst anses vara orsakad av det terrestra ekosystemets respons på klimatförändringar och antropogen aktivitet. En bättre förståelse för det terrestra ekosystemets funktion ger därför inblick i den globala koldioxidcykeln och klimatförändringen.
Ekosystemmodeller tillämpar kunskap om ekologiska processer (e.g. fotosyntes, respiration, kolallokering och andra växtfysiologiska och mikrobiella processer) för att simulera nettoprimärproduktion, ackumulering av biomassa, dött organiskt material och markkol i markbundna ekosystem världen över. Dessa modeller används i stor utsträckning för att undersöka möjligheter och ge ökad förståelse för de komplexa interaktionerna mellan biom och flöden av kol, näringsämnen och vatten genom ekosystem över tiden som svar på klimatförändringar och störningar. Ekosystemmodeller möjliggör också att projicera utvecklingen av kolcykeln under olika scenarier av framtida potentiell koldioxidkoncentration. Nuvarande studier har dock visat på stor osäkerhet vid förutsägelser av tidigare och nuvarande markbunden koldynamik och även stor osäkerhet i framtida prognoser. Dessa osäkerheter, som härrör från modellstruktur, parametrar och indata, begränsar vår förmåga att korrekt bedöma ekosystemmodellernas prestanda samt vår förståelse av ekosystemens svar på miljöförändringar.
Denna avhandling avser att utreda osäkerheter i modellering av markbunden koldynamik, vilket hjälper till med förbättring av modeller. En modern ekosystemmodell, LPJ-GUESS, har använts som modelleringsplattform för denna studie. Osäkerhet orsakad av klimatdata vid modellbaserade uppskattningar av markbunden primärproduktion analyseras och kvantifieras för olika ekosystem. Olika klimatvariabler identifieras som de främsta bidragsgivarna till den totala klimatinducerade osäkerheten. Denna avhandling utvärderar också lämpligheten hos nuvarande klimatdataset med avseende på specifikt forskningssyfte och studieområde. Dessutom tillämpas en matrismetod som omorganiserar ekosystemmodellernas ekvationssystem till en matrisekvation och bibehåller alla ursprungliga mekanismer och processer relaterade till kolcykeln. Matrismetoden tillämpas för att identifiera vilka ekologiska processer som bidrar mest till avvikelser mellan modellresultat och data med hänseende till markbaserade flöden och lagring av kol.
Att identifiera och minska osäkerheten vid uppskattningar av den markbundna kolcykeln via ett modelleringsförfarande gör att vi bättre kan förstå, kvantifiera och förutspå effekterna av klimatförändringar och antropogen aktivitet på det markbundna ekosystemet, men det är också av ökande relevans i samband med klimatpolitik. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a104fc98-7cbc-49cd-b3f0-4ac91dd0a08d
- author
- Wu, Zhendong LU
- supervisor
- opponent
-
- Professor Sitch, Stephen, University of Exeter, Exeter, UK
- organization
- publishing date
- 2018-09
- type
- Thesis
- publication status
- published
- subject
- keywords
- Global carbon cycle, Ecosystem modeling, Uncertainty, LPJ-GUESS, Traceability Framework, Model-data fusion, Climate data
- pages
- 174 pages
- publisher
- Lund University, Faculty of Science, Department of Physical Geography and Ecosystem Science
- defense location
- Auditorium Pangea, Geocentrum II, Sölvegatan 12, Lund
- defense date
- 2018-10-19 13:00:00
- ISBN
- 9789185793976
- 9789185793983
- language
- English
- LU publication?
- yes
- id
- a104fc98-7cbc-49cd-b3f0-4ac91dd0a08d
- date added to LUP
- 2018-09-20 11:05:08
- date last changed
- 2021-11-25 14:33:44
@phdthesis{a104fc98-7cbc-49cd-b3f0-4ac91dd0a08d,
abstract = {{The terrestrial ecosystem sequesters about one-third of anthropogenic emissions each year, thereby providing a critical ecosystem service that slows the rate of increase of atmospheric carbon dioxide and helps mitigate climate change. Observed atmospheric carbon dioxide concentrations exhibit a large inter-annual variability which is considered to be caused primarily by the response of the terrestrial ecosystem to climate change and anthropogenic activity. A better understanding of the functioning of the terrestrial ecosystem is therefore required to improve our ability to predict the global carbon cycle and climate change.<br/>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, biomass accumulation, litterfall and soil carbon amongst others, in terrestrial ecosystems worldwide. These models are widely applied to explore, analyze and further our understanding of the complex interactions among biomes as well as the flows of carbon, nutrients and water through ecosystems over time in response to climate change and disturbances. Ecosystem models also allow the projection of the evolution of the carbon cycle under different scenarios of future possible carbon dioxide concentrations. However, current studies have demonstrated large uncertainties in predictions of past and present terrestrial carbon dynamics which limits our confidence in projections of future changes. These uncertainties, originating from model structure, parameters and data that drives the model, greatly limits our ability to accurately assess the performance of ecosystem models as well as our understanding of the response of ecosystems to environmental changes.<br/>This thesis aims to analyze these caveats by disentangling the causes of uncertainties in modeling terrestrial carbon dynamics to inform future model improvement. A state-of-the-art ecosystem model LPJ-GUESS is employed as the model platform for this study. Climate data induced uncertainty in model-based estimations of terrestrial primary productivity are analyzed and quantified for different ecosystems. Also, different climate variables are identified as the main contributors to total climate induced uncertainty in different regions. In addition, this thesis assesses the suitability of contemporary climate datasets with respect to a given research purpose and study area, and quantifies the effect of land use and land cover changes on the terrestrial carbon sink. Moreover, a matrix approach, which reorganizes the carbon balance equations of the ecosystem models into one matrix equation while preserving dynamically modeled carbon cycle processes and mechanisms, is applied to identify which ecological processes contribute most strongly to model-data disagreement in term of terrestrial carbon storage and flux.<br/>Identifying and reducing uncertainty in estimations of the terrestrial carbon cycle via a modeling approach enables us better understand, quantify, and forecast the effects of climate change and anthropogenic activity on the terrestrial ecosystem, but is also of increasing relevance in the context of climate change mitigation policies.}},
author = {{Wu, Zhendong}},
isbn = {{9789185793976}},
keywords = {{Global carbon cycle; Ecosystem modeling; Uncertainty; LPJ-GUESS; Traceability Framework; Model-data fusion; Climate data}},
language = {{eng}},
publisher = {{Lund University, Faculty of Science, Department of Physical Geography and Ecosystem Science}},
school = {{Lund University}},
title = {{Modelling the terrestrial carbon cycle – drivers, benchmarks, and model-data fusion}},
url = {{https://lup.lub.lu.se/search/files/51836618/kappa_final_ZhendongWu.pdf}},
year = {{2018}},
}