Applying LPJ-GUESS on the Arctic: A model evaluation and benchmarking study
(2023) In Student thesis series INES NGEM01 20231Dept of Physical Geography and Ecosystem Science
- Abstract
- Warming in the Arctic occurs at a much higher rate than the global average, which has a considerable impact on the Arctic terrestrial carbon cycle. Permafrost thawing can release substantial amounts of carbon, whilst tundra shrubification and tree-line advance, on the other hand, may compensate for this. To gain a better understanding of the Arctic carbon cycle in the future, global dynamic vegetation models (DGVMs) can be used to simulate vegetation properties and dynamics.
The aim of this study was to evaluate the performance of LPJ-GUESS, a DGVM, when it is applied on the Arctic to gain a better understanding how well the model is able to capture certain key Arctic-related processes and variables. The study focused primarily on gross... (More) - Warming in the Arctic occurs at a much higher rate than the global average, which has a considerable impact on the Arctic terrestrial carbon cycle. Permafrost thawing can release substantial amounts of carbon, whilst tundra shrubification and tree-line advance, on the other hand, may compensate for this. To gain a better understanding of the Arctic carbon cycle in the future, global dynamic vegetation models (DGVMs) can be used to simulate vegetation properties and dynamics.
The aim of this study was to evaluate the performance of LPJ-GUESS, a DGVM, when it is applied on the Arctic to gain a better understanding how well the model is able to capture certain key Arctic-related processes and variables. The study focused primarily on gross primary productivity (GPP), ecosystem respiration (Reco), active layer thickness (ALT) and snow depth. A total of 20 (sub-)Arctic FLUXNET sites were included. The model was forced with a bias-corrected climate forcing based on meteorological observations for each site. Different simulations were evaluated, including an upland, wetland and wet forest run.
This study has shown that LPJ-GUESS tends to underestimate GPP and Reco, especially for high Arctic sites (>70ºN). ALT at the end of the season (August/September) is largely overestimated for the upland simulation, whereas it is underestimated for wetlands. Running the model as a wet forest (i.e. wetland with tree PFTs) resulted in a very good fit for ALT. However, it also led to a large decrease in the modelled GPP and Reco. Snow depth was poorly captured by the model, with large underestimations at most sites.
In light of these insights, it is evident that refining the LPJ-GUESS model remains essential for comprehending the intricate dynamics of the Arctic carbon cycle. Furthermore, this study accentuates the capacity and promise associated with the utilization of DGVMs in emulating vegetation attributes and behaviours. (Less) - Popular Abstract
- The Arctic is warming up much faster than the rest of the world, and this has significant effects on the balance of carbon in the region. As the previously frozen ground melts, substantial amounts of carbon are released into the air, further contributing to climate change. Yet interestingly, the growth of more vegetation, like shrubs and trees, might help offset these carbon emissions. To better understand how all of this will evolve in the future, scientists are using models to simulate how plants and carbon interact in the Arctic.
The aim of this study was to see how well one of these models, called LPJ-GUESS, could capture important ecosystem processes in the Arctic. Variables that were considered included things like how much carbon... (More) - The Arctic is warming up much faster than the rest of the world, and this has significant effects on the balance of carbon in the region. As the previously frozen ground melts, substantial amounts of carbon are released into the air, further contributing to climate change. Yet interestingly, the growth of more vegetation, like shrubs and trees, might help offset these carbon emissions. To better understand how all of this will evolve in the future, scientists are using models to simulate how plants and carbon interact in the Arctic.
The aim of this study was to see how well one of these models, called LPJ-GUESS, could capture important ecosystem processes in the Arctic. Variables that were considered included things like how much carbon plants take up, how much they release, how deep the ground thaws in the summer, and how much snow accumulates. The model was tested at 20 sites in the Arctic and predictions were compared to observations.
The study found that LPJ-GUESS often underestimated how much carbon plants were taking up and releasing, especially in the high Arctic regions. The model also had performed poorly when predicting how deep the ground thawed at different times of the year, and it did not do a good job with estimating snow depth.
Given these findings, it is clear that improving the LPJ-GUESS model is crucial for truly understanding the complex ways carbon behaves in the Arctic. Additionally, this study highlights the potential of using models to mimic how plants behave, which could greatly enhance our understanding of how the Arctic carbon cycle might change in the future. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9139908
- author
- Knapen, Margot Jeanne LU
- supervisor
-
- Stefan Olin LU
- organization
- course
- NGEM01 20231
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Physical Geography and Ecosystem Analysis, LPJ-GUESS, Arctic, FLUXNET, Gross Primary Productivity, Ecosystem Respiration, Permafrost, Active Layer Thickness, Snow Depth
- publication/series
- Student thesis series INES
- report number
- 630
- language
- English
- id
- 9139908
- date added to LUP
- 2023-10-12 09:28:25
- date last changed
- 2023-10-12 09:28:25
@misc{9139908,
abstract = {{Warming in the Arctic occurs at a much higher rate than the global average, which has a considerable impact on the Arctic terrestrial carbon cycle. Permafrost thawing can release substantial amounts of carbon, whilst tundra shrubification and tree-line advance, on the other hand, may compensate for this. To gain a better understanding of the Arctic carbon cycle in the future, global dynamic vegetation models (DGVMs) can be used to simulate vegetation properties and dynamics.
The aim of this study was to evaluate the performance of LPJ-GUESS, a DGVM, when it is applied on the Arctic to gain a better understanding how well the model is able to capture certain key Arctic-related processes and variables. The study focused primarily on gross primary productivity (GPP), ecosystem respiration (Reco), active layer thickness (ALT) and snow depth. A total of 20 (sub-)Arctic FLUXNET sites were included. The model was forced with a bias-corrected climate forcing based on meteorological observations for each site. Different simulations were evaluated, including an upland, wetland and wet forest run.
This study has shown that LPJ-GUESS tends to underestimate GPP and Reco, especially for high Arctic sites (>70ºN). ALT at the end of the season (August/September) is largely overestimated for the upland simulation, whereas it is underestimated for wetlands. Running the model as a wet forest (i.e. wetland with tree PFTs) resulted in a very good fit for ALT. However, it also led to a large decrease in the modelled GPP and Reco. Snow depth was poorly captured by the model, with large underestimations at most sites.
In light of these insights, it is evident that refining the LPJ-GUESS model remains essential for comprehending the intricate dynamics of the Arctic carbon cycle. Furthermore, this study accentuates the capacity and promise associated with the utilization of DGVMs in emulating vegetation attributes and behaviours.}},
author = {{Knapen, Margot Jeanne}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Applying LPJ-GUESS on the Arctic: A model evaluation and benchmarking study}},
year = {{2023}},
}