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Evaluation of modeling ecosystem seasonality in the University of Victoria Earth System Climate Model

Eisele, Annika (2013) BIOM01 20131
Degree Projects in Biology
Abstract
Abstract

Investigating climate model ability to simulate ecosystem seasonality, for instance causes and dynamics of phytoplankton blooms in the North Atlantic, is of major importance, because interannual and seasonal variations in bloom timing, duration and intensity caused by anthropogenic induced global climate change, can lead to species shifts and alterations in the trophic food web and biogeochemical cycles, which otherwise may remain undetected on an annual scale.

The seasonal variability of the North Atlantic spring bloom is highly related to surface pCO2 (Takahashi et al., 2002; Corbiere et al., 2007; Signorini et al., 2012) and bloom dynamics have a substantial role in carbon sequestration (Watson et al., 1991; Townsend et... (More)
Abstract

Investigating climate model ability to simulate ecosystem seasonality, for instance causes and dynamics of phytoplankton blooms in the North Atlantic, is of major importance, because interannual and seasonal variations in bloom timing, duration and intensity caused by anthropogenic induced global climate change, can lead to species shifts and alterations in the trophic food web and biogeochemical cycles, which otherwise may remain undetected on an annual scale.

The seasonal variability of the North Atlantic spring bloom is highly related to surface pCO2 (Takahashi et al., 2002; Corbiere et al., 2007; Signorini et al., 2012) and bloom dynamics have a substantial role in carbon sequestration (Watson et al., 1991; Townsend et al., 1994). Hence it is necessary to improve predictions of seasonal variability in ecosystem models in order to simulate future global warming more precisely.

To evaluate the accuracy of the University of Victoria Earth System climate model (UVic model) simulations of timing, forcing factors and limitations (e.g. mixed layer depth, temperature, irradiance, nutrients, zooplankton grazing pressure) to the North Atlantic spring bloom, model output has been compared to observations from MODIS satellite images, WOA09 data, IFREMER records and PAP measurements.

The results showed that the UVic model simulated phytoplankton growth rates inaccurately and estimated the spring bloom start approximately one month too late. The model consistently underestimated actual temperature values, but temperature changes were significantly correlated between observations and predictions. The mixed layer shallowed earlier, more and patchier in practice than in the model. The simulated bloom is limited by solar radiance in early spring, while nutrient limitations are pivotal during summer. At specific study sites temperature was detected as driving factor for bloom formation. The role of the mixed layer depth on bloom dynamics was not simulated adequately by UVic.

Therefore especially simulations of the mixed layer depth and associated shoaling processes need to be optimized, maybe by improved parameterization of eddies and wind stress, to achieve accurate predictions of bloom dynamics and related forcing factors. This is important to provide precise simulations of phytoplankton bloom dynamics in the North Atlantic region for solid predictions about CO2 sequestration, biological pump magnitude and other biological and physical interaction processes with respect to seasonal variability. (Less)
Abstract
Popular science summary

Ecosystem seasonality in climate models

Climate change has a major effect on the timing, intensity and duration of seasonal events in Earth´s ecosystems. These changes may remain undetected when analyzing them on an annual scale, as current climate models usually do. Therefore it is necessary to investigate the impact of ecosystem seasonality on climate processes to improve model simulations.

The phytoplankton spring bloom in the North Atlantic is such a seasonal event. Its seasonal variability affects biogeochemical cycles. This bloom is for example an important contributor to oceanic CO2 uptake in the North Atlantic. Phytoplankton is also a major food source for zooplankton and larval fish. Thus,... (More)
Popular science summary

Ecosystem seasonality in climate models

Climate change has a major effect on the timing, intensity and duration of seasonal events in Earth´s ecosystems. These changes may remain undetected when analyzing them on an annual scale, as current climate models usually do. Therefore it is necessary to investigate the impact of ecosystem seasonality on climate processes to improve model simulations.

The phytoplankton spring bloom in the North Atlantic is such a seasonal event. Its seasonal variability affects biogeochemical cycles. This bloom is for example an important contributor to oceanic CO2 uptake in the North Atlantic. Phytoplankton is also a major food source for zooplankton and larval fish. Thus, variations in bloom dynamics can lead to alterations in the oceanic food web and changes in species compositions.

The aim of this project is to analyse model simulations about timing, forcing factors and limitations to the North Atlantic spring bloom. Therefore observations were compared with predictions of the University of Victoria Earth System Climate model (UVic model) (Weaver et al., 2001; Eby et al., 2009; Keller et al., 2012).

Does the UVic model predict the spring bloom correctly?
No! The UVic model simulated the bloom start approximately one month too late and predicted the bloom dynamics inaccurately. The irregular distribution of the actual bloom with patchy and temporary peaks was simulated too uniform by the model.

Which factors control the bloom?
The algae growth in the model is controlled by temperature. The UVic model consistently underestimated actual ocean temperature values. In contrast, phytoplankton blooms in the real ocean seem to be caused by shoaling processes in the upper water column. The actual mixed layer shallowed earlier and patchier than predicted by the model.
The simulated bloom was limited in early spring by light, while in summer nutrient limitation was pivotal. In summer also zooplankton can terminate the bloom by grazing on phytoplankton. The UVic model showed a high correlation between both parameters.

Observations showed that in the North Atlantic phytoplankton bloom patches are correlated with eddies. This pattern is not simulated by the UVic model and lead to incorrect predictions. Furthermore water column mixing depths and shoaling processes are major drivers for an algae bloom formation. Their simulations in the UVic model need to be improved. A better simulation of phytoplankton bloom dynamics will provide a useful tool to investigate the role of ecosystem seasonality on climate processes.

Advisor: Per Carlson
Master´s Degree Project 30 credits in Biology 2013
Department of Biology, Lund University (Less)
Please use this url to cite or link to this publication:
author
Eisele, Annika
supervisor
organization
course
BIOM01 20131
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
4193682
date added to LUP
2013-12-09 09:16:56
date last changed
2014-01-08 09:38:40
@misc{4193682,
  abstract     = {Popular science summary

Ecosystem seasonality in climate models 

Climate change has a major effect on the timing, intensity and duration of seasonal events in Earth´s ecosystems. These changes may remain undetected when analyzing them on an annual scale, as current climate models usually do. Therefore it is necessary to investigate the impact of ecosystem seasonality on climate processes to improve model simulations. 

The phytoplankton spring bloom in the North Atlantic is such a seasonal event. Its seasonal variability affects biogeochemical cycles. This bloom is for example an important contributor to oceanic CO2 uptake in the North Atlantic. Phytoplankton is also a major food source for zooplankton and larval fish. Thus, variations in bloom dynamics can lead to alterations in the oceanic food web and changes in species compositions. 

The aim of this project is to analyse model simulations about timing, forcing factors and limitations to the North Atlantic spring bloom. Therefore observations were compared with predictions of the University of Victoria Earth System Climate model (UVic model) (Weaver et al., 2001; Eby et al., 2009; Keller et al., 2012). 

Does the UVic model predict the spring bloom correctly? 
No! The UVic model simulated the bloom start approximately one month too late and predicted the bloom dynamics inaccurately. The irregular distribution of the actual bloom with patchy and temporary peaks was simulated too uniform by the model. 

Which factors control the bloom? 
The algae growth in the model is controlled by temperature. The UVic model consistently underestimated actual ocean temperature values. In contrast, phytoplankton blooms in the real ocean seem to be caused by shoaling processes in the upper water column. The actual mixed layer shallowed earlier and patchier than predicted by the model. 
The simulated bloom was limited in early spring by light, while in summer nutrient limitation was pivotal. In summer also zooplankton can terminate the bloom by grazing on phytoplankton. The UVic model showed a high correlation between both parameters. 

Observations showed that in the North Atlantic phytoplankton bloom patches are correlated with eddies. This pattern is not simulated by the UVic model and lead to incorrect predictions. Furthermore water column mixing depths and shoaling processes are major drivers for an algae bloom formation. Their simulations in the UVic model need to be improved. A better simulation of phytoplankton bloom dynamics will provide a useful tool to investigate the role of ecosystem seasonality on climate processes. 

Advisor: Per Carlson 
Master´s Degree Project 30 credits in Biology 2013 
Department of Biology, Lund University},
  author       = {Eisele, Annika},
  language     = {eng},
  note         = {Student Paper},
  title        = {Evaluation of modeling ecosystem seasonality in the University of Victoria Earth System Climate Model},
  year         = {2013},
}