Understanding modelled sea-air CO2 flux biases in the Southern Ocean through the seasonal cycle

• Main Author: Mongwe, Ndunisani Precious
• Other Authors: Monteiro, Pedro M S, Vichi, Marcello
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: University of Cape Town 2018
• Subjects: Oceanography; Southern Ocean
• Online Access: http://hdl.handle.net/11427/29260; https://open.uct.ac.za/bitstream/11427/29260/1/thesis_sci_2018_mongwe_ndunisani_precious.pdf

The Southern Ocean forms a vital component of the earth system as a sink of CO2 and heat, taking over 40% of the annual oceanic CO2 uptake (75% of global heat uptake), slowing down the accumulation of CO2 in the atmosphere and thus the rate of climate change. However, recent studies based on the Coupled Model Intercomparison Project version 5 (CMIP5) Earth System Models (ESMs) show that CMIP5 ESMs disagree on the phasing of the seasonal cycle of the CO2 flux (FCO2) and compare poorly with available observation estimates in the Southern Ocean. Notwithstanding these differences, the seasonal cycle is a dominant mode of CO2 variability in the Southern Ocean, and hence this is an important bias. Previous studies suggest that these biases of FCO2 in ESMs might be a significant limitation to the long-term simulation of CO2 characteristics in the Southern Ocean. Consequently, this study has three primary objectives: first, to develop a process-based diagnostic method to analyze and isolate key biases and their underlaying mechanisms in the model-observations seasonal cycle of FCO2 differences for forced ocean models and ESMs. Second, to use this framework to examine sources of biases responsible for the limited skill of CMIP5 models in simulating the seasonal cycle of FCO2 with respect to observed estimates. Thirdly, to investigate how these present-day biases in the seasonality and drivers of CO2 in CMIP5 ESMs affect modelled longterm changes in the mechanisms of CO2 uptake in the Southern Ocean. In the first part of the dissertation, an objective diagnostic framework was established to analyze model-observation biases in the seasonal scale of FCO2 using the NEMO PISCES ORCA2LP model output, and Takahashi et al. (2009) observed estimates. The diagnostic framework focuses on examining the relative contributions of the competing drivers (SST and DIC) and related processes (solubility, biological and mixing) to instantaneous monthly changes in surface pCO2 (and FCO2) at the seasonal scale. In the second part of the ...