NEMO modelling environment
NEMO is an ocean modelling environment principally developed in Institute Pierre Simon- Laplace (ISPL), France, with the participation of several European research centres2. Its main core is the OPA model, for ocean dynamics and thermodynamics (Madec ). OPA is a primitive equation model aimed to cover from regional to global ocean scales and to be coupled to models representing important ocean-related processes as sea-ice dynamics and thermodynamics (LIM model) or passive tracers dynamics (TOP). Ocean biogeochemical cycles are also represented in NEMO environment thanks to PISCES model (Aumont and Bopp ), which is embedded in TOP.
NEMO system is used for a wide range of applications: from studies on fundamental physical oceanography (e.g. studies on turbulence and ocean energetics) and ocean and ice biogeochemistry, to operational forecast (e.g. ECMWF and Mercator) and global Climate Change projections. Actually, NEMO constitutes the ocean component of several Earth System Models (ESM) as IPSL one, the IPSL-CM5 (Dufresne et al.  and http://www.icmc.ipsl.fr).
Biogeochemical parametrisations in PISCES
The PISCES biogeochemical model (Aumont ) aims to simulate processes governing ocean primary production and other carbon cycle related processes. PISCES resolves 24 prognostic variables (see figure 3.1), including two phytoplankton (nanophytoplankton and diatoms), two zooplankton (micro- and mesozooplankton) and the main limiting nutrients in the global ocean (N, P, Si and Fe). The PISCES formulation is based on the assumption that marine organic matter is present in the global ocean in a relatively constant composition: for each atom of P there is 16 atoms of N and 106 of C 3. Phytoplankton growth is limited by the external concentrations of these three nutrients. Such assumptions are the foundations of Monod type of biogeochemical models (Monod ). However, PISCES is not a fully Monod model as Si and Fe quotas are variable4. The nutrient-depending formulation allows for the adaptation of PISCES to a large number biogeochemical environments and so, to better represent global ocean variability.
Water-column (1D) biogeochemical model
In this section, I will describe the main features of the water column (i.e., 1D) PISCES configuration I developed during this PhD. This offline and 1D configuration was created to address how ocean physics controls Southern Ocean phytoplankton seasonal cycles (and hence, spring blooms at the ocean surface). The same question, generalized to any high-latitude region, is one of the longest and most explored questions on modern biogeochemical oceanography and has inspired a number of studies during the past decades (Sverdrup , Siegel , Behrenfeld , Taylor and Ferrari , Ferrari et al. ). Most of these studies and theories are based on the North-Atlantic region and we wanted to contrast these theories to the very specific biogeochemical conditions of the Southern Ocean. A biogeochemical offline configuration was assumed to be a good approach to the bloom onset question because it allowed us to apply strict controls over the physical environment.
We aimed to answer this question via a sensitivity analysis approach, with the objective of defining clear links between physical oceanic features and phytoplankton dynamics throughout the seasonal cycle. The robustness of such an approach was based on taking into account a large number of runs from which statistical relationships between variables could be confidently established. Such a methodology required a modelling framework simple enough to identify the impact of an isolated physical variable over biogeochemistry, yet complex enough to reproduce observations under realistic physical scenarios and efficient enough (in terms of time and computing resources) to be repeated for a large number of times.
Characterisation of distinct bloom phenology regimes in the Southern ocean (Article)
Satellite observations and studies based on in situ observations have shown that phytoplankton distribution in the Southern Ocean displays patchy regional variability (5.1.a) and a wide range of distinct seasonal cycle regimes (e.g. Moore and Abbott ; Arrigo et al. ; Thomalla et al. ; Chiswell et al. ; Franks ; Carranza and Gille ). While phytoplankton biomass and the associated primary productivity fluctuate according to season (e.g. Arrigo et al. ) and location, the environmental conditions that drive these patterns are poorly understood. These misunderstandings hamper our ability to apprehend how seasonality might be modified in the future (e.g. Henson et al. ) and the associated implications for Southern Ocean food webs. A unique aspect of the Southern Ocean circulation is the presence of a strong eastward, circumpolar current, the Antarctic Circumpolar Current (ACC). On the northern edge of the ACC, subtropical gyres flow counterclockwise, and their intense and energetic western boundary currents join the northern branches of the ACC in the western Atlantic, Indian, and Pacific basins.
The ACC and the western boundary currents have a profound influence on the physical and geochemical characteristics of the Southern Ocean (Rintoul et al. ). They form meridional dynamical barriers (Sall´ee et al. ), which split the Southern Ocean into a number of distinct zones. Four main zones can be described, from north to south: the subtropical region, around Southern Ocean bloom phenology 86 30° S, characterized by stratified surface layers (5.1.c), and relatively weak wind and buoyancy forcing; the subantarctic region, directly north of the ACC, which is characterized by very deep mixed-layers (5.1.c), intense winds, large buoyancy forcing, and the presence of the energetic western boundary currents; the ACC region, characterized by the top-to-bottom and large circumpolar current; and the subpolar region, south of the ACC, characterized by the seasonal presence of sea-ice, and a relatively stratified surface layer.
These dynamical zones of the Southern Ocean correspond to specific geochemical regions (e.g. Longhurst et al. ). The surface layer of the subtropical region have low macronutrient concentrations (5.1.d), the subantarctic, ACC and subpolar regions are generally considered as macronutrient rich, iron limited regions (e.g. Martin et al. ; Boyd et al. ), although silicic acid is notably much lower in the subantarctic region than the ACC (e.g. Sarmiento et al. ). Another notable aspect of the subantarctic zone is that it contains numerous continental sources of iron (Boyd and Ellwood ), with the presence of continental plateau and many subantarctic islands, in the lee of the western boundary currents flowing eastward.
At present, it is not clear how the specific dynamical and geochemical regions of the Southern Ocean relate to the patchy phytoplankton distribution in the Southern Ocean (5.1; e.g. Thomalla et al. ; Chiswell et al. ). The aim of this study is to use a range of physical and biochemical observational products to shed light on the general chlorophyll bloom patterns in the Southern Ocean, and link these patterns to the distinct dynamical and geochemical regions of the Southern Ocean.
Our aim of describing regional variability of the Southern Ocean phytoplankton seasonal cycle falls within the more general context of the mechanisms associated with onset and duration of phytoplankton blooms. These mechanisms remain much debated despite decades of research (e.g. Sverdrup ; Evans and Parslow ; Townsend et al. ; Huisman et al. ; Behrenfeld ; Mahadevan et al. ; Taylor and Ferrari ; Ferrari et al. ). This debate arises from the wide diversity, and often inter-related, factors that control phytoplankton blooms, which range from physical (e.g. solar irradiance and the intensity of surface layer mixing), biological (e.g. growth or grazing rates) to chemical (e.g. availability or cycling of nutrients) factors.
Table of contents :
1 General Context
1.1 High-latitude bloom theories
1.2 The Southern Ocean
1.3 Specific objectives and structure
2.2 Observations in the Indian Sector of the Southern Ocean
2.2.1 KERFIX station
2.2.2 KEOPS project
2.2.3 Kerguelen elephant seals
2.3 Ocean colour data in the Southern Ocean
2.4 Argo floats
3.2 NEMO modelling environment
3.3 Coupled Earth system models
3.4 Regional forced models
3.5 Water-column (1D) biogeochemical model
4 Bloom dynamics: mechanisms and phenology
4.2 Onset, intensification and decline of phytoplankton blooms in the Southern Ocean (Article)
5 Bloom phenology in the Souhtern Ocean
5.2 Characterisation of distinct bloom phenology regimes in the Southern ocean (Article)
5.3 Integrated view of bloom phenology regimes using a regional model
6 Sensitivity of Southern Ocean primary production to Climate Change
6.2 CMIP5 projections in the Southern Ocean
6.2.1 Projected trends on primary production
6.2.2 Projected changes in the MLD and its influence on #PP
6.3 Mechanics of mixing and iron supply control over primary production
6.3.1 Winter and summer stratification influence on primary production
6.3.2 How vertical iron supply controls community structure?
6.3.3 Winter mixing and vertical iron supply as coupled drivers
6.3.4 What is the net effect of summer stratification?
6.4 Shedding light on hidden future drivers
6.5 Summary and Conclusions
Conclusions and Perspectives
A Rapid establishment of the CO2 sink associated with Kerguelen’s 2 bloom observed
during the KEOPS2/OISO20 cruise. (Article)