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Mixed layer depth variability and phytoplankton phenology in the Mediterranean Sea H. Lavigne 1, F. D’Ortenzio 1, M. Ribera d’Alcalà 2, H. Claustre 1 1.Laboratoire.

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Presentation on theme: "Mixed layer depth variability and phytoplankton phenology in the Mediterranean Sea H. Lavigne 1, F. D’Ortenzio 1, M. Ribera d’Alcalà 2, H. Claustre 1 1.Laboratoire."— Presentation transcript:

1 Mixed layer depth variability and phytoplankton phenology in the Mediterranean Sea H. Lavigne 1, F. D’Ortenzio 1, M. Ribera d’Alcalà 2, H. Claustre 1 1.Laboratoire d’Océanographie de Villefranche, France 2.Stazione Zoologica A. Dohrn, Naples, Italy 45 th Liège Colloquium – May 17 th 2013

2 It is now well recognized that the phytoplankton phenology is impacted by mixed layer depth (MLD) variability (blooms events are good examples). However, it is still challenging to observe and characterize the impact of MLD on phytoplankton (MLD and phytoplankton biomass change rapidly, low availability of the phytoplankton biomass data). Merging in situ MLD data and ocean color chlorophyll-a concentration ([Chl] SAT ) data represents a way to explore interactions between MLD annual cycle and phytoplankton phenology. DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION

3 Present analysis was performed on the Mediterranean Sea because  Data availability (weak cloud coverage for [Chl] SAT, and important CTD sampling).  contrasting biogeochemical regimes co-exist over the basin. Present analysis was based on :  The generation of concomitant MLD and [Chl] SAT annual cycles.  Spatial averages computed in established bioregions.  Description of MLD and [Chl] SAT cycles based on a new set of metrics. DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION

4 DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION Data SeaWiFS (1998 – July 2007) et MODIS- Aqua (July ) Level 3, 8-day, 9km Standard NASA algorithm (O’Reilly et al., 2007) Satellite surface chlorophyll-a concentration ([Chl] SAT ) Historical database (D’Ortenzio et al updated with Coriolis) profiles of temperature and salinity Computation of MLD (criteria in density difference 0.03 kg m -3 ) Mixed Layer Depth (MLD) calculated from in situ CTD measurements. The data density is not sufficient to work with a regular mesh grid. A bioregionalization was used instead.

5 The geographical framework - Starting point the bioregionalization of the Mediterranea Sea proposed by D’Ortenzio and Ribera d’Alcalà (2009). DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION bloom no bloom intermittent 3 mains kinds of dynamics appeared Result from a k- means cluster analysis based on the seasonal cycle of SeaWiFS chlorophyll- a concentration Source: D’Ortenzio et Ribera D’alcalà (2009)

6 DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION Med NW - Bloom bioregion Ionian - No bloom bioregion Climatological scale All MLD and [Chl] SAT observations are mixed to produce a climatological cycle. Interannual scale Data are averaged for each year separately. spatial temporal Data processing MLD and [Chl] SAT observations Generation of concomitant MLD and [Chl] SAT annual cycles at 8-day resolution

7 DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION CHL-MAX: annual maximum of [Chl] SAT MLD-MAX: annual maximum of MLD ΔINIT: Time lag between the initiation of mixing and the initiation of [Chl] SAT increase (determination of the initiation date: annual median + 5%; Siegel et al., 2002). ΔMAX: Time lag between the date of MLD maxima and the date of [Chl] SAT maxima. Metrics to describe annual MLD and [Chl] SAT cycles July year n June year n+1 MLD [Chl] SAT MLD-MAX CHL-MAX ΔINIT ΔMAX

8 DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION The climatological scale Med NW - Bloom Ionian - No Bloom MLD-MAXCHL-MAXΔINITΔMAX 185 m 0.99 mg m days MLD-MAXCHL-MAXΔINITΔMAX 90 m 0.22 mg m days8 days

9 DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION The interannual scale : the analysis of annual cycles The shape of MLD and [Chl] SAT cycles vary from year to year. For 4 cycles out of 5, the succession MLD deepening followed by [Chl] SAT increase and decay is repeated. Cycle 2006/2007 is anomalous. The shape of MLD and [Chl] SAT cycles are fairly similar to the climatology. The absolute values, especially for MLD and [Chl] SAT peaks, are variable. Med NW - Bloom Ionian – No Bloom

10 bioregionMLD-MAXCHL-MAXΔINITΔMAX Med NW - Bloom 368m [119 – 524] 1.44 mg m -3 [0.84 – 1.72] 14 days [0 – 40] 31 days [(-16) – (+72)] Ionian - No Bloom 108m [76 – 158] 0.25 mg m -3 [0.19 – 0.30] 17 days [(-8) – (+32)] 11 days [(-56) – (+88)] DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION The interannual scale: the analysis of metrics The metric MLD-MAX is highly variable, by comparison to CHL-MAX. MLD-MAX and CHL-MAX are both higher in the “Bloom” bioregion than in the “No Bloom” bioregion. ΔINIT is relatively stable and similar for the “Bloom” and “No Bloom” bioregions. ΔMAX is more variable, especially for the “No Bloom” bioregion. ΔMAX is higher for the “Bloom” than for the “No Bloom” bioregion.

11 DATA & METHODS RESULTS DISCUSSION CONCLUSION INTRODUCTION Summary  Metrics are a powerful tool to identify patterns in the MLD and [Chl] SAT cycles.  These patterns are relatively consistent between interannual and climatological analyses.  Metrics analysis confirmed that in the Mediterranean Sea stronger biomass accumulation matches with areas where winter MLDs are the deepest.  Metrics analysis revealed temporal differences between main MLD and [Chl] SAT events (measured with Δ INIT and Δ MAX). c ΔINITΔMAX BLOOM~30 days NO BLOOM~30 days~0 days How we can explain the ΔMAX difference and the ΔINIT of 30 days?

12 DATA & METHODS DISCUSSION CONCLUSION INTRODUCTION RESULTS Do light and nutrient availability can explain the ΔINIT and ΔMAX values in the « Bloom » and « No Bloom » bioregions? P rNUT P rLIGHT Probability that the MLD is deeper than the nitracline depth.  Empirical estimation (MLD and nitracline datasets are confronted) Nitracline data set: Nitracline = isoline 1µM Calculated from a dataset of 5318 nitrates profiles (MEDAR, SESAME projects). Probability that the MLD is deeper than the nitracline depth.  Empirical estimation (MLD and nitracline datasets are confronted) Nitracline data set: Nitracline = isoline 1µM Calculated from a dataset of 5318 nitrates profiles (MEDAR, SESAME projects). Probability that the MLD is above the critical depth (D cr, Sverdrup 1953).  Empirical estimation (MLD dataset is compared to a climatological estimation of the critical depth, calculation method Siegel et al ) Probability that the MLD is above the critical depth (D cr, Sverdrup 1953).  Empirical estimation (MLD dataset is compared to a climatological estimation of the critical depth, calculation method Siegel et al ) SeaWiFS climatology 1.3 mol photon m -2 d -1

13 DATA & METHODS DISCUSSION CONCLUSION INTRODUCTION RESULTS Med NW - Bloom Ionian – No Bloom Do light and nutrient availability can explain the ΔINIT and ΔMAX values in the « Bloom » and « No Bloom » bioregions? Med NW - Bloom Ionian – No Bloom P rNUT P rLIGHT

14 DATA & METHODS CONCLUSION INTRODUCTION RESULTS DISCUSSION Δ INIT ♦ BLOOM : ~ 30 days ♦ NO BLOOM : ~ 30 days Δ MAX ♦ BLOOM: ~ 30 days ♦ NO BLOOM : ~ 0 days Hypothesis : [ Chl ] SAT increase only when the MLD is below the nitracline ( November ) Hypothesis : Episodically, a deficit of light could limit the growth during winter. Hypothesis : Light is always available and irregular nutrients supplies by mixing sustain phytoplankton growth during winter.

15 Metrics are a powerful tool to identify phenological patterns and characterize the influence of the mixed layer. The relevance of metrics ΔINIT and ΔMAX was emphasized. In the Mediterranean Sea, we proposed some hypotheses to explain their behaviors. (Lavigne et al. JGR, in revision) The proposed phenological metrics could be particularly adapted for profiling floats observations. DATA & METHODS CONCLUSION INTRODUCTION RESULTS DISCUSSION Conclusions and Perspectives

16 Thanks to Fabrizio D’Ortenzio (my supervisor), Loïc Houpert (CEFREM, Perpignan FRANCE) and Rosario Lavezza (SZN, Napoli, Italy) for their help on CTD and nitrate data. Thank you to for your attention Contact:

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18 Method Lavigne et al., 2012


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