Eleonora Rinaldi , Bruno Buongiorno Nardelli , Gianluca Volpe, Rosalia Santoleri

1. Chlorophyll distribution and variability in the Sicily Channel (Mediterranean Sea) as seen by remote sensing data Eleonora Rinaldi, Bruno BuongiornoNardelli, Gianluca Volpe, RosaliaSantoleri Institute for Atmospheric Sciences and Climate (ISAC)National Research Council (CNR) eleonora.rinaldi@artov.isac.cnr.it

2. Objectives To investigate if and how the biological processes observed in the Sicilian Channel and associated variability can be linked to physical processes over different temporal scales • Given their resolution spatial and temporal coverage satellite data represent the best choice to explore this issue. • The parameters considered are CHL data (as a proxy of phytoplankton biomass), Sea Surface Temperature and altimeter-derived Kinetic Energy.

3. Study Area • Twolayer system • MAW (top 200 m) flowseastwards • LIWflowswestwards Sicily CapoPassero Main Circulation • NearcapeBone MAW split in • Atlantic TunisianCurrent (ATC) that • circulatesaround the TunisianCoast • Atlantic IonianStream (AIS) thatflows • in the central and northernregions • of the Cannel • South of Pantelleria the AIS bifurcates • A principalveinflowsnortheastward • While a weakerstreamflowsalong the • TunisianShelf Tunisia MAW AIS Cape Bon Pantelleria ATC

4. Study Area • From a biogeochemical point of view • The entire basin can be considered as a mesotrophic area • The Chlorophyll concentration values ranging • 0.04 - 0.5 mg CHL m-3 at sub-basin scale • 0.01-10 mg CHL m-3 on a daily basis. • Higher values of phytoplankton biomass are found in coastal areas whereas off-shore regions exhibit a more pronounced variability. • The most productive region of the Channel has been generally identified as the wind driven upwelling area, along the south-eastern coasts of Sicily.

5. Data The dataset covers the time period spanning from 1998 to 2006 using WEEKLY data at 1/16° resolution • SST •  ISAC-GOS optimally interpolated (OISST) re-analysis product (Marullo et al. 2007) • Kinetic Energy • MADT data distributed by AVISO • The KE has been calculated as: • Where U and V are meridional and zonal geostrophic current components. • Chlorophyll data •  MedOC4 ocean colour algorithmto Level-3 remote sensing reflectance acquired and distributed by ISAC-GOS

6. Methods EmpiricalOrthogonalFunction (EOF) werecalculatedfrom the timeseriesof Log10 (CHL), SST, and KE. Thisstatisticaltechnique allows to find the recurrent patterns of space-time variability (EOF modes) in a time series, giving an estimation of the amount of variance associated with each mode. The EOF analysis requires complete time series of input maps, with no data voids. SST and KE data considered  already interpolated LCHL maps have significant data voids due to the presence of persistent cloud cover. Consequently, it was necessary to apply an interpolation algorithm to the LCHL time series. The reconstruction of missing data was performed iteratively following the Data INterpolating Empirical Orthogonal Functions (DINEOF) method [Beckers and Rixen (2003), Beckers et al. (2006)].

7. Methods • It is important to underline that EOFs mode are not necessarily related to physical or biological processes. • (e.g. often a single process can be spread over different modes, in other cases more than one process can contribute to the variance explained by a single mode) • However, in many cases, EOF modes reflect the typical patterns associated with a specific process (e.g. Buongiorno Nardelli and Santoleri 2004, 2005; Buongiorno Nardelliet al. 2003, 2010, Garcia and Garcia, 2008; Primpaset al., 2010) • To isolate and identify the different physical and biological processes acting in the Channel • EOFs are estimated for each variable separately • Spatial correlation and temporal lagged-correlation analyses between identified patterns and amplitudes are then used to define the timing of the covariability between physical and biological processes (all correlation values were tested for significance through the Student’s test)

8. Chl, SST and KE climatologies Chla [mg m-3] • Highest values of Chla present along • Sicilian and Tunisian coasts • Weak signature of the Capo Passero • filament 0.1 1998 1999 2000 2001 2002 2003 2004 2005 2006 KE [m2 s-2] 180 • Highest value of KE associated with MAW path • Maximum values correspond to MAW and to the Capo Passero jet 160 140 120 100 1998 1999 2000 2001 2002 2003 2004 2005 2006 SST [°C] 28 26 24 • Meridional gradient due to • Latitudinal variations • Different water masses 22 20 18 16 1998 1999 2000 2001 2002 2003 2004 2005 2006

9. First Chlorophyll mode Chl First Mode • The spatial pattern shows positive values everywhere meaning that there are simultaneous seasonal • variations in the whole channel • The strongest signal is present at the strait entrance and downstream Cap Bon (Tunisian Coast). • High variability characterizes also the area off-shore Capo Passero; • Clear annual cycle: highest (positive) signal November-March, lowest (negative) June-October • Interannual variability • Maxima decrease up until 2002, the signal increase up to 2005 and decrease during 2006 • Minima show less evident interannual variability

10. First Chlorophyll mode Chl First Mode • This mode is significantly CORRELATED to the 1st Mode of KE • Temporal r = 0.70 with a lag of 6 weeks • Space r = 0.42 • The Chl signal is maximum in winter when the MAW inflow is more intense • The area off-shore Capo Passero shows a similar seasonal variability. • Indeed an increase of intensity of the AIS filament during winter corresponds • to an increment of Chl concentration. • The upwelling regions are more productive with respect to the surrounding areas during summer KE First Mode

11. First Chlorophyll mode Chl First Mode • The significant correlations between the first modes of Chl and KE suggest that ̴ 80% of the biological variability in the channel is due to the advection of MAW KE First Mode

12. Second Chlorophyll mode Chl Second Mode • The pattern shows two regions of opposite values. • Positive values of anomalies are present in the northern regions of the Channel while negative values • characterize the southern region. • The corresponding amplitude varies between negative and positive values depending on season • Interannual variability • 1998 and 2002 the spring bloom is absent • Maximum 1999, 2000 and 2005

13. Second Chlorophyll mode Chl Second Mode • This mode is CORRELATED to the 2st Mode of SST • Temporal r = 0.52 with a lag of 18 weeks • Space r = -0.92 • The second SST EOF mode (pattern and amplitude) shows an inversebehaviour with respect to the second mode of Chl. • Highest values of Chl are found during spring in correspondence of the areas of the lowest winter SST. • Looking at the SST as a proxyofsurfacestratification(Behrenfeldet al. [2006]), a deeper upper mixed layer during winter thus results in a higher production in spring, possibly due to the enhanced nutrient flux into the euphotic zone (Doney, 2006). SST Second Mode

14. Primary Production decline is correlated to a general positive trend in the surface temperature from Behrenfeld et al. [2006] from Wilson & Coles [2005] the deepening of the upper mixed layer does not reach the nutricline upper mixed layer deepens seasonally to reach the nutrient pool from Doney [2006]

15. Second Chlorophyll mode Chl Second Mode • 3% of phytoplankton variability can be reasonably be associated with the water column stratification dynamics, and thus on the nutrient flux into the upper mixed layer SST Second Mode

16. Conclusion While EOF decomposition does not necessarily identify processes, in this study it provides means of interpreting the covariability in the physical and biological fields, at different spatial and temporal scales, suggesting a number of mechanisms linking the upper ocean dynamics to ecosystem functioning in the Channel of Sicily The phytoplankton biomass surface variability is strongly linked to the surface circulation and to the upper mixed layer dynamics • The correlation between the first modes of Chl and KE suggests that the advection from the northern coasts of Africa associated with MAW flow has a significant impact on the phytoplankton concentration in the Channel. • The significant correlation between the second modes of CHL and SST suggests an inverse relationship between spring phytoplankton concentration and the winter upper mixed layer deepening