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

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 Cape Bon MAW Pantelleria AIS ATC Two layer system MAW (top 200 m) flows eastwards LIW flows westwards Near cape Bone MAW split in Atlantic Tunisian Current (ATC) that circulates around the Tunisian Coast Atlantic Ionian Stream (AIS) that flows in the central and northern regions of the Cannel South of Pantelleria the AIS bifurcates A principal vein flows northeastward While a weaker stream flows along the Tunisian Shelf Sicily Tunisia Capo Passero Main Circulation

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 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 algorithm to Level-3 remote sensing reflectance acquired and distributed by ISAC-GOS The dataset covers the time period spanning from 1998 to 2006 using WEEKLY data at 1/16° resolution

6. Empirical Orthogonal Function (EOF) were calculated from the time series of Log 10 (CHL), SST, and KE. This statistical technique 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)]. Methods

7. 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 Nardelli et al. 2003, 2010, Garcia and Garcia, 2008; Primpas et 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 Highest values of Chla present along Sicilian and Tunisian coasts Weak signature of the Capo Passero filament Highest value of KE associated with MAW path Maximum values correspond to MAW and to the Capo Passero jet Meridional gradient due to Latitudinal variations Different water masses Chla [mg m -3 ] 0.1 KE [m 2 s -2 ] 100 120 140 160 180 1998 1999 2000 2001 2002 2003 2004 2005 2006 SST [°C] 1998 1999 2000 2001 2002 2003 2004 2005 2006 16 18 20 22 24 26 28

9. 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 First Chlorophyll mode Chl First Mode

10. First Chlorophyll mode This mode is significantly CORRELATED to the 1 st 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 Chl First Mode KE First Mode

11. First Chlorophyll mode 11 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 Chl First Mode KE First Mode

12. Second Chlorophyll 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 Chl Second Mode

13. Second Chlorophyll mode This mode is CORRELATED to the 2 st 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 inverse behaviour 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 proxy of surface stratification (Behrenfeld et 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). Chl Second Mode SST Second Mode

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

15. Second Chlorophyll mode Chl Second Mode SST 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

16. Conclusion 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 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