• Keine Ergebnisse gefunden

A 10 year long time series of SeaWiFS data shows spatial and temporal variability of phytoplankton blooms in the Scotia Sea region

N/A
N/A
Protected

Academic year: 2022

Aktie "A 10 year long time series of SeaWiFS data shows spatial and temporal variability of phytoplankton blooms in the Scotia Sea region"

Copied!
1
0
0

Wird geladen.... (Jetzt Volltext ansehen)

Volltext

(1)

A 10 year long time series of SeaWiFS data shows spatial and temporal variability of phytoplankton blooms in the Scotia Sea region

I. Borrione, R. Schlitzer

Alfred Wegener Institute for Polar and Marine Research – GERMANY Ines.Borrione@awi.de, Reiner.Schlitzer@awi.de

Conclusions

A 10-year long time series of Ocean Color Data in the Scotia Sea shows inter- annual variability, especially close to coastal and island ecosystems.

Cloud cover, sea ice extent, and low solar elevation limit data coverage so that analysis of satellite images needs to be limited to Austral spring and summer.

Seasonal productivity increases in Aug/Sept reaching its maximum extent and intensity in December.

Little inter-annual variability is found upstream the Drake Passage, east of the South Sandwich Islands, and in the region surrounding the Shackelton Fracture

Zone where productivity is kept low.

SST contours, shelf regions and circulation patterns may control bloom extension and distribution.

Introduction

The Scotia Sea is a High Nutrient Low Chlorophyll region experiencing intense summer phytoplankton blooms. The average depth is greater than 2000m with a complex bathymetry due to ridges, plateaus and islands. The ACC flows through the region carrying a uniquely high nutrient content. Continental shelves may supply the current with iron, thus enhancing spring and summer primary productivity. A synoptic study of such a vast region is possible only via satellite imagery, although winter low solar elevation does not allow measurements between April and July.

Methods

SeaWiFS estimates of surface Chl-a were obtained from the Goddard Distributed Active Center. Level-3, monthly composites at 9 km resolution were retrieved for the period between January 1998 and December 2007. Each monthly Chl-a image was combined into a climatological average to study seasonal and inter-annual variability.

Available data were elaborated with NCO software and displayed with Ocean Data View.

Seasonal variability

Figures 1(a-f): Climatological average of September to February.

The sequence of images indicate a clear seasonal evolution of blooms in the Scotia Sea regions. Chl-a concentrations start increasing in austral spring especially along shallower bathymetries.

Maximum concentrations and bloom extensions are reached in Austral summer (Dec./Jan). Units are mg/m3.

Inter-annual variability

Figures 2(a-f): Chl-a monthly composites of December from 1998 to 2003.The comparison of the 6 images show strong inter-annual variability, especially close to island ecosystem, along shelf regions, and the marginal ice zone. In white are missing values due to clouds and/or sea-ice cover.Units are mg/m3.

Bloom controlling factors

Figure 3.a Climatological average of December. SST and 500m bathymetry contours. Aqua-MODIS derived SST contours (black solid line) confine the plume extending east of the South Georgia island. The western limit and the shape of the intense bloom (arrow) appears to be regulated by circulation patterns as shown by trajectories of deployed surface drifters (Figure 3.bas from Meredith et al. (2003) GRL, vol. 30, n. 20). The 500m bathymetry contour (white solid line in Fig. 3-a) limits the extension of the Argentine (Chl-a

> 4mg/m3) Antarctic Peninsula bloom. Units are mg/m3

Fig. 3-a 3-b

Ave Jan. Ave Feb.

1-f 1-c 1-d

1-e

1-b

Ave Nov. Ave Dec.

Ave Oct.

Ave Sept.

Fig. 1-a

Fig. 2-a

Dec. 1998 Dec. 1999

Dec. 2000 Dec. 2001

Dec. 2002

2-b

2-d 2-c

2-e 2-f

Dec. 2003

Referenzen

ÄHNLICHE DOKUMENTE

SCCP and MCCP levels as well as congener group patterns (n-alkane chain length, chlorine content) could be evaluated by electron capture negative ionization low resolution

From left to right, 100 days of an ultrasonic sounder time series from the automatic weather station AWS9 (height above surface) [van den Broeke et al., 2004b] at site DML05, near

The following variables were used in the pairwise correlation matrix: archaeal HS1392, G1 HSIJC12, G11 HSarchacul, prokaryote abundance, leucine incorporation rate, chl a,

Abstract Temporal disaggregation methods are used to disaggregate low frequency time series to higher frequency series, where either the sum, the average, the first or the last value

Mann's test allows us to confirm that, on the average, in 43 % and 32.3 % of cases - following the appearance of trends in the mean values and in the variances - there

Horizontal transects of spectral irradiance measure- ments under sea ice reveal the spatial variability of light conditions as a function of snow cover, sediment load,

ANP, Antarctic Peninsula; CSS, central Scotia Sea; DP, Drake Passage; ESS, East Scotia Sea; MEB, Maurice Ewing Bank; PB, Protector Basin; Pow, Powell Basin; JB, Jane Basin; SAAR,

In the sixth chapter of the thesis the prediction of the lability of preferred orientations in the rodent visual cortex is tested experimentally. In collaboration with the Max