Inter-annual variations in the SeaWiFS global chlorophyll-a concentration (1997-2007)

• Published in: Deep Sea Research Part I: Oceanographic Research Papers
• Main Authors: VANTREPOTTE Vincent, MELIN Frederic
• Language: English
• Published: PERGAMON-ELSEVIER SCIENCE LTD 2011
• Subjects: Kendall; Northeast Atlantic
• Online Access: https://publications.jrc.ec.europa.eu/repository/handle/JRC63447; https://doi.org/10.1016/j.dsr.2011.02.003

The SeaWiFS data set covering the period 1997-2007 is used to develop a framework for a comprehensive description of the inter-annual variations in chlorophyll-a concentration (Chla). For each grid cell, the monthly Chla series is decomposed into seasonal, irregular and trend-cycle terms with the Census X-11 technique that is an iterative band-pass filter algorithm. This approach allows variations in the annual cycle, while the trend-term isolates the multi-annual evolution in the mean level of the signal. The patterns with relatively large inter-annual variations are selected using the variance due to the trend-term with respect to the total variance, and are compared with maps of monotonic trends derived by a non-parametric Kendall analysis. Most of these patterns are identified in the subtropical domain (30S-30N), even though there are patterns with strong variations at mid-latitudes, particularly in the Northeast Atlantic and South of Australia. The time series found within each pattern of interest are found coherent. Conversely, the ensemble of spatially averaged time series of Chla trend-terms shows a diversity of evolutions, with rather monotonic changes for all or part of the period, abrupt shifts or low-frequency oscillations, sometimes coupled with a modification in the amplitude of the annual cycle. Some of these series are correlated with climate indices, and those in subtropical regions usually show a negative correlation with the equivalent trend-term calculated for sea surface temperature. The identified inter-annual signals should be further monitored with longer time series and can serve as test cases for biogeochemical models. JRC.H.5 - Land Resources Management