Increased phytoplankton blooms detected by ocean color

Transcription

1 Increased phytoplankton blooms detected by ocean color Mati Kahru & B. Greg Mitchell Scripps Institution of Oceanography/ University of California San Diego La Jolla, CA ASLO Aquatic Sciences Meeting, Nice, 2009/01/26

2 Detection of change is a hot topic due to the threat of global warming and climate change Examples of famous time series showing significant changes: Reconstructed estimates of Northern Hemisphere mean temperature changes over the past millennium, the MBH98 reconstruction (Mann et al., 1998) The Keeling curve (after Charles Keeling) showing the variation in concentration of atmospheric carbon dioxide since 1958 at the Mauna Loa Observatory in Hawaii In Ocean Color we do not have time series of comparable length and accuracy OC satellite data provide large-scale synoptic coverage of the World Ocean biosphere and are therefore essential in detecting such change if it were to happen Satellite-based ocean color sensors have provided global coverage of the world ocean since 1978 but have a 10-year gap between

3 Detection of change with Ocean Color (OC) Several authors have tried to compare the old (CZCS, ) data with the new OC data (Gregg & Conkright, 2002; Gregg et al., 2003; Antoine et al., 2005; Kahru et al., 2007) but the results are inconclusive Sensor calibration and inter-calibration, especially with the old data will remain a big problem The period of the new OC data since Nov-1996 (OCTS) is becoming longer and the detection trends in OC time series is now becoming possible Following Kahru and Mitchell, 2008 (EOS); and we search for trends in annual maxima based on monthly composites; we call this bloom magnitude Bloom magnitude is typically caused by the annual bloom (e.g. the spring bloom) or Harmful Algal Blooms (HAB)

4 1-Jan 31-Jan 2-M ar 1-A pr 1-M ay 31-M ay 30-Jun 30-Jul 29-A ug 28-Sep 28-O ct 27-N ov 27-D ec C hl. a (m g m -2 ) Bloom magnitude and Harmful Algal Blooms Interannual variations in the spring bloom in central North Sea: depth-integrated Chl-a in 1991 (green), 1997 (blue), and mean for (black). From Nielsen and St. John, Wimsoft 2008

5 Monthly time series of Net Primary Production in the Gulf of California

6 Detecting global changes in phytoplankton bloom magnitude To detect change, we want to use as long time period as possible To ensure maximum consistency we use SeaWiFS data whenever possible We use OCTS data of January-June 1997 (skip the 2 months of OCTS data in 1996), SeaWiFS data from Sept when available In 2008 SeaWiFS had intermittent problems, therefore we substitute 2008 Jan-Mar, July with MODIS-Aqua total = 142 months, 12 years ( )

7 Detecting global changes in bloom magnitude Sensor calibration/intercalibration is still a major issue Merged products becoming available (NASA: SeaWiFS/MODISA, GlobColour: SeaWiFS/MODISA/MERIS SeaWiFS SeaWiFS/MODISA MERIS

8 Detecting global changes in bloom magnitude Using a program wam_annual_max find global datasets of annual Max, Min, Valid Counts, Day of the maximum

9 Detecting global changes in bloom magnitude For each pixel we estimate trend and its significance using the Sen nonparametric trend estimator: Areas with significant (90% level) increase are red, areas with significant decrease are in blue, gray

10 Detecting global changes in bloom magnitude Zooming in to specific areas of significant changes Eastern boundary currents: Washington-Oregon-California, Peru Malvinas Current =======

11 Detecting global changes in bloom magnitude Zoom in to specific areas of significant changes <==Arafura Sea Caspian Sea, Aral Sea, Arabian Sea, SW India Arafura Sea, Chl-a

12 Detecting global change Few areas show significant changes in annual Minima (previous examples were all for annual Maxima): limited areas in Bering Sea, White Sea, Sea of Okhotsk, Caspian Sea, Azov Sea, Baltic Sea, Hudson Bay, Lake Maracaibo in Venezuela

13 Detecting global changes in bloom magnitude What is causing the apparent increase in bloom magnitude in eastern Boundary currents? Increased upwelling? 500 Changes in Upwelling Index: N, 122 W 33 N, 119 W 30 N, 119 W Schwing et al., 1996; data from Pacific Fisheries Environmental Laboratory, NOAA,

14 Thank you!

15 References Kahru, M., B.G. Mitchell, Ocean color reveals increased blooms in various parts of the World. EOS, Trans. AGU, Vol. 89, N 18, p. 170, PDF Kahru, M., O. P. Savchuk, and R. Elmgren, Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: interannual and spatial variability, Mar. Ecol. Prog. Ser., 343:15-23, doi: /meps06943, PDF Kahru, M., R. Kudela, M. Manzano-Sarabia and B.G. Mitchell (2009), Trends in primary production in the California Current detected with satellite data, J. Geophys. Res., in press. PDF