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 occur 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 but the results are inconclusive (Gregg et al., 2003: -6%; Antoine et al., 2005: +22%; Kahru et al., 2007: Baltic) 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 of trends in OC time series is now finally becoming possible Following Kahru and Mitchell, 2008 (EOS); and we search for trends in annual maxima (Chl or NPP) based on monthly composites; we call this bloom magnitude Bloom magnitude is typically determined by the annual bloom (e.g. the spring bloom) or Harmful Algal Bloom (HAB) events

4 1-Jan 31-Jan 2-Mar 1-Apr 1-May 31-May 30-Jun 30-Jul 29-Aug 28-Sep 28-Oct 27-Nov 27-Dec Chl. a (mg m -2 ) Bloom magnitude and timing varies from year to year 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: trend?

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 ( ) 12 yr x 12 mo = 144, missing 2 mo (1997 July-Aug)

7 Detecting global changes in bloom magnitude Sensor calibration/intercalibration is still a major issue Efforts to merge data from different sensors (NASA: SeaWiFS/MODISA, GlobColour: SeaWiFS/MODISA/MERIS SeaWiFS SeaWiFS/MODISA MERIS

8 Detecting global changes in bloom magnitude Find annual Max, Min, Valid Counts, Day of the maximum for each pixel in global datasets,

9 Detecting global changes in bloom magnitude For each pixel we estimate the trend and its significance using the Sen slope estimator: Areas with significant (90% level) increase are red, significant decrease are in blue. White = no significant change. Sen slope is a nonparametric estimate of the slope that involves computing slopes for all the pairs of time points and then using the median of these slopes as an estimate of the overall slope. Similar estimates of the slope were obtained with the linear least squares regression but the Sen slope estimator is preferable because of its less sensitivity to outliers.

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 showing significant changes Some of the observed trend in bloom magnitude is attributable to the strong El Niño of in the start of the time series. However, bloom magnitudes have increased in some areas even after <==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? Changes in Upwelling Index: no change in the North, decrease in the South 36 N, 122 W 33 N, 119 W 30 N, 119 W 45 N,125 W Schwing et al., 1996; data from Pacific Fisheries Environmental Laboratory, NOAA,

14 Conclusions Ocean color data from 1997 to 2008 show increased phytoplankton blooms in certain areas of the World, e.g. eastern boundary upwelling systems and in a number of areas with known eutrophication. No evidence of increased upwelling; the cause of the increased blooms in eastern boundary currents remains unclear The increased blooms off Oregon are linked to the increase in the dead zones of oxygen-depleted water [Service, 2004, 2007]. In the Arabian Sea the increased biomass and productivity are related to the intensification of the southwest monsoon due to global warming [Goes et al., 2005] We present global maps at & and hope that experts familiar with conditions in specific areas can interpret these patterns 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, Trends in primary production in the California Current detected with satellite data, J. Geophys. Res., 114, doi: /2008jc004979, PDF