Challenges in modeling spatiotemporally varying phytoplankton blooms in the Northwestern Arabian Sea and Gulf of Oman

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Abstract

Recent years have shown an increase in harmful algal blooms in the Northwest Arabian Sea and Gulf of Oman, raising the question of whether climate change will accelerate this trend. This has led us to examine whether the Earth System Models used to simulate phytoplankton productivity accurately capture bloom dynamics in this region - both in terms of the annual cycle and interannual variability. Satellite data (SeaWIFS ocean color) show two climatological blooms in this region, a wintertime bloom peaking in February and a summertime bloom peaking in September. On a regional scale, interannual variability of the wintertime bloom is dominated by cyclonic eddies which vary in location from one year to another. Two coarse (1°) models with the relatively complex biogeochemistry (TOPAZ) capture the annual cycle but neither eddies nor the interannual variability. An eddy-resolving model (GFDL CM2.6) with a simpler biogeochemistry (miniBLING) displays larger interannual variability, but overestimates the wintertime bloom and captures eddy-bloom coupling in the south but not in the north. The models fail to capture both the magnitude of the wintertime bloom and its modulation by eddies in part because of their failure to capture the observed sharp thermocline and/or nutricline in this region. When CM2.6 is able to capture such features in the Southern part of the basin, eddies modulate diffusive nutrient supply to the surface (a mechanism not previously emphasized in the literature). For the model to simulate the observed wintertime blooms within cyclones, it will be necessary to represent this relatively unusual nutrient structure as well as the cyclonic eddies. This is a challenge in the Northern Arabian Sea as it requires capturing the details of the outflow from the Persian Gulf - something that is poorly done in global models.

Figures

  • Figure 1. Monthly average for region from 56–66◦ E, 15–26◦ N: (a) climatological surface chlorophyll a (SeaWIFS) for a nominal year of 2001; (b) nitrate (WOA09) over top 100 m; (c) temperature over top 100 m; (d) WOA09 seasonal mixed layer depth in meters – black line shows result from World Ocean Atlas, red line from ARGO climatology (ARGO, 2015).
  • Figure 2. Monthly variation of organic matter in SeaWiFS satellite data between 1998 and 2005 within 56–66◦ E, 15–26◦ N (large region); and 60–62◦ E, 22–26◦ N (small region) (a) and (d) chlorophyll; (b) and (e) particulate backscatter; (c) and (f) CDOM.
  • Figure 3. Satellite chlorophyll a in mg m−3 (colors) and sea-surface height anomaly (SSHA, contours) in cm (contour interval= 5 cm) in the Gulf of Oman and Northwest Arabian Sea over the course of 2001.
  • Figure 4. Chlorophyll a in mg m−3 (colors) and sea surface height anomaly (SSHA, contours) in cm (contour interval= 5 cm) in the Gulf of Oman and Northwest Arabian Sea during November in different years.
  • Figure 5. Monthly cross-correlation with AVISO SSHA between 1998 and 2005 within 56–66◦ E and 15–26◦ N. for (a) satellite-estimated chlorophyll; (b) satellite estimated BBP; (c) satellite-estimated CDOM.
  • Figure 6. Average monthly cross-correlation with observed SSHA and average monthly values between 1998 and 2005 within 56–66◦ E and 15–26◦ N for (a, b) satellite-estimated chlorophyll; (c, d) satellite-estimated backscatter; (e, f) satellite-estimated CDOM.
  • Figure 7. Monthly variation of organic matter in satellite data between 1998 and 2005 and GFDL models (8 characteristic years) within 56– 66◦ E, 15–26◦ N (a) chlorophyll from GFDL models and GSM5 algorithm. (b) PO4 from the BLING and miniBLING simulations, NO3/16 from the TOPAZ simulations and observed PO4 from WOA09. (c) NO3 from the TOPAZ simulations and observed NO3 fromWOA09.
  • Figure 8. Modeled biomass in CM2.6 in P units (mol P kg −1) vs. (a) Mixed layer irradiance (Wm−2); (b) Light-Saturated photosynthesis rate (carbon specific) (s−1) 56–66◦ E, 15–26◦ N for January of year 195. In the model, biomass is a function of the growth rate smoothed over several days, and the light-saturated photosynthesis rate indicates the extent to which this growth rate is controlled by nutrient limitation.

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APA

Sedigh Marvasti, S., Gnanadesikan, A., Bidokhti, A. A., Dunne, J. P., & Ghader, S. (2016). Challenges in modeling spatiotemporally varying phytoplankton blooms in the Northwestern Arabian Sea and Gulf of Oman. Biogeosciences, 13(4), 1049–1069. https://doi.org/10.5194/bg-13-1049-2016

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