Primary Production using Ocean Color Remote Sensing Watson Gregg NASA/Global Modeling and Assimilation Office watson.gregg@nasa.gov
Classification of Ocean Color Primary Production Methods Carr, M.-E., et al., 2005. A comparison of global estimates of marine primary production from ocean color. Deep-Sea Research, in press. 4 Categories Wavelength and Depth Integrated WIDI Wavelength-Integrated and Depth-Resolved WIDR Wavelength and Depth Resolved WRDR General Circulation Models GCM New: Behrenfeld et al 2005: multi-algorithm approach PP = C x µ x Z eu x h
Ocean Color-Based Algorithms Advantages: derived directly from satellite data wide use community familiarity and legacy high spatial resolution Disadvantages: require external data sets dependent on data set quality/accuracy include only a limited number of processes GCM s (Carr terminology) Advantages: include all relevant processes, physically consistent independent of data set accuracy Disadvantages: complex relatively new error propagation poor spatial resolution generally poor accuracy
NASA Ocean Biogeochemical Model (NOBM) Winds, ozone, relative humidity, pressure, precip. water, cloud cover, cloud liquid water path Radiative Model Spectral Irradiance Abundances Atmospheric Forcing Data Dust (Fe) Sea Ice Layer Depths Biogeochemical Processes Model Biogeo constituents Winds, SST, Shortwave radiation Temperature Current Velocities Advection/ Diffusion Circulation Model Layer Depths Spectral Radiance Primary Production Chlorophyll, Nutrients, DOC, DIC, pco 2 Nutrients Biogeochemical Processes Model Ecosystem Component Phytoplankton Biogeochemical Processes Model Carbon Component Winds, Surface pressure pco2 (air) Si NO 3 NH 4 Fe Iron Detritus Silica Detritus Herbivores N/C Detritus Diatoms Dissolved Organic Carbon Chlorophytes Cyanobacteria Coccolithophores Phytoplankton Herbivores N/C Detritus pco2 (water) Dissolved Inorganic Carbon
North Pacific North Atlantic North Central Pacific North Central Atlantic North Indian Equatorial Indian Equatorial Pacific Equatorial Atlantic Chlorophyll (mg m -3 ) South Indian South Pacific South Atlantic Antarctic Day of Year Statistically positively correlated (P < 0.05) all 12 basins Gregg, W.W., 2002. Tracking the SeaWiFS record with a coupled physical/biogeochemical/radiative model of the global oceans. Deep-Sea Research II 49: 81-105. Gregg, W.W., P. Ginoux, P.S. Schopf, and N.W. Casey, 2003. Phytoplankton and Iron: Validation of a global three-dimensional ocean biogeochemical model. Deep-Sea Research II, 50: 3143-3169.
Assimilation of Satellite Ocean Chlorophyll Conditional Relaxation Analysis Method 2 2 M = M,S Advantages: Very strongly weighted toward data, less susceptible to model errors Fast Disadvantages Very susceptible to data errors
60.0 RMS 50.0 RMS % log Error 40.0 30.0 20.0 10.0 0.0 Antarct S Indian S Pacific S Atlantic Eq Indian Eq Pacific Eq Atlantic N Cen Pacific N Cen Atlantic RMS % 38.5 27.5 12.7 20.1 24.0 16.8 48.5 24.7 35.8 55.7 31.6 33.1 31.0 Med/ Black Sea N Pacific N Atlantic Global 50.0 40.0 Bias 30.0 Bias % log 20.0 10.0 0.0-10.0-20.0-30.0 Antarct S Indian S Pacific S Atlantic Eq Indian Eq Pacific Eq Atlantic N Cen Pacific N Cen Atlantic Med/ Black Sea N Pacific N Atlantic Bias % -18.1-7.0-3.0 10.0 12.4 0.5 29.4 0.9 14.3 44.7 4.1 7.9 6.2 Global
V4.1 To keep assimilation model bounded requires: 1) Smoothing of data (25% monthly mean, 75% daily weight) 2) Increase model weighting relative to data regionally
0.5 0.25 V5.1 0.85 0.25 0.5 0.25 0.25 0.5
M Assimilation occurs daily at model midnight
Feb. 1, 2003
Fig. 5. Assimilation model chlorophyll (mg m -3 ), SeaWiFS mean chlorophyll, and the difference (Assimilation-SeaWiFS) for March 2001
30 Annual RMS Log Error vs SeaWiFS 25 RMS Log Error (%) 20 15 10 5 0 Free Run SeaWiFS- Assimilation Aqua-Assimilation Terra-Assimilation In situ-assimilation
Comparison with In Situ Data from NODC/SeaBASS RMS log Error log Bias N SeaWiFS 26.5% 0.6% 2133 Free-run Model 43.4% -5.4% 4471 Assimilation Model 28.4% 0.9% 4471
North Pacific North Atlantic North Central Pacific North Central Atlantic Chlorophyll (mg m -3 ) North Indian Equatorial Indian Equatorial Pacific Equatorial Atlantic South Indian South Pacific South Atlantic Antarctic Red = model monthly mean Diamonds = SeaWiFS monthly mean
48 Global Annual Primary Production 47 Free-run 46 45 44 SeaWiFS V4.1 SeaWiFS V4.1 Assim PgCy -1 43 42 41 40 SeaWiFS V5.1 Assim SeaWiFS V5.1 Aqua V1.0 Assim 39 38 Aqua V1.0 1998 1999 2000 2001 2002 2003 2004
How the Assimilation Works C T = C i, where C = chlorophyll advection -- C i = (K C i ) - VC i - (ws) i C i + µ i C i gh sc i t i = 1 = diatoms i = 2 = chlorophytes i = 3 = cyanobacteria i = 4 = coccolithophores diffusion sinking growth grazing senescence PP = Φ µ i C i dz, Φ = carbon:chlorophyll ratio
Phytoplankton Functional Group Primary Production 60 Contribution to Global Primary Production 50 Percent of total 40 30 20 10 0 diatoms chlorophytes cyanobacteria coccolithophores
Summary Assimilation improves biomasses and distributions of total phytoplankton (chlorophyll) and primary production (less so), but has limited capability for phytoplankton relative abundances Future satellite products, such as PIC, may help Assimilation can compensate for satellite data errors Assimilation using LwN is a new development with potential New methods such as EnKF can potentially improve and extend forecast skill Assimilation requires stable data set, for which biases (especially) and random errors are understood CDR s must weigh stability vs. data set improvement. SeaWiFS has undergone 5 reprocessings in 7 years.