Carbon-based primary production & phytoplankton physiology from ocean color data Toby Westberry1, Mike Behrenfeld1, Emmanuel Boss2, Dave Siegel3, Allen Milligan1 1Department of Botany & Plant Pathology, Oregon State University, 2School of Marine Sciences, University of Maine, 3Institute for Computational Earth System Science, University of California Santa Barbara 1. Introduction 2. Model Historically, net primary production (NPP) has been modeled as a function of chlorophyll concentration, allowing for a natural application to ocean color satellite data. However, cellular chlorophyll content is highly variable and is affected by photoacclimation and nutrient stress which act to confound global NPP model results. An approach to alleviate these limitations was recently introduced providing satellite NPP estimates based on conversion of backscattering to phytoplankton carbon and mixed layer phytoplankton growth rates (m, day-1) derived from chlorophyll:carbon ratios (Carbon Based Productivity Model (CBPM), see Behrenfeld et al., 2005).  Here, the CBPM is extended to provide full vertical profiles of m and NPP given knowledge of the mixed layer depth and the nitracline depth, which allow appropriate parameterization of photoacclimation and nutrient stress through the water column. This depth resolved approach accurately reconstructs the underwater light field, and produces biological profile data (C, Chl, NPP) which are broadly consistent with laboratory- and field-based measurements. Direct validation using regional in situ datasets of phytoplankton chlorophyll:carbon, cellular growth rates, and measured NPP rates support the findings presented here. Conceptual model (ex., strongly stratified case) Some details - within ML, phytoplankton are acclimated to median PAR in ML. Below this, they are acclimated to ambient PAR - Kd(490) is expanded to give Kd(l) using Austin & Petzold (1986) - Kd(l,z) below ML is estimated from [Chl] (Morel, 1988) + difference between this method & Austin & Petzold (1986) at surface - each property (Kd,PAR,Chl,C,m,NPP) is dependent on values above it (=iterative!) z=0 Uniform z=MLD Propagation (and relaxation) of surface nutrient stress Photoacclimated Chl:C Nutrient-limited &/or light-limited + photoacclimation y0 z=zNO3 Light-limited + photoacclimation - Concept of a non-zero minimum Chl:C (y0) when m=0 is important! - can be seen in in situ data (e.g., Laws & Bannister (1980)) - required to make model formulations self-consistent Light limitation Nutrient stress z=∞ Relative PAR Relative NO3 2 main ingredients for modeling NPP : 1) biomass & 2) physiology NPP ~ [biomass] x physiologic rate Can have any combination of above processes depending on PAR(z) and depths of mixed layer and nitracline General Most often the case: Key Points Invert ocean color data to estimate [Chl] & bbp(443) (Garver & Siegel, 1997; Maritorena et al., 2001) Relate bbp(443) to carbon biomass (mg C m-2) Use satellite Chl:C to infer phytoplankton physiology in mixed layer (photoacclimation & nutrient stress) Iteratively propagate properties below mixed layer as (PAR,zMLD, zNO3) keeping track of photoacclimation to variable nutrient and light stress Data Sources & implementation NPP ~ [Chl] x Pbopt x … (e.g., Antoine & Morel, 1996; Behrenfeld & Falkowski, 1997; others) Intercept represents stable component of bbp due to background particles (i.e., bacteria, colloids, detritus, etc.) and scalar was chosen such that phytoplankton C ~30% of scattering-based POC estimates Chl-based bbp (m-1) INPUTS Problem: [Chl] does not adequately characterize “biomass” for modeling NPP due to photoacclimation and nutrient- stress related changes in cellular chlorophyll - SeaWiFS: nLw(l) [Chl], bbp(443) PAR Kd(490) Kd(l) - FNMOC: MLD - WOA 2001: [NO3] ZNO3 OUTPUTS Maritorena et al. (2001) - Chl(z), C(z), & Chl:C(z) - m(z) - NPP(z) - Kd(l,z), PAR(z), Zeu? Austin & Petzold (1986) Chl (mg m-3) Solution: estimate biomass independently of physiology NPP ~ [C] x m Can estimate these from ocean color measurements C-based NO3>0.5mM Chl : C m (divisions d-1) **1° x1° monthly mean climatologies (1999-2004)** Scattering (cp or bbp) Ratio of Chl to scattering (Chl:C) Carbon-Based Production Model (CBPM) Ig (Ein m-2 h-1) 3. Data & Results Phytoplankton growth rates, mz=0 Depth-integrated NPP temporal patterns Example, single station (pixel: Summer (Jun-Aug) Ex. 1. North Atlantic Ex 2. Tropical Atlantic Western N. Atl Eastern Pacific (20°N, -110°E, Aug) All data Oligotrophic gyres (L0) CBPM In N. Atlantic, both the onset and peak of the spring bloom occur 1-2 months later than Chl- based model indicates Seasonality is amplified at higher latitudes & dampened in tropics (difference btwn summer & winter) Subsurface profile features: variable Kd(l,z) subsurface Chl a maximum realistic 1% light levels variable C biomass realistic NPP profiles VGPM m represents a net community growth rate and reflects growth, respiration, and other losses (grazing, mixing, etc.) of a mixed population Surface phytoplankton growth rates are broadly consistent with expected values Median values of m are ~0.4 Open ocean growth rates range from ~0.1-1.0 divisions d-1 Depth (m) Eastern N. Atl Winter (Dec-Feb) # occur. m (d-1) m (d-1) CBPM VGPM Summary/Conclusions Mean NPP profiles We have developed a spectral- & depth-resolved NPP model based on independent C & Chl estimates from ocean color data Ability to distinguish DChl due to photoacclimation v. growth Estimates of the phytoplankton growth rate, m, are also derived as part of this approach and provide valuable insight into phytoplankton physiology Spatial & temporal patterns in NPP are markedly different than if using a Chl-based model (e.g., VGPM of Behrenfeld & Falkowski, 1997) Ongoing validation with various diagnostics (PAR, Chl, m, NPP) suggest the model is performing well m (d-1) Validation example: NPP at Hawaii Ocean Time-series Depth-integrated NPP spatial patterns Summer (Jun-Aug) Summer (Jun-Aug) Large spatial differences between C-based and Chl-based NPP estimates (e.g., N. Atlantic, Eq. upwelling) lead to redistribution of NPP in time and space If classify pixels according to average seasonal Chl variance (see above image), such that L0 L4 ~Oligotrophic ~High Lat. we can look at mean m & NPP profiles within those regions Depth (m) ∫NPP VGPM This model (Pg C yr-1) Annual 45 49 Gyres 5 (11%) 10 (20%) High latitudes 19 (42%) 13 (27%) Subtropics 18 (40%) 23 (47%) Southern Ocean (q<-50°S) 2 (4%) 3 (6%) Depth (m) Winter (Dec-Feb) Winter (Dec-Feb) NPP (mg C m-3 d-1) **Annual NPP (and % of total) in each region** C-based NPP (mg C m-2 d-1) Chl-based NPP (mg C m-2 d-1)