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© The Authors, 2010. This article is distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Biogeosciences 7 (2010): 621-640, doi:10.5194/bg-7-621-2010, Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years). Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected., S. A. H. was supported by NASA grants NNG06GE77G and NNX07AL81G. J. L. S. and C. B. acknowledge support from the Carbon Mitigation Initiative funded by BP Amoco. S. C. D. and I. L. were supported by NSF grant EF-0424599. L. B. acknowledges support from the ANR-GlobPhy and FP7-MEECE projects.

Author Posting. © American Geophysical Union, 2010. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 24 (2010): GB3020, doi:10.1029/2009GB003655., The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth-integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ 14C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll-a was derived from high-performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll-a time series were available., This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G).

Author Posting. © American Geophysical Union, 2004. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 18 (2004): GB3019, doi:10.1029/2003GB002146., We incorporate multinutrient and size-structured ecosystem dynamics into a three-dimensional ocean general circulation model for the North Atlantic. The model reproduces the magnitude and general spatial and temporal patterns in nutrients, chlorophyll and primary production seen in in situ (BATS, NABE, and OWSI) and satellite (SeaWiFS) data, showing substantial improvements over prior basin-scale simulations. Model skill is evaluated quantitatively against SeaWiFS data using a Taylor diagram approach. Model-data correlation R for the overall surface chlorophyll time-space distribution is ∼0.6, with comparable model and observed total variability. The agreement relative to satellite-based primary production is somewhat weaker (0.2 < R < 0.5). The simulations capture observed ecological characteristics, e.g., the dominance of picoplankton and episodic diatom blooms in the subtropics, nutrient-controlled plankton succession at higher latitudes, and associated seasonal/depth changes in new and regenerated production and particle export. In a sensitivity experiment that mimics behavior of simpler single-species models, removal of diatom silica limitation leads to major shifts in community structure and export and larger model-data errors similar to previous model studies. Model results also suggest that episodic diatom blooms at BATS may be related to interannual variations in the southward transport of nutrients, mainly SiO3, and plankton cells., Support for this work was provided by NASA SeaWiFS grant W-19,223 and NSF JGOFS SMP grant 0222033.

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 22 (2008): GB4027, doi:10.1029/2007GB003167., Observational studies report a rapid decline of ocean CO2 uptake in the temperate North Atlantic during the last decade. We analyze these findings using ocean physical-biological numerical simulations forced with interannually varying atmospheric conditions for the period 1979–2004. In the simulations, surface ocean water mass properties and CO2 system variables exhibit substantial multiannual variability on sub-basin scales in response to wind-driven reorganization in ocean circulation and surface warming/cooling. The simulated temporal evolution of the ocean CO2 system is broadly consistent with reported observational trends and is influenced substantially by the phase of the North Atlantic Oscillation (NAO). Many of the observational estimates cover a period after 1995 of mostly negative or weakly positive NAO conditions, which are characterized in the simulations by reduced North Atlantic Current transport of subtropical waters into the eastern basin and by a decline in CO2 uptake. We suggest therefore that air-sea CO2 uptake may rebound in the eastern temperate North Atlantic during future periods of more positive NAO, similar to the patterns found in our model for the sustained positive NAO period in the early 1990s. Thus, our analysis indicates that the recent rapid shifts in CO2 flux reflect decadal perturbations superimposed on more gradual secular trends. The simulations highlight the need for long-term ocean carbon observations and modeling to fully resolve multiannual variability, which can obscure detection of the long-term changes associated with anthropogenic CO2 uptake and climate change., S. C. Doney and I. D. Lima were supported by NASA grant NNG05GG30G.

Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 76 (2009): 95-112, doi:10.1016/j.jmarsys.2008.05.015., We present a generalized framework for assessing the skill of global upper ocean ecosystem-biogeochemical models against in-situ field data and satellite observations. We illustrate the approach utilizing a multi-decade (1979-2004) hindcast experiment conducted with the Community Climate System Model (CCSM-3) ocean carbon model. The CCSM-3 ocean carbon model incorporates a multi-nutrient, multi-phytoplankton functional group ecosystem module coupled with a carbon, oxygen, nitrogen, phosphorus, silicon, and iron biogeochemistry module embedded in a global, threedimensional ocean general circulation model. The model is forced with physical climate forcing from atmospheric reanalysis and satellite data products and time-varying atmospheric dust deposition. Data-based skill metrics are used to evaluate the simulated time-mean spatial patterns, seasonal cycle amplitude and phase, and subannual to interannual variability. Evaluation data include: sea surface temperature and mixed layer depth; satellite derived surface ocean chlorophyll, primary productivity, phytoplankton growth rate and carbon biomass; large-scale climatologies of surface nutrients, pCO2, and air-sea CO2 and O2 flux; and time-series data from the Joint Global Ocean Flux Study (JGOFS). Where the data is sufficient, we construct quantitative skill metrics using: model-data residuals, time-space correlation, root mean square error, and Taylor diagrams., This work was supported in part by grants from the NSF/ONR National Ocean Partnership Program (N000140210370), the NASA Ocean Biology and Biogeochemistry Program (NNX07AL80G), and the NSF Center for Microbial Oceanography Research and Education (C-MORE).

Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 76 (2009): 113-133, doi:10.1016/j.jmarsys.2008.05.010., Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983- 1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (< 0.2 gC m-2d-2) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison six years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill., This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G), as well as by numerous other grants to the various participating investigators

© 2009 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Biogeosciences 6 (2009): 779-794, doi: 10.5194/bg-6-779-2009, Phytoplankton photosynthesis links global ocean biology and climate-driven fluctuations in the physical environment. These interactions are largely expressed through changes in phytoplankton physiology, but physiological status has proven extremely challenging to characterize globally. Phytoplankton fluorescence does provide a rich source of physiological information long exploited in laboratory and field studies, and is now observed from space. Here we evaluate the physiological underpinnings of global variations in satellite-based phytoplankton chlorophyll fluorescence. The three dominant factors influencing fluorescence distributions are chlorophyll concentration, pigment packaging effects on light absorption, and light-dependent energy-quenching processes. After accounting for these three factors, resultant global distributions of quenching-corrected fluorescence quantum yields reveal a striking consistency with anticipated patterns of iron availability. High fluorescence quantum yields are typically found in low iron waters, while low quantum yields dominate regions where other environmental factors are most limiting to phytoplankton growth. Specific properties of photosynthetic membranes are discussed that provide a mechanistic view linking iron stress to satellite-detected fluorescence. Our results present satellite-based fluorescence as a valuable tool for evaluating nutrient stress predictions in ocean ecosystem models and give the first synoptic observational evidence that iron plays an important role in seasonal phytoplankton dynamics of the Indian Ocean. Satellite fluorescence may also provide a path for monitoring climate-phytoplankton physiology interactions and improving descriptions of phytoplankton light use efficiencies in ocean productivity models., This work was supported by grants from the NASA Ocean Biology and Biogeochemistry Program and the NSF Biological Oceanography Program.

© 2008 Author(s). This article is distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Biogeosciences 5 (2008): 875-899, doi:10.5194/bg-5-875-2008, A three dimensional, time-evolving field of atmospheric potential oxygen (APO ~O2/N2+CO2) was estimated using surface O2, N2 and CO2 fluxes from the WHOI ocean ecosystem model to force the MATCH atmospheric transport model. Land and fossil carbon fluxes were also run in MATCH and translated into O2 tracers using assumed O2:CO2 stoichiometries. The modeled seasonal cycles in APO agree well with the observed cycles at 13 global monitoring stations, with agreement helped by including oceanic CO2 in the APO calculation. The modeled latitudinal gradient in APO is strongly influenced by seasonal rectifier effects in atmospheric transport. An analysis of the APO-vs.-CO2 mass-balance method for partitioning land and ocean carbon sinks was performed in the controlled context of the MATCH simulation, in which the true surface carbon and oxygen fluxes were known exactly. This analysis suggests uncertainty of up to ±0.2 PgC in the inferred sinks due to variability associated with sparse atmospheric sampling. It also shows that interannual variability in oceanic O2 fluxes can cause large errors in the sink partitioning when the method is applied over short timescales. However, when decadal or longer averages are used, the variability in the oceanic O2 flux is relatively small, allowing carbon sinks to be partitioned to within a standard deviation of 0.1 Pg C/yr of the true values, provided one has an accurate estimate of long-term mean O2 outgassing., We acknowledge the support of NASA grant NNG05GG30G and NSF grant ATM0628472.