Search

A multiplatform assessment of the Ocean–Atmosphere Spectral Irradiance Model (OASIM) radiative model focussed on the Mediterranean Sea is presented. BOUée pour l'acquiSition d'une Série Optique à Long termE (BOUSSOLE) mooring and biogeochemical Argo (BGC-Argo) float optical sensor observations are combined with model outputs to analyse the spatial and temporal variabilities in the downward planar irradiance at the ocean-atmosphere interface. The correlations (r) between the data and model are always higher than 0.6. At the scale of the BOUSSOLE sampling (15-minute temporal resolution), the root mean square difference (RMSD) oscillates at approximately 30~40 % of the averaged model output and is reduced to approximately 10 % when the daily variability is filtered out. Both BOUSSOLE and BGC-Argo indicate that the bias is high for the irradiance at 412 nm, whereas it decreases to less than 5 % at the other wavelengths. Analysis of atmospheric input data indicates that the model skill is strongly affected by cloud dynamics and seasonality. High skills are observed during summer when the cloud cover is low.

A recent revision of the NASA global ocean colour record shows changes in global ocean chlorophyll trends. This new 18-year time series now includes three global satellite sensors, the Sea-viewing Wide Field of view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite (VIIRS). The major changes are radiometric drift correction, a new algorithm for chlorophyll, and a new sensor VIIRS. The new satellite data record shows no significant trend in global annual median chlorophyll from 1998 to 2015, in contrast to a statistically significant negative trend from 1998 to 2012 in the previous version. When revised satellite data are assimilated into a global ocean biogeochemical model, no trend is observed in global annual median chlorophyll. This is consistent with previous findings for the 1998-2012 time period using the previous processing version and only two sensors (SeaWiFS and MODIS). Detecting trends in ocean chlorophyll with satellites is sensitive to data processing options and radiometric drift correction. The assimilation of these data, however, reduces sensitivity to algorithms and radiometry, as well as the addition of a new sensor. This suggests the assimilation model has skill in detecting trends in global ocean colour. Using the assimilation model, spatial distributions of significant trends for the 18-year record (1998-2015) show recent decadal changes. Most notable are the North and Equatorial Indian Oceans basins, which exhibit a striking decline in chlorophyll. It is exemplified by declines in diatoms and chlorophytes, which in the model are large and intermediate size phytoplankton. This decline is partially compensated by significant increases in cyanobacteria, which represent very small phytoplankton. This suggests the beginning of a shift in phytoplankton composition in these tropical and subtropical Indian basins.

Observations taken over the last few decades indicate that dramatic changes are occurring in the ArcticBoreal Zone (ABZ), which are having significant impacts on ABZ inhabitants, infrastructure, flora and fauna, and economies. While suitable for detecting overall change, the current capability is inadequate for systematic monitoring and for improving processbased and largescale understanding of the integrated components of the ABZ, which includes the cryosphere, biosphere, hydrosphere, and atmosphere. Such knowledge will lead to improvements in Earth system models, enabling more accurate prediction of future changes and development of informed adaptation and mitigation strategies. In Duncan et al. (2020), we review the strengths and limitations of current spacebased observational capabilities for several important ABZ components and make recommendations for improving upon these current capabilities. We recommend an interdisciplinary and stepwise approach to develop a comprehensive ABZ Observing Network (ABZON), beginning with an initial focus on observing networks designed to gain processbased understanding for individual ABZ components and systems that can then serve as the building blocks for a comprehensive ABZON.

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches.

We compared the functional response of a biogeochemical data assimilation model versus an empirical satellite-derived algorithm in describing the variation of four phytoplankton (diatoms, cyanobacteria, coccolithophores and chlorophytes) groups globally and in 12 major oceanographic basins. Global mean differences of all groups were within ~ 15% of an independent observation data base for both approaches except for satellite-derived chlorophytes. Diatoms and cyanobacteria concentrations were significantly (p < 0.05) correlated with the independent observation data base for both methods. Coccolithophore concentrations were only correlated with the in situ data for the model approach and the chlorophyte concentration was only significantly correlated to the in situ data for the satellite-derived approach. Using monthly means from 1998–2007, the seasonal variation from the satellite-derived approach and model were significantly correlated in 11 regions for diatoms and in 9 for coccolithophores but only in 3 and 2 regions for cyanobacteria and chlorophytes. Most disagreement on the seasonal variation of phytoplankton composition occurred in the North Pacific and Antarctic where, except for diatoms, no significant correlation could be found between the monthly mean concentrations derived from both approaches. In these two regions there was also an overestimate of diatom concentration by the model of ~ 60% whereas the satellite-derived approach was closer to in situ data (8–26% underestimate). Chlorophytes were the group for which both approaches differed most and that was furthest from the in situ data. These results highlight the strengths and weaknesses of both approaches and allow us to make some suggestions to improve our approaches to understanding phytoplankton dynamics and distribution.

Identifying major trends in biogeochemical composition of the oceans is essential to improve our understanding of biological responses to climate forcing. Using the NASA Ocean Biogeochemical Model combined with ocean color remote sensing data assimilation, we assessed the trends in phytoplankton composition (diatoms, cyanobacteria, coccolithophores, and chlorophytes) at a global scale for the period 1998–2012. We related these trends in phytoplankton to physical conditions (surface temperature, surface photosynthetically available radiation (PAR), and mixed layer depth (MLD)) and nutrients (iron, silicate, and nitrate). We found a significant global decline in diatoms (−1.22% yr−1, p

The NASA PACE mission is a hyper-spectral radiometer planned for launch in the next decade. It is intended to provide new information on ocean biogeochemical constituents by parsing the details of high resolution spectral absorption and scattering. It is the first of its kind for global applications and as such, poses challenges for design and operation. To support pre-launch mission development and assess on-orbit capabilities, the NASA Global Modeling and Assimilation Office has developed a dynamic simulation of global water-leaving radiances, using an ocean model containing multiple ocean phytoplankton groups, particulate detritus, particulate inorganic carbon (PIC), and chromophoric dissolved organic carbon (CDOC) along with optical absorption and scattering processes at 1 nm spectral resolution. The purpose here is to assess the skill of the dynamic model and derived global radiances. Global bias, uncertainty, and correlation are derived using available modern satellite radiances at moderate spectral resolution. Total chlorophyll, PIC, and the absorption coefficient of CDOC (aCDOC), are simultaneously assimilated to improve the fidelity of the optical constituent fields. A 5-year simulation showed statistically significant (P < 0.05) comparisons of chlorophyll (r = 0.869), PIC (r = 0.868), and aCDOC (r = 0.890) with satellite data. Additionally, diatoms (r = 0.890), cyanobacteria (r = 0.732), and coccolithophores (r = 0.716) were significantly correlated with in situ data. Global assimilated distributions of optical constituents were coupled with a radiative transfer model (Ocean-Atmosphere Spectral Irradiance Model, OASIM) to estimate normalized water-leaving radiances at 1 nm for the spectral range 250–800 nm. These unassimilated radiances were within −0.074 mW cm−2 μm−1 sr−1 of MODIS-Aqua radiances at 412, 443, 488, 531, 547, and 667 nm. This difference represented a bias of −10.4% (model low). A mean correlation of 0.706 (P < 0.05) was found with global distributions of MODIS radiances. These results suggest skill in the global assimilated model and resulting radiances. The reported error characterization suggests that the global dynamical simulation can support some aspects of mission design and analysis. For example, the high spectral resolution of the simulation supports investigations of band selection. The global nature of the radiance representations supports investigations of satellite observing scenarios. Global radiances at bands not available in current and past missions support investigations of mission capability.

Quantifying change in ocean biology using satellites is a major scientific objective. We document trends globally for the period 1998–2012 by integrating three diverse methodologies: ocean color data from multiple satellites, bias correction methods based on in situ data, and data assimilation to provide a consistent and complete global representation free of sampling biases. The results indicated no significant trend in global pelagic ocean chlorophyll over the 15 year data record. These results were consistent with previous findings that were based on the first 6 years and first 10 years of the SeaWiFS mission. However, all of the Northern Hemisphere basins (north of 10° latitude), as well as the Equatorial Indian basin, exhibited significant declines in chlorophyll. Trend maps showed the local trends and their change in percent per year. These trend maps were compared with several other previous efforts using only a single sensor (SeaWiFS) and more limited time series, showing remarkable consistency. These results suggested the present effort provides a path forward to quantifying global ocean trends using multiple satellite missions, which is essential if we are to understand the state, variability, and possible changes in the global oceans over longer time scales.

Reanalysis products from MERRA, NCEP2, NCEP1, and ECMWF were used to force an established ocean biogeochemical model to estimate air–sea carbon fluxes (FCO2) and partial pressure of carbon dioxide (pCO2) in the global oceans. Global air–sea carbon fluxes and pCO2 were relatively insensitive to the choice of forcing reanalysis. All global FCO2 estimates from the model forced by the four different reanalyses were within 20% of in situ estimates (MERRA and NCEP1 were within 7%), and all models exhibited statistically significant positive correlations with in situ estimates across the 12 major oceanographic basins. Global pCO2 estimates were within 1% of in situ estimates with ECMWF being the outlier at 0.6%. Basin correlations were similar to FCO2. There were, however, substantial departures among basin estimates from the different reanalysis forcings. The high latitudes and tropics had the largest ranges in estimated fluxes among the reanalyses. Regional pCO2 differences among the reanalysis forcings were muted relative to the FCO2 results. No individual reanalysis was uniformly better or worse in the major oceanographic basins. The results provide information on the characterization of uncertainty in ocean carbon models due to choice of reanalysis forcing.

Sensitivities of the oceanic biological pump within the GISS (Goddard Institute for Space Studies ) climate modeling system are explored here. Results are presented from twin control simulations of the air–sea CO2 gas exchange using two different ocean models coupled to the same atmosphere. The two ocean models (Russell ocean model and Hybrid Coordinate Ocean Model, HYCOM) use different vertical coordinate systems, and therefore different representations of column physics. Both variants of the GISS climate model are coupled to the same ocean biogeochemistry module (the NASA Ocean Biogeochemistry Model, NOBM), which computes prognostic distributions for biotic and abiotic fields that influence the air–sea flux of CO2 and the deep ocean carbon transport and storage. In particular, the model differences due to remineralization rate changes are compared to differences attributed to physical processes modeled differently in the two ocean models such as ventilation, mixing, eddy stirring and vertical advection. GISSEH(GISSER) is found to underestimate mixed layer depth compared to observations by about 55% (10%) in the Southern Ocean and overestimate it by about 17% (underestimate by 2%) in the northern high latitudes. Everywhere else in the global ocean, the two models underestimate the surface mixing by about 12–34%, which prevents deep nutrients from reaching the surface and promoting primary production there. Consequently, carbon export is reduced because of reduced production at the surface. Furthermore, carbon export is particularly sensitive to remineralization rate changes in the frontal regions of the subtropical gyres and at the Equator and this sensitivity in the model is much higher than the sensitivity to physical processes such as vertical mixing, vertical advection and mesoscale eddy transport. At depth, GISSER, which has a significant warm bias, remineralizes nutrients and carbon faster thereby producing more nutrients and carbon at depth, which eventually resurfaces with the global thermohaline circulation especially in the Southern Ocean. Because of the reduced primary production and carbon export in GISSEH compared to GISSER, the biological pump efficiency, i.e., the ratio of primary production and carbon export at 75 m, is half in the GISSEH of that in GISSER, The Southern Ocean emerges as a key region where the CO2 flux is as sensitive to biological parameterizations as it is to physical parameterizations. The fidelity of ocean mixing in the Southern Ocean compared to observations is shown to be a good indicator of the magnitude of the biological pump efficiency regardless of physical model choice.

Modeling and monitoring plankton functional types (PFTs) is challenged by the insufficient amount of field measurements of ground truths in both plankton models and bio-optical algorithms. In this study, we combine remote sensing data and a dynamic plankton model to simulate an ecologically sound spatial and temporal distribution of phyto-PFTs. We apply an innovative ecological indicator approach to modeling PFTs and focus on resolving the question of diatom–coccolithophore coexistence in the subpolar high-nitrate and low-chlorophyll regions. We choose an artificial neural network as our modeling framework because it has the potential to interpret complex nonlinear interactions governing complex adaptive systems, of which marine ecosystems are a prime example. Using ecological indicators that fulfill the criteria of measurability, sensitivity and specificity, we demonstrate that our diagnostic model correctly interprets some basic ecological rules similar to ones emerging from dynamic models. Our time series highlight a dynamic phyto-PFT community composition in all high-latitude areas and indicate seasonal coexistence of diatoms and coccolithophores. This observation, though consistent with in situ and remote sensing measurements, has so far not been captured by state-of-the-art dynamic models, which struggle to resolve this "paradox of the plankton". We conclude that an ecological indicator approach is useful for ecological modeling of phytoplankton and potentially higher trophic levels. Finally, we speculate that it could serve as a powerful tool in advancing ecosystem-based management of marine resources.

Results from twin control simulations of the preindustrial CO2 gas exchange (natural flux of CO2) between the ocean and the atmosphere are presented here using the NASA-GISS climate model, in which the same atmospheric component (modelE2) is coupled to two different ocean models, the Russell ocean model and HYCOM. Both incarnations of the GISS climate model are also coupled to the same ocean biogeochemistry module (NOBM) which estimates prognostic distributions for biotic and abiotic fields that influence the air-sea flux of CO2. Model intercomparison is carried out at equilibrium conditions and model differences are contrasted with biases from present day climatologies. Although the models agree on the spatial patterns of the air-sea flux of CO2, they disagree on the strength of the North Atlantic and Southern Ocean sinks mainly because of kinematic (winds) and chemistry (pCO2) differences rather than thermodynamic (SST) ones. Biology/chemistry dissimilarities in the models stem from the different parameterizations of advective and diffusive processes, such as overturning, mixing and horizontal tracer advection and to a lesser degree from parameterizations of biogeochemical processes such as gravitational settling and sinking. The global meridional overturning circulation illustrates much of the different behavior of the biological pump in the two models, together with differences in mixed layer depth which are responsible for different SST, DIC and nutrient distributions in the two models and consequently different atmospheric feedbacks (in the wind, net heat and freshwater fluxes into the ocean).

The importance of including directional and spectral light in simulations of ocean radiative transfer was investigated using a coupled biogeochemical-circulation-radiative model of the global oceans. The effort focused on phytoplankton abundances, nutrient concentrations and vertically-integrated net primary production. The importance was approached by sequentially removing directional (i.e., direct vs. diffuse) and spectral irradiance and comparing results of the above variables to a fully directionally and spectrally-resolved model. In each case the total irradiance was kept constant; it was only the pathways and spectral nature that were changed. Assuming all irradiance was diffuse had negligible effect on global ocean primary production. Global nitrate and total chlorophyll concentrations declined by about 20% each. The largest changes occurred in the tropics and sub-tropics rather than the high latitudes, where most of the irradiance is already diffuse. Disregarding spectral irradiance had effects that depended upon the choice of attenuation wavelength. The wavelength closest to the spectrally-resolved model, 500 nm, produced lower nitrate (19%) and chlorophyll (8%) and higher primary production (2%) than the spectral model. Phytoplankton relative abundances were very sensitive to the choice of non-spectral wavelength transmittance. The combined effects of neglecting both directional and spectral irradiance exacerbated the differences, despite using attenuation at 500 nm. Global nitrate decreased 33% and chlorophyll decreased 24%. Changes in phytoplankton community structure were considerable, representing a change from chlorophytes to cyanobacteria and coccolithophores. This suggested a shift in community function, from light-limitation to nutrient limitation: lower demands for nutrients from cyanobacteria and coccolithophores favored them over the more nutrient-demanding chlorophytes. Although diatoms have the highest nutrient demands in the model, their relative abundances were generally unaffected because they only prosper in nutrient-rich regions, such as the high latitudes and upwelling regions, which showed the fewest effects from the changes in radiative simulations. The results showed that including directional and spectral irradiance when simulating the ocean light field can be important for ocean biology, but the magnitude varies with variables and regions. The quantitative results are intended to assist ocean modelers when considering improved irradiance representations relative to other processes or variables associated with the issues of interest.

A new empirical approach is developed for ocean color remote sensing. Called the Empirical Satellite Radiance-In situ Data (ESRID) algorithm, the approach uses relationships between satellite water-leaving radiances and in situ data after full processing, i.e., at Level-3, to improve estimates of surface variables while relaxing requirements on post-launch radiometric re-calibration. The approach is evaluated using SeaWiFS chlorophyll, which is the longest time series of the most widely used ocean color geophysical product. The results suggest that ESRID 1) drastically reduces the bias of ocean chlorophyll, most impressively in coastal regions, 2) modestly improves the uncertainty, and 3) reduces the sensitivity of global annual median chlorophyll to changes in radiometric re-calibration. Simulated calibration errors of 1% or less produce small changes in global median chlorophyll (less than 2.7%). In contrast, the standard NASA algorithm set is highly sensitive to radiometric calibration: similar 1% calibration errors produce changes in global median chlorophyll up to nearly 25%. We show that 0.1% radiometric calibration error (about 1% in water-leaving radiance) is needed to prevent radiometric calibration errors from changing global annual median chlorophyll more than the maximum interannual variability observed in the SeaWiFS 9-year record (+/- 3%), using the standard method. This is much more stringent than the goal for SeaWiFS of 5% uncertainty for water leaving radiance. The results suggest ocean color programs might consider less emphasis of expensive efforts to improve post-launch radiometric re-calibration in favor of increased efforts to characterize in situ observations of ocean surface geophysical products. Although the results here are focused on chlorophyll, in principle the approach described by ESRID can be applied to any surface variable potentially observable by visible remote sensing.

Ocean phytoplankton, detrital material, and water absorb and scatter light spectrally. The Ocean- Atmosphere Spectral Irradiance Model (OASIM) is intended to provide surface irradiance over the oceans with sufficient spectral resolution to support ocean ecology, biogeochemistry, and heat exchange investigations, and of sufficient duration to support inter-annual and decadal investigations. OASIM total surface irradiance (integrated 200 nm to 4 microns) was compared to in situ data and three publicly available global data products at monthly 1-degree resolution. OASIM spectrally-integrated surface irradiance had root mean square (RMS) difference= 20.1 W/sq m (about 11%), bias=1.6 W/sq m (about 0.8%), regression slope= 1.01 and correlation coefficient= 0.89, when compared to 2322 in situ observations. OASIM had the lowest bias of any of the global data products evaluated (ISCCP-FD, NCEP, and ISLSCP 11), and the best slope (nearest to unity). It had the second best RMS, and the third best correlation coefficient. OASIM total surface irradiance compared well with ISCCP-FD (RMS= 20.7 W/sq m; bias=-11.4 W/sq m, r=0.98) and ISLSCP II (RMS =25.2 W/sq m; bias= -13.8 W/sq m; r=0.97), but less well with NCEP (RMS =43.0 W/sq m ;bias=-22.6 W/sq m; x=0.91). Comparisons of OASIM photosynthetically available radiation (PAR) with PAR derived from SeaWiFS showed low bias (-1.8 mol photons /sq m/d, or about 5%), RMS (4.25 mol photons /sq m/d ' or about 12%), near unity slope (1.03) and high correlation coefficient (0.97). Coupled with previous estimates of clear sky spectral irradiance in OASIM (6.6% RMS at 1 nm resolution), these results suggest that OASIM provides reasonable estimates of surface broadband and spectral irradiance in the oceans, and can support studies on ocean ecosystems, carbon cycling, and heat exchange.

There is growing recognition that rigorous skill assessment is required to understand the ability of ocean biological models to represent ocean processes and distributions. Statistical analysis of model results with observations represents the most quantitative form of skill assessment, and this principle serves as well for data assimilation models. However, skill assessment for data assimilation requires special consideration. This is because there are three sets of information in the free-run model, data, and the assimilation model, which uses Data assimilation information from both the flee-run model and the data. Intercom parison of results among the three sets of information is important and useful for assessment, but is not conclusive since the three information sets are intertwined. An independent data set is necessary for an objective determination. Other useful measures of ocean biological data assimilation assessment include responses of unassimilated variables to the data assimilation, performance outside the prescribed region/time of interest, forecasting, and trend analysis. Examples of each approach from the literature are provided. A comprehensive list of ocean biological data assimilation and their applications of skill assessment, in both ecosystem/biogeochemical and fisheries efforts, is summarized.

Chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) is assimilated into the three-dimensional global NASA Ocean Biogeochemical Model (NOBM) for the period 1998–2004 in order to obtain an improved representation of chlorophyll in the model. The assimilation is performed by the SEIK filter, which is based on the Kalman filter algorithm. The filter is implemented to univariately correct the concentration of surface total chlorophyll. A localized filter analysis is used and the filter is simplified by using a static state error covariance matrix. The assimilation provides daily global surface chlorophyll fields and improves the chlorophyll estimates relative to a model simulation without assimilation. The comparison with independent in situ data over the seven years also shows a significant improvement of the chlorophyll estimate. The assimilation reduces the RMS log error of total chlorophyll from 0.43 to 0.32, while the RMS log error is 0.28 for the in situ data considered. That is, the global RMS log error of chlorophyll estimated by the model is reduced by the assimilation from 53% to 13% above the error of SeaWiFS. Regionally, the assimilation estimate exhibits smaller errors than SeaWiFS data in several oceanic basins.

Although modem ocean color sensors, such as MODIS and SeaWiFS are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a "truth field", which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25deg longitude by 0.67deg latitude. The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and >5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (

Coccolithophores are important ecological and geochemical components of the global oceans. A global three-dimensional model was used to simulate their distributions in a multi-phytoplankton context. The realism of the simulation was supported by comparisons of model surface nutrients and total chlorophyll with in situ and satellite observations. Nitrate, silica, and dissolved iron surface distributions were positively correlated with in situ data across major oceanographic basins. Global annual departures were +18.9% for nitrate (model high), +5.4% for silica, and +45.0% for iron. Total surface chlorophyll was also positively correlated with satellite and in situ data sets across major basins. Global annual departures were −8.0% with Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (model low), +1.1% with Aqua, and −17.1% with in situ data. Global annual primary production estimates were within 1% and 9% of estimates derived from SeaWiFS and Aqua, respectively, using a common primary production algorithm. Coccolithophore annual mean relative abundances were 2.6% lower than observations, but were positively correlated across basins. Two of the other three phytoplankton groups, diatoms and cyanobacteria, were also positively correlated with observations. Distributions of coccolithophores were dependent upon interactions and competition with the other phytoplankton groups. In this model, coccolithophores had a competitive advantage over diatoms and chlorophytes by virtue of a greater ability to utilize nutrients and light at low values. However, their higher sinking rates placed them at a disadvantage when nutrients and light were plentiful. In very low nutrient conditions, such as the mid-ocean gyres, coccolithophores were unable to compete with the efficient nutrient utilization capability and low sinking rate of cyanobacteria. Comparisons of simulated coccolithophore distributions with satellite-derived estimates of calcite concentration and coccolithophore blooms showed some agreement, but also areas of departure. Vast blooms observed in the North Atlantic were well-represented by the model. However, model coccolithophores were nearly absent in the North Pacific, while calcite estimates suggested widespread abundance in summer. In situ observations supported the satellite calcite, suggesting a deficiency in the model. New satellite estimates of phytoplankton groups indicated good agreement of diatoms in one case, and poor agreement in general in another. Comparisons of phytoplankton group primary production with other models showed wide disparity. The divergence among models and satellite estimates is common for such an emerging field of research. The quantitative comparisons with in situ observations are encouraging, but disparities with model and satellite estimates suggested that further research is needed.

In this paper results are presented from a simple offline assimilation system that uses radiances from the Moderate Resolution Imaging Spectroradiometer (MODIS) channels that sense atmospheric aerosols over land and ocean. The MODIS information is directly inserted into the Goddard Chemistry and Aerosol Radiation Transport model (GOCART), which simulates the following five aerosol types: dust, sea salt, black carbon, organic carbon, and sulfate. The goal is to produce three-dimensional fields of these aerosol types for radiative forcing calculations. Products from this assimilation system are compared with ground-based measurements of aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Insertion of MODIS radiances draws the GOCART model closer to the AERONET AOD. However, there are still uncertainties with surface reflectivity over moderately bright surfaces and with the amount of absorbing aerosol. Also described is the assimilation cycle. The forward model takes the aerosol information from the GOCART model and calculates radiances based on optical parameters of the aerosol type, satellite viewing angle, and the particle growth from relative humidity. Because the GOCART model is driven by previously assimilated meteorology, these forward model radiances can be directly compared with the observed MODIS level-2 radiances. The offline assimilation system simply adjusts the aerosol loading in the GOCART model so that the observed minus forward model radiances agree. Minimal change is made to the GOCART aerosol vertical distribution, size distribution, and the ratio of the five different aerosol types. The loading in the GOCART model is updated with new MODIS observations every 6 h. Since the previously assimilated meteorology provides surface wind speed, radiance sensitivity to wind speed over rough ocean is taken into account. Over land the dark target approach, also used by the MODIS–atmosphere group retrieval, is used. If the underlying land surface is deemed dark enough, the surface reflectances at the 0.47- and 0.66-μm wavelengths are constant multiples of the observed 2.13-μm reflectance. Over ocean the assimilation AOD compares well with AERONET, over land less so. The results herein are also compared with AERONET-retrieved single-scattering albedo. This research is part of an ongoing effort at NASA Goddard to integrate aerosols into the Goddard Modeling and Assimilation Office (GMAO) products.

As part of the Global Carbon Cycle research effort, an agency-wide planning initiative was organized between October 2000 and June 2001 by the NASA Goddard Space Flight Center at the behest of the Associate Administrator for Earth Science. The goal was to define future research and technology development activities needed for implementing a cohesive scientific observation plan. A timeline for development of missions necessary to acquire the selected new measurements was laid out, and included missions for low–medium density terrestrial biomass/coastal ocean, and global ocean carbon. This paper will begin with the scientific justification and measurement requirements for these specific activities, lightly touch on the options for having separate low Earth orbiting missions, and follow-up in more detail with a combined implementation study centered on a hyperspectral imager at geosynchronous altitudes, highlighting both its merits and challenges.

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll data set was compared to comprehensive archives of in situ chlorophyll data from NASA and NOAA. The global comparison indicated a root mean square (RMS) log error of 31%, with a coefficient of determination ( r 2 ) of 0.76, using 4168 data points where in situ and SeaWiFS data were coincident and collocated. RMS log error for open ocean (defined as bottom depth>200 m) was 27.7% with r 2 =0.72, compared to 33% RMS log error and r 2 =0.60 on the coasts, indicating a deterioration of quality of the SeaWiFS data set in coastal regions. All of the Pacific oceanographic basins generally showed very good agreement with SeaWiFS, as did the South Atlantic basin. However, poorer agreement was found in the Mediterranean/Black Seas, Equatorial Atlantic, and the Antarctic. Optical complexity arising from riverine inputs, Saharan dust, and anomalous oceanic constituents contributed to the differences observed in the Atlantic, where an overestimation by SeaWiFS occurred. The Antarctic indicated a pronounced negative bias, indicating an underestimation, especially for chlorophyll concentrations greater than about 0.15 mg m −3 . The results provide a comprehensive global and geographic analysis of the SeaWiFS data set, which will assist data users and policy makers in assessing the uncertainty of estimates of global and regional ocean chlorophyll and primary production.

The JGOFS program and NASA ocean-color satellites have provided a wealth of data that can be used to test and validate models of ocean biogeochemistry. A coupled three-dimensional general circulation, biogeochemical, and radiative model of the global oceans was validated using these in situ data sources and satellite data sets. Biogeochemical processes in the model were determined from the influences of circulation and turbulence dynamics, irradiance availability, and the interactions among four phytoplankton functional groups (diatoms, chlorophytes, cyanobacteria, and coccolithophores) and four nutrients (nitrate, ammonium, silica, and dissolved iron). Annual mean log-transformed dissolved iron concentrations in the model were statistically positively correlated on basin scale with observations ðPo0:05Þ over the eight (out of 12) major oceanographic basins where data were available. The model tended to overestimate in situ observations, except in the Antarctic where a large underestimate occurred. Inadequate scavenging and excessive remineralization and/or regeneration were possible reasons for the overestimation. Basin scale model chlorophyll seasonal distributions were positively correlated with SeaWiFS chlorophyll in each of the 12 oceanographic basins ðPo0:05Þ: The global mean difference was 3.9% (model higher than SeaWiFS). The four phytoplankton groups were initialized as homogeneous and equal distributions throughout the model domain. After 26 years of simulation, they arrived at reasonable distributions throughout the global oceans: diatoms predominated high latitudes, coastal, and equatorial upwelling areas, cyanobacteria predominated the mid-ocean gyres, and chlorophytes and coccolithophores represented transitional assemblages. Seasonal patterns exhibited a range of relative responses: from a seasonal succession in the North Atlantic with coccolithophores replacing diatoms as the dominant group in mid-summer, to successional patterns with cyanobacteria replacing diatoms in mid-summer in the central North Pacific. Diatoms were associated with regions where nutrient availability was high. Cyanobacteria predominated in quiescent regions with low nutrients. While the overall patterns of phytoplankton functional group distributions exhibited broad qualitative agreement with in situ data, quantitative comparisons were mixed. Three of the four phytoplankton groups exhibited statistically significant correspondence across basins. Diatoms did not. Some basins exhibited excellent correspondence, while most showed moderate agreement, with two functional groups in agreement with data and the other two in disagreement.

Chlorophyll climatologies derived from historical in situ data, Coastal Zone Color Scanner data (CZCS) and SeaWiFS (Version 3) data were inter-compared to evaluate their strengths and weaknesses in...

An important focus of climate-change research is the understanding of the role of ecosystems in shaping climate. Central to this aim is the identification of any feedbacks by which ecosystems may moderate anthropogenic forcing of climate. One possible ecosystem feedback involves the marine food-web and the biogenic sulfur compound dimethylsulfide (DMS). DMS is produced by algae containing the precursor compound dimethylsulfoniopropionate (DMSP), and once ventilated to the atmosphere can be transformed to sulfate aerosols and global climate. It was hypothesized that an increase in biogenically produced sulfate aerosols leading to formation of more cloud condensation nuclei (CCN), and brighter clouds, could stabilize the climate against perturbations due to greenhouse warming. Although a large database of DMS seawater measurements exist, attempts to statistically correlate DMS concentrations with other biological parameters, such as chlorophyll a or nutrients, have failed. This underscores the complex and dynamic nature of the DMS cycle, and means that simple regression-type predictive models are unlikely to be useful, except at local scales. Regional-scale simulations of the DMS cycle have involved multi-parameter, deterministic formulations based on ecological food-web approaches but with the added challenge of properly simulating the behavior of coupled sulfur and nitrogen (or carbon) cycles. Here we review the current DMS modeling approaches, outline the parameterization of key processes, and identify areas where our knowledge is poor and improvements should be made. Model skill can only be assessed against detailed regional and global data sets, however data have not always been collected in a form suitable for model parameter estimation or model calibration/validation. DMS time series, which are essential for calibration of seasonal or multi-annual simulations, are rare. We discuss the minimum requirements for a successful future integration of observational and theoretical efforts.

The historical archives of in situ (National Oceanographic Data Center) and satellite (Coastal Zone Color Scanner (CZCS)) chlorophyll data were combined using the blended analysis method of Reynolds [1988] in an attempt to construct an improved climatological seasonal representation of global chlorophyll distributions. The results of the blended analysis differed dramatically from the CZCS representation: Global chlorophyll estimates increased 8–35% in the blended analysis depending upon season. Regional differences were even larger, up to 140% in the equatorial Indian Ocean in summer (during the southwest monsoon). Tropical Pacific chlorophyll values increased 25–41%. The results suggested that the CZCS generally underestimates chlorophyll. Regional and seasonal differences in the blended analysis were sufficiently large as to produce a different representation of global chlorophyll distributions than otherwise inferred from CZCS data alone. Analyses of primary production and biogeochemical cycles may be substantially impacted by these results.

The Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) has observed multiple years of routine global chlorophyll observations from space. The mission was launched into a record El Nino event, which eventually gave way to one of the most intense and longest-lasting La Nina events ever recorded. The SeaWiFS chlorophyll record captured the response of ocean phytoplankton to these significant events in the tropical Indo-Pacific basins, but also indicated significant interannual variability unrelated to the El Nino/La Nina events. This included large variability in the North Atlantic and Pacific basins, in the North Central and equatorial Atlantic, and milder patterns in the North Central Pacific. This SeaWiFS record was tracked with a coupled physical/biogeochemical/radiative model of the global oceans using near-real-time forcing data such as wind stresses, sea surface temperatures, and sea ice. This provided an opportunity to offer physically and biogeochemically meaningful explanations of the variability observed in the SeaWiFS data set, since the causal mechanisms and interrelationships of the model are completely understood. The coupled model was able to represent the seasonal distributions of chlorophyll during the SeaWiFS era, and was capable of differentiating among the widely different processes and dynamics occurring in the global oceans. The model was also reasonably successful in representing the interannual signal, especially when it was large, such as the El Nino and La Nina events in the tropical Pacific and Indian Oceans. The model provided different phytoplankton group responses for the different events in these regions: diatoms were predominant in the tropical Pacific during the La Nina, but other groups were predominant during El Nino. The opposite condition occurred in the tropical Indian Ocean. Both situations were due to the different responses of the basins to El Nino. Interannual variability in the North Pacific was exhibited as an increase in the spring bloom in 1999 and 2000 relative to 1998. This resulted in the model from a shallower and more rapidly shoaling mixed layer, producing more average irradiance in the water column and preventing herbivore populations to keep pace with increasing phytoplankton populations. However, several aspects of the interannual cycle were not well-represented by the model. Explanations range from inherent model deficiencies, to monthly averaging of forcing fields, to biases in SeaWiFS atmospheric correction procedures.

A framework is developed for examining spatial patterns of interannual variability in springtime chlorophyll concentrations as a response to physical changes. A simpli"ed, two-layer bio-physical model reveals regional responses to interannual variability of convective mixing. Vertical mixing can promote productivity in the surface waters through enhanced nutrient supply, but also can retard productivity due to the transport of phytoplankton below Sverdrup's critical depth. The balance of these processes determines the regimes of response in the two-layer model. The regimes may be identi"ed by the ratio of the thickness of Sverdrup's critical layer during spring and the end of winter mixed layer, h /h . The responses predicted by the simpli"ed model are found in a more sophisticated four-compartment, nitrogen-based ecosystem model, driven by a general circulation model of the North Atlantic. Anomalously strong convective mixing leads to enhanced chlorophyll concentrations in regions of shallow mixed layers (h /h &1), such as the subtropics. In contrast, in the subpolar regions, where mixed layers are deeper (h /h ;1), the sensitivity to convective mixing is weaker, and increased mixing can lead to lower phytoplankton abundances. The numerical model also reveals regions of more complex behavior, such as the inter-gyre boundary, where advective supply of nutrients plays a signi"cant role on interannual timescales. Preliminary analyses of in situ and remote

An automated method has been developed for performing navigation assessment on satellite-based Earth sensor data. The method utilizes islands as targets which can be readily located in the sensor data and identified with reference locations. The essential elements are an algorithm for classifying the sensor data according to source, a reference catalogue of island locations, and a robust pattern-matching algorithm for island identification. The algorithms were developed and tested for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), an ocean colour sensor. This method will allow navigation error statistics to be automatically generated for large numbers of points, supporting analysis over large spatial and temporal ranges.

[1] The effect of climate variability on the phytoplankton community was assessed for the tropical and sub-tropical Pacific Ocean between 1998 and 2007 using an established biogeochemical assimilation model. The tropical and sub-tropical phytoplankton communities exhibited a wide range of responses to climate variability, from radical shifts in the Equatorial Pacific, to changes of only a couple of phytoplankton groups in the North Central Pacific, to no significant changes in the South Pacific. In the Equatorial Pacific, climate variability represented by ENSO dominated the variability of phytoplankton. Here, nitrate, chlorophyll and all of the 4 phytoplankton types (diatoms, cyanobacteria, chlorophytes and coccolithophores) were strongly correlated (p

A closed-form solution for geolocation of satellite-based Earth sensor data has been developed. The algorithms are exact, depending only upon the assumption of an ellipsoidal Earth model, and rely on vector and matrix algebra to improve computational efficiency. The values computed are the geodetic coordinates of observed locations (latitude and longitude) and the sensor and solar angles required by radiative transfer models (sensor and solar zenith and azimuth angles). An improvement in computation speed of a factor of two is achieved over a spherical trigonometry solution, along with a much greater accuracy on an ellipsoidal Earth. As much as a factor of six increase in speed is obtained over the more accurate iterative solution for just geodetic latitude and longitude.

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.

[1] Two ocean color missions, SeaWiFS and MODIS, overlap in time and are processed with consistent methods. Global annual median chlorophyll from SeaWiFS and MODIS differ by 12.2%. These discrepancies exceed the maximum observed interannual variability globally and in every major oceanographic basin. Estimates of trends are affected as well. For 1998–2007 the SeaWiFS global trend is −2.6% (not statistically significant). Substitution of MODIS for SeaWiFS in 2003–2007 produces a −18% significant trend. A new approach that incorporates in situ data improves the consistency of the two sensor data sets. The global difference is −0.6% and the 10-year trend of SeaWiFS and MODIS agrees with standalone SeaWiFS (−3.3%, not significant). In oceanographic basins where sampling biases are small the differences are less than the maximum observed interannual variability. The approach improves the consistency of multiple successive ocean color missions and represents a step toward scientifically reliable Climate Data Records.

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 ∼ 100014C 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 g C m- 2d- 1) 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 6 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. © 2008 Elsevier B.V.

We assessed the geometric and radiometric performance of the ocean color and temperature scanner (OCTS) using data acquired over the United States. Initial results indicated a geometric offset in the along-track direction of 4–5 pixels that was attributed to a tilt bias. OCTS radiometric data appeared to suffer from near-field and possibly far-field scatter effects. Analysis of radiometric stability was inconclusive because of daily variability and the absence of a full seasonal cycle. Comparison of OCTS-computed water-leaving radiances with colocated in situ measurements showed that the prelaunch calibration required adjustment from -2% to +13%. Minor modification of OCTS data processing based on these results and avoidance of near-field scatter effects can enable improved and more-reliable OCTS data for quantitative scientific analyses.

The ocean color and temperature scanner (OCTS) collected global ocean color data from November 1996 to June 1997. Analyses of OCTS imagery indicate three features that impair scientific research uses: (1) band misalignments, (2) image striping, and (3) image noise. These are due to (1) band offsets in the sensor design, (2) detector radiometric response variability, and (3) primarily cloud contamination, respectively. Methods are analyzed to ameliorate the effects of each that facilitate use of OCTS ocean color data for quantitative scientific analyses.

Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data.

International audience; The third primary production algorithm round robin (PPARR3) compares output from 24 models that estimate depth-integrated primary production from satellite measurements of ocean color, as well as seven general circulation models (GCMs) coupled with ecosystem or biogeochemical models. Here we compare the global primary production fields corresponding to eight months of 1998 and 1999 as estimated from common input fields of photosynthetically-available radiation (PAR), sea-surface temperature (SST), mixed-layer depth, and chlorophyll concentration. We also quantify the sensitivity of the ocean-color-based models to perturbations in their input variables. The pair-wise correlation between ocean-color models was used to cluster them into groups or related output, which reflect the regions and environmental conditions under which they respond differently. The groups do not follow model complexity with regards to wavelength or depth dependence, though they are related to the manner in which temperature is used to parameterize photosynthesis. Global average PP varies by a factor of two between models. The models diverged the most for the Southern Ocean, SST under 10 degrees C, and chlorophyll concentration exceeding 1 mg Chlm(-3). Based on the conditions under which the model results diverge most, we conclude that current ocean-color-based models are challenged by high-nutrient low-chlorophyll conditions, and extreme temperatures or chlorophyll concentrations. The GCM-based models predict comparable primary production to those based on ocean color: they estimate higher values in the Southern Ocean, at low SST, and in the equatorial band, while they estimate lower values in eutrophic regions (probably because the area of high chlorophyll concentrations is smaller in the GCMs). Further progress in primary production modeling requires improved understanding of the effect of temperature on photosynthesis and better parameterization of the maximum photosynthetic rate. (c) 2006 Elsevier Ltd. All rights reserved.