Modeling and Assimilating Ocean Color Radiances

Radiances are the source of information from ocean color sensors to produce estimates of biological and geochemical constituents. They potentially provide information on various other aspects of global biological and chemical systems, and there is considerable work involved in deriving new information from these signals. Each derived product, however, contains errors that are derived from the application of the radiances, above and beyond the radiance errors. A global biogeochemical model with an explicit spectral radiative transfer model is used to investigate the potential of assimilating radiances. The results indicate gaps in our understanding of radiative processes in the oceans and their relationships with biogeochemical variables. Most important, detritus optical properties are not well characterized and produce important effects of the simulated radiances. Specifically, there does not appear to be a relationship between detrital biomass and its optical properties, as there is for chlorophyll. Approximations are necessary to get beyond this problem. In this reprt we will discuss the challenges in modeling and assimilation water-leaving radiances and the prospects for improving our understanding of biogeochemical process by utilizing these signals

Does Ocean Color Data Assimilation Improve Estimates of Global Ocean Inorganic Carbon?

Ocean color data assimilation has been shown to dramatically improve chlorophyll abundances and distributions globally and regionally in the oceans. Chlorophyll is a proxy for phytoplankton biomass (which is explicitly defined in a model), and is related to the inorganic carbon cycle through the interactions of the organic carbon (particulate and dissolved) and through primary production where inorganic carbon is directly taken out of the system. Does ocean color data assimilation, whose effects on estimates of chlorophyll are demonstrable, trickle through the simulated ocean carbon system to produce improved estimates of inorganic carbon? Our emphasis here is dissolved inorganic carbon, pC02, and the air-sea flux. We use a sequential data assimilation method that assimilates chlorophyll directly and indirectly changes nutrient concentrations in a multi-variate approach. The results are decidedly mixed. Dissolved organic carbon estimates from the assimilation model are not meaningfully different from free-run, or unassimilated results, and comparisons with in situ data are similar. pC02 estimates are generally worse after data assimilation, with global estimates diverging 6.4% from in situ data, while free-run estimates are only 4.7% higher. Basin correlations are, however, slightly improved: r increase from 0.78 to 0.79, and slope closer to unity at 0.94 compared to 0.86. In contrast, air-sea flux of C02 is noticeably improved after data assimilation. Global differences decline from -0.635 mol/m2/y (stronger model sink from the atmosphere) to -0.202 mol/m2/y. Basin correlations are slightly improved from r=O.77 to r=0.78, with slope closer to unity (from 0.93 to 0.99). The Equatorial Atlantic appears as a slight sink in the free-run, but is correctly represented as a moderate source in the assimilation model. However, the assimilation model shows the Antarctic to be a source, rather than a modest sink and the North Indian basin is represented incorrectly as a sink rather than the source indicated by the free-run model and data estimates

Interannual Variation in Phytoplankton Primary Production at a Global Scale

We used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of four phytoplankton groups to the total primary production. First, we assessed the contribution of each phytoplankton groups to the total primary production at a global scale for the period 1998-2011. Globally, diatoms contributed the most to the total phytoplankton production ((is)approximately 50%, the equivalent of 20 PgCy1). Coccolithophores and chlorophytes each contributed approximately 20% ((is) approximately 7 PgCy1) of the total primary production and cyanobacteria represented about 10% ((is) approximately 4 PgCy1) of the total primary production. Primary production by diatoms was highest in the high latitudes ((is) greater than 40 deg) and in major upwelling systems (Equatorial Pacific and Benguela system). We then assessed interannual variability of this group-specific primary production over the period 1998-2011. Globally the annual relative contribution of each phytoplankton groups to the total primary production varied by maximum 4% (1-2 PgCy1). We assessed the effects of climate variability on group-specific primary production using global (i.e., Multivariate El Nio Index, MEI) and "regional" climate indices (e.g., Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability as indicated by significant correlation (p (is) less than 0.05) between the MEI and the group-specific primary production from all groups except coccolithophores. In the Atlantic, climate variability as indicated by NAO was significantly correlated to the primary production of 2 out of the 4 groups in the North Central Atlantic (diatoms/cyanobacteria) and in the North Atlantic (chlorophytes and coccolithophores). We found that climate variability as indicated by SAM had only a limited effect on group-specific primary production in the Southern Ocean. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale

Forecasting Ocean Chlorophyll in the Equatorial Pacific

Using a global ocean biogeochemical model combined with a forecast of physical oceanic and atmospheric variables from the NASA Global Modeling and Assimilation Office, we assess the skill of a chlorophyll concentrations forecast in the Equatorial Pacific for the period 2012-2015 with a focus on the forecast of the onset of the 2015 El Nio event. Using a series of retrospective 9-month hindcasts, we assess the uncertainties of the forecasted chlorophyll by comparing the monthly total chlorophyll concentration from the forecast with the corresponding monthly ocean chlorophyll data from the Suomi-National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite. The forecast was able to reproduce the phasing of the variability in chlorophyll concentration in the Equatorial Pacific, including the beginning of the 2015-2016 El Nio. The anomaly correlation coefficient (ACC) was significant (p less than 0.05) for forecast at 1-month (R=0.33), 8-month (R=0.42) and 9-month (R=0.41) lead times. The root mean square error (RMSE) increased from 0.0399 microgram ch1 L(exp -1) for the 1-month lead forecast to a maximum of 0.0472 microgram ch1 L(exp -1) for the 9-month lead forecast indicating that the forecast of the amplitude of chlorophyll concentration variability was getting worse. Forecasts with a 3-month lead time were on average the closest to the S-NPP VIIRS data (23% or 0.033 microgram ch1 L(exp -1)) while the forecast with a 9-month lead time were the furthest (31% or 0.042 microgram ch1 L(exp -1)). These results indicate the potential for forecasting chlorophyll concentration in this region but also highlights various deficiencies and suggestions for improvements to the current biogeochemical forecasting system. This system provides an initial basis for future applications including the effects of El Nio events on fisheries and other ocean resources given improvements identified in the analysis of these results

An Assessment of SeaWiFS and MODIS Ocean Coverage

Ocean coverages of SeaWiFS and MODIS were assessed for three seasons by considering monthly mean values of surface winds speeds and cloud cover. Mean and maximum coverages combined SeaWiFS and MODIS by considering combined coverages for ten-degree increments of the MODIS orbital mean anomaly. From this analysis the mean and maximum combined coverages for SeaWiFS and MODIS were determined for one and four-day periods for spring, summer, and winter seasons. Loss of coverage due to Sun glint and cloud cover were identified for both the individual and combined cases. Our analyses indicate that MODIS will enhance ocean coverage for all three seasons examined. ne combined SeaWiFS/MODIS show an increase of coverage of 42.2% to 48.7% over SeaWiFS alone for the three seasons studied; the increase in maximum one day coverage ranges from 47.5% to 52.0%. The increase in four-day coverage for the combined case ranged from 31.0% to 35.8% for mean coverage and 33.1 % to 39.2% for maximum coverage. We computed meridional distributions of coverages by binning the data into five-degree latitude bands. Our analysis shows a strong seasonal dependence of coverage. In general the meridional analysis indicates that increase in coverages for SeaWiFS/MODIS over SeaWiFS alone are greatest near the solar declination

Sensitivity of Simulated Global Ocean Carbon Flux Estimates to Forcing by Reanalysis Products

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

Global Trends in Ocean Phytoplankton: A New Assessment Using Revised Ocean Colour Data

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

Technical Report Series on Global Modeling and Data Assimilation

MERRA products were used to force an established ocean biogeochemical model to estimate surface carbon inventories and fluxes in the global oceans. The results were compared to public archives of in situ carbon data and estimates. The model exhibited skill for ocean dissolved inorganic carbon (DIC), partial pressure of ocean CO2 (pCO2) and air-sea fluxes (FCO2). The MERRA-forced model produced global mean differences of 0.02% (approximately 0.3 microns) for DIC, -0.3% (about -1.2 (micro) atm; model lower) for pCO2, and -2.3% (-0.003 mol C/sq m/y) for FCO2 compared to in situ estimates. Basin-scale distributions were significantly correlated with observations for all three variables (r=0.97, 0.76, and 0.73, P<0.05, respectively for DIC, pCO2, and FCO2). All major oceanographic basins were represented as sources to the atmosphere or sinks in agreement with in situ estimates. However, there were substantial basin-scale and local departures

The Effects of Chlorophyll Assimilation on Carbon Fluxes in a Global Biogeochemical Model

In this paper, we investigated whether the assimilation of remotely-sensed chlorophyll data can improve the estimates of air-sea carbon dioxide fluxes (FCO2). Using a global, established biogeochemical model (NASA Ocean Biogeochemical Model, NOBM) for the period 2003-2010, we found that the global FCO2 values produced in the free-run and after assimilation were within -0.6 mol C m(sup -2) y(sup -1) of the observations. The effect of satellite chlorophyll assimilation was assessed in 12 major oceanographic regions. The region with the highest bias was the North Atlantic. Here the model underestimated the fluxes by 1.4 mol C m(sup -2) y(sup -1) whereas all the other regions were within 1 mol C m(sup -2) y(sup -1) of the data. The FCO2 values were not strongly impacted by the assimilation, and the uncertainty in FCO2 was not decreased, despite the decrease in the uncertainty in chlorophyll concentration. Chlorophyll concentrations were within approximately 25% of the database in 7 out of the 12 regions, and the assimilation improved the chlorophyll concentration in the regions with the highest bias by 10-20%. These results suggest that the assimilation of chlorophyll data does not considerably improve FCO2 estimates and that other components of the carbon cycle play a role that could further improve our FCO2 estimates

CATLAC - Calibration and Validation Analysis Tool of Local Area Coverage for the SeaWiFS Mission

Calibration and validation Analysis Tool of Local Area Coverage (CATLAC) is an analysis package for selecting and graphically displaying Earth and space targets for calibration and validation activities on a polar orbiting satellite. The package is written in the Interactive Data Language (IDL) and includes a graphical user interface. Although it is designed specifically for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission, the package can be used for analysis on other Earth-viewing missions. An individual can use text or graphical methods in CATLAC to select Earth targets to be scanned by a satellite. Additional onboard calibration activities (such as observations of the moon, or solar irradiance from a solar diffuser), which use data recorder time, can also be specified. All information pertinent to the creation of a command schedule can be written to a file which is read by a command scheduler. The scheduler can be invoked and the Local Area Coverage (LAC) recording periods can be visually verified using CATLAC. The schedule can also be verified by examining record and error files written by the scheduler