Filling the Gap in the Ocean Color Record Watson Gregg and Nancy Casey

1. NASA Ocean Biogeochemical Model (NOBM) Winds SST Shortwave Radiation Dust (Fe) Sea Ice Winds, ozone, humidity,pressure, precip. water, clouds, aerosols Radiative Model (OASIM) 2 2 Ed Es Ed Es Layer Depths IOP, Eu Biogeochemical Processes Model Circulation Model Temperature, Layer Depths Advection-diffusion Chlorophyll,Phytoplankton Groups Primary Production Nutrients DOC, DIC, pCO2 Spectral Irradiance/Radiance Outputs: Figure 2. Interactions among the main components of NOBM, nominal outputs, and forcing fields. IOP indicates inherent optical properties. Ed represents spectral downwelling direct irradiance, Es is downwelling diffuse irradiance, and Eu is upwelling irradiance. Filling the Gap in the Ocean Color Record Watson Gregg and Nancy Casey NASA/Global Modeling and Assimilation Office watson.gregg@nasa.gov ABSTRACT A critical gap in ocean color data will occur if Aqua-MODIS and SeaWiFS fail before VIIRS is operational. We seek to fill the gap using an intensified field campaign with a global assimilation model. Our objective is to maintain the integrity of global annual mean values of chlorophyll. We seek a solution that produces <3% difference in the global annual mean using field sampling, and with no major basin having a difference >10% from the assimilation of SeaWiFS. 10% field sampling achieves our objectives. This corresponds to about 1500 field observations per day. In this case they are evenly distributed throughout the global oceans. If we observe where and when the largest differences occur, we can reduce our field sampling requirement substantially. Our results suggest that an intensified field campaign combined with a global assimilation model can maintain the integrity of our ocean color time series. Introduction Can we mitigate a gap in remote sensing ocean color observations through field sampling and a global assimilation model? We investigate using sub-sampled SeaWiFS data for 1999 and the GMAO ocean color assimilation model (Gregg, 2008). Methods SeaWiFS 1999 data were sub-sampled by 10%, 5%, 2% and 1%, assimilated into the GMAO model, and compared with global and basin means from full assimilation (no sub-sampling). The basins are shown in Figure 1. Results Table 1. Difference in annual mean for 1999 from full SeaWiFS assimilation. Global Sampling % Difference Maximum difference by basin 10% sampling (about 1500 obs/day) -2.3% -7.6% North Pacific 1% sampling (about 150 obs/day) 1.4% -21.9% North Indian Using targeted sampling by basin and month, we can refine our sampling strategy, and reduce the required amount. Shown below is targeted sampling as a percent of SeaWiFS, by month. Month Basin 1 2 3 4 5 6 7 8 9 10 11 12 North Atlantic 2% 1% 1% 2% 1% 1% 2% 10% 10% 2% 10% 5% North Pacific 2% 1% 5% 5% 10% 10% 10% 10% 10% 10% 10% 2% North Central Atlantic 5% 1% 5% 2% 2% 2% 2% 2% 2% 2% 2% 2% North Central Pacific 2% 5% 1% 0% 5% 1% 1% 1% 1% 5% 5% 1% North Indian 2% 10% 10% 10% 10% 2% 2% 10% 2% 2% 2% 2% Equatorial Atlantic 5% 10% 2% 10% 2% 10% 2% 2% 2% 5% 1% 5% Equatorial Pacific 5% 5% 0% 0% 0% 0% 1% 1% 1% 1% 1% 1% Equatorial Indian 1% 2% 2% 2% 1% 1% 5% 5% 5% 1% 1% 5% South Atlantic 0% 0% 0% 1% 1% 1% 1% 1% 1% 1% 5% 2% South Pacific 2% 2% 5% 0% 1% 5% 5% 2% 5% 5% 5% 5% South Indian 10% 0% 0% 0% 1% 1% 1% 1% 1% 5% 2% 2% Antarctic 2% 2% 5% 5% 5% 5% 5% 5% 5% 5% 5% 2% Figure 1. Major oceanographic basins. Coupled General Circulation/Biogeochemical/Radiative Model of the Global Oceans A diagrammatic representation of a fully coupled general circulation/ biogeochemical/radiative model illustrates the complex interactions among the three major components (Gregg et al., 2003; Gregg and Casey 2007). The Ocean General Circulation Model (OGCM) is a reduced gravity representation of circulation fields, extending from near the South Pole to 72o N, in increments of 2/3o latitude and 1 1/4o longitude. The model contains 14 vertical layers, in quasi-isopycnal coordinates. The biogeochemical model utilizes the circulation fields and the vertical mixing processes to produce horizontal and vertical distributions of constituents. The model contains 4 phytoplankton groups, which differ in maximum growth rates, sinking rates, nutrient requirements, and optical properties to help represent the extreme variety of environments encountered in a global model (Figure 2). Phytoplankton are ingested by a separate herbivore component, which also contributes to the ammonium field through excretion. This produces the statistics in Table 2. Table 2. Difference in annual mean for 1999 from full SeaWiFS assimilation, using targeted sampling. Global Difference Maximum difference by basin -0.2% -7.8% North Pacific Further reductions in sampling are possible using finer targeted sampling. References . Gregg, W.W., 2008. Assimilation of SeaWiFS ocean chlorophyll data into a three- dimensional global ocean model. Journal of Marine Systems 69: 205-225. Gregg, W.W. and N.W. Casey, 2007. Modeling coccolithophores in the global oceans. Deep-Sea Research II 54: 447-477. Gregg, W.W., P. Ginoux, P.S. Schopf, and N.W. Casey, 2003. Phytoplankton and iron: Validation of a global three-dimensional ocean biogeochemical model. Deep-Sea Research II 50: 3143-3169. Assimilation Methodology The assimilation methodology uses the conditional relaxation analysis method, commonly known as the blended analysis. This method assumes that the satellite chlorophyll data represent a truth field, and the model spatial distributions are realistic. Biases in the model are removed using the satellite data, while enforcing matching of Laplacian’s with and without the inserted satellite data: M = M,S where M is the model chlorophyll and S is the satellite chlorophyll. The method is heavily weighted toward the data, and thus data errors is SeaWiFS are a critical issue in application. In our application, SeaWiFS daily data was weighted at 25% with monthly means at 75%. Additionally, an error-weighting field was constructed, based on analysis of SeaWiFS errors (Gregg and Casey, 2004).