The third primary production algorithm round robin (PPARR3) compares output from 24 models that estimate depthintegrated 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 C, and chlorophyll concentration exceeding 1 mg Chl m À3 . 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. r

The lognormal distribution is presented as a useful model for bio-optical variability at a variety of spatial and temporal scales. A parametric statistical framework is presented for using the lognormal model to assess the effects of heterogeneity and scale on closure. Variability at small scales affects but is unresolved by large-scale measurements. An assumed lognormal distribution allows one to integrate over small-scale variability to predict large-scale measurements. Examples are presented to demonstrate how knowledge of the variance can be incorporated into models to relate measurements made at different scales. 1. 13,237 13,238 CAMPBELL: LOGNORMAL VARIABILITY IN BIO-OPTICS area, and volume), and even some of the more difficult to measure properties, such as the quantum yields of photosynthesis and fluorescence, all seem to have lognormal distributions within certain sampling domains. Why is this so? There are several processes that might explain why a variable is lognormally distributed [Shimizu and Crow, , 1957] which is an analogue to the additive central limit theorem for normal or Gaussian random variables. According to the central limit theorem, a random variable will be normally distributed if it can be represented as the sum of independent random variables. The larger the number of independent variables in the sum, the closer their sum is to being normal or Gaussian. However, the independent variables being summed need not be Gaussian. 1988]. Perhaps the most relevant to bio-optical processes is the "law of proportionate effect" [Aitchison and BrownThe lognormal analogue to this is as follows: a random variable will be lognormally distributed if it can be expressed as the product of independent random variables. By taking its logarithm, the product is transformed to a sum, and the sum is normally distributed according to the central limit theorem.

Increasing atmospheric CO2 is likely to cause a corresponding increase in oceanic acidity by lowering pH by 0.20.5 pH units by the end of the 21st century [Royal Society, 2005]. In light of increasing acidity, there are growing concerns about the future health of a variety of marine organisms, particularly shellfish, which in the United States is a $1.6 billion industry. Shellfish predominantly inhabit coastal regions, and in addition to the projected stress caused by the global trend in ocean acidification, some coastal ecosystems receive persistent or episodic acid inputs as a result of interactions with river water, bottom sediments, or atmospheric deposition of terrigenous materials. Most river plumes are acidic relative to the receiving ocean, and river water is mixed extensively over the continental shelf. Moreover, the chemical nature and magnitude of discharge are changing rapidly due to climate change and land‐use practices.

No abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) will add a significant new capability for investigating the 70% of the earth's surface that is covered by oceans, in addition to contributing to the continuation of a decadal scale time series necessary for climate change assessment in the oceans. Sensor capabilities of particular importance for improving the accuracy of ocean products include high SNR and high stability for narrower spectral bands, improved onboard radiometric calibration and stability monitoring, and improved science data product algorithms. Spectral bands for resolving solar-stimulated chlorophyll fluorescence and a split window in the 4-m region for SST will result in important new global ocean science products for biology and physics. MODIS will return full global data at 1-km resolution. The complete suite of Levels 2 and 3 ocean products is reviewed, and many areas where MODIS data are expected to make significant, new contributions to the enhanced understanding of the oceans' role in understanding climate change are discussed. In providing a highly complementary and consistent set of observations of terrestrial, atmospheric, and ocean observations, MODIS data will provide important new information on the interactions between earth's major components. I. INTRODUCTION U SE OF satellite image data to investigate oceanic processes has become an essential component of oceanographic research and monitoring. Data from the Coastal Zone Color Scanner (CZCS) provided the first demonstration of the ability to observe the abundance and distribution of phyto-Manuscript