New Model for Vertical Distribution of PAR

Unfortunately, the primary productivity using the empirical equation (8) exhibited overestimates of primary productivity in the East China Sea and in the water off the Sanriku. On the East China Sea, the Seikai-ku Fishery Research Laboratory conducted in situ and simulated in situ incubations to estimate primary productivities for the water located on the eastern end of the continental shelf. The water on the eastern end of the continental shelf was the case-I water, where the concentration of suspended particles or colored dissolved organic matter is low and is different from the continental shelf case-II water. Off the Sanriku, the Hokkaido-ku Fishery Research Laboratory conducted in situ and simulated in situ incubations to estimate primary productivities for the Kuroshio originated water, the Oyashio, and the mixing region between two waters. The water off the Sanriku exhibits a variation in primary productivity with a variation in chlorophyll a concentration, of which water is also classified as the case-I water with a low concentration of suspended particles and colored dissolved organic matter. Although the water for in situ measurements was case-I water, the primary productivity was estimated higher than in situ measurement with higher chlorophyll a concentration.

A distribution of diffused attenuation coefficient at 490 nm (Kd490) and chlorophyll a concentration were surveyed from the satellite observation data, to check validity of the unique empirical equation for PAR by

Figure 5: Plots of chlorophyll a concentration and diffused attenuation coefficient at 490 nm (Kd490) for each pixels observed by SeaWiFS around Japan in July 2001. Data were selected from the monthly mean of level-3 products of SeaWiFS project. Plots of chlorophyll a concentration less than 0.5 mgm~3 exhibits a deep chlorophyll maximum more than 50 m and is corresponding to the case-I water. Kd490 exhibits a larger divergence in a higher chlorophyll a concentration.

Figure 5: Plots of chlorophyll a concentration and diffused attenuation coefficient at 490 nm (Kd490) for each pixels observed by SeaWiFS around Japan in July 2001. Data were selected from the monthly mean of level-3 products of SeaWiFS project. Plots of chlorophyll a concentration less than 0.5 mgm~3 exhibits a deep chlorophyll maximum more than 50 m and is corresponding to the case-I water. Kd490 exhibits a larger divergence in a higher chlorophyll a concentration.

chlorophyll a concentration on the surface. Fig. 5 indicates a scatter diagram between Kd490 and chlorophyll a concentration for the monthly composite data of SeaWiFS in July 2001 on the northwestern Pacific waters including the East China Sea, the Kuroshio along the Japan Islands, and water off Sanriku. Points are classified into two groups by a chlorophyll a concentration of 0.5 mgm~3, which is a threshold of chlorophyll a concentration giving a deep chlorophyll maximum more than 50 m. Kd490 at chlorophyll a concentration of 1.0mgm~3 shows a variation of 7 0.02m-1. Similarly, Kd490 exhibits variation of 7 0.05 m-1 at 5.0 mgm~3, 7 0.12 m_1 at 10.0mgm~3, and 70.15m-1 at 20.0mgm~3. A less variation of Kd490 at lower chlorophyll a concentration suggests that optical properties in the open water, like the case-I water, could be represented by a unique equation between chlorophyll a concentration and Kd490. In contrast, a large variation of Kd490 at higher chlorophyll a concentration, which is corresponding to the coastal water, suggests a difficulty in representing a relationship between Kd490 and chlorophyll a concentration with a unique equation. A combination of phytoplankton, suspended particles, and colored dissolved organic matter are cause of variations in Kd490 and chlorophyll a concentration using multispectrum remote sensing data at the current algorithm.

Figs. 6a and b show vertical distributions of PAR estimated by equations (7) and (10), respectively.

PAR vertical distribution for two algorithm based on Chl-a and Kd

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Figure 6: Vertical distributions of PAR. Solid lines show vertical distributions of PAR for three different chlorophyll a concentrations in the surface. The solid lines in Fig. 6a were computed by equation (7). The solid lines on Fig. 6b were computed by equation (10). For both figures, possible pairs of diffused attenuation coefficients at 490nm(Kd490) for three different chlorophyll a concentrations are plotted by dashed lines based on equation (6). Kd490 was selected from Fig. 5 for 0.028 and 0.033 m-1, 0.065 and 0.085 m-1, and 0.2 and 0.35m-1 for 0.05, 0.065, and 5.0mgm-3 of chlorophyll a concentration, respectively. Equation (10) gives the vertical distributions of PAR within the variation of Kd490s for corresponding chlorophyll a concentration.

According to Fig. 6a, the equation (7) exhibits an underestimate of PAR for chlorophyll a concentration of 0.05 and 5.0 mgm-3, where PAR did not penetrate deeper relative to PARs estimated by Kd490. In addition, an overestimate of PAR was observed for a chlorophyll a concentration of 0.5mgm-3, where the PAR penetrates into deeper waters.

So as to avoid those over- and underestimates of vertical distributions of PAR on Fig. 6a, a new empirical equation was proposed to estimate vertical distributions of PAR based on chlorophyll a concentration to minimize errors in the previous equation (7) and to find an optimum distribution of PAR as follows:

PAR%c(z) = exp{(—0.0018 C + 0.022 C2 - 0.11 C0 - 0.024)Z} PAR%(0)

where C0 is chlorophyll a concentration in the surface, mgm-3, Z is the depth in m. Fig. 6b shows vertical distributions of PAR estimated by equation (10) with solid lines. The solid lines are observed within a pair of two Kd490s of dashed lines for each combination. The proposed equation (10) provides the optimum vertical distributions of PAR with a unique solution for each chlorophyll a concentration.

Fig. 7 shows a scatter diagram between in situ and model-estimated primary productivities. The correlation coefficient was 0.768, which was a significant improvement of the correlation coefficient from 0.307 in Fig. 4. Although there are some underestimates at some stations, Fig. 7 exhibits a good correlation between in situ and model-estimated primary productivities. The underestimates of primary productivity were observed at three a.

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Figure 7: Scatter diagram of primary productivity between the model-estimated and in situ measured primary productivity based on the vertical distribution of PAR in equation (10). The correlation coefficient between the model-estimated and the in situ primary productivity was 0.768.

different regions, especially at stations exhibiting higher primary productivity. As the major group of measurement exhibited a good correlation, the primary productivity model proposed in this study is working in a good manner.

But, the spatial and temporal difference between in situ measurement and model calculation could be one reason for the error. In situ measurement is given for one station, where a limited amount of water mass is a subject for measurement. There is a difficulty of repeated measurement to get a mean value because of an incubation period of 12 or 24 h. In situ measurement will be conducted on the day with the PAR for the day, which could not be a mean PAR for a month or some period because of a weather condition for the day. In contrast, the model is applied to chlorophyll a distribution, sea-surface temperature, and PAR, which are temporally averaged for 1 month and spatially averaged for 9 x 9 km2. The major error was observed in high primary productivity region, which is corresponding to a high chlorophyll a concentration. There are uncertainties of representing the water mass for 9 x 9 km2 in space, because of a small possibility of equally distribution of high chlorophyll a concentration on the corresponding area. There are also uncertainties of continuous presence of high chlorophyll a concentration for the period corresponding to in situ measurement and monthly average of satellite observation. For these reasons, the spatial and temporal average by satellite observation may be the significant error for this validation using in situ measurement.

It is ideal to use simultaneous satellite observation with in situ measurement including chlorophyll a distribution, sea-surface temperature, and PAR. Unfortunately, chlorophyll a distribution observed by a polar orbit satellite has a possibility of cloud presence within a pass over the in situ measurement station, which covers the region once a day. Because of this less opportunity of chlorophyll a observation by polar orbit satellites, weekly or monthly composite for chlorophyll a concentration is applied, although a daily composite dataset is provided. In contrast, sea-surface temperature observation by polar orbit satellite has several opportunities within a day by a few NOAA polar orbit satellites and high opportunity of observation. The daily composite sea-surface temperature dataset is not available globally, but locally. A global sea-surface temperature dataset is provided on a weekly or monthly composite because of cloud presence. PAR is also provided on a weekly or monthly basis because of reducing uncertainties of error.

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