Ocean & Fishery Resources

Application of Ocean Reanalysis to the Diagnosis of Two Pelagic Squid Stocks

Introduction

Toward the creation of a variety of social benefits in the ocean and climate fields, we have been currently developing a leading-edge 4-dimensional variational (4D-VAR) data assimilation system and enhancing the ability to work as an interactive platform capable of linking ocean/climate studies with biogeochemistry and fishery science up to the level of full descriptions of essential processes. Data assimilation studies have so far shown that among a variety of assimilation methods, the 4D-VAR approach solves the minimization problem of the model-data misfit while satisfying the model equations using the Lagrange multiplier and thereby it can provide the best possible time-trajectory fit to the observations, leading to creation of a dynamically self-consistent dataset capable of offering more information and forecast potential on the dynamical state than can be derived from models or data alone.

Dynamical ocean state estimation by 4D-VAR data assimilation

The advanced 4D-VAR ocean data assimilation system using the GFDL MOM3 for the ocean GCM has been successfully developed. The model resolution is 1 degree in lat./lon. with 36 vertical levels. The assimilated elements are temperature and salinity from the FNMOC Dataset, OISST values, and ARGO float data, TAO/TRITON data, PIRATA data, and TOPEX/POSEIDON sea-surface height (SSH) anomaly data. In addition, the World Ocean Database 2001 were used as the background data for region without observational coverage. The global synthesis of available observational records and the numerical model produces a dynamically consistent time-varying dataset which exhibits realistic features of the major ocean variability such as the life cycle of the 1997/98 ENSO.

Figure1. Longitude-time section of optimized TAUx,SSH,SST within 2S-2N in 1990s (upper), and NINO3 SST time sequences derived from observation, simulation and assimilation (lower).

Figure 1 (upper) shows the improved longitude-time section of the zonal wind stress, SSH anomalies and sea surface temperatures (SST) averaged within 2N-2S, which has good consistency with the previous knowledge. The RMS error of optimized NINO3 SST during 1990-2000 is 0.73K, which is much more accurate than in the simulation case (Figure 1,lower).

Application of 4D-VAR products to the diagnosis of jumbo flying squid stock off Peru

An accurate ocean state estimation is one of the most important factors for fishery stock assessment. In parallel with the release of the high-quality reanalysis datasets obtained by our 4D-VAR assimilation experiments, we are now seeking for the high-impact applications that warrant social benefits such as fishery stock assessment as below. Jumbo flying squid is the largest species in the ommastrephid squids that has a life span of around one year. A sea area offshore of Peru is one of the major fishing grounds. Previous studies suggest that the feeding condition when they are young and small is considered to be very important for their survival and highly depends on the interannual variation of the ocean environment.

Figure2. Interannual variation of jumbo flying squid CPUE off Peru and NINO1-2 SST anomaly (Waluda et al.,2006; Ichii et al.,2002)

Since the early 1990s, there has been an increase in Japanese fishing fleets off Peru. The fluctuation in the squid abundance varies largely from year to year (Figure 2), and the possible relationship with El Nino has been pointed out in some reports. Most recently, however, severe decrease occurred in 1996 just one year before the historically biggest El Nino. The cause of this timing has been unknown yet. Here, we examine the statistical relationship between jumbo flying squid catch and ocean circulation by using the 4D-VAR ocean reanalysis dataset. Consequently, the close relationship is found between the jumbo flying squid CPUE (catch per unit effort) and the thermocline depth variation (Figure 3, left) around the fishing area. The vertical profiles of lag-correlation time series between squid CPUE and the interannual component of water temperature there shows the persistent relationship beyond one year in the depth from 30 to 100m (Figure 3, right). Further analysis demonstrates that this apparent signal is part of the interannual variation of the Peruvian current and the coastal upwelling. Figure 4 shows the lag-correlation between the squid catch and the one-year-lead components of the Peruvian current. The weakened Peruvian current and upwelling off the Peru and Chile coast corresponds to the decrease in squid catch. These results suggest that the persistent forcing over one year by the Peruvian current strongly affects the squid abundance off Peru. We are now developing the statistical model to reconstruct the jumbo flying squid catch variation on the basis of these results.

CPUE

Figure 3. Spatial distribution of correlation coefficients between jumbo flying squid CPUE and thermocline deprh (left), and vertical profiles of lag- correlation time series between squid CPUE and temperature at 7S85W(right).

Figure 4. Lag-regression between jumbo flying squid CPUE and 1-year lead T,U,V,W offshore of South America (upper), and lag-correlation time series between squid CPUE and major components of Peruvian current (lower).

Application of 4D-VAR products to the diagnosis of neon flying squid stock in the North Pacific

Neon flying squid has a wide-spread distribution in the north Pacific. This species also has a 1-year lifespan and migrates between spawning grounds and feeding grounds. The north Pacific population is comprised both the winter-to-spring spawning cohort and the autumn spawning cohort, of which the autumn cohort has the greater importance in fishery because of its larger size. As shown in Figure 5, the stock level was low during the period of the large-scale driftnet fishing. But, after a global moratorium on all large-scale pelagic driftnet fishing on the high seas at the end of 1992, the squid stock increased rapidly.

Figure 5. Interannual variation of neon flying squid CPUE in autumn cohort (Ichii et al.,2007).

The stock level, again, became low after 1999 because of the productivity change associated with ocean regime shift. Previous studies have tried to find the relationship between the change of the squid stocks after 1993 and the ocean regime shift by using satellite derived SST. Figure 6 (left) shows the north-south migration of the SST18°C isotherm along the date line derived by satellite and 4D-VAR reanalysis as a good proxy for Transiton Zone Chlorophyll Front (TZCF) position that could strongly affect the feeding condition of neon flying squid. The estimated CPUE variation in neon flying squid by regression analysis with these TZCF proxy indices couldn't show good agreement with observation ( 6 right) despite the well-reproduced regeme shift. But the clear relationship between the squid CPUE and the ocean temperature and salinity variations along 160W can be seen around the thermocline (Figure 7, right upper), and the reconstructed time series show good agreement with observation. These results suggest that the survival of young squids could be strongly affected by the variation of the time-varying subtropical upper ocean structure. We will make further analysis to gain more insight into such relationships by collaboration with fishery scientists on the 4D-VAR data assimilation research platform.

Figure 6. North-sourth variation of SST18(°C) line along the date line derived by satellite observation (left upper) and 4D-VAR ocean reanalysis (left lower), and the estimated CPUE variation in neon flying squid (autumn cohort) by regression analysis with Transition Zone Chlorophyll Front (TZCF) proxies (right).

Correlation coefficients between the neon flying squid CPUE and ocean temperature and salinity variation

Figure 7. Vertical profiles of correlation coefficients between the neon flying squid CPUE and ocean temperature and salinity variation along 160W (right upper) , and the estimated squid CPUE variation by regression analysis with thermocline temperature and salinity (right lower).