West Indian Ocean variability and East African fish catch

Marine Environmental Research 70 (2010) 162e170

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Marine Environmental Research journal homepage: www.elsevier.com/locate/marenvrev

West Indian Ocean variability and East African fish catch M. Jury a, b, *, T. McClanahan c, J. Maina c a

University of Zululand, South Africa University of Puerto Rico, Mayaguez, PR, USA c Marine Programs, Wildlife Conservation Society, NY, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 March 2010 Received in revised form 12 April 2010 Accepted 16 April 2010

We describe marine climate variability off the east coast of Africa in the context of fish catch statistics for Tanzania and Kenya. The time series exhibits quasi-decadal cycles over the period 1964e2007. Fish catch is up when sea surface temperature (SST) and atmospheric humidity are below normal in the tropical West Indian Ocean. This pattern relates to an ocean Rossby wave in one phase of its eastewest oscillation. Coastal-scale analyses indicate that northward currents and uplift on the shelf edge enhance productivity of East African shelf waters. Some of the changes are regulated by the south equatorial current that swings northward from Madagascar. The weather is drier and a salty layer develops in high catch years. While the large-scale West Indian Ocean has some impact on East African fish catch, coastal dynamics play a more significant role. Climatic changes are reviewed using 200 years of past and projected data. The observed warming trend continues to increase such that predicted SST may reach 30 C by 2100 while SW monsoon winds gradually increase, according to a coupled general circulation model simulation with a gradual doubling of CO2. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Fish catch variability Physical oceanography East Africa

1. Introduction Close to one-third of the world’s population lives in countries next to the Indian Ocean, yet this region produces only 10% of global fish catch (Okemwa and Sted,1995). Western Indian Ocean countries are in a developing state with annual economic production of w$ 3 billion. The coastal zone is an important source of renewable food production and tourism activities. Increasingly coastal habitats are being degraded and natural resources over-exploited, causing conflict over resource use around coastal cities such as Dar es Salaam and Mombasa (Ohman et al., 2002). Marine resource utilization cuts across a diverse range of people and significantly affects socio-economic development in the region (Van der Elst et al., 2005). It creates challenges in the application of technology, design of sustainable scientific research, and the development of management strategies. Estimating changes in production, abundance, and catch rate are key issues for reliable fish stock assessment. Marine environmental conditions are known to play a role in biological renewal and fluctuate on many time and space scales. In order to improve the management of marine and coastal resources for East Africa, we investigate the regional ocean climate and coastal circulation and examine relationships with commercial fish catch (Laevastu, 1993)

* Corresponding author. University of Puerto Rico, Mayaguez, PR, USA. E-mail address: [email protected] (M. Jury). 0141-1136/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.marenvres.2010.04.006

using ocean reanalysis products to describe water properties and circulation off the East African coast (Fig. 1). 1.1. Marine-climate patterns The Indian monsoon regulates the climate by instilling an annual cycle that affects the marine ecology. The coast of Tanzania and Kenya, our focus here, is largely concave and the shelf is relatively narrow. Around Madagascar southeasterly winds prevail throughout the year, while East African coastal winds alternate according to the monsoon, being from the north at 3 m s1 from December to February and from the south in excess of 5 m s1 between April and October. The coast channels the wind, producing acceleration around Cape Delgado at 11 S. The axis of strongest wind extends from the tip of Madagascar toward the Kenyan coast, overlying the incoming south equatorial current (SEC). An axis of ‘blue’ low-chlorophyll water marks the SEC (Fig. 1a). Primary production increases northward along the Kenya coast, where an upwelling circulation occurs in the JuneeSeptember monsoon season. Coastal waters are affected by river discharges that increase nutrients and turbidity. A stable thermocline associated with light wind can lead to greater nitrogen fixation and increased productivity (Bryceson, 1982). Whilst the coastal winds are important, remote influences can modulate marine resources through changes induced by water mass advection and vertical motion. A see-saw of the thermocline is known to occur in the Indian Ocean (Saji et al., 1999) in response to

M. Jury et al. / Marine Environmental Research 70 (2010) 162e170

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Fig. 1. (a) The mean annual SeaWifs chlorophyll concentration and 0e200 m current vectors (largest ¼ 0.4 m/s), (b) SODA2.4 mean annual 0e200 m salinity, and (c) mean annual nitrate concentration from model assimilated observations.

large-scale coupled oceaneatmosphere Rossby waves that take 3e5 years to travel from Australia to Madagascar (White et al., 2004; Jury and Huang, 2004; Menard et al., 2007). Changes in zonal winds over the East Indian Ocean initiate this process and drive SST variability over the West Indian Ocean about a year later (Jury et al., 2002). This see-saw is partially modulated by the global El Nino Southern Oscillation (ENSO) and contributes to direct changes in water temperatures and sea levels, indirect changes in salinity due to shifts in rainfall and evaporation, and corresponding eastewest shifts of the mid-ocean tuna catch (Marsac and Le Blanc,1999a,b; White et al., 2004; Lu et al., 2008). Catch is significantly higher during times when the thermocline shoals, the mixed layer is shallow, and nutrients are brought into the euphotic zone (Marsac, 2001). Sequences of tuna catch and sub-surface temperature exhibit slow westward propagation in conjunction with the ocean Rossby wave (White et al., 2004). Yet associations are known to be irregular in time, and dependent on species and catch method. The zonally propagating ocean Rossby wave affects the entire basin, but its amplitude is damped near the coast where meridional currents are strong. There is also a slower decadal component to this oscillation that has received some attention in the context of tropical cyclone variability east of Madagascar (Chang-Seng, 2004). 2. Data and methods 2.1. Marine-climate data Our ability to analyze regional climates has benefited from a long history of routine observations associated with national weather services and measurements from oceanographic and commercial ships (Fig. 2a). These have been centrally archived to enable the production of interpolated fields at monthly time scales. Recent advances in our understanding of the ocean have taken place through modeling and satellite data. Prior to 1980, only ship’s data were routinely available to describe ocean conditions, with coverage adequate for climatological mean descriptions. Since then, satellites have provided wider coverage based on thermal infrared imagery, altimetric height, scatterometer winds, and ocean colour (Andrefouet and Riegl, 2004). Advances in numerical ocean modeling have been made that combine in-situ observations and satellite fields in a monthly

assimilation. The ocean reanalysis fields considered here derive from the GFDL modular ocean model. The ocean dynamics are driven by coupling with the atmosphere that makes use of surface wind stress and fluxes from ECMWF reanalysis at a grid resolution of w2 . Ocean temperature observations are assimilated from in situ sea surface temperature (SST) measurements of the ship-based COADS archive, temperature profile measurements and, since 1981, with satellite-estimated SST fields (Reynolds and Smith, 1994). In situ SST measurements exceed 90,000 per year in the Indian Ocean while the density of temperature profiles off East Africa (Fig. 2a) has been adequate since 1962. The subsurface data is concentrated near the coasts, along key lines from Durban, Madagascar and Mauritius to Mombasa. The simple ocean data assimilation (SODA) version 2.4 analysis of ocean currents, employed here, makes use of steric adjustments between upper ocean heat content and sea surface height anomalies from the Topex/Poseidon satellite altimeter. We consider temperature, salinity, currents and vertical motion fields as maps and depth sections with respect to fish catch, and study the seasonal cycle and natural variability using a mathematical cluster analysis technique known as singular value decomposition (SVD). The SVD analysis is an eigenvector decomposition of the covariance matrix within a single input field with variability reduced to modes that each has unique character. For each mode there is a spatial pattern of loadings and scores that describe its temporal fluctuations. The goal here is to characterize the leading patterns of regional to coastal scale inter-annual variability, to determine whether the findings specific to fish catch are also naturally occurring. For the atmospheric data, we make use of the NCEP reanalysis (Kalnay et al., 1996) available via the IRI climate library website. From this source we analyze those patterns that are repetitious, through composite averaging, making use of SST, precipitable water (humidity) and surface winds. The reanalysis fields are considered for the West Indian Ocean and East African shelf region, to understand relationships between long-lived anomalies in regional ocean conditions and fisheries abundance in the coastal zone. 2.2. Fish catch analysis The catch of marine fish along the coast of East Africa has been compiled by FAO since the 1950s and stored in various databases.

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Fig. 2. (a) West Indian Ocean 0e200 m depth-averaged observation density (1964e2007) with grey scale from 1 to 10 profiles/degree/month, (b) mean sea surface temperature from MODIS (2000e2007). Box (a) outlines coastal zone that is analyzed in detail in (b) where place names are given.

There has been some analysis of the factors that influence catch, including human effort and oceanographic conditions. The annual catch data in many countries often appears suspect due to weak collection and reporting systems. We have compared different sources of data, such as national statistics from the respective country fisheries services and the FAO dataset; and find reasonable agreement. Earlier research found coherent fluctuations of fish catch across the SW Indian Ocean (Jury, 2005). The statistics we employ here are fish catch landed within coastal and shelf waters of Tanzania and Kenya over the period 1964e2007, based on annual records in the FAO data base. Different species may exhibit unique responses to marine climatic signals, and dominant species often compete with each other for environmental niches (Jury, 2006). With this in mind, the time series for the various Tanzania/Kenya fish species were analyzed. Fluctuations of the dominant fish species were found to be positively correlated and to exhibit co-varying spectral cycles in a way that suggests an aggregate response by the coastal ecosystem. Thus we employ an all-species catch index as our reference variable (Fig. 3a). Although there has been a tripling of population over the period 1964e2007 that could affect catch effort (McClanahan and Maina, 2002), trends appear to stabilize early in the record, so adjustments were deemed unnecessary. The two country’s marine fish catch was summed (Tanzania having the bigger share) to create a time series for analysis with respect to the marine environment. Indices of SST and ocean climatic fields were extracted over wide and narrow domains and cross-correlations were computed with respect to annual catch data. Composite fields were made by averaging groups of high catch and low catch years as in Jury (2005). The fields were subtracted to produce ‘high minus low’ composite maps. The years included in the high composite (cf. Fig. 3a) are: 1967e68, 1974e75e76e77e78, 1989e90, 1996, 2004e05; and in the low composite: 1969e70e71e72e73, 1981e82, 1992e93e94e95, 2006. The difference between high and low catch years is about 20,000 tons.

past the northern tip of Madagascar impinges on the Tanzanian coast at 11 S (Schott, 1983; Swallow et al., 1983). Much of the SEC turns northward bringing fresh, low productive (blue) water to the shelf. A notable feature is the chlorophyll-rich water found along the coast of Somalia. Values >0.5 mg m3 are found around the mouth of large rivers, recessed bays and south of the capes. Some of the coastal signal is turbidity from river discharge. The ocean reanalysis data indicates that the northward current strengthens

3. Results 3.1. The marine climate and fisheries Fig. 1a and b illustrates the mean ocean color, currents and salinity. The incoming south equatorial current (SEC) that sweeps

Fig. 3. (a) Time series of East Africa fish catch with 2nd order trend and (b) wavelet spectra with contours at 10% power levels and cone of validity.


along the coast of Kenya. The mean northward coastal current is overlain by a shallow wind-driven southward current in January. In July the northward current is stronger and extends to 150 m depth. The thermocline generally lies near 140 m depth, but rises to 100 m around Zanzibar at 5 S. Near the equator is a strong zonal current (Wyrtki, 1973). The ocean reanalysis data are underpinned by a reasonable data density along the coast and 200 km offshore from Kenya and Somalia (Fig. 2a). However, in the zone north of the Comoros Islands (12S) and east of 43E, data density is lower. Since 1980 satellites have provided useful data inputs at the surface and in the atmosphere. The high resolution SST field for the coastal zone of Tanzania and Kenya (Fig. 2b) shows a warm region south of Dar es Salaam at 7S along the coast, and offshore to the south of 4S. Yet along the coast, there is a cooler band extending from 7S northward pointing to interaction between the SEC and the islands of Mafia, Zanzibar and Pemba, and offshore Ekman transport. Further north off Kenya the cool band broadens, particularly during the period of seasonal upwelling around July. We will analyze the seasonal cycle in Section 3.4, here we continue our focus on mean conditions. The water chemistry map for nitrate is given in Fig. 2c averaged over the 0e200 m layer. This indicates an offshore maximum that reaches shoreward between 6 and 9S associated with cyclonic shear along the northward edge of the SEC. Low nutrient values are found in the zone south of 11S. Apart from a coastal maximum between the islands of Mafia and Zanzibar, the concave coastal zone is rather nutrient-poor on average. North of Lamu Kenya nutrient levels rise, particularly in the upwelling season. The region to the northeast of Madagascar is where the mid-ocean thermocline ridge is located, and consequently where nutrients are pooled. The FAO fish catch time series is given in Fig. 3a, based on a monitoring system that tracks a largely artisanal fishery over the shelf. There are quite a number of low catch years before 1975, when most of the species were unidentified. Since then, the record exhibits little trend and irregular oscillations that are quantified using wavelet spectral analysis in Fig. 3b. In the 1970s w3 year cycles were prevalent, whereas in the 1990s a w6 year cycle was noted. Throughout most of the record the dominant cycle was around 12 years. Although it is found to be significant, the record length only just captures this signal. The catch record is significantly negatively correlated with the number of tropical cyclone days east of Madagascar (r ¼ 0.37 for N ¼ 43). Inspection of the individual species records points to a gradual decrease in mean trophic level that may be related to over-fishing.

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offshore zone. The currents in respect of high catch exhibit two cyclonic gyres along 6S and a cyclonic gyre around 12S. In between these the SEC is stronger than usual and sweeps northward along the East African coast. In the equatorial zone, currents with respect to high catch are westward. The composite analysis for the coastal zone is given in Fig. 6aec. Northeasterly wind stress differences suggest a lengthening of the NE monsoon season and a strengthening in its amplitude relative to the SW monsoon. Such a wind will generate onshore Ekman transport in the surface layer. Shelf currents move toward the coast around 6S advecting waters that promote fish catch. Dry conditions are found along the coastal strip from Dar es Salaam northward according to CPC soil moisture anomalies. Depth sections are given in Fig. 7aed, covering the shelf zone off Zanzibar and Pemba. Meridional currents in respect of higher catch are northward in the upper 150 m despite the southward wind stress, representing an enhancement of the mean coastal (and SEC) current (Swallow et al., 1991). There is a weak undercurrent flowing southward from 200 to 600 m off the shelf. Anomalous 0e100 m zonal currents are toward the coast due to onshore Ekman transport and the cyclonic ocean gyre. Along the shelf edge 40e41.5E

3.2. Composite difference maps and depth sections Composite difference maps are constructed for ocean climate fields in respect of high and low fish catch. Widespread, coherent but weak signals emerge for SST and precipitable water (vertically integrated humidity). The entire West Indian Ocean is w0.2 C cooler (Fig. 4a) consistent with a lifted thermocline that also anticipates higher catch in the mid-ocean fishery (White et al., 2004; Lu et al., 2008). Cooling over such a wide area derives from the coupled Rossby wave that lifts the thermocline and accelerates monsoon winds. With the lower SST, precipitable water is less over the western Indian Ocean, so rainfall is limited before and during high catch. Dry subsiding air along a NWeSE axis suppresses tropical cyclones to the east of Madagascar. The atmospheric ‘imprint’ is brought to the coast by the SEC. The high minus low catch maps for winds, sea surface height and currents are given in Fig. 5aec at regional scale. The atmospheric contrasts include upper westerly winds that help to create a zonal overturning circulation and surface easterly winds in the

Fig. 4. Composite analysis of differences between years with high and low catch for (a) SST (C), (b) precipitable water (mm). Diagonal line marks the axis of the coupled Rossby wave.

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Fig. 5. Composite maps analyzed as differences between high and low catch for: (a) 200 hPa wind (m/s with key vector), (b) sea surface height (m) and (c) 0e200 m ocean currents, for the West Indian Ocean.

there is upward motion in the layer 100e400 m that joins currents moving off the shelf. The upward motion likely enhances primary production. Temperature and salinity (Fig 7c and d) patterns are fresh and cool above 100 m. There is a warm salty layer from 150 to 200 m in years with high fish catch. This layer is unlikely to be governed by surface fluxes, and could therefore be an advective signal. We investigate these links and the issue of seasonality in the following section, using SeaWifs satellite Chlorophyll and modelassimilated nitrate as reference variables. 3.3. Seasonal cycle, ocean productivity and inter-relationships The seasonal cycle along the East African coast is pronounced due to the monsoon reversal. In the previous sections we have used multi-year periods to represent high and low catch. However this may obscure some of the processes linking the physics to the

biochemistry and ecology. Thus an analysis of consecutive monthly data to describe the mean annual cycle is needed (Fig. 8aed). SeaWifs chlorophyll and Nitrate rise to a peak in August-September at the end of the SW monsoon season when northward wind stress and offshore Ekman transport is present. There is a secondary chlorophyll maximum in January when wind stress is southward. Standard deviations of chlorophyll tend to follow the absolute values, but are high in January. Northward currents and wind stress are strongest in AprileMay at the onset of the SW monsoon, but vertical uplift peaks much later in July and November and tends to follow currents. Sea temperatures are highest in AprileMay and fall rapidly during the SW monsoon season to a minimum in August, when thermal stratification is weakest. Salinity in the coastal zone is lowest following the MarcheMay long rains and rise to a maximum in November following dry weather during the SW monsoon season.

Fig. 6. Composite maps analyzed as differences between high and low catch for: (a) wind stress, (b) 0e200 m ocean currents and soil moisture (yellow to brown 5 to 15 mm) and (c) 0e200 m sea temperature. Dashed box in (c) identifies area for analysis of depth sections and seasonal cycle.


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Fig. 7. Composite depth sections averaged 4e7S analyzed as differences between high and low catch for: (a) meridional currents (m/s), (b) zonal currents and vertical motion as vectors (m/s), (c) temperature (C), (d) salinity (g/kg). The TeS structure has an anomalous warm salty layer near 150 m. View is to north with coast on the left. Vertical motion in (b) exaggerated 100-fold.

b 0.10

0.3 0.06 0.2

0.02

0.1

East Africa

6

0.10

12

0.00

15

-0.10 M ar A pr M ay J un J ul A ug S ep Oct N ov D ec J an F eb M ar

M ar Apr M ay J un J ul Aug Sep Oc t Nov Dec J an Feb M ar

-0.02

9

months

months

0.3

0.2

0.2 M ar A pr M ay J un J ul A ug S ep Oct N ov D ec J an F eb M ar

-0.2

23

35.2

22

35.1

21

35.0 M ar A pr M ay J un J ul A ug S ep Oct N ov D ec J an F eb M ar

0.4 0.6

35.3 sea temp salinity

S alinity (g k g-1)

vert vel. Vcurrent

V c ur r en t ( m s - 1)

V er t v el ( m d- 1)

d 24

0.5 1.0

S ea T em p ( C )

c

0.20

T10-200 Vstress

V s tr e s s ( N m - 2 )

Chl a Nitrate

dT /dz ( C )

East Africa 0.4 Nitrate (m g m -3)

Chl-a (m g m -3)

a

Fig. 8. Mean annual cycle over the shelf from Dar es Salaam to Mombasa for: (a) chlorophyll from SeaWifs and Nitrate from ocean model, (b) ECMWF meridional wind stress and 10e200 m temperature gradient (inverted), (c) 0e200 m meridional currents and 0e500 m vertical velocity, (d) 0e200 sea temperature and salinity from SODA2.4.

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Table 1 Cross-correlations between various physical and bio-chemical variables for consecutive monthly data in the period 1997e2007 for the shelf between Dar es Salaam and Mombasa. Variables (in order) are chlorophyll, nitrate, NeS wind stress, NeS current 0e200 m, vertical motion 0e500 m, salinity 0e200 m, sea temperature 0e20 m, temperature change with depth 10e200 m, and (wind) mixed layer depth. Chl a Chl a N Vstress Vcurrent Vert.mot. Salinity T10 dT/dz MLD

N

Vstress

Vcurrent Vert.mot. Salinity T10

0.75 0.30 0.17 0.15 0.07 0.44 0.04 0.25 0.06 0.24 0.20 0.03 0.64 0.25 0.69 0.86 0.29 0.04 0.64 0.78 0.31 0.12 0.59 0.84 0.31 0.01

0.07 0.24 0.14 0.27

0.04 0.06 0.01

dT/dz

0.89 0.92 0.86

To determine how important the physical conditions are for biochemical productivity, cross-correlations and multi-variate analysis was carried out on consecutive monthly data in the period 1997e2007 for data averaged over the shelf from Dar es Salaam to Mombasa (Table 1). Chlorophyll and Nitrate are best linked with sea temperatures (r ¼ 0.69 and 0.86, respectively) which are in turn closely associated with mixed layer depth (e0.92), but crosscorrelations between sea temperatures and other variables are weak. Meridional winds and currents are better associated with salinity (r < 0) than sea temperatures. Multi-variate regression scatterplots are given in Fig. 9a and b. The model for chlorophyll uses salinity, near surface sea temperature and mixed layer depth to account for 61% of variance. All environmental variables have a negative coefficient, suggesting that fresh, cool conditions in a shallow mixed layer contribute to productivity. The model for nitrate employs meridional wind stress and also sea temperature and mixed layer depth, accounting for 80% of variance. Various environmental indices were formulated from the above composite analyses, and by singular value decomposition (SVD) applied at regional scale. Time scores that describe the ‘natural’ variability for ocean and atmosphere modes over the West Indian Ocean were cross-correlated with the fish catch. The mode-2 patterns for 1000e850 hPa geopotential height, precipitable water and zonal wind stress are illustrated in Fig. 10aec. These atmospheric modes associated with fish catch exhibit significant eastewest dipole patterns (Yeshanew, 2004), yet the large-scale sub-surface ocean modes (for T, S, U, V, W) were of little consequence. A multi-variate regression of large-scale oceaneatmosphere variables onto fish catch gave a poor fit suggesting

that the zonal see-saw that affects the mid-ocean fishery has little influence on the East African shelf. Considering this outcome, SVD was repeated at local-scale (2e9S/39e42E) on sub-surface ocean data. The resulting scores were more significant in respect of fish catch. Many of the composite features (i.e. current gyre in Fig. 5c, TeS layer in Fig. 7c and d) were picked up by SVD as naturally-occurring and dominant modes of coastal variability, supporting our interpretations from the composite technique ‘targeted’ by fish catch. Time scores for coastal mode-1 U and V currents are compared with fish catch in Fig. 11a. Through much of the 44-year record (except the 1990s), periods of high catch coincide with or follow periods of anomalous northward currents linked to the cyclonic gyre located at 6S, 43E (cf. Fig. 5c), with r ¼ þ0.41 significant at 90% confidence. Wavelet analysis (Fig. 11b) indicates that currents contribute 3, 6 and 12 year oscillations that correspond with fish catch (cf. Fig. 3b). While the FAO statistics appear to reflect changes in abundance, and both targeted and natural signals inter-relate, the major part of catch fluctuation is unresolved likely due to the low levels of productivity (cf. Fig. 8a). 3.4. Climate change Global climate change is likely to impact marine fisheries via increased oxygen consumption rates, changes in foraging and migration, and community changes in coral reefs (Roessig et al., 2004). Projections of further impacts on the distribution and abundance of fishes associated with relatively small temperature changes will undoubtedly affect coastal communities who harvest these stocks. In this context, we analyze ship-derived NOAA SST over the past hundred years and those projected by the Geophysical Fluid Dynamics Lab (GFDL) coupled model for the next hundred years (Fig. 12). SST observations near Zanzibar show a 1 C 2nd order warming trend that, when projected over the 21st century, fits the GFDL-model simulated SST for a gradual doubling of CO2. By 2100 mean SST are predicted to reach 30 C, contributing to coral bleaching amongst other ecosystem stresses. Meridional winds from COADS (ships) exhibit rapid oscillations over the 20th century, while the GFDL model predicts weaker fluctuations in the 21st century. There is an upward trend in winds indicating the SW monsoon may strengthen along the East African coast. Belkin (2009) has shown that observed upward SST trends in the Somali upwelling zone are less than those for the south equatorial region analyzed here.

Fig. 9. Multivariate regression of environmental variables onto (a) SeaWifs chlorophyll and (b) assimilated nitrate. MLD ¼ mixed layer depth.


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Fig. 10. Singular value decomposition of regional-scale atmospheric mode-2 patterns subsequently related to fish catch: (a) 1000e850 hPa geopotential height, (b) NCEP precipitable water, and (c) ECMWF zonal wind stress.

4. Conclusion We have evaluated marine environmental conditions using model-assimilated, satellite and in situ observations in the context of an aggregate index of coastal fisheries abundance. Small changes in primary productivity off the east African shelf appear to be governed by advection patterns contributed by the south equatorial current. The SEC entrains water masses from the western Indian Ocean (latitudes 6e14 S) toward the East African coast at speeds >0.2 m s1. It swings northward in an axis that is characterized by low salinity. Composite results in respect of years with higher coastal fish catch indicate that cooler SSTs and dry weather extend 3000 km across the tropical West Indian Ocean. Under such a regime, open-ocean and coastal inputs of fresh water decline and a deep salty layer develop that is advected shoreward and consequently affects the fisheries. Accelerated currents promote uplift, cooling SSTs and bringing enhanced nutrients to the shelf. While the eastewest oscillation of the Indian Ocean has some impact on East African fish catch, coastal dynamics play a more significant role. Uplift over the shelf joins flow moving seaward at depths of 100e400 m. Surface flow is onto the coast in response to southward wind stress. Further work is needed to unravel the ecosystem processes underlying the bio-physical links, including analysis per species and trophic level, and a seasonal analysis of fish catch that distinguishes how the alternating monsoon affects productivity.

Fig. 11. Time scores based on SVD analysis of depth sections for (a) coastal mode-1 zonal and meridional currents and East African fish catch, and (b) wavelet spectra for V current (as in Fig. 3b).

Fig. 12. 20th century observations and 21st century GFDL model A1B projections for SST (blue) and meridional wind in the East African coastal zone. Trends are consistent in the past and future eras.

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