Colored Version of Some Figures

   
Fig. 1.4.

Distribution of global sea surface temperature superimposed by the surface current vectors representing the modeled annual average global ocean circulation in the top 66 m in 2001 ( http://www.atmos.umd.edu/~ocean/ ) based on data assimilation (Carton et al., 2000a,b).

   
Fig. 2.2.9. Sea surface pCO2 versus distance offshore. (A) Variability is high inshore due to intermittent coastal upwelling which is most intense in spring and summer. Offshore variability is due primarily to seasonal temperature differences. (B) Estimate of annual sea to air CO2 flux based on approximately 150 cruises covering the inner 60 km and 30 cruises covering the remainder of the section. The top of the bottom figure delineates subregions of the section (see Fig. 2.2.5): Monterey Bay (MB), the first Rossby radius region of active upwelling (RR), the coastal transition zone (CTZ), and the CC (offshore of the CUS). Mean annual air/Sea CO2 flux is near-zero within the CUS.
   
Fig. 2.6.6. (a) Water fluxes, (b) inorganic nitrogen and (c) particulate organic nitrogen fluxes, and (d) nitrogen biogeochemical budget for the Ría de Arousa during the upwelling season 1989. QS and QB are the horizontal surface and bottom convective flows at the outer boundary of the ría, respectively; QS and MZ are the vertical convective and turbulent mixing flows, respectively; QR is the continental runoff; NEP and R are the net ecosystem production and respiration, respectively; DON and PON are the net export of dissolved and particulate organic nitrogen to the adjacent self; M is the net export of organic nitrogen associated to the extraction of mussels cultured on hanging ropes; and SED is the net export of organic nitrogen to the sediments of the ría. Water fluxes in m3 s–1, nitrogen fluxes in g N s–1, NEP, R, M, DON, PON and SED in mg N m–2 d–1.
   
Fig. 2.6.8. Distribution of (a) pCO2 (in µatm), (b) temperature (in °C) and (c) salinity in the surface waters of the NW Iberian margin in January and July 1998, corresponding respectively to downwelling and upwelling conditions
   
Fig. 3.2.4. The average conditions of sea surface temperature and salinity distributions in the study area (Data from NODC, 1998).
   
Fig. 3.2.12. Spatial variation of primary productivity measured by Gong et al. (2003) in the ECS shelf in winter (December 1997) and summer (July 1998). The dashed line indicates the boundary between two nutrient regimes in the ECS shelf.
   
Fig. 3.2.14. Fugacity of dissolved CO2 (µatm) in surface seawater in the ECS (Tsunogai, 1999). (a) February 1993; (b) August 1994.
   
Fig. 3.4.1. Brazilian Coast classification.
   
Fig. 3.4.3. Ocean color images of distribution of long-term (1978–1986) average pigment concentration obtained by Nimbus 7–CZCS sensor. (a) Summer, (b) fall, (c) winter, (d) spring
   
Fig. 3.4.4. The Godoi (2005) interpretation to the Schmid et al. (1995) AVHRR image used to describe the Vitória cyclonic eddy. The former author interpreted the Vitória eddy as part of a baroclinic Rossby wave in which cyclones (troughs or “Low oceanographic pressure centers”) succeed anticyclones (crests or “High oceanographic pressure centers”).
   
Fig. 3.4.5. Schematic representation of the thermocline-pycnocline displacements during cyclonic (upper panel) and anticyclonic events. Courtesy of R. A. de Mattos (IOUSP, Brazil).
   
Fig. 3.4.8. A schematic representation of a Brazil Current propagating cyclone. According to Campos et al. (2000).
   
Fig. 4.4.3. A sea surface temperature snapshot off the WA coast on 11 May 2006. The high temperatures have been used to represent the core of the LC and perimeter the anticyclonic eddies from the LC.
   
Fig. 5.3.1. Map of the Northwest Atlantic continental shelf region, showing the major features referred to in the text. Transects A, B and C are labeled.
   
Fig. 5.3.2. Major current systems in the region. Dashed arrows indicate area of mixing between shelf-slope waters and Gulf Stream waters. Bathymetric contours (100, 200, 1000, 2000, 3000 and 4000m) are given, with the 200m isobath indicated by the heavier line.
   
Fig. 5.4.3. Hydrographic parameter profiles in a shelf-slope section at 38ºS: (a) Temperature; (b) Salinity; (c) Nitrate (modified from Carreto et al., 1995).
   
Fig. 5.4.6. Primary production (g C m-2 yr-1) in the South Atlantic (after Pfeifer et al., 2001).
   
Fig. 5.6.1. New Zealand marine bathymetry and terrestrial topography (CANZ 1996). Numbers indicate places discussed in the text. (1) Hikurangi Trough; (2) Alpine Fault and Southern Alps; (3) Ruakumara Peninsula; (4) Hauraki and Taupo Volcanic zones; (5) Hauraki Gulf and northeast shelf; (6) Otago; (7) Kaikoura Canyon; (8) Hokitika Canyon; (9) Cook Canyon; (10) Farewell Spit; (11) Pelorus Sound; (12) Manukau Harbour.
   
Fig. 5.6.2. Specific and regional sediment yields predicted by the New Zealand sediment yield model (Hicks et al. 2004). Specific yields (t km-2 y-1) are shown by colored terrestrial areas, and regional yields (Mt y-1) are shown for the areas enclosed within blue lines around the coastline. Yields stored in lake catchments are given in parentheses.
   
Fig. 5.6.5. Major surface current systems around New Zealand (Carter et al. 1998). Abbreviations: TF, SF: Tasman and Subtropical Fronts; WAUC, EAUC: West and East Auckland Currents; NCE, ECE, WE: North Cape, East Cape and Wairarapa Eddies; DC, WC, SC: Durville, Westland and Southland Currents.
   
Fig. 6.4.3. The temporal patterns of organic carbon flux in the west Antarctic Peninsula, 64º 29’S, 64º 02’W (Ducklow et al., 2008).
   
Fig. 6.4.7. Estimate of carbon vertical flux according to the AWI inverse model (Usbeck et al., 2003). The vertical flux of carbon to 133 m ranges from 0 - 4 mol C m-2 y-1 for Antarctic shelves.
   
Fig. 7.4.2. The lateral and vertical exchanges of water fluxes (in km3 yr-1) within the Black Sea and the Turkish Straits System. The average salinities used in the computations are shown in brackets. Qr, Qp. Qe denote the fluxes due to river discharge, precipitation and evaporative loss.
   
Fig. 7.4.4. The total organic carbon (TOC) budget of the Black Sea - Turkish Straits System. The average concentrations (in g m-3) at various sections of the system are given in brackets. The TOC fluxes are given in mega tons yr-1.
   
Fig. 7.8.2. Time series of Mississippi River discharge (blue) and [NO3+NO2] (open circle, red) (A), and the ratio of NOX to PO4 (B) for the period from 1967 through July 2007. Nutrient data were provided by the U.S. Geological Survey from St. Francisville, LA (USGS Stations #07373420 and #07373423) and Luling, LA (USGS Station #07374400). The thick black trend lines represent a Savitsky-Golay second order polynomial smoothing using a 35 mo window. The horizontal black line in B represents the “Redfield Ratio” of 16:1 N:P by moles.
   
Fig. 7.8.3. The correlation of [NO3+NO2] with discharge (A), and the time series of [NO3+NO2] flux from 1967 through July 2007 (B). Regression line represents a geometric mean regression. Nutrient data were provided by the U.S. Geological Survey from St. Francisville, LA (USGS Stations #07373420 and #07373423) and Luling, LA (USGS Station #07374400). Nutrient flux data were estimated using the approach of Lohrenz et al. (2008). Discharge data were from the U.S. Army Corp of Engineers monitoring site at Tarbert Landing, MS (Gage ID 01100). Thick lines in A represent a Savitsky-Golay second order polynomial smoothing using a 35 mo window.
   
Fig. 7.8.4. Time series of Mississippi River discharge and TAlk (data period; 1990s) (A), the correlation of TAlk with discharge (data period; 1964-2000) (B), and the correlation of TAlk flux with discharge (data period; 1964-2000) (C) at St. Francisville (USGS Station#: 07373420), LA. Data are from USGS web page, “Water-Quality Data for the Nation” ( http://waterdata.usgs.gov/nwis/qw ). In the correlation equations in Fig. 7.8.4B, TAlk is in µM.
   
Fig. 7.8.5. Conceptual representation of major plume processes and hypothetical relationships to air-sea fluxes of CO2.
   
Fig. 7.8.6. Relationship of average daily-integrated primary production in plume impacted waters to riverborne NOX flux at the Southwest Pass of the Mississippi delta for the period of 1988-1994 (modified after Lohrenz et al, 1997).
   
Fig. 7.8.7. Relationship between particle export and photic-zone-integrated primary production in the Mississippi plume. Data from Redalje et al. (1994).
   
Fig. 7.8.8. Surface water DIC, TAlk and pCO2 in the Mississippi River and Atchafalaya River system in August 1998 (Cai unpublished data).
   
Fig. 7.8.9. DIC, TAlk and pH values measured in the Mississippi River plume in June 2003 (Cai unpublished data). Solid lines are regression lines and dashed lines are either conservative mixing line (DIC) or lines (pH and pCO2) predicted from conservation mixing. In the mixing calculation river end-member DIC and TAlk (not presented) are used. Data from deep water samples are not excluded in the regression. This created an obvious offset in the pCO2 case. Also River end-member value (900 µatm) is used to set the intercept in the pCO2 regression.
   
Fig. 9.1. Human activities along the watershed-coast continuum. Symbols for diagrams courtesy of the Integration and Application Network ( http://ian.umces.edu/symbols ) University of Maryland Center for Environmental Science.
   
Fig. 9.6. Modified sustainable livelihoods framework based on the Sustainable Livelihoods Approach framework (Scoones 1998, DFID 2000), and incorporating elements from the Millenium Ecosystem Assessment conceptual framework (MEA 2003), and the IDS STEPS Centre framework (Leach 2007).
   
Fig. 12.6. Southern California Bight surface temperature and currents for the two finer-resolution sub-domains in a 3-level (i.e. 18+6+2 km) configuration. Note the strong influences of island and coastline topography around Santa Monica Bay in the 2 km sub-domain. (Marchesiello et al., 2002b.)
   
Fig. 12.7. Present biogeochemical model configuration for U.S. West Coast Carbon Cycle modeling program. Not shown are the boxes for Oxygen, TIC, and Alkalinity. Carbon, Nitrogen, Phosphate, and Oxygen pathways are presently being resolved. All of the model pathways follow stoichiometric balances while maintaining variable C:N:P ratios for different model variables.
   
Fig. 12.13. Figure outlining the individual watersheds of the continental U.S. that ultimately flow into the Gulf of Mexico or the Eastern U.S. coast.
   
Fig. 14.2. Exchange time of budgeted systems vs system area (log scale)
   
Fig. 14.3. ΔDIP and ΔDIN as a function of system exchange time (log-log plots) (a) Sites for which ΔDIP < 0; (b) Sites for which ΔDIP > 0; (c) Sites for which ΔDIN < 0; (d) Sites for which ΔDIN > 0
   
Fig. 14.4. Apparent net production (P-R) and apparent nitrogen fixation minus denitrification (Nfix-denit) as a function of system exchange time (log-log plots) (a) Sites for which P-R < 0; (b) Sites for which P-R > 0; (c) Sites for which Nfix-denit < 0; (d) Sites for which Nfix-denit > 0
   
Fig. 14.5. Apparent net production (P-R) vs system area (log-log plots). (a) Sites for which P-R < 0; (b) Sites for which P-R > 0
   
Fig. A.1.1. The World Data Center for Marine Environmental Sciences (WDC-MARE) and its information system PANGAEA are strictly organized in terms of technical and scientific design. The network concept uses client/server technology through Intranet/Internet communication. Remote sites are (a) groups of clients using a subserver (mirror site); (b) single clients inter-connected with the main server; (c) stand-alone devices for temporary connection to the network (e.g., on research vessels). Any client has full access to the information system.
   
Fig. A.1.2. PANGAEA’s (simplified) data model is converted into a straightforward, flexible RDBMS scheme. The hierarchy of the data model is classified into four levels that redraw the evolution of analytical values. The PROJECT level contains all meta-information on the project, its scientists and affiliated institutions; the CAMPAIGN level consists of the regional objective and its task basis; the sub-level SITE serves as the superimposed layer of sampling areas; the level EVENT accommodates equipment and includes the sub-level SAMPLE that reflects the custody; the level DATA comprises analytical data sets and series, analytical methods, variables and units, and their publication. The data model is universal and can be employed for any scientific, geo-coded data. The arrows show principle relations between tables.
   
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