Table of Contents

• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

CTD profile data

Sea-Bird SBE 9 Data File:
FileName = G:Data21000101.hex
Software Version Seasave V 7.0h
Temperature SN = 2131
Conductivity SN = 2881
Number of Bytes Per Scan = 41
Number of Voltage Words = 4
Number of Scans Averaged by the Deck Unit = 1
Append System Time to Every Scan
System UpLoad Time = Aug 18 2007 01:25:56
NMEA Latitude = 54 35.96 N
NMEA Longitude = 134 03.38 W
NMEA UTC (Time) = Aug 18 2007 01:25:53
Store Lat/Lon Data = Append to Every Scan
Ship: Thomas G. Thompson
Cruise name: NRL #4
Cruise number: TN208
nquan = 25                               
nvalues = 200                        
units = specified
name 0 = prDM: Pressure, Digiquartz [db]
name 1 = depSM: Depth [salt water, m]
name 2 = t090C: Temperature [ITS-90, deg C]
name 3 = t190C: Temperature, 2 [ITS-90, deg C]
name 4 = c0mS/cm: Conductivity [mS/cm]
name 5 = c1mS/cm: Conductivity, 2 [mS/cm]
name 6 = altM: Altimeter [m]
name 7 = latitude: Latitude [deg]
name 8 = longitude: Longitude [deg]
name 9 = timeS: Time, Elapsed [seconds]
name 10 = timeY: Time, System [seconds]
name 11 = flECO-AFL: Fluorescence, Wetlab ECO-AFL/FL [mg/m^3]
name 12 = par: PAR/Irradiance, Biospherical/Licor
name 13 = sbeox0ML/L: Oxygen, SBE 43 [ml/l]
name 14 = xmiss: Beam Transmission, Chelsea/Seatech/Wetlab CStar [%]
name 15 = sigma-é00: Density [sigma-theta, Kg/m^3]
name 16 = sigma-t00: Density [sigma-t, Kg/m^3 ]
name 17 = sigma-é11: Density, 2 [sigma-theta, Kg/m^3]
name 18 = sigma-t11: Density, 2 [sigma-t, Kg/m^3 ]
name 19 = potemp090C: Potential Temperature [ITS-90, deg C]
name 20 = potemp190C: Potential Temperature, 2 [ITS-90, deg C]
name 21 = sal00: Salinity [PSU]
name 22 = sal11: Salinity, 2 [PSU]
name 23 = svCM: Sound Velocity [Chen-Millero, m/s]
name 24 = flag: flag
span 0 =      2.000,    103.000      
span 1 =      1.980,    102.049      
span 2 =     6.3851,    15.1536      
span 3 =     6.3859,    15.6442      
span 4 =  33.069511,  37.810101      
span 5 =  33.071484,  38.161823      
span 6 =       0.94,     124.52      
span 7 =   54.59930,   54.59938      
span 8 = -134.05646, -134.05640      
span 9 =     81.537,    854.430      
span 10 = 1187400437, 1187401210     
span 11 =    -0.0742,     6.2105     
span 12 = 7.3999e+00, 9.3234e+01     
span 13 =    3.72266,    7.10852     
span 14 =    77.4994,    97.1311     
span 15 =    22.2944,    26.0131     
span 16 =    22.2943,    26.0120     
span 17 =    22.1385,    26.0138     
span 18 =    22.1384,    26.0127     
span 19 =     6.3763,    15.1533     
span 20 =     6.3772,    15.6439     
span 21 =    30.2677,    33.1114     
span 22 =    30.2021,    33.1124     
span 23 =    1475.56,    1501.69     
span 24 = 0.0000e+00, 0.0000e+00     
interval = decibars: 1               
start_time = Aug 18 2007 01:25:56
bad_flag = -9.990e-29
sensor 0 = Frequency  0  temperature, primary, 2131, 23-jan-07
sensor 1 = Frequency  1  conductivity, primary, 2881, 17-jan-07, cpcor = -9.5700e-08
sensor 2 = Frequency  2  pressure, 34901, 01-Feb-2007
sensor 3 = Frequency  3  temperature, secondary, 1703, 23-jan-07
sensor 4 = Frequency  4  conductivity, secondary, 0855, 17-jan-07, cpcor = -9.5700e-08
sensor 5 = Extrnl Volt  2  WET Labs, ECO_AFL
sensor 6 = Extrnl Volt  3  transmissometer, primary, CST-401DR, 09-feb-2007
sensor 7 = Extrnl Volt  4  Oxygen, SBE, primary, 0023, 12 oct 2006
sensor 8 = Extrnl Volt  5  altimeter
sensor 9 = Extrnl Volt  6  irradiance (PAR), primary, 4747, 10-July-2006
datcnv_date = Aug 18 2007 03:36:31, 5.37e
datcnv_in = g:data21000101.hex g:data21000101.CON
datcnv_skipover = 0
wildedit_date = Aug 18 2007 03:36:32, 5.37e
wildedit_in = g:data21000101.cnv
wildedit_pass1_nstd = 2.0
wildedit_pass2_nstd = 20.0
wildedit_pass2_mindelta = 0.000e+000
wildedit_npoint = 100
wildedit_vars = prDM depSM t090C t190C c0mS/cm c1mS/cm flECO-AFL par sbeox0ML/L xmiss
wildedit_excl_bad_scans = yes
celltm_date = Aug 18 2007 03:36:33, 5.37e
celltm_in = g:data21000101.cnv
celltm_alpha = 0.0300, 0.0300
celltm_tau = 7.0000, 7.0000
celltm_temp_sensor_use_for_cond = primary, secondary
filter_date = Aug 18 2007 03:36:33, 5.37e
filter_in = g:data21000101.cnv
filter_low_pass_tc_A = 0.030
filter_low_pass_tc_B = 0.150
filter_low_pass_A_vars = prDM
filter_low_pass_B_vars = c0mS/cm c1mS/cm
loopedit_date = Aug 18 2007 03:36:34, 5.37e
loopedit_in = g:data21000101.cnv
loopedit_minVelocity = 0.000                                                                                           
loopedit_surfaceSoak: minDepth = 5.0, maxDepth = 20, useDeckPress = 1                                                  
loopedit_excl_bad_scans = yes
binavg_date = Aug 18 2007 03:36:34, 5.37e
binavg_in = g:data21000101.cnv
binavg_bintype = decibars
binavg_binsize = 1
binavg_excl_bad_scans = yes
binavg_skipover = 0
binavg_surface_bin = no, min = 0.000, max = 0.000, value = 0.000
Derive_date = Aug 18 2007 03:36:34, 5.37e
Derive_in = g:data21000101.cnv g:data21000101.CON
file_type = ascii

CTD cast data processed using standard (NODC) methods.
See (.pdf): Rovegno, PS, CA Edwards, and KW Bruland, 2009, Observations of a Kenai eddy and Sitka eddy in the Northern Gulf of Alaska. J. Geophys. Res. 114: C11012.

BCO-DMO Processing Notes
- Awk written to reformat original .cnv files contributed by Ralph Kudela
- AWK: TN210_CTDcnv_2_bcodmo.awk
- space delimited reformatted to tab delimited
- all records with "#" or "*" ignored
- blank lines ignored
- BCO-DMO header o/p from routine

Influence of continental margin iron on phytoplankton species composition and production in the northern Gulf of Alaska (part of Enhanced Phytoplankton Biomass-Northwest Gulf of Alaska)

The northern Gulf of Alaska (GOA) is among the ocean's most productive ecosystems and supports a rich coastal fisheries. Although strong cross-shelf gradients in phytoplankton (chlorophyll decreasing offshore) have been identified, yet the specific factors that regulate and control primary production have only been hypothesized. Cross-shelf patterns in primary production/species composition are consistent with a gradient of iron availability (Strom et al., in press), but this has yet to be rigorously tested. In collaboration with the NSF-funded project Mixing of iron-rich coastal waters with nutrient-rich HNLC waters leading to enhanced phytoplankton biomass: a focus on the northwest Gulf ofAlaska (K. Bruland), this project will examine the influence of cross-shelf exchange and physico-chemical gradients on phytoplankton distributions, physiology, and assemblage structure in the northern GOA, making use of complementary high-resolution iron data and building on the results from previous studies. The proposed work directly complements studies accomplished by the US GLOBEC Coastal Gulf of Alaska (CGOA) program, and is essential to link Bruland's study of trace metal dynamics and speciation to key biological processes. Bruland's project seeks to quantify the inputs of iron from the Copper River, AK, and to characterize and assess the interactions among river inputs and shelf/offshore systems. The quasi-synoptic sampling scheme enables characterization at the mesoscale, the dominant scale of variability in the region. The station grid allows quasi-synoptic sampling while remaining flexible to take advantage of interesting mesoscale features. Should a mesoscale eddy be present, the study will focus on the role that eddy circulation plays in facilitating the offshore transport (suggested by Stabeno et al., 2004) of bio-active trace metals.

This project aims to provide a detailed examination of the phytoplankton rates, assemblage structure, and response to cross-shelf transport/ mixing across gradients in iron and light to better parameterize satellite observations and future modeling efforts. Specific questions include: 1) Do cross-shelf gradients in iron correspond to patterns of carbon assimilation, nutrient uptake, new production, and species composition of phytoplankton in the northern GOA? 2) Do iron and light interact to structure species assemblages and patterns of carbon assimilation? 3) How do frontal regions influence phytoplankton distributions and physiology? 4) What bio-optical properties characterize the different water masses (inshore/offshore), and how well do satellite observations describe phytoplankton standing stocks and rates? 5) What chemical characteristics define the deep Fe-source identified by Lam et al. (2006), and what is the bioavailability of this material when mixed with near-surface waters? This study will provide the first concurrent measurements of iron concentration and phytoplankton physiological parameters in the waters of the northern GOA.

Broader Impacts. The proposed work will be extremely valuable in testing hypotheses arising from many years of effort by GLOBEC CGOA. Data from this study will further our understanding of ocean-climate interactions in an economically and ecologically important region. The results could have far-reaching implications for our basic understanding of coupled biogeochemical cycles in shelf ecosystems. This will have both direct and indirect impacts on our understanding of carbon cycling, as well as how other researchers parameterize regional and global biogeochemical models.