Carbon Dioxide, Hydrographic and Chemical Data Obtained During the Time Series Line P Cruises in the North-East Pacific Ocean

Miller, Lisa A.; Christian, J. R.; Davelaar, Marty; Johnson, William K.; LINGUANTI, J.

doi:10.3334/cdiac/otg.clivar_line_p_2009

Cited by 4 publications

(5 citation statements)

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“…10, the MLD anomaly clearly showed the typical pattern of ENSO events (Alexander et al, 2002), and the MLD was approximately 10 m deeper in 2008 than in 2003 in the region. CLIVAR Repeat Section Line P data provided by Miller et al (2010) showed that surface (< 10 m) DIC concentration in station P in February 2003 is about 35 µmol kg −1 lower than in February 2008. By using CO2SYS program (Lewis and Wallace, 1998;Robbins et al, 2010), the estimated pCO sea 2 difference between February 2003 and February 2008 in the region caused by the changes of surface DIC, TA, temperature and salinity, is about 14 µatm.…”

Section: Difference Of Pco Sea 2 Distributions During Enso Eventsmentioning

confidence: 99%

Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a self-organizing map neural network technique

et al. 2013

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Abstract. This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM) originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) – are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

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Section: Difference Of Pco Sea 2 Distributions During Enso Eventsmentioning

confidence: 99%

Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a self-organizing map neural network technique

et al. 2013

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“…Details about the use of these data are presented in sections 3.1.1 and 3.3. The specific cruise data used in this analysis include Climate Variability and Predictability (CLIVAR) Line P16 North in 2006, CLIVAR Line P01 in 2007, and Line P cruises between 1994 applying Pacific Ocean Interior Carbon (PACIFICA) Database corrections when appropriate ( Figure 2, http://cdiac.ornl.gov/oceans/PACIFICA/) [Fukasawa et al, 2007;Feely et al, 2008Feely et al, , 2011Miller et al, 2010]. In addition, 1°optimally interpolated data fields from the 2013 World Ocean Atlas (WOA) are used herein and discussed in section 3.3 [Garcia et al, 2013a[Garcia et al, , 2013bLocarnini et al, 2013;Zweng et al, 2013].…”

Section: Repeat Hydrography Datamentioning

confidence: 99%

Net community production and calcification from 7 years of NOAA Station Papa Mooring measurements

Fassbender

Sabine

Cronin

2016

Global Biogeochemical Cycles

104

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Seven years of near-continuous observations from the Ocean Station Papa (OSP) surface mooring were used to evaluate drivers of marine carbon cycling in the eastern subarctic Pacific. Processes contributing to mixed layer carbon inventory changes throughout each deployment year were quantitatively assessed using a time-dependent mass balance approach in which total alkalinity and dissolved inorganic carbon were used as tracers. By using two mixed layer carbon tracers, it was possible to isolate the influences of net community production (NCP) and calcification. Our results indicate that the annual NCP at OSP is 2 ± 1 mol C m À2 yr À1 and the annual calcification is 0.3 ± 0.3 mol C m À2 yr À1. Piecing together evidence for potentially significant dissolved organic carbon cycling in this region, we estimate a particulate inorganic carbon to particulate organic carbon ratio between 0.15 and 0.25. This is at least double the global average, adding to the growing evidence that calcifying organisms play an important role in carbon export at this location. These results, coupled with significant seasonality in the NCP, suggest that carbon cycling near OSP may be more complex than previously thought and highlight the importance of continuous observations for robust assessments of biogeochemical cycling.

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“…For verification, we used DIC concentrations independently observed from research vessels at ocean time series stations: K2 (47°N, 160°E) [ Wakita et al ., ], KNOT (44°N, 155°E) [ Wakita et al ., ], Papa (50°N, 145°W) [ Miller et al ., ], and ALOHA (23°N, 158°W) [ Keeling et al ., ]. We downloaded the data from the Carbon Dioxide Information Analysis Center (http://cdiac.ornl.gov/oceans/).…”

Section: Datamentioning

confidence: 99%

“…Global pCO 2 observations by other international institutes archived in such as the Surface Ocean CO 2 Atlas [Pfeil et al, 2013] and the Lamont-Doherty Earth Observatory database [Takahashi et al, 2013] were not used here, and including those data will be a next step. [Miller et al, 2010], and ALOHA (23°N, 158°W) . We downloaded the data from the Carbon Dioxide Information Analysis Center (http://cdiac.ornl.gov/oceans/).…”

Section: Datamentioning

confidence: 99%

North Pacific dissolved inorganic carbon variations related to the Pacific Decadal Oscillation

Yasunaka

Nojiri

Nakaoka

et al. 2014

Geophysical Research Letters

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Carbon Dioxide, Hydrographic and Chemical Data Obtained During the Time Series Line P Cruises in the North-East Pacific Ocean

Cited by 4 publications

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Estimating temporal and spatial variation of ocean surface <i>p</i>CO<sub>2</sub> in the North Pacific using a self-organizing map neural network technique

Estimating temporal and spatial variation of ocean surface <i>p</i>CO<sub>2</sub> in the North Pacific using a self-organizing map neural network technique

Net community production and calcification from 7 years of NOAA Station Papa Mooring measurements

North Pacific dissolved inorganic carbon variations related to the Pacific Decadal Oscillation

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Carbon Dioxide, Hydrographic and Chemical Data Obtained During the Time Series Line P Cruises in the North-East Pacific Ocean

Cited by 4 publications

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Estimating temporal and spatial variation of ocean surface &lt;i&gt;p&lt;/i&gt;CO&lt;sub&gt;2&lt;/sub&gt; in the North Pacific using a self-organizing map neural network technique

Estimating temporal and spatial variation of ocean surface &lt;i&gt;p&lt;/i&gt;CO&lt;sub&gt;2&lt;/sub&gt; in the North Pacific using a self-organizing map neural network technique

Net community production and calcification from 7 years of NOAA Station Papa Mooring measurements

North Pacific dissolved inorganic carbon variations related to the Pacific Decadal Oscillation

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Estimating temporal and spatial variation of ocean surface <i>p</i>CO<sub>2</sub> in the North Pacific using a self-organizing map neural network technique

Estimating temporal and spatial variation of ocean surface <i>p</i>CO<sub>2</sub> in the North Pacific using a self-organizing map neural network technique