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Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM) ArchiMer
Rödenbeck, C.; Bakker, D. C. E.; Gruber, N.; Iida, Y.; Jacobson, A. R.; Jones, S.; Landschützer, P.; Metzl, N.; Nakaoka, S.; Olsen, A.; Park, G.-h.; Peylin, P.; Rodgers, K. B.; Sasse, T. P.; Schuster, U.; Shutler, J. D.; Valsala, V.; Wanninkhof, R.; Zeng, J..
Using measurements of the surface-ocean CO2 partial pressure (pCO2) and 14 different pCO2 mapping methods recently collated by the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative, variations in regional and global sea–air CO2 fluxes have been investigated. Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCO2 seasonality, in line with previous estimates. In terms of interannual variability (IAV), all mapping methods estimate the largest variations to occur in the Eastern equatorial Pacific. Despite considerable spead in the detailed variations, mapping methods with closer match to the data also tend to be more consistent with each other. Encouragingly, this includes...
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Ano: 2015 URL: https://archimer.ifremer.fr/doc/00293/40407/38967.pdf
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A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink ArchiMer
Landschuetzer, P.; Gruber, N.; Bakker, D. C. E.; Schuster, U.; Nakaoka, S.; Payne, M. R.; Sasse, T. P.; Zeng, J..
The Atlantic Ocean is one of the most important sinks for atmospheric carbon dioxide (CO2), but this sink has been shown to vary substantially in time. Here we use surface ocean CO2 observations to estimate this sink and the temporal variability from 1998 through 2007 in the Atlantic Ocean. We benefit from (i) a continuous improvement of the observations, i. e. the Surface Ocean CO2 Atlas (SOCAT) v1.5 database and (ii) a newly developed technique to interpolate the observations in space and time. In particular, we use a two-step neural network approach to reconstruct basin-wide monthly maps of the sea surface partial pressure of CO2 (pCO(2)) at a resolution of 1 degrees x 1 degrees From those, we compute the air-sea CO2 flux maps using a standard gas...
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Ano: 2013 URL: https://archimer.ifremer.fr/doc/00264/37568/36705.pdf
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A novel method for diagnosing seasonal to inter-annual surface ocean carbon dynamics from bottle data using neural networks ArchiMer
Sasse, T. P.; Mcneil, B. I.; Abramowitz, Ag.
The ocean's role in modulating the observed 1–7 Pg C yr−1 inter-annual variability in atmospheric CO2 growth rate is an important, but poorly constrained process due to current spatio-temporal limitations in ocean carbon measurements. Here, we investigate and develop a non-linear empirical approach to predict inorganic CO2 concentrations (total carbon dioxide (CT) and total alkalinity (AT)) in the global ocean mixed layer from hydrographic properties (temperature, salinity, dissolved oxygen and nutrients). The benefit of this approach is that once the empirical relationship is established, it can be applied to hydrographic datasets that have better spatio-temporal coverage, and therefore provide an additional constraint to diagnose ocean carbon dynamics...
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Ano: 2013 URL: https://archimer.ifremer.fr/doc/00661/77335/78774.pdf
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Quantifying the influence of CO2 seasonality on future aragonite undersaturation onset ArchiMer
Sasse, T. P.; Mcneil, B. I.; Matear, R. J.; Lenton, A..
Ocean acidification is a predictable consequence of rising atmospheric carbon dioxide (CO2), and is highly likely to impact the entire marine ecosystem - from plankton at the base of the food chain to fish at the top. Factors which are expected to be impacted include reproductive health, organism growth and species composition and distribution. Predicting when critical threshold values will be reached is crucial for projecting the future health of marine ecosystems and for marine resources planning and management. The impacts of ocean acidification will be first felt at the seasonal scale, however our understanding how seasonal variability will influence rates of future ocean acidification remains poorly constrained due to current model and data...
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Ano: 2015 URL: https://archimer.ifremer.fr/doc/00293/40372/38980.pdf
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