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Archive for model

Identifying the water mass composition of a sample has never been so easy!

Posted by mmaheigan 
· Thursday, August 31st, 2023 

When we collect seawater in any point of the ocean, we are collecting a mix of water masses from different origin that traveled until there keeping their salinity and temperature properties. The Atlantic Ocean is likely the most complex basin in term of water masses containing more than 15 in its depths. Some of them were “born” in the North Atlantic Ocean, others in the Southern Ocean, even in the Mediterranean Sea! And when we collect a seawater sample we can know which water masses are there, where they come from, what happened to each of them during their journey to us, what story can they tell us.

The variation of any non-conservative property (such as dissolved organic carbon or nutrients) in the deep open ocean depends on the mixing of those water masses and on the biogeochemical processes affecting it (such as heterotrophic respiration). But the effect of the water mass mixing is usually very high, so in order to study the biogeochemical processes, it is necessary to remove that effect.

On the other hand, estimating the contribution of the water masses composing a sample is useful to trace the distribution of each water mass identifying the depth of maximum water mass contribution or the depth-range where the water mass is dominant contributing > 50%. Ocean biogeochemists and microbiologists can get more out of their data estimating the impact of water mass mixing on the variability of any chemical (e.g. inorganic nutrients and dissolved organic carbon) or biological (e.g. prokaryotic heterotrophic abundance and production) property.

Knowing the contribution of each water mass to each sample was not an easy task and required expertise on the origin, circulation and mixing patterns of the water masses present in the study area. This could be even harder in very complex oceanic basin such as the deep Atlantic Ocean. The most commonly used methodology is the Optimum Multi-Parameter (OMP) analysis that was first applied by Tomczak in 1981. However, this methodology is time consuming and requires availability of a large set of quality-controlled chemical variables (e.g. nutrients, oxygen,..) together with a deep knowledge of the oceanography of the studied area. Those chemical variables are not always available or do not have the required quality by contrast to potential temperature and salinity that are high standard core variables in any cruise or database. In a recent research article, we applied multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). The model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Potential temperature and salinity are the most commonly collected and curated variables in oceanography both from oceanographic cruises and autonomous vehicles (e.g. ARGO) avoiding the use of less commonly measured chemical variables which also require longer and time-consuming analyses of both the water samples and the data.

Figure 1. A16 section for the contribution of the water masses (A) AAIW5, (B) ENACW12, (C) AAIW3, (D) MW, (E) LSW, (F) ISOW, (G) CDW and (H) WSDW obtained with the Extremely Randomized Trees algorithm. Ocean Data View software (Schlitzer, 2015).

We also provide the code with instructions where any user can easily introduce the required variables (latitude, longitude, depth, temperature and salinity) of the chosen Atlantic samples and obtain the water mass proportion of each one in a fast and easy way. Actually, it would allow the user to obtain this information in real time during a cruise.

New research using other methods like OMP and its variants can be incorporated to the existing model increasing its accuracy and prediction capacity. Help us to improve the model and increase its spatial resolution!

Ocean biogeochemists and microbiologists can benefit from this tool even if they do not have a deep knowledge of the oceanography of the studied area. Identifying the water masses composition of a sample has never been so easy!

Author
Cristina Romera-Castillo (Instituto de Ciencias del Mar-CSIC, Barcelona, Spain)

Twitter: @crisrcas

Unveiling the Past and Future of Ocean Acidification: A Novel Data Product covering 10 Global Surface OA Indicators

Posted by mmaheigan 
· Wednesday, August 23rd, 2023 

Accurately predicting future ocean acidification (OA) conditions is crucial for advancing research at regional and global scales, and guiding society’s mitigation and adaptation efforts.

As an update to Jiang et al. 2019, this new model-data fusion product:
1. Utilizes an ensemble of 14 distinct Earth System Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) along with three recent observational ocean carbon data products –>instead of relying on just one model (i.e., the GFDL-ESM2M) this approach reduces potential projection biases in OA indicators.
2. Eliminates model biases using observational data, and model drift with pre-Industrial controls.
3. Covers 10 OA indicators, an expansion from the usual pH, acidity, and buffer capacity.
4. Incorporates the new Shared Socioeconomic Pathways (SSPs).

The use of the most recent observational datasets and a large Earth System Model ensemble is a major step forward in the projection of future surface ocean OA indicators and provides critical information to guide OA mitigation and adaptation efforts.

Figure X. Temporal changes of global average surface ocean OA indicators as reconstructed and projected from 14 CMIP6 Earth System Models after applying adjustments with observational data: (a) fugacity of carbon dioxide (fCO2), (b) total hydrogen ion content ([H+]total), (c) carbonate ion content ([CO32-]), (d) total dissolved inorganic carbon content (DIC), (e) pH on total scale (pHT), (f) aragonite saturation state (Ωarag), (g) total alkalinity content (TA), (h) Revelle Factor (RF), and (i) calcite saturation state (Ωcalc). The asterisk signs on the left-side y-axes show the values in 1750. The numbers along right-side y-axes, i.e., 1-1.9, 1-2.6, 2-4.5, 3-7.0, and 5-8.5, indicate the shared socioeconomic pathway SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. These are missing from panel g because the trajectories were more dependent on the model than the SSP.

Authors
Li-Qing Jiang (University Maryland)
John Dunne (NOAA/Geophysical Fluid Dynamics Laboratory)
Brendan R. Carter (University of Washington)
Jerry F. Tjiputra (NORCE Norwegian Research Centre Bjerknes)
Jens Terhaar (Woods Hole Oceanographic Institution)
Jonathan D. Sharp (University of Washington)
Are Olsen (University of Bergen and Bjerknes Centre for Climate Research)
Simone Alin (NOAA/Pacific Marine Environmental Laboratory)
Dorothee C. E. Bakker (University of East Anglia)
Richard A. Feely (NOAA/Pacific Marine Environmental Laboratory)
Jean-Pierre Gattuso (Sorbonne Université)
Patrick Hogan (NOAA/National Centers for Environmental Information)
Tatiana Ilyina (Max Planck Institute for Meteorology)
Nico Lange (GEOMAR Helmholtz Centre for Ocean Research)
Siv K. Lauvset (NORCE Norwegian Research Centre)
Ernie R. Lewis (Brookhaven National Laboratory)
Tomas Lovato (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici)
Julien Palmieri (National Oceanography Centre)
Yeray Santana-Falcón (Université de Toulouse)
Jörg Schwinger (NORCE Norwegian Research Centre)
Roland Séférian (Université de Toulouse)
Gary Strand (US National Center for Atmospheric Research)
Neil Swart (Canadian Centre for Climate Modelling and Analysis)
Toste Tanhua (GEOMAR Helmholtz Centre for Ocean Research)
Hiroyuki Tsujino (JMA Meteorological Research Institute)
Rik Wanninkhof (NOAA/Atlantic Oceanographic Meteorological Laboratory)
Michio Watanabe (Japan Agency for Marine-Earth Science and Technology)
Akitomo Yamamoto (Japan Agency for Marine-Earth Science and Technology)
Tilo Ziehn (CSIRO Oceans and Atmosphere)

Twitter:
@JiangLiqing, @JensTerhaar, @jpGattuso, @j_d_sharp, @AreOlsen, @SimoneAlin, @Dorothee_Bakker, @RFeely, @ilitat, @sivlauvset, @yeraysf, @TosteTanhua,

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Funding for the Ocean Carbon & Biogeochemistry Project Office is provided by the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA). The OCB Project Office is housed at the Woods Hole Oceanographic Institution.