Level up ocean carbon observations: Successful implementation of a novel autonomous total alkalinity analyzer on a commercial Ship of Opportunity

2020 ◽  
Author(s):  
Katharina Seelmann ◽  
Tobias Steinhoff ◽  
Arne Körtzinger
Keyword(s):  
Ocean Acidification ◽  
Main Part ◽  
Sea Surface ◽  
Total Alkalinity ◽  
Global Ocean ◽  
Observation System ◽  
Successful Implementation ◽  
Gas Sampling ◽  
Ocean Carbon

<p>The observation and documentation of the marine carbon cycle is of utmost importance because of probable future changes such as ocean acidification, warming or deoxygenation. Over decades, ship-based observatories (Ships of Opportunity – SOOP) equipped with sensors measuring the CO<sub>2</sub> partial pressure (<em>p</em>CO<sub>2</sub>) in the surface seawater form the backbone of the global ocean carbon observation system. However, one severe shortcoming of the current carbon-SOOP observatory is the fact that it mostly only measures <em>p</em>CO<sub>2</sub> which is required to calculate the net air-sea CO<sub>2</sub> flux. Full insight into the marine CO<sub>2</sub> system for important aspects such as net biological production, ocean acidification, and marine calcification requires the measurement of two out of the four measurable variables of the marine CO<sub>2</sub> system which are <em>p</em>CO<sub>2</sub>, total alkalinity (<em>A</em><sub>T</sub>), dissolved inorganic carbon (<em>C</em><sub>T</sub>) and pH. The so far common workaround is the calculation of <em>A</em><sub>T</sub> from sea surface temperature and sea surface salinity using established parameterizations. Unfortunately, this procedure leads to high uncertainties and is particularly prone to regional bias. Therefore, autonomous <em>A</em><sub>T </sub>measurements are necessary. Our study describes the implementation of a novel autonomous analyzer for seawater <em>A</em><sub>T</sub>, the CONTROS HydroFIA<sup>®</sup> TA system (Kongsberg Maritime Contros GmbH, Kiel, Germany) on a North Atlantic SOOP line based on the merchant vessel M/V <em>Atlantic Sail</em> (Atlantic Container Line). The first main part of this work deals with the installation of the analyzer, for which several circumstances must be taken into account: 1) The system’s typical drift behavior, 2) stabilization measurements and cleaning procedures, and 3) the waste handling. We present our installation in detail and how we handle the named issues. Another major problem during automated long-term campaigns is the provision of sufficient reference seawater for regular quality assurance measurements and subsequent drift correction. We tested ten different container types and materials with minimum 5L volume (e.g. gas sampling bags) for their suitability as long-term seawater storage. As a result, only one gas sampling bag based on polyvinylidene fluoride (PVDF) featured the high-quality requirements and was chosen as reference seawater storage. The second main part focusses on the measured sea surface <em>A</em><sub>T </sub>data from the first four unattended measurement campaigns. In order to prove the success of the installation, we compared the measurements with 1) discrete samples (taken manually only during the first two transits), and 2) calculated <em>A</em><sub>T </sub>values based on established parameterization. The gained results show very promising consistency between the measured values and the <em>A</em><sub>T </sub>range and variability of the monitored region. We conclude that the implementation of the CONTROS HydroFIA<sup>®</sup> TA system on a SOOP line was successful and brings ocean carbon observations to a new level.</p>

scholarly journals Enhance Ocean Carbon Observations: Successful Implementation of a Novel Autonomous Total Alkalinity Analyzer on a Ship of Opportunity

2020 ◽  
Vol 7 ◽  
Author(s):  
Katharina Seelmann ◽  
Tobias Steinhoff ◽  
Steffen Aßmann ◽  
Arne Körtzinger
Keyword(s):  
Large Volume ◽  
Polyvinylidene Fluoride ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
Total Alkalinity ◽  
Spatiotemporal Variability ◽  
Global Ocean ◽  
Observation System ◽  
Successful Implementation ◽  
Ocean Carbon

Over recent decades, observations based on merchant vessels (Ships of Opportunity—SOOP) equipped with sensors measuring the CO2 partial pressure (pCO2) in the surface seawater formed the backbone of the global ocean carbon observation system. However, the restriction to pCO2 measurements alone is one severe shortcoming of the current SOOP observatory. Full insight into the marine inorganic carbon system requires the measurement of at least two of the four measurable variables which are pCO2, total alkalinity (TA), dissolved inorganic carbon (DIC), and pH. One workaround is to estimate TA values based on established temperature-salinity parameterizations, but this leads to higher uncertainties and the possibility of regional and/or seasonal biases. Therefore, autonomous SOOP-based TA measurements are of great interest. Our study describes the implementation of a novel autonomous analyzer for seawater TA, the CONTROS HydroFIAⓇ TA system (-4H-JENA engineering GmbH, Germany) for unattended routine TA measurements on a SOOP line operating in the North Atlantic. We present the installation in detail and address major issues encountered with autonomous measurements using this analyzer, e.g., automated cleaning and stabilization routines, and waste handling. Another issue during long-term deployments is the provision of reference seawater in large-volume containers for quality assurance measurements and drift correction. Hence, a stable large-volume seawater storage had to be found. We tested several container types with respect to their suitability to store seawater over a time period of 30 days without significant changes in TA. Only one gas sampling bag made of polyvinylidene fluoride (PVDF) satisfied the high stability requirement. In order to prove the performance of the entire setup, we compared the autonomous TA measurements with TA from discrete samples taken during the first two trans-Atlantic crossings. Although the measurement accuracy in unattended mode (about ± 5 μmol kg^-1) slightly deteriorated compared to our previous system characterization, its overall uncertainty fulfilled requirements for autonomous TA measurements on SOOP lines. A comparison with predicted TA values based on an established and often used parameterization pointed at regional and seasonal limitations of such TA predictions. Consequently, TA observations with better coverage of spatiotemporal variability are needed, which is now possible with the method described here.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

The In situ Global Ocean Observing System for Climate (and Other Needs)

2015 ◽  
Vol 49 (2) ◽  
pp. 112-121
Author(s):  
Stephen R. Piotrowicz ◽  
David M. Legler
Keyword(s):  
Weather Forecasting ◽  
Spatial Scales ◽  
The Arctic ◽  
Global Ocean ◽  
Observation System ◽  
Ocean Management ◽  
Accuracy And Precision ◽  
Weather And Climate ◽  
Ocean Observing

AbstractThe Global Ocean Observing System (GOOS) is the international observation system that ensures long-term sustained ocean observations. The ocean equivalent of the atmospheric observing system supporting weather forecasting, GOOS, was originally developed to provide data for weather and climate applications. Today, GOOS data are used for all aspects of ocean management as well as weather and climate research and forecasting. National Oceanic and Atmospheric Administration (NOAA), through the Climate Observation Division of the Office of Oceanic and Atmospheric Research/Climate Program Office, is a major supporter of the climate component of GOOS. This paper describes the eight elements of GOOS, and the Arctic Observing Network, to which the Climate Observation Division is a major contributor. In addition, the paper addresses the evolution of the observing system as rapidly evolving new capabilities in sensors, platforms, and telecommunications allow observations at unprecedented temporal and spatial scales with the accuracy and precision required to address questions of climate variability and change.

scholarly journals OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification

2021 ◽  
Vol 13 (2) ◽  
pp. 777-808
Author(s):  
Luke Gregor ◽  
Nicolas Gruber
Keyword(s):  
Ocean Acidification ◽  
Total Alkalinity ◽  
Global Ocean ◽  
Carbonate System ◽  
Data Set ◽  
Saturation State ◽  
Total Uncertainty ◽  
Mean Squared Errors ◽  
Surface Ocean ◽  
Basin Scale

Abstract. Ocean acidification has profoundly altered the ocean's carbonate chemistry since preindustrial times, with potentially serious consequences for marine life. Yet, no long-term, global observation-based data set exists that allows us to study changes in ocean acidification for all carbonate system parameters over the last few decades. Here, we fill this gap and present a methodologically consistent global data set of all relevant surface ocean parameters, i.e., dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO2 (pCO2), pH, and the saturation state with respect to mineral CaCO3 (Ω) at a monthly resolution over the period 1985 through 2018 at a spatial resolution of 1∘×1∘. This data set, named OceanSODA-ETHZ, was created by extrapolating in time and space the surface ocean observations of pCO2 (from the Surface Ocean CO2 Atlas, SOCAT) and total alkalinity (TA; from the Global Ocean Data Analysis Project, GLODAP) using the newly developed Geospatial Random Cluster Ensemble Regression (GRaCER) method (code available at https://doi.org/10.5281/zenodo.4455354, Gregor, 2021). This method is based on a two-step (cluster-regression) approach but extends it by considering an ensemble of such cluster regressions, leading to improved robustness. Surface ocean DIC, pH, and Ω were then computed from the globally mapped pCO2 and TA using the thermodynamic equations of the carbonate system. For the open ocean, the cluster-regression method estimates pCO2 and TA with global near-zero biases and root mean squared errors of 12 µatm and 13 µmol kg−1, respectively. Taking into account also the measurement and representation errors, the total uncertainty increases to 14 µatm and 21 µmol kg−1, respectively. We assess the fidelity of the computed parameters by comparing them to direct observations from GLODAP, finding surface ocean pH and DIC global biases of near zero, as well as root mean squared errors of 0.023 and 16 µmol kg−1, respectively. These uncertainties are very comparable to those expected by propagating the total uncertainty from pCO2 and TA through the thermodynamic computations, indicating a robust and conservative assessment of the uncertainties. We illustrate the potential of this new data set by analyzing the climatological mean seasonal cycles of the different parameters of the surface ocean carbonate system, highlighting their commonalities and differences. Further, this data set provides a novel constraint on the global- and basin-scale trends in ocean acidification for all parameters. Concretely, we find for the period 1990 through 2018 global mean trends of 8.6 ± 0.1 µmol kg−1 per decade for DIC, −0.016 ± 0.000 per decade for pH, 16.5 ± 0.1 µatm per decade for pCO2, and −0.07 ± 0.00 per decade for Ω. The OceanSODA-ETHZ data can be downloaded from https://doi.org/10.25921/m5wx-ja34 (Gregor and Gruber, 2020).

scholarly journals Current estimates of K<sub>1</sub>* and K<sub>2</sub>* appear inconsistent with measured CO<sub>2</sub> system parameters in cold oceanic regions

Ocean Science ◽  
2020 ◽  
Vol 16 (4) ◽  
pp. 847-862 ◽  
Author(s):  
Olivier Sulpis ◽  
Siv K. Lauvset ◽  
Mathilde Hagens
Keyword(s):  
Surface Water ◽  
Ocean Acidification ◽  
Carbonic Acid ◽  
Dissolved Inorganic Carbon ◽  
Dissociation Constants ◽  
Total Alkalinity ◽  
Global Ocean ◽  
Polar Regions ◽  
Carbonate System ◽  
System Variables

Abstract. Seawater absorption of anthropogenic atmospheric carbon dioxide (CO2) has led to a range of changes in carbonate chemistry, collectively referred to as ocean acidification. Stoichiometric dissociation constants used to convert measured carbonate system variables (pH, pCO2, dissolved inorganic carbon, total alkalinity) into globally comparable parameters are crucial for accurately quantifying these changes. The temperature and salinity coefficients of these constants have generally been experimentally derived under controlled laboratory conditions. Here, we use field measurements of carbonate system variables taken from the Global Ocean Data Analysis Project version 2 and the Surface Ocean CO2 Atlas data products to evaluate the temperature dependence of the carbonic acid stoichiometric dissociation constants. By applying a novel iterative procedure to a large dataset of 948 surface-water, quality-controlled samples where four carbonate system variables were independently measured, we show that the set of equations published by Lueker et al. (2000), currently preferred by the ocean acidification community, overestimates the stoichiometric dissociation constants at temperatures below about 8 ∘C. We apply these newly derived temperature coefficients to high-latitude Argo float and cruise data to quantify the effects on surface-water pCO2 and calcite saturation states. These findings highlight the critical implications of uncertainty in stoichiometric dissociation constants for future projections of ocean acidification in polar regions and the need to improve knowledge of what causes the CO2 system inconsistencies in cold waters.

scholarly journals Design and Application of Buoy Single Point Mooring System with Electro-Optical-Mechanical (EOM) Cable

2020 ◽  
Vol 8 (9) ◽  
pp. 672
Author(s):  
Junwei Yu ◽  
Shaowei Zhang ◽  
Wencai Yang ◽  
Yongzhi Xin ◽  
Huaining Gao
Keyword(s):  
Power Transmission ◽  
Wind Wave ◽  
Hydrodynamic Force ◽  
Single Point ◽  
Sea Surface ◽  
Observation System ◽  
Near Shore ◽  
Transmission Link ◽  
Scientific Phenomena

This paper presents the design of a hybrid system named the Mooring Buoys Observation System for Benthic with Electro Optical Mechanical Cable (MBOSBC) for long-term mooring buoy observation in deep oceans. MBOSBC is comprised of three main modules: a surface buoy, a benthic node, and an Electro-Optical-Mechanical (EOM) cable. The Surface buoy provides energy for the entire system, the Benthic node is used to observe scientific phenomena on the seabed, and the Electro-Optical-Mechanical (EOM) cable connects the sea surface buoy and the benthic observation node, serving as the information and power transmission link. This paper provides design criteria for a single point mooring buoy using an EOM cable. The environmental load of the buoy under varying wind, wave, and current conditions is analyzed and the mooring hydrodynamic force of EOM is calculated. After calculating the load of the EOM cable under extreme marine conditions, the hydrodynamic force needed for the system is analyzed. After physically testing the strength of the designed EOM cable, a near-shore test of MBOSBC was carried out, in order to verify that the system has the expected function.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

2009 ◽  
Vol 6 (2) ◽  
pp. 1129-1171
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

scholarly journals Modulation of ocean acidification by decadal climate variability in the Gulf of Alaska

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Claudine Hauri ◽  
Rémi Pagès ◽  
Andrew M. P. McDonnell ◽  
Malte F. Stuecker ◽  
Seth L. Danielson ◽  
...  
Keyword(s):  
Ocean Acidification ◽  
Ocean Circulation ◽  
Large Scale ◽  
Dissolved Inorganic Carbon ◽  
Model Simulation ◽  
Gulf Of Alaska ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Global Ocean ◽  
Wind Stress Curl

AbstractUptake of anthropogenic carbon dioxide from the atmosphere by the surface ocean is leading to global ocean acidification, but regional variations in ocean circulation and mixing can dampen or accelerate apparent acidification rates. Here we use a regional ocean model simulation for the years 1980 to 2013 and observational data to investigate how ocean fluctuations impact acidification rates in surface waters of the Gulf of Alaska. We find that large-scale atmospheric forcing influenced local winds and upwelling strength, which in turn affected ocean acidification rate. Specifically, variability in local wind stress curl depressed sea surface height in the subpolar gyre over decade-long intervals, which increased upwelling of nitrate- and dissolved inorganic carbon-rich waters and enhanced apparent ocean acidification rates. We define this sea surface height variability as the Northern Gulf of Alaska Oscillation and suggest that it can cause extreme acidification events that are detrimental to ecosystem health and fisheries.

scholarly journals An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021

2021 ◽  
Vol 13 (12) ◽  
pp. 5565-5589
Author(s):  
Siv K. Lauvset ◽  
Nico Lange ◽  
Toste Tanhua ◽  
Henry C. Bittig ◽  
Are Olsen ◽  
...  
Keyword(s):  
Quality Control ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
Data System ◽  
Total Alkalinity ◽  
The Arctic ◽  
Global Ocean ◽  
Isotopic Tracers ◽  
Data Product ◽  
Ocean Carbon

Abstract. The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2021 is an update of the previous version, GLODAPv2.2020 (Olsen et al., 2020). The major changes are as follows: data from 43 new cruises were added, data coverage was extended until 2020, all data with missing temperatures were removed, and a digital object identifier (DOI) was included for each cruise in the product files. In addition, a number of minor corrections to GLODAPv2.2020 data were performed. GLODAPv2.2021 includes measurements from more than 1.3 million water samples from the global oceans collected on 989 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For this annual update, adjustments for the 43 new cruises were derived by comparing those data with the data from the 946 quality controlled cruises in the GLODAPv2.2020 data product using crossover analysis. Comparisons to estimates of nutrients and ocean CO2 chemistry based on empirical algorithms provided additional context for adjustment decisions in this version. The adjustments are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent with to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments. The original data, their documentation, and DOI codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/GLODAPv2_2021/, last access: 7 July 2021). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/ttgq-n825 (Lauvset et al., 2021). These bias-adjusted product files also include significant ancillary and approximated data and can be accessed via https://www.glodap.info (last access: 29 June 2021). These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2021 methods and provides a broad overview of the secondary quality control procedures and results.

scholarly journals Arctic Ocean acidification over the 21st century co-driven by anthropogenic carbon increases and freshening in the CMIP6 model ensemble

2021 ◽  
Vol 18 (6) ◽  
pp. 2221-2240
Author(s):  
Jens Terhaar ◽  
Olivier Torres ◽  
Timothée Bourgeois ◽  
Lester Kwiatkowski
Keyword(s):  
Ocean Acidification ◽  
Arctic Ocean ◽  
21St Century ◽  
Sea Surface ◽  
Total Alkalinity ◽  
The Arctic ◽  
Cmip5 Models ◽  
The Arctic Ocean ◽  
Anthropogenic Carbon ◽  
Projected Changes

Abstract. The uptake of anthropogenic carbon (Cant) by the ocean leads to ocean acidification, causing the reduction of pH and the saturation states of aragonite (Ωarag) and calcite (Ωcalc). The Arctic Ocean is particularly vulnerable to ocean acidification due to its naturally low pH and saturation states and due to ongoing freshening and the concurrent reduction in total alkalinity in this region. Here, we analyse ocean acidification in the Arctic Ocean over the 21st century across 14 Earth system models (ESMs) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared to the previous model generation (CMIP5), models generally better simulate maximum sea surface densities in the Arctic Ocean and consequently the transport of Cant into the Arctic Ocean interior, with simulated historical increases in Cant in improved agreement with observational products. Moreover, in CMIP6 the inter-model uncertainty of projected changes over the 21st century in Arctic Ocean Ωarag and Ωcalc averaged over the upper 1000 m is reduced by 44–64 %. The strong reduction in projection uncertainties of Ωarag and Ωcalc can be attributed to compensation between Cant uptake and total alkalinity reduction in the latest models. Specifically, ESMs with a large increase in Arctic Ocean Cant over the 21st century tend to simulate a relatively weak concurrent freshening and alkalinity reduction, while ESMs with a small increase in Cant simulate a relatively strong freshening and concurrent total alkalinity reduction. Although both mechanisms contribute to Arctic Ocean acidification over the 21st century, the increase in Cant remains the dominant driver. Even under the low-emissions Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6), basin-wide averaged Ωarag undersaturation in the upper 1000 m occurs before the end of the century. While under the high-emissions pathway SSP5-8.5, the Arctic Ocean mesopelagic is projected to even become undersaturated with respect to calcite. An emergent constraint identified in CMIP5 which relates present-day maximum sea surface densities in the Arctic Ocean to the projected end-of-century Arctic Ocean Cant inventory is found to generally hold in CMIP6. However, a coincident constraint on Arctic declines in Ωarag and Ωcalc is not apparent in the new generation of models. This is due to both the reduction in Ωarag and Ωcalc projection uncertainty and the weaker direct relationship between projected changes in Arctic Ocean Cant and changes in Ωarag and Ωcalc.

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