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Marine heatwaves (MHWs) represent anomalously warm temperature conditions of seawater that may affect marine life and ocean biogeochemistry. Under such conditions, phytoplankton communities may modify their structure and functions, and their resilience is not assured. This study characterizes the impact of MHWs on the phytoplankton spring bloom in the North‐Western Mediterranean Sea. Here, we synergistically combine autonomous observations from BioGeoChemical‐Argo floats, satellite‐based and marine ecosystem model data, and show that MHW events occurring during winter drastically inhibit phytoplankton carbon biomass in spring by up to 70%. Such reduction is related to the enhanced stratification of the water column under MHWs which hinders the renewal of nutrients from deep‐ocean reservoirs, thus preventing surface phytoplankton from blooming. This process negatively impacts particulate organic carbon stocks within the mixed layer, while severe events cause an earlier shift of phytoplankton phenology that provokes changes in zooplankton biomass distribution. Plain Language Summary: Marine heatwaves (MHWs) are described as an abnormal and prolonged increase of ocean temperatures. These events may occur in all the oceans, and are becoming more frequent than before. Such increase in water temperature might not be tolerated by organisms, which must need to adapt themselves to the new environmental conditions. Consequently, marine ecosystem health is endangered. Here, we analyze the effect that MHWs have on the growth of small algae called phytoplankton. Phytoplankton are vital microscopic organisms for ecosystems that use sunlight to produce organic carbon through photosynthesis. At middle and high latitudes, phytoplankton massively grows (i.e., blooms) once a year at the sea surface, and introduces a major carbon flux into the ecosystem that sustains larger animals. Through combining observations acquired by different platforms (satellites, autonomous in situ BioGeoChemical‐Argo robots, and ecosystem models), we comprehensively study how MHWs affect phytoplankton carbon production during spring blooms and trophic chains at mid‐latitudes. Results show that MHW events occurring in winter lead to a large decrease in phytoplankton carbon biomass (up to 70%) in spring. Winter MHW events, driven by local atmospheric conditions, intensify water column stratification thus hindering the deep‐ocean nutrient transport to the surface, which is essential for phytoplankton to bloom. Key Points: Marine heatwaves (MHWs) intensify water stratification leading to reduction in nutrient supply which inhibits surface phytoplankton spring bloomMHWs lead to a phytoplankton community shift toward smaller cells, while increasing the transparency of surface watersMHWs decrease carbon stocks within the mixed layer, while intense ones shift phytoplankton phenology and affect zooplankton [ABSTRACT FROM AUTHOR]

The role of coastal oceans in regulating atmospheric carbon dioxide remains poorly quantified and understood. Here, we use a two‐step neural network approach to generate estimates from sparse observational data in the coastal Northeast Pacific Ocean at an unprecedented spatial resolution of 1/12° with coverage in the nearshore (0–25 km offshore). We compiled partial pressure of carbon dioxide (pCO2) observations as well as a range of predictor variables including satellite‐based and physical oceanographic reanalysis products. With the predictor variables representing processes affecting pCO2, we created non‐linear relationships to interpolate observations from 1998 to 2019. Compared to in situ shipboard and mooring observations, our coastal pCO2 product captures broad spatial patterns and seasonal cycle variability well. A sensitivity analysis identifies that the parameters responsible for the neural network's ability to capture regional pCO2 variability are associated with mechanistic processes, including mixed layer deepening, mesoscale eddies, and gyre upwelling. Using wind speed and atmospheric CO2, we calculated air‐sea CO2 fluxes. We report an anticorrelation between annual air‐sea CO2 flux and its seasonal amplitude with the relationship driven by circulation, opposing seasonal upwelling/relaxation versus downwelling, and the effects of winter mixing and primary productivity. We show that the inclusion of nearshore net outgassing fluxes lowers the overall regional net flux. Overall, our results suggest that the region is a net sink (−0.7 mol m−2 yr−1) for atmospheric CO2 with trends indicating increasing oceanic uptake due to strong connectivity to subsurface waters. Plain Language Summary: The importance of the coastal ocean as a hub of exchange for carbon between terrestrial ecosystems, the open ocean, and the atmosphere is still unclear. In this study, we investigate how much carbon dioxide moves between the ocean and the atmosphere in the coastal Northeast Pacific. We use a mathematical technique (i.e., machine learning) to transform limited observational data to a high‐resolution estimate of this exchange across the entire region. We found this method effectively captured the big picture patterns and seasonal changes in ocean carbon dioxide levels. We report that the coastal Northeast Pacific absorbs slightly more carbon dioxide than it releases, helping regulate atmospheric levels of this greenhouse gas. However, there are large differences regionally with some coastal zones absorbing substantial amounts of carbon dioxide and others releasing the gas, such as the nearshore. We report a trend of increasing ocean uptake over time, suggesting the region may play an increasingly important role in reducing atmospheric carbon dioxide levels. This study provides valuable baseline information for efforts to reduce carbon dioxide in the atmosphere through artificially enhancing ocean uptake in the region. Key Points: The coastal Northeast Pacific is a net sink for atmospheric CO2 with increasing air‐sea pCO2 disequilibrium trends in most of the regionRegional processes drive net annual air‐sea CO2 flux to be anticorrelated with air‐sea CO2 flux seasonal amplitudeEstimated pCO2 reproduces observed seasonal cycle phase and amplitude well along with broad spatial patterns of variability [ABSTRACT FROM AUTHOR]

Abstract Marine heatwaves (MHWs) represent anomalously warm temperature conditions of seawater that may affect marine life and ocean biogeochemistry. Under such conditions, phytoplankton communities may modify their structure and functions, and their resilience is not assured. This study characterizes the impact of MHWs on the phytoplankton spring bloom in the North‐Western Mediterranean Sea. Here, we synergistically combine autonomous observations from BioGeoChemical‐Argo floats, satellite‐based and marine ecosystem model data, and show that MHW events occurring during winter drastically inhibit phytoplankton carbon biomass in spring by up to 70%. Such reduction is related to the enhanced stratification of the water column under MHWs which hinders the renewal of nutrients from deep‐ocean reservoirs, thus preventing surface phytoplankton from blooming. This process negatively impacts particulate organic carbon stocks within the mixed layer, while severe events cause an earlier shift of phytoplankton phenology that provokes changes in zooplankton biomass distribution.

In order to understand the oceans role as a global carbon sink, we must accurately quantify the amount of carbon exchanged at the air‐sea interface. A widely used machine learning neural network product, the SOM‐FFN, uses observations to reconstruct a monthly, 1° × 1° global CO2 flux estimate. However, uncertainties in neural network and interpolation techniques can be large, especially in seldom‐sampled regions. Here, we present a three‐dimensional (latitude, longitude, time) gridded product for our SOM‐FFN observational data set consisting of uncertainties (pCO2 mapping, transfer velocity, wind) and biases (pCO2 mapping). We find that polar regions are dominated by uncertainty from gas exchange transfer velocity, with an average 48.7% contribution. In contrast, for subtropical regions, wind product choice contributes an average 50.0%. Regions with fewer observations correlate with higher uncertainty and biases, illustrating the importance of maintaining and expanding existing measurements. Plain Language Summary: The ocean plays an important role in regulating climate and the carbon cycle by absorbing and releasing carbon through the air‐sea interface. In order to better understand these dynamics, we need to accurately quantify the amount of carbon exchanged between the ocean and atmosphere reservoirs, known as our air‐sea carbon flux. Since the data can't be retrieved by satellites, it is challenging to get a global scale monthly product, so interpolation techniques such as neural networks are used. While these techniques have proven to provide robust observation‐based estimates, uncertainties can be high, especially in regions where few observations are available. We calculate the uncertainty and bias created while using a two‐step neural network machine learning method, the SOM‐FFN. We find the sources of flux uncertainty vary regionally, with subtropical uncertainty dominated by choice of wind product but polar uncertainty influenced most by the coefficient chosen for the air‐sea gas exchange transfer. Areas with fewer observations correlate with higher uncertainty and bias. This analysis provides important motivation for maintaining and increasing global ocean carbon observations, and is an important step toward closing the carbon budget through accurate quantification of the fluxes at the air‐sea interface. Key Points: We analyze a new explicit spatial quantification of bias and uncertainty in the air‐sea CO2 exchange from observation‐based SOM‐FFN methodWe find variations in seasonal uncertainty with higher magnitude in boreal wintertime and larger uncertainty in less‐observed regionsFlux uncertainty is dominated by the exchange transfer velocity in polar regions and wind reanalysis estimates in the subtropics [ABSTRACT FROM AUTHOR]

Abstract In order to understand the oceans role as a global carbon sink, we must accurately quantify the amount of carbon exchanged at the air‐sea interface. A widely used machine learning neural network product, the SOM‐FFN, uses observations to reconstruct a monthly, 1° × 1° global CO2 flux estimate. However, uncertainties in neural network and interpolation techniques can be large, especially in seldom‐sampled regions. Here, we present a three‐dimensional (latitude, longitude, time) gridded product for our SOM‐FFN observational data set consisting of uncertainties (pCO2 mapping, transfer velocity, wind) and biases (pCO2 mapping). We find that polar regions are dominated by uncertainty from gas exchange transfer velocity, with an average 48.7% contribution. In contrast, for subtropical regions, wind product choice contributes an average 50.0%. Regions with fewer observations correlate with higher uncertainty and biases, illustrating the importance of maintaining and expanding existing measurements.

[[12]] examine the role of the Southern Ocean in setting global ocean heat uptake in coupled climate models, as well as its impacts on climate metrics such as the transient climate response where it exerts an impact greater than its proportional area. [[7]] document how a new type of dataset coming from sailboats, particularly racing craft, navigating the Southern Ocean can help fill the observational gap and be useful in our understanding of Southern Ocean carbon budget. Crucially, the response of future Southern Ocean heat and carbon uptake to projected global warming scenarios, and the nature of the feedbacks that these may generate, are poorly understood and even more poorly constrained by observations. Keywords: oceanography; Southern Ocean; ocean carbon EN oceanography Southern Ocean ocean carbon 1 5 5 05/09/23 20230626 NES 230626 The Southern Ocean is an extreme environment. [Extracted from the article]

The 5-year Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports (ORCHESTRA) programme and its 1-year extension ENCORE (ENCORE is the National Capability ORCHESTRA Extension) was an approximately 11-million-pound programme involving seven UK research centres that finished in March 2022. The project sought to radically improve our ability to measure, understand and predict the exchange, storage and export of heat and carbon by the Southern Ocean. It achieved this through a series of milestone observational campaigns in combination with model development and analysis. Twelve cruises in the Weddell Sea and South Atlantic were undertaken, along with mooring, glider and profiler deployments and aircraft missions, all contributing to measurements of internal ocean and air–sea heat and carbon fluxes. Numerous forward and adjoint numerical experiments were developed and supported by the analysis of coupled climate models. The programme has resulted in over 100 peer-reviewed publications to date as well as significant impacts on climate assessments and policy and science coordination groups. Here, we summarize the research highlights of the programme and assess the progress achieved by ORCHESTRA/ENCORE and the questions it raises for the future. This article is part of a discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'. [ABSTRACT FROM AUTHOR]

The ocean holds vast quantities of carbon that it continually exchanges with the atmosphere through the air-sea interface. Because of its enormous size and relatively rapid exchange of carbon with the atmosphere, the ocean controls atmospheric CO2 concentration and thereby Earth's climate on timescales of tens to thousands of years. This review examines the basic functions of the ocean's carbon cycle, demonstrating that the ocean carbon inventory is determined primarily by the mass of the ocean, by the chemical speciation of CO2 in seawater, and by the action of the solubility and biological pumps that draw carbon into the ocean's deeper layers, where it can be sequestered for decades to millennia. The ocean also plays a critical role in moderating the impacts of climate change by absorbing an amount of carbon equivalent to about 25% of anthropogenic CO2 emissions over the past several decades. However, this also leads to ocean acidification and reduces the chemical buffering capacity of the ocean and its future ability to take up CO2. This review closes with a look at the uncertain future of the ocean carbon cycle and the scientific challenges that this uncertainty brings. [ABSTRACT FROM AUTHOR]

The ocean is one of the most important sinks for anthropogenic CO2 emissions. Here, I use an ocean circulation inverse model (OCIM), ocean biogeochemical models, and pCO2 interpolation products to examine trends and variability in the oceanic CO2 sink. The OCIM quantifies the impacts of rising atmospheric CO2, changing sea surface temperatures, and gas transfer velocities on the oceanic CO2 sink. Together, these effects account for an oceanic CO2 uptake of 2.2 ± 0.1 PgC yr−1 from 1994 to 2007, and a net increase in the oceanic carbon inventory of 185 PgC from 1780 to 2020. However, these effects cannot account for the majority of the decadal variability shown in data‐based reconstructions of the ocean CO2 sink over the past 30 years. This implies that decadal variability of the ocean CO2 sink is predominantly driven by changes in ocean circulation or biology that act to redistribute both natural and anthropogenic carbon in the ocean. Plain Language Summary: The ocean currently absorbs about 25% of industrial CO2 emissions, but there are numerous factors that can cause variation in the rate of ocean CO2 uptake over time. This study uses a combination of observation‐based models to investigate the causes of variability in the rate of ocean CO2 uptake. The results show that the long‐term trend of ocean CO2 uptake is driven mainly by increasing atmospheric CO2 concentrations, but the variability from that trend is due mostly to changes in the distribution of CO2 in the ocean that are caused by changes in ocean circulation and biology. Key Points: An ocean circulation inverse model is used to quantify abiotic air‐sea CO2 fluxes from 1780 to 2020Increasing atmospheric pCO2 drives the multidecadal trend in ocean CO2 uptake, which averages 2.7 PgC yr−1 from 2010 to 2020Ocean circulation and biology are implicated as the main drivers of decadal variability of the ocean CO2 sink [ABSTRACT FROM AUTHOR]

The ocean has absorbed about 25% of the carbon emitted by humans to date. To better predict how much climate will change, it is critical to understand how this ocean carbon sink will respond to future emissions. Here, we examine the ocean carbon sink response to low emission (SSP1-1.9, SSP1-2.6), intermediate emission (SSP2-4.5, SSP5-3.4-OS), and high emission (SSP5-8.5) scenarios in CMIP6 Earth System Models and in MAGICC7, a reduced-complexity climate carbon system model. From 2020–2100, the trajectory of the global-mean sink approximately parallels the trajectory of anthropogenic emissions. With increasing cumulative emissions during this century (SSP5-8.5 and SSP2-4.5), the cumulative ocean carbon sink absorbs 20%–30% of cumulative emissions since 2015. In scenarios where emissions decline, the ocean absorbs an increasingly large proportion of emissions (up to 120% of cumulative emissions since 2015). Despite similar responses in all models, there remains substantial quantitative spread in estimates of the cumulative sink through 2100 within each scenario, up to 50 PgC in CMIP6 and 120 PgC in the MAGICC7 ensemble. We demonstrate that for all but SSP1-2.6, approximately half of this future spread can be eliminated if model results are adjusted to agree with modern observation-based estimates. Considering the spatial distribution of air-sea CO _2 fluxes in CMIP6, we find significant zonal-mean divergence from the suite of newly-available observation-based constraints. We conclude that a significant portion of future ocean carbon sink uncertainty is attributable to modern-day errors in the mean state of air-sea CO _2 fluxes, which in turn are associated with model representations of ocean physics and biogeochemistry. Bringing models into agreement with modern observation-based estimates at regional to global scales can substantially reduce uncertainty in future role of the ocean in absorbing anthropogenic CO _2 from the atmosphere and mitigating climate change.

The decline in global emissions of carbon dioxide due to the COVID‐19 pandemic provides a unique opportunity to investigate the sensitivity of the global carbon cycle and climate system to emissions reductions. Recent efforts to study the response to these emissions declines has not addressed their impact on the ocean, yet ocean carbon absorption is particularly susceptible to changing atmospheric carbon concentrations. Here, we use ensembles of simulations conducted with an Earth system model to explore the potential detection of COVID‐related emissions reductions in the partial pressure difference in carbon dioxide between the surface ocean and overlying atmosphere (ΔpCO2), a quantity that is regularly measured. We find a unique fingerprint in global‐scale ΔpCO2 that is attributable to COVID, though the fingerprint is difficult to detect in individual model realizations unless we force the model with a scenario that has four times the observed emissions reduction. Plain Language Summary: The COVID‐19 pandemic is slowing the rate of fossil fuel use, and thus impacting the carbon dioxide concentration in the atmosphere. Here we explore what this change in fossil fuel use does to carbon in the ocean. We use a climate model to estimate the change in ocean‐atmosphere carbon exchange and ocean acidity. Since we don't yet know how much we will slow our fossil fuel use due to COVID, we make several informed guesses and see how our model ocean responds to each. We use the model to investigate whether the change that we model would be detectable in real world observations. We find that it is nearly impossible to detect a COVID‐related change in ocean acidity with observations. It might be possible to detect a COVID‐related change in ocean‐atmosphere carbon exchange, but only if we use the most extreme guess, and only if we have enough observation stations in place to record it. Key Points: COVID‐related emissions reductions will be imperceptible in surface ocean pH observationsThe CanESM5 COVID ensemble predicts a unique fingerprint of COVID‐related emissions reductions in global mean ΔpCO2 (pCO2oc ‐ pCO2atm)The fingerprint is potentially detectable in global‐scale observations of ΔpCO2, but only with large emissions reductions [ABSTRACT FROM AUTHOR]

In situ sensors for environmental chemistry promise more thorough observations, which are necessary for high confidence predictions in earth systems science. However, these can be a challenge to interpret because the sensors are strongly influenced by temperature, humidity, pressure, or other secondary environmental conditions that are not of direct interest. We present a comparison of two statistical learning methods—a generalized additive model and a long short-term memory neural network model for bias correction of in situ sensor data. We discuss their performance and tradeoffs when the two bias correction methods are applied to data from submersible and shipboard mass spectrometers. Both instruments measure the most abundant gases dissolved in water and can be used to reconstruct biochemical metabolisms, including those that regulate atmospheric carbon dioxide. Both models demonstrate a high degree of skill at correcting for instrument bias using correlated environmental measurements; the difference in their respective performance is less than 1% in terms of root mean squared error. Overall, the long short-term memory bias correction produced an error of 5% for O2 and 8.5% for CO2 when compared against independent membrane DO and laser spectrometer instruments. This represents a predictive accuracy of 92–95% for both gases. It is apparent that the most important factor in a skillful bias correction is the measurement of the secondary environmental conditions that are likely to correlate with the instrument bias. These statistical learning methods are extremely flexible and permit the inclusion of nearly an infinite number of correlates in finding the best bias correction solution.

Abstract: Previous studies found large biases between individual observational and model estimates of historical ocean anthropogenic carbon uptake. We show that the largest bias between the Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble mean and between two observational estimates of ocean anthropogenic carbon is due to a difference in start date. After adjusting the CMIP5 and observational estimates to the 1791–1995 period, all three carbon uptake estimates agree to within 3 Pg of C, about 4% of the total. The CMIP5 ensemble mean spatial bias compared to the observations is generally smaller than the observational error, apart from a negative bias in the Southern Ocean and a positive bias in the Southern Indian and Pacific Oceans compensating each other in the global mean. This dipole pattern is likely due to an equatorward and weak bias in the position of Southern Hemisphere westerlies and lack of mode and intermediate water ventilation. [ABSTRACT FROM AUTHOR]

While the number of surface ocean CO2 partial pressure ( pCO2) measurements has soared the recent decades, the Southern Ocean remains undersampled. Williams et al. (2017) now present pCO2 estimates based on data from pH-sensor equipped Bio-Argo floats, which have been measuring in the Southern Ocean since 2014. The authors demonstrate the utility of these data for understanding the carbon cycle in this region, which has a large influence on the distribution of CO2 between the ocean and atmosphere. Biogeochemical sensors deployed on autonomous platforms hold the potential to shape our view of the ocean carbon cycle in the coming decades. [ABSTRACT FROM AUTHOR]

Prediction of the substantial biologically mediated carbon flows in a rapidly changing and acidifying ocean requires model simulations informed by observations of key carbon cycle processes on the appropriate spatial and temporal scales. From 2000 to 2004, the National Oceanographic Partnership Program (NOPP) supported the development of the first low-cost, fully autonomous ocean profiling Carbon Explorers, which demonstrated that year-round, real-time observations of particulate organic carbon (POC) concentration and sedimentation could be achieved in the world's ocean. NOPP also initiated the development of a particulate inorganic carbon (PIC) sensor suitable for operational deployment across all oceanographic platforms. As a result, PIC profile characterization that once required shipboard sample collection and shipboard or shore-based laboratory analysis is now possible to full ocean depth in real time using a 0.2-W sensor operating at 24 Hz. NOPP developments further spawned US Department of Energy support to develop the Carbon Flux Explorer, a free vehicle capable of following hourly variations of PIC and POC sedimentation from the near surface to kilometer depths for seasons to years and capable of relaying contemporaneous observations via satellite.We have demonstrated the feasibility of real-time, low-cost carbon observations that are of fundamental value to carbon prediction and that, when further developed, will lead to a fully enhanced global carbon observatory capable of real-time assessment of the ocean carbon sink, a needed constraint for assessment of carbon management policies on a global scale.