The oceans mitigate climate change by absorbing approximately 25% of anthropogenic carbon emissions. Decadal variability in the ocean carbon sink, such as a weakening in the 1990s and a strengthening in the 2000s, has been suggested by pCO2-based reconstructions, but its causes remain poorly understood. This variability is also not well represented in climate models, raising concerns about our ability to accurately project future changes. To address potential biases from sparse observational data, machine learning methods have been applied to surface pCO2 and interior dissolved inorganic carbon (DIC), but global reconstructions of full-depth DIC remain lacking. We aim to determine whether ocean carbon sink variability is real and to understand the role of interior DIC inventory changes in the carbon budget. Using neural networks trained on GLODAPv2.2023 observations and predictors like atmospheric CO2, location, temperature, and salinity from EN4 analysis, we reconstruct full-depth global DIC distributions from the 1990s to the 2010s using a residual neural network (ResNet). Validation through prediction of independent datasets show an improvement over previous products. Validation with the ECCO-Darwin dataset results in an average RMSE of 15.1 µmol/kg and bias of -0.3 µmol/kg. The global average uncertainty is 16.85 µmol/kg. The global change in the DIC inventory exhibits pronounced peaks in decadal variability, especially in the early 2000s driven primarily by intermediate waters at depths of 300-1200 m, particularly in the Atlantic, Indian, and Southern Oceans, and to a lesser extent in the Pacific. The accumulation rate of DIC increases steadily from the mid-2000s.
Continue reading ‘The spatiotemporal distribution of dissolved inorganic carbon in the global ocean interior: reconstructed through machine learning’Posts Tagged 'globalmodeling'
The spatiotemporal distribution of dissolved inorganic carbon in the global ocean interior: reconstructed through machine learning
Published 22 January 2025 Science ClosedTags: chemistry, globalmodeling, modeling
Oceanic enrichment of ammonium and its impacts on phytoplankton community composition under a high-emissions scenario
Published 21 January 2025 Science ClosedTags: biogeochemistry, biological response, globalmodeling, modeling, phytoplankton
Ammonium (NH4+) is an important component of the ocean’s dissolved inorganic nitrogen (DIN) pool, especially in stratified marine environments where intense recycling of organic matter elevates its supply over other forms. Using a global ocean biogeochemical model with good fidelity to the sparse NH4+ data that is available, we project increases in the NH4+:DIN ratio in over 98% of the ocean by the end of the 21st century under a high-emission scenario. This relative enrichment of NH4+ is driven largely by circulation changes, and secondarily by warming-induced increases in microbial metabolism, as well as reduced nitrification rates due to pH decreases. Supplementing our model projections with geochemical measurements and phytoplankton abundance data from Tara Oceans, we demonstrate that shifts in the form of DIN to NH4+ may impact phytoplankton communities by disadvantaging nitrate-dependent taxa like diatoms while promoting taxa better adapted to NH4+. This could have cascading effects on marine food webs, carbon cycling, and fisheries productivity. Overall, the form of bioavailable nitrogen emerges as an potentially underappreciated driver of ecosystem structure and function in the changing ocean.
Continue reading ‘Oceanic enrichment of ammonium and its impacts on phytoplankton community composition under a high-emissions scenario’Advanced deep learning technique for estimating global surface ocean calcium carbonate saturation (Ωcal)
Published 18 December 2024 Science ClosedTags: chemistry, globalmodeling, methods, modeling
Highlights
- The study employed FFNN, RF, and TabNet ML models to estimate Ωcal using in-situ measurements and satellite-derived data.
- The TabNet model outperformed with significantly low errors (RMSE=0.39, MNB=0.0058, MRE=0.019) and high accuracy (R2=0.96).
- SST strongly correlated with Ωcal (r=0.98), SSS moderately (r=0.49), and Chla showed a weak negative correlation (r=-0.27).
- The study observed seasonal Ωcal variability, with higher values in summer months, notably in temperate and polar regions.
- Emphasized a declining trend of Ωcal from 2012 to 2022, likely influenced by changing oceanic and atmospheric conditions.
Abstract
The accurate estimation of surface ocean calcium carbonate saturation (Ωcal) is crucial for understanding the impacts of ocean acidification (OA) on marine ecosystems, particularly for calcifying organisms. This study investigates the estimation of global surface ocean Ωcal using machine learning (ML) models and satellite-derived data. Three ML models such as feed-forward neural networks (FFNN), random forests (RF), and Tabularnet (TabNet) were employed to estimate Ωcal, utilizing in-situ and satellite measurements of sea surface temperature (SST), sea surface salinity (SSS), and Chlorophyll-a concentration (Chla). Among these, the TabNet model exhibited superior performance, with a root-mean-square error (RMSE) of 0.39, mean relative error (MRE) of 0.019, mean normalized bias (MNB) of 0.0058 and coefficient of determination (R2) of 0.96. SST showed a strong positive correlation with Ωcal (r = 0.95), while SSS and Chla exhibited moderate positive (r = 0.49) and weak negative (r = −0.27) correlations, respectively. The study revealed significant spatiotemporal variability in Ωcal, driven by seasonal changes and ocean circulation patterns. Sensitivity analysis highlighted the robustness of the TabNet model, maintaining high predictive capability despite variations in SST, SSS, and Chla. The TabNet model high accuracy provides a valuable tool for monitoring and forecasting changes in ocean chemistry, informing conservation efforts and policy-making. This study emphasizes the importance of advanced ML models in marine science and their potential for enhancing our understanding of global oceanic processes.
Continue reading ‘Advanced deep learning technique for estimating global surface ocean calcium carbonate saturation (Ωcal)’Progression of ocean interior acidification over the industrial era
Published 4 December 2024 Science ClosedTags: chemistry, globalmodeling, modeling
Ocean acidification driven by the uptake of anthropogenic CO2 represents a major threat to ocean ecosystems, yet little is known about its progression beneath the surface. Here, we reconstruct the history of ocean interior acidification over the industrial era on the basis of observation-based estimates of the accumulation of anthropogenic carbon. Across the top 100 meters and from 1800 to 2014, the saturation state of aragonite (Ωarag) and pH = −log[H+] decreased by more than 0.6 and 0.1, respectively, with nearly 50% of the progression occurring over the past 20 years. While the magnitude of the Ωarag change decreases uniformly with depth, the magnitude of the [H+] increase exhibits a distinct maximum in the upper thermocline. Since 1800, the saturation horizon (Ωarag = 1) shoaled by more than 200 meters, approaching the euphotic zone in several regions, especially in the Southern Ocean, and exposing many organisms to corrosive conditions.
Continue reading ‘Progression of ocean interior acidification over the industrial era’Migrating is not enough for modern planktonic foraminifera in a changing ocean
Published 20 November 2024 Science ClosedTags: abundance, globalmodeling, modeling, mortality, otherprocess, phytoplankton
Rising carbon dioxide emissions are provoking ocean warming and acidification1,2, altering plankton habitats and threatening calcifying organisms3, such as the planktonic foraminifera (PF). Whether the PF can cope with these unprecedented rates of environmental change, through lateral migrations and vertical displacements, is unresolved. Here we show, using data collected over the course of a century as FORCIS4 global census counts, that the PF are displaying evident poleward migratory behaviours, increasing their diversity at mid- to high latitudes and, for some species, descending in the water column. Overall foraminiferal abundances have decreased by 24.2 ± 0.1% over the past eight decades. Beyond lateral migrations5, our study has uncovered intricate vertical migration patterns among foraminiferal species, presenting a nuanced understanding of their adaptive strategies. In the temperature and calcite saturation states projected for 2050 and 2100, low-latitude foraminiferal species will face physicochemical environments that surpass their current ecological tolerances. These species may replace higher-latitude species through poleward shifts, which would reduce low-latitude foraminiferal diversity. Our insights into the adaptation of foraminifera during the Anthropocene suggest that migration will not be enough to ensure survival. This underscores the urgent need for us to understand how the interplay of climate change, ocean acidification and other stressors will impact the survivability of large parts of the marine realm.
Continue reading ‘Migrating is not enough for modern planktonic foraminifera in a changing ocean’Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning
Published 15 October 2024 Science ClosedTags: chemistry, globalmodeling, modeling
Global measurements of ocean pCO2 are critical to monitor and understand changes in the global carbon cycle. However, pCO2 observations remain sparse as they are mostly collected on opportunistic ship tracks. Several approaches, especially based on direct learning, have been used to upscale and extrapolate sparse point data to dense estimates using globally available input features. However, these estimates tend to exhibit spatially heterogeneous performance. As a result, we propose a physics-informed transfer learning workflow to generate dense pCO2 estimates that are grounded in real-world measurements and remain physically consistent. The models are initially trained on dense input predictors against pCO2 estimates from Earth system model simulation, and then fine-tuned to sparse SOCAT observational data. Compared to the benchmark direct learning approach, our transfer learning framework shows major improvements of up to 56-92%. Furthermore, we demonstrate that using models that explicitly account for spatiotemporal structures in the data yield better validation performances by 50-68%. Our strategy thus presents a new monthly global pCO2 estimate that spans for 35 years between 1982-2017.
Continue reading ‘Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning’Linking cumulative carbon emissions to observable climate impacts
Published 10 October 2024 Science ClosedTags: globalmodeling, mitigation, modeling, policy, regionalmodeling, review
Anthropogenic CO2 emissions are causing climate change, and impacts of climate change are already affecting every region on Earth. The purpose of this review is to investigate climate impacts that can be linked quantitatively to cumulative CO2 emissions (CE), with a focus on impacts scaling linearly with CE. The reviewed studies indicate a proportionality between CE and various observable climate impacts such as regional warming, extreme daily temperatures, heavy precipitation events, seasonal changes in temperature and precipitation, global mean precipitation increase over ocean, sea ice decline in September across the Arctic Ocean, surface ocean acidification, global mean sea level rise, different marine heatwave characteristics, changes in habitat viability for non-human primates, as well as labour productivity loss due to extreme heat exposure. From the reviewed literature, we report estimates of these climate impacts resulting from one trillion tonne of CE (1 Tt C). These estimates are highly relevant for climate policy as they provide a way for assessing climate impacts associated with every amount of CO2 emitted by human activities. With the goal of expanding the number of climate impacts that could be linked quantitatively to CE, we propose a framework for estimating additional climate impacts resulting from CE. This framework builds on the transient climate response to cumulative emissions (TCRE), and it is applicable to climate impacts that scale linearly with global warming. We illustrate how the framework can be applied to quantify physical, biological, and societal climate impacts resulting from CE. With this review, we highlight that each tonne of CO2 emissions matters in terms of resulting impacts on natural and human systems.
Continue reading ‘Linking cumulative carbon emissions to observable climate impacts’Marine carbon sink dominated by biological pump after temperature overshoot
Published 3 October 2024 Science ClosedTags: chemistry, globalmodeling, modeling
In the event of insufficient mitigation efforts, net-negative CO2 emissions may be required to return climate warming to acceptable limits as defined by the Paris Agreement. The ocean acts as an important carbon sink under increasing atmospheric CO2 levels when the physico-chemical uptake of carbon dominates. However, the processes that govern the marine carbon sink under net-negative CO2 emission regimes are unclear. Here we assessed changes in marine CO2 uptake and storage mechanisms under a range of idealized temperature-overshoot scenarios using an Earth system model of intermediate complexity over centennial timescales. We show that while the fate of CO2 from physico-chemical uptake is very sensitive to future atmospheric boundary conditions and CO2 is partly lost from the ocean at times of net-negative CO2 emissions, storage associated with the biological carbon pump continues to increase and may even dominate marine excess CO2 storage on multi-centennial timescales. Our findings imply that excess carbon that is attributable to the biological carbon pump needs to be considered carefully when quantifying and projecting changes in the marine carbon sink.
Continue reading ‘Marine carbon sink dominated by biological pump after temperature overshoot’Site-Specific multiple stressor assessments based on high frequency surface observations and an earth system model
Published 1 August 2024 Science ClosedTags: chemistry, globalmodeling, methods, modeling
Abstract
Global Earth system models are often enlisted to assess the impacts of climate variability and change on marine ecosystems. In this study, we compare high frequency (daily) outputs of potential ecosystem stressors, such as sea surface temperature and surface pH, and associated variables from an Earth system model (GFDL ESM4.1) with high frequency time series from a global network of moorings to directly assess the capacity of the model to resolve local biogeochemical variability on time scales from daily to interannual. Our analysis indicates variability in surface temperature is most consistent between ESM4.1 and observations, with a Pearson correlation coefficient of 0.93 and bias of 0.40°C, followed by variability in surface salinity. Physical variability is reproduced with greater accuracy than biogeochemical variability, and variability on seasonal and longer time scales is more consistent between the model and observations than higher frequency variability. At the same time, the well-resolved seasonal and longer timescale variability is a reasonably good predictor, in many cases, of the likelihood of extreme events. Despite limited model representation of high frequency variability, model and observation-based assessments of the fraction of days experiencing surface T-pH and T-Ωarag multistressor conditions show reasonable agreement, depending on the stressor combination and threshold definition. We also identify circumstances in which some errors could be reduced by accounting for model biases.
Key Points
- Physical and biogeochemical variability from moorings and GFDL ESM4.1 output is most consistent on seasonal and longer time scales
- More high frequency (daily to monthly) variability is present in observations than in ESM output
- Despite missing high frequency variability, ESM output can be applied to accurately quantify multiple stressor events
Plain Language Summary
Ocean ecosystems are under stress from changing temperature and acidity due to the human-driven increase in global atmospheric carbon dioxide. Global Earth system models (ESMs) are used to study the effects of climate variability and change on marine ecosystems. However, computing power and storage constraints limit the level of detail represented by these simulations. Some short timescale variability present in the real world is missing from ESM output. Despite this inconsistency, we show that at an array of sites where daily observation data from ocean moorings is available, models accurately capture observed spatial patterns in estimates of the amount of time the locations experience combined temperature and acidification stress. We also demonstrate circumstances in which some model errors can be reduced through bias correction.
Continue reading ‘Site-Specific multiple stressor assessments based on high frequency surface observations and an earth system model’Ocean carbon sink assessment via temperature and salinity data assimilation into a global ocean biogeochemistry model
Published 31 July 2024 Science ClosedTags: chemistry, globalmodeling, methods, modeling
Global ocean biogeochemistry models are frequently used to derive a comprehensive estimate of the global ocean carbon uptake. These models are designed to represent the most important processes of the ocean carbon cycle, but the idealized process representation and uncertainties in the initialization of model variables lead to errors in their predictions. Here, observations of ocean physics (temperature and salinity) are assimilated into the ocean biogeochemistry model FESOM-REcoM over the period 2010–2020 to study the effect on the air-sea CO2 flux and other biogeochemical variables. While the free running model already represents temperature and salinity rather well, the assimilation further improves it and hence influences the modeled ecosystem and CO2 fluxes. The assimilation has mainly regional effects on the air-sea CO2 flux, with the largest imprint of assimilation in the Southern Ocean during winter. South of 50° S, winter CO2 outgassing is reduced and thus the mean CO2 uptake increases by 0.18 Pg C yr-1 through the assimilation. Other particularly strong regional effects on the air-sea CO2 flux are located in the area of the North Atlantic Current. Yet, the effect on the global ocean carbon uptake is a comparatively small increase by 0.05 Pg C yr-1 induced by the assimilation, yielding a global mean uptake of 2.78 Pg C yr-1 for the period 2010–2020.
Continue reading ‘Ocean carbon sink assessment via temperature and salinity data assimilation into a global ocean biogeochemistry model’From nutrients to fish: impacts of mesoscale processes in a global CESM-FEISTY eddying ocean model framework
Published 26 July 2024 Science ClosedTags: abundance, biological response, BRcommunity, chemistry, community composition, fish, globalmodeling, modeling, otherprocess, phytoplankton, review, zooplankton
The ocean sustains ecosystems that are essential for human livelihood and habitability of the planet. The ocean holds an enormous amount of carbon, and serves as a critical source of nutrition for human societies worldwide. Climate variability and change impacts marine biogeochemistry and ecosystems. Thus, having state-of-the-art simulations of the ocean, which include marine biogeochemistry and ecosystems, is critical for understanding the role of climate variability and change on the ocean biosphere. Here we present a novel global eddy-resolving (0.1° horizontal resolution) simulation of the ocean and sea ice, including ocean biogeochemistry, performed with the Community Earth System Model (CESM). The simulation is forced by the atmospheric dataset based on the Japanese Reanalysis (JRA-55) product over the 1958 – 2021 period. We present a novel configuration of the CESM marine ecosystem model in this simulation which includes two zooplankton classes: microzooplankton and mesozooplankton. This novel planktonic food web structure facilitates “offline” coupling with the Fisheries Size and Functional Type (FEISTY) model. FEISTY is a size- and trait-based model of fish functional types contributing to fisheries. We present an evaluation of the ocean biogeochemistry, marine ecosystem (including fish types), and sea ice in this high-resolution simulation compared to available observations and a corresponding low resolution (nominal 1°) simulation. Our analysis offers insights into environmental controls on trophodynamics within the ocean. We find that this high resolution simulation provides a realistic reconstruction of nutrients, oxygen, sea ice, plankton and fish distributions over the global ocean. On global and large regional scales the high-resolution simulation is comparable to the standard 1° simulation, but on smaller scales, explicitly resolving the mesoscale dynamics is shown to be important for accurately capturing trophodynamic structuring, especially in coastal ecosystems. We show that fine scale ocean features leave imprints on ocean ecosystems, from plankton to fish, from the tropics to polar regions. This simulation also offers insights on ocean acidification over the past 64 years, as well as how large scale climate variations may impact upper trophic levels. The data generated by the simulations are publicly available and will be a fruitful community resource for a large variety of oceanographic science questions.
Continue reading ‘From nutrients to fish: impacts of mesoscale processes in a global CESM-FEISTY eddying ocean model framework’Estimation of spatiotemporal variability of global surface ocean DIC fields using ocean color remote sensing data
Published 20 June 2024 Science ClosedTags: chemistry, globalmodeling, methods, modeling
The estimation of dissolved inorganic carbon (DIC) in global surface ocean waters is crucial for understanding air-sea carbon dioxide (CO2) flux rates, ocean acidification, and climate change. DIC magnitude and spatiotemporal variability are influenced by various physical and biogeochemical processes. Due to dynamic variations in ocean surface water, estimating DIC through in situ data alone is challenging. Ocean color remote sensing offers high spatial and temporal resolution data with extensive synoptic views. Over decades, multiple DIC approaches have emerged using in situ and satellite observations but are limited to specific regions due to improper model parameter selection and sparse in situ measurements. To address this, we propose a novel multiparametric regression (MPR) approach that relates DIC as a function of sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chla) concentration. Utilizing in situ data from the Global Ocean Data Analysis Project (GLODAP), the trends of DIC with SST, SSS, and Chla were analyzed to develop MPR regression equations. The validation results indicated that the proposed regression approach accurately estimates DIC in global surface ocean waters. This approach offers benefits, such as DIC estimates at any spatiotemporal resolutions, easy implementation, and cost-effective alternatives to in situ measurements. Additionally, seasonal and interannual variations of global DIC fields were demonstrated through satellite oceanographic data, enhancing monitoring of ocean acidification and climate change scenarios.
Continue reading ‘Estimation of spatiotemporal variability of global surface ocean DIC fields using ocean color remote sensing data’The carbonate pump feedback on alkalinity and the carbon cycle in the 21st century and beyond
Published 13 June 2024 Science ClosedTags: chemistry, globalmodeling, modeling, review
Ocean acidification is likely to impact all stages of the ocean carbonate pump, i.e. the production, export, dissolution and burial of biogenic CaCO3. However, the associated feedback on anthropogenic carbon uptake and ocean acidification has received little attention. It has previously been shown that Earth system model (ESM) carbonate pump parameterizations can affect and drive biases in the representation of ocean alkalinity, which is critical to the uptake of atmospheric carbon and provides buffering capacity towards associated acidification. In the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we show divergent responses of CaCO3 export at 100 m this century, with anomalies by 2100 ranging from −74 % to +23 % under a high-emission scenario. The greatest export declines are projected by ESMs that consider pelagic CaCO3 production to depend on the local calcite/aragonite saturation state. Despite the potential effects of other processes on alkalinity, there is a robust negative correlation between anomalies in CaCO3 export and salinity-normalized surface alkalinity across the CMIP6 ensemble. Motivated by this relationship and the uncertainty in CaCO3 export projections across ESMs, we perform idealized simulations with an ocean biogeochemical model and confirm a limited impact of carbonate pump anomalies on 21st century ocean carbon uptake and acidification. However, we highlight a potentially abrupt shift, between 2100 and 2300, in the dissolution of CaCO3 from deep to subsurface waters when the global-scale mean calcite saturation state reaches about 1.23 at 500 m (likely when atmospheric CO2 reaches 900–1100 ppm). During this shift, upper ocean acidification due to anthropogenic carbon uptake induces deep ocean acidification driven by a substantial reduction in CaCO3 deep dissolution following its decreased export at depth. Although the effect of a diminished carbonate pump on global ocean carbon uptake and surface ocean acidification remains limited until 2300, it can have a large impact on regional air–sea carbon fluxes, particularly in the Southern Ocean.
Continue reading ‘The carbonate pump feedback on alkalinity and the carbon cycle in the 21st century and beyond’Column-compound extremes in the global ocean
Published 6 June 2024 Science ClosedTags: globalmodeling, methods, modeling
Abstract
Marine extreme events such as marine heatwaves, ocean acidity extremes and low oxygen extremes can pose a substantial threat to marine organisms and ecosystems. Such extremes might be particularly detrimental (a) when they are compounded in more than one stressor, and (b) when the extremes extend substantially across the water column, restricting the habitable space for marine organisms. Here, we use daily output of a hindcast simulation (1961–2020) from the ocean component of the Community Earth System Model to characterize such column-compound extreme events (CCX), employing a relative threshold approach to identify extremes and requiring them to extend vertically over at least 50 m. The diagnosed CCX are prevalent, occupying worldwide in the 1960s about 1% of the volume contained within the top 300 m. Over the duration of our simulation, CCX become more intense, last longer, and occupy more volume, driven by the trends in ocean warming and ocean acidification. For example, the triple CCX expanded 39-fold, now last 3-times longer, and became 6-times more intense since the early 1960s. Removing this effect with a moving baseline permits us to better understand the key characteristics of CCX, revealing a typical duration of 10–30 days and a predominant occurrence in the Tropics and high latitudes, regions of high potential biological vulnerability. Overall, the CCX fall into 16 clusters, reflecting different patterns and drivers. Triple CCX are largely confined to the tropics and the North Pacific and tend to be associated with the El Niño-Southern Oscillation.
Key Points
- Column-compound extremes (CCX)- extremes in multiple parameters within the top 300 m—may reduce habitable space by up to 75%
- From 1961 to 2020, CCX have become more intense, longer, and occupy more volume, driven by the trends in ocean warming and acidification
- Triple CCX are confined to the tropics and the North Pacific and tend to be associated with ENSO
Plain Language Summary
The global ocean is becoming warmer, more acidic, and losing oxygen due to climate change. On top of this trend, sudden increases in temperature, or drops in pH or oxygen adversely affect marine organisms when they cannot quickly adapt to these extreme conditions. These conditions are worse for marine organisms when such extremes occur together in the vertical water column, leading to column-compound extreme (CCX) events, severely reducing the available habitable space. To investigate such CCX, we used a numerical model simulation of the global ocean during the historical period of 1961–2020. Singular extreme events are identified primarily with relative percentile thresholds, while CCX require a 50 m minimum depth threshold in the water column. We find that CCX have been increasing in volume, occupying up to 20% of the global ocean volume toward 2020. We then remove the climate trend to better understand the drivers behind CCX. Many CCX occur in the tropics and high latitudes, lasting 10–30 days and reducing habitable space by up to 75%. This study is the first to systematically detect compound extremes in the water column and may form the basis for determining their detrimental effects on marine organisms and ecosystems.
Continue reading ‘Column-compound extremes in the global ocean’Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry
Published 21 May 2024 Science ClosedTags: chemistry, globalmodeling, methods, modeling
Ocean CO2 uptake and acidification in response to human activities are driven primarily by the rise in atmospheric CO2, but are also modulated by climate change. Existing work suggests that this `climate effect’ influences the uptake and storage of anthropogenic carbon and acidification via the global increase in ocean temperature, although some regional responses have been attributed to changes in circulation or biological activity. Here, we investigate spatial patterns in the climate effect on surface-ocean acidification (and the closely related carbonate chemistry) in an Earth System Model under a rapid CO2-increase scenario, and identify another culprit. We show that the amplification of the hydrological cycle, a robustly simulated feature of climate change, is largely responsible for the spatial patterns in this climate effect at the sea surface. This `hydrological effect’ can be understood as a subset of the total climate effect which includes warming, hydrological cycle amplification, circulation and biological changes. We demonstrate that it acts through two primary mechanisms: (i) directly diluting or concentrating dissolved ions by adding or removing freshwater and (ii) altering the sea surface temperature, which influences the solubility of dissolved inorganic carbon (DIC) and acidity of seawater. The hydrological effect opposes acidification in salinifying regions, most notably the subtropical Atlantic, and enhances acidification in freshening regions such as the western Pacific. Its single strongest effect is to dilute the negative ions that buffer the dissolution of CO2, quantified as `Alkalinity’. The local changes in Alkalinity, DIC, and pH linked to the pattern of hydrological cycle amplification are as strong as the (largely uniform) changes due to warming, explaining the weak increase in pH and DIC seen in the climate effect in the subtropical Atlantic Ocean.
Continue reading ‘Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry’A global monthly field of seawater pH over 3 decades: a machine learning approach
Published 20 May 2024 Science ClosedTags: biogeochemistry, chemistry, globalmodeling, methods, modeling
The continuous uptake of anthropogenic CO2 by the ocean leads to ocean acidification, which is an ongoing threat to the marine ecosystem. The ocean acidification rate was globally documented in the surface ocean but limited below the surface. Here, we present a monthly four-dimensional 1°×1° gridded product of global seawater pH, derived from a machine learning algorithm trained on pH observations at total scale and in-situ temperature from the Global Ocean Data Analysis Project (GLODAP). The constructed pH product covers the years 1992–2020 and depths from the surface to 2 km on 41 levels. Three types of machine learning algorithms were used in the pH product construction, including self-organizing map neural networks for region dividing, a stepwise algorithm for predictor selection, and feed-forward neural networks (FFNN) for non-linear relationship regression. The performance of the machine learning algorithm was validated using real observations by a cross validation method, where four repeating iterations were carried out with 25 % varied observations for each evaluation and 75 % for training. The constructed pH product is evaluated through comparisons to time series observations and the GLODAP pH climatology. The overall root mean square error between the FFNN constructed pH and the GLODAP measurements is 0.028, ranging from 0.044 in the surface to 0.013 at 2000 m. The pH product is distributed through the data repository of the Marine Science Data Center of the Chinese Academy of Sciences at http://dx.doi.org/10.12157/IOCAS.20230720.001 (Zhong et al., 2023).
Continue reading ‘A global monthly field of seawater pH over 3 decades: a machine learning approach’Thermal and nutrient stress drove Permian-Triassic shallow marine extinctions
Published 17 May 2024 Science ClosedTags: globalmodeling, modeling, paleo, review
Impact Statement: What are the biggest consequences of climate change for marine ecosystems? Is it deoxygenation, thermal stress, ocean acidification, or any combination thereof? The Permian-Triassic climate crisis was an episode of severe and rapid climate warming with similarities to the worst-case projected scenarios for the near future. To better understand which consequences of this climate event led to one of the most severe biodiversity crisis ever, we implemented a novel approach of statistically integrating high-resolution fossil data with high-resolution geochemical data. Our results demonstrate that for equatorial, marine ecosystems, oxygen isotope (temperature proxy) and cadmium isotope (primary productivity proxy) dynamics best explain the marine extinction. This suggests that the biggest threats to past and modern biodiversity in these settings are the impacts of thermal and nutrient stress, as well as associated trophic knock-on effects.
Abstract: The Permian-Triassic climate crisis can provide key insights into the potential impact of horizon threats to modern-day biodiversity. This crisis coincides with the same extensive environmental changes that threaten modern marine ecosystems (i.e., thermal stress, deoxygenation and ocean acidification), but the primary drivers of extinction are currently unknown. To understand which factors caused extinctions, we conducted a data analysis to quantify the relationship (anomalies, state-shifts and trends) between geochemical proxies and the fossil record at the most intensively studied locality for this event, the Meishan section, China. We found that δ18O apatite (paleotemperature proxy) and δ 32 114/110Cd (primary productivity proxy) best explain changes in species diversity and species composition in Meishan’s paleoequatorial setting. These findings suggest that the physiological stresses induced by ocean warming and nutrient availability played a predominant role in driving equatorial marine extinctions during the Permian-Triassic event. This research enhances our understanding of the interplay between environmental changes and extinction dynamics during a past climate crisis, presenting an outlook for extinction threats in the worst-case “Shared Socioeconomic Pathways (SSP5-8.5) scenario.
Continue reading ‘Thermal and nutrient stress drove Permian-Triassic shallow marine extinctions’Response of ocean acidification to atmospheric carbon dioxide removal
Published 28 March 2024 Science ClosedTags: chemistry, globalmodeling, methods, mitigation, modeling
Artificial CO2 removal from the atmosphere (also referred to as negative CO2 emissions) has been proposed as a potential means to counteract anthropogenic climate change. Here we use an Earth system model to examine the response of ocean acidification to idealized atmospheric CO2 removal scenarios. In our simulations, atmospheric CO2 is assumed to increase at a rate of 1% per year to four times its pre-industrial value and then decreases to the pre-industrial level at a rate of 0.5%, 1%, 2% per year, respectively. Our results show that the annual mean state of surface ocean carbonate chemistry fields including hydrogen ion concentration ([H+]), pH and aragonite saturation state respond quickly to removal of atmospheric CO2. However, the change of seasonal cycle in carbonate chemistry lags behind the decline in atmospheric CO2. When CO2 returns to the pre-industrial level, over some parts of the ocean, relative to the pre-industrial state, the seasonal amplitude of carbonate chemistry fields is substantially larger. Simulation results also show that changes in deep ocean carbonate chemistry substantially lag behind atmospheric CO2 change. When CO2 returns to its pre-industrial value, the whole-ocean acidity measured by [H+] is 15%-18% larger than the pre-industrial level, depending on the rate of CO2 decrease. Our study demonstrates that even if atmospheric CO2 can be lowered in the future as a result of net negative CO2 emissions, the recovery of some aspects of ocean acidification would take decades to centuries, which would have important implications for the resilience of marine ecosystems.
Continue reading ‘Response of ocean acidification to atmospheric carbon dioxide removal’Anthropogenic climate change drives non-stationary phytoplankton internal variability
Published 19 March 2024 Science ClosedTags: Arctic, biological response, globalmodeling, modeling, North Atlantic, North Pacific, phytoplankton, regionalmodeling
Earth system models suggest that anthropogenic climate change will influence marine phytoplankton over the coming century with light-limited regions becoming more productive and nutrient-limited regions less productive. Anthropogenic climate change can influence not only the mean state but also the internal variability around the mean state, yet little is known about how internal variability in marine phytoplankton will change with time. Here, we quantify the influence of anthropogenic climate change on internal variability in marine phytoplankton biomass from 1920 to 2100 using the Community Earth System Model 1 Large Ensemble (CESM1-LE). We find a significant decrease in the internal variability of global phytoplankton carbon biomass under a high emission (RCP8.5) scenario and heterogeneous regional trends. Decreasing internal variability in biomass is most apparent in the subpolar North Atlantic and North Pacific. In these high-latitude regions, bottom-up controls (e.g., nutrient supply, temperature) influence changes in biomass internal variability. In the biogeochemically critical regions of the Southern Ocean and the equatorial Pacific, bottom-up controls (e.g., light, nutrients) and top-down controls (e.g., grazer biomass) affect changes in phytoplankton carbon internal variability, respectively. Our results suggest that climate mitigation and adaptation efforts that account for marine phytoplankton changes (e.g., fisheries, marine carbon cycling) should also consider changes in phytoplankton internal variability driven by anthropogenic warming, particularly on regional scales.
Continue reading ‘Anthropogenic climate change drives non-stationary phytoplankton internal variability’Multi-month forecasts of marine heatwaves and ocean acidification extremes
Published 21 February 2024 Science ClosedTags: chemistry, globalmodeling, modeling
Marine heatwaves (MHW) and ocean acidification extreme events (OAX) are periods during which temperature and acidification reach extreme levels, endangering ecosystems. As the threats from MHW and OAX grow with climate change, there is need for skillful predictions of events months-to-years in advance. Previous work has demonstrated that climate models can predict marine heatwaves up to 12 months in advance in key regions, but no studies have attempted to predict OAX. Here we use the Community Earth System Model (CESM) Seasonal-to-Multiyear Large Ensemble (SMYLE) to make predictions of both MHW and OAX events. We find that CESM SMYLE skillfully predicts discrete MHW and OAX events up to 1 year in advance. Skill is highest in the tropical and northeast Pacific, reflecting the contribution of El Niño-Southern Oscillation. A forecast generated in late 2023 during the 2023-24 ENSO event finds high likelihood for widespread MHWs and OAX in 2024.
Continue reading ‘Multi-month forecasts of marine heatwaves and ocean acidification extremes’
