Posts Tagged 'regionalmodeling'

Retrieving monthly and interannual total-scale pH (pHT) on theEast China Sea shelf using an artificial neural network:ANN-pHT-v1 (update)

While our understanding of pH dynamics has strongly progressed for open-ocean regions, for marginal seas such as the East China Sea (ECS) shelf progress has been constrained by limited observations and complex interactions between biological, physical and chemical processes. Seawater pH is a very valuable oceanographic variable but not always measured using high-quality instrumentation and according to standard practices. In order to predict total-scale pH (pH(T)) and enhance our understanding of the seasonal variability of pHT on the ECS shelf, an artificial neural network (ANN) model was developed using 11 cruise datasets from 2013 to 2017 with coincident observations of pHT, temperature (T), salinity (S), dissolved oxygen (DO), nitrate (N), phosphate (P) and silicate (Si) together with sampling position and time. The reliability of the ANN model was evaluated using independent observations from three cruises in 2018, and it showed a root mean square error accuracy of 0.04. The ANN model responded to T and DO errors in a positive way and S errors in a negative way, and the ANN model was most sensitive to S errors, followed by DO and T errors. Monthly water column pHT for the period 2000-2016 was retrieved using T, S, DO, N, P and Si from the Changjiang biology Finite-Volume Coastal Ocean Model (FVCOM). The agreement is good here in winter, while the reduced performance in summer can be attributed in large part to limitations of the Changjiang biology FVCOM in simulating summertime input variables.

Continue reading ‘Retrieving monthly and interannual total-scale pH (pHT) on theEast China Sea shelf using an artificial neural network:ANN-pHT-v1 (update)’

Physical and biogeochemical drivers of alongshore pH and oxygen variability in the California Current System

In the California Current System (CCS), the nearshore environment experiences natural exposure to low pH and reduced oxygen in response to coastal upwelling. Anthropogenic impacts further decrease pH and oxygen below biological thresholds, making the CCS particularly vulnerable to ocean acidification and hypoxia. Results from a coupled physical‐biogeochemical model reveal a strongly heterogeneous alongshore pattern of nearshore pH and oxygen in the central CCS, both in their long‐term means and trends. This spatial structuring is explained by an interplay between alongshore variability in local upwelling intensity and subsequent primary production, modulated by nearshore advection and regional geostrophic currents. The model solution suggests that the progression of ocean acidification and hypoxia will not be spatially homogeneous, thereby highlighting the need to consider subregional processes when assessing natural and anthropogenic impacts on coastal ecosystems in eastern boundary current upwelling regions.

Continue reading ‘Physical and biogeochemical drivers of alongshore pH and oxygen variability in the California Current System’

Permian–Triassic mass extinction pulses driven by major marine carbon cycle perturbations

The Permian/Triassic boundary approximately 251.9 million years ago marked the most severe environmental crisis identified in the geological record, which dictated the onwards course for the evolution of life. Magmatism from Siberian Traps is thought to have played an important role, but the causational trigger and its feedbacks are yet to be fully understood. Here we present a new boron-isotope-derived seawater pH record from fossil brachiopod shells deposited on the Tethys shelf that demonstrates a substantial decline in seawater pH coeval with the onset of the mass extinction in the latest Permian. Combined with carbon isotope data, our results are integrated in a geochemical model that resolves the carbon cycle dynamics as well as the ocean redox conditions and nitrogen isotope turnover. We find that the initial ocean acidification was intimately linked to a large pulse of carbon degassing from the Siberian sill intrusions. We unravel the consequences of the greenhouse effect on the marine environment, and show how elevated sea surface temperatures, export production and nutrient input driven by increased rates of chemical weathering gave rise to widespread deoxygenation and sporadic sulfide poisoning of the oceans in the earliest Triassic. Our findings enable us to assemble a consistent biogeochemical reconstruction of the mechanisms that resulted in the largest Phanerozoic mass extinction.

Continue reading ‘Permian–Triassic mass extinction pulses driven by major marine carbon cycle perturbations’

Nordic Seas acidification

Being windows to the deep ocean, the Nordic Seas play an important role in transferring anthropogenic carbon, and thus ocean acidification, to the abyss. Due to its location in high latitudes, it is further more sensitive to acidification compared with many other oceanic regions. Here we make a detailed investigation of the acidification of the Nordic Seas, and its drivers, since pre-Industrial to 2100 by using in situ measurements, gridded climatological data, and simulations from one Earth System Model (ESM). In the last 40 years, pH has decreased by 0.11 units in the Nordic Seas surface waters, a change that is twice as large as that between 1850–1980. We find that present trends are larger than expected from the increase in atmospheric CO2 alone, which is related to a faster increase in the seawater pCO2 compared with that of the atmosphere, i.e. a weakening of the pCO2 undersaturation of the Nordic Seas. The pH drop, mainly driven by an uptake of anthropogenic CO2, is significant all over the Nordic Seas, except for in the Barents Sea Opening, where it is counteracted by a significant increase in alkalinity. We also find that the acidification signal penetrates relatively deep, in some regions down to 2000 m. This has resulted in a significant decrease in the aragonite saturation state, which approaches undersaturation at 1000–2000 m in the modern ocean. Future scenarios suggest an additional drop of 0.1–0.4 units, depending on the emission scenario, in surface pH until 2100. In the worst case scenario, RCP8.5, the entire water column will be undersaturated with respect to aragonite by the end of the century, threatening Nordic Seas cold-water corals and their ecosystems. The model simulations suggest that aragonite undersaturation can be avoided at depths where the majority of the cold-water corals live in the RCP2.6 and RCP4.5 scenarios. As these results are based on one model only, we request additional observational and model studies to better quantify the transfer of anthropogenic CO2 to deep waters and its effect on future pH in the Nordic Seas.

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Modeled impacts of sea ice exchange processes on Arctic Ocean carbon uptake and acidification (1980–2015)

Marine organisms and ecosystems face multiple, temporally variable stressors in a rapidly changing world. Realistic experiments that incorporate these aspects of physiological stress are important for advancing our ability to understand, predict, and manage their ecological impacts. However, the experimental systems needed to conduct such experiments can be costly. Here, we describe a low‐cost, modular control system that can be used with seawater sensors and actuators to dynamically manipulate multiple seawater variables. It enables researchers to run a variety of realistic multiple‐stressor, variable exposure experiments with a range of marine organisms. This tank controller system is based on the open‐source Arduino prototyping platform and features a custom‐made circuit board with a 16‐bit analog‐to‐digital converter, a real‐time clock, a MicroSD memory card reader, a high‐voltage transistor array, and solderless screw terminal connectors for easy connection of sensors, actuators, and power supplies. The assembly and use of this controller system does not require extensive electronics engineering or programming experience, and each module can be assembled for under 80 USD in parts. To demonstrate the system’s capabilities, we present seawater manipulations from experiments involving (1) simultaneous manipulations of dissolved oxygen and pH; (2) fluctuating dissolved oxygen levels; and (3) a controlled stepwise decrease in dissolved oxygen at different temperatures. The low cost and high customizability of this Arduino‐based control system can contribute to expanding capacities for running global change experiments for researchers and students worldwide.

Continue reading ‘Modeled impacts of sea ice exchange processes on Arctic Ocean carbon uptake and acidification (1980–2015)’

Long‐term changes of carbonate chemistry variables along the North American East Coast

Decadal variability of carbonate chemistry variables has been studied for the open ocean using observations and models, but less is known about the variations in the coastal ocean due to observational gaps and the more complex environments. In this work, we use a Bayesian‐neural‐network approach to reconstruct surface carbonate chemistry variables for the Mid‐Atlantic Bight (MAB) and the South Atlantic Bight (SAB) along the North American East Coast from 1982 to 2015. The reconstructed monthly time series data suggest that the rate of fCO2 increase in the MAB (18 ± 1 μatm per decade) is faster than those in the SAB (14 ± 1 μatm per decade) and the open ocean (14 ± 1 μatm per decade). Correspondingly, pH decreases faster in the MAB. The observed stagnation in the aragonite saturation state, Ωarag decrease during 2005–2015 in the MAB, is attributed to the intrusion of water from southern and offshore regions with high Ωarag, which offsets the decrease expected from anthropogenic CO2 uptake. Furthermore, seasonal asymmetry in the evolution of long‐term change leads to the faster change in the amplitudes of the seasonal cycle in carbonate chemistry variables in coastal waters than those in the open ocean. In particular, the increase in the seasonal‐cycle amplitude of dissolved inorganic carbon in the MAB is 2.9 times larger than that of the open ocean. This leads to the faster increase in the season‐cycle amplitude of Ωarag and earlier occurrence of undersaturation in coastal waters as acidification continues.

Continue reading ‘Long‐term changes of carbonate chemistry variables along the North American East Coast’

Effects of nearshore processes on carbonate chemistry dynamics and ocean acidification

Time series from open ocean fixed stations have robustly documented secular changes in carbonate chemistry and long-term ocean acidification (OA) trends as a direct response to increases in atmospheric carbon dioxide (CO2). However, few high-frequency coastal carbon time series are available in reef systems, where most affected tropical marine organisms reside. Seasonal variations in carbonate chemistry at Cheeca Rocks (CR), Florida, and La Parguera (LP), Puerto Rico, are presented based on 8 and 10 years of continuous, high-quality measurements, respectively. This dissertation synthesizes autonomous and bottle observations to model carbonate chemistry and to understand how physical and biological processes affect seasonal carbonate chemistry at both locations. The autonomous carbonate chemistry and oxygen observations are used to examine a mass balance approach using a 1-D model to determine net rates of ecosystem calcification and production (NEC and NEP) from communities close (<5km) to the buoys. The results provide evidence to suggest that seasonal response between benthic metabolism and seawater chemistry at LP is attenuated relative to that at CR because their differences in benthic cover and how benthic metabolism modifies the water chemistry. Simple linear trends cannot explain the feedback between metabolism and reef water chemistry using long-term observations over natural variations. The effects of community production on partial pressure of CO2 (pCO2sw) make these interactions complex at short- and long-term scales. Careful consideration should be taken when inferring local biogeochemical processes, given that pCO2sw (and presumably pH) respond on much shorter time and local scales than dissolved inorganic carbon (DIC) and total alkalinity (TA). The observations highlight the need for more comprehensive observing systems that can reliably measure both the fast-response (pCO2sw, pH) and slow-response (DIC) carbon pools.

Continue reading ‘Effects of nearshore processes on carbonate chemistry dynamics and ocean acidification’

A regional neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED

A regional neural network-based method, “CANYON-MED” is developed to estimate nutrients and carbonate system variables specifically in the Mediterranean Sea over the water column from pressure, temperature, salinity, and oxygen together with geolocation and date of sampling. Six neural network ensembles were developed, one for each variable (i.e., three macronutrients: nitrates (NO−33-), phosphates (PO3−443-) and silicates (SiOH4), and three carbonate system variables: pH on the total scale (pHT), total alkalinity (AT), and dissolved inorganic carbon or total carbon (CT), trained using a specific quality-controlled dataset of reference “bottle” data in the Mediterranean Sea. This dataset is representative of the peculiar conditions of this semi-enclosed sea, as opposed to the global ocean. For each variable, the neural networks were trained on 80% of the data chosen randomly and validated using the remaining 20%. CANYON-MED retrieved the variables with good accuracies (Root Mean Squared Error): 0.73 μ–1 for NO−33-, 0.045 μ–1 for PO3−443- and 0.70 μ–1 for Si(OH)4, 0.016 units for pHT, 11 μ–1 for AT and 10 μ–1 for CT. A second validation on the ANTARES independent time series confirmed the method’s applicability in the Mediterranean Sea. After comparison to other existing methods to estimate nutrients and carbonate system variables, CANYON-MED stood out as the most robust, using the aforementioned inputs. The application of CANYON-MED on the Mediterranean Sea data from autonomous observing systems (integrated network of Biogeochemical-Argo floats, Eulerian moorings and ocean gliders measuring hydrological properties together with oxygen concentration) could have a wide range of applications. These include data quality control or filling gaps in time series, as well as biogeochemical data assimilation and/or the initialization and validation of regional biogeochemical models still lacking crucial reference data. Matlab and R code are available at https://

Continue reading ‘A regional neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED’

Configuration and skill assessment of the coupled biogeochemical model for the carbonate system in the Bay of Bengal


  • A coupled physical-biogeochemical model (ROMS-PISCES) has been set up for the Bay of Bengal region to emulate the carbonate chemistry of this region.
  • The model has been run and rigorously evaluated using the available data sets and 8 statistical indices have been used to evaluate model skills.
  • The effect of wind stress and E-P has been evaluated through two numerical experiments, which uses two different bulk formulae to calculate the wind stresses.
  • The model is excellent in simulating the spatial heterogeneity and temporal variation of all the carbonate parameters thus giving a basis for further studies like the effect of physical dynamics, forecasting, etc.


The Bay of Bengal is a semi-enclosed ocean basin situated in the eastern part of the North Indian Ocean. Though the physical dynamical features of the Bay of Bengal have been studied and measured in detail, the carbonate chemistry of this basin has been less explored, and very few reliable data-sets exist. This paucity of data has emerged as a major challenge in modeling and understanding the carbonate system parameters for this region. In this study, a coupled physical-biogeochemical (ROMS-PISCES) model has been configured and run to emulate the surface carbonate system parameters (DIC, TALK, pCO2, and pH) for the Bay of Bengal region. Model skill assessment analysis has been performed using available observational data-sets. Two different numerical experiments have been performed (WB indicating the use of default bulk formulae of ROMS to calculate wind stress and WoB indicating the calculated wind stresses of QuikSCAT climatology product using different bulk formula), to understand which one reproduces the carbonate parameters better. Both the numerical experiments are rigorously compared for physical as well as carbonate system parameters. The numerical experiments have been passed through exhaustive statistical analysis by comparing it with the observed data-sets. The temperature, the primary driver affecting pH and pCO2 has been reproduced by both the experiments excellently, and the correlation value is more than 0.9 with RAMA buoy data (15o N, 90o E). The salinity, when compared with the NIOA climatology data, shows that the WoB experiment has better captured both the spatial and temporal variation of salinity. Both the numerical experiments have been compared individually with three sets of observed carbonate data. The WoB run has been found to emulate carbonate system parameters satisfactorily than the WB run. The pCO2 and pH show a good positive correlation with RAMA data and the values are 0.87, and 0.93, respectively.


Continue reading ‘Configuration and skill assessment of the coupled biogeochemical model for the carbonate system in the Bay of Bengal’

Downscaling global ocean climate models improves estimates of exposure regimes in coastal environments

Climate change is expected to warm, deoxygenate, and acidify ocean waters. Global climate models (GCMs) predict future conditions at large spatial scales, and these predictions are then often used to parameterize laboratory experiments designed to assess biological and ecological responses to future change. However, nearshore ecosystems are affected by a range of physical processes such as tides, local winds, and surface and internal waves, causing local variability in conditions that often exceeds global climate models. Predictions of future climatic conditions at local scales, the most relevant to ecological responses, are largely lacking. To fill this critical gap, we developed a 2D implementation of the Regional Ocean Modeling System (ROMS) to downscale global climate predictions across all Representative Concentration Pathway (RCP) scenarios to smaller spatial scales, in this case the scale of a temperate reef in the northeastern Pacific. To assess the potential biological impacts of local climate variability, we then used the results from different climate scenarios to estimate how climate change may affect the survival, growth, and fertilization of a representative marine benthic invertebrate, the red abalone Haliotis rufescens, to a highly varying multi-stressor environment. We found that high frequency variability in temperature, dissolved oxygen (DO), and pH increases as pCO2 increases in the atmosphere. Extreme temperature and pH conditions are generally not expected until RCP 4.5 or greater, while frequent exposure to low DO is already occurring. In the nearshore environment simulation, strong RCP scenarios can affect red abalone growth as well as reduce fertilization during extreme conditions when compared to global scale simulations.

Continue reading ‘Downscaling global ocean climate models improves estimates of exposure regimes in coastal environments’

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Ocean acidification in the IPCC AR5 WG II

OUP book