Posts Tagged 'regionalmodeling'

Seasonal patterns of surface inorganic carbon system variables in the Gulf of Mexico inferred from a regional high-resolution ocean biogeochemical model (update)

Uncertainties in carbon chemistry variability still remain large in the Gulf of Mexico (GoM), as data gaps limit our ability to infer basin-wide patterns. Here we configure and validate a regional high-resolution ocean biogeochemical model for the GoM to describe seasonal patterns in surface pressure of CO2 (pCO2), aragonite saturation state (ΩAr), and sea–air CO2 flux. Model results indicate that seasonal changes in surface pCO2 are strongly controlled by temperature across most of the GoM basin, except in the vicinity of the Mississippi–Atchafalaya river system delta, where runoff largely controls dissolved inorganic carbon (DIC) and total alkalinity (TA) changes. Our model results also show that seasonal patterns of surface ΩAr are driven by seasonal changes in DIC and TA, and reinforced by the seasonal changes in temperature. Simulated sea–air CO2 fluxes are consistent with previous observation-based estimates that show CO2 uptake during winter–spring, and CO2 outgassing during summer–fall. Annually, our model indicates a basin-wide mean CO2 uptake of 0.35 molm−2yr−1, and a northern GoM shelf (< 200 m) uptake of 0.93 molm−2yr−1. The observation and model-derived patterns of surface pCO2 and CO2 fluxes show good correspondence; thus this study contributes to improved constraints of the carbon budget in the region.

Continue reading ‘Seasonal patterns of surface inorganic carbon system variables in the Gulf of Mexico inferred from a regional high-resolution ocean biogeochemical model (update)’

A sediment trap evaluation of B/Ca as a carbonate system proxy in asymbiotic and nondinoflagellate hosting planktonic foraminifera

The ratio of boron to calcium (B/Ca) in a subset of foraminifera has been shown to covary with seawater carbonate chemistry, making this geochemical signature a promising proxy for carbon cycle science. Some studies suggest complications with the B/Ca proxy in photosymbiont‐bearing planktonic foraminifera, while relatively few studies have investigated B/Ca in species that lack large dinoflagellate symbionts. For the first time, we use a sediment trap time series to evaluate B/Ca of subtropical and subpolar planktonic foraminifera species that are asymbiotic (Globigerina bulloides and Neogloboquadrina incompta) and a species that hosts small intrashell photosymbionts (Neogloboquadrina dutertrei). We find that B/Ca measurements across size fractions indicate overall little to no size‐dependent uptake of boron that has previously been reported in some symbiont‐bearing foraminifera. Neogloboquadrina incompta and N. dutertrei B/Ca are strongly correlated with calcite saturation, pH, and carbonate ion concentration, which is in good agreement with the limited number of published core top results. While G. bulloides B/Ca trends with seasonal fluctuations in carbonate chemistry, during discrete periods considerable B/Ca offsets occur when a cryptic G. bulloides species is known to be seasonally present within the region. We confirm presence and significant B/Ca offset between cryptic species by individual LA‐ICP‐MS analyses. This finding calls into question the use of traditional morphological classification to lump what might be genetically distinct species for geochemical analyses. Our overall results highlight the utility of G. bulloides, N. incompta, and N. dutertrei B/Ca while bringing to light new considerations regarding divergent geochemistry of cryptic species.

Continue reading ‘A sediment trap evaluation of B/Ca as a carbonate system proxy in asymbiotic and nondinoflagellate hosting planktonic foraminifera’

The importance of environmental exposure history in forecasting Dungeness crab megalopae occurrence using J-SCOPE, a high-resolution model for the US Pacific Northwest

The Dungeness crab (Metacarcinus magister) fishery is one of the highest value fisheries in the US Pacific Northwest, but its catch size fluctuates widely across years. Although the underlying causes of this wide variability are not well understood, the abundance of M. magister megalopae has been linked to recruitment into the adult fishery 4 years later. These pelagic megalopae are exposed to a range of ocean conditions during their dispersal period, which may drive their occurrence patterns. Environmental exposure history has been found to be important for some pelagic organisms, so we hypothesized that inclusion of recent environmental exposure history would improve our ability to predict inter-annual variability in M. magister megalopae occurrence patterns compared to using “in situ” conditions alone. We combined 8 years of local observations of M. magister megalopae and regional simulations of ocean conditions to model megalopae occurrence using a generalized linear model (GLM) framework. The modeled ocean conditions were extracted from JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE), a high-resolution coupled physical-biogeochemical model. The analysis included variables from J-SCOPE identified in the literature as important for larval crab occurrence: temperature, salinity, dissolved oxygen concentration, nitrate concentration, phytoplankton concentration, pH, aragonite, and calcite saturation state. GLMs were developed with either in situ ocean conditions or environmental exposure histories generated using particle tracking experiments. We found that inclusion of exposure history improved the ability of the GLMs to predict megalopae occurrence 98% of the time. Of the six swimming behaviors used to simulate megalopae dispersal, five behaviors generated GLMs with superior fits to the observations, so a biological ensemble of these models was constructed. When the biological ensemble was used for forecasting, the model showed skill in predicting megalopae occurrence (AUC = 0.94). Our results highlight the importance of including exposure history in larval occurrence modeling and help provide a method for predicting pelagic megalopae occurrence. This work is a step toward developing a forecast product to support management of the fishery.

Continue reading ‘The importance of environmental exposure history in forecasting Dungeness crab megalopae occurrence using J-SCOPE, a high-resolution model for the US Pacific Northwest’

Effects of climate change and fishing on the Pearl River Estuary ecosystem and fisheries

Climate change poses a challenge to the management of marine ecosystems and fisheries. Estuarine ecosystems in particular are exposed to a broad range of environmental changes caused by the effects of climate change both on land and in the ocean, and such ecosystems have also had a long history of human disturbance from over-exploitation and habitat changes. In this study, we examine the effects of climate change and fishing on the Pearl River Estuary (PRE) ecosystem using Ecopath with Ecosim. Our results show that changes in net primary production and ocean warming are the dominant climatic factors impacting biomass and fisheries productivity in the PRE. Additionally, physiological changes of fishes and invertebrates that are induced by climate change were projected to be modified by trophic interactions. Overall, our study suggests that the combined effects of climate change and fishing will reduce the potential fisheries catches in the PRE. Reducing fishing efforts can reduce the impacts of climate change on selected functional groups; however, some prey fishes are expected to experience higher predation mortality and consequently decreases in biomass under low fishing intensity scenarios. Thus, our study highlights the non-linearity of the responses of estuarine ecosystems when climate change interacts with other human stressors.

Continue reading ‘Effects of climate change and fishing on the Pearl River Estuary ecosystem and fisheries’

A regional hindcast model simulating ecosystem dynamics, inorganic carbon chemistry and ocean acidification in the Gulf of Alaska

The coastal ecosystem of the Gulf of Alaska (GOA) is especially vulnerable to the effects of ocean acidification and climate change that can only be understood within the context of the natural variability of physical and chemical conditions. Controlled by its complex bathymetry, iron enriched freshwater discharge, and wind and solar radiation, the GOA is a highly dynamic system that exhibits large inorganic carbon variability from subseasonal to interannual timescales. This variability is poorly understood due to the lack of observations in this expansive and remote region. To improve our conceptual understanding of the system, we developed a new model set-up for the GOA that couples the three-dimensional Regional Oceanic Model System (ROMS), the Carbon, Ocean Biogeochemistry and Lower Trophic (COBALT) ecosystem model, and a high resolution terrestrial hydrological model. Here, we evaluate the model on seasonal to interannual timescales using the best available inorganic carbon observations. The model was particularly successful in reproducing observed aragonite oversaturation and undersaturation of near-bottom water in May and September, respectively. The largest deficiency of the model is perhaps its inability to adequately simulate spring time surface inorganic carbon chemistry, as it overestimates surface dissolved inorganic carbon, which translates into an underestimation of the surface aragonite saturation state at this time. We also use the model to describe the seasonal cycle and drivers of inorganic carbon parameters along the Seward Line transect in under-sampled months. As such, model output suggests that a majority of the near-bottom water along the Seward Line is seasonally under-saturated with regard to aragonite between June and January, as a result of upwelling and remineralization. Such an extensive period of reoccurring aragonite undersaturation may be harmful to CO2 sensitive organisms. Furthermore, the influence of freshwater not only decreases aragonite saturation state in coastal surface waters in summer and fall, but simultaneously also decreases surface pCO2, thereby decoupling the aragonite saturation state from pCO2. The full seasonal cycle and geographic extent of the GOA region is undersampled, and our model results give new and important insights for months of the year and areas that lack in situ inorganic carbon observations.

Continue reading ‘A regional hindcast model simulating ecosystem dynamics, inorganic carbon chemistry and ocean acidification in the Gulf of Alaska’

Climate‐induced changes in the suitable habitat of cold‐water corals and commercially important deep‐sea fishes in the North Atlantic

The deep sea plays a critical role in global climate regulation through uptake and storage of heat and carbon dioxide. However, this regulating service causes warming, acidification and deoxygenation of deep waters, leading to decreased food availability at the seafloor. These changes and their projections are likely to affect productivity, biodiversity and distributions of deep‐sea fauna, thereby compromising key ecosystem services. Understanding how climate change can lead to shifts in deep‐sea species distributions is critically important in developing management measures. We used environmental niche modelling along with the best available species occurrence data and environmental parameters to model habitat suitability for key cold‐water coral and commercially important deep‐sea fish species under present‐day (1951–2000) environmental conditions and to project changes under severe, high emissions future (2081–2100) climate projections (RCP8.5 scenario) for the North Atlantic Ocean. Our models projected a decrease of 28%–100% in suitable habitat for cold‐water corals and a shift in suitable habitat for deep‐sea fishes of 2.0°–9.9° towards higher latitudes. The largest reductions in suitable habitat were projected for the scleractinian coral Lophelia pertusa and the octocoral Paragorgia arborea, with declines of at least 79% and 99% respectively. We projected the expansion of suitable habitat by 2100 only for the fishes Helicolenus dactylopterus and Sebastes mentella (20%–30%), mostly through northern latitudinal range expansion. Our results projected limited climate refugia locations in the North Atlantic by 2100 for scleractinian corals (30%–42% of present‐day suitable habitat), even smaller refugia locations for the octocorals Acanella arbuscula and Acanthogorgia armata (6%–14%), and almost no refugia for P. arborea. Our results emphasize the need to understand how anticipated climate change will affect the distribution of deep‐sea species including commercially important fishes and foundation species, and highlight the importance of identifying and preserving climate refugia for a range of area‐based planning and management tools.

Continue reading ‘Climate‐induced changes in the suitable habitat of cold‐water corals and commercially important deep‐sea fishes in the North Atlantic’

Simulation of factors affecting Emiliania huxleyi blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection

The observed warming in the Arctic is more than double the global average, and this enhanced Arctic warming is projected to continue throughout the 21st century. This rapid warming has a wide range of impacts on polar and sub-polar marine ecosystems. One of the examples of such an impact on ecosystems is that of coccolithophores, particularly Emiliania huxleyi, which have expanded their range poleward during recent decades. The coccolithophore E. huxleyi plays an essential role in the global carbon cycle. Therefore, the assessment of future changes in coccolithophore blooms is very important.

Currently, there are a large number of climate models that give projections for various oceanographic, meteorological, and biochemical variables in the Arctic. However, individual climate models can have large biases when compared to historical observations. The main goal of this research was to select an ensemble of climate models that most accurately reproduces the state of environmental variables that influence the coccolithophore E. huxleyi bloom over the historical period when compared to reanalysis data. We developed a novel approach for model selection to include a diverse set of measures of model skill including the spatial pattern of some variables, which had not previously been included in a model selection procedure. We applied this method to each of the Arctic and sub-Arctic seas in which E. huxleyi blooms have been observed. Once we have selected an optimal combination of climate models that most skilfully reproduce the factors which affect E. huxleyi, the projections of the future conditions in the Arctic from these models can be used to predict how E. huxleyi blooms will change in the future.

Here, we present the validation of 34 CMIP5 (fifth phase of the Coupled Model Intercomparison Project) atmosphere–ocean general circulation models (GCMs) over the historical period 1979–2005. Furthermore, we propose a procedure of ranking and selecting these models based on the model’s skill in reproducing 10 important oceanographic, meteorological, and biochemical variables in the Arctic and sub-Arctic seas. These factors include the concentration of nutrients (NO3, PO4, and SI), dissolved CO2 partial pressure (pCO2), pH, sea surface temperature (SST), salinity averaged over the top 30 m (SS30 m), 10 m wind speed (WS), ocean surface current speed (OCS), and surface downwelling shortwave radiation (SDSR). The validation of the GCMs’ outputs against reanalysis data includes analysis of the interannual variability, seasonal cycle, spatial biases, and temporal trends of the simulated variables. In total, 60 combinations of models were selected for 10 variables over six study regions using the selection procedure we present here. The results show that there is neither a combination of models nor one model that has high skill in reproducing the regional climatic-relevant features of all combinations of the considered variables in target seas. Thereby, an individual subset of models was selected according to our model selection procedure for each combination of variable and Arctic or sub-Arctic sea. Following our selection procedure, the number of selected models in the individual subsets varied from 3 to 11.

The paper presents a comparison of the selected model subsets and the full-model ensemble of all available CMIP5 models to reanalysis data. The selected subsets of models generally show a better performance than the full-model ensemble. Therefore, we conclude that within the task addressed in this study it is preferable to employ the model subsets determined through application of our procedure than the full-model ensemble.

Continue reading ‘Simulation of factors affecting Emiliania huxleyi blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection’


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