scholarly journals Bottom-Up Drivers for Global Fish Catch Assessed with Reconstructed Ocean Biogeochemistry from an Earth System Model

Climate ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 83
Author(s):  
Hyo-Jong Song ◽  
Jong-Yeon Park
Keyword(s):  
Chlorophyll Concentration ◽  
Coastal Ecosystems ◽  
Earth System Model ◽  
System Model ◽  
Global Ocean ◽  
Earth System ◽  
Bottom Up ◽  
Fish Catch ◽  
Ocean Biogeochemistry

Identifying bottom-up (e.g., physical and biogeochemical) drivers for fish catch is essential for sustainable fishing and successful adaptation to climate change through reliable prediction of future fisheries. Previous studies have suggested the potential linkage of fish catch to bottom-up drivers such as ocean temperature or satellite-retrieved chlorophyll concentration across different global ecosystems. Robust estimation of bottom-up effects on global fisheries is, however, still challenging due to the lack of long-term observations of fisheries-relevant biotic variables on a global scale. Here, by using novel long-term biological and biogeochemical data reconstructed from a recently developed data assimilative Earth system model, we newly identified dominant drivers for fish catch in globally distributed coastal ecosystems. A machine learning analysis with the inclusion of reconstructed zooplankton production and dissolved oxygen concentration into the fish catch predictors provides an extended view of the links between environmental forcing and fish catch. Furthermore, the relative importance of each driver and their thresholds for high and low fish catch are analyzed, providing further insight into mechanistic principles of fish catch in individual coastal ecosystems. The results presented herein suggest the potential predictive use of their relationships and the need for continuous observational effort for global ocean biogeochemistry.

scholarly journals Data-constrained assessment of ocean circulation changes since the middle Miocene in an Earth system model

2021 ◽  
Vol 17 (5) ◽  
pp. 2223-2254
Author(s):  
Katherine A. Crichton ◽  
Andy Ridgwell ◽  
Daniel J. Lunt ◽  
Alex Farnsworth ◽  
Paul N. Pearson
Keyword(s):  
Greenhouse Gas ◽  
Ocean Circulation ◽  
North Atlantic ◽  
Middle Miocene ◽  
Earth System Model ◽  
System Model ◽  
Global Ocean ◽  
Earth System ◽  
Cooling Trend

Abstract. Since the middle Miocene (15 Ma, million years ago), the Earth's climate has undergone a long-term cooling trend, characterised by a reduction in ocean temperatures of up to 7–8 ∘C. The causes of this cooling are primarily thought to be due to tectonic plate movements driving changes in large-scale ocean circulation patterns, and hence heat redistribution, in conjunction with a drop in atmospheric greenhouse gas forcing (and attendant ice-sheet growth and feedback). In this study, we assess the potential to constrain the evolving patterns of global ocean circulation and cooling over the last 15 Ma by assimilating a variety of marine sediment proxy data in an Earth system model. We do this by first compiling surface and benthic ocean temperature and benthic carbon-13 (δ13C) data in a series of seven time slices spaced at approximately 2.5 Myr intervals. We then pair this with a corresponding series of tectonic and climate boundary condition reconstructions in the cGENIE (“muffin” release) Earth system model, including alternative possibilities for an open vs. closed Central American Seaway (CAS) from 10 Ma onwards. In the cGENIE model, we explore uncertainty in greenhouse gas forcing and the magnitude of North Pacific to North Atlantic salinity flux adjustment required in the model to create an Atlantic Meridional Overturning Circulation (AMOC) of a specific strength, via a series of 12 (one for each tectonic reconstruction) 2D parameter ensembles. Each ensemble member is then tested against the observed global temperature and benthic δ13C patterns. We identify that a relatively high CO2 equivalent forcing of 1120 ppm is required at 15 Ma in cGENIE to reproduce proxy temperature estimates in the model, noting that this CO2 forcing is dependent on the cGENIE model's climate sensitivity and that it incorporates the effects of all greenhouse gases. We find that reproducing the observed long-term cooling trend requires a progressively declining greenhouse gas forcing in the model. In parallel to this, the strength of the AMOC increases with time despite a reduction in the salinity of the surface North Atlantic over the cooling period, attributable to falling intensity of the hydrological cycle and to lowering polar temperatures, both caused by CO2-driven global cooling. We also find that a closed CAS from 10 Ma to present shows better agreement between benthic δ13C patterns and our particular series of model configurations and data. A final outcome of our analysis is a pronounced ca. 1.5 ‰ decline occurring in atmospheric (and ca. 1 ‰ ocean surface) δ13C that could be used to inform future δ13C-based proxy reconstructions.

scholarly journals Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model

2015 ◽  
Vol 12 (11) ◽  
pp. 3301-3320 ◽  
Author(s):  
K. B. Rodgers ◽  
J. Lin ◽  
T. L. Frölicher
Keyword(s):  
Southern Ocean ◽  
Marine Ecosystem ◽  
Marine Ecosystems ◽  
Earth System Model ◽  
System Model ◽  
Global Ocean ◽  
Earth System ◽  
Biological Productivity ◽  
The Tropics ◽  
Ecosystem Drivers

Abstract. Marine ecosystems are increasingly stressed by human-induced changes. Marine ecosystem drivers that contribute to stressing ecosystems – including warming, acidification, deoxygenation and perturbations to biological productivity – can co-occur in space and time, but detecting their trends is complicated by the presence of noise associated with natural variability in the climate system. Here we use large initial-condition ensemble simulations with an Earth system model under a historical/RCP8.5 (representative concentration pathway 8.5) scenario over 1950–2100 to consider emergence characteristics for the four individual and combined drivers. Using a 1-standard-deviation (67% confidence) threshold of signal to noise to define emergence with a 30-year trend window, we show that ocean acidification emerges much earlier than other drivers, namely during the 20th century over most of the global ocean. For biological productivity, the anthropogenic signal does not emerge from the noise over most of the global ocean before the end of the 21st century. The early emergence pattern for sea surface temperature in low latitudes is reversed from that of subsurface oxygen inventories, where emergence occurs earlier in the Southern Ocean. For the combined multiple-driver field, 41% of the global ocean exhibits emergence for the 2005–2014 period, and 63% for the 2075–2084 period. The combined multiple-driver field reveals emergence patterns by the end of this century that are relatively high over much of the Southern Ocean, North Pacific, and Atlantic, but relatively low over the tropics and the South Pacific. For the case of two drivers, the tropics including habitats of coral reefs emerges earliest, with this driven by the joint effects of acidification and warming. It is precisely in the regions with pronounced emergence characteristics where marine ecosystems may be expected to be pushed outside of their comfort zone determined by the degree of natural background variability to which they are adapted. The results underscore the importance of sustained multi-decadal observing systems for monitoring multiple ecosystems drivers.

scholarly journals MESMO 2: a mechanistic marine silica cycle and coupling to a simple terrestrial scheme

2013 ◽  
Vol 6 (2) ◽  
pp. 477-494 ◽  
Author(s):  
K. Matsumoto ◽  
K. Tokos ◽  
A. Huston ◽  
H. Joy-Warren
Keyword(s):  
Coupled Model ◽  
Earth System Model ◽  
System Model ◽  
Important Class ◽  
Earth System ◽  
Carbon Export ◽  
Ocean Ventilation ◽  
Key Features ◽  
Ocean Biogeochemistry ◽  
Silica Cycle

Abstract. Here we describe the second version of Minnesota Earth System Model for Ocean biogeochemistry (MESMO 2), an earth system model of intermediate complexity, which consists of a dynamical ocean, dynamic-thermodynamic sea ice, and energy moisture balanced atmosphere. The new version has more realistic land ice masks and is driven by seasonal winds. A major aim in version 2 is representing the marine silica cycle mechanistically in order to investigate climate-carbon feedbacks involving diatoms, a critically important class of phytoplankton in terms of carbon export production. This is achieved in part by including iron, on which phytoplankton uptake of silicic acid depends. Also, MESMO 2 is coupled to an existing terrestrial model, which allows for the exchange of carbon, water and energy between land and the atmosphere. The coupled model, called MESMO 2E, is appropriate for more complete earth system simulations. The new version was calibrated, with the goal of preserving reasonable interior ocean ventilation and various biological production rates in the ocean and land, while simulating key features of the marine silica cycle.

scholarly journals Progress towards a probabilistic Earth system model: examining the impact of stochasticity in the atmosphere and land component of EC-Earth v3.2

2019 ◽  
Vol 12 (7) ◽  
pp. 3099-3118 ◽  
Author(s):  
Kristian Strommen ◽  
Hannah M. Christensen ◽  
Dave MacLeod ◽  
Stephan Juricke ◽  
Tim N. Palmer
Keyword(s):  
Climate Model ◽  
Weather Forecasting ◽  
Southern Oscillation ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Beneficial Impact ◽  
The Impact ◽  
Mean State

Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

scholarly journals Long-Term Climate Simulations Using the IITM Earth System Model (IITM-ESMv2) With Focus on the South Asian Monsoon

2018 ◽  
Vol 10 (5) ◽  
pp. 1127-1149 ◽  
Author(s):  
P. Swapna ◽  
R. Krishnan ◽  
N. Sandeep ◽  
A. G. Prajeesh ◽  
D. C. Ayantika ◽  
...  
Keyword(s):  
South Asian ◽  
Asian Monsoon ◽  
Earth System Model ◽  
System Model ◽  
South Asian Monsoon ◽  
Earth System ◽  
The South ◽  
Climate Simulations

scholarly journals MESMO 2: a mechanistic marine silica cycle and coupling to a simple terrestrial scheme

2012 ◽  
Vol 5 (3) ◽  
pp. 2999-3033 ◽  
Author(s):  
K. Matsumoto ◽  
K. S. Tokos ◽  
A. Huston ◽  
H. Joy-Warren
Keyword(s):  
Coupled Model ◽  
Earth System Model ◽  
System Model ◽  
Important Class ◽  
Earth System ◽  
Carbon Export ◽  
Ocean Ventilation ◽  
Key Features ◽  
Ocean Biogeochemistry ◽  
Silica Cycle

Abstract. Here we describe the second version of Minnesota Earth System Model for Ocean biogeochemistry (MESMO 2), an earth system model of intermediate complexity, which consists of a dynamical ocean, dynamic-thermodynamic sea ice, and energy moisture balanced atmosphere. The new version has more realistic land ice masks and is driven by seasonal winds. A major aim in version 2 is representing the marine silica cycle mechanistically in order to investigate climate-carbon feedbacks involving diatoms, a critically important class of phytoplankton in terms of carbon export production. This is achieved in part by including iron, on which phytoplankton uptake of silicic acid depends. Also, MESMO 2 is coupled to an existing terrestrial model, which allows for the exchange of carbon, water, and energy between land and the atmosphere. The coupled model, called MESMO 2E, is appropriate for more complete earth system simulations. The new version was calibrated with the goal of preserving reasonable interior ocean ventilation and various biological production rates in the ocean and land, while simulating key features of the marine silica cycle.

scholarly journals Coupling of a sediment diagenesis model (MEDUSA) and an Earth system model (CESM1.2): a contribution toward enhanced marine biogeochemical modelling and long-term climate simulations

2019 ◽  
Author(s):  
Takasumi Kurahashi-Nakamura ◽  
André Paul ◽  
Guy Munhoven ◽  
Ute Merkel ◽  
Michael Schulz
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Coupled Model ◽  
Earth System Model ◽  
System Model ◽  
Future Application ◽  
Coupling Scheme ◽  
Earth System ◽  
Climate Simulations

Abstract. We developed a coupling scheme for the Community Earth System Model version 1.2 (CESM1.2) and the Model of Early Diagenesis in the Upper Sediment of Adjustable complexity (MEDUSA), and explored the effects of the coupling on solid components in the upper sediment and on bottom seawater chemistry by comparing the coupled model's behaviour with that of the uncoupled CESM having a simplified treatment of sediment processes. CESM is a fully-coupled atmosphere-ocean-sea ice-land model and its ocean component (the Parallel Ocean Program version 2, POP2) includes a biogeochemical component (BEC). MEDUSA was coupled to POP2 in an off-line manner so that each of the models ran separately and sequentially with regular exchanges of necessary boundary condition fields. This development was done with the ambitious aim of a future application for long-term (spanning a full glacial cycle; i.e., ~ 105 years) climate simulations with a state-of-the-art comprehensive climate model including the carbon cycle, and was motivated by the fact that until now such simulations have been done only with less-complex climate models. We found that the sediment-model coupling already had non-negligible immediate advantages for ocean biogeochemistry in millennial-time-scale simulations. First, the MEDUSA-coupled CESM outperformed the uncoupled CESM in reproducing an observation-based global distribution of sediment properties, especially for organic carbon and opal. Thus, the coupled model is expected to act as a better bridge between climate dynamics and sedimentary data, which will provide another measure of model performance. Second, in our experiments, the MEDUSA-coupled model and the uncoupled model had a difference of 0.2‰ or larger in terms of δ13C of bottom water over large areas, which implied potential significant model biases for bottom seawater chemical composition due to a different way of sediment treatment. Such a model bias would be a fundamental issue for paleo model–data comparison often relying on data derived from benthic foraminifera.

scholarly journals Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2

2019 ◽  
Author(s):  
Kristian Strommen ◽  
Hannah M. Christensen ◽  
David MacLeod ◽  
Stephan Juricke ◽  
Tim N. Palmer
Keyword(s):  
Climate Model ◽  
Weather Forecasting ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Beneficial Impact ◽  
The Mean ◽  
The Impact ◽  
Mean State

Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model, two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic earth-system model in atmosphere-only mode. Stochastic parametrisations have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as ENSO, North Atlantic weather regimes and the Indian monsoon. Stochasticity in the land-component has been shown to improve variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied, It is shown that the inclusion all three schemes notably change the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to large changes in the model's energy budget. This implies that in order to keep the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

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