scholarly journals Evaluating the ocean biogeochemical components of Earth system models using atmospheric potential oxygen and ocean color data

2015 ◽  
Vol 12 (1) ◽  
pp. 193-208 ◽  
Author(s):  
C. D. Nevison ◽  
M. Manizza ◽  
R. F. Keeling ◽  
M. Kahru ◽  
L. Bopp ◽  
...  
Keyword(s):  
Net Primary Production ◽  
Ocean Color ◽  
Seasonal Cycles ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Earth System ◽  
System Models ◽  
Color Data ◽  
Ocean Biogeochemistry ◽  
Earth System Models ◽  
Potential Oxygen

Abstract. The observed seasonal cycles in atmospheric potential oxygen (APO) at a range of mid- to high-latitude surface monitoring sites are compared to those inferred from the output of six Earth system models (ESMs) participating in the fifth phase of the Coupled Model Intercomparison Project phase 5 (CMIP5). The simulated air–sea O2 fluxes are translated into APO seasonal cycles using a matrix method that takes into account atmospheric transport model (ATM) uncertainty among 13 different ATMs. Three of the ocean biogeochemistry models tested are able to reproduce the observed APO cycles at most sites, to within the large TransCom3-era ATM uncertainty used here, while the other three generally are not. Net primary production (NPP) and net community production (NCP), as estimated from satellite ocean color data, provide additional constraints, albeit more with respect to the seasonal phasing of ocean model productivity than overall magnitude. The present analysis suggests that, of the tested ocean biogeochemistry models, the community ecosystem model (CESM) and the Geophysical Fluid Dynamics Laboratory (GFDL) ESM2M are best able to capture the observed APO seasonal cycle at both northern and southern hemispheric sites. In most models, discrepancies with observed APO can be attributed to the underestimation of NPP, deep ventilation or both in the northern oceans.

scholarly journals Evaluating the ocean biogeochemical components of earth system models using atmospheric potential oxygen (APO) and ocean color data

2014 ◽  
Vol 11 (6) ◽  
pp. 8485-8529
Author(s):  
C. D. Nevison ◽  
M. Manizza ◽  
R. F. Keeling ◽  
M. Kahru ◽  
L. Bopp ◽  
...  
Keyword(s):  
Transport Model ◽  
Net Primary Production ◽  
Ocean Color ◽  
Seasonal Cycles ◽  
Earth System ◽  
System Models ◽  
Color Data ◽  
Ocean Biogeochemistry ◽  
Earth System Models ◽  
Potential Oxygen

Abstract. The observed seasonal cycles in atmospheric potential oxygen (APO) at a range of mid to high latitude surface monitoring sites are compared to those inferred from the output of 6 Earth System Models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The simulated air–sea O2 fluxes are translated into APO seasonal cycles using a matrix method that takes into account atmospheric transport model (ATM) uncertainty among 13 different ATMs. Half of the ocean biogeochemistry models tested are able to reproduce the observed APO cycles at most sites, to within the current large ATM uncertainty, while the other half generally are not. Net Primary Production (NPP) and net community production (NCP), as estimated from satellite ocean color data, provide additional constraints, albeit more with respect to the seasonal phasing of ocean model productivity than the overall magnitude. The present analysis suggests that, of the tested ocean biogeochemistry models, CESM and GFDL ESM2M are best able to capture the observed APO seasonal cycle at both Northern and Southern Hemisphere sites. In the northern oceans, the comparison to observed APO suggests that most models tend to underestimate NPP or deep ventilation or both.

scholarly journals Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of height-structured competition

2015 ◽  
Vol 12 (9) ◽  
pp. 2655-2694 ◽  
Author(s):  
E. S. Weng ◽  
S. Malyshev ◽  
J. W. Lichstein ◽  
C. E. Farrior ◽  
R. Dybzinski ◽  
...  
Keyword(s):  
Atmospheric Co2 ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Earth System ◽  
Modeling Framework ◽  
Light Water ◽  
System Models ◽  
C Cycle ◽  
Competition For Light ◽  
Individual Trees ◽  
Earth System Models

Abstract. The long-term and large-scale dynamics of ecosystems are in large part determined by the performances of individual plants in competition with one another for light, water, and nutrients. Woody biomass, a pool of carbon (C) larger than 50% of atmospheric CO2, exists because of height-structured competition for light. However, most of the current Earth system models that predict climate change and C cycle feedbacks lack both a mechanistic formulation for height-structured competition for light and an explicit scaling from individual plants to the globe. In this study, we incorporate height-structured competition for light, competition for water, and explicit scaling from individuals to ecosystems into the land model version 3 (LM3) currently used in the Earth system models developed by the Geophysical Fluid Dynamics Laboratory (GFDL). The height-structured formulation is based on the perfect plasticity approximation (PPA), which has been shown to accurately scale from individual-level plant competition for light, water, and nutrients to the dynamics of whole communities. Because of the tractability of the PPA, the coupled LM3-PPA model is able to include a large number of phenomena across a range of spatial and temporal scales and still retain computational tractability, as well as close linkages to mathematically tractable forms of the model. We test a range of predictions against data from temperate broadleaved forests in the northern USA. The results show the model predictions agree with diurnal and annual C fluxes, growth rates of individual trees in the canopy and understory, tree size distributions, and species-level population dynamics during succession. We also show how the competitively optimal allocation strategy – the strategy that can competitively exclude all others – shifts as a function of the atmospheric CO2 concentration. This strategy is referred to as an evolutionarily stable strategy (ESS) in the ecological literature and is typically not the same as a productivity- or growth-maximizing strategy. Model simulations predict that C sinks caused by CO2 fertilization in forests limited by light and water will be down-regulated if allocation tracks changes in the competitive optimum. The implementation of the model in this paper is for temperate broadleaved forest trees, but the formulation of the model is general. It can be expanded to include other growth forms and physiologies simply by altering parameter values.

scholarly journals Harnessing big data to rethink land heterogeneity in Earth system models

2018 ◽  
Vol 22 (6) ◽  
pp. 3311-3330 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Marjolein H. J. Van Huijgevoort ◽  
Elena Shevliakova ◽  
Sergey Malyshev ◽  
Paul C. D. Milly ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Grid Cell ◽  
Environmental Data ◽  
Distributed Model ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Earth System ◽  
Carbon Cycles ◽  
System Models ◽  
Clustering Approach ◽  
Earth System Models

Abstract. The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth system models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and computational constraints, these data are underused for global applications. As a proof of concept, this study explores how to effectively and efficiently harness these data in Earth system models over a 1/4∘ (∼ 25 km) grid cell in the western foothills of the Sierra Nevada in central California. First, a novel hierarchical multivariate clustering approach (HMC) is introduced that summarizes the high-dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this clustering approach impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled using HMC. The results over the test domain show that (1) the observed similarity over the landscape makes it possible to converge on the macroscale response of the fully distributed model with around 300 sub-grid land model tiles; (2) assembling the sub-grid tile configuration from available environmental data can have a large impact on the macroscale states and fluxes of the water, energy, and carbon cycles; for example, the defined subsurface connections between the tiles lead to a dampening of macroscale extremes; (3) connecting the fine-scale grid to the model tiles via HMC enables circumvention of the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth system models.

scholarly journals Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution

Ocean Science ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. 561-575 ◽  
Author(s):  
Tihomir S. Kostadinov ◽  
Svetlana Milutinović ◽  
Irina Marinov ◽  
Anna Cabré
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Ocean Color ◽  
Earth System ◽  
System Models ◽  
Size Classes ◽  
Carbon Biomass ◽  
Earth System Models

Abstract. Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the “unit of accounting” in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size – picophytoplankton (0.5–2 µm in diameter), nanophytoplankton (2–20 µm) and microphytoplankton (20–50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield  ∼  0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.

Causes of uncertainty in China's net primary production over the 21st century projected by the CMIP5 Earth system models

2015 ◽  
Vol 36 (5) ◽  
pp. 2323-2334 ◽  
Author(s):  
Tao Wang ◽  
Xin Lin ◽  
Yongwen Liu ◽  
Sarah Dantec-Nédélec ◽  
Catherine Ottlé
Keyword(s):  
Primary Production ◽  
21St Century ◽  
Net Primary Production ◽  
Earth System ◽  
System Models ◽  
Earth System Models

scholarly journals Harnessing Big Data to Rethink Land Heterogeneity in Earth System Models

2017 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Marjolein H. J. Van Huijgevoort ◽  
Elena Shevliakova ◽  
Sergey Malyshev ◽  
Paul C. D. Milly ◽  
...  
Keyword(s):  
Grid Cell ◽  
Environmental Data ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Fine Scale ◽  
Earth System ◽  
Carbon Cycles ◽  
System Models ◽  
Data Driven Approach ◽  
Scale Grid ◽  
Earth System Models

Abstract. The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth System models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and the computational constraints of macroscale models, until now these data have been underutilized for global applications. As a proof of concept, this study explores over a 1/4 degree (~ 25 km) grid cell in southeastern California how to effectively and efficiently harness these data in Earth System models. First, a novel hierarchical multivariate clustering approach (HMC) is used to summarize the high dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this data-driven approach to assemble the model tiles impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled by HMC. The results over the 1/4 degree macroscale grid cell and the underlying 30-meter fine-scale grid in southeastern California show that: 1) the observed similarity over the landscape makes it possible to robustly account for the role of multi-scale heterogeneity in the macroscale states and fluxes with around 300 sub-grid land model tiles; 2) assembling the sub-grid tiles from observed data, at times, leads to noticeable differences in the macroscale water, energy, and carbon cycles; for example, explicit subsurface interactions between the tiles leads to a dampening of macroscale extremes; 3) connecting the fine-scale grid to the model tiles via HMC enables circumventing the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth System models.

scholarly journals Scaling from individuals to ecosystems in an Earth System Model using a mathematically tractable model of height-structured competition for light

2014 ◽  
Vol 11 (12) ◽  
pp. 17757-17860 ◽  
Author(s):  
E. S. Weng ◽  
S. Malyshev ◽  
J. W. Lichstein ◽  
C. E. Farrior ◽  
R. Dybzinski ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimal Allocation ◽  
Atmospheric Co2 ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Earth System ◽  
Light Water ◽  
System Models ◽  
C Cycle ◽  
Competition For Light ◽  
Earth System Models

Abstract. The long-term and large scale dynamics of ecosystems are in large part determined by the performances of individual plants in competition with one another for light, water and nutrients. Woody biomass, a pool of carbon (C) larger than 50% of atmospheric CO2, exists because of height-structured competition for light. However, most of the current Earth System Models that predict climate change and C cycle feedbacks lack both a mechanistic formulation for height-structured competition for light and an explicit scaling from individual plants to the globe. In this study, we incorporate height-structured competition and explicit scaling from individuals to ecosystems into the land model (LM3) currently used in the Earth System Models developed by the Geophysical Fluid Dynamics Laboratory (GFDL). The height-structured formulation is based on the Perfect Plasticity Approximation (PPA), which has been shown to accurately scale from individual-level plant competition for light, water and nutrients to the dynamics of whole communities. Because of the tractability of the PPA, the coupled LM3–PPA model is able to include a large number of phenomena across a range of spatial and temporal scales, and still retain computational tractability, as well as close linkages to mathematically tractable forms of the model. We test a range of predictions against data from temperate broadleaved forests in the northern USA. The results show the model predictions agree with diurnal and annual C fluxes, growth rates of individual trees in the canopy and understory, tree size distributions, and species-level population dynamics during succession. We also show how the competitively optimal allocation strategy – the strategy that can competitively exclude all others – shifts as a function of the atmospheric CO2 concentration. This strategy is referred as an evolutionary stable strategy (ESS) in the ecological literature and is typically not the same as a productivity- or growth-maximizing strategy. Model simulations predict that C sinks caused by CO2 fertilization in forests limited by light and water will be down-regulated if allocation tracks changes in the competitive optimum. The implementation of the model in this paper is for temperate broadleaved forest trees, but the formulation of the model is general. It can be expanded to include other growth forms and physiologies simply by altering parameter values.

scholarly journals Inter-comparison of phytoplankton functional type phenology metrics derived from ocean color algorithms and Earth System Models

2017 ◽  
Vol 190 ◽  
pp. 162-177 ◽  
Author(s):  
Tihomir S. Kostadinov ◽  
Anna Cabré ◽  
Harish Vedantham ◽  
Irina Marinov ◽  
Astrid Bracher ◽  
...  
Keyword(s):  
Ocean Color ◽  
Earth System ◽  
Functional Type ◽  
System Models ◽  
Earth System Models

scholarly journals Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution

2015 ◽  
Vol 12 (3) ◽  
pp. 573-644 ◽  
Author(s):  
T. S. Kostadinov ◽  
S. Milutinović ◽  
I. Marinov ◽  
A. Cabré
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Ocean Color ◽  
Standing Stock ◽  
Uncertainty Budget ◽  
Earth System ◽  
Particle Volume ◽  
System Models ◽  
Earth System Models

Abstract. Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the "unit of accounting" in Earth System models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing algorithms to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size – picophytoplankton (0.5–2 μm in diameter), nanophytoplankton (2–20 μm) and microphytoplankton (20–50 μm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e. oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have large biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield on average ~0.2–0.3 Gt of C, consistent with analogous estimates from two other ocean color algorithms, and several state-of-the-art Earth System models. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, because the PSD-based algorithm is not a priori empirically constrained and introduces improvement over the assumptions of the other approaches. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients.

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