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Author: Nabir Mamnun
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Category :
Languages : en
Pages : 0
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Predicting climate change necessitates a thorough understanding of marine biogeochemical (BGC) processes and the coupling between marine ecosystems and the global carbon cycle. Ocean BGC models are tools employed for this purpose. However, current ocean models used to simulate and thus better understand the ocean BGC processes are highly uncertain in their parameterization. This work delves into research to quantify uncertainties that arise in ocean BGC models and obtain improved parameters to reduce those uncertainties utilizing the BGC ocean model Regulated Ecosystem Model Version 2. A Global Sensitivity Analysis (GSA) is performed to identify which parameters most influence the uncertainty of model outputs in a one-dimensional (1-D) configuration at two ocean sites in the North Atlantic (BATS) and the Mediterranean Sea (DYFAMED). This work finds that the grazing parameter, the maximum chlorophyll-to-nitrogen ratio, the photosynthesis parameters, and the chlorophyll degradation rate are significant for BGC simulation. This dissertation uses ensemble data assimilation to estimate the most important BGC process parameters. First, data assimilation experiments are carried out in a 1-D model using an ensemble Kalman Filter to estimate preselected BGC parameters at BATS and DYFAMED stations. Subsequently, the scope and application of experiments are broadened to a global scale 3-D model by incorporating spatial variations in parameter values. Replacing the default parameter values with the optimal values obtained in this work improves the model outcomes in both 1-D and 3-D configurations. This work underscores the importance of spatially varying parameter optimization and highlights the potential benefits of incorporating spatially varying BGC parameters in regional and global 3-D BGC models. Through such rigorous scientific endeavors, we inch closer to a more coherent understanding of the complex interplay between the ocean BGC processes and the carbon cycle.
Author: Nabir Mamnun
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Predicting climate change necessitates a thorough understanding of marine biogeochemical (BGC) processes and the coupling between marine ecosystems and the global carbon cycle. Ocean BGC models are tools employed for this purpose. However, current ocean models used to simulate and thus better understand the ocean BGC processes are highly uncertain in their parameterization. This work delves into research to quantify uncertainties that arise in ocean BGC models and obtain improved parameters to reduce those uncertainties utilizing the BGC ocean model Regulated Ecosystem Model Version 2. A Global Sensitivity Analysis (GSA) is performed to identify which parameters most influence the uncertainty of model outputs in a one-dimensional (1-D) configuration at two ocean sites in the North Atlantic (BATS) and the Mediterranean Sea (DYFAMED). This work finds that the grazing parameter, the maximum chlorophyll-to-nitrogen ratio, the photosynthesis parameters, and the chlorophyll degradation rate are significant for BGC simulation. This dissertation uses ensemble data assimilation to estimate the most important BGC process parameters. First, data assimilation experiments are carried out in a 1-D model using an ensemble Kalman Filter to estimate preselected BGC parameters at BATS and DYFAMED stations. Subsequently, the scope and application of experiments are broadened to a global scale 3-D model by incorporating spatial variations in parameter values. Replacing the default parameter values with the optimal values obtained in this work improves the model outcomes in both 1-D and 3-D configurations. This work underscores the importance of spatially varying parameter optimization and highlights the potential benefits of incorporating spatially varying BGC parameters in regional and global 3-D BGC models. Through such rigorous scientific endeavors, we inch closer to a more coherent understanding of the complex interplay between the ocean BGC processes and the carbon cycle.
Author: Alexander G. Kalmikov
Publisher:
ISBN:
Category : Climatic changes
Languages : en
Pages : 160
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Book Description
Quantifying uncertainty and error bounds is a key outstanding challenge in ocean state estimation and climate research. It is particularly difficult due to the large dimensionality of this nonlinear estimation problem and the number of uncertain variables involved. The "Estimating the Circulation and Climate of the Oceans" (ECCO) consortium has developed a scalable system for dynamically consistent estimation of global time-evolving ocean state by optimal combination of ocean general circulation model (GCM) with diverse ocean observations. The estimation system is based on the "adjoint method" solution of an unconstrained least-squares optimization problem formulated with the method of Lagrange multipliers for fitting the dynamical ocean model to observations. The dynamical consistency requirement of ocean state estimation necessitates this approach over sequential data assimilation and reanalysis smoothing techniques. In addition, it is computationally advantageous because calculation and storage of large covariance matrices is not required. However, this is also a drawback of the adjoint method, which lacks a native formalism for error propagation and quantification of assimilated uncertainty. The objective of this dissertation is to resolve that limitation by developing a feasible computational methodology for uncertainty analysis in dynamically consistent state estimation, applicable to the large dimensionality of global ocean models. Hessian (second derivative-based) methodology is developed for Uncertainty Quantification (UQ) in large-scale ocean state estimation, extending the gradient-based adjoint method to employ the second order geometry information of the model-data misfit function in a high-dimensional control space. Large error covariance matrices are evaluated by inverting the Hessian matrix with the developed scalable matrix-free numerical linear algebra algorithms. Hessian-vector product and Jacobian derivative codes of the MIT general circulation model (MITgcm) are generated by means of algorithmic differentiation (AD). Computational complexity of the Hessian code is reduced by tangent linear differentiation of the adjoint code, which preserves the speedup of adjoint checkpointing schemes in the second derivative calculation. A Lanczos algorithm is applied for extracting the leading rank eigenvectors and eigenvalues of the Hessian matrix. The eigenvectors represent the constrained uncertainty patterns. The inverse eigenvalues are the corresponding uncertainties. The dimensionality of UQ calculations is reduced by eliminating the uncertainty null-space unconstrained by the supplied observations. Inverse and forward uncertainty propagation schemes are designed for assimilating observation and control variable uncertainties, and for projecting these uncertainties onto oceanographic target quantities. Two versions of these schemes are developed: one evaluates reduction of prior uncertainties, while another does not require prior assumptions. The analysis of uncertainty propagation in the ocean model is time-resolving. It captures the dynamics of uncertainty evolution and reveals transient and stationary uncertainty regimes. The system is applied to quantifying uncertainties of Antarctic Circumpolar Current (ACC) transport in a global barotropic configuration of the MITgcm. The model is constrained by synthetic observations of sea surface height and velocities. The control space consists of two-dimensional maps of initial and boundary conditions and model parameters. The size of the Hessian matrix is 0(1010) elements, which would require 0(60GB) of uncompressed storage. It is demonstrated how the choice of observations and their geographic coverage determines the reduction in uncertainties of the estimated transport. The system also yields information on how well the control fields are constrained by the observations. The effects of controls uncertainty reduction due to decrease of diagonal covariance terms are compared to dynamical coupling of controls through off-diagonal covariance terms. The correlations of controls introduced by observation uncertainty assimilation are found to dominate the reduction of uncertainty of transport. An idealized analytical model of ACC guides a detailed time-resolving understanding of uncertainty dynamics. Keywords: Adjoint model uncertainty, sensitivity, posterior error reduction, reduced rank Hessian matrix, Automatic Differentiation, ocean state estimation, barotropic model, Drake Passage transport.
Author:
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ISBN:
Category :
Languages : en
Pages : 44
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Uncertainty quantificatio in climate models is challenged by the sparsity of the available climate data due to the high computational cost of the model runs. Another feature that prevents classical uncertainty analyses from being easily applicable is the bifurcative behavior in the climate data with respect to certain parameters. A typical example is the Meridional Overturning Circulation in the Atlantic Ocean. The maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. We develop a methodology that performs uncertainty quantificatio in the presence of limited data that have discontinuous character. Our approach is two-fold. First we detect the discontinuity location with a Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve location in presence of arbitrarily distributed input parameter values. Furthermore, we developed a spectral approach that relies on Polynomial Chaos (PC) expansions on each sides of the discontinuity curve leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification and propagation. The methodology is tested on synthetic examples of discontinuous data with adjustable sharpness and structure.
Author: Ralph C. Smith
Publisher: SIAM
ISBN: 161197321X
Category : Computers
Languages : en
Pages : 400
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Book Description
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers can find data used in the exercises and other supplementary material.
Author: Geoffrey T. Evans
Publisher: Springer Science & Business Media
ISBN: 3642846025
Category : Science
Languages : en
Pages : 351
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Book Description
Key biogeochemical events in the ocean take place in less than a second, are studied in experiments lasting a few hours, and determine cycles that last over seasons or even years. Models of the controlling processes thus have to take into account these time scales. This book aims at achieving consensus among these controlling processes at all relevant time scales. It helps understand the global carbon cycle including the production and breakdown of solved organic matter and the production, sinking and breakdown of particles. The emphasis on considering all time scales in submodel formulation is new and of interest to all those working in global ocean models and related fields.
Author: Philippe Renard
Publisher: Frontiers Media SA
ISBN: 2889636747
Category :
Languages : en
Pages : 177
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Book Description
Numerical models of flow and transport processes are heavily employed in the fields of surface, soil, and groundwater hydrology. They are used to interpret field observations, analyze complex and coupled processes, or to support decision making related to large societal issues such as the water-energy nexus or sustainable water management and food production. Parameter estimation and uncertainty quantification are two key features of modern science-based predictions. When applied to water resources, these tasks must cope with many degrees of freedom and large datasets. Both are challenging and require novel theoretical and computational approaches to handle complex models with large number of unknown parameters.
Author: Vivian Scott
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Languages : en
Pages :
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In this thesis I explore the effect of parameter uncertainty in ocean biogeochemical models on the calculation of carbon uptake by the ocean. The ocean currently absorbs around a quarter of the annual anthropogenic CO2 emissions to the atmosphere [Scholes et al., 2009], slowing the increase in radiative forcing associated with the increasing atmospheric CO2 concentration. Ocean biogeochemical models have been developed to study the role of the ocean ecosystem in this process. Such models consist of a greatly simplified representation of the hugely complex ocean ecosystem. This simplification requires extensive parameterisation of the biological processes that convert inorganic carbon to and from organic carbon in the ocean. The HadOCC ocean biogeochemical model is a Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model that is used to represent the role of the ocean ecosystem in the global carbon cycle in the HadCM3 and FAMOUS GCMs. HadOCC uses twenty parameters to control the processes of biological growth, mortality, grazing and detrital sinking that control the uptake and cycling of carbon in the ocean ecosystem. These parameters represent highly complex and in some cases incompletely understood biological processes, and as a result are uncertain in value. A sensitivity analysis is performed to identify the HadOCC parameters that due to uncertainty in value have the greatest possible effect on the exchange of CO2 between the atmosphere and the ocean--the air-sea CO2 flux. These are found to be the parameters that control phytoplankton growth in the well lit surface ocean, the formation of carbonate by marine organisms and the sinking of biological detritus. The uncertainty in these parameters is found to cause changes to the air-sea CO2 flux calculated by the FAMOUS GCM. The initial effect of these changes is equivalent to the order of the error of current estimates of the net annual carbon uptake by the ocean (2.2 ± 0.3 Pg C y-1 [Gruber et al., 2009], 2.2 ± 0.5 Pg C y-1 [Denman et al., 2007]). This indicates that while the effect of ocean biogeochemical parameter uncertainty is non-negligible, it is within the bounds of the uncertainty of the total (inorganic and organic) ocean carbon system, and is considerably less than the uncertainty in the carbon uptake of the terrestrial biosphere [Houghton, 2007]. However, as the ocean plays a crucial role in the global carbon cycle and the regulation of the Earth's climate, further understanding and better modelling of the role of the ocean ecosystem in the global carbon cycle and its reaction to anthropogenic climate forcing remains important.
Author: Jan Peter Hessling
Publisher: BoD – Books on Demand
ISBN: 9535132792
Category : Computers
Languages : en
Pages : 228
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Book Description
Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.
Author: Michael J.R. Fasham
Publisher: Springer Science & Business Media
ISBN: 3642558445
Category : Science
Languages : en
Pages : 324
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Book Description
Oceans account for 50% of the anthropogenic CO2 released into the atmosphere. During the past 15 years an international programme, the Joint Global Ocean Flux Study (JGOFS), has been studying the ocean carbon cycle to quantify and model the biological and physical processes whereby CO2 is pumped from the ocean's surface to the depths of the ocean, where it can remain for hundreds of years. This project is one of the largest multi-disciplinary studies of the oceans ever carried out and this book synthesises the results. It covers all aspects of the topic ranging from air-sea exchange with CO2, the role of physical mixing, the uptake of CO2 by marine algae, the fluxes of carbon and nitrogen through the marine food chain to the subsequent export of carbon to the depths of the ocean. Special emphasis is laid on predicting future climatic change.
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ISBN: 9789535132806
Category :
Languages : en
Pages :
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