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Mathematical Paradigms of Climate Science

  • Book
  • © 2016

Overview

Editors:
  1. Fabio Ancona

    1. Dipartimento di Matematica, Università degli Studi di Padova, Padova, Italy

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  2. Piermarco Cannarsa

    1. Dipartimento di Matematica, Università di Roma - Tor Vergata, Roma, Italy

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  3. Christopher Jones

    1. Department of Mathematics, University of North Carolina, Chapel Hill, USA

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  4. Alessandro Portaluri

    1. Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Torino, Italy

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  • Provides an overview of cutting-edge mathematical theories and techniques that promise to play a central role in climate science
  • Offers a truly interdisciplinary approach, bringing together researchers with distinct views and methods
  • Emphasizes the growing importance of data assimilation techniques for climate modelling

Part of the book series: Springer INdAM Series (SINDAMS, volume 15)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-x
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  3. Mathematical Techniques

    1. Front Matter

      Pages 33-33
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    2. Source Reconstruction by Partial Measurements for a Class of Hyperbolic Systems in Cascade

      • Fatiha Alabau-Boussouira, Piermarco Cannarsa, Masahiro Yamamoto
      Pages 35-50

    3. Remarks on Stochastic Navier-Stokes Equations

      • Franco Flandoli
      Pages 51-65

    4. Remarks on Long Time Versus Steady State Optimal Control

      • Alessio Porretta, Enrique Zuazua
      Pages 67-89

  4. Paleoclimate

    1. Front Matter

      Pages 91-91
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    2. Effects of Additive Noise on the Stability of Glacial Cycles

      • Takahito Mitsui, Michel Crucifix
      Pages 93-113

    3. On the Calibration of Lévy Driven Time Series with Coupling Distances and an Application in Paleoclimate

      • Jan Gairing, Michael Högele, Tetiana Kosenkova, Alexei Kulik
      Pages 115-136

  5. Data Assimilation

    1. Front Matter

      Pages 137-137
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    2. Data Assimilation for Geophysical Fluids: The Diffusive Back and Forth Nudging

      • Didier Auroux, Jacques Blum, Giovanni Ruggiero
      Pages 139-174

    3. Deterministic Treatment of Model Error in Geophysical Data Assimilation

      • Alberto Carrassi, Stéphane Vannitsem
      Pages 175-213

    4. Decreasing Flow Uncertainty in Bayesian Inverse Problems Through Lagrangian Drifter Control

      • Damon McDougall, Chris K. R. T. Jones
      Pages 215-228
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About this book

This book, featuring a truly interdisciplinary approach, provides an overview of cutting-edge mathematical theories and techniques that promise to play a central role in climate science. It brings together some of the most interesting overview lectures given by the invited speakers at an important workshop held in Rome in 2013 as a part of MPE2013 (“Mathematics of Planet Earth 2013”). The aim of the workshop was to foster the interaction between climate scientists and mathematicians active in various fields linked to climate sciences, such as dynamical systems, partial differential equations, control theory, stochastic systems, and numerical analysis. Mathematics and statistics already play a central role in this area. Likewise, computer science must have a say in the efforts to simulate the Earth’s environment on the unprecedented scale of petabytes. In the context of such complexity, new mathematical tools are needed to organize and simplify the approach. The growing importance of data assimilation techniques for climate modeling is amply illustrated in this volume, which also identifies important future challenges.

Editors and Affiliations

  • Dipartimento di Matematica, Università degli Studi di Padova, Padova, Italy

    Fabio Ancona

  • Dipartimento di Matematica, Università di Roma - Tor Vergata, Roma, Italy

    Piermarco Cannarsa

  • Department of Mathematics, University of North Carolina, Chapel Hill, USA

    Christopher Jones

  • Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Torino, Italy

    Alessandro Portaluri

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