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Black Box Identification of Earth's Climate System

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Black Box Identification of Earth's Climate System Black Box Identification of Earth’s Climate System Philippe de Larminat To cite this version: Philippe de Larminat. Black Box Identification of Earth’s Climate System. 2019. hal-02178554 HAL Id: hal-02178554 https://hal.archives-ouvertes.fr/hal-02178554 Preprint submitted on 10 Jul 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Black Box Identification of Earth’s Climate System* Philippe de Larminat** * Submitted to E&E (Energy and Environment journal) ** Independent Scientist. He was Professor at Ecole Centrale, Nantes, France Abstract Climate models allow simulating and evaluating the responses of Earth's climate to imbalance factors (known as forcing factors): human, volcanic and solar activities. The purpose of this paper is to assess the respective contributions of these factors only from observations, modern or paleoclimatic, without resorting to any a priori quantification of energy flows generated by forcing factors, nor of the coefficients of sensitivity of global temperatures to these energy fluxes. For this purpose, an energy balance model is calibrated from classical identification techniques (in the sense of systems theory). It is a black box approach, i.e. based exclusively on observations. Despite the diversity and relative inaccuracy of the models obtained, the simulated contributions of the forcing factors are found to be incompatible with the generally accepted levels. The sensitivity to solar activity appears to be underestimated in a ratio up to 6 or 15; volcanic activity is overestimated at least in a ratio of two. More importantly, the contribution of human activity to recent warming could be negligible compared to that of the internal climate variability. These results are based on objective probabilities, directly derived from observations, which in science prevail over any theoretical speculation. Although they must obviously be received with caution, they cannot be ignored, and we conclude by giving some explanations to the deviations from the prevailing consensus. Keywords: Anthropogenic Warming, System Identification, black box, energy balance model, radiative forcing, millennial simulations, objective probabilities. ** Philippe de Larminat (Graduate Engineer, 1964, Ph. D., 1972). He was Professor at the Institut National des Sciences Appliquées (Rennes, France) and Ecole Centrale (Nantes). He is the author of 6 books and more than 100 papers in journals and international conferences. Since 2001, he is an independent consultant and author of several patents (e.g. power plants control, satellite guidance). His research interests include mathematical modeling, system identification, signal processing and control theory. Since 2012, he is conducting a pioneer work on identification of the Earth climate system. 1 Table of contents 1. Introduction ................................................................................................................................. 4 1.1. Paper’s content ..................................................................................................................... 4 1.2. Modeling approaches ........................................................................................................... 4 1.3. Objective and Subjective Probabilities ................................................................................ 5 2. Development of an energy balance model .................................................................................. 6 2.1. Basic principles .................................................................................................................... 6 2.2. State equations and transfer function ................................................................................... 7 2.3. Comments ............................................................................................................................ 8 3. Radiative forcings ....................................................................................................................... 8 3.1. Quantification of radiative forcings ..................................................................................... 8 3.2. A priori forcings (APF) ........................................................................................................ 9 4. Global climate observations ...................................................................................................... 11 4.1. Global mean surface temperature ...................................................................................... 11 4.2. Ocean heat content ............................................................................................................. 12 5. A priori simulations................................................................................................................... 13 5.1. A priori forcings and models ............................................................................................. 13 5.2. Paleoclimate simulation ..................................................................................................... 13 5.3. Oceans Heat Content simulations ...................................................................................... 14 6. Climate system identification .................................................................................................... 15 6.1. Identification ...................................................................................................................... 15 6.2. Output Error Method ......................................................................................................... 15 6.3. Variance of estimations ..................................................................................................... 15 6.4. Estimating the feedback climate coefficient ...................................................................... 16 7. Identification over the historical period .................................................................................... 17 7.1. Parametric estimation ........................................................................................................ 17 7.2. Simulations ........................................................................................................................ 17 7.3. Millennial hindcast ............................................................................................................ 18 7.4. Comments .......................................................................................................................... 19 8. Millennium identifications ........................................................................................................ 19 8.1. Dynamical parametres ....................................................................................................... 20 8.2. Millenary identification and simulations ........................................................................... 20 8.3. Estimated parameters ......................................................................................................... 21 8.4. Equilibrium Climate Sensitivity and Transient Climate Response ................................... 22 9. Discussion ................................................................................................................................. 23 9.1. Caveat ................................................................................................................................ 23 9.2. Ergodicity ........................................................................................................................... 23 9.3. Solar impact ....................................................................................................................... 23 2 9.4. Internal variability .............................................................................................................. 24 10. Conclusion................................................................................................................................. 24 References ........................................................................................................................................... 24 Appendix. Estimating the parametric uncertainty variance ................................................................ 28 3 1. Introduction 1.1. Paper’s content This paper continues the effort initiated by de Larminat (2014, 2016) on identification of Earth’s climate system. It is organized as follows. After the present introduction, which deals with objective and subjective probabilities, section 2 develop a simplified energy balance model (EBM) which is identifiable from millennial global climate data. Radiative forcing data for human, volcanic and solar activities are detailed in Section 3. The output observations – global surface temperature and ocean heat content – are presented in section 4, and in section 5, the millennial simulations from an a priori defined model are compared with the results of PMIP31. Section 6 discusses
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