Under certain scenarios on the subject of CO2 emissions, by the end of the century the atmospheric concentration could triple its pre-industrial level.
The very large numerical models intended to anticipate the corresponding climate evolutions are designed and quantified from the laws of physics. However, little is generally known about these: genesis of clouds, terms of the greenhouse effect, solar activity intervention, etc.
This book deals with the issue of climate modeling in a different way: using proven techniques for identifying black box-type models. Taking climate observations from throughout the millennia, the global models obtained are validated statistically and confirmed by the resulting simulations.
This book thus brings constructive elements that can be reproduced by anyone adept at numerical simulation, whether an expert climatologist or not. It is accessible to any reader interested in the issues of climate change.
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Maße
Höhe: 240 mm
Breite: 161 mm
Dicke: 13 mm
Gewicht
ISBN-13
978-1-84821-777-5 (9781848217775)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Philippe de Larminat was previously Professor at Ecole-Centrale-Nantes, France. The author of numerous publications and books in the field of control-command, he is an expert in the modeling of processes.
Autor*in
IRCCyN, Nantes, France
CHAPTER 1. INTRODUCTION 1
1.1. Context 1
1.2. Identification 3
1.3. Expectations and results 5
1.4. Contents of the work 6
CHAPTER 2. CLIMATIC DATA 11
2.1. Sources 11
2.2. Global temperature 12
2.2.1. Modern temperatures 12
2.2.2. Pre-industrial temperature 13
2.2.3. Paleotemperatures 14
2.3. Concentration of CO2 in the atmosphere 17
2.4. Solar activity 18
2.5. Volcanic activity 26
CHAPTER 3. THE WAR OF THE GRAPHS 29
3.1. History 29
3.2. Inconsistent controversies 35
3.3. Usable data 38
CHAPTER 4. FORMULATING AN ENERGY BALANCE MODEL 41
4.1. State models and transmittance 41
4.2. Structure of an energy balance model 44
4.3. Specificity of EBMs 47
4.4. Dynamic parametrization 49
CHAPTER 5. PRESUMED PARAMETERS 55
5.1. Terminology 55
5.2. Climate sensitivity Sclim 57
5.3. Coefficient of radiative forcing ?N1 58
5.4. The climate feedback coefficient ?UEG 58
5.5. Sensitivity to irradiance S2 59
5.6. Sensitivity to volcanic activity S3 61
5.7. Climate or anthropogenic sensitivity 61
5.8. Review of uncertainties 63
CHAPTER 6. IDENTIFICATION METHOD 67
6.1. The current state of affairs 67
6.2. Output error method 69
6.3. Estimating the error variance 70
6.4. Hypothesis test and confidence regions 72
6.5. Conditions of application 73
CHAPTER 7. PARTIAL RESULTS 75
7.1. A selection of data 75
7.2. Free identification 77
7.3. Forced identifications 81
7.4. Statistical analysis 86
CHAPTER 8. OVERALL RESULTS 91
8.1. Preliminary comments 91
8.2. Regions and intervals of confidence 93
8.3. Hypothesis test 96
8.4. Comments 97
CHAPTER 9. HISTORIC MINUSCULE SIMULATIONS 99
9.1. Overview of IPCC simulations 99
9.2. Comparative simulations 100
9.3. Representative concentration pathways (RCPs) 102
9.4. Comparative radiative forcing 105
CHAPTER 10. LONG-TERM CLIMATE PROJECTIONS 107
10.1. IPCC scenarios and projections 107
10.2. EBM compatible scenarios 109
10.3. Long-term projections 110
10.4. A disaster scenario 113
CHAPTER 11. SHORT-TERM PREDICTIONS 115
11.1. Decadal time scale predictions by GCM 115
11.2. The climate's natural variability 117
11.3. State estimate and prediction 120
11.4. Decadal time scale predictions by EBM 123
11.5. A posteriori predictions 124
CHAPTER 12. CONCLUSIONS 129
12.1. On the identification 129
12.2. Climate sensitivity 130
12.3. Solar activity 131
12.4. Predictive capacity 132
12.5. The climate change in question 133
12.6. Prospects 133
BIBLIOGRAPHY 135
INDEX 141
