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.

• CHAPTER 1. INTRODUCTION 11.1. Context 11.2. Identification 31.3. Expectations and results 51.4. Contents of the work 6CHAPTER 2. CLIMATIC DATA 112.1. Sources 112.2. Global temperature 122.2.1. Modern temperatures 122.2.2. Pre-industrial temperature 132.2.3. Paleotemperatures 142.3. Concentration of CO2 in the atmosphere 172.4. Solar activity 182.5. Volcanic activity 26CHAPTER 3. THE WAR OF THE GRAPHS 293.1. History 293.2. Inconsistent controversies 353.3. Usable data 38CHAPTER 4. FORMULATING AN ENERGY BALANCE MODEL 414.1. State models and transmittance 414.2. Structure of an energy balance model 444.3. Specificity of EBMs 474.4. Dynamic parametrization 49CHAPTER 5. PRESUMED PARAMETERS 555.1. Terminology 555.2. Climate sensitivity Sclim 575.3. Coefficient of radiative forcing ?Ñ1 585.4. The climate feedback coefficient ?ÜG 585.5. Sensitivity to irradiance S2 595.6. Sensitivity to volcanic activity S3 615.7. Climate or anthropogenic sensitivity 615.8. Review of uncertainties 63CHAPTER 6. IDENTIFICATION METHOD 676.1. The current state of affairs 676.2. Output error method 696.3. Estimating the error variance 706.4. Hypothesis test and confidence regions 726.5. Conditions of application 73CHAPTER 7. PARTIAL RESULTS 757.1. A selection of data 757.2. Free identification 777.3. Forced identifications 817.4. Statistical analysis 86CHAPTER 8. OVERALL RESULTS 918.1. Preliminary comments 918.2. Regions and intervals of confidence 938.3. Hypothesis test 968.4. Comments 97CHAPTER 9. HISTORIC MINUSCULE SIMULATIONS 999.1. Overview of IPCC simulations 999.2. Comparative simulations 1009.3. Representative concentration pathways (RCPs) 1029.4. Comparative radiative forcing 105CHAPTER 10. LONG-TERM CLIMATE PROJECTIONS 10710.1. IPCC scenarios and projections 10710.2. EBM compatible scenarios 10910.3. Long-term projections 11010.4. A disaster scenario 113CHAPTER 11. SHORT-TERM PREDICTIONS 11511.1. Decadal time scale predictions by GCM 11511.2. The climate’s natural variability 11711.3. State estimate and prediction 12011.4. Decadal time scale predictions by EBM 12311.5. A posteriori predictions 124CHAPTER 12. CONCLUSIONS 12912.1. On the identification 12912.2. Climate sensitivity 13012.3. Solar activity 13112.4. Predictive capacity 13212.5. The climate change in question 13312.6. Prospects 133BIBLIOGRAPHY 135INDEX 141

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.

• CHAPTER 1. INTRODUCTION 11.1. Context 11.2. Identification 31.3. Expectations and results 51.4. Contents of the work 6CHAPTER 2. CLIMATIC DATA 112.1. Sources 112.2. Global temperature 122.2.1. Modern temperatures 122.2.2. Pre-industrial temperature 132.2.3. Paleotemperatures 142.3. Concentration of CO2 in the atmosphere 172.4. Solar activity 182.5. Volcanic activity 26CHAPTER 3. THE WAR OF THE GRAPHS 293.1. History 293.2. Inconsistent controversies 353.3. Usable data 38CHAPTER 4. FORMULATING AN ENERGY BALANCE MODEL 414.1. State models and transmittance 414.2. Structure of an energy balance model 444.3. Specificity of EBMs 474.4. Dynamic parametrization 49CHAPTER 5. PRESUMED PARAMETERS 555.1. Terminology 555.2. Climate sensitivity Sclim 575.3. Coefficient of radiative forcing ?Ñ1 585.4. The climate feedback coefficient ?ÜG 585.5. Sensitivity to irradiance S2 595.6. Sensitivity to volcanic activity S3 615.7. Climate or anthropogenic sensitivity 615.8. Review of uncertainties 63CHAPTER 6. IDENTIFICATION METHOD 676.1. The current state of affairs 676.2. Output error method 696.3. Estimating the error variance 706.4. Hypothesis test and confidence regions 726.5. Conditions of application 73CHAPTER 7. PARTIAL RESULTS 757.1. A selection of data 757.2. Free identification 777.3. Forced identifications 817.4. Statistical analysis 86CHAPTER 8. OVERALL RESULTS 918.1. Preliminary comments 918.2. Regions and intervals of confidence 938.3. Hypothesis test 968.4. Comments 97CHAPTER 9. HISTORIC MINUSCULE SIMULATIONS 999.1. Overview of IPCC simulations 999.2. Comparative simulations 1009.3. Representative concentration pathways (RCPs) 1029.4. Comparative radiative forcing 105CHAPTER 10. LONG-TERM CLIMATE PROJECTIONS 10710.1. IPCC scenarios and projections 10710.2. EBM compatible scenarios 10910.3. Long-term projections 11010.4. A disaster scenario 113CHAPTER 11. SHORT-TERM PREDICTIONS 11511.1. Decadal time scale predictions by GCM 11511.2. The climate’s natural variability 11711.3. State estimate and prediction 12011.4. Decadal time scale predictions by EBM 12311.5. A posteriori predictions 124CHAPTER 12. CONCLUSIONS 12912.1. On the identification 12912.2. Climate sensitivity 13012.3. Solar activity 13112.4. Predictive capacity 13212.5. The climate change in question 13312.6. Prospects 133BIBLIOGRAPHY 135INDEX 141

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.

• CHAPTER 1. INTRODUCTION 11.1. Context 11.2. Identification 31.3. Expectations and results 51.4. Contents of the work 6CHAPTER 2. CLIMATIC DATA 112.1. Sources 112.2. Global temperature 122.2.1. Modern temperatures 122.2.2. Pre-industrial temperature 132.2.3. Paleotemperatures 142.3. Concentration of CO2 in the atmosphere 172.4. Solar activity 182.5. Volcanic activity 26CHAPTER 3. THE WAR OF THE GRAPHS 293.1. History 293.2. Inconsistent controversies 353.3. Usable data 38CHAPTER 4. FORMULATING AN ENERGY BALANCE MODEL 414.1. State models and transmittance 414.2. Structure of an energy balance model 444.3. Specificity of EBMs 474.4. Dynamic parametrization 49CHAPTER 5. PRESUMED PARAMETERS 555.1. Terminology 555.2. Climate sensitivity Sclim 575.3. Coefficient of radiative forcing ?Ñ1 585.4. The climate feedback coefficient ?ÜG 585.5. Sensitivity to irradiance S2 595.6. Sensitivity to volcanic activity S3 615.7. Climate or anthropogenic sensitivity 615.8. Review of uncertainties 63CHAPTER 6. IDENTIFICATION METHOD 676.1. The current state of affairs 676.2. Output error method 696.3. Estimating the error variance 706.4. Hypothesis test and confidence regions 726.5. Conditions of application 73CHAPTER 7. PARTIAL RESULTS 757.1. A selection of data 757.2. Free identification 777.3. Forced identifications 817.4. Statistical analysis 86CHAPTER 8. OVERALL RESULTS 918.1. Preliminary comments 918.2. Regions and intervals of confidence 938.3. Hypothesis test 968.4. Comments 97CHAPTER 9. HISTORIC MINUSCULE SIMULATIONS 999.1. Overview of IPCC simulations 999.2. Comparative simulations 1009.3. Representative concentration pathways (RCPs) 1029.4. Comparative radiative forcing 105CHAPTER 10. LONG-TERM CLIMATE PROJECTIONS 10710.1. IPCC scenarios and projections 10710.2. EBM compatible scenarios 10910.3. Long-term projections 11010.4. A disaster scenario 113CHAPTER 11. SHORT-TERM PREDICTIONS 11511.1. Decadal time scale predictions by GCM 11511.2. The climate’s natural variability 11711.3. State estimate and prediction 12011.4. Decadal time scale predictions by EBM 12311.5. A posteriori predictions 124CHAPTER 12. CONCLUSIONS 12912.1. On the identification 12912.2. Climate sensitivity 13012.3. Solar activity 13112.4. Predictive capacity 13212.5. The climate change in question 13312.6. Prospects 133BIBLIOGRAPHY 135INDEX 141