Front Cover -- Sub-seasonal to Seasonal Prediction: The Gap Between Weather and Climate Forecasting -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgements -- Part I: Setting the Scene -- Chapter 1: Introduction: Why Sub-seasonal to Seasonal Prediction (S2S)? -- 1. History of Numerical Weather and Climate Forecasting -- 2. Sub-seasonal to Seasonal Forecasting -- 2.1. The Discovery of Sources of Sub-seasonal to Seasonal Predictability Associated With Atmosphere, Ocean, and Land Proc ... -- 2.2. Improvements in Numerical Weather Forecasting -- 2.3. Development of Seamless Prediction -- 2.4. Demand From Users for S2S Forecasts -- 3. Recent National and International Efforts on Sub-seasonal to Seasonal Prediction -- 4. Structure of This Book -- Chapter 2: Weather Forecasting: What Sets the Forecast Skill Horizon? -- 1. Introduction -- 2. The Basics of Numerical Weather Prediction -- 2.1. The Atmosphere as a Dynamical System -- 2.2. Predictability -- 2.3. Scale-Dependent Behavior -- 2.4. Coupled Systems -- 3. The Evolution of NWP Techniques -- 3.1. Computational Infrastructure -- 3.2. Observing Systems -- 3.3. Data Assimilation -- 3.4. Modeling -- 3.5. Improvements in Forecast Performance -- 3.6. Weather Versus Climate Prediction -- 4. Enhancement of Predictable Signals -- 4.1. Spatiotemporal Aggregation -- 4.2. Ensemble Averaging -- 4.3. Removal of Systematic Errors -- 5. Ensemble Techniques: Brief Introduction -- 5.1. Background -- 5.2. Methodology -- 5.3. Use of Ensembles -- 6. Expanding the Forecast Skill Horizon -- 7. Concluding Remarks: Lessons for S2S Forecasting -- Acknowledgments -- Chapter 3: Weather Within Climate: Sub-seasonal Predictability of Tropical Daily Rainfall Characteristics -- 1. Introduction -- 2. Data and Methods -- 2.1. Daily Rainfall and OLR -- 2.2. S2S Forecasts.

2.3. Method to Estimate the Spatial Coherence -- 3. Results -- 3.1. Daily Rainfall Characteristics of the Indian Summer Monsoon -- 3.2. Sub-seasonal Modulation of Spatial Coherence Across India -- 3.3. Sub-seasonal Modulation of Spatial Coherence Over the Whole Tropical Zone -- 3.4. Skill and Spatial Coherence of S2S Reforecasts -- 4. Discussion and Concluding Remarks -- Chapter 4: Identifying Wave Processes Associated With Predictability Across Time Scales: An Empirical Normal Mode Approach -- 1. Introduction -- 2. Partitioning Atmospheric Behavior Using Its Conservation Properties -- 2.1. Partitioning Variability: Background State and Wave Activity -- 2.2. Wave Activity Conservation Laws -- 2.3. The Implications of Wave-Activity Conservation for Modes of Variability -- 3. The ENM Approach to Observed Data and Models and Its Relevance to S2S Dynamics and Predictability -- 3.1. ENMs: Bridging Principal Component, Normal Modes, and Conservation Laws -- 3.2. ENM in Applications Relevant to Predictability Across Time Scales -- 3.3. ENM Application to the Atmospheric S2S Variability -- 4. Conclusion -- Acknowledgments -- Part II: Sources of S2S Predictability -- Chapter 5: The Madden-Julian Oscillation -- 1. Introduction -- 2. The Real-Time Multivariate MJO Index -- 3. Observed MJO Structure -- 4. The Relationship Between the MJO and Tropical and Extratropical Weather -- 5. Theories and Mechanisms for MJO Initiation, Maintenance, and Propagation -- 6. The Representation of the MJO in Weather and Climate Models -- 7. MJO Prediction -- 7.1. Sub-seasonal and Interannual Variations in Forecast Skill -- 8. Future Priorities for MJO Research for S2S Prediction -- 8.1. Linking Theory and Modeling -- 8.2. MJO Initiation -- 8.3. Predicting the Impacts of the MJO -- Acknowledgments.

Chapter 6: Extratropical Sub-seasonal to Seasonal Oscillations and Multiple Regimes: The Dynamical Systems View -- 1. Introduction and Motivation -- 2. Multiple Midlatitude Regimes and Low-Frequency Oscillations -- 2.1. The Case for Multiple Regimes and Their Classification -- 2.2. Theoretical Basis of Multiple Regimes -- Rossby Wave Propagation and Interference -- 3. Extratropical Oscillations in the S2S Band -- 3.1. Phenomenological Description -- Variations of Geopotential Height -- Oscillatory Features in Time and Space -- 3.2. Topographic Instability and Hopf Bifurcation -- 4. Low-Order, Data-Driven Modeling, Dynamical Analysis, and Prediction -- 4.1. Background and Methodological LOM Developments -- 4.2. Dynamical Diagnostics and Empirical Prediction on S2S Scales -- 4.3. LFV and Multilayer Stochastic Closure: A Simple Illustration -- 5. Concluding Remarks -- Acknowledgments -- Chapter 7: Tropical-Extratropical Interactions and Teleconnections -- 1. Introduction -- 2. Tropical Influence on the Extratropical Atmosphere -- 2.1. Observed MJO Influences -- 2.2. Extratropical Atmospheric Response to Tropical Thermal Forcing -- 3. Extratropical Influence on the Tropics -- 3.1. Extratropical Influences on Tropical Convection and the MJO -- 3.2. Diagnosing Intraseasonal Extratropical Influences on the Tropics -- 4. Tropical-Extratropical, Two-Way Interactions -- 4.1. Forcing of Extratropical Waves Through Two-Way Interactions -- 4.2. Three-Dimensional Instability Theory -- 5. Summary and Discussion -- Appendix. Technical Matters Relating to Section 4.2 -- Chapter 8: Land Surface Processes Relevant to Sub-seasonal to Seasonal (S2S) Prediction -- 1. Introduction -- 2. Process of Land-Atmosphere Interaction -- 2.1. Surface Fluxes -- 2.2. Land-Surface States -- 2.3. Boundary Layer (BL) Response -- 2.4. Timescales.

3. A Brief History of Land-Surface Models -- 3.1. Origin and Evolution of Land-Surface Models -- 3.2. LSMs at Operational Forecast Centers -- 3.3. LSM Initialization and Data Assimilation -- 4. Predictability and Prediction -- 5. Improving Land-Driven Prediction -- 5.1. Validation -- 5.2. Initialization -- 5.3. Unconsidered Elements -- 5.4. Coupled Land-Atmosphere Model Development -- Chapter 9: Midlatitude Mesoscale Ocean-Atmosphere Interaction and Its Relevance to S2S Prediction -- 1. Introduction -- 2. Data and Models -- 2.1. Uncoupled Integrations -- 2.2. Coupled Integrations -- 3. Mesoscale Ocean-Atmosphere Interaction in the Atmospheric Boundary Layer -- 4. Local Tropospheric Response -- 5. Remote Tropospheric Response -- 6. Impact on Ocean Circulation -- 7. Implications for S2S Prediction -- 8. Summary and Conclusions -- Acknowledgments -- Chapter 10: The Role of Sea Ice in Sub-seasonal Predictability -- 1. Introduction -- 2. Sea Ice in the Coupled Atmosphere-Ocean System -- 2.1. Sea Ice Physics -- 2.2. Sea Ice Observations -- 2.3. Sea Ice in Models and Reanalyses -- 3. Sea Ice Distribution, Seasonality, and Variability -- 4. Sources of Sea Ice Predictability at the Sub-seasonal to Seasonal Timescale -- 4.1. Persistence -- 4.2. Other Mechanisms -- 5. Sea Ice Sub-seasonal to Seasonal Predictability and Prediction Skill in Models -- 5.1. Potential Sea Ice Predictability -- 5.2. Skill of Sea Ice Prediction Systems at Sub-seasonal Timescales -- 5.2.1. Short-Term Predictions -- 5.2.2. Sub-seasonal to Seasonal Predictions -- 6. Impact of Sea Ice on Sub-seasonal Predictability -- 6.1. Impacts in the Polar Regions -- 6.2. Impacts Outside Polar Regions -- 7. Concluding Remarks -- Acknowledgments -- Chapter 11: Sub-seasonal Predictability and the Stratosphere -- 1. Introduction -- 2. Stratosphere-Troposphere Coupling in the Tropics.

2.1. How Does the QBO Influence the Tropical Troposphere? -- 2.2. Predictability Related to Tropical Stratosphere-Troposphere Coupling -- 3. Stratosphere-Troposphere Coupling in the Extratropics -- 3.1. An Overview of Polar Vortex Variability -- 3.2. What Drives Polar Vortex Variability? -- 3.3. How Does Stratospheric Polar Vortex Variability Influence Surface Climate? -- 3.4. Other Manifestations of Extratropical Stratosphere-Troposphere Coupling -- 4. Predictability Related to Extratropical Stratosphere-Troposphere Coupling -- 4.1. How Accurately Can the Polar Stratosphere be Predicted? -- 4.2. S2S Extratropical Forecast Skill Associated With Strong and Weak Polar Vortex Events -- 4.3. S2S Extratropical Forecast Skill Associated With Stratosphere-Troposphere Pathways -- 5. Summary and Outlook -- 5.1. What Determines How Well a Model Represents Stratosphere-Troposphere Coupling? -- 5.1.1. Role of Model Lid Height and Vertical Resolution -- 5.1.2. Influence of the Tropospheric State and Biases -- 5.1.3. Influence of Different Drivers on Stratosphere-Troposphere Coupling Efficacy -- 5.2. How Can We Use Sub-seasonal Prediction Data in New Ways to Study Stratospheric Dynamics and Stratosphere-Troposphere ... -- Part III: S2S Modeling and Forecasting -- Chapter 12: Forecast System Design, Configuration, and Complexity -- 1. Introduction -- 2. Requirements and Constraints of the Operational Sub-seasonal Forecast -- 3. Effect of Ensemble Size and Lagged Ensemble -- 3.1. Effect of Ensemble Size -- 3.2. Uncertainty of Skill Estimate -- 3.3. Effect of LAF Ensemble -- 4. Real-Time Forecast Configuration -- 5. Reforecast Configuration -- 6. Summary and Concluding Remarks -- Acknowledgments -- Chapter 13: Ensemble Generation: The TIGGE and S2S Ensembles -- 1. Global Sub-seasonal and Seasonal Prediction Is an Initial Value Problem.

2. Ensembles Provide More Complete and Valuable Information Than Single States.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.