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

2.1. Reliability and Accuracy of an Ensemble

2.2. Value of Single and Ensemble-Based Probabilistic Forecasts

3. A Brief Introduction to Data Assimilation

4. A Brief Introduction to Model Uncertainty Simulation

5. An Overview of Operational, Global, Sub-seasonal, and Seasonal Ensembles, and Their Initialization and Generation Methods

5.1. The TIGGE Global, Medium-Range Operational Ensembles

5.1.1. BMRC-ENS

5.1.2. CMA-ENS

5.1.3. The CPTEC-ENS

5.1.4. The ECMWF-ENS

5.1.5. The JMA-ENS

5.1.6. The KMA-ENS

5.1.7. The MSC-ENS

5.1.8. The NCEP-ENS

5.1.9. The UKMO-ENS

5.2. The S2S Global, Monthly Ensembles

5.2.1. The BMRC Monthly Ensemble

5.2.2. The CMA-BCC Monthly Ensemble

5.2.3. The ECCC Monthly Ensemble

5.2.4. The ECMWF Monthly Ensemble

5.2.5. The HMRC Monthly Ensemble

5.2.6. The ISAC-CNR Monthly Ensemble

5.2.7. The JMA Monthly Ensemble

5.2.8. The KMA Monthly Ensemble

5.2.9. The MF Monthly Ensemble

5.2.10. The NCEP Monthly Ensemble

5.2.11. The UKMO Monthly Ensemble

5.3. Does an Ensemble Performance Depend on its Configuration?

6. Ensembles: Considerations About Their Future

7. Summary and Key Lessons

Chapter 14: GCMs With Full Representation of Cloud Microphysics and Their MJO Simulations

1. Introduction

2. Global CRM

3. Superparameterized GCM

4. GCM With Full Representation of Cloud Microphysics and Scale-Adaptive Convection

5. Summary and Conclusion

Acknowledgments

Chapter 15: Forecast Recalibration and Multimodel Combination

1. Introduction

2. Statistical Methods for Forecast Recalibration

3. Regression Methods

3.1. Model Output Statistics

3.2. Nonhomogeneous Gaussian Regression

3.3. Comparing Recalibration Models

3.4. Further Remarks on Recalibration

4. Forecast Combination

4.1. Hierarchical Linear Regression

4.2. Why Is It So Hard to Beat the Recalibrated Multimodel Mean?

5. Concluding Remarks

Acknowledgments

Chapter 16: Forecast Verification for S2S Timescales

1. Introduction

2. Factors Affecting the Design of Verification Studies

2.1. Target Audience

2.2. Forecast Type and Parameters

2.3. Nature of Available Observations

2.4. Identification of Appropriate Methods and Metrics

3. Observational References

4. Review of the Most Common Verification Measures

4.1. Metrics for Continuous Deterministic Forecasts

4.2. Verification Methods for Categorical Deterministic Forecasts

4.3. Verification Measures for Probability Forecasts

4.4. Spatial Methods

5. Types of S2S Forecasts and Current Verification Practices

5.1. Deterministic S2S Forecast Verification Practices

5.2. Probabilistic S2S Forecast Verification Practices

5.3. Madden and Julian Oscillation (MJO) Forecast Verification

6. Summary, Challenges, and Recommendations in S2S Verification

Part IV: S2S Applications

Chapter 17: Sub-seasonal to Seasonal Prediction of Weather Extremes

1. Introduction

2. Prediction of Large-Scale, Long-Lasting Extreme Events

2.1. Heat Waves/Cold Spells

2.1.1. Heat Wave/Cold Spell Prediction Over Europe

2.1.2. Heat-Wave Prediction in Australia

2.2. Drought Prediction

3. Prediction of Mesoscale Events

3.1. Tropical Cyclones

3.2. Heavy Precipitation/Flooding

3.3. Tornadoes/Thunderstorms

3.4. Windstorms

4. Display and Verification of Sub-seasonal Forecasts of Extreme Events

5. Conclusions

Chapter 18: Pilot Experiences in Using Seamless Forecasts for Early Action: The ``Ready-Set-Go!´´ Approach in

1. Introduction

2. Why Sub-seasonal?

3. Case Study: Peru El Niño

3.1. Outline of the Early Action Protocols (EAPs) and Products Used

3.2. Preconditions for FbF Action

3.3. Forecast Thresholds

4. Reflections on the Use of S2S Forecasts

5. Conclusions

Chapter 19: Communication and Dissemination of Forecasts and Engaging User Communities

1. Introduction

2. Sector-Specific Methods and Practices in S2S Forecast Communication, Dissemination, and Engagement

2.1. Availability to the Public

2.1.1. Improving S2S Public Service Through Community Engagement: Example From the Australian Bureau of Meteorology

2.2. Current S2S Research and Applications for Weather- and Climate-Sensitive Sectors

2.2.1. Agricultural Sector

2.2.2. Energy and Water Management Sectors

2.2.3. Natural Hazards and Disaster Risk Reduction (DRR)

2.2.4. Health Sector

3. Guiding Principles for Improved Communication Practices

4. Summary and Recommendations for Future Research

Chapter 20: Seamless Prediction of Monsoon Onset and Active/Break Phases

1. Introduction

2. Extended-Range Forecast of Monsoon Sub-seasonal Variability

3. Monsoon Onset and Identification of Active/Break Spells

3.1. Criteria for Monsoon Onset Over Kerala (MOK)

3.2. Active/Break Spells Associated With MISOs

4. Demonstration of Seamless Sub-seasonal Prediction

4.1. Phase-Dependent Skill of Large-Scale MISO Indices

4.2. MISO Forecast Ensemble Spread Versus RMSE

4.3. Forecast Skill of Five Homogeneous Regions

4.4. The Forecast Skill of Active and Break Spells for Meteorological Subdivisions

4.5. Feasibility of MME Prediction to Further Smaller Spatial Scales

4.6. Application of MME to the Forecast of Extreme Events: An Example

5. Conclusions

5.1. Prospects and Problems

5.2. Future Directions for Spatially Seamless Sub-seasonal Prediction

Acknowledgments

Chapter 21: Lessons Learned in 25 Years of Informing Sectoral Decisions With Probabilistic Climate Forecasts

1. Introduction

2. Learning and Understanding the Status Quo

2.1. Characterization of Uncertainties and Associated Exposure

2.2. Explicitness of Options and Associated Upside and Downside

2.3. Identification of Stakeholders and Associated Incentives

2.4. Summary

3. Embedding a Probabilistic Climate Forecast Into Decisions

3.1. Shifting Climate Uncertainty

3.2. Assessing Changes in Risk and Options

3.3. Involvement With Stakeholders

4. Examples

4.1. The Management of the Interconnected Electric System

4.2. The Ministry of Agriculture and Fisheries and Three Recent Droughts

5. Final Remarks

Chapter 22: Predicting Climate Impacts on Health at Sub-seasonal to Seasonal Timescales

1. Introduction

1.1. Climate Impacts on Health

1.2. Toward S2S Predictions in Health

2. Case Studies

2.1. Malaria (Tompkins and Thomson)

2.2. Dengue (Lowe)

2.3. Meningitis (Martiny, Roucou, and Nakazawa)

2.4. Heat Waves (Nissan and Lowe)

3. Operationalization: Challenges and Opportunities

3.1. Data Access and Usage

3.2. Operationalization of Climate Information

3.3. Interaction Through Workshops

4. Outlook

Acknowledgments

Chapter 23: Epilogue

References

Index

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