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耶鲁大学-南京信息工程大学大气环境中心 Yale-NUIST Center on Atmospheric Environment Sensitivity of Bowen ratio of water bodies to air temperature Wang Wei YNCenter Video Conference November 15, 2012

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Page 1: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

耶鲁大学-南京信息工程大学大气环境中心

Yale-NUIST Center on Atmospheric Environment

Sensitivity of Bowen ratio of water bodies to air temperature

Wang WeiYNCenter Video Conference

November 15, 2012

Page 2: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Outline

1. Introduction2. ResultsObservationModelingPrediction3. Conclusion

Page 3: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Why choosing water bodies?

Inland water bodies occupy  4% of the continent surface, oceans cover 71% of the earth surface; (Downing et al., 2006)

Energy partitioning between latent and sensible heat flux of water bodies play a crucial role in determining hydrological cycle, weather and climate change; (Wilson et al., 2002)

Rare knowledge on variability of energy partitioning for water bodies, especially under global warming.

1. INTRODUCTION

Page 4: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

(Trenberth et al., 2007)

1. INTRODUCTION

Page 5: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Why choosing air temperature?

The most important climatological factors influencing land‐atmosphere interaction;

Negative relationship between Bowen Ratio and air temperature; (Brutsaert 1982)

Alternating energy partitioning as air temperature increasing.

1 1 1

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1. INTRODUCTION

Page 6: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Simple lake model

(Developed by Lee, 2012)

4(1 )( )

( )

s

a p H s a

a E s a

K L T H EH c C u T T

E C u q q

Linearization of Ts and qs

Solution of Ts

Meteorological drives

H, λE, ββ slightly sensitive to wind speedβ not sensitive to radiationβ decrease with air temperature

1. INTRODUCTION

Page 7: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Previous studies

(Wilson et al., 2002; Cho, J., T. Oki, et al., 2012)

1. INTRODUCTION

Page 8: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Objective

To quantify the Bowen Ratio’s sensitivity to air temperature from temporal and spatial perspective;

To understand how Bowen Ratio varying as global warming.

1. INTRODUCTION

Page 9: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Dataset1. INTRODUCTION

5 scenarios of CM2.1: emission rate order A1FI>A2>A1B>B1>S1.

Page 10: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Literature results (Annual)2.RESULTS

Page 11: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Global distribution of Bowen ratio2.RESULTS

Page 12: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Zonal average (Annual)2.RESULTS

Page 13: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Three lakes cases (monthly)2.RESULTS

Page 14: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Zonal sensitivity2.RESULTS

Page 15: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Global ocean since 1985 (Annual)2.RESULTS

Page 16: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Merra inputs and MLW 2011 observation

2.RESULTS

Page 17: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

2.RESULTS

Simple lake model driven by Merra(1979‐2011)

Page 18: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

CM2.1 prediction (2001‐2100)2.RESULTS

Including ice sheet

Page 19: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Summary2.RESULTS

Sensitivity calculated from Brutsaert formula is about 0.023/℃.

Page 20: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Conclusion

Bowen ratio is more (less) sensitive to air temperature at high (low) latitudes or at low (high) temperature, with a spatial average sensitivity of ‐0.02 per ℃;

The Bowen Ratio’s sensitivity to air temperature is about ‐0.088 per ℃ during the past 27 years over global oceans;

All the scenarios have significantly underestimated the decreasing rate of Bowen ratio for global warming over oceans. 

3.  Conclus ion

Page 21: Wang Wei YNCenterVideo Conference November · 2019. 12. 18. · Wang Wei YNCenterVideo Conference November 15, 2012. Outline 1. Introduction 2. Results Observation Modeling Prediction

Thank you!

Suggestions and questions are welcome.

耶鲁大学-南京信息工程大学大气环境中心

Yale-NUIST Center on Atmospheric Environment