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Investigation of Atmospheric Recycling Rate from Observation and Model James Trammell 1 , Xun Jiang 1 , Liming Li 2 , Maochang Liang 3 , Jing Zhou 4 , and Yuk L. Yung 5. 1 Department of Earth & Atmospheric Sciences, Univ. of Houston 2 Department of Physics, Univ. of Houston - PowerPoint PPT Presentation
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Investigation of Atmospheric Recycling Rate from Observation and Model
James Trammell1, Xun Jiang1, Liming Li2, Maochang Liang3, Jing Zhou4, and Yuk L. Yung5
1 Department of Earth & Atmospheric Sciences, Univ. of Houston
2Department of Physics, Univ. of Houston
3 Research Center for Environmental Changes, Academia Sinica
4Department of Physics, Beijing Normal University
5 Division of Geological & Planetary Sciences, Caltech
AGU Fall Meeting, Dec 3, 2012
Motivation• To understand the hydrological cycle as a response to
global warming
• To quantitatively simulate the precipitation trend in order to predict the variation of precipitation in the future
• To better understand the physics behind the temporal variation and spatial pattern of precipitation
• To alleviate, forecast, and prepare for the consequences of drought in one area and flooding in another
Data
Water VaporSpecial Sensor Microwave/Imager (SSM/I) (V6)
Spatial: 0.25º× 0.25º; Temporal: 1988-present
PrecipitationGlobal Precipitation Climatology Project (GPCP) (V2.1)
Spatial: 2.5º× 2.5º; Temporal: 1979-2009 SSM/I (V6) Spatial: 0.25º× 0.25º; Temporal: 1988-present
Recycling Rate
Total Monthly Precipitation (P)
Recycling Rate (R) = _________________________________________
Mean Precipitable Water Vapor (W)
_ _ __
∆R / R = ∆P / P - ∆W / W
(The ratio of temporal variation to time mean)
[Chahine et al., 1997]
Trends in Oceanic Precipitation, Water Vapor, and Recycling Rates [Li et al., ERL
2011]
SSM/I: 0.13 ± 0.63 %/decade GPCP: 0.33 ± 0.54 %/decade
SSM/I: 0.97 ± 0.37 %/decade
Recycling 2 = (GPCP P)/(SSM/I W)
Recycling 2: -0.65 ± 0.51 %/decade
Recycling 1 = (SSM/I P)/(SSM/I W)
Recycling 1: -0.82 ± 1.11 %/decade
Deseasonalized & Lowpass Filtered Time Series
ENSO Signals have been removed by a multiple regression method.
Recycling RatePositive at ITCZ // Negative at two sides of ITCZ
Recycling Rate1 = (SSM/I Precipitation)/(SSM/I H2O)
GISS Model
NASA Goddard Institute for Space Studies (GISS) Model
Historic Run – Historic greenhouse gases are included.
Control Run – Concentrations of greenhouse gases are fixed.
Can the current atmospheric models quantitatively capture the characteristics of precipitation and water vapor from the
observational study?
Observation / Historic Run Comparison
Deseasonalized / Lowpass Filtered Precipitation
Observation GISS Historic Run
Oceanic Precipitation, Water Vapor, and Recycling Rates
Deseasonalized & Lowpass Filtered Time Series
ENSO Signals have been removed by a multiple regression method.
Dashed line is the GISS historic run comparison with the observations.
Trends for GISS run
(A)P: 0.80 ± 0.29 %/decade(B)W: 1.78 ± 0.48 %/decade(C)R: -0.55 ± 0.34 %/decade
% change in precipitation (A), water vapor (B), and recycling rate (C)
GISS ComparisonDeseasonalized / Lowpass Filtered Precipitation
Historic Run Control Run (fixed)
2.36 ± 1.17 mm/decade
-0.14 ± 0.22 mm/decade -0.02 ± 0.20 mm/decade
0.12 ± 1.04 mm/decade
GISS ComparisonDeseasonalized / Lowpass Filtered Column Water
Historic Run Control Run (fixed)
1.12 ± 0.17 mm/decade
0.55 ± 0.09 mm/decade
0.03 ± 0.12 mm/decade
-0.01 ± 0.08 mm/decade
Conclusions- Observations and GISS historic run
- Recycling rate has increased in the ITCZ and decreased in the neighboring regions over the past two decades- Temporal variation is stronger in precipitation than in water vapor, which results to the positive (negative) trend of recycling rate in the high (low) precipitation region. - GISS model captures the observed precipitation, water vapor, and recycling rate trends
- Historic and control run comparison- suggests that the increasing greenhouse gas forcing affects the temporal and spatial variation of precipitation, contributing to precipitation extremes
- Future Work - use the GISS model to explore the physics driving the temporal and spatial variability of precipitation- investigate whether the model can capture the observed spatial pattern
Acknowledgments
• NASA ROSES (NASA Energy and Water Cycle Study)
• Moustafa T Chahine (JPL), Edward T Olsen (JPL), Eric J Fetzer (JPL), Luke Chen (JPL)
Thank You!!