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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
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Overview
• Motivation
• Data
• Observational Study
• GISS Model Results • Conclusions
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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
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Data
I. Water VaporSpecial Sensor Microwave/Imager (SSM/I) (V6)
Spatial: 0.25º× 0.25º; Temporal: 1988-present
II. Precipitation1. Global Precipitation Climatology Project (GPCP) (V2.1)
Spatial: 2.5º× 2.5º; Temporal: 1979-2009
2. SSM/I (V6) Spatial: 0.25º× 0.25º; Temporal: 1988-present
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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]
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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.
Lowpass filter has been applied to remove high frequency signals.
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Recycling RatePositive at ITCZ // Negative at two sides of ITCZ
Recycling Rate1 = (SSM/I Precipitation)/(SSM/I H2O)
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Temporal Variations of PrecipitationWet Areas
Dry Areas
8.0 ± 2.4 mm/decade
-1.3 ± 0.88 mm/decade
ENSO Signals have been removed by a multiple regression method.
Lowpass filter has been applied to remove high frequency signals.
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GISS Model
NASA Goddard Institute for Space Studies (GISS)-HYCOM 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?
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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)
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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
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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
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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 qualitatively
- Historic and control run comparison- suggests that the increasing greenhouse gas forcing affects the temporal variation of precipitation, contributing to precipitation extremes
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Acknowledgments
• NASA ROSES-2010 NEWS grant NNX13AC04G
• Eric J Fetzer (JPL), Moustafa T Chahine (JPL), Edward T Olsen (JPL), Luke Chen (JPL)
Thank You!!
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16
Spatial Pattern of the Mean Precipitation for 1988-2008
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Ensemble Runs
- 5 different colors represent 5 different initial conditions, all with the historic run forcing
- Black line is the control run
- Some weakness in the “dry” area