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
3 Research
of Physics, Univ. of Houston
Center for Environmental Changes, Academia Sinica
4Department
5 Division
of Physics, Beijing Normal University
of Geological & Planetary Sciences, Caltech
AGU Fall Meeting, Dec 3, 2012
Overview
• Motivation
• Data
• Observational Study
• GISS Model Results
• Conclusions
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
I. Water Vapor
Special Sensor Microwave/Imager (SSM/I) (V6)
Spatial: 0.25º× 0.25º; Temporal: 1988-present
II. Precipitation
1. 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
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]
Deseasonalized & Lowpass Filtered Time Series
SSM/I: 0.13 ± 0.63 %/decade
GPCP: 0.33 ± 0.54 %/decade
SSM/I: 0.97 ± 0.37 %/decade
Recycling 1 = (SSM/I P)/(SSM/I W)
Recycling 1: -0.82 ± 1.11 %/decade
Recycling 2 = (GPCP P)/(SSM/I W)
Recycling 2: -0.65 ± 0.51 %/decade
ENSO Signals have been removed by a multiple regression method.
Lowpass filter has been applied to remove high frequency signals.
Recycling Rate
Positive at ITCZ // Negative at two sides of ITCZ
Recycling Rate1 = (SSM/I Precipitation)/(SSM/I H2O)
Temporal Variations of Precipitation
Wet Areas
8.0 ± 2.4 mm/decade
-1.3 ± 0.88 mm/decade
Dry Areas
ENSO Signals have been removed by a multiple regression method.
Lowpass filter has been applied to remove high frequency signals.
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?
Oceanic Precipitation, Water Vapor, and Recycling
Rates
% change in precipitation (A), water vapor (B), and recycling rate (C)
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
Deseasonalized & Lowpass Filtered Time Series
ENSO Signals have been removed by a multiple regression method.
GISS Comparison
Deseasonalized / Lowpass Filtered Precipitation
0.12 ± 1.04 mm/decade
2.36 ± 1.17 mm/decade
-0.02 ± 0.20 mm/decade
-0.14 ± 0.22 mm/decade
Control Run (fixed)
Historic Run
GISS Comparison
Deseasonalized / Lowpass Filtered Column Water
0.03 ± 0.12 mm/decade
1.12 ± 0.17 mm/decade
-0.01 ± 0.08 mm/decade
0.55 ± 0.09 mm/decade
Control Run (fixed)
Historic Run
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
Acknowledgments
• NASA ROSES-2010 NEWS grant NNX13AC04G
• Eric J Fetzer (JPL), Moustafa T Chahine (JPL), Edward
T Olsen (JPL), Luke Chen (JPL)
Thank You!!
Spatial Pattern of the Mean Precipitation for 1988-2008
16
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