Investigation of Atmospheric Recycling Rate from Observation and Model James Trammell 1, Xun Jiang...

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

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 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

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.

Lowpass filter has been applied to remove high frequency signals.

Recycling RatePositive at ITCZ // Negative at two sides of ITCZ

Recycling Rate1 = (SSM/I Precipitation)/(SSM/I H2O)

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.

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

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 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!!

16

Spatial Pattern of the Mean Precipitation for 1988-2008

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