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 3 Research Center for Environmental Changes, Academia Sinica 4 Department 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 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: 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: ± 0.51 %/decade Recycling 1 = (SSM/I P)/(SSM/I W) Recycling 1: ± 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 Rate Positive at ITCZ // Negative at two sides of ITCZ Recycling Rate1 = (SSM/I Precipitation)/(SSM/I H2O)

Temporal Variations of Precipitation Wet 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: ± 0.34 %/decade % change in precipitation (A), water vapor (B), and recycling rate (C)

GISS Comparison Deseasonalized / Lowpass Filtered Precipitation Historic Run Control Run (fixed) 2.36 ± 1.17 mm/decade ± 0.22 mm/decade ± 0.20 mm/decade 0.12 ± 1.04 mm/decade

GISS Comparison Deseasonalized / 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 ± 0.08 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

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