GLACE: The Global Land-Atmosphere Coupling Experiment.

Part I: Overview

Wenxian ZhangSchool of Earth and Atmospheric Sciences

Georgia Institute of Technology

Background

• Precipitation Land surface moisture

• Numerical models

vs. observations

• AGCMs

• Model dependence

Background

• Land-atmosphere coupling strength• K02: Four-model intercomparison (Koster et al.,

2002) - Four independent AGCM modeling groups - One-month simulation - The same time series of surface prognostic variables - Quantification of the response of precipitation - A marked disparity in the coupling strength

Motivations

• To quantify the land-atmosphere coupling strength of the twelve AGCMs

- Participation from a wider range of models

- Separation of the effects of “fast” and “low”

reservoirs

- Effect on air temperature• To document the coupling strengths of the

participating models for future study

Experimental Design

• Three ensemble

- Write

- Read

- Subsurface• Sixteen members• 1 June – 31 August

1994• The same SST

Experimental Design

Experimental Design

Ω Diagnostic

• Time series of six-day totals• P(t): 14 six-day totals for each simulation• :The ensemble mean time series

• :The temporal standard deviation• :The standard deviation of the ensemble mean time series

( )P t

16

1

1( ) ( )

16 ii

P t P t

p

P

Ω Diagnostic

• The degree to which the sixteen precipitation time series generated by the ensemble members are similar

• The relative contributions of boundary forcing and internal chaotic variability to the generation of precipitation

2 2

2

16

15

PP

pP

Ω Diagnostic

Figure 2 of Koster et al., 2002: Time series of precipitation produced by NSIPP’s R ensemble. (top) Grid cell for which Ω is high. (bottom) Grid cell for which Ω is low

Ω Diagnostic

Precipitation

Conclusions

• The range of coupling strengths is large.

• The multimodel “hot spots” of land-atmosphere coupling is determined.