Impact of atmospheric variability on soil moisture-precipitation coupling

1. Impact of atmospheric variability on soil moisture-precipitation coupling Jiangfeng Wei with support from Paul Dirmeyer, ZhichangGuo, and Li Zhang Center for Ocean-Land-Atmosphere Studies Maryland, USA

3. Motivation • Uncertainty of land surface models • significantly different output at the same forcing (e.g., PILPS, GSWP) • Complexity of land-atmosphere interaction • full of nonlinear processes • uncertainties in land simulations may be brought to atmosphere • Sources of the signals are hard to trace in the complex system • e.g. GLACE “hotspots”

4. Global Land-Atmosphere Coupling Experiment 16-member ensembles for 1 June- 31 August of 1994 (SST prescribed) Ensemble W: control integrations Ensemble S: soil moisture is given the same as one member of W Ω measures the similarity (or predictability) of the time series in 16 ensemble members, and is equivalent to the percentage of variance caused by the slowly varying oceanic, radiative, and land surface processes. Ω(S)-Ω(W) is the predictability come from the prescribed subsurface soil moisture, and is a measure of land-atmosphere coupling strength in GLACE. Koster, R. D., and Coauthors 2004: Regions of strongcouplingbetweensoilmoisture and precipitation, Science, 305, 1138-1140. Koster, R. D., and Coauthors, 2006: GLACE: The Global Land-AtmosphereCouplingExperiment. Part I: Overview, J. Hydrometeorol., 7, 590–610.

5. Models Each AGCM is coupled to the three land models. Totally six model configurations (combinations): COLA-SSiB, COLA-CLM, COLA-Noah, GFS-SSiB, GFS-CLM, GFS-Noah .

6. Results from GLACE-type experiments Values for COLA AGCM are larger than for GFS, showing the dominant impact of the AGCM. Ω(W), Ω(S), and Ω(S)-Ω(W) have similar patterns, with larger values over tropical land. Spatial correlations among Ω are 0.43-0.71, and among Ω(S)-Ω(W) are 0-0.29. Results indicate that the land-atmosphere coupling strength may be strongly influenced by the external forcing (e.g. SST).

7. Decompose of precipitation variance using Fourier transform All the model simulations underestimate the high-frequency (fast synoptic) variance and overestimate the low-frequency (intraseasonal) variance. More low-frequency variance in COLA AGCM. The percentages for the theoretical white noise are 69%, 21%, and 10%, indicating that the precipitation time series follow a red spectrum.

8. Power precipitation White noise frequency

9. Decompose of precipitation variance using Fourier transform global mean spatial correlation with Ω(W) spatial correlation with Ω(S)-Ω(W) Good correspondence between low-frequency intraseasonal variability and precipitation predictability (Ω).

10. JJA lag-2-pentad autocorrelation of precipitation (ACR) ACR has higher spatial correlation with Ω than intraseasonal variance. global mean spatial correlation with Ω(W)

11. Conceptual relationships Based on the above analysis, we can build a conceptual relationship: F:the impact of low-frequency external forcing (F≈ACR) α:the impact of soil moisture α0 is a constant, andα0>>α. Thus,Ωis largely determined by F. Then the coupling strength α(S)-α(W)is the difference ofαbetween the two ensembles, and is the impact of soil moisture on the coupling strength.

12. S’ experiment: COLA-SSiB reads soil moisture from GFS-SSiB GFS-SSiB reads soil moisture from COLA-SSiB Exchange prescribed soil moisture to separate the impact of soil moisture and atmosphere Difference in soil moisture variability has some impact on land-atmosphere coupling, but the characteristics of the atmosphere appear to be more important.

13. Physical mechanisms More low-frequency variation of precipitation Atmospheric path Land path More sustained dry and wet period Larger soil moisture memory and more sustained ET Stronger impact of soil moisture on precipitation

14. Conceptual relationships Based on the above analysis, we can build a conceptual relationship: F:the impact of low-frequency external forcing (F≈ACR) α:the impact of soil moisture α0 is a constant, andα0>>α. Thus,Ωis largely determined by F. Then the coupling strength α(S)-α(W)is the difference ofαbetween the two ensembles, and is the impact of soil moisture on the coupling strength. α(S)-α(W)can be further expanded to SM->ET and and ET->P

15. Lag-2-pentad ACR for GLACE models

16. Lag-2-pentad ACR

17. Correlation across 12 GLACE models

18. Conceptual relationships Based on the above analysis, we can build a conceptual relationship: F:the impact of low-frequency external forcing (F≈ACR) α:the impact of soil moisture α0 is a constant, andα0>>α. Thus,Ωis largely determined by F. Then the coupling strength α(S)-α(W)is the difference ofαbetween the two ensembles, and is the impact of soil moisture on the coupling strength. α(S)-α(W)can be further expanded to SM->ET and and ET->P

19. Coupling strength Ω(S)-Ω(W)

20. Summary • For our six model configurations, the choice of AGCMs is the main reason for the substantially different precipitation variability, predictability, and land-atmosphere coupling strength among the configurations. The impact of different land models is secondary. • Intraseasonal precipitation variability, which is mainly a property of the AGCM, can impact land-atmosphere coupling both directly in the atmosphere and indirectly through soil moisture response to precipitation. • Models generally overestimate the low-frequency component of precipitation. The calibrated coupling strength shows a similar global pattern, but is significantly weaker over some regions.

21. References • Wei, J., P. A. Dirmeyer, and Z. Guo, 2010: How much do different land modelsmatter for climate simulation? Part II: A decomposedview of land-atmospherecouplingstrength. J. Climate. 23, 3135-3145. • Wei, J. and P. A. Dirmeyer, 2010: Towardunderstanding the large-scaleland-atmospherecoupling in the models: Roles of differentprocesses, Geophys. Res. Lett., 37, L19707, doi:10.1029/2010GL044769. • Wei, J., P. A. Dirmeyer, Z. Guo, and Li Zhang, 2011: Impact of atmosphericvariability on soilmoisture-precipitationcoupling, in ClimateVariability, published by Intech. Questions or comments tojianfeng@iges.org.