Jiangfeng Wei with support from Paul Dirmeyer, Zhichang Guo, and Li Zhang Center for Ocean-Land-Atmosphere Studies Maryland, USA

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2 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”

3 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 strong coupling between soil moisture and precipitation, Science, 305, Koster, R. D., and Coauthors, 2006: GLACE: The Global Land-Atmosphere Coupling Experiment. Part I: Overview, J. Hydrometeorol., 7, 590–610.

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. 4

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 , and among Ω(S)-Ω(W) are Results indicate that the land-atmosphere coupling strength may be strongly influenced by the external forcing (e.g. SST). 5

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. 6

frequency Power White noise precipitation 7

Decompose of precipitation variance using Fourier transform global mean spatial correlation with Ω(S)-Ω(W) spatial correlation with Ω(W) Good correspondence between low- frequency intraseasonal variability and precipitation predictability (Ω). 8

JJA lag-2-pentad autocorrelation of precipitation (ACR) global mean spatial correlation with Ω(W) ACR has higher spatial correlation with Ω than intraseasonal variance. 9

10 Conceptual relationships Based on the above analysis, we can build a conceptual relationship: α 0 is a constant, and α 0 >>α. Thus, Ω is largely determined by F. Then the coupling strength α(S)-α(W) is the difference of α between the two ensembles, and is the impact of soil moisture on the coupling strength. F: the impact of low-frequency external forcing (F≈ACR) α: the impact of soil moisture

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. 11

Physical mechanisms More low-frequency variation of precipitation More sustained dry and wet period Stronger impact of soil moisture on precipitation Larger soil moisture memory and more sustained ET Land path Atmospheric path 12

Conceptual relationships Based on the above analysis, we can build a conceptual relationship: α 0 is a constant, and α 0 >>α. Thus, Ω is largely determined by F. Then the coupling strength α(S)-α(W) is the difference of α between 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 F: the impact of low-frequency external forcing (F≈ACR) α: the impact of soil moisture 13

Lag-2-pentad ACR for GLACE models 14

Lag-2-pentad ACR 15

Correlation across 12 GLACE models 16

Conceptual relationships Based on the above analysis, we can build a conceptual relationship: α 0 is a constant, and α 0 >>α. Thus, Ω is largely determined by F. Then the coupling strength α(S)-α(W) is the difference of α between 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 F: the impact of low-frequency external forcing (F≈ACR) α: the impact of soil moisture 17

Coupling strength Ω(S)-Ω(W) 18

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. 19

References Wei, J., P. A. Dirmeyer, and Z. Guo, 2010: How much do different land models matter for climate simulation? Part II: A decomposed view of land-atmosphere coupling strength. J. Climate. 23, Wei, J. and P. A. Dirmeyer, 2010: Toward understanding the large-scale land-atmosphere coupling in the models: Roles of different processes, Geophys. Res. Lett., 37, L19707, doi: /2010GL Wei, J., P. A. Dirmeyer, Z. Guo, and Li Zhang, 2011: Impact of atmospheric variability on soil moisture-precipitation coupling, in Climate Variability, published by Intech. 20 Questions or comments to