How much do different land models matter for climate simulation?

1. How much do different land models matter for climate simulation? Jiangfeng Wei with support from Paul Dirmeyer, Zhichang Guo, Li Zhang, Vasu Misra, and James Kinter COLA/IGES

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 simulation may be brought to atmosphere Sources of the signals are hard to trace in the complex system • e.g. GLACE “hotspots”

3. Models

4. Experiments Exp I(individually coupled runs) AGCM AGCM AGCM SSiB CLM Noah Exp C(combined run, interactive ensemble) AGCM Same atmospheric forcing for 3 land models Average fluxes from 3 land models SSiB CLM Noah can be seen as 3 offline simulations with same forcing

5. All the simulations start from April 1, 1982 and end on January 1, 2005 (close to 23 years). The last 18 years of data is used for analysis. The atmospheric initial condition is from NCEP/NCAR reanalysis, and land initial conditions are from long-term offline simulations.

6. Tropical land (25S-25N) Northern land (25N-70N)

7. Inter-model differences of JJA climatology

8. Can land-atmosphere interaction amplify the uncertainties from land models? Define Var(I) and Var(C) are the inter-model (3 cases) variances of fluxes from land to atmosphere in experiments I and C, respectively. Var(I): land model difference + land-atmosphere feedback Var(C): land model difference only :the percentage of inter-model variance caused by land-atmosphere feedback If Var(I)Var(C), 01, else, <0.

9. : the percentage of inter-model variance caused by land-atmosphere feedback 01: land-atmosphere interaction can amplify the spread caused by land model differences <0: the spread decreases when coupled to the AGCM

10. : the percentage of inter-model variance caused by land-atmosphere feedback For the colored area (>0): (LH) has much larger value than for (SH) because LH is more strongly influenced by precipitation. The largest value of (LH) is generally over semi-arid regions, where precipitation uncertainties influence LH most. For SH, only about half of the inter-model spread is caused by the different forcings over land and another half is from LSS differences.

11. 1987-2004 JJA interannual variation LH-SWnet Water limiting LHSWnet Energy limiting The evaporation regime largely determines how the spread of LH among LSSs changes.

12. Std dev of Tmin, Tmax, and DTR(= Tmax- Tmin) among 3 LSSs “Pure” influence of land model uncertainties Land uncertainty + feedback • More impact on Tmax than on Tmin, mainly through LH • Interaction can decrease the tropical uncertainties through a negative feedback: Higher T -> stronger convection -> more P and cloud-> more ET and less radiation-> lower T • Interaction can increase the middle to high latitude uncertainties through a positive feedback (if warm): less P and cloud -> drier soil -> less ET -> higher T and less P, or the complex snow-atmosphere-cloud feedback (if cold).

13. Summary for part I The choice of LSS has significant impact on the model hydrological cycle. The evaporation regime largely determines how the spread of LH among LSSs changes. In coupled GCM simulations, most of the LH uncertainties over semi-arid areas are caused by the precipitation difference and LSS differences have very little influence, while only about half of the inter-model differences of SH over land are caused by the forcing difference and another half is from LSS differences. The uncertainties of LH among the LSSs have strong influence on surface temperature, and it has more influence on Tmax than on Tmin. The influence is stronger in dry regions/seasons, where LH has more uncertainty. Land-atmosphere interaction can weaken the influence of LSS uncertainties in the tropics, but may strengthen their influence in middle to high latitudes.

14. Memory of land models Noah model has lower memory of LH than the other two models

15. Causes of low LH memory in Noah model: Tropics: percentage of canopy interception is too high Middle to high latitudes: high percentage of interception and low memory of vegetation transpiration

16. What’s the role of land in this precipitation persistence? How to highlight it? • Does precipitation variability impact land-atmosphere coupling? If does, how does this model overestimation of precipitation persistence affect the estimated coupling strength? The memory of land model does not has a significant effect on the global pattern of precipitation persistence, but regional effect may exist. All the models have overestimated the precipitation persistence.

17. Global Land-Atmosphere Coupling Experiment Koster et al. (2004, 2006) 16-member ensembles for 1 June- 31 August of 1994 (SST prescribed) Ensemble W: control integrations Ensemble S: subsurface 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. (S)-(W) is the predictability come from the prescribed subsurface soil moisture, and is a measure of land-atmosphere coupling strength in GLACE.

18. Ω shows similar patterns for 3 models, with largest values in the tropical rain belt where the SST forcing has strongest influence. • The patterns of W and S are very close, large differences ((S)- (W)) mainly over the regions with high  values. This indicates that the land-atmosphere coupling strength is strongly influenced by external forcing. • COLA-Noah has very weak land-atmosphere coupling.

19. (Pegion and Kirtman 2008). Percentage of intraseasonal (30–100 day) precipitation variance calculated from the CMAP for the years 1982–2002.

20. (W) global mean spatial correlation with (W) spatial correlation with (S)-(W)   intraseasonal variance? Obs.

21. Calculated with the intraseasonal component of precipitation time series • Most of the precipitation predictability () and land-atmosphere coupling strength ((S)-(W)) are associated with the intraseasonal component of precipitation in the models, although they only account for a small percentage (~20%) of the total variance.

22. Results from models participating in GLACE All have high spatial correlation with , supporting our theory. All models have overestimated the percentage of intraseasonal variance.

23. Conceptual relationships Based on the above analysis, we can build a conceptual relationship: F: the impact of low-frequency external forcing :the impact of soil moisture 0 is a constant, and 0>>. Thus, the spatial variation of  is largely determined by F. Then the coupling strength (S)-(W) is the difference of  between the two ensembles, and is the “pure” impact of soil moisture on the coupling strength (i.e. without the influence of external forcing). It is evident that both F and (S)-(W) can impact the coupling strength greatly.

24. For the three individually coupled models, F is similar and (S)-(W) causes the large difference in coupling strength. Noah model should have the smallest (S)-(W). If F is equivalent to the percentage of intraseasonal variance, it should have been overestimated by the models. We can adjust (S)-(W) as < 1

25. Adjustment of the GLACE coupling strength Another physical explanation: More low-frequency variation of precipitation (rains too frequently at reduced intensity) Precipitation has prolonged impact on soil moisture Less runoff, more ET, increased soil moisture memory Overestimation of land-atmosphere coupling Difficult to evaluate by comparing with observation. Hard to say whether it is more realistic.

26. Summary for part II Different land models or subsurface soil moisture have little influence on the global pattern of precipitation predictability () and variance distribution because of the stronger control of other factors. The regional effect of soil moisture can be highlighted by the difference of  from two ensembles, which shows contrasting patterns for the three models. Most of the precipitation predictability and land-atmosphere coupling strength are associated with the intraseasonal component of precipitation in the models. Most models have overestimated the low-frequency variance percentage and underestimated the high-frequency variance percentage of precipitation. Based on the findings, we adjust the land-atmosphere coupling strength estimated by GLACE. It is found that the adjusted coupling strengths are generally weaker than that from GLACE but the patterns are nearly the same.

27. References • Wei, J., P. A. Dirmeyer, Z. Guo, L. Zhang, and V. Misra, 2009: How much do different land models matter for climate simulation? Part I: Climatology and variability. COLA Tech. Rep. 273. 35pp. [Available online at ftp://grads.iges.org/pub/ctr/ctr273_ms.pdf] • Wei, J., P. A. Dirmeyer, and Z. Guo, 2009: How much do different land models matter for climate simulation? Part II: A decomposed view of land-atmosphere coupling strength. COLA Tech. Rep. 274. 27pp. [Available online at ftp://grads.iges.org/pub/ctr/ctr274_ms.pdf]