1. SAC Meeting – 12 April 2010 Land Surface Impact on Sub-seasonal Prediction Zhichang Guo and Paul Dirmeyer

2. SAC Meeting – 12 April 2010 Second Global Land-Atmosphere Coupling Experiment (GLACE-2) GLACE-2 is a project jointly sponsored within WCRP by GEWEX and CLIVAR. It is designed to evaluate the land surface contribution to sub-seasonal and seasonal prediction. This project is being completed with a large number of state-of-the- art global forecasting systems in a coordinated, comprehensive, and systematic manner.

3. SAC Meeting – 12 April 2010 Motivation for GLACE-2 For soil moisture initialization to add to sub-seasonal or seasonal forecast skill, two criteria must be satisfied: 1.An initialized surface anomaly must be “remembered” into the forecast period, and 2.The atmosphere must be able to respond to the surface anomaly. Addressed by GLACE Addressed by GLACE2: the full initialization forecast problem

4. SAC Meeting – 12 April 2010 GLACE-2 : Experiment Overview Perform ensembles of retrospective seasonal forecasts realistic initial land surface states Prescribed, observed SSTs realistic initial atmospheric states Evaluate forecasts against observations; Evaluate signal-to- noise ratio Series 1: Perform ensembles of retrospective seasonal forecasts realistic initial land surface states Prescribed, observed SSTs realistic initial atmospheric states Evaluate forecasts against observations; Evaluate signal-to- noise ratio Series 2: “Randomize” land Initialization! Step 1 Step 2

5. SAC Meeting – 12 April 2010 GLACE-2 : Measures of predictability and forecast skill Step 3: Compare skill and predictability in two sets of forecasts; isolate contribution of realistic land initialization. Forecast skill, Series 1 Forecast skill, Series 2 Forecast skill due to land initialization Signal-to-noise ratio, Series 1 Signal-to-noise ratio, Series 2 Predictability due to land initialization

6. SAC Meeting – 12 April 2010 Baseline: 100 Forecast Start Dates Apr 01 1986 1987 1988 1989 1990 Each ensemble consists of 10 simulations, each running for 2 months. 1000 2-month simulations for each series (realistic and random ICs). 1991 1992 1993 1994 1995 Apr 15 May 01 May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 10 Years

7. SAC Meeting – 12 April 2010 GLACE-2 COLA AGCM Experiments, 250 Forecast Start Dates Apr 01 1982 1983 1984 Each ensemble consists of 10 simulations, each running for 3 months. 2500 3-month simulations for each series (realistic and random ICs). Atmospheric initial states: NCEP-NCAR Reanalysis. Land surface initial states: SSiB offline simulations (GOLD, driven by Princeton meteorology force data, monthly observations + reanalysis synoptic and diurnal cycle). ….… ……. 2004 2005 2006 Apr 15 May 01 May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 ……. 25 Years

8. SAC Meeting – 12 April 2010 Participant List Group/ModelPoints of Contact# models S. Seneviratne, E. Davin E. Wood, L. Luo Z. Guo, P. Dirmeyer R. Koster, S. Mahanama 2 B. van den Hurk T. Gordon J.-H. Jeong T. Yamada 2 1 1 1 1 1 1 13 models(10 finished) 1G. Balsamo, F. Doblas-Reyes M. Boisserie1 1 B. Merryfield 01. NASA/GSFC (USA): GMAO seasonal forecast system (old and new) NSIPP 02. COLA (USA): COLA GCM, NCAR/CAM GCM 03. Princeton (USA): NCEP GCM 04. IACS (Switzerland): ECHAM GCM 05. KNMI (Netherlands): ECMWF 06. ECMWF 07. GFDL (USA): GFDL system (1/2 completed) 08. U. Gothenburg (Sweden): NCAR 09. CCSR/NIES/FRCGC (Japan): CCSR GCM 10. FSU/COAPS 11. CCCma Green: Finished baseline forecasts

9. SAC Meeting – 12 April 2010 Key notes for experiment and data analysis Baseline simulations: 10 years (1986-1995), 10 member ensembles, 10 start dates (1st and 15th of Apr-Aug), 2-month forecast. COLA AGCM: 25 years (1982-2006), 10 member ensembles, 10 start dates (1st and 15th of Apr-Aug), 3-month forecast. 2 cases (Realistic Land IC minus Random gives impact of initial soil state on forecast). Focus on land surface IC contribution to the predictability and forecast skill of temperature and precipitation. Focus on sub-seasonal: examine daily and 15-day periods. Global simulations - here we concentrate on results over North America.

10. SAC Meeting – 12 April 2010 Measures for Predictability: STR = variability of ensemble mean total variability SNR = variability of ensemble mean variability about ensemble mean Measures for Land Impacts on Predictability: variability of ensemble mean for realistic IC variability of ensemble mean for random IC Realistic ICRandom IC Month: June Lead:31-45 Solid lines: Ensemble mean Assume same noises for both realistic and random cases, this is equivalent to the ratio of SNR. We use the following metric to evaluate land impact on predictability signal for realistic IC signal for random IC Log 10

11. SAC Meeting – 12 April 2010 Land Impacts on Air Temperature Potential Predictability COLA AGCM Regions above 95% significance level are dotted. Land impacts are stronger in June and July, weaker in May and August, and weakest in April. Land impacts on potential predictability persists through the 2- month forecast periods.

12. SAC Meeting – 12 April 2010 Land Impacts on Precipitation Potential Predictability COLA AGCM Similar figure for precipitation, impacts on precipitation predictability are weaker than air temperature. Land impacts are relatively stronger in June and July, weaker in other months. Land impacts on potential predictability persists through the 2- month forecast periods.

13. SAC Meeting – 12 April 2010 Land Impacts on Air Temperature Potential Predictability NCEP AGCM COLA AGCM Land has impact for all months (April-August) with comparable strength. But the response for impacts are slower than COLA AGCM (Realistic Land IC in NCEP AGCM has no significant impacts on temperature predictability for the first 15 days). Geographic patterns of land impacts change with lead time and month (Impacts for COLA AGCM tend to be locked in certain areas)

14. SAC Meeting – 12 April 2010 Land Impacts on Air Temperature Potential Predictability ECMWF AGCM COLA AGCMNCEP AGCM Similar to COLA AGCM: land impacts have seasonal dependence, and persists through the 2-month forecast periods (weaker for the first 15 days). ECMWF: stronger in April and May, and weaker in JJA. COLA: stronger in JJA, and weaker in April and May. NCEP: comparable strength for all months (AMJJA), but the impacts are slower than COLA AGCM.

15. SAC Meeting – 12 April 2010 Forecast Skill measure: r 2 when regressed against observations COLA AGCM - 25 years. Compute r 2 from N points in scatter plot, one point for each of the N independent forecasts. (N=25*3*2=150 for MJJ) Forecast skill, Series 1 Forecast skill, Series 2 Forecast skill gain due to realistic land initialization

16. SAC Meeting – 12 April 2010 Land Impacts on Air Temperature Forecast Skill Multi-model Analysis The multi-model average of air temperature forecast has been correlated against observations for series 1 and 2. The r 2 difference indicates where the air temperature forecast can get benefits from realistic land IC (common to most models) Overall, land IC has significant positive impacts for at least 45 days.

17. SAC Meeting – 12 April 2010 Land Impacts on Precipitation Forecast Skill Multi-model Analysis Impacts of land IC on precipitation forecast skill are weaker than air temperature. But, in general, land IC still has positive impacts on precipitation forecast skill.

18. SAC Meeting – 12 April 2010 Land Impacts on Air Temperature Forecast Skill Weighted Multi- model Analysis Multi-model Analysis Using prior knowledge of individual model’s forecast skill, the weighted multi- model average of forecasted air temperature has been calculated, and correlated against observations. The geographic pattern of land impacts is similar to that of multi-model analysis. It did improve the forecast skills for both series 1 and 2, though it did not make further improvement on r 2 differences.

19. SAC Meeting – 12 April 2010 Areal average of weights used for the weighted multi-model analysis has been computed over North America for both series 1 and 2. The figures show inter-model differences of forecast skill. Weights for AGCMs

20. SAC Meeting – 12 April 2010 Inter-model Comparison Models appear to differ in their ability to extract skill from land initialization. For most AGCMs, there exists certain common areas where land IC tends to have significant impacts on temperature forecast skill.

21. SAC Meeting – 12 April 2010 Motivation for GLACE-2 For soil moisture initialization to add to subseasonal or seasonal forecast skill, two criteria must be satisfied: 1.An initialized surface anomaly must be “remembered” into the forecast period, and 2.The atmosphere must be able to respond to the surface anomaly. Addressed by GLACE Addressed by GLACE2: the full initialization forecast problem

22. SAC Meeting – 12 April 2010 Forecast Skill, Coupling Strength, and Soil Moisture Memory Impacts of land surface IC on air temperature forecast skill are highly related to the soil moisture memory. Impacts of land surface IC on precipitation forecast skill are related to both of the soil moisture and land- atmosphere coupling strength.

23. SAC Meeting – 12 April 2010 Temperature Forecast Skill and Soil Moisture Memory Areas with longer soil moisture memory tend to have stronger ability to extract skills from realistic land surface initialization.

24. SAC Meeting – 12 April 2010 Land Impacts on Air Temperature Forecast Skill Temporal Variability of Land Impacts With COLA-AGCM, GLACE- 2 experiments have been extended to 25 yrs (1982- 2006). This animation shows the land impacts on air temperature forecast skill with 10-year moving window. It indicates that impacts of land IC on forecast skill have temporal variability.

25. SAC Meeting – 12 April 2010 Land Impacts on Precipitation Forecast Skill Temporal Variability of Land Impacts Similar animation for precipitation forecast skill. For some years, impacts of land surface IC on precipitation forecast skill are much stronger than other years.

26. SAC Meeting – 12 April 2010 Air temperature forecast in the realistic series has been replaced with forecasted air temperature in the random series during dry, neutral, and wet years, respectively. Degradation indicates the relative importance of land surface initialization during dry, neutral, and wet years. Asymmetry impacts of land surface on sub-seasonal prediction for dry and wet years Dry Years vs. Wet Years

27. SAC Meeting – 12 April 2010 Summary Contribution of land surface initialization to sub-seasonal predictability and forecast skill is highly model-dependent. Multi-model analysis reveals the regions where realistic land surface initialization could contribute to sub-seasonal forecast skill (western and northern parts of the USA for air temperature, and northern parts of the USA for precipitation). Significant contribution of land surface initialization to sub-seasonal air temperature prediction is found over areas where soil moisture has longer memory. Moderate contribution of land surface initialization to seasonal precipitation prediction is found over limited areas where soil moisture has longer memory as well as it exhibits large land-atmosphere coupling strength. Asymmetry impacts of land surface on sub-seasonal forecast (impacts during dry years are much stronger than wet years).

28. SAC Meeting – 12 April 2010 Thank You!

29. SAC Meeting – 12 April 2010

30. Inter-model Comparison Models appear to differ in their ability to extract skill from land initialization. For most AGCMs, there exists certain common areas where land tends to have significant impacts on precipitation forecast skill.

31. SAC Meeting – 12 April 2010 Realistic IC Random IC Decay of skill with time of GLACE-2 forecasts over the region 124.25-96.25W, 18.0- 46.0N. 15-day running means are shown for runs with realistic land initialization (solid), random land initialization (dashed) and the difference (dotted). Horizontal line shows the 95% confidence threshold for significance. Decay of Skill

32. SAC Meeting – 12 April 2010 Land Impacts on Precipitation Potential Predictability NCEP AGCM COLA AGCM Similar figure for precipitation. Land has impact for all months (April-August) with comparable strength. Weaker impacts for the first 15 days.