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Land Surface Impact on Sub-seasonal Prediction

Land Surface Impact on Sub-seasonal Prediction. Zhichang Guo and Paul Dirmeyer. Second Global Land-Atmosphere Coupling Experiment (GLACE-2).

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Land Surface Impact on Sub-seasonal Prediction

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  1. Land Surface Impact on Sub-seasonal Prediction Zhichang Guo and Paul Dirmeyer

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

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

  5. 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. Predictability due to land initialization Signal-to-noise ratio, Series 1 Signal-to-noise ratio, Series 2 Forecast skill due to land initialization Forecast skill, Series 2 Forecast skill, Series 1

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

  7. GLACE-2 COLA AGCM Experiments, 250 Forecast Start Dates May 01 May 15 Aug 15 Aug 01 Apr 15 Jun 15 Jun 01 Apr 01 Jul 15 Jul 01 1982 1983 1984 ……. 25 Years ……. ….… ……. 2004 2005 2006 • 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).

  8. Participant List Group/Model # models Points of Contact 2 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 R. Koster, S. Mahanama Z. Guo, P. Dirmeyer 2 E. Wood, L. Luo 1 1 S. Seneviratne, E. Davin 1 B. van den Hurk 1 G. Balsamo, F. Doblas-Reyes 1 T. Gordon 1 J.-H. Jeong 1 T. Yamada 1 M. Boisserie 1 B. Merryfield Green: Finished baseline forecasts 13 models(10 finished)

  9. 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. Realistic IC Random IC Measures for Predictability: variability of ensemble mean STR = total variability variability of ensemble mean SNR = variability about ensemble mean Measures for Land Impacts on Predictability: variability of ensemble mean for realistic IC variability of ensemble mean for random IC 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 Log Month: June Lead:31-45 Solid lines: Ensemble mean 10 signal for random IC

  11. 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. 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. Land Impacts on Air Temperature Potential Predictability NCEP 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) COLA AGCM

  14. Land Impacts on Air Temperature Potential Predictability ECMWF 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. NCEP AGCM COLA AGCM

  15. Forecast Skill measure: r2 when regressed against observations COLA AGCM - 25 years. Compute r2 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. 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 r2 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. 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. Weighted Multi-model Analysis Land Impacts on Air Temperature Forecast Skill 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 r2 differences. Multi-model Analysis

  19. Weights for AGCMs 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.

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

  22. 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. 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. 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. 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. Dry Years vs. Wet Years 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

  27. 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. Thank You!

  29. 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.

  30. Decay of Skill Realistic 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. Random IC Realistic IC Random IC

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

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