Toward Improving Predictability through Transferability Experiments

1. Toward Improving Predictability through Transferability Experiments E. S. Takle1, B. Rockel2, W. J. Gutowski, Jr.1, J. Roads3, R. W. Arritt1, I. Meinke2, and C. Jones4 1Iowa State University, Ames, IA 2GKSS Research Centre, Geesthacht , Germany 3Scripps Institution of Oceanography, UCSD,LaJolla, CA 4Université du Québec à Montréal gstakle@iastate.edu Indo-US Workshop on High-Performance Computing for Regional Weather and Climate 30 June - 2 July 2005, NCAR, Boulder, CO

2. “Transferability” is proposed as the next step beyond “model intercomparison projects” (MIPs) for advancing our understanding of the global energy balance and the global water cycle by use of models

3. ARCMIP GLIMPSE Regional Model Intercomparison Projects BALTIMOS GKSS/ICTS PRUDENCE SGMIP QUIRCS RMIP PIRCS AMMA IRI/ARC LA PLATA

4. Lessons Learned from MIPs • No single model stands out as being best at simulating all variables • Most MIPs have helped individual modelers identify specific shortcomings of their models • Regional models run in climate mode simulate real sequences of events if such events are strongly coupled to the large-scale flow (stratiform precip vs. convective precip)

5. Project to Intercompare Regional Climate Simulations (PIRCS) Experiment PIRCS 1a

6. Lessons Learned… • MIP ensemble means frequently are closer to observations than any individual model • MIP ensembles recognize extreme events but fail to capture the magnitude of such extremes • Models generally capture well the diurnal and seasonal cycles of temperature, although with larger error under extreme cold and stably stratified surface conditions (ARCMIP)

7. Project to Intercompare Regional Climate Simuations (PIRCS) Experiment PIRCS 1b Precipitation (mm/day)

8. Lessons Learned… • MIP ensemble means frequently are closer to observations than any individual model • MIP ensembles recognize extreme events but fail to capture the magnitude of such extremes • Models generally capture well the diurnal and seasonal cycles of temperature, although with larger error under extreme cold and stably stratified surface conditions (ARCMIP)

9. Lessons Learned… • Most models produce too many high-level clouds, too few mid-level clouds, and too many low clouds • There is wide disagreement on partitioning between convective and stratiform precipitation even when precipitation totals agree • Some but not all models can capture the diurnal cycle of precipitation even with nocturnal convection • All models tend to produce to many light rain events and not enough high-intensity rain events

10. Lessons Learned… • All models (with 50 km resolution) fail to capture the timing between maximum and minimum 3-hourly precipitation accumulation from MCSs • Seasonal cycles of precipitation are captured over a wide range of climate regimes • Precipitation generally is underestimated in both extreme events and very moist climates

11. Transferability Objective Regional climate model transferability experiments are designed to advance the science of high-resolution climate modeling by taking advantage of continental-scale observations and analyses.

12. Objective Regional climate model transferability experiments are designed to advance the science of high-resolution climate modeling by taking advantage of continental-scale observations and analyses. MIPs have helped modelers eliminate major model deficiencies. Coordinated studies with current models can advance scientific understanding of global water and energy cycles.

13. Use of Regional Models to Study Climate • How portable are our models?

14. Use of Regional Models to Study Climate • How portable are our models? • How much does “tuning” limit the general applicability to a range of climatic regions?

15. Use of Regional Models to Study Climate • How portable are our models? • How much does “tuning” limit the general applicability to a range of climatic regions? • Can we recover some of the generality of “first-principles” models by examining their behavior on a wide range of climates?

16. Slide source: J Roads

17. Transferability Working Group Slide source: J Roads

18. Transferability Working Group (TWG) Overall Objective To understand physical processes underpinning the global energy budget, the global water cycle, and their predictability through systematic intercomparisons of regional climate simulationson several continentsand throughcomparisonof these simulated climateswith coordinated continental-scale observationsand analyses

19. Types of Experiments • Multiple models on multiple domains (MM/MD) • Hold model choices constant for all domains

20. Types of Experiments • Multiple models on multiple domains (MM/MD) • Hold model choices constant for all domains • Not • Single models on single domains • Single models on multiple domains • Multiple models on single domains

21. ARCMIP GLIMPSE TRANSFERABILITY EXPERIMENTS FOR ADDRESSING CHALLENGES TO UNDERSTANDING GLOBAL WATER CYCLE AND ENERGY BUDGET BALTEX BALTIMOS BALTEX GKSS/ICTS PRUDENCE MAGS SGMIP QUIRCS RMIP PIRCS CAMP GAPP GAPP GAME GAME AMMA LBA LBA IRI/ARC CATCH MDB LA PLATA MDB

22. Specific Objectives of TWG • Provide a framework for systematic evaluation of simulations of dynamical and climate processes arising in different climatic regions

23. Specific Objectives of TWG • Provide a framework for systematic evaluation of simulations of dynamical and climate processes arising in different climatic regions • Evaluate “transferability”, that is, quality of model simulations in “non-native” regions

24. Specific Objectives of TWG • Provide a framework for systematic evaluation of simulations of dynamical and climate processes arising in different climatic regions • Evaluate “transferability”, that is, quality of model simulations in “non-native” regions • “Meta-comparison” among models and among domains

25. We recognize that… • The water cycle introduces exponential, binary, and other non-linear processes into the climate system

26. We recognize that… • The water cycle introduces exponential, binary, and other non-linear processes into the climate system • Water cycle processes occur on a wide range of scales, many being far too small to simulate in global or regional models

27. We recognize that… • The water cycle introduces exponential, binary, and other non-linear processes into the climate system • Water cycle processes occur on a wide range of scales, many being far too small to simulate in global or regional models • The water cycle creates spatial heterogeneities that feed back strongly on the energy budget and also the circulation system

28. Strategy • Identify key processes relating to the water cycle and energy budget that express themselves to different degrees in different climatic regions

29. Strategy • Identify key processes relating to the water cycle and energy budget that express themselves to different degrees in different climatic regions • Create hypotheses that can be tested by use of MM/MD experiments.

30. GEWEX CSEs overlain to indicate correlation between "hotspots" as identified by Koster et al. (2004) and GEWEX CSEs. Dashed circle over India indicates a major "hotspot" that is not a CSE, but dialog is beginning with Indian Meteorological Department on joint experiments. Locations of “hotspots” having high land-atmosphere coupling strength as identified by Koster et al. (2004) with GEWEX Continental Scale Experiments overlain.

31. Considerations for Developing Hypotheses • Exploit the availability of CEOP data • Vertical profiles at isolated points • Components of energy budget and hydrological cycle • Sub-daily data • High-resolution observations of events • Recognize the limitations of reanalyses in data-sparse regions

32. Slide source: B. Rockel

33. Static stability (CAPE) Diurnal timing Seasonal patterns Spatial patterns Monsoon characteristics Diurnal timing of precip Onset timing Precip spatial patterns Snow processes Rain-snow partitioning Snow-water equivalent Snowmelt Snow-elevation effects Soil moisture Frozen soils Cloud formation Candidate Issues Highly Relevant to Hypotheses on the Water and Energy Cycles

34. Expected Outcomes • Improved understanding of the water cycle and its feedbacks on the energy budget and circulation system

35. Expected Outcomes • Improved understanding of the water cycle and its feedbacks on the energy budget and circulation system • Improved capability to model climate processes at regional scales

36. Expected Outcomes • Improved understanding of the water cycle and its feedbacks on the energy budget and circulation system • Improved capability to model climate processes at regional scales • Improved applicability to impacts models

37. Simulating Future Climates with Models Trained on Current Climates FCA Climates FCA=Future, region A Variable or Process 2 Variable or Process 1

38. Simulating Future Climates with Models Trained on Current Climates FCA Climates FCA=Future, region A CCA=Current, region A Variable or Process 2 CCA Variable or Process 1

39. Simulating Future Climates with Models Trained on Current Climates FCA Climates FCA=Future, region A CCA=Current, region A Variable or Process 2 Model Simulations CCA, model 1 (on its home domain) CCA Variable or Process 1

40. Simulating Future Climates with Models Trained on Current Climates FCA Climates FCA=Future, region A CCA=Current, region A Variable or Process 2 Model Simulations CCA, model 1 CCA, model 2 CCA Variable or Process 1

41. Simulating Future Climates with Models Trained on Current Climates FCA Climates CCB FCA=Future, region A CCA=Current, region A CCB=Current, region B Variable or Process 2 Model Simulations CCA, model 1 CCA, model 2 CCA Variable or Process 1

42. Simulating Future Climates with Models Trained on Current Climates FCA Climates CCB FCA=Future, region A CCA=Current, region A CCB=Current, region B Variable or Process 2 Model Simulations CCA, model 1 CCA, model 2 CCB, model 2 (on its home domain) CCA Variable or Process 1

43. Simulating Future Climates with Models Trained on Current Climates FCA Climates CCB FCA=Future, region A CCA=Current, region A CCB=Current, region B Variable or Process 2 Model Simulations CCA, model 1 CCA, model 2 CCB, model 2 CCB, model 1 CCA Variable or Process 1

44. Simulating Future Climates with Models Trained on Current Climates FCA Climates CCB FCA=Future, region A CCA=Current, region A CCB=Current, region B Variable or Process 2 Model Simulations CCA, model 1 CCA, model 2 CCB, model 2 CCB, model 1 CCA Fully spanning FCA requires: More models More domains Variable or Process 1

45. Plan of Work • Phase 0: Write an article for BAMS summarizing lessons learned from various “MIPs” and describe how transferability experiments will provide new insight on the global climate system, particularly the water cycle and energy budget, report preliminary results

46. Plan of Work • Phase 0: Write an article for BAMS summarizing lessons learned from various “MIPs” and describe how transferability experiments will provide new insight on the global climate system, particularly the water cycle and energy budget, report preliminary results • Phase 1: Conduct pilot studies

47. Transferability Domains and CSE Reference Sites Slide source: B. Rockel

48. Plan of Work • Phase 0: Write an article for BAMS summarizing lessons learned from various “MIPs” and describe how transferability experiments will provide new insight on the global climate system, particularly the water cycle and energy budget, report preliminary results • Phase 1: Conduct pilot studies • Phase 2: Perform sensitivity studies on key processes relating to the water cycle. Create and test hypotheses by MM/MD

49. Plan of Work • Phase 0: Write an article for BAMS summarizing lessons learned from various “MIPs” and describe how transferability experiments will provide new insight on the global climate system, particularly the water cycle and energy budget, report preliminary results • Phase 1: Conduct pilot studies • Phase 2: Perform sensitivity studies on key processes relating to the water cycle. Create and test hypotheses by MM/MD • Phase 3: Prediction, global change, new parameterizations

50. Transferability Consolidates Lessons Learned from Modeling and Observations • Models: Use experience gained from simulating “home domains”