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LUSciD-LLNL UCSD/SIO Scientific Data Project:

LUSciD-LLNL UCSD/SIO Scientific Data Project:. Climate Studies. SIO LLNL SDSC Tim Barnett Doug Rotman Reagan Moore David Pierce Dave Bader Leesa Brieger Dan Cayan Ben Santer Amit Chourasia Hugo Hidalgo Peter Gleckler Mary Tyree Krishna AcutaRao. Objective.

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LUSciD-LLNL UCSD/SIO Scientific Data Project:

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  1. LUSciD-LLNL UCSD/SIO Scientific Data Project: Climate Studies SIOLLNLSDSC Tim Barnett Doug Rotman Reagan Moore David Pierce Dave Bader Leesa Brieger Dan Cayan Ben Santer Amit Chourasia Hugo Hidalgo Peter Gleckler Mary Tyree Krishna AcutaRao

  2. Objective • Can we detect a global warming signal in main hydrological features of the western United States?

  3. Program Elements • Control run: Natural variability CCSM3 from NCAR on Thunder. (approx 4.5 TB) • Downscaling: 12 km grid over west for spatial resolution (control+anthro; another 5 TB) • Hydrological modeling: The downscaled data on rainfall, temperature, terrain, etc. force a hydrological model for time histories of steam flows and snow pack evolution in the western US (control+anthro: another 5 TB). • Detection and attribution (D&A) analysis.

  4. Deliverables Year 1 • Complete a long GCM control run and begin statistical downscaling for selected geographic regions.…..DONE • Begin VIC simulations with both downscaled data and PCM forced realizations.……………………….....…..DONE • Implement a data grid linking resources between LLNL and SDSC. The data grid will be used to manage the simulation output that is generated.………………….…..DONE (1st order)

  5. Deliverables (con’t) Year 2 • Complete downscaling of Control. • Complete VIC run on downscaled Control run. • Prepare paper on downscaling intercomparisons • Begin preliminary D&A analysis. • Develop a digital library for publishing results, and integrating with PCMDI Year 3 • Complete D&A analysis. • Write a paper describing the results.

  6. Change in California Snowfall

  7. Change in Snow Water Equivalent • Observed, 1950-2003 Courtesy P. Mote

  8. River flow earlier in the year

  9. Runoff already coming earlier

  10. Projected change by 2050

  11. Key Question Do the signals we see happen naturally or are they human-induced? To answer, we need to know the levels and scales of natural variability in the western hydrological cycle.

  12. ACCOMPLISHMENTS: Year 1

  13. Long GCM control run • CCSM3 with finite volume dynamical core (“-FV”) • Atmospheric resolution is 1.25ox1o with 26 vertical levels • Ocean resolution is 320x384 stretched grid with 40 levels (so-called “gx1v3” grid; averages 1 1/8ox0.5o) • 760 years of a long pre-industrial control run transferred to SDSC

  14. CCSM3-FV: Temperature, DJF

  15. CCSM3-FV: Temperature, JJA

  16. CCSM3-FV: Precipitation, DJF

  17. CCSM3-FV: Precipitation, JJA

  18. CCSM3-FV: Precip Variablity, DJF

  19. CCSM3-FV: Precip Variablity, JJA

  20. CCSM3-FV and El Nino

  21. CCSM3-FV and La Nina

  22. Next steps with CCSM3-FV • Dynamical downscaling • Provisional plan is to use COAMPS model • First tests underway with 20-yr segment of CCSM3-FV

  23. Statistical downscaling • Uses “analogue” technique: • Start with daily CCSM3-FV data on coarse grid, and daily obs. data on fine grid (Mauer et al. 2002; PRISM data disaggregated to daily level using daily obs) • Coarsen obs to model grid • Compare model field to coarsened obs • 30 closest matches (least RMSE) and optimal weights found • Weights applied to obs on original fine grid • Hidalgo et. al 2006, J. Climate, submitted

  24. CCSM3-FV downscaling: Examples

  25. CCSM3-FV downscaling: Precipitation monthly EOFs

  26. CCSM3-FV downscaled: T-max EOFs vs. Obs

  27. Runoff applied to river flow model

  28. Sacramento River at Sacramento Columbia River at the Dalles Colorado River at Lees Ferry

  29. Next steps with statistical downscaling • Have processed ~100 yrs of the 760 yrs available • Process rest of CCSM3-FV control run • Evaluate observed changes in hydrology against this estimate of unforced variability

  30. PCM/VIC runs (Andy Wood, UW) • Historical simulations with estimated GHG and sulfate aerosols • 4 ensemble members covering 1880-1999

  31. PCM/VIC:Trend in Snow Water Equivalent

  32. PCM/VIC: Trend in T-air

  33. PCM/VIC:River flow

  34. Amit Chourasia, SDSC, for the LUSciD project

  35. PCM/VIC:River flow Amplitude shows strong decadal variability Phase shows flow earlier in the year for some, but not all, rivers

  36. Next steps for PCM/VIC • Process other ensemble members to reduce natural internal decadal variability • Is the forced change statistically significant? • How does it compare to observations?

  37. Cooperative project #1: Ocean Heat Content SIOLLNL Tim Barnett Krishna AchutaRao David Pierce Peter Gleckler Ben Santer Karl Taylor

  38. Motivation • Can GHG and sulfate aerosol forcing explain the warming signal in the world’s oceans? YES! (surprisingly well)

  39. PCM Signal Strength & Noise

  40. Inter-Model Comparison: N. HemPCM signal + HadCM3 noise

  41. What about other models? • 38 realizations of 20th century climate from 15 coupled models in the IPCC AR4 archive are being analyzed. • Work in progress • Krishna AchutaRao; David Pierce; Peter Gleckler; Tim Barnett

  42. MRI CGCM 2.3a NCAR CCSM 3.0

  43. Preliminary findings • Most models show a detectable warming signal in all the ocean basins with some exceptions • NCAR CCSM 3.0 shows large natural variability in the North Atlantic • Details of signal penetration in some ocean basins vary • More complex picture than the previous study (Barnett et al. 2005) that considered two models • Does the fidelity of model heat uptake relate to climate sensitivity?

  44. Heat uptake vs. climate sensitivity Note: Plot shows only a subset of the 15 models analyzed.

  45. Volcanic Eruptions and Heat Content • P. Gleckler1, K. AchutaRao1, T. Barnett2, D. Pierce2, B.D. Santer1 , K. Taylor1, J. Gregory3, and T. Wigley4 (1PCMDI 2UCSD/SIO, 3U.Reading, 4NCAR) • How do volcanic eruptions affect ocean heat content? • Can this give insight into how ocean heat content anomalies are formed and propagate?

  46. Heat Content (1022 J) Background • Volcanic eruptions substantially reduced 20th Century ocean warming and thermal expansion. • Recovery from Krakatoa (1883) takes decades. • Effect of Pinatubo is much weaker than Krakatoa because it occurs against backdrop of substantial ocean warming. • Models including V forcing agree more closely with late 20th Century observations than those without V • Gleckler et al., Nature, 2006 Krakatoa Pinatubo Heat Content Depth (m) Temperature

  47. Cooperative project #2: Atmospheric water vapor SIOLLNLJPL Tim Barnett Peter Gleckler Eric Fetzer David Pierce Ben Santer

  48. Water vapor a key greenhouse gas • How well do models simulate it? • New 3-D satellite data set available • Compare to AR-4 model fields in PCMDI database

  49. Annual mean: models too moist

  50. Fractional errors greater with height

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