LUSciD-LLNL UCSD/SIO Scientific Data Project: 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 Climate Studies
Jan 12, 2016
LUSciD-LLNL UCSD/SIO Scientific Data Project:
SIO LLNL SDSCTim Barnett Doug Rotman Reagan MooreDavid Pierce Dave Bader Leesa BriegerDan Cayan Ben Santer Amit ChourasiaHugo Hidalgo Peter GlecklerMary Tyree Krishna AcutaRao
Climate Studies
Objective
Can we detect a global warming signal in main hydrological features of the western United States?
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.
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)
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.
Change in California Snowfall
Change in Snow Water Equivalent
Observed, 1950-2003
Courtesy P. Mote
River flow earlier in the year
Runoff already coming earlier
Sierra snow pack
Now and ………………….………….future?
Projected change by 2050
Global model (orange dots) vs. Regional model grid
(green dots)
Downscaling: the motivation
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.
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
CCSM3-FV: Temperature, DJF
CCSM3-FV: Temperature, MAM
CCSM3-FV: Temperature, JJA
CCSM3-FV: Temperature, SON
CCSM3-FV: Precipitation, DJF
CCSM3-FV: Precipitation, MAM
CCSM3-FV: Precipitation, JJA
CCSM3-FV: Precipitation, SON
CCSM3-FV: Precip Variablity, DJF
CCSM3-FV: Precip Variablity, MAM
CCSM3-FV: Precip Variablity, JJA
CCSM3-FV: Precip Variablity, SON
CCSM3-FV and El Nino
CCSM3-FV and La Nina
Next steps with CCSM3-FV
…(Tim: are we done with CCSM3-FV? Will it be dynamically downscaled? Will there be historical runs with anthro forcing?)
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
Example: Jan 1st, M.Y. 240
PREVISIONARYMAY HAVE ERROR
CCSM3-FV downscaling: Precipitation monthly EOFs
CCSM3-FV downscaling: T-max monthly EOFs
CCSM3-FV downscaling: T-min monthly EOFs
CCSM3-FV downscaled: Precipitation EOFs vs. Obs
CCSM3-FV downscaled: T-max EOFs vs. Obs
CCSM3-FV downscaled: T-min EOFs vs. Obs
Spectrum of Precipitation PC#1
Observed M.Y. 240-289 M.Y. 290-339
(x axis is cycles per month)
Spectrum of T-max PC#1
Observed M.Y. 240-289 M.Y. 290-339
(x axis is cycles per month)
Spectrum of T-min PC#1
Observed M.Y. 240-289 M.Y. 290-339
(x axis is cycles per month)
Runoff applied to river flow model
Sacramento River at Sacramento
Columbia River at the Dalles
Colorado River at Lees Ferry
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
PCM/VIC runs (Andy Wood, UW)
Historical simulations with estimated GHG and sulfate aerosols
3 ensemble members covering 1880-1999
PCM/VIC:Trend in Snow Water Equivalent
PCM/VIC: Trend in T-air
PCM/VIC:River flow
PCM/VIC:River flow
Amplitude shows strong decadal variability
Phase shows flow earlier in the year for some, but not all, rivers
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?
Cooperative projects #1:
SIO LLNL
Tim Barnett Krishna AchutaRao
David Pierce Peter Gleckler
Ben Santer
Karl Taylor
Ocean Heat Content
Motivation
Can GHG and sulfate aerosol forcingexplain the warming signal?
YES!
(surprisingly well)
PCM Signal Strength & Noise
HadCM3 Signal Strength & Noise
Inter-Model Comparison: N. HemPCM signal + HadCM3 noise
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 AchutaRoa; David Pierce; Peter Gleckler; Tim Barnett
NCAR CCSM 3.0
MRI CGCM 2.3a
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?
Note: Plot shows only a subset of the 15 models analyzed.
Heat uptake vs. climate sensitivity
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?
BackgroundH
eat
Conte
nt
(10
22
J) • 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
Depth
(m
)
Heat Content
Temperature
Work in progress:Evolution of the Krakatoa anomaly GISS-HYCOM NCAR CCSM3
•6 realizations of CCSM3, GISS-HYCOM (and GFDL2.1)
•Large inter-model differences
• Uncertainties associated with external forcings, model physics and initial conditions
•S/N analysis (in-progress) should help isolate model responses to eruptions - necessary to evaluate realism
Decadal average ocean heat content anomalies: zonally integrated cross-sections
Heat content (1016 Joules/m)
Cooperative projects #2:
SIO LLNL JPL
Tim Barnett Peter Gleckler Eric Fetzer
David Pierce Ben Santer
Atmospheric water vapor
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
Annual mean: models too moist
Fractional errors greater with height
Seasonal cycle in North Pacific
Blue = 10 yrs of model
Red = 3 yrs of AIRS
Summary
Goal: can we detect a global warming signal in main hydrological features of the western United States?
Long CCSM3-FV control run for estimate of natural internal variability is done CCSM3-FV simulation comparable to other NCAR-series models
Statistical downscaling to give river flow is progressing
PCM runs give another estimate of natural varaibility, and also in this case of the forced signal
Other cooperative projects (ocean heat content, atmospheric water vapor) progressing well