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

Projected change by 2050

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.

ACCOMPLISHMENTS:

Year 1

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, JJA

CCSM3-FV: Precipitation, DJF

CCSM3-FV: Precipitation, JJA

CCSM3-FV: Precip Variablity, DJF

CCSM3-FV: Precip Variablity, JJA

CCSM3-FV and El Nino

CCSM3-FV and La Nina

Next steps with CCSM3-FV

Dynamical downscaling Provisional plan is to use COAMPS model

First tests underway with 20-yr segment of CCSM3-FV

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

CCSM3-FV downscaling: Examples

CCSM3-FV downscaling: Precipitation monthly EOFs

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

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

4 ensemble members covering 1880-1999

PCM/VIC:Trend in Snow Water Equivalent

PCM/VIC: Trend in T-air

PCM/VIC:River flow

Amit Chourasia, SDSC, for the LUSciD project

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 project #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 in the world’s

oceans?

YES!

(surprisingly well)

PCM 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 AchutaRao; 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

Cooperative project #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

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

Sierra snow pack

Now and ………………….………….future?

Global model (orange dots) vs. Regional model grid

(green dots)

Downscaling: the motivation

CCSM3-FV: Temperature, MAM

CCSM3-FV: Temperature, SON

CCSM3-FV: Precipitation, MAM

CCSM3-FV: Precipitation, SON

CCSM3-FV: Precip Variablity, MAM

CCSM3-FV: Precip Variablity, SON

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-min EOFs vs. Obs

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)

HadCM3 Signal Strength & Noise

Seasonal cycle in North Pacific

Blue = 10 yrs of model

Red = 3 yrs of AIRS

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)

Spectrum of Precipitation PC#1

Observed M.Y. 240-289 M.Y. 290-339

(x axis is cycles per month)