GFDL’s CM2 Climate Models

1. Description of new CM2 climate models and IPCC experiments

2. Simulations of 19th through 23rd centuries (a) Surface air temperature (b) Global ocean temperature and heat content(c) Sahel precipitation changes

Material preparado por Thomas L. Delworth Geophysical Fluid Dynamics Laboratory (GFDL)/NOAA

http://www.cdc.noaa.gov/cgi-bin/Composites/printpage.pl

Es una pagina interactiva del CDC donde muy rapidamente uno puede calcular composites or regressions de campos atmosfericos u oceanicos.Desde 1958 hasta el presente (campos mensuales).

Ncview es una herramienta que les permite ver muy rapidamente campos que tengan en netcdf (mucho mas rapido y en animacion que GRADS) libre.

Ferret es una programa para visualizar y hcer todo tipo de graficos ( a la grads pero creo muy superior) libre para linux .

Matlab es muy general y muy bueno pero mas restringido que ferret para graficos atmosfericos mu oceanicos.

The models used …• New generation of atmosphere, ocean, land and sea ice models developed

at GFDL over the last several years.

• Atmosphere model referred to as “AM2”.

• Coupled model referred to as “CM2.0” and “CM2.1”. A complete suite of experiments has been conducted for the IPCC 2007 report.

– AM2 atmosphere (2o horizontal, 24 levels)– MOM4 ocean model, 1o horizontal, 0.3o at Equator, 50 levels)– Sea ice, land models

• Detailed descriptions of these models available in GAMDT (2004), Delworth

et al. (2005), available on the web athttp://data1.gfdl.noaa.gov/nomads/forms/deccen/CM2.X/references

Model output available at http://data1.gfdl.noaa.gov

CM2.0

CM2.1

SST Errors (Annual mean Model minus Observations)

Main algorithmic differences between FV and Bgrid cores

horizontal advection of momentum:FV (vorticity)

vs centered (u,v)

Vertical coordinate and vertical tracer advection:Lagrangian with ppm remapping

vs Eulerian ppm

Polar filtering “details”

Known differences in atmosphere-only (AMIP) modein fv vs. bgrid

Poleward shift of jets and surface westerliesesp. North Atlantic

Stronger subtropical easterlies

Stronger hydrological cycle (3%)

Drier Amazon

More eddy activity in polar latitudes

850mb uDJF

coupledamip

2.0

2.1

Transient eddy v’2 300mb DJF

bgrid fv

Transient eddy v’2 300mb DJF

2.0 2.1

Equatorward drift of jets upon coupling: smaller in CM2.1 than CM2.0;

SH shift partly forced from equator, partly locally in SH(P. Kushner – need to check with latest version)

Arctic pressures also improve in CM2.1

CM2.1 inherits too strong Pacific winds from AM2_fv:(eddy angular momentum fluxes stronger;

and mean tropical rainfall larger than in AM2p13)

DJF stationary eddy significantly degraded by coupling over N. America/N.Atlantic

(presumably related to redistribution of tropical rain)Somewhat worse in CM2.1 than CM2.0

AM2/LM2:comparison to other models

Differences in annual mean precipitation from CMAP (Xie-Arkin)

Desviacion ClimaticaClimate drift

• Coupled models are typically constructed from atmosphere and ocean components that have been independently developed.

• Stand-alone atmosphere and ocean components are tightly constrained by observed boundary conditions.

• When atmosphere and ocean components are coupled, the resulting climate will often drift away from a realistic state.

Material: Anthony Broccolli (Rutgers University

Climate Drift in GFDL CM2

Zonal Mean SST Error from CM2_a10o2 [K]

Causes of Climate DriftFlux Difference [W m-2]

AGCM vs. OGCMCM2_a10o2 SST Error [K]

Causes of Climate Drift

• Imbalances between atmosphere-ocean heat fluxes simulated by AGCM and OGCM when both are run with observed SSTs.

• Climate feedbacks triggered by flux imbalances. (Ex: CM2_a10o2 cooling pattern in midlatitude N.H. → southward shift in westerlies → error in position of western boundary currents)

Flux Corrections/Adjustments

• One ad hoc approach to reducing climate drift is to adjust for differences in atmospheric and oceanic component fluxes by adding a compensating flux at each grid point.

• This method is known as flux correction (Sausen et al. 1986) or flux adjustment (Manabe et al. 1991).

Calculating Flux Adjustments

• The goal is to determine artificial heat and water fluxes that vary seasonally and spatially but do not depend on the state of the model.

• Method 1: GFDL Three-Step

• Method 2: Coupled Restore

• Method 3: Offline Flux Difference

Method 1: GFDL Three-Step

• Step 1: Run the AGCM with climatological SSTs, archiving the heat and water fluxes.

• Step 2: Run the OGCM with the fluxes from step 1, while simultaneously restoring to observed T and S.

Method 1: GFDL Three-Step

• Step 1: Run the AGCM with climatological SSTs, archiving the heat and water fluxes.

• Step 2: Run the OGCM with the fluxes from step 1, while simultaneously restoring to observed T and S.

)(...

)(...

SSt

S

TTt

T

obs

obs

Restoring terms

Method 1: GFDL Three-Step

• Step 1: Run the AGCM with climatological SSTs, archiving the heat and water fluxes.

• Step 2: Run the OGCM with the fluxes from step 1, while simultaneously restoring to observed T and S.

• Step 3: Couple the AGCM and OGCM without restoring, using the archived restoring terms from step 2 as flux adjustments.

Flux Adjustment: Pros and Cons

Cons• Flux adjustments are nonphysical.

• There is no guarantee that coupled model biases are invariant over different climate states.

• Flux adjustments could distort climate feedbacks.

Flux Adjustment: Pros and Cons

Pros• Flux adjustments minimize climate drift

that would distort climate feedbacks if left unchecked.

• Flux adjustments allow sensitivity experiments to be performed while better models (i.e., those with smaller errors) are under development.

Design of Coupled Model Experiments

• Equilibrium: The goal is to determine the climate that is in equilibrium with a given set of climate forcings. (Example: What climate state is in equilibrium with twice the preindustrial level of atmospheric CO2?)

• Transient: The goal is to investigate the time-dependent response of the climate to a given (often time-dependent) change. (Example: How will the climate change in response to projected increases in CO2 and other human-induced climate forcings?)

100 200 300 400 500 600 700 800 Model Year

Minimal climate drift after spinup

CM2.0

CM2.1

W m

-2

(8 member ensemble of1861-2000 experiments)

Ensemble simulations of the 20th century

• ALL – includes ghgs, strat ozone, anthrop. aerosols, land use, solar,volcanoes

• ANTHRO

• NATURAL

• AEROSOL (anthropogenic aerosols)

• WMMGO3 (well mixed ghgs, stratospheric ozone)

Knutson et al., submitted

3-member ensemble mean CM2.0

OBS SST 77-95 minus 49-66

5-member ensemble mean CM2.1

ALL FORCINGS

NATURAL

ANTHRO

Krakatau

AEROSOLS (ANTHRO)

WMGGO3

ObservedPrecipitation(mm/month)

Data from Univ. ofEast Anglia, Climatic ResearchUnit (CRU)

Seasonal migration of Intertropical Convergence Zone (ITCZ)

January

July

1950-2000 trends in observed and simulated precipitation

(JAS)

Observed Simulated

(Atmosphere model forced with observed SSTs 1950-2000)

GLOBAL SSTs

ATLONLY

INDIANONLY

PACONLY

Simulated effect of Observed 1950-2000 SST trend on African precipitation

1950-2000 linear trend in precipitation

CM2.0 20th century historical run

Observed

CM2.1 20th century historical run

Future perspective

CM2.0 CM2.1

20th centuryhistorical runs1951-2000

A1B scenarios2001-2050

CM2.0CM2.1

Observations

Model ensemblemean

21st centuryscenarios

Summary/Conclusions

1. Suite of 20th century simulations with new GFDL climate models. Output freely available on the Web. http://data1.gfdl.noaa.gov

2. Simulated air temperature trends largely consistent with observed trends.

3. Simulated ocean temperature and heat content changes “consistent” with observations. Strong role for aerosols (natural and anthropogenic) in simulated 19th and 20th century changes.

4. GFDL models (a) simulated the 20th century Sahel drought as a

response to anthropogenic forcing, and (b) project the Sahel drying to continue into the future. This is generally not seen in other coupled models, and is thus highly uncertain.

Comparing the climate sensitivity of AM2/LM2 to the new NCAR CAM2

Ensemble of the world’s climate models (IPCC 2001)

Low cloud behavior in models run with observed SSTs (1952-1997)

Analysis performed by Joel Norris (UCSD)

Cubed Sphere CoreLat-Lon Core

Dynamics: non-hydrostatic Cubed Sphere FV core Physics: GFDL AM 2.1 (identical to CM2.1 for IPCC AR4) “Cold” start (isothermal, dry, wind-less) from 00Z Jan 1, 1980 Forced with “observed” SST

The C360 (~ 28 km) tropical cyclone-climate model

3-hourly precipitation: 1-31 August 1980

Corridas de sensibilidad al incremento de CO2

Vientos la componente u en 850hPa

Precipitacion

Ozono en la baja estratosfera

Final notes: The “cubed sphere” core development is in a mature stage.

But the application side of the work has just been started…..the codes that contains the cubed sphere model within GFDL’s atmosphere-ocean-land-ice coupling system were released internally in Aug 2007.

Experimental dynamical seasonal hurricane/typhoon predictions will be made at GFDL (starting FY07-08) at global ¼ (C360) to 1/8 deg (C720) resolutions.

With the non-hydrostatic cubed sphere dynamical core, it is feasible to make global cloud resolving runs at 4~5km resolution with existing massively parallel computers in the US (e.g., DOE/Oak Ridge or NASA Columbia?). The model is fast enough to be used for real-time10-day hurricane predictions – a potential breakthrough for hurricane forecasting

FIN