Intra-seasonal Seasonal Interannual Intra-seasonal Seasonal Interannual ISI Research at COLA Paul...

Intra-seasonalSeasonal

Interannual

ISI Research at COLA

Paul Dirmeyer

COLA Scientific Advisory Committee - George Mason University 2

ISI Outline• Introduction• COLA Multi-Model Leadership• El Niño and Ocean-Driven Predictability• Monsoons – Ocean/Land Contrast• Land-Climate Interactions

26 April 2012

Introduction• There is a 20+ year history at

COLA in ISI climate research.• ISI seamlessly bridges from

weather to decadal+ climate – “Weather is the climate’s delivery system”.

• Question: where does climate predictability come from?

• It is difficult to categorize by timescale, component, phenomenon because it’s all intertwined.

ISI

after Ghil 2002

26 April 2012


COLA Leadership in Multi-Model Projects• A key element of COLA’s history has been its central role

in ISI multi-model and multi-institutional experiments.• Institutional diversity was intrinsic in the COLA AGCM.

– The original COLA model was a version of the NMC operational forecast model with parameterizations from GFDL (sub-grid atmospheric physics) and NASA (land surface).

– Version 2 of the COLA AGCM coupled the NCAR dynamical core with the “COLA Physics Package” of diverse lineage.

– Also COLA coupled model, Poseidon OGCM, SSiB … much institutional expertise in modeling.

26 April 2012

COLA Leadership in Multi-Model Projects

1984 1988 1992 1996 2000 2004 2008 2012

Atm+Ocean

Atm+Land

Atmosphere

Land

Ocean

Dynamical Extended Range Forecasts (DERF)

Global Soil Wetness Project (GSWP)

Project to Inter-compare Land-Surface Parameterization Schemes (PILPS 2d)

Experimental Long-Lead Forecast Bulletin

Dynamical Seasonal Prediction (DSP)

Climate of the Twentieth Century (C20C)

Interactive Ensemble

Retrospective ENSO Forecasts with MOM

GSWP-2 + Multi-Model Land Analysis

Global Land-Atmosphere System Study (GLACE)

Multi-Model Ensemble (MME)

Land Multi-Model Interactive Ensemble

GLACE-2

Project Athena

Ocean Multi-Model Analysis

Multi-ODA Initialization

National Multi-Model Ensemble (NMME)

Nearly all projects include international participation



Project to Inter-compare Land Surface Parameterization Schemes (PILPS 2d)










GLACE-2

Project Athena





1984 1988 1992 1996 2000 2004 2008 2012

GFDL

NMC/NCEP

Both

NOAA Models

NOAA model run by COLA













GLACE-2

Project Athena





1984 1988 1992 1996 2000 2004 2008 2012

CCM/CCSM

NCAR Models

NCAR model run by COLAor components













GLACE-2

Project Athena





1984 1988 1992 1996 2000 2004 2008 2012

GSFC

NASA Models


Predictability and Prediction on ISI Scales

• ENSO underpins ISI predictability and prediction.• El Niño remains more “potentially predictable” than

actually predictable.• Spectra between models and observations still do

not match (model fidelity) – this is one of the motivations for multi-model approaches.

26 April 2012


Brief History of ENSO Research at COLA• ENSO investigation in AMIP runs (diagnostic)• ENSO's dominant impact in DSP skill, mid-latitudes• Predictability of ENSO• Ocean dynamics and ENSO• IE and the destructive role of atmospheric noise• ENSO in MMEs (diagnostic)• Effect of super-parameterization of convection on ENSO• ENSO effect on low-frequency patterns / weather regimes• Ocean MMA and the intrinsic vs. ENSO-forced variability• ENSO in a changing climate and mid-latitude response to ENSO in a changing climate• Pacemaker experiments• Multi-ocean-analysis initialization• ENSO, diabatic heating, and monsoon response

26 April 2012


ODA Heat Content Agreement

moderate high low

SignalNoise

€

=σEnsMean2

σ Intra−Ens2

Ocean analyses from: ECMWF (ORA-S3, COMBINE-NV), NCEP (GODAS, CFSR), UMCP/TAMU (SODA) and GFDL (ECDA).

26 April 2012

1979-2007

Zhu et al. 2012 GRL


What Does This Uncertainty Mean for Forecasts?

• One model: CFSv2 [GFSv2 (T126 L64) + MOM4 (½°×½°; ¼°lat ±10°; L40)]

– 4-member ensembles: 1-4 April 1979-2007– 12 month forecasts

• Four ODAs: Ocean ICs (anomaly initialization) from each: COMBINE-NV, ORA-S3, CFSR, GODAS

• “Fifth” ODA: Mean of the four above (“AVEoci”)

26 April 2012


Niño 3.4 Validation

• Ensemble mean performs as well or better than best single-ODA initialization run at nearly all leads for both correlation and root mean square error.

• AVEoci is middle of the pack – no bargain/economy.

26 April 2012

Lead (months)

Lead (months)


Changing ENSO/Monsoon Linkages

• 1997 developing El Niño (strong summer Niño3) did not translate into a poor monsoon, as expected – is the ENSO/monsoon relationship changing, and how?

• GCM cumulus parameterizations struggle to simulate the response to SST, so investigations based on manipulation of SST anomalies are handicapped.

• Solution: bypass the problem and specify “observed” diabatic heating anomalies in the atmosphere associated with the SSTs.

26 April 2012

Jang & Straus 2012a,b (in review JAS,

JClim)


No Indian Ocean Heating Indian Ocean Heating Included

Inserting Idealized Heating in CAM3

• Full set of model parameterizations are retained – model can have non-linear moist feedbacks

• Idealized vertical structure to added diabatic heating, but a realistic horizontal structure

Added Heating for 1997 Monsoon

26 April 2012Wm-2 Wm-2


1997 Exp with IO

1997 Exp without IOERA40

• With added Indian Ocean heating the monsoon response is closer to normal, as observed!

26 April 2012

JJAS Anomalous 850 hPa y Response

17

El Niño and Monsoons

• ENSO remains the “big gorilla” – the baseline source of global predictability.

• Other processes and elements of the climate system modulate and modify regionally.

• Monsoons are a classic example; particularly South Asia- For prediction, a big difficult

problem with huge societal impacts on a large population.

26 April 2012


Monsoons• Fennessy's early work with COLA AGCM• Role of spring Eurasian snow cover• Idealized land-sea and orographic effects• Fixed sun vs fixed SST• Linear prediction• I-S variability• South American monsoon• Indian Ocean modes• MJO interaction with monsoon• Connection to ENSO – modulation• Changes in changing climate• Dynamical vs. statistical prediction of monsoon• Extremes and circulation changes

26 April 2012


Dynamical Models Outperform Statistical

• The skill in forecasts of all-India monsoon rainfall from May ICs with dynamical models (ENSEMBLES Project) is statistically significant, and greater than empirical forecasts based on observed SST.

26 April 2012

DelSole & Shukla 2012: GRL

ISMR=India Summer Monsoon

Rainfall


ENSO/ISMR Relationship

• The “breakdown” in the ENSO/ISMR relationship may be a sampling issue.– Model ensemble mean

hindcasts (solid; colors) do not exhibit the breakdown in correlation.

– Individual ensemble members (dash-dot) do show apparent breakdowns when sampled like the observations.

26 April 2012

DelSole & Shukla 2012: GRL


MERRA QIBT• We apply the quasi-isentropic back

trajectory method* to MERRA data and observed precipitation to estimate sources of surface evaporation supplying precipitation over all land locations 60°S-90°N.

• Example (left) of 1979-2005 JJA moisture source for rain over the DC area, the 3 driest years, and the 3 wettest years.

• The “blobs” are effectively PDFs – we can use relative entropy to compare.

26 April 2012

*Dirmeyer and Brubaker 1999; 2007

Climo.

Dry

Wet

ppm – normalized so global integral = 106


Drought Years vs. Climatology• Recall RE=0 if two

distributions are identical.• Maps show RE between

climatological evaporative moisture source calculated at each point and the source for the 3 driest years.

• Small values ≈ circulation changes are not associated with drought. Must be another cause.

26 April 2012

Classic monsoon areas tend to low RE values

Dirmeyer et al. (in prep)


Wet Years Signal

• Wet years show similar large-scale patterns.

• Note that the highest RE values are usually over arid regions – require a circulation change to bring in moisture.26 April 2012


“Relative Empathy” With Circulation• The ratio of the REs (log of

ratio shown) indicates “droughts” are more likely than “floods” to be associated with circulation changes (different evaporative sources).

• Implies wet spells are either more locally driven or more random in nature

• Time-scales come into play also.

26 April 2012

25

Our Evolving Understanding of Land-Climate Interactions

• They could matter…– Land cover change (deforestation, desertification, etc.)– Soil moisture sensitivity studies (perturbed BCs, e.g. GLACE)– Breaking the water cycle (specified SM, flux replacement)

• They do matter...– GSWP-1; realistic SM BCs improve simulation, “wrong year”

degrades simulation– GLACE-2; realistic SM ICs improve hindcasts

• How it works…– Feedback pathways, coupling indices, “rebound”…26 April 2012 COLA Scientific Advisory Committee - George Mason University

COLA Scientific Advisory Committee - George Mason University

GLACE-2 Forecasts• Multi-model prediction skill: r2(realistic SM IC) minus r2(random SM IC).• Significant skill

improvement over a large part of North America, especially for extreme soil moisture anomalies.

Temperature Skill

Precipitation Skill

10 models, 1986-1995, only forecasts in JJA considered here.

Koster, Dirmeyer, Guo

et al. 2010: GRL26 April 2012


Predictability in a Changing Climate• We have begun exploring systematically ISI

predictability and prediction using CCSM4– Long 50-year simulations for current, pre-industrial and RCP85.– 15 years chosen for ensemble “forecasts” with randomized land ICs vs.

small (“realistic” or similar to the climate series being forecast) perturbations (May, June, July and December ICs).

– Can separate land IC role from ocean/atmosphere, and see how roles change in a changing climate.

• This is a “perfect model” study – plan to do actual forecast experiments for current climate scenario.

26 April 2012


Land ICs Signal• The impact of “realistic” land

surface initialization on the first week of the forecast (signal/signal ratio) is evident in precipitation over land.

• There is a hint that stronger impacts are present in the current climate than in pre-industrial…

26 April 2012

€

σ realistic IC2

σ random IC2

Ratio is: , where s2 is the interannual

variance of the ensemble mean precipitation.


Sensitivity to Changing Climate

• Over most land areas, in all four months examined, the positive impact of “realistic” land initialization on the simulation has increased (signal/signal ratios increase) from 19th century to today.

• What is the cause of this change in predictability (also evident in temperature, not shown)?

26 April 2012

Dirmeyer et al. (in prep)


Is Land Cover Change the Driver?• We find a suspicious

correspondence between the pattern of improved predictability from land ICs (top) and the pattern of prescribed land use change from 1850 to 2000 scenarios.

• We are still exploring this link.

26 April 2012

Change in T2m Predictability*

Land Cover Change (DAlbedo)

*Predictability defined as number of days in forecast lead 31-60 (1 June ICs) where the land ICs have significant impact on T2m interannual variance.

Kumar et al. (in prep)


Summary

• ISI is an integral and enduring element of COLA’s research mission.

• ISI is far from a solved problem – progress is being made on many fronts, and there is still much we do not fully understand.

• We continue to explore the role of the slowly-varying boundary conditions (ocean and land) in climate predictability and prediction.

• Climate change adaptation is only meaningful if our models can capture the changing regional impacts of ENSO and other boundary-forced climate anomalies. 26 April 2012

Thank You

Intra-seasonal Seasonal Interannual Intra-seasonal Seasonal Interannual ISI Research at COLA Paul...

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