Intra-seasonal Seasonal Interannual Intra-seasonal Seasonal Interannual ISI Research at COLA Paul...
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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 Scientific Advisory Committee - George Mason University 4
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
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)
COLA Leadership in Multi-Model Projects
1984 1988 1992 1996 2000 2004 2008 2012
GFDL
NMC/NCEP
Both
NOAA Models
NOAA model run by COLA
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)
COLA Leadership in Multi-Model Projects
1984 1988 1992 1996 2000 2004 2008 2012
CCM/CCSM
NCAR Models
NCAR model run by COLAor components
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)
COLA Leadership in Multi-Model Projects
1984 1988 1992 1996 2000 2004 2008 2012
GSFC
NASA Models
COLA Scientific Advisory Committee - George Mason University 9
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
COLA Scientific Advisory Committee - George Mason University 10
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
COLA Scientific Advisory Committee - George Mason University 11
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
COLA Scientific Advisory Committee - George Mason University 12
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
COLA Scientific Advisory Committee - George Mason University 13
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)
COLA Scientific Advisory Committee - George Mason University 14
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)
COLA Scientific Advisory Committee - George Mason University 15
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
COLA Scientific Advisory Committee - George Mason University 16
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
COLA Scientific Advisory Committee - George Mason University 18
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
COLA Scientific Advisory Committee - George Mason University 19
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
COLA Scientific Advisory Committee - George Mason University 20
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
COLA Scientific Advisory Committee - George Mason University 21
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
COLA Scientific Advisory Committee - George Mason University 22
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)
COLA Scientific Advisory Committee - George Mason University 23
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
COLA Scientific Advisory Committee - George Mason University 24
“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
COLA Scientific Advisory Committee - George Mason University 27
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
COLA Scientific Advisory Committee - George Mason University 28
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.
COLA Scientific Advisory Committee - George Mason University 29
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)
COLA Scientific Advisory Committee - George Mason University 30
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)
COLA Scientific Advisory Committee - George Mason University 31
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