COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Intra-Seasonal to Inter-Annual Predictabilty and Prediction

(Acknowledgements)

Deepthi Achuthavarier Youkyoung JangEric Altshuler Jim KinterBen Cash V. KrishnamurthyTim DelSole Sanjiv KumarPaul Dirmeyer Julia ManganelloMike Fennessy Cristiana StanZhichang Guo David StrausBohua Huang Jieshun Zhu

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Intra-Seasonal to Inter-Annual Predictabilty and Prediction

Overarching Framework for Seasonal Predictability – COLA’s Role

Role of Oceanic initial Conditions in ENSO Re-forecasts

Seamless Prediction: The Role of Resolution

Strategies for Doing Research with Flawed Parameterizations

Predictability in a Changing Climate: Past, Present and Future

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Overarching framework for Seasonal Predictability COLA’s Role

“Predictability in the Midst of Chaos” Scientific Basis for Seasonal Predictability

Slowly varying tropical SST and land surface act as forcing function for the seasonal mean circulation and intra-seasonal fluctuations (storm tracks, blocking, weather regimes)

Thus:

In coupled prediction, ocean and land initial conditions must be specified from observations/analyses!

Need to know the sensitivities to uncertainties in the initial conditions of atmosphere, ocean and land (land not well studied)

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Bulletin of the Americal Meteorological Society Vol. 81, No. 11, November 2000

Spatial Variance of midlatitude geopotential due to tropical SST forcing: Probabilistic view from ensembles

Compile a large number of samples of GCM integrations, where a sample is obtained by randomly drawing one ensemble member for each calendar win- ter. (Each sample is a series of seasonal means, comparable to observations.)

JFM SST time series from Maximum Correlation Analysis (SVD) between tropical Pacific SST and 500 hPa mid-latitude geopotential fields in PNA region

Geopotential height variance explained computed by regression onto SST time series

Slowly varying tropical SST as forcing function

DSP and PROVOST (European partner)DSP: Multi-agency, multi-model, multi-institution

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Pacific North American Height variance explained by tropical SST (winter mean)

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Circulation Regimes: Chaotic Variability versus SST-Forced PredictabilityDavid M. Straus, Susanna Corti, Franco MolteniJournal of ClimateVolume 20, Issue 10 (May 2007) pp. 2251-2272

Synoptic-Eddy Feedbacks and Circulation Regime AnalysisDavid M. StrausMonthly Weather ReviewVolume 138, Issue 11 (November 2010) pp. 4026-4034

Tropical SST Forcing, seasonal mean climate and low-frequency intraseasonal fluctuations

Straus, D.M., S. Corti, and F. Molteni, 2007: J. Clim. 20, 2251-2272Straus, D.M. 2010: Mon Wea. Rev. 138, 4026-4034

Frequency of occurrence depends on SST

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Role of Oceanic Initial Conditions in ENSO Re-forecasts

• Model CFS version 2 provided by NCEP EMC

• Hindcast Experiments: 1) ATM/LND/ICE initial data from CFSRR

2) Four sets of forecasts differing in OCN initial data from ODA products: ECMWF COMBINE-NV, ECMWF ORA-S3, NCEP CFSR, NCEP GODAS

3) Anomaly Initialization for OCN initial state

4) 12-month hindcast starting 01 April for 1979-2007 (4 ensemble members)

• Validation Datasets:

SST -- ERSST v3.

Heat Content (HC) -- Ensemble Mean (EM) of six ODAs (above 4 ODAs + SODA + GFDL ECDA)

CFSv2 SST Predictive Skill (April ICs) Correlation for ICs from 4 ODAs

2-month forecast lead 5-month forecast lead 11-month forecast lead

ODA

1

ODA

2O

DA 3

ODA

4

Leading Months Leading Months

( oC )

Prediction Skill of the Nino3.4 Index

Combine-NV CFSR Super_Ensemble

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Forecast Equatorial Heat Content Anomaly vs. OBS COMBINE-NV ORA-S3 CFSR GODAS

OBS

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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ENSO Forecast Summary

• ENSO prediction skill can depend significantly on the ODA used to initialize the ocean.

• The slightly worse performance of the prediction initializing from CFSR is attributed to its slight difference in the upper ocean heat content, possibly in the off-equatorial domain.

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Are we still dependent upon and/or limited by parameterizations of convection and other processes?

The Athena Project

ECMWF Integrated Forecast System (IFS) - AGCM

- 13-month runs at a variety of horizontal resolutions: T159 (125 km), T511 (39 km), T1279 (16 km) , T2047 (10 km)

- AMIP runs (1961-2007) at a variety of horizontal resolutions

- No re-tuning of convective parameterizations

NICAM (almost no parameterizations)

- Seasonal runs

Seamless Prediction: The Role of Resolution

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Manganello, et al., 2011: Tropical Cyclone Climatology in a 10-km Global Atmospheric GCM: Toward Weather-Resolving Climate Modelling.

Atlantic Tropical CyclonesTrack genesis in left panelsTrack densities in right panels

Higher resolution is necessary

OBS

T2047

T1279

T511

T156

GENESISDENSITY

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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black line: Observedgreen line – T159 (multiplied by 10)red line – T1279 (multiplied by 2)dashed line – Nino 3.4 (multiplied by -1)

Power Dissipation Index North Atlantic (May-Nov 1975-2007) from AMIP and Obs

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

Indian Monsoon JJAS Precipitation IFS (reduced to N80) 1961-2008, T2047 1990-2008

TRMM 1998-2009 (mm/day)

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TRMM

T159

T511

T1279

T2047

Increased resolution only makes the systematic error worse !

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Strategies for Doing Research with Flawed Parameterizations

Replace them:“Super-parameterization SP-CCSM” - embed a two-dimensional slab of one-

dimensional cloud-resolving models in CCSM3 T42 – these replace the conventional convection parameterizations (South American Monsoon)

Supplement them:Idealized added heating put into CAM3 to circumvent model’s poor moist response to

SST anomalies (ENSO / Indian Monsoon relationship)

Remove them:Try to resolve everything explicitly – (NICAM)

Stochastic Parameterizations – Augment existing parameterizations

Oscillatory Modes in South American Monsoon System

SP-CCSM: CCSM with embedded cloud-resolving models

Observation

CCSM No intraseasonaloscillation

Intra-Seasonal Oscillation (MJO)

Inter-Seasonal Oscillation (NAO)

Multi-ChannelSingular Spectrum Analysis of OLR

period ~ 60 d period ~ 120 d

Added Heating for 1997 Monsoon

Inserting idealized additional heating into CAM3

- Proxy for SST-forcing of tropics during developing warm ENSO event in JJAS

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

- Use idealized vertical stucture, and a realistic horizontal structure

No Indian Ocean Heating Indian Ocean Heating Included

JJAS Mean 850 hPa Streamfunction Response

1997 Exp without IO

1997 Exp with IO

ERA40

Note: With added IO heating the Monsoon response is closer to normal, as observed !

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Predictability in a Changing Climate: Past, Present and Future

Evolution of uncertainty (spread of pdf) from initial state synoptic weather intra-seasonal time scales in the fully coupled system.

Questions:

Does the evolution of uncertainty through atmosphere, land and ocean depend systematically on the climate: Recent past, present and future climates?

What particular coupled pathways of uncertainty evolution are initiated by uncertainty in the initial land states?

(Will our ability to forecast ISI time scales get better or worse in the future?)

What 20th Century ISI phenomena can we re-forecast with current coupled models?

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Predictability in a Changing Climate

Design Considerations:

Predictability and prediction skill are both model-dependent: Use both CCSM4 (1o x 1o) and CFSv2

Baseline runs from recent past, present and future climates needed.

Methodologies for introducing both “small” and “large” uncertainties in land initial states are needed (unique aspect of this design)

Predictability (“perfect model”) runs and predictions should be made for multiple starting times of year, with adequate ensemble size.

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Predictability in a Changing Climate

CCSM4 1ox1o Predictability Experiments:

50-year baseline run from pre-industrial 1850 forcing conditions and ICs50-year baseline run from 2000 forcing and ICs50-year baseline run from 2050 scenario forcing and ICs

For each baseline run:Define four classes based on calendar date (01 Dec, 01 May, 01 Jun, 01 Jul)

For each calendar date: Choose 15 key years from the appropriate baseline run, based on ENSO-criterion

Each calendar date + key year define a start date from the baseline run. For each start date:Construct 14 “large” land surface perturbations (15 IC states altogether)Construct 14 “small” land surface perturbations (15 IC states altogether)

For each IC state run the model for 90 days (12 months for 01 Dec, 01 Jun)

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Predictability in a Changing Climate

Small land surface perturbations

14 new land states must be defined for each start date from the baseline run.

Subclass one: land states taken from 1,2,3, … ,7 days previous to the start date

Subclass two: land states taken from 0.5, 1.5, …., 6.5 days previous (defined by linear interpolation )

Each horizontal black line represents a baseline run Each orange circle represents a key year

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Predictability in a Changing Climate

Large land surface perturbations

14 new land states must be defined for each start date from the baseline run.

These land states are taken from the same calendar date but from the 14 other key years

Each horizontal black line represents a baseline run Each column of blue circles represents a key year

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Evolution of small and large land errors (1850 baseline run)Soil Moisture Root Zone (all land)

Shaded region are 95% uncertainty range for respective mean

Common atmosphere IC forces early convergence of pdfSo

il M

oist

ure

(roo

t zon

e) rm

s err

or

Large perturbations

Small perturbations

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Evolution of small and large land errors (2000 baseline run)Soil Moisture Root Zone (all land)

Shaded region are 95% uncertainty range for the respective mean

Soil

Moi

stur

e (r

oot z

one)

rms e

rror

Large perturbations

Small perturbations

Signal/Total

• Initial land state has three regimes of impact on temperature predictability:

1. First two weeks: steady significant global impact.2. Second two weeks: rapid decay of effects.3. Beyond 30 days: limited to a few regions.

CCSM-4

Days from May 1

Predictability from Coupling

• Top: CCSM4 (1850) correlation between initial ½ day soil moisture perturbations and 1-day T2m anomalies.

• Bottom: GSWP2 seasonal index of coupling between soil moisture and evaporation.

• Red shading links high land IC impacts on atmosphere (top) to strong land-atmosphere coupling (bottom).

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Contrary to the paradigm of rapid tropical error growth followed by early saturation, Tropical wind errors continue to grow even after day 30, and saturate later than extratropical errors.

The predictability time is thus seen to be ‘greater’ in tropics than further poleward,especially for the planetary waves.

We need to better understand the nature of tropical planetary waves beyond the MJO (the “background spectrum”)

Results from an AGCM with specified SST

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Normalized Error growth in u-rotational (1 – 60 days)

PW:m = 1-5

SW:m = 6-20

TROPICSSH MIDLAT

Planetary Waves

m=1-5

Medium Waves

m=6-20

200 mb top

850 mb bot

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Error growth 1 – 60 days – udiv

PW:m = 1-5

SW:m = 6-20

Normalized Error growth in u-divergent (1 – 60 days)

TROPICSSH MIDLAT

Planetary Waves

m=1-5

Medium Waves

m=6-20

200 mb top

850 mb bot

COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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Predictability in a Changing Climate

Preliminary Results

- Land-atmosphere coupling at daily time scales has the same structure as longer time sensitivites of land-atmosphere coupling

- Confirmation of enhanced theoretical predictability in the tropics on a wide range of space and time scales

- Little or no systematic difference seen between predictability properties based on 1850 and 2000 baseline CCSM4 runs

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Intra-Seasonal to Inter-Annual Predictabilty and Prediction

Conclusions (1)

Uncertainty in the ocean initial conditions remain a major factor in ENSO predictability

Seamless approach for Intra-seasonal to Inter-annual time scales:High resolution is critical for coherent structures (blocking, tropical cyclones) BUTModel pararmeterizations remain a stumbling block

Stochastic parameterization technique to be exploited (in future work)

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Intra-Seasonal to Inter-Annual Predictabilty and Prediction

Conclusions (2)

Basic research using “super-parameterization” and techniques for adding idealized heating has given insights into the predictability of the Indian and South American monsoons

Predictability in a Changing Climate: How do fundamental predictability properties change as the climate changes? (ongoing work)