WP4.4: Sources of predictability in current and future climates

Laurent Terray (CERFACS)

Participants: CERFACS, CGAM, CNRM, DMI(nc), ECMWF, IfM, IPSL, ISAC(nc)

Thanks to: R.Sutton (CGAM), H.Douville (CNRM), F.Doblas-reyes (ECMWF), S.Corti (ISAC), B.Christiansen (DMI), J.P.Duvel (IPSL)

Main objectives

• To develop methodologies and tools to exploit existing seasonal to decadal hindcasts for identifying and understanding the sources of predictability in current and future climates

• To assess and understand the main factors which influence the predictability of the climate system at different time scales

• To improve the understanding of the interaction between anthropogenic climate change and natural climate variability modes and of the possible changes in predictability at all time scales

Work Plan for months 1-18• T4.4a: design a general framework for the analysis of

participating models (ALL)• T4.4b: assess the role of snow and soil moisture in the

predictability of climate (CNRM)• T4.4c: assess the potential predictability of the North Atlantic

region at seasonal to decadal timescales (ALL)• T4.4d: investigate the vertical structure of weather and climate

regimes in several re-analysis products and the potential role of the stratosphere (DMI, ISAC, CERFACS)

• D4.4.1: Synthesis of current estimates and mechanisms of predictability on seasonal to decadal timescales, including understanding the influence of ocean initial conditions, and with a focus on the North Atlantic European sector (month 18)

• M4.4.1: development of methodologies to explore climate variability and predictability, for use with the ENSEMBLES system(month 18)

• M4.4.2: Assessment of climate variability and predictability in exixting simulations to provide benchmark against which the ENSEMBLES system can be judged (month 18)

CGAM contribution to WP4.4

Northern European temperatures

observations forecasts

Initial condition information is ignored in current climate forecasts

Source: Anne Pardaens, Hadley Centre / PREDICATE R.Sutton

Evidence from FP5 PREDICATE project of decadal predictability in the THC

Control simulations

Perturbed runs

Source: Mat Collins,

What mechanisms determine the extent of predictability in ocean and atmosphere variables?

R.Sutton

Questions and Methods1. What mechanisms determine the predictability of the

Atlantic THC, and related aspects of climate, in current climate models?

2. To which aspects of the ocean initial conditions are forecasts of the THC, and related aspects of climate, most sensitive?

and later in the project:3. How do initial conditions and changing external

forcings combine to determine the evolution of climate on decadal timescales?

Methods:• Further analysis of PREDICATE ensemble integrations• New ensemble integrations with HadCM3 model

(larger ensembles)• A new methodology to estimate empirical singular

vectors for the THC. (addresses question 2.)

R.Sutton

CNRM contribution to WP4.4

Questions and MethodsExplore the predictability associated to land surface

anomalies1. What is the influence of soil moisture conditions on

atmospheric seasonal predictability? And later in the project2. Assess the influence of snow conditions on seasonal (to

interannual?) predictabilityMethods:• Preliminary step: produce a 10-yr global monthly mean

soil moisture climatology using the 3-hourly atmospheric forcing provided by GSWP-2.

• run ensembles of global atmospheric simulations with the ARPEGE AGCM(prescribed observed SSTs from 1986 to 1995 and with GSWP-2 vs climatological initial conditions).

Influence of soil moisture relaxation towards GSWP-1 on the JJAS Z500 stationary eddy anomalies simulated by the ARPEGE AGCM

Observedanomalies

FreeSoil

moisture

RelaxedSoil

moisture

Douville & Chauvin (2000), Climate Dyn.,16,719-736; Douville H. (2OO2), J.Climate,15,701-720

Control (interactive soil moisture and

ERA15 initial conditions)

Douville (2004), Climate Dyn.,22,429-446

Impact of climatological

initial conditionsfor soil moisture

Impact of climatological

boundary conditions for soil moisture

ECMWF contribution to WP4.4

Questions and Methods1. Focus on predictability of current climates

2. Influence of anthropogenic forcing upon the seasonal-to interannual predictability of natural modes of variability (ENSO, NAO, PNA) to explain the latest results (see below)

ECMWF’s effort will take place after month 18

• Links to WP5.3 (Assessment of forecast quality)

2-4 months lead time (DJF)

Southern Europe DEMETER hindcasts

2-4 (DJF)

PrecipitationT2m

4-6 (FMA)

Nov start date

IPSL contribution to WP4.4

Questions and Methods Intraseasonal convective and dynamical perturbations have a large impact on the

Asian monsoon activity and on the triggering of ENSO1. What is the predictability of the intra-seasonal activity in the Indo-Pacific

region2. Study the seasonal predictability of the intra-seasonal oscillation in the Indo-

Pacific region in current and future climates

Methods:• Develop an operational tool to test the seasonal forecast of the intraseasonal

oscillation in the tropics and use this tool to assess the skill of the different global ESMs

• First 18 months (RT5): Use DEMETER simulations to develop a diagnostic tool (based on the Local Mode Analysis) to infer the skill of seasonal hindcasts in describing the intraseasonal oscillation in the Indo-Pacific region.

• Remaining time up to 5 years (WP4.4): Analysis of the seasonal predictability in current and future climates using the core ENSEMBLES simulations (links with potential changes in ENSO activity)

20

10

0

-10

120100806040

240

240

220

200224-161%Period:49±14.2std Max:0.078936Memb:11

20

10

0

-10

120100806040

240

240

220

200229-160%Period:48±18.8std Max:0.065812Memb:9

20

10

0

-10

120100806040

240

240

220

200224-143%Period:34.5±12.6std Max:0.056733Memb:5

Variability of the ISO patterns between hindcasts members: Internal Variability

• Example for the CNRM model in January 2002

– One member (member 9) give a reasonable pattern

– One member (member 5) with low organisation (weak %var), unrealistic pattern at too short time scale

OLR-NOAA

Member 9

Member 5

DMI contribution to WP4.4

Questions and Methods

Evidence for nonlinear regime behaviour has been found in both the stratosphere and the troposphere and strong evidence has been reported for a stratospheric regime shift in the late half of the 1970ies

1. What is the atmospheric regime behaviour in the recent period ? What is the vertical extent of the regimes ?

2. Are there any connections between the stratospheric and tropospheric regimes (polar vortex strength and the NAO-AO)?

Methods:Critical assessment of the standard algorithms (k-means, mixture models) used to perform clustering (nature of the underlying probability distribution) - link with WP4.3, KNMI ?Use of the ERA40 datasetLater in the project: analysis of the core ENSEMBLES simulations for current and future climate (Any of the core ENSEMBLES modelswith high-res in the stratosphere ??)

Bimodality in the tropospheric wave amplitude index

Bimodality in the strength of the stratospheric vortex

Change in 1990

Change in 1979

Christiansen JAS 2005

Christiansen 2003J. Climate

What is the connection?

Wave amplitude indexdefined by Hansen and Sutera

ISAC contribution to WP4.4

Questions and Methods1. What is the vertical and thermal structure of (global, hemispheric-scale)

circulation regimes for the current climate?

2. Explore the potential role of weather regimes and non-linearity in the

emerging anthropogenic signal.

Later in the project:

Verification of regime structure in present and future climate core ENSEMBLES

simulations.

Interaction between natural and forced variability

Regime response to anthropogenic forcing and SST anomalies

Troposphere-stratosphere connection [Collaboration with DMI]Methods:Study of the extended winter(Oct-Apr) with reanalysis datasets (NCEP and ERA40)Diagnostic tools: multivariate EOF analysis, Pdf estimators and clustering techniques

Clusters

48-98

50yr

48-73

25yr

74-98

25yr

Enso-out

28yr

Nino-out

35yr

Nina-out

42yr

2 78% 52% 56% 88% 85% 76%

3 97% 90% 97% 94% 95% 79%

4 95% 76% 99% 98% 98% 71%

5 81% 74% 90% 92% 96% 71%

6 83% 67% 81% 88% 88% 79%

Changes in cluster frequency and significance when different periods (corresponding to different external forcings: ENSO and climate signal) are considered.

Multivariate combined EOF analysis

Data NCEP reanalysis

Clustering in the first 2-EOFs phase space

K-means algorithm 3-cluster partition

positive NAM

It suggests that the associated tropical heating anomalies reorganize the mid-latitude circulation sufficiently to disrupt the “normal” regime behaviour.

CERFACS contribution to WP4.4

Questions and Methods

1. What are the physical processes associated to climate predictability of the North Atlantic – European sector at various timescales ? (Focus on SST influence and interaction between the different ocean basins) (months 1-18)

2. What is the influence of anthropogenic forcing upon the levels of predictability of the major climate modes ? (months 19-60)

Methods:

Analyses of existing integrations (e.g PREDICATE and DEMETER) and coordinated experiments (to be discussed)

Assess the relevance of various predictability measures to improve the understanding of physical mechanisms (e.g relative entropy Kleeman 2002 Stephenson and Doblas-reyes 2000)

Analyses of the core ENSEMBLES integrations

Two ensembles of 40 members with the NCAR AGCMOne CTRL and one forced with 2003 TATL diabatic heating

Summer (JJA) weather regimes (daily timescale) from NCEP-NCAR Reanalysis (1950-2002)

… associated to an increase of warm days (exceeding the 95% percentile) over France (data from Météo-France)

Percentage of days exceeding the 95 % climatological threshold for a given regime

Z500 anomalies (m)

0% 5%(clim)

10%(x2)

15%(x3)

20%(x4)

Weather regimes and local climate 2003 heat wave: a process study

A B

A B+ represent 80% Of 2003 summer days

A B

Tropical Atlantic forcing?

Rainy Dry

OLR anomalies for June 2003

Simulated changes Of warm regime occurrenceFor JJA 2003 in responseTo the tropical Atlantic diabatic heating forcing

Cassou et al. 2004

Influence of anthropogenic forcing on the NAO

Current climate Perturbed climate

NAO+

NAO-

GHG forcing

PRUDENCE simulations: series ofTime-slice exp. With ARPEGE (high res. Over Europe, 50 km) forced by:Observed SST and GHG (1960-1999) And SST (from 2 CGCMs) and SRESScenarios (2070-2099)

Terray et al. Jclimate 2004

Remarks

• Existing simulations: PREDICATE, DEMETER, AR4, Others … Need a list of available model data

• Coordinated experiments: to be discussed soon …• Need good coordination with WP4.2 and WP5.3• Utility and limitations of regime analysis algorithms

(interaction with WP4.3, others… e.g downscaling)