WP4.4: Sources of predictability in current and future climates Laurent Terray (CERFACS)

1. WP4.4: Sources of predictability in current and future climatesLaurent 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)

2. 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

3. 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)

4. CGAM contribution to WP4.4

5. Initial condition information is ignored in current climate forecasts observations forecasts Northern European temperatures R.Sutton Source: Anne Pardaens, Hadley Centre / PREDICATE

6. 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

7. Questions and Methods • What mechanisms determine the predictability of the Atlantic THC, and related aspects of climate, in current climate models? • 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: • 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

8. CNRM contribution to WP4.4

9. Questions and Methods Explore the predictability associated to land surface anomalies • What is the influence of soil moisture conditions on atmospheric seasonal predictability? And later in the project • Assess the influence of snow conditions on seasonal (to interannual?) predictability Methods: • 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).

10. Observed anomalies Free Soil moisture Relaxed Soil moisture Influence of soil moisture relaxation towards GSWP-1 on the JJAS Z500 stationary eddy anomalies simulated by the ARPEGE AGCM Douville & Chauvin (2000), Climate Dyn.,16,719-736; Douville H. (2OO2), J.Climate,15,701-720

11. Control (interactive soil moisture and ERA15 initial conditions) Impact of climatological initial conditions for soil moisture Impact of climatological boundary conditions for soil moisture Douville (2004), Climate Dyn.,22,429-446

12. ECMWF contribution to WP4.4

13. Questions and Methods • Focus on predictability of current climates • 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)

14. Southern Europe DEMETER hindcasts T2m Precipitation Nov start date 2-4 (DJF) 4-6 (FMA)

15. IPSL contribution to WP4.4

16. Questions and Methods Intraseasonal convective and dynamical perturbations have a large impact on the Asian monsoon activity and on the triggering of ENSO • What is the predictability of the intra-seasonal activity in the Indo-Pacific region • Study the seasonal predictability of the intra-seasonal oscillationin 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 theseasonal predictability in current and future climates using the core ENSEMBLES simulations (links with potential changes in ENSO activity)

17. Variability of the ISO patterns between hindcasts members: Internal Variability OLR-NOAA • 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 Member 9 Member 5

18. DMI contribution to WP4.4

19. 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 • What is the atmospheric regime behaviour in the recent period ? What is the vertical extent of the regimes ? • 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 dataset Later in the project: analysis of the core ENSEMBLES simulations for current and future climate (Any of the core ENSEMBLES models with high-res in the stratosphere ??)

20. Bimodality in the tropospheric wave amplitude index Christiansen JAS 2005 Wave amplitude index defined by Hansen and Sutera Change in 1990 Bimodality in the strength of the stratospheric vortex Christiansen 2003 J. Climate Change in 1979 What is the connection?

21. ISAC contribution to WP4.4

22. Questions and Methods • What is the vertical and thermal structure of (global, hemispheric-scale) circulation regimes for the current climate? • 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

23. Multivariate combined EOF analysis Data NCEP reanalysis Clustering in the first 2-EOFs phase space K-means algorithm 3-cluster partition positive NAM Changes in cluster frequency and significance when different periods (corresponding to different external forcings: ENSO and climate signal) are considered. It suggests that the associated tropical heating anomalies reorganize the mid-latitude circulation sufficiently to disrupt the “normal” regime behaviour.

24. CERFACS contribution to WP4.4

25. Questions and Methods • 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) • 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

26. Rainy Dry B B B Z500 anomalies (m) 0% 5% (clim) 10% (x2) 15% (x3) 20% (x4) Weather regimes and local climate 2003 heat wave: a process study Summer (JJA) weather regimes (daily timescale) fromNCEP-NCAR Reanalysis (1950-2002) A + represent 80% Of 2003 summer days Tropical Atlantic forcing? A … associated to an increase of warm days (exceeding the 95% percentile) over France (data from Météo-France) OLR anomalies for June 2003 Two ensembles of 40 members with the NCAR AGCM One CTRL and one forced with 2003 TATL diabatic heating Simulated changes Of warm regime occurrence For JJA 2003 in response To the tropical Atlantic diabatic heating forcing Cassou et al. 2004 A Percentage of days exceeding the 95 % climatological threshold for a given regime

27. Influence of anthropogenic forcing on the NAO Perturbed climate Current climate GHG forcing NAO- NAO+ PRUDENCE simulations: series of Time-slice exp. With ARPEGE (high res. Over Europe, 50 km) forced by: Observed SST and GHG (1960-1999) And SST (from 2 CGCMs) and SRES Scenarios (2070-2099) Terray et al. Jclimate 2004

28. 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)