Transcript Slide 1

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
Initial condition information is
ignored in current climate forecasts
Northern European temperatures
observations
Source: Anne Pardaens, Hadley Centre / PREDICATE
forecasts
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 Methods
1.
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 Methods
Explore the predictability associated to land surface
anomalies
1. What is the influence of soil moisture conditions on
atmospheric seasonal predictability?
And later in the project
2. 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).
Influence of soil moisture relaxation towards GSWP-1 on the JJAS
Z500 stationary eddy anomalies simulated by the ARPEGE AGCM
Observed
anomalies
Free
Soil
moisture
Relaxed
Soil
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)
Impact of
climatological
initial conditions
for soil moisture
Impact of
climatological
boundary conditions
for soil moisture
Douville (2004), Climate Dyn.,22,429-446
ECMWF contribution to
WP4.4
Questions and Methods
1. 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
T2m
Nov start date
2-4 (DJF)
4-6 (FMA)
Precipitation
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 ENSO
1. What is the predictability of the intra-seasonal activity in the IndoPacific region
2. 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)
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
20
2002
24-1
61%
P eriod:
49±14.2
s td Max:
0.078936
Memb:11
10
240
0
220
-10
240
40
60
80
100
120
Member 9
20
20
2002
10 24-1
43%
P eriod:
34.5±12.6
0 s td Max:
0.056733
Memb:5
-10
10
240
0
220
-10
2002
29-1
60%
P eriod:
48±18.8
s td Max:
0.065812
Memb:9
240
220
240
240
40
60
80
Member 5
100
120
40
60
80
100
120
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 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 ??)
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?
ISAC contribution to
WP4.4
Questions and Methods
1.
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
Multivariate combined
EOF analysis
Data NCEP reanalysis
Clustering in the first 2EOFs phase space
K-means algorithm
3-cluster partition
positive NAM
Cluster
s
48-98
50yr
48-73
25yr
74-98
25yr
Ensoout
28yr
Ninoout
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.
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
Weather regimes and local climate
2003 heat wave: a process study
Summer (JJA) weather regimes (daily timescale) from
NCEP-NCAR Reanalysis (1950-2002)
A
+
B
represent 80%
Of 2003 summer days
Tropical Atlantic forcing?
Rainy
A
Dry
B
Z500 anomalies (m)
… 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
A
B
0%
5%
(clim)
10% 15% 20%
(x2) (x3) (x4)
Percentage of days exceeding the
95 % climatological threshold for a given regime
Simulated changes Of
warm regime occurrence
For JJA 2003 in response
To the tropical Atlantic
diabatic heating forcing
Cassou et al. 2004
Influence of anthropogenic forcing on the NAO
Perturbed climate
Current climate
GHG forcing
NAONAO+
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
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)