The ENSEMBLES Project: Prospects for down scaling over

SEE and understanding the uncertainties in climate

projections

Jens H. Christensen, DMI

UNCERTAINTIES IN CLIMATE CHANGE PROJECTIONS

• Uncertainty in future emissions– use a range of SRES emissions scenarios

• Uncertainty due to observational limitations– use multiple means of validation

• Natural variability– use a number of different initial conditions (ensembles)

• Uncertainty in the response of the climate system– use a range of climate modelling systems

– AND/OR assess confidence in climate projections (better models)

• Need for a large-scale coordinated effort

The ENSEMBLES RCM approach

CNRM DMI ETHZ GKSS HC ICTP KNMI MPI SMHI UCM

A2+HadAM3H 3 1 1 3 1 1 1 1 1

A2+ECHAM4 1 1

A2+ARPEGE3 1

B2+HadAM3H 1 1 1 1

B2+ECHAM4 1 1

B2+ARPEGE3 3

PRUDENCE GCM-RCM

Christensen et al. (2007)

Rockel domains

PRUDENCE model bias

Jacob et al. 2007

ENSEMBLES GCM-RCM MatrixGlobal model

Regional model METO-HC MPIMET IPSL CNRM NERSC CGCM3 Total number

METO-HC 1950-2100£ 1950-2100

1950-2050*

1

2 (4)MPIMET 1950-2100 1950-2050* 2CNRM 1950-2050 2

DMI 1950-2100 1950-2050* 2ETH 1950-2050 1

KNMI 1950-2050 1

ICTP 1950-2050 1

SMHI 1950-2050 1950-2050* 2

UCLM 1950-2050 1C4I 1950-2050 1

GKSS** 1950-2050* 1Met.No** 1950-2050* 1CHMI** 1950-2050* 1

OURANOS** 1Total

(1950-2050) 4 (6) 6 2 3 2 18 (20)

ENSEMBLES GCM-RCM MatrixERA40 METO-HC MPIMET IPSL CNRM NERSC CGCM3 Total

numberMETO-HC 1950-2100£ 1950-2100

1950-2050*

1

2 (4)MPIMET 1950-2100 1950-2050* 2CNRM 1950-2050 2

DMI 1950-2100 1950-2050* 2ETH 1950-2050 1

KNMI 1950-2050 1

ICTP 1950-2050 1

SMHI 1950-2050 1950-2050* 2

UCLM 1950-2050 1C4I 1950-2050 1

GKSS** 1950-2050* 1Met.No** 1950-2050* 1CHMI** 1950-2050* 1

OURANOS** 1

1960-2002 4 (6) 6 2 3 2 18 (20)

Making use of it all?

• ANOVA analysis of data withinPRUDENCE

• Producing weights for each RCM/GCM pair

Temperature: RegCM3-CRU

Simulations vs. Observations

ENSEMBLES GCM-RCM MatrixERA40 METO-HC MPIMET IPSL CNRM NERSC CGCM3 Total

numberMETO-HC 1950-2100£ 1950-2100

1950-2050*

1

2 (4)MPIMET 1950-2100 1950-2050* 2CNRM 1950-2050 2

DMI 1950-2100 1950-2050* 2ETH 1950-2050 1

KNMI 1950-2050 1

ICTP 1950-2050 1

SMHI 1950-2050 1950-2050* 2

UCLM 1950-2050 1C4I 1950-2050 1

GKSS** 1950-2050* 1Met.No** 1950-2050* 1CHMI** 1950-2050* 1

OURANOS** 1

1960-2002 4 (6) 6 2 3 2 18 (20)

ENSEMBLES GCM-RCM MatrixERA40 METO-HC MPIMET IPSL CNRM NERSC CGCM3 Total

numberMETO-HC 1950-2100£ 1950-2100

1950-2050*

1

2 (4)MPIMET 1950-2100 1950-2050* 2CNRM 1950-2050 2

DMI 1950-2100 1950-2050* 2ETH 1950-2050 1

KNMI 1950-2050 1

ICTP 1950-2050 1

SMHI 1950-2050 1950-2050* 2

UCLM 1950-2050 1C4I 1950-2050 1

GKSS** 1950-2050* 1Met.No** 1950-2050* 1CHMI** 1950-2050* 1

OURANOS** 1

1960-2002 4 (6) 6 2 3 2 18 (20)

ENSEMBLES GCM-RCM MatrixERA40 METO-HC MPIMET IPSL CNRM NERSC CGCM3 Total

numberMETO-HC 1950-2100£ 1950-2100

1950-2050*

1

2 (4)MPIMET 1950-2100 1950-2050* 2CNRM 1950-2050 2

DMI 1950-2100 1950-2050* 2ETH 1950-2050 1

KNMI 1950-2050 1

ICTP 1950-2050 1

SMHI 1950-2050 1950-2050* 2

UCLM 1950-2050 1C4I 1950-2050 1

GKSS** 1950-2050* 1Met.No** 1950-2050* 1CHMI** 1950-2050* 1

OURANOS** 1

1960-2002 4 (6) 6 2 3 2 18 (20)

ENSEMBLES GCM-RCM MatrixERA40 METO-HC MPIMET IPSL CNRM NERSC CGCM3 Total

numberMETO-HC 1950-2100£ 1950-2100

1950-2050*

1

2 (4)MPIMET 1950-2100 1950-2050* 2CNRM 1950-2050 2

DMI 1950-2100 1950-2050* 2ETH 1950-2050 1

KNMI 1950-2050 1

ICTP 1950-2050 1

SMHI 1950-2050 1950-2050* 2

UCLM 1950-2050 1C4I 1950-2050 1

GKSS** 1950-2050* 1Met.No** 1950-2050* 1CHMI** 1950-2050* 1

OURANOS** 1

1960-2002 4 (6) 6 2 3 2 18 (20)

ENSEMBLES GCM-RCM MatrixERA40 METO-HC MPIMET IPSL CNRM NERSC CGCM3 Total

numberMETO-HC 1950-2100£ 1950-2100

1950-2050*

1

2 (4)MPIMET 1950-2100 1950-2050* 2CNRM 1950-2050 2

DMI 1950-2100 1950-2050* 2ETH 1950-2050 1

KNMI 1950-2050 1

ICTP 1950-2050 1

SMHI 1950-2050 1950-2050* 2

UCLM 1950-2050 1C4I 1950-2050 1

GKSS** 1950-2050* 1Met.No** 1950-2050* 1CHMI** 1950-2050* 1

OURANOS** 1

1960-2002 4 (6) 6 2 3 2 18 (20)

The ENSEMBLES RCM weights are based on the following specific metrics, deduced

from the ERA40 driven simulations at 25 km

• f1: large scale circulation based on a weather regime classification (PI Meteo France)

• f2: meso-scale signal based on seasonal temperature and precipitation analysis (PI ICTP)

• f3: probability density distribution match of daily and monthly temperature and precipitation analysis (PI DMI/SMHI)

• f4: extremes in terms of re-occurrence periods for temperature and precipitation (PI KNMI/Hadley)

• f5: trend analysis for temperature (PI MPI)• f6: representation of the annual cycle in temperature

and precipitation (PI CUNI)

Full RCM weight

∏=

=6

1i

n

iRCM fw i

GCM weights

• The RCM weights introduced are basically independent on GCM performance as they are all based on the ERA40 experiments.

• The final weights in the RCM/GCM matrix, therefore needs to include element of assessing skill of the GCM as well.– The quality of the GCMs used as driving models

for the regional climate change production runs will also have a characteristic assigned weights deduced by the GCM teams

Final system

• Convert discrete data set into a continuous PDFs of climate change variables. – This will be done using a Gaussian Kernel

algorithm applied to the discrete dataset with the aim to take into account the GCM/RCM model specific weights

Tests of system

• The robustness can be tested using sub-periods of the 1961 - 2002 ERA driven simulations,– i.e. establishing the weights based on the

period 1961 - 1990 and using the period 1991 - 2002 for validation purposes.