climate models: from projections to predictions of climate...

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Climate models: From projections to predictions of climate change

Jens Hesselbjerg ChristensenDanish Meteorological Institute

Seminar on Climate Change Impacts onWater Resources: 23-9-2008 - Geocenter


Acknowledgements

PRUNDENCE and ENSEMBLES partners– EU Framework Programmes 5 & 6– http://prudence.dmi.dk 2001-2004– http://ensembles-eu.metoffice.com 2004-2009

Ole Bøssing Christensen, Martin Drews, Philippe Lucas-Picher, Fredrik Boberg - DMI


Outline

Uncertainties related to regional climate changeState-of-the-artUsing high resolution informationThe ENSEMBLES regional climate modeling product


Uncertainties in climate change projections

Uncertainty due to observational limitations

• use multiple means of validation

Uncertainty in future emissions

• use a range of SRES emissions scenarios

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


Source: Hadley Centre

Global temperature rise

Signal

Noise


3B2+ARPEGE3

11B2+ECHAM4

1111B2+HadAM3H

1A2+ARPEGE3

11A2+ECHAM4

1111/113113A2+HadAM3H

UCMSMHIMPIKNMI/met.noICTPHCGKSSETHDMICNRM

50km time slice

PRUDENCE RCM


Schär et al. (2004)

Heat wave summer 2003


Schär et al. (2004)


xxxxxx….

xxxxxx….

xxxxxx….

xxxxxxxxx….

xxxxxxxxxGCM2

xxxxxxxxxGCM1

....….….….RCM3RCM2RCM125kmTrans.

ENSEMBLES idea


ENSEMBLES GCM-RCM Matrix

1

1950-2050*

CGCM3

1OURANOS**

18 (20)23264 (6)Total (1950-2050)

11950-2050*CHMI**

11950-2050*Met.No**

11950-2050*GKSS**

11950-2050C4I

11950-2050UCLM

21950-2050*1950-2050SMHI

11950-2050ICTP

11950-2050KNMI

11950-2050ETH

21950-2050*1950-2100DMI

21950-2050CNRM

21950-2050*1950-2100MPIMET

2 (4)1950-21001950-2100£METO-HC

Total numberNERSCCNRMIPSLMPIMETMETO-HCGlobal modelRegional model


Rockel domains



1

1950-2050*

CGCM3

1OURANOS**

18 (20)23264 (6)1960-2002

11950-2050*CHMI**

11950-2050*Met.No**

11950-2050*GKSS**

11950-2050C4I

11950-2050UCLM

21950-2050*1950-2050SMHI

11950-2050ICTP

11950-2050KNMI

11950-2050ETH

21950-2050*1950-2100DMI

21950-2050CNRM

21950-2050*1950-2100MPIMET

2 (4)1950-21001950-2100£METO-HC

Total numberNERSCCNRMIPSLMPIMETMETO-HCERA40


Christensen et al. GRL 2008



1

1950-2050*

CGCM3

1OURANOS**

18 (20)23264 (6)1960-2002

11950-2050*CHMI**

11950-2050*Met.No**

11950-2050*GKSS**

11950-2050C4I

11950-2050UCLM

21950-2050*1950-2050SMHI

11950-2050ICTP

11950-2050KNMI

11950-2050ETH

21950-2050*1950-2100DMI

21950-2050CNRM

21950-2050*1950-2100MPIMET

2 (4)1950-21001950-2100£METO-HC

Total numberNERSCCNRMIPSLMPIMETMETO-HCERA40


Making sense of it all?

Chris Ferro (UniReading) tought us in PRUDENCE: ANOVA (Analysis of variance)

Producing weights for each RCM/GCM pair


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


The weights

0.00001150.00001300.00000220.00000670.00001860.00004650.00000520.00000860.00000810.00000930.00001370.00000830.0000055

0.0910.0890.0920.0810.0950.0900.0890.0960.0940.0910.0910.0930.089

0.3610.5440.2980.2140.3600.7890.3120.2950.2420.2600.5370.2410.224

0.07800.07800.07370.07700.07550.08200.07300.07730.07690.08020.07680.07270.0789

0.0520.0410.0130.0550.0710.0930.0300.0530.0560.0560.0520.0650.042

0.08660.08390.08540.09110.08870.08590.08580.07420.08250.08760.07000.07860.0833

CHMICNRMDMI

ETHZICTPKNMI

METNOMETOHC

MPISMHIUCLM

OURANOSC4I

Wf6f5f4f3f2f1Model


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 weight,

• These precise weights are still to be deduced


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 also the GCM/RCM model specific weights


<p05>=0.7(0.6,0.8)

<p50>=1.1(1.0,1.2)

<p95>=1.4(1.3,1.5)

<med>=1.1

<s>=0.2

<x>=1.1

Temperature[ºC] (ann)

<p05>=-0.9(-1.9,0.2)

<p50>=2.4(1.7,3.0)

<p95>=5.6(4.5,6.6)

<med>=2.5

<s>=2.0

<x>=2.4

Precipitation [%] (ann)

0.9(0.7,1.0)0.7(0.6,0.8)0.6(0.5,0.8)0.6(0.5,0.8)

1.2(1.2,1.3)1.1(1.0,1.1)1.0(0.9,1.1)1.0(1.0,1.1)

1.6(1.5,1.7)1.4(1.3,1.5)1.4(1.3,1.5)1.4(1.3,1.6)

1.21.01.01.1

0.20.20.20.2

1.21.11.01.0

(SON)(JJA)(MAM)Temperature[ºC] (DJF)

-5.3(-7.5,-3.0)

-12.2(-14.1,-10.3)

-0.0(-1.2,1.1)

3.7(1.7,5.7)

1.5(0.0,2.9)-6.4(-7.5,-5.2)3.6(2.9,4.4)9.8(8.5,11.1)

8.3(6.1,10.5)-0.6(-2.3,1.2)7.3(6.1,8.4)15.9(13.8,17.9)

1.7-73.310.1

4.13.52.23.7

1.5-6.43.69.8

(SON)(JJA)(MAM)Precipitation [%] (DJF)

Example: DenmarkChange /°C global warming

1.0 2.00.0

0% 5%-5%


Conclusions

Regional information provided by high resolution modeling adds credibility in projectionsCombining multiple simulations allows to assess uncertaintyElaborated model quality checks will reduce model spread – not necessarily uncertainty!

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