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Precipitation variations over the Iberian peninsula under climate change conditions C. Rodríguez Puebla y S. Nieto Dept. de Física General y de la Atmósfera.

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Presentation on theme: "Precipitation variations over the Iberian peninsula under climate change conditions C. Rodríguez Puebla y S. Nieto Dept. de Física General y de la Atmósfera."— Presentation transcript:

1 Precipitation variations over the Iberian peninsula under climate change conditions C. Rodríguez Puebla y S. Nieto Dept. de Física General y de la Atmósfera Universidad de Salamanca 28/10/2008 Seminario CLIVAR_Es, Madrid 11-13 Febrero 2009

2 Motivation Why precipitation shows a trend to decrease over the Iberian peninsula? Trenberth, et al. 2007: Observations: Surface and Atmospheric Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge 2

3 IPCC report 2007 28/10/2008 3

4 Observed precipitation time series (DJFM) Datos de la AEMet Kendall’Z = -2.2 Datos grid (CRU-Ensembles)‏ Kendall’Z = -1.2 Precipitation trend AEMetPrecipitation trend Cru_Ensemble) 4

5 Links between precipitation (DJFM) and the NAO for observations Precipi (mm/mes)‏ NAO pattern for DJFM (shaded) Correlation pattern between mean precipitation over the IP and sea level pressure (SLP) R= -0.67 ± 0.06 Lamb & Peppler 1987, BAMS Hurrel et al. 2003, Geophysical Monograph vol. 134 Precip.NAO Mean68 mm0 std23.41 Kurtosis-0.2-0.4 Skewness0.5-0.1 Kendall’Z-2.153.6 Sens’s-0.43 mm/mes 0.02 5 R= -0.95 ± 0.002

6 NAO effects on precipitation (DJFM) EOF1 of precipitation_OBS (shaded) and correlation between NAO and precipitation(contour lines) Var=74% R= -0.67 ±0.06 6 R= -0.92 ± 0.03

7 28/10/2008 7 What models tell us about the trend of precipitation ? 7

8 Data: WCRP CMIP3, PCMDI http://www-pcmdi.llnl.gov/ Model namesCenter, CountryResolution and References BCCR-BCM2.0 Bjerknes Centre for Climate Research, Norway T63 L31 (Deque et al., 1994) CGCM3.1 (T63) Canadian Centre for Climate Modelling and Analysis, Canada T63 L31 (Flato et al., 2000) CSIRO-Mk3.5 Commonwealth Scientific and Industrial Research Organisation, Australia T63 L18 (Gordon and Coauthors, 2002) ECHAM5/MPI-OM Max Planck Institute for Meteorology, Germany T63 L31 (Roeckner and Coauthors, 2003) ECHO-G Meteorological Institute of the University of Bonn, Meteorological Research Institute of the Korea, Germany, Korea T30 L19 FGOALS-g1.0 National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, China T42 L26 GFDL-CM2.1 NOAA/Geophysical Fluid Dynamics Laboratory, USA 2.0 o X 2.5 o L24 (Delworth et al., 2006) INGV-SXG Instituto Nazionale di Geofisica e Vulcanologia, Italy T106 L19 INM-CM3.0 Institute for Numerical Mathematics, Russia 4 o X 5 o L21 IPSL-CM4 Institut Pierre Simon Laplace, France 2.5 o x 3.75 o L19 MIROC3.2(medres) Center for Climate System Research (University of Tokyo, Japan T42 L20 MRI-CGCM2.3.2 Meteorological Research Institute, Japan T42 L30 PCM National Center for Atmospheric Research, USA T42 L26 (Kiehl et al., 2004) UKMO-HadCM3 Hadley Centre for Climate Prediction and Research/Met. Office, UK 2.5 o X 3.75 o L19 (Pope et al., 2007) UKMO_HadGEM1 Hadley Centre for Climate Prediction and Research/Met. Office, UK 1.3 o X 1.9 o (Martin et al., 2006) 8

9 Experiments Forcing 20C3MSimulations to the year 2000, starting end 1800 with pre-industrial conditions, with natural and anthropogenic forcing. SRES A221 st simulations with strong CO 2 forcing, approximately 820 ppm to the year 2100. SRES A1B21 st simulations with medium CO 2 forcing approximately 700 ppm to the year 2100. SRES B121 st simulations with low CO 2 forcing approximately 550 ppm to the year 2100. 9

10 Mean precipitation patterns over the Iberian peninsula 10 CRU-ENSEMBLES 20C3M SRES A2 SRES A1B

11 Precipitation simulations averaged over the Iberian peninsula: 20C3M and SRES A2 Multi-model precipitation averaged over the IP 11 Precipitation trend

12 Precipitation simulations averaged over the Iberian peninsula: 20C3M and SRES A1B Multi-model precipitation averaged over the IP 12 Precipitation trend

13 Precipitation simulations averaged over the Iberian peninsula: 20C3M and SRES B1 Multi-model precipitation averaged over the IP 20C3MA2A1BB1 Kendall’Z-0.4-4.8-4.7-1.2 Sen’s (mm/mes) -0.012-0.09-0.08-0.02 13

14 Relative precipitation changes: (SRES– 20C3M)*100/20C3M 14 SRES A2SRES A1B SRES B1

15 Mehods to obtain the NAO 1. EOF (Bretherton, 1992, Wallace 1981) 2. Selection of stations (Jones, 1997) 3. SLP averaged over areas (Stephenson 2006, Kuzmina 2005) 4. Project the SLP of model data onto the NAO pattern from reanalysis data. 15 28/10/2008 Multi- model 20C3MSRESA2SRESA1BSRESB1 R (1 and 4)0.99

16 Precipitation response to NAO 28/10/2008 16 Model names 20C3M Correlation NAO/precipitation(IP) SRES A2 Correlation NAO/precipitation(IP) SRES A1B Correlation NAO/precipitation(IP) SRES B1 Correlation NAO/precipitation(IP) BCCR-BCM2.0-0.67 ± 0.05 -0.68± 0.04 -0.60 ± 0.04 -0.74 ± 0.03 CGCM3.1 (T63)-0.62 ± 0.04 -0.59 ± 0.04 -0.66 ± 0.03 -0.55 ± 0.04 CSIRO-Mk3.5-0.52 ± 0.06 -0.68 ± 0.04 -0.60 ± 0.03 -0.58 ± 0.04 ECHAM5/MPI-0.62 ± 0.07 -0.78 ± 0.04 -0.70 ± 0.04 -0.77 ± 0.04 ECHO-G-0.64 ± 0.05 -0.69 ± 0.03 -0.70 ± 0.04 -0.78 ± 0.03 FGOALS-g1.0-0.73 ± 0.03-0.60 ± 0.02 -0.53 ± 0.03 GFDL-CM2.1 -0.80 ± 0.04 -0.77 ± 0.03 -0.76 ± 0.03 -0.69 ± 0.04 INGV-SXG-0.72 ± 0.04 -0.74 ± 0.03 -0.70 ± 0.03 INM-CM3.0-0.68 ± 0.06 -0.57 ± 0.04 -0.58 ± 0.04 -0.60 ± 0.04 IPSL-CM4 -0.76 ± 0.04 -0.69 ± 0.04 -0.78 ± 0.04 MIROC3.2(medres)-0.57 ± 0.05 -0.71 ± 0.03 -0.74 ± 0.03 -0.69 ± 0.03 MRI-CGCM2.3.2 -0.79 ± 0.05 -0.90 ± 0.03 -0.85 ± 0.03 - 0.83 ± 0.04 PCM-0.65 ± 0.05 -0.71 ± 0.04 -0.75 ± 0.04 UKMO-HadCM3-0.68 ± 0.06 -0.74 ± 0.03 -0.67 ± 0.04 -0.79 ± 0.04 UKMO_HadGEM1-0.57 ± 0.06 -0.61 ± 0.04 -0.62 ± 0.05

17 Precipitation response to NAO for multi-models 28/10/2008 17 20C3MA2A1BB1 R-0.64-0.76-0.77-0.71 b (slope)-0.07-0.12 -0.11 a (cte) 2.13 2.02 1.992.07

18 28/10/2008 18 NAO pattern for DJFM (shaded) Correlation pattern between mean precipitation over the IP and sea level pressure (SLP) EOF1 of precipitation_model (shaded) and Correlation between NAO and precipitation (contour lines) Links between precipitation and NAO 20C3M R= -0.907 ± 0.004R= -0.89 ± 0.03

19 28/10/2008 19 NAO pattern for DJFM (shaded) Correlation pattern between mean precipitation over the IP and sea level pressure (SLP) EOF1 of precipitation_model (shaded) and Correlation between NAO and precipitation (contour lines) Links between precipitation and NAO SRES A2 R= -0.979 ± 0.001R= -0.84 ± 0.04

20 28/10/2008 20 NAO pattern for DJFM (shaded) Correlation pattern between mean precipitation over the IP and sea level pressure (SLP) EOF1 of precipitation_model (shaded) and Correlation between NAO and precipitation (contour lines) Links between precipitation and NAO SRES A1B R= -0.979 ± 0.001R= -0.92 ± 0.02

21 Links between precipitation and NAO SRES B1 28/10/2008 21 NAO pattern for DJFM (shaded) Correlation pattern between mean precipitation over the IP and sea level pressure (SLP) EOF1 of precipitation_model (shaded) and Correlation between NAO and precipitation (contour lines) R= -0.978 ± 0.001R= -0.91 ± 0.02

22 Summary and Conclusions Observed precipitation over the Iberian peninsula shows:  a trend to decrease in DJFM  opposite correlation with NAO 21 st century simulations indicate a decrease in precipitation with greater significance for strong CO 2 forcing. The links between precipitation and NAO will become more significant for 21 st century simulations with strong forcing of CO 2. Therefore, the rate to decrease of Iberian precipitation will accelerate under climate change conditions. 22

23 Acknowledments To the AEMet and Institute of Meteorology and Geophysics of Portugal for precipitation data. EU-FP6 project ENSEMBLES (http://www.ensembles- eu.org) and ECA&D (http://eac.knmi.nl.) for precipitation data. To the modelling groups for the model data CMIP3 and PCMDI that archives and distributes the data. To the NCEP/NCAR for the reanalysis data and the CPC for SLP and teleconnection indices. This research is supported with National and Regional projects CGL2005-06600-CO3- 01/CLI, CGL2008- 04619/CLI and CyL SA123A08. To the software developers GrADS and CDAT

24 Thank you very much for your attention

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