Warming patterns in regional climate change projections over the Iberian Peninsula

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A set of four regional climate change projections over the Iberian Peninsula has been performed. Simulations were driven by two General Circulation Models (consisting of two versions of the same atmospheric model coupled to two different ocean models) under two different SRES scenario. The XXI century has been simulated following a full-transient approach with a climate version of the mesoscale model MM5. An Empirical Orthogonal Function analysis (EOF) is applied to the monthly mean series of daily maximum and minimum 2-metre temperature to extract the warming signal. The first EOF is able to capture the spatial structure of the warming. The obtained warming patterns are fairly dependent on the month, but hardly change with the tested scenarios and GCM versions. Their shapes are related to geographical parameters, such as distance to the sea and orography. The main differences among simulations mostly concern the temporal evolution of the warming. The temperature trend is stronger for maximum temperatures and depends on the scenario and the driving GCM. This asymmetry, as well as the different warming rates in summer and winter, leads to a continentalization of the climate over the IP.
Vier regionale Projektionen des Klimawandels im Bereich der Iberischen Halbinsel werden vorgestellt. Die zu Grunde liegenden numerischen Simulationen wurden durch die Ergebnisse aus je zwei unterschiedlichen globalen Zirkulationsmodellen (GCM) angetrieben, welche jeweils das identische Atmosphärenmodul mit unterschiedlichen Ozeanmodulen kombinieren. Dabei wurden für zwei SRES-Szenarios behandelt. Das XXI. Jahrhundert wurde zeitabhängig mit einer klimatauglichen Version des ursprünglich mesoskaligen MM5-Modells simuliert. Die resultierenden Zeitreihen der täglichen Maximaltemperatur und Minimaltemperatur in 2 m Höhe wurden mit der Methode der empirischen orthogonalen Funktionen (EOF) analysiert, um das Signal der Erwärmung zu extrahieren. Die erste EOF gibt die räumliche Struktur des Erwärmungsmusters wieder. Diese Muster sind deutlich monatsabhängig, unterscheiden sich jedoch kaum für die beiden Szenarios und die Versionen des antreibenden GCM. Ihre Eigenschaften hängen mit geographischen Parametern zusammen, wie zum Beispiel dem Abstand zur Küste und der Orographie. Die wichtigsten Unterschiede zwischen den Simulationen betreffen die zeitliche Entwicklung der Erwärmung. Dieser Trend ist ausgeprägter für die Maximaltemperaturen, und hängt von den Szenarios und den antreibenden GCM ab. Die zunehmenden täglichen Differenzen in Kombination mit den unterschiedlichen Erwärmungsraten in Sommer und Winter bedeuten eine Kontinentalisierung des Klimas der Iberischen Halbinsel.
© by Gebrüder Borntraeger 2010. Lund Regional-Scale Climate Modelling Workshop (2nd. 2009. Lund, Sweden). This work was funded by the Spanish Ministry of the Environment (project ESCENA, Ref. 20080050084265) and the Spanish Ministry of Science and Technology (project SPECMORE-CGL2008-06558-C02-02/CLI). The authors also gratefully acknowledge funding from the Euro-Mediterranean Institute of Water (IEA) and the Regional Agency for Science and Technology of Murcia (Fundación Séneca, Ref. 00619/PI/04 and 11047/EE1/09). J.J. GÓMEZ NAVARRO thanks the Spanish Ministry of Education for his Doctoral scholarship (AP2006- 04100). Thanks to the Max Planck Institute and DKRZ for providing the access and computational support necessary to get the GCM simulation data employed in this work. Also thanks to the professor Volker RATH for translating the abstract into German.
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BOO, K.-O., W.-T. KWON, H.-J. BAEK, 2004: Change of extreme events of temperature and precipitation over Korea using regional projection of future climate change. – Geophys. Res. Lett. 33, L01701. CHEN, F., J. DUDHIA, 2001a: Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. – Mon. Wea. Rev. 129, 569–585. —, —, 2001b: Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part II: Preliminary Model Validation. – Mon. Wea. Rev. 129, 587–604. DEQUÉ, M., R. JONES, M. WILD, F. GIORGI, J. CHRISTENSEN, D. HASSELL, P. VIDALE, B. ROCKEL, D. JACOB, E. KJELLSTROM, DE M. CASTRO, F. KUCHARSKI, VAN DEN B. HURK, 2005: Global high resolution versus Limited Area Model climate change projections over Europe: quantifying confidence level from PRUDENCE results. – Climate Dynam. 25, 653–670. DEQUÉ, M., D.P. ROWELL, D. LUETHI, F. GIORGI, J.H. CHRISTENSEN, B. ROCKEL, D. JACOB, E. KJELLSTROM, M. DE CASTRO, B. VAN DEN HURK, 2007: An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. – Climatic Change 81 (Suppl. 1), 53–70. DIFFENBAUGH, N.S., J.S. PAL, F. GIORGI, X. GAO, 2007: Heat stress intensification in the Mediterranean climate change hotspot. – Geophys. Res. Lett. 34, L1706, DOI:10.1029/2007GL030000. DUDHIA, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. – J. Atmos. Sci. 46, 3077–3107. DUDHIA, J., 1993: A nonhydrostatic version of the Penn StateNCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. – Mon. Wea. Rev. 121, 1493–1513. FERNÁNDEZ, J., J.P. MONTÁVEZ, J. SÁENZ, J. F. GONZÁLEZ ROUCO, E. ZORITA, 2007: Sensitivity of the MM5 mesoscale model to physical parameterizations for regional climate studies: Annual cycle. – J. Geophys. Res. Atmos. 112, D04101. FONT-TULLOT, I., 2000: Climatología de España y Portugal – Ediciones Universidad de Salamanca, Salamanca. GIORGI, F., 2005: Climate change prediction. – Climate Change 73, 239–265. GIORGI, F., 2006: Climate change hot-spots. – Geophys. Res. Lett. 33, 11217–11222. GIORGI, F., J.W. HURREL, M.R. MARINUCCI, 1997: Elevation dependency of the surface climate change signal: A model study. – J. Climate 10, 288–296. GIORGI, F., X. BI, J. PAL, 2004a: Mean, interannual variability and trends in a regional climate change experiment over Europe. I. Present-day climate (1961–1990). – Climate Dynam. 22, 733–756. GIORGI, F., X. BI, J. PAL, 2004b: Mean, interannual variability and trends in a regional climate change experiment over Europe. II: climate change scenarios (2071-2100). – Climate Dynam. 23, 839–858. GÓMEZ NAVARRO, J.J., J. MONTÁVEZ, S. JÉREZ, J. GARCÍA VALERO, 2009: On the relationship between warming and orography in regional climate change projections (in Spanish). – In: Clima en España: Pasado. Presente y futuro. Contribución a un informe de Evaluación del Cambio Climático Regional: Resúmenes del congreso, 132, Madrid. GRELL, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. – Mon. Wea. Rev. 121, 764–787. GRELL, G.A., J. DUDHIA, D.R. STAUFFER, 1994: A description of the fifth-generation penn state/ncar mesoscale model (mm5). – Technical Report NCAR/TN-398+STR, National Center for Atmospheric Research. HANNACHI, A., I.T. JOLLIFFE, D.B. STEPHENSON, 2007: Empirical orthogonal functions and related techniques in atmospheric science: A review. – Int. J. Climatol. 27, 1119–1152. HAWKINS, E., R. SUTTON, 2009: The potential to narrow uncertainty in regional climate predictions. – Bull. Amer. Meteor. Soc. 90, 1095–1107. HONG, S.Y., H.L. PAN, 1996: Nonlocal boundary layer vertical diffusion in a medium–range forecast model. – Mon. Wea. Rev. 124, 2322–2339. HOUGHTON, J.T., Y. DING, D.J. GRIGGS, M. NOGUER, VAN DER P.J. LINDEN, X. DAI, K. MASKELL, C.A. JOHNSON, editors, 2001: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. – Cambridge University Press, New York, 881. IPCC, 2007: 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, New York. JACOB, D., L. BARRING, O.B. CHRISTENSEN, J. H. CHRISTENSEN, DE M. CASTRO, M. DEQUÉ, F. GIORGI, S. HAGEMANN, G. LENDERINK, B. ROCKEL, E. SÁNCHEZ, C. SCHAER, S. I. SENEVIRATNE, S. SOMOT, A. VAN ULDEN, B. VAN DEN HURK, 2007: An inter-comparison of regional climate models for Europe: model performance in present-day climate. – Climatic Change 81(Suppl. 1), 31–52. JÉREZ, S., J.P. MONTÁVEZ, P. JIMÉNEZ GUERRERO, J.J. GÓMEZ NAVARRO, J.F. GONZÁLEZ ROUCO, 2009: Influence of soil moisture-near surface temperature feedback on present and future climate simulations over the Iberian Peninsula. – In: 21st Century Challenges in Regional-scale Climate Modelling, number 41, 261–262, Lund University. JUNGCLAUS, J.H., N. KEENLYSIDE, M. BOTZET, H. HAAK, J.J. LUO, M. LATIF, J. MAROTZKE, U. MIKOLAJEWICZ, E. ROECKNER, 2006: Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. – J. Climate 19, 3952–3972. KITTEL, T., F. GIORGI, G. MEEHL, 1998: Intercomparison of regional biases and doubled CO2-sensitivity of coupled atmosphere-ocean general circulation model experiments. – Climate Dynam. 14, 1–15. LEGUTKE, S., R. VOSS, 1999: The Hamburg atmosphereocean coupled circulation model ECHO-G. – Technical report, DKRZ. LIANG, X.-Z., K.E. KUNKEL, G.A. MEEHL, R.G. JONES, J.X.L. WANG, 2008: Regional climate models downscaling analysis of general circulation models present climate biases propagation into future change projections. – Geophys. Res. Lett. 35, L08709, DOI:10.1029/2007GL032849. LORENZ, E. N., 1956: Empirical Orthogonal Functions and Statistical Weather Prediction. – Technical report, Massachusetts Institute of Technology. MLAWER, E.J., S.J. TAUBMAN, P.D. BROWN, M. J. IACONO, S.A. CLOUGH, 1997: Radiative transfer for inhomogeneous atmospheres: Rrtm, a validated correlatedmodel for the longwave. – J. Geophys. Res. 102, 16663–16682. MONTÁVEZ, J. P., J. FERNÁNDEZ, J. F. GONZÁLEZ ROUCO, J. SÁENZ, E. ZORITA, F. VALERO, 2006: Climate change projections over the Iberian Peninsula (in Spanish). – In: V Asamblea Hispano Portuguesa de geodesia y geofísica, Sevilla. MONTÁVEZ, J.P., S. JÉREZ, J.J. GÓMEZ NAVARRO, J.F. GONZÁLEZ ROUCO, 2008: Evaluation of soil models coupled to a RCM in simulating the Iberian Peninsula Climate. – In: Eos Trans. AGU. Fall Meet. Suppl., volume 89, GC53A–0706. NUNEZ, M.N., S.A. SOLMAN, M. FERNANDA CABRE, 2009: Regional climate change experiments over southern South America. II: Climate change scenarios in the late twenty-first century. – Climate Dynam. 32, 1081–1095. RAISANEN, J., 2001: CO2-induced climate change in CMIP2 experiments: Quantification of agreement and role of internal variability. – Journal of Climate 14(9), 2088–2104. RAISANEN, J., U. HANSSON, A. ULLERSTIG, R. DOSCHER, L. GRAHAM, C. JONES, H. MEIER, P. SAMUELSSON, U. WILLEN, 2004: European climate in the late twenty-first century: regional simulations with two driving global models and two forcing scenarios. – Climate Dynam. 22, 13–31. RIND, D., R. GOLDBERG, J. HANSEN, C. ROSENZWEIG, R. RUEDY, 1990: Potential evapotranspiration and the likelihood of future drought. – J. Geophys. Res. Atmos. 95, 9983–10004. SOLMAN, S.A., M.N. NUNEZ, M.F. CABRE, 2008: Regional climate change experiments over southern South America. I: present climate. – Climate Dynam. 30, 533–552. TRIGO, R., J. PALUTIKOF, 2001: Precipitation scenarios over Iberia: A comparison between direct GCM output and different downscaling techniques. – J. Climate 14, 4422–4446. VON STORCH, H., 1995: Inconsistencies at the interface of climate impact studies and global climate research. -Meteorol. Z. 4, 72–80. VON STORCH, H., F. ZWIERS, 2007: Statistical Analysis in Climate Research – Cambridge University Press. WILBY, R., T. WIGLEY, D. CONWAY, P. JONES, B. HEWITSON, J. MAIN, D. WILKS, 1998: Statistical downscaling of general circulation model output: A comparison of methods. – Water Resour. Res. 34, 2995–3008. XOPLAKI, E., J. GONZÁLEZ ROUCO, J. LUTERBACHER, H. WANNER, 2004: Wet season Mediterranean precipitation variability: influence of large-scale dynamics and trends. – Climate Dynam. 23, 63–78 ZORITA, E., J. GONZÁLEZ ROUCO, H. VON STORCH, J. MONTÁVEZ, F. VALERO, 2005: Natural and anthropogenic modes of surface temperature variations in the last thousand years. – Geophys. Res. Lett. 32, L08707, Doi:10.1029/2004GL021563.