Actividades en downscaling/cambio

climático en la UPV/EHU

Jon SáenzDepto. Física Aplicada IIFac. Ciencia y TecnologíaUniversidad País Vasco

jon.saenz@ehu.eshttp://www.ehu.es/eolo

Colaboradores

• Jesús Fernández (post-doc en GKSS) y Eduardo Zorita GKSS Forschungszentrum.

• F. Valero y F. González-Rouco, UCM.

• J. P. Montávez, U. Murcia

• F. J. Doblas-Reyes, ECMWF.

• J. M. Gutiérrez, U. Cantabria

• C. Rodríguez-Puebla y M. D. Frías, U. Salamanca.

• C. Llasat, U. Barcelona.

Estudios de clima presente/pasado relevantes para cambio climático

• Comparación técnicas estadísticas y dinámicas.

• Nuevas metodologías downscaling estadístico.

• Regionalización estadística y dinámica con ensembles multimodelo en “clima presente”.

• Análisis del realismo de las PDF regionalizadas.

• Sensibilidad a parametrizaciones MM5 anidado a Reanálisis.

• Reanálisis ibérico (colaboración con UM) modelo suelo y asimilación de datos 3DVAR

Otras actividades cambio climático

• Uso de simulaciones globales realizadas en GKSS/DKRZ (JFGR, UCM) para:

• Downscaling estadístico (expone JFGR, UCM).

• Ensemble dinámico mono-escenario (expone JPM, UMurcia).

• Ensembles de simulaciones multi-modelo, SDM con simulaciones AR4.

Tesis Jesús Fernández, 2004

• Comparación de métodos estadísticos y dinámicos en la Cornisa Cantábrica

• Desarrollo de nuevas metodologías estadísticas

• Dirigida por J. Sáenz (UPV) y E. Zorita (GKSS), Jesús es actualmente post-doc en GKSS.12 Data sets

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Figure 2.1: The 17 stations from the UCM dataset used in this study. Those also presentin the INM daily dataset are surrounded by a circle. The key to the abbreviations andfurther information can be found in Table 2.1. The CRU gridded data set grid points overland are labelled with crosses.

best available option in all cases, but as a result of their free (or cheap) access they arewidely used among the climatological community.

2.1 Precipitation

Precipitation is the focus variable of this study. Data were needed for calibrating statisticaldownscaling models and for validating both the statistical and dynamical approaches.

2.1.1 UCM monthly precipitation data

This precipitation data were kindly provided by J. F. Gonzalez Rouco from the UniversidadComplutense de Madrid (UCM). Gonzalez-Rouco et al. (2001) homogenised and performedquality tests on a set of station data covering southwestern Europe and northern Africa.The resulting data set comprises monthly total precipitation at 92 stations during theperiod 1899–1989. Another 45 secondary stations were used in the homogenisation of thisdata set to fill the gaps and/or extend their temporal extent. The stations of the UCMdata set used for this study are shown in Figure 2.1. They cover not only the area closeto the coast, but also interior stations isolated by mountain ranges from the influence ofthe sea. The details about geographical location and altitude of each station are given inTable 2.1.

The UCM data set was selected because of its length. In the formulation of a statisticalmodel, the length of the database used is of paramount importance. This data set fulfilsthis requirement. We selected all the stations in the UCM data base which are placed inthe area [10!W, 1!W]× [42!N, 44!N], corresponding to the Cantabrian Coast (Figure 2.1).A total of 17 stations are in the area. Even though the station density over the area

Tesis JF - 1

Desarrollo de nuevas metodologías, análogos en el espacio de los CCCs, Climate Research, 24:199-213, 2003.

4.1 Validation of the downscaled precipitation 37

Cantabrian Coast with a skill similar to that reached by the PCA–Analog SDM whenusing 4 degrees of freedom and, therefore, a much filled phase-space.

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E = 4 NpreE = 2) in gray. The lower part shows the variance

skill.

The comparison of the PCA–Analog SDM and the CCA–Analog SDM should takeplace in a search space of the same dimension where the search library equally fills thephase space. Thus, in Figure 4.7 the PCA–Analog SDM has been reduced to 2 degreesof freedom by projecting over the 2 leading EOFs. In this case the skill improvement ofthe CCA–Analog SDM is apparent. But, in this comparison, the predictor signal enteringeach SDM is di!erent. While the CCA–Analog SDM makes use of 84% of the variance ofthe predictor field, the PCA–Analog SDM has only 61%.

Since the reconstruction of the precipitation is determined by the SLP analogs selection,the explanation of the CCA–Analog improvement should be in the SLP analog selectiondi!erences. Figure 4.8 shows the temporal correlation at each grid point of the NMC SLPpredictor field and the SLP field reconstructed by the selected analogs (in this case thepredictor itself is reconstructed by the analogs found and compared with its own basepatterns) by means of the ordinary PCA–space analog search and the CCA–space searchduring the independent period 1961–1989. Correlations are very high over a wide area in

Tesis JF - 2

1960-1989, MM5 anidado en NNRA,135x45x15 km24 niveles, sensibilidad 6 parametrizacioness.

46 Dynamical downscaling: The MM5 mesoscale model

from a high resolution data set of the U.S. Geological Survey (USGS; Loveland et al. 2000).These data are reinterpolated by this module to the resolution of the di!erent domains.

The USGS data set provides 24 di!erent land uses including water bodies. Di!erentvalues of the surface albedo, moisture availability, emissivity, roughness length, thermalinertia and heat capacity are assigned to each land use. Some of these values are seasonallydependent with di!erent values applying in summer and winter.

Three di!erent domains were nested and in the following will be referred to as Atlantic

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Figure 5.2: Dot grid points of the Atlantic (D1), Iberian (D2) and Cantabrian (D3)domains used in the study. For reference, the grid points of the input NNR data outsidethe coarser domain are also shown (crosses).

5.1 MM5 modules 49

also generated by this module. In the present application, 24 levels were used with !values: 1.00, 0.99, 0.98, 0.96, 0.93, 0.89, 0.85, 0.80, 0.75, 0.70, 0.65, 0.60, 0.55, 0.50, 0.45,0.40, 0.35, 0.30, 0.25, 0.20, 0.15, 0.10, 0.05, 0.00. The increase in vertical resolution fromthe original NNR pressure levels can be appreciated in Figure 5.4.

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This module was not originally prepared to handle more than 1000 temporal records(less than 9 months when using 6–hourly data) and its source code was modified to processlarger files. Besides, a driver was necessary to process the split input from REGRID.

After this step, the input data were prepared for the core module: MM5.

5.1.4 MM5

Several different physical schemes and parameterisations can be selected to run the MM5.In addition to realistic physical schemes, the model allows for a number of sensitivityswitches which, for example, let the model run dry (no latent heat release), switch offthe coriolis parameter, fix the SST, etc. The huge amount of possibilities that the modeloffers has two faces: on one hand, make the model very flexible but, on the other, theuser is forced to carefully select the combination best suited to his problem. For thispurpose, a sensitivity analysis on several key parameterisations was carried out and is

Tesis JF - 3

Simulación dinámica. Comparación con sondeos (12 horas), variabilidad intradiaria

5.3 MM5 sensitivity study: Results 67

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ary conditions and nudging are, thus, unrealistic from this level up. It is also true thatat those missing levels the observed moisture is very small. Figure 5.15 shows the wintermean profiles of temperature and mixing ratio showing the small amount of water vapourin the upper levels (note the logarithmic scale). The model shows a great ability to risethe moisture to the upper levels, where the simulated mixing ratio is higher than observed

Tesis JF - 4 Comparación SDM-DDM

Simulación dinámica. Comparación con pluviómetros, variabilidad mensual

Simulaciones ibéricas

• Colaboración GKSS, UMurcia, UCM, UPV/EHU

• 16 miembros en el conjunto, distintas parametrizaciones.

• Forzadas por NNRA (y ERA40)

• 1985-1989

Conjunto de simulaciones ibéricas

Forzadas por reanálisis, 16 miembros, 1985-1989,colaboración GKSS, UMurcia, UCM, UPV/EHU

La escurridiza realidad

Realismo de las PDFs

Fernández & Sáenz: Searching for analogs in CCA space

The χ2 statistic measures the deviation of a PDF fromnormality (Wilks 1995). χ2 is essentially the meansquared distance to a normal PDF with the same meanand variance.Using the monthly total precipitation isinsufficient to normalize the PDFs (Fig. 16). The PCA-Analog and CCA-Analog reconstructed precipitationPDFs show a level of nonnormality similar to that of theobserved precipitation. The PDF of the CCA DMreconstructed precipitation is more normal than that ofthe observed precipitation (note the logarithmic scale),even though it is not statistically significant at the 5%significance level. The statistic values at Gijón and ACoruña (GJN and CRN) are closely reproduced by thelinear model, because observed precipitation is morenormally distributed at these 2 stations.

GCMs represent the most useful approach to getinformation about the future evolution of the large-scale climate. But, as long-term climate prediction isnot an initial value problem, temporal correlation of along-range simulation with observed variables cannotbe expected. The same is true for the downscaledinformation. The information derived from GCMsneeds to be interpreted in a probabilistic way. The cor-rectness of multiyear averages, variance, or trendsimplies the conservation of the PDF. If a downscalingmodel does not preserve the non-normality of the PDF(as shown in the case of the CCA DM for theCantabrian Coast), any change in the PDF due to forc-ing (e.g. greenhouse gases, volcanism, SST tropicalforcing) will be obscured by the normalization of thePDF that has resulted from downscaling. No clear con-clusions can be drawn from the CCA DM downscaledprecipitation PDF in a climate change experimentsince the climate change effects on the PDF and theDM-induced normalization are merged in the changeof the PDF.

5. CONCLUSIONS

A new phase space for the selection of atmosphericanalogs when used for downscaling has been pro-posed. The standard PCA technique for dimensionalityreduction of the phase space is insensitive to localfields in downscaling. The approach presented hereconsists in a projection onto the space spanned by theleading CCA spatial patterns of the predictor, and thisshould select circulations induced by the predictorfield relevant to the local predictand field.

The new approach finds d.f. of the North Atlantic cir-culation with greater relevance to precipitation overthe Cantabrian Coast. When working with a reducednumber of dimensions in the phase space, the direc-tions selected by the CCA-Analog DM are more accu-rate and yield higher predictive skills than the stan-dard approach (projection onto the EOFs).

This improvement by the CCA-Analog DM methodderives from the automatic selection of the large-scaledomains of interest for the precipitation. The low sen-sitivity of the correlation and variance skills to differentpredictor regions supports this idea.

The PCA truncation in the PCA-Analog DM is some-times carried out as a routine truncation of the noise,and a high number of EOFs is considered, in order tomake sure that nearly 100% of the variance is takeninto account (Luksch & von Storch 1999, Timbal &McAvaney 2001). The quality of the analogs dependson this truncation, because the volume of the phasespace is being increased, while the number of patternsin the search library is maintained constant. Theseauthors locate their truncations in the stable part ofFig. 10 (right panel), but this procedure does not guar-antee maximum skill or better analogs. Moreover, forabout 8 d.f. the search in standardized coordinates

(with very low skill for high order trun-cations) shows average correlation skillsnever attained by the search in vari-ance-carrying coordinates and with lessdispersion between stations (data notshown).

The results in correlation skill of theCCA-Analog DM are similar to those ofthe CCA DM and there is no gain inusing the non-linear (more costly)model. The analog methods have bettervariance skill. Even after smoothing, thelevel of reproduced variability is higherthan the linearly reconstructed one, andthe average variance skill is very closeto one when no smoothing is applied(but this reduces the correlation skill).

The linear CCA DM fails to reproducethe non-normality of the precipitation

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Fig. 16. χ2 normality test and confidence level thresholds of normal distributionfor observed and reconstructed precipitation data using the linear CCA DM, the

PCA-Analog DM and the CCA-Analog DM. See Fig. 1 for abbreviations

PDFs del ensemble AMIP2

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multi–model ensemble downscaled by means of CCAnalogs (third column) and the direct AMIP2 ensemble precipitation esti-

mate interpolated to the 0.5! grid (rightmost column).

OBS CCA AnaCCA GCM M.O.

PDFs - SDM vs DDM80 Dynamical vs statistical approaches (and more)

grid point and this area is larger than for the MM5 grid. For the interior stations (rightside of the Figure) the more normal output of the CCA estimate is clear.

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Figure 6.6: Kernel–based PDF estimates of the monthly precipitation anomaly observedaccording to the UCM dataset, simulated by the NNR model and downscaled by CCA,CCA–Analogs and MM5. Left: Coruna, right: Reinosa.

A more visually appealing representation of the ability of each downscaling methodin producing monthly precipitation anomalies is presented in Figure 6.6. An interior(Reinosa) and a coastal (A Coruna) station were selected. For each of them the precipi-tation PDFs were estimated through a kernel–based density estimator (Silverman 1986).The Epanechnikov kernel was used to minimise the quadratic error of the estimation.Least squares cross validation did not lead to a stable selection of the bandwidth and thefollowing expression h = min(std, iqr/1.34)/n1/5 was used instead (Silverman 1986). Itcombines the standard deviation and interquartilic range to adjust the bandwidth to thespread of the distribution despite of its shape. The n refers to the number of time records.

In Coruna, the MM5 estimate is the closest to the observed PDF, even though theCCA presents at this station the same !2 value. The well fitted variance of the MM5 forthis station (see Figure 6.4) really helps in producing a very reasonable estimate of thePDF. In Reinosa, the coarse NNR estimate presents the most similar !2 value, but thisis, again, not enough to achieve a good fitted PDF. In this case the analog method showsthe better performance representing the PDF, although the correlation skill is poor. Thenormal distribution of the CCA estimate is also apparent from these Figures.

Therefore, each method presents advantages and drawbacks. That is the main reasonwhy all of them are in use. The SDMs are fast and, in the stations more attached tothe large–scale driving field, the correlation skill is as good as the costly dynamical esti-mate. Analog methods perform even better than the dynamical approach in reproducingthe variability of the series. They are in general poorer than CCA in correlation skill.

Depende del lugar

Coruña Reinosa

Aplicaciones predicción estacional

Statistical DS of ensembles Statistical downscaling of maximum temperature over the Iberian Peninsula from the results of the DEMETER multi-model seasonal prediction project

In collaboration with:Group C. Rodríguez Puebla (USal)

E. Zorita (GKSS)

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M.D. Frías, J. Fernández, J. Sáenz, C. Rodríguez-Puebla (2005) “Operational predictability of ...”, Tellus A, in press

Reproducibility

Spatial averaged reproducibility of SLP from the DEMETER models:

!"#$%&'$()*$+,&-./0123/045&6/02.70!8

9#":+,)(&;),+<+,&-=/7012=>7045&=>?70@2=>?70!8

M. D. Frías et al., 2005, Tellus 57A.Colaboración con U. Salamanca.

Proyecto RAMSHES

• Proyecto coordinado (J. Sáenz), Plan Nacional, 2002-2005

• Grupos coordinados:

• UB: Dra. Llasat

• UCM: Dr. Valero (incluye U. Murcia)

• UPV: Dr. Sáenz

• U. Salamanca: Dra. Rodríguez-Puebla

Objetivos RAMSHES

• Regionalización estadística y dinámica, 500 años dentro de integración global ECHO-G de GKSS/DKRZ.

• Regionalización dinámica MM5 500 años.

• Generación de climatologías sintéticas LARGAS.

• Estudio aplicabilidad MM5.

• Aplicaciones prácticas (agricultura, extremos hídricos).

• Evaluaciones en términos de PDFs, predictibilidad 2ª especie.

RAMSHES-1

Generador de ruido “consistente”:Llueve más en Coruña que en Almería

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A. Precipitación A. TemperaturaSoria

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La línea de base es comparable con la evolución forzada.

A. Precipitación A. TemperaturaSoria

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Presente - Reanálisis Ibérico

• Parte del proyecto InVENTO, coordinado por J. Navarro (CIEMAT), con partners de CIEMAT, Universidades de Cantabria, Rovira Virgili, UPM, UCM, GKSS, UM, UPV/EHU.

• Objetivo inicial Reanálisis Ibérico: Entrenar modelos estadísticos de downscaling.

• Downscaling dinámico MM5 con modelo de suelo y asimilación 3DVAR de observaciones.

• Disponibilidad pública WWW (10x10 km2).

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Proyectos, publicaciones

• Proyecto RAMSHES, Plan Nacional I+D+i 2002-2005

• Proyecto InVENTO, Plan Nacional I+D+i 2005-2008

• J. Fernández y J. Sáenz, 2003, Climate Research, 24(3):199–213.

• M.D. Frías, J. Fernández, J. Sáenz y C. Rodríguez-Puebla, 2005, Tellus A, 57:448-463.

• J. Fernández, J. Sáenz, E. Zorita y F. J. Doblas-Reyes, 2005, “On the ability of Statistical Downscaling Methods driven by Atmospheric General Circulation Models to represent proper statistics of winter monthly precipitation”, remitido a Climate Dynamics.

• J. Fernández, J. P. Montávez, J. Sáenz, J. F. González-Rouco y E. Zorita, “Sensitivity of MM5 Mesoscale Model to Physical Parameterizations for Regional Climate Studies: Monthly Seasonal Cycle”, remitido a Journal of Geophysical Research.