Modelos colectivos de downscaling probabilístico con Redes Bayesianas

Ponencia presentada en: XXX Jornadas Científicas de la AME y el IX Encuentro Hispano Luso de Meteorología celebrado en Zaragoza, del 5 al 7 de mayo de 2008

Snow trends in Northern Spain: analysis and simulation with statistical downscaling methods

In this study we analyze and simulate (with statistical downscaling techniques) the snow trends observed in the Northern Iberian Peninsula using daily snow occurrence (DSO) data from a network of 33 stations ranging from 60 to 1350 m. We first analyze the annual snow frequency measured as the annual number of snow days (NSD), obtaining a significant decreasing trend since the mid-seventies with a NSD reduction of about 50%; moreover, this magnitude is similar for low and high stations and for winter and spring separately. Then, we analyze the existing correlations with mean temperature and precipitation occurrence obtaining different relationships depending on the season and elevation. Finally, we simulate the observed trends using the connection of DSO with large-scale fields simulated by a General Circulation Model; for this purpose we apply an analog-based statistical downscaling method to obtain an estimation of DSO, working in perfect prognosis conditions using reanalysis data. On the one hand, the downscaling method is able to estimate/predict the DSO with typical values of hit and false alarm rates around 60% and 2%, respectively. On the other hand, the annual frequency obtained by averaging the DSO estimations reproduces very well both the observed trends and the high inter-annual variability. These promising results open the possibility to future research in seasonal or climate change projections of snow frequency.The authors are also grateful to the University of Cantabria, CSIC and the Comisión Interministerial de Ciencia y Tecnología (CICYT, CGL-2007-64387/CLI and CGL2005-06966-C07-02/CLI) for partial support of this work

Reassessing statistical downscaling techniques for their robust application under climate change conditions

The performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies. To this end, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performances of 12 different SD methods (from the analog, weather typing, and regression families) for downscaling minimum and maximum temperatures in Spain. First, a calibration of these methods is performed in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including near-surface temperature data (in particular 2-m temperature), which appropriately discriminate cold episodes related to temperature inversion in the lower troposphere. Although regression methods perform best in terms of correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late twenty-first century as given by a global climate model [the ECHAM5-Max Planck Institute (MPI) model]. In this case, the different downscaling methods provide warming values with differences in the range of 1°C, in agreement with the robustness significance values. Therefore, the proposed test is a promising technique for detecting lack of robustness in statistical downscaling methods applied in climate change studies.Thiswork has been funded by the Spanish I1D1i 2008-11 Program: A strategic action for energy and climate change (ESTCENA, code 200800050084078) and the project CGL2010-21869 (EXTREMBLES). S.B. was supported by a JAE PREDOC grant (CSIC, Spain). The authors would like to especially thank the three anonymous reviewers who helped to considerably improve this manuscript

Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods

This is the second in a pair of papers in which the performance of Statistical Downscaling Methods (SDMs) is critically re-assessed with respect to their robust applicability in climate change studies. Whereas Part I focused on temperatures (Gutierrez et al., 2013), the present manuscript deals with precipitation and considers an ensemble of twelve SDMs from the analog, weather typing, and regression (GLM) families. In the first part, we assess the performance of the methods with perfect (reanalysis) predictors, screening different geographical domains and predictor sets. To this aim, standard accuracy and distributional similarity scores, and a test for extrapolation capability based on dry observed historical periods are considered. As in Part I, the results are highly dependent on the predictor sets, with optimum configurations including information of middle tropospheric humidity (in particular Q850). As a result of this analysis, deficient SDMs are discarded in order to properly assess the spread (uncertainty) of future climate projections, avoiding the noise introduced by unsuitable models. In the second part, the resulting ensemble of SDMs is applied to four Global Circulation Models (GCMs) from the ENSEMBLES (CMIP3) project to obtain historical (1961-2000, 20C3M scenario) and future (2001-2100, A1B) regional projections. The obtained results are compared with those produced by an ensemble of Regional Climate Models (RCMs) driven by almost the same GCMs in the ENSEMBLES project. In general, the mean signal is similar with both methodologies (with the exception of Summer, where the RCMs project drier conditions) but the spread is larger for the SDM results. Finally, the contribution of the GCM and SDM-derived components to the total spread is assessed using a simple analysis of variance previously applied to the ENSEMBLES RCM ensemble. Results show that the main contributor to the spread is the choice of the GCM, except for the autumn results in the Atlantic sub-region of Spain and the Autumn and Summer results in the Mediterranean sub-region, where the choice of the SDM dominates the uncertainty during the second half of the 21st century due mainly to the different projections obtained from different families of SDM techniques. The most noticeable difference with the RCMs is the magnitude of the interaction terms, which is larger in all cases in the present study.This work has been funded by the strategic action for energy and climate change by the Spanish R&D 2008–2011 program ‘‘Programa coordinado para la generación de escenarios regionalizados de cambio climático: Regionalización Estadística (esTcena),’’ code 200800050084078, and the project CGL2015-66583-R (MINECO/FEDER). The RCM simulations used in this study were obtained from the European Union–funded FP6 Integrated Project ENSEMBLES (Contract 505539)

Intense precipitation events in the Central Range of the Iberian Peninsula

Intense orographic precipitation associated with the Central Range was analysed using data of maximum accumulated precipitation in 24 h, occurring between 1958 and 2010. The 18 selected episodes were associated with a southwesterly tropospheric flow, a low-level jet, and high moisture flux at low levels. The observed moisture flux was higher than 100 (m g(s kg)−1) and the dry and wet Froude numbers were greater than 1. The selected area to study this synoptic situation was Gredos, broad and high range, which is located in the eastern part of the Central Range and generates a leeward “orographic shadow”. The effect of the Central Range on the spatial distribution of precipitation on the Iberian Peninsula plateau results in a sharp increase in precipitation in the south of the Central Range, followed by a decrease to the north of this range

Statistical downscaling in the tropics can be sensitive to reanalysis choice: A case study for precipitation in the Philippines

This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a generalized linear model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested separately with two distinct reanalyses (ERA-Interim and JRA-25) using a cross-validation scheme over the period 1981–2000. When the observed and downscaled time series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal–bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a global climate model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to “delta-change” estimates differing by up to 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is an important contributor to the uncertainty of local-scale climate change projections and, consequently, should be treated with as much care as other better-known sources of uncertainty (e.g., the choice of the GCM and/or downscaling method). Implications of the results for the entire tropics, as well as for the model output statistics downscaling approach are also briefly discussed.The authors are grateful to the free distribution of the ECMWF ERA-Interim (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim), JMA JRA-25 (http://jra.kishou.go.jp/JRA-25/index_en.html), and MPI-ECHAM5 data (http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=ENSEMBLES_MPEH5_SRA1B_3_D) and acknowledge PAGASA for the observational data provided. This study was supported by the EU projects QWeCI and SPECS, funded by the European Commission through the Seventh Framework Programme for Research under Grant Agreements 243964 and 308378, respectively. RM also acknowledges the EU project EUPORIAS, funded by the European Commission through the Seventh Framework Programme for Research under Grant Agreement 308291. SB is grateful to the CSIC-JAE-Predoc Program for financial support

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of “virtual” neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware

Tendencias de ocurrencia de nieve en el Norte de España. Análisis y simulación con modelos de downscaling estadístico

Ponencia presentada en: XXX Jornadas Científicas de la AME y el IX Encuentro Hispano Luso de Meteorología celebrado en Zaragoza, del 5 al 7 de mayo de 2008

Characterization of mutant versions of the R-RAS2/TC21 GTPase found in tumors

The R-RAS2 GTP hydrolase (GTPase) (also known as TC21) has been traditionally considered quite similar to classical RAS proteins at the regulatory and signaling levels. Recently, a long-tail hotspot mutation targeting the R-RAS2/TC21 Gln72 residue (Q72L) was identified as a potent oncogenic driver. Additional point mutations were also found in other tumors at low frequencies. Despite this, little information is available regarding the transforming role of these mutant versions and their relevance for the tumorigenic properties of already-transformed cancer cells. Here, we report that many of the RRAS2 mutations found in human cancers are highly transforming when expressed in immortalized cell lines. Moreover, the expression of endogenous R-RAS2Q72L is important for maintaining optimal levels of PI3K and ERK activities as well as for the adhesion, invasiveness, proliferation, and mitochondrial respiration of ovarian and breast cancer cell lines. Endogenous R-RAS2Q72L also regulates gene expression programs linked to both cell adhesion and inflammatory/immune-related responses. Endogenous R-RAS2Q72L is also quite relevant for the in vivo tumorigenic activity of these cells. This dependency is observed even though these cancer cell lines bear concurrent gain-of-function mutations in genes encoding RAS signaling elements. Finally, we show that endogenous R-RAS2, unlike the case of classical RAS proteins, specifically localizes in focal adhesions. Collectively, these results indicate that gain-of-function mutations of R-RAS2/TC21 play roles in tumor initiation and maintenance that are not fully redundant with those regulated by classical RAS oncoproteins