Jon Sáenz

Sensitivity of the MM5 mesoscale model to physical parameterizations for regional climate studies: Annual cycle

We present an analysis of the sensitivity to different physical parameterizations of a high-resolution simulation of the MM5 mesoscale model over the Iberian Peninsula. Several (16) 5-year runs of the MM5 model with varying parameterizations of microphysics, cumulus, planetary boundary layer and longwave radiation have been carried out. The results have been extensively compared with observational precipitation and surface temperature data. The parameterization uncertainty has also been compared with that related to the boundary conditions and the varying observational data sets. The annual cycles of precipitation and surface temperature are well reproduced. The summer season presents the largest deviations, with a 5 K cold bias in the southeast and noticeable precipitation errors over mountain areas. The cold bias seems to be related to the surface, probably because of the excessive moisture availability of the five-layer soil scheme used. No parameterization combination was found to perform best in simulating both precipitation and surface temperature in every season and subregion. The Kain-Fritsch cumulus scheme was found to produce unrealistically high summer precipitation. The longwave radiation parameterizations tested were found to have little impact on our target variables. Other factors, such as the choice of boundary conditions, have an impact on the results as large as the selection of parameterizations. The range of variability in the MM5 physics ensemble is of the same order of magnitude as the observational uncertainty, except in summer, when it is larger and probably related to the inaccuracy of the model to reproduce the summer precipitation over the area.

Interpretation of interannual winter temperature variations over southwestern Europe

A great part of winter temperature variability over southwestern Europe (SWE) shows a very low correlation with the North Atlantic or Arctic Oscillation indices. The patterns of winter mean surface air temperature over SWE were obtained from gridded and instrumental data. The first mode is highly correlated with the East Atlantic (EA) pattern and explains temperature variations of the same sign over the whole domain. The second mode, however, is correlated with the North Atlantic Oscillation (NAO) and shows a dipolar structure with significant negative values in northern Africa and the southern Iberian Peninsula and positive values toward northern Europe. Thus the NAO only influences the meridional gradient of temperature over the area, and the second mode explains a significantly lower fraction of variance than the first mode. Both modes of temperature variability depend on the stationary component of the sensible heat fluxes but cannot be explained through the eddy sensible heat fluxes. It is well known that precipitation depends on the existence of baroclinic systems over the area. According to the results in this paper, this does not hold for temperature. This explains the different sensitivities of SWE precipitation and temperature to the North Atlantic Oscillation.

Operational predictability of monthly average maximum temperature over the Iberian Peninsula using DEMETER simulations and downscaling

The multi-model ensemble for seasonal to interannual prediction developed in the European Union project DEMETER has been used to quantify the predictability of monthly average maximum temperature that could be achieved operationally over the Iberian Peninsula. Statistical downscaling based on canonical correlation analysis is applied to increase the spatial resolution available from the global models. The downscaling is based on empirical connections between the North Atlantic sea level pressure and monthly average maximum temperature over the Iberian Peninsula. The maximum temperature estimated from the multi-model ensemble and the single models is compared to the observations. The statistical downscaling model skill is characterized by means of the correlation, variance fraction and the Brier skill score. The results suggest the following: the downscaling model works properly when driven by observed large-scale fields in terms of the correlation and the variance fraction scores, despite some problems owing to sample degeneracy; the predictability is almost limited to February, which is one of the initialization months of the DEMETER ensemble, and it is lost when this month is not considered as starting month. This result is supported by the fact that the areally averaged reproducibility is lower during non-initialization months. In any case, the analysis of the variance test performed reveals that the monthly average maximum temperature is scarcely predictable. Finally, the results also support the advantage of using a multi-model ensemble approach instead of single models participating in DEMETER.

Geophysical data analysis using Python

A set of routines designed for geophysical data analysis that make extensive use of the numerical extensions to the computer language Python are presented. The routines perform some typical tasks during multivariate analysis of geophysical fields, such as principal component analysis and related tasks (truncation rules by means of analytical and Monte Carlo techniques). Other functions perform singular value decomposition of covariance matrices and canonical correlation analysis for coupled variability of geophysical fields. Other parts of the package allow access to a library of statistical distribution functions, multivariate digital filters, time-handling routines, kernel-based probability density function estimation and differential operators over the sphere for gridded data sets. As they rely on the numerical extensions to the Python language, they are fast for numerical analysis. The programs make the analysis of geophysical data sets both easier and faster.

Reconstruction of sea surface temperature by means of DINEOF: a case study during the fishing season in the Bay of Biscay

The Spanish surface fishery operates mainly during the summer season in the waters of the Bay of Biscay. Sea surface temperature (SST) data recovered from satellite images are being used to improve the operational efficiency of fishing vessels (e.g. reduce search time and increase catch rate) and to improve the understanding of the variations in catch distribution and rate needed to properly manage fisheries. The images used for retrieval of SST often present gaps due to the existence of clouds or satellite malfunction periods. The data gaps can totally or partially affect the area of interest. Within this study, an application of a technique for the reconstruction of missing data called DINEOF (data interpolating empirical orthogonal functions) is analysed, with the aim of testing its applicability in operational SST retrieval during summer months. In this case study, the Bay of Biscay is used as the target area. Three months of SST Moderate Resolution Imaging Spectroradiometer (MODIS) images, ranging from 1 May 2006 to 31 July 2006, were used. The main objective of this work is to test the overall performance of this technique, under potential operational use for the support of the fleet during the summer fishing season. The study is designed to analyse the sensitivity of the results of this technique to several details of the methodology used in the reconstruction of SST, such as the number of empirical orthogonal functions (EOFs) retained, the handling of the seasonal cycle or the length (number of images) of the SST database used. The results are tested against independent SST data from International Comprehensive Ocean–Atmosphere Data Set (ICOADS) ship reports and standing buoys and estimations of the error of the reconstructed SST fields are given. Conclusions show that over this area three months of data are enough for efficient SST reconstruction, which yields four EOFs as the optimal number needed for this case study. An extended EOF experiment with SST and SST with a lag of one day was carried out to analyse whether the autocorrelation of the SST data allows better performance in the SST reconstruction, although the experiment did not improve the results. The validation studies show that the reconstructed SSTs can be trusted, even when the amount of missing data is very high. The mean absolute deviation maps show that the error is greatest near to the coast and mainly in the upwelling areas close to the French and north-western Spanish coasts.

Using neural networks for short-term prediction of air pollution levels

The present paper focuses on the prediction of hourly levels up to 8 hours ahead for five pollutants (SO2, CO, NO2, NO and O3) and six locations in the area of Bilbao (Spain. To that end, 216 models based on neural networks (NN) have been built. The database used to fit the NNs has been historical records of the traffic, meteorological and air pollution networks existing in the area corresponding to year 2000. Then, the models have been tested on data from the same networks but corresponding to year 2001. At a first stage, for each of the 216 cases, 100 models based on different types of neural networks have been built using data corresponding to year 2000. The final identification of the best model has been made under the criteria of simultaneously having at a 95% confidence level the best values of R2, d1, FA2 and RMSE when applied to data of year 2001. The number of hourly cases in which due to gaps in data predictions have been possible range from 11% to 38% depending on the sensor. Depending on the pollutant, location and number of hours ahead the prediction is made, different types of models have been selected. The use of these models based on NNs can provide Bilbaos air pollution network originally designed for diagnosis purposes, with short-term, real time forecasting capabilities. The performance of these models at the different sensors in the area range from a maximum value of R2=0.88 for the prediction of NO2 1 hour ahead, to a minimum value of R2=0.15 for the prediction of ozone 8 hours ahead. These boundaries and the limitation in the number of cases that predictions are possible represent the maximum forecasting capability that Bilbaos network can provide in real-life operating conditions.

Comparison of the Performance of Different Analog-Based Bayesian Probabilistic Precipitation Forecasts over Bilbao, Spain

This study evaluates the performance of different analog-based downscaling models for probabilistic quantitative precipitation forecasts over the metropolitan area of Bilbao, Spain. The analog-based statistical downscaling models used are a probability of the exceedance models in which the probability is derived from the probabilities given by a set of analogs and three Bayesian models. Results show that the differences in the performance of the models are subtle. The simplest model, which makes no use of Bayesian methods, performs better than the other models in the forecast of very low precipitation events, very likely due to the quality of the found analogs, since these categories are very populated in the phase space and forecast is very easy. However, as precipitation rates increase, Bayesian models perform better than the simple one based on probability of exceedance from individual analogs. The Bayesian models involving precipitation in the computation of the likelihood show in general the smallest biases in the forecast versus observed probabilities.

Wave Energy Forecasting at Three Coastal Buoys in the Bay of Biscay

In 2008, the first commercial wave farm came online in Portugal. As with other types of renewable energy, the electricity obtained from waves has the drawback of intermittency. Knowing a few hours ahead how much energy waves will hold can contribute to a better management of the electricity grid. In this work, three types of statistical models have been used to create up to 24-h forecasts of the zonal and meridional components of wave energy flux (WEF) levels at three directional buoys located off the coast in the Bay of Biscay. Each models performance has been compared at a 95% confidence level with the simplest prediction (persistence of levels), along with the forecasts provided by the physics-based WAve Modeling (WAM) wave model at the nearest grid point. The results indicate that for forecasting horizons between 3 and roughly 16 h ahead, the statistical models built on random forests (RFs) outperform the rest, including WAM and persistence.

Multi-objective environmental model evaluation by means of multidimensional kernel density estimators

We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to single and multi-core processors. They compute multivariate PDFs by means of kernels, the optimal bandwidth using smoothed bootstrap and the index of agreement between multidimensional PDFs. Their use is illustrated with two case-studies. The first one assesses the ability of seven global climate models to reproduce the seasonal cycle of zonally averaged temperature. The second case study analyzes the ability of an oceanic reanalysis to reproduce global Sea Surface Temperature and Sea Surface Height. Results show that the proposed methodology is robust to variations in the optimal bandwidth used. The technique is able to process multivariate datasets corresponding to different physical dimensions. The methodology is very sensitive to the existence of a bias in the model with respect to observations. The performance index based on the area under two PDFs is extended to several dimensions.The evaluation of the performance of models can be done for several variables, resulting in a single skill score.A fast and parallel implementation that allows to apply the method with highly dimensional problems is presented.The method is illustrated with two case-studies.The sensitivity of the results to the bias between models and observations or the bandwidth is presented.

Comparison of the Main Features of the Zonally Averaged Surface Air Temperature as Represented by Reanalysis and AR4 Models

The ability exhibited by seven coupled global climate models of the Climate Model Intercomparison Project 3 used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change to simulate the meridional profiles of the current daily zonally averaged surface air temperature (ZASAT) is analysed. The expansion in the second order of these profiles of the zonally averaged surface air temperature by Legendre polynomials was compared to the same expansion carried out over the profiles provided by European and American reanalysis from 1961 to 1998. According to the theoretical support provided by the one-dimensional energy balance models, the Legendre coefficients corresponding to the ZASAT profile can be qualitatively interpreted as the independent modes that represent the meridional energy flux from the equator to the poles. Three models may be considered as the models that best reproduce the meridional structure of current zonally averaged surface air temperature although the differences between the models are not really large.