Pere Quintana-Seguí

Testing MOS precipitation downscaling for ENSEMBLES regional climate models over Spain

[1] Model Output Statistics (MOS) has been recently proposed as an alternative to the standard perfect prognosis statistical downscaling approach for Regional Climate Model (RCM) outputs. In this case, the model output for the variable of interest (e.g. precipitation) is directly downscaled using observations. In this paper we test the performance of a MOS implementation of the popular analog methodology (referred to as MOS analog) applied to downscale daily precipitation outputs over Spain. To this aim, we consider the state‐of‐the‐art ERA40‐driven RCMs provided by the EU‐funded ENSEMBLES project and the Spain02 gridded observations data set, using the common period 1961–2000. The MOS analog method improves the representation of the mean regimes, the annual cycle, the frequency and the extremes of precipitation for all RCMs, regardless of the region and the model reliability (including relatively low‐performing models), while preserving the daily accuracy. The good performance of the method in this complex climatic region suggests its potential transferability to other regions. Furthermore, in order to test the robustness of the method in changing climate conditions, a cross‐validation in driest or wettest years was performed. The method improves the RCM results in both cases, especially in the former.

Testing instrumental and downscaled reanalysis time series for temperature trends in NE of Spain in the last century

In the context of climatic temperature studies, more often than not a time series is affected by artificial inhomogeneities. To overcome such limitation, we propose a new simple methodology in which promising results point not only toward the detection of unknown inhomogeneous periods but also toward the possibility of reconstructing the uncertain portion of the series. It is based on a parsimonious statistical downscaling (Multiple Linear Regression) of the large-scale 20CR reanalysis data. This method is successfully applied upon two long-range temperature series from a couple of centennial observatories (Ebre and Fabra, NE of Spain) which do not have nearby suitable temperature series to compare with. Results of trend analysis point to a clear signal of warming, with a larger rate of increase for the maximum temperature (respect to the minimum one), for the more recent decades (respect to the whole available period), and for the original series (respect to the reconstructed ones).

Seasonal predictability of water resources in a Mediterranean freshwater reservoir and assessment of its utility for end-users

In this study we explore the seasonal predictability of water resources in a Mediterranean environment (the Boadella reservoir, in north-eastern Spain). Its utility for end-users is assessed through the analysis of economic value curve areas (EVA). Firstly, we have built monthly multiple linear regression (MLR) models for the in-flow, out-flow and volume anomalies by identifying the underlying relationships between these predictands and their potential predictors, both meteorological and human influenced: rainfall, maximum and minimum temperatures, reservoir volume and discharge. Subsequently, we have forecast the monthly anomalies with these models for the period 1981-2010 (up to seven months ahead). We have tested the aforementioned models with four strategies in a leave-one-out cross-validation procedure (LOOCV): a) Climatology (Clim.), b) persistence (Pers.), c) antecedent observations+climatology (A+Clim.), d) antecedent observations+European Centre for Medium-range Weather Forecasts (ECMWF) System 4 anomalies (A+S4). Climatology is the operational strategy against which the other approximations are compared. The second and third approaches only use observations as input data. Finally, the last one combines both observations and ECMWF System 4 forecasts. The LOOCV revealed that reservoir volume is the variable best described by the MLR models, followed by in-flow and out-flow anomalies. In the case of volume anomalies, the predictability displayed provides added value with respect to climatology with a minimum of four months in advance. For in-flow and out-flow this is true at one month ahead, and regarding the latter variable we encounter enhanced predictability also at longer horizons for the summer months, when water demands peak (a valuable result for end-users). Hence, there is a window of opportunity to develop future operational frameworks that could outperform the use of climatology for these variables and forecast horizons.

Use of bias correction techniques to improve seasonal forecasts for reservoirs - A case-study in northwestern Mediterranean

In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter.