Quality assurance of surface wind observations from automated weather stations

Meteorological data of good quality arc important for understanding both global and regional climates In this respect, great efforts have been made to evaluate temperature- and precipitation-related records This study summarizes the evaluations made to date of the quality of wind speed and direction records acquired at 41 automated weather stations in the northeast of the Iberian Peninsula Observations were acquired from 1992 to 2005 at a temporal resolution of 10 and 30 min A quality assurance system was imposed to select) the records for 1) manipulation errors associated with storage and management of the data. 2) consistency limits to to ensure that observations ale within their natural limits of variation, and 3) temporal consistency to assess abnormally low/high variations in the individual time series In addition. the most important biases of the dataset are analyzed and corrected wherever possible A total of 1 8% wind speed and 3 7% wind direction records was assumed invalid. pointing to specific problems in wind measurement The study not only tiles to contribute to the science with the creation of a wind damsel of unmoved quality. but it also reports on potential errors that could be plc:sent in other wind dataset

Event selection for dynamical downscaling: a neural network approach for physically-constrained precipitation events

This study presents a new dynamical downscaling strategy for extreme events. It is based on a combination of statistical downscaling of coarsely resolved global model simulations and dynamical downscaling of specific extreme events constrained by the statistical downscaling part. The method is applied to precipitation extremes over the upper Aare catchment, an area in Switzerland which is characterized by complex terrain. The statistical downscaling part consists of an Artificial Neural Network (ANN) framework trained in a reference period. Thereby, dynamically downscaled precipitation over the target area serve as predictands and large-scale variables, received from the global model simulation, as predictors. Applying the ANN to long term global simulations produces a precipitation series that acts as a surrogate of the dynamically downscaled precipitation for a longer climate period, and therefore are used in the selection of events. These events are then dynamically downscaled with a regional climate model to 2 km. The results show that this strategy is suitable to constraint extreme precipitation events, although some limitations remain, e.g., the method has lower efficiency in identifying extreme events in summer and the sensitivity of extreme events to climate change is underestimated

Internal and external variability in regional simulations of the Iberian Peninsula climate over the last millennium

In this study we analyse the role of internal variability in regional climate simulations through a comparison of two regional paleoclimate simulations for the last millennium. They share the same external forcings and model configuration, differing only in the initial condition used to run the driving global model simulation. A comparison of these simulations allows us to study the role of internal variability in climate models at regional scales, and how it affects the long-term evolution of climate variables such as temperature and precipitation. The results indicate that, although temperature is homogeneously sensitive to the effect of external forcings, the evolution of precipitation is more strongly governed by random unpredictable internal dynamics. There are, however, some areas where the role of internal variability is lower than expected, allowing precipitation to respond to the external forcings. In this respect, we explore the underlying physical mechanisms responsible for it. This study identifies areas, depending on the season, in which a direct comparison between model simulations of precipitation and climate reconstructions would be meaningful, but also other areas where good agreement between them should not be expected even if both are perfect

Evaluation of two MM5-PBL parameterizations for solar radiation and temperature estimation in the South-Eastern area of the Iberian Peninsula

We study the relative performance of two different MM5-PBL parameterizations (Blackadar and MRF) simulating hourly values of solar irradiance and temperature in the south-eastern part of the Iberian Peninsula. The evaluation was carried out throughout the different seasons of the year 2005 and for three different sky conditions: clear-sky, broken-clouds and overcast conditions. Two integrations, one per PBL parameterization, were carried out for every sky condition and season of the year and results were compared with observational data. Overall, the MM5 model, both using the Blackadar or MRF PBL parameterization, revealed to be a valid tool to estimate hourly values of solar radiation and temperature over the study area. The influence of the PBL parameterization on the model estimates was found to be more important for the solar radiation than for the temperature and highly dependent on the season and sky conditions. Particularly, a detailed analysis revealed that, during broken-clouds conditions, the ability of the model to reproduce hourly changes in the solar radiation strongly depends upon the selected PBL parameterization. Additionally, it was found that solar radiation RMSE values are about one order of magnitude higher during broken-clouds and overcast conditions compared to clear-sky conditions. For the temperature, the two PBL parameterizations provide very similar estimates. Only under overcast conditions and during the autumn, the MRF provides significantly better estimates

Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm

This study analyzed the sensitivity of atmospheric rivers (ARs) to aerosol treatment in regional climate simulations. Three experiments covering the Iberian Peninsula for the period from 1991 to 2010 were examined: (1) an experiment including prescribed aerosols (BASE); (2) an experiment including direct and semi-direct aerosol effects (ARI); and (3) an experiment including direct, semi-direct, and indirect aerosol effects (ARCI). A new regional-scale AR identification algorithm, AIRA, was developed and used to identify around 250 ARs in each experiment. The results showed that spring and autumn ARs were the most frequent, intense, and long-lasting and that ARs could explain up to 30 % of the total accumulated precipitation. The inclusion of aerosols was found to redistribute precipitation, with increases in the areas of AR occurrence. The analysis of common AR events showed that the differences between simulations were minimal in the most intense cases and that a negative correlation existed between mean direction and mean latitude differences. This implies that more zonal ARs in ARI or ARCI with respect to BASE could also be linked to northward deviations. The joint analysis and classification of dust and sea salt aerosol distributions allowed for the common events to be clustered into eight main aerosol configurations in ARI and ARCI. The sensitivity of ARs to different aerosol treatments was observed to be relevant, inducing spatial deviations and integrated water vapor transport (IVT) magnitude reinforcements/attenuations with respect to the BASE simulation depending on the aerosol configuration. Thus, the correct inclusion of aerosol effects is important for the simulation of AR behavior at both global and regional scales, which is essential for meteorological predictions and climate change projections.</p

An assessment of aerosol optical properties from remote-sensing observations and regional chemistry–climate coupled models over Europe

Atmospheric aerosols modify the radiative budget of the Earth due to their optical, microphysical and chemical properties, and are considered one of the most uncertain climate forcing agents. In order to characterise the uncertainties associated with satellite and modelling approaches to represent aerosol optical properties, mainly aerosol optical depth (AOD) and Ångström exponent (AE), their representation by different remote-sensing sensors and regional online coupled chemistry–climate models over Europe are evaluated. This work also characterises whether the inclusion of aerosol–radiation (ARI) or/and aerosol–cloud interactions (ACI) help improve the skills of modelling outputs.Two case studies were selected within the EuMetChem COST Action ES1004 framework when important aerosol episodes in 2010 all over Europe took place: a Russian wildfire episode and a Saharan desert dust outbreak that covered most of the Mediterranean Sea. The model data came from different regional air-quality–climate simulations performed by working group 2 of EuMetChem, which differed according to whether ARI or ACI was included or not. The remote-sensing data came from three different sensors: MODIS, OMI and SeaWIFS. The evaluation used classical statistical metrics to first compare satellite data versus the ground-based instrument network (AERONET) and then to evaluate model versus the observational data (both satellite and ground-based data).Regarding the uncertainty in the satellite representation of AOD, MODIS presented the best agreement with the AERONET observations compared to other satellite AOD observations. The differences found between remote-sensing sensors highlighted the uncertainty in the observations, which have to be taken into account when evaluating models. When modelling results were considered, a common trend for underestimating high AOD levels was observed. For the AE, models tended to underestimate its variability, except when considering a sectional approach in the aerosol representation. The modelling results showed better skills when ARI+ACI interactions were included; hence this improvement in the representation of AOD (above 30 % in the model error) and AE (between 20 and 75 %) is important to provide a better description of aerosol–radiation–cloud interactions in regional climate models