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International audience; PMIP contributed to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports (ARs) by placing current climate change into a wider context, evaluating climate model performance in very different climatic states, and constraining climate sensitivity based on paleoclimates.

This report describes the sensitivity studies performed with the mesoscale model WRF in preparation of the mesoscale wind atlas production runs. The objective of this work was to find a model setup that is not just a best practice setup but well-founded and based on scientific evaluation. We started with performing some initial sensitivity experiments changing the PBL scheme and the initialisation of the model. The work was distributed among several partners, each conducting the same set of experiments but on a different domain. The objective of this first phase was to ensure that everybody speaks the same language in terms of applying WRF in the context of NEWA. The results were analysed and compared in terms of the mean wind climate. To draw conclusions regarding the quality of the experiments, the results of one domain were compared to tall mast observations. Overall the model showed a good performance with slightly better results for one of the two tested PBL schemes (MYNN) and weekly initialisation of simulations (compared to daily). In the next phase, further sensitivity tests were conducted for one of the previously defined domains, varying a multitude of parameters as e.g. model version, vertical resolution, forcing data and land surface parameterisation. These studies showed that virtually each parameter change is affecting the results in some way, while significant effects on the wind climate are mostly obtained by changes in physical parameterisation e.g. PBL scheme, representation of the land surface and surface roughness. However, also non-physical parameters as the simulation length and the domain size affects the results considerably. The results suggest to use rather small domains and not too long simulations (in the order of 1–2 weeks). One of the objectives of NEWA is to create a probabilistic wind atlas, i.e. to provide uncertainty information to the mesoscale wind atlas (see Deliverables D3.1 and D4.4). This will be achieved by generating an ensemble of WRF simulations with different model configurations. While the final ensemble to be run over the complete NEWA domain will only include a few members, a much larger ensemble was run for a smaller sub-domain to find the ensemble members that generate the largest spread and will be used in the final NEWA ensemble. A second objective of this initial large ensemble was to find an optimal setup for the mesoscale production run. Based on the experience gained in the previous sensitivity experiments, a 47-member ensemble was assembled and run. The individual members were compared against each other, as well as against tall mast observations. Different metrics were explored to assess the performance of the members, i.e. not only the usual statistical measures as RMSE, BIAS and correlation but also metrics that compare the wind speed distributions. In the final part of this report we present the ultimate WRF setup for the NEWA production run that was run between August 2018 and March 2019 on the MareNostrum supercomputer in Barcelona.

Tis Article contains errors in the Discussion section.

This report explores the quantification of uncertainty in the NEWA production run and probabilistic ensemble. Uncertainty quantification relies on the availability of model simulations and observations. In areas where observations are not available, uncertainty addresses the quantification of the variability resulting from the sensitivity of the model chain tool resulting from the sampling of all the available model configurations tested. Uncertainty can be here regarded as the quantification of model sensitivity or model spread. In areas with availability of observations, the realism of the model ensemble is evaluated by establishing metrics of model-data comparison. Therefore, uncertainty is understood here as the result of the contributions of model sensitivity to different model setups, and of model errors in a model-data comparison framework. The first part of this report explores the uncertainty derived from model sensitivity subjected to the decisions taken regarding the use of different models setups and how these produce variability in model output. The range of this variability has been regarded as spread in model output and has been quantified in various manners. The second part of this report addresses how model performance can be characterised with the data at hand and whether decisions regarding selection of a given model setup for a production run can be taken on the basis of model performance in a variety of situations, using different variables and datasets as observational targets: wind data from tall masts in the Vestas database; wind speed profiles from tall masts and offshore lidars; surface (10-meter) wind data; satellite data and reanalysis outputs.

Ponencia presentada en: XI Congreso de la Asociación Española de Climatología celebrado en Cartagena entre el 17 y el 19 de octubre de 2018. [ES]Este trabajo proporciona un primer análisis de la variabilidad de la temperatura a escalas interanuales y decadales en la Sierra de Guadarrama (SG), un área protegida de alta montaña del Sistema Central en la Península Ibérica. Se utilizan datos observacionales de estaciones situadas en la zona y una simulación regional de alta resolución (1km) del modelo Weather Research and Forecasting (WRF) para analizar la variabilidad de la temperatura durante el periodo 2000-2015. La comparación entre ambos conjuntos de datos permite evaluar el grado de realismo con el que las simulaciones representan la variabilidad observada. Los resultados muestran que el modelo tiene tendencia a infraestimar los valores medios y las anomalías de las temperaturas observacionales en las estaciones de mayor altitud. Se observa un gradiente vertical de temperatura media de -3.95ºC/km, que es sobreestimado por el modelo (-6.52ºC/km).La variabilidad de las anomalías térmicas aumenta con la altitud para las observaciones y, en menor medida, para las simulaciones. Se evalúa el valor añadido que ofrece WRF frente al uso del reanálisis ERA Interim, que proporciona las condiciones iniciales y de contorno a la simulación regional. Se observa un menor sesgo de las temperaturas que proporciona el modelo regional en comparación con el reanálisis. Se lleva a cabo un Análisis de Componentes Principales (PCA) sobre el campo de anomalías de temperatura de WRF para la evaluación de su variabilidad. Este análisis proporciona un primer modo muy dominante que explica el 94% de la varianza total y cuya componente principal muestra una gran correlación con las anomalías observacionales. La variabilidad de la temperatura en la SG muestra una gran relación con la temperatura en el interior de la Península Ibérica y con una gran parte del suroeste de Europa. El patrón de regresión entre la temperatura proporcionada por la simulación y la que procede del reanálisis permite obtener una estimación de la variabilidad de la temperatura en la SG en los últimos 40 años. [EN]This work provides a first assessment of temperature variability at interannual and decadal timescales in the Sierra de Guadarrama (SG), a high mountain protected area of the Central System in the Iberian Peninsula. Observational data from stations located in the area and simulated data from a high-resolution configuration (1 km) of the Weather Research and Forecasting (WRF) model, fed from ERA Interim inputs, are used in order to analyse the temperature variability in the period 2000-2015. The comparison between both datasets allows for the evaluation of the realism of the model simulations. Results show that the model tends to underestimate observational mean temperatures and anomalies at high altitude stations. A linear mean temperature vertical gradient of -3.95ºC/km is observed and overestimated by the model (-6.52ºC/km).The variability of temperature anomalies for both the observations and, to a lesser extent, the simulations increases with height. The added value that WRF offers against the use of ERA Interim is evaluated. Results show that WRF provides a better performance than the reanalysis, as it shows smaller biases with the observational temperature anomalies. A Principal Component Analysis (PCA) is performed over the temperature anomalies field of WRF for the assessment of its variability. This analysis provides a very dominant first mode that explains the 94% of the total variance and whose PC shows a large correlation with the observational anomalies. Temperature variability in the SG shows a large relationship with temperature in the midland Iberian Peninsula and broadly over south-western Europe. The regression patterns between WRF and the reanalysis are calculated in order to obtain an estimate of the temperature variability over the SG during the last 40 years.

The pre-industrial millennium is among the periods selected by the Paleoclimate Model Intercomparison Project (PMIP) for experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and the fourth phase of the PMIP (PMIP4). The past1000 transient simulations serve to investigate the response to (mainly) natural forcing under background conditions not too different from today, and to discriminate between forced and internally generated variability on interannual to centennial timescales. This paper describes the motivation and the experimental set-ups for the PMIP4-CMIP6 past1000 simulations, and discusses the forcing agents orbital, solar, volcanic, and land use/land cover changes, and variations in greenhouse gas concentrations. The past1000 simulations covering the pre-industrial millennium from 850 Common Era (CE) to 1849 CE have to be complemented by historical simulations (1850 to 2014 CE) following the CMIP6 protocol. The external forcings for the past1000 experiments have been adapted to provide a seamless transition across these time periods. Protocols for the past1000 simulations have been divided into three tiers. A default forcing data set has been defined for the Tier 1 (the CMIP6 past1000) experiment. However, the PMIP community has maintained the flexibility to conduct coordinated sensitivity experiments to explore uncertainty in forcing reconstructions as well as parameter uncertainty in dedicated Tier 2 simulations. Additional experiments (Tier 3) are defined to foster collaborative model experiments focusing on the early instrumental period and to extend the temporal range and the scope of the simulations. This paper outlines current and future research foci and common analyses for collaborative work between the PMIP and the observational communities (reconstructions, instrumental data).

The spatial context is criticalwhen assessing present-day climate anomalies, attributing them to potential forcings and making statements regarding their frequency and severity in a long-term perspective. Recent international initiatives have expanded the number of high-quality proxy-records and developed new statistical reconstruction methods. These advances allow more rigorous regional past temperature reconstructions and, in turn, the possibility of evaluating climate models on policy-relevant, spatiotemporal scales. Here we provide a new proxy-based, annually-resolved, spatial reconstruction of the European summer (June-August) temperature fields back to 755 CE based on Bayesian hierarchical modelling (BHM), together with estimates of the European mean temperature variation since 138 BCE based on BHM and composite-plus-scaling (CPS). Our reconstructions compare well with independent instrumental and proxy-based temperature estimates, but suggest a larger amplitude in summer temperature variability than previously reported. Both CPS and BHM reconstructions indicate that the mean 20th century European summer temperature was not significantly different from some earlier centuries, including the 1st, 2nd, 8th and 10th centuries CE. The 1st century (in BHM also the 10th century) may even have been slightly warmer than the 20th century, but the difference is not statistically significant. Comparing each 50 yr period with the 1951-2000 period reveals a similar pattern. Recent summers, however, have been unusually warm in the context of the last two millennia and there are no 30 yr periods in either reconstruction that exceed the mean average European summer temperature of the last 3 decades (1986-2015 CE). A comparison with an ensemble of climate model simulations suggests that the reconstructed European summer temperature variability over the period 850-2000 CE reflects changes in both internal variability and external forcing on multi-decadal time-scales. For pan-European temperatures we find slightly better agreement between the reconstruction and the model simulations with high-end estimates for total solar irradiance. Temperature differences between the medieval period, the recent period and the Little Ice Age are larger in the reconstructions than the simulations. This may indicate inflated variability of the reconstructions, a lack of sensitivity and processes to changes in external forcing on the simulated European climate and/or an underestimation of internal variability on centennial and longer time scales.