An RCM multi-physics ensemble over Europe: Multi-variable evaluation to avoid error compensation

ABSTRACT:Regional Climate Models (RCMs) are widely used tools to add detail to the coarse resolution of global simulations. However, these are known to be affected by biases. Usually, published model evaluations use a reduced number of variables, frequently precipitation and temperature. Due to the complexity of the models, this may not be enough to assess their physical realism (e.g. to enable a fair comparison when weighting ensemble members). Furthermore, looking at only a few variables makes difficult to trace model errors. Thus, in many previous studies, these biases are de- scribed but their underlying causes and mechanisms are often left unknown. In this work the ability of a multi-physics ensemble in reproducing the observed climatologies of any variables over Europe is analysed. These are temperature, precipitation, cloud cover, ra- diative fluxes and total soil moisture content. It is found that, during winter, the model suffers a significant cold bias over snow covered regions. This is shown to be re- lated with a poor representation of the snow-atmosphere interaction, and is amplified by an albedo feedback. It is shown how two members of the ensemble are able to alleviate this bias, but by generating a too large cloud cover. During summer, a large sensitivity to the cumulus parameterization is found, related to large differences in the cloud cover and short wave radiation flux. Results also show that small errors in one variable are sometimes a result of error compensation, so the high dimensionality of the model evaluation problem cannot be disregarded.This work was partially supported by Projects EXTREMBLES (CGL2010-21869) and CORWES (CGL2010-22158-C02), funded by the Spanish R&D Programme. WRF4G (CGL2011-28864) provided the framework to run the model; this Spanish R&D project is co-funded by the European Regional Development Fund (ERDF). Partial support from the 7th European Framework Programme (FP7) through Grant 308291 (EUPORIAS) is also acknowledged

Sensitivity of El Niño intensity and timing to preceding subsurface heat magnitude

Despite extensive ongoing efforts on improving the long-term prediction of El Niño-Southern Oscillation, the predictability in state-of-the-art operational schemes remains limited by factors such as the spring barrier and the influence of atmospheric winds. Recent research suggests that the 2014/15 El Niño (EN) event was stalled as a result of an unusually strong basin-wide easterly wind burst in June, which led to the discharge of a large fraction of the subsurface ocean heat. Here we use observational records and numerical experiments to explore the sensitivity of EN to the magnitude of the heat buildup occurring in the ocean subsurface 21 months in advance. Our simulations suggest that a large increase in heat content during this phase can lead to basin-wide uniform warm conditions in the equatorial Pacific the winter before the occurrence of a very strong EN event. In our model configuration, the system compensates any initial decrease in heat content and naturally evolves towards a new recharge, resulting in a delay of up to one year in the occurrence of an EN event. Both scenarios substantiate the non-linear dependency between the intensity of the subsurface heat buildup and the magnitude and timing of subsequent EN episodes

Advantages of using a fast urban boundary layer model as compared to a full mesoscale model to simulate the urban heat island of Barcelona

1991-959

Using WRF to generate high resolution offshore wind climatologies

Ponencia presentada en: VIII Congreso de la Asociación Española de Climatología celebrado en Salamanca entre el 25 y el 28 de septiembre de 2012.Recently, the demand of gridded wind datasets over sea areas has increased due to the ongoing development of offshore wind farms. Currently available reanalysis datasets do not have enough resolution to deal with complex coastlines and coastal topography, and these do interact with the winds and meteorological systems well into the open sea. Here we present the main characteristics of a high resolution wind climatology that has been produced using the Weather Research and Forecasting model to downscale the ERA-INTERIM reanalysis. The simulations were carried out in a domain covering the Mediterranean basin and most of Europe, and thus areas with different wind regimes. The model has been kept close to the driving reanalysis by restarting it daily, as this running mode provided better results than nudging techniques. Results show that WRF is able to produce realistic offshore wind climatologies, probabilistic wind distributions and annual cycle. It also reproduces well-known regional winds remarkably well.This paper is a contribution to the financed projects by the Spanish government CORWES (CGL2010-22158-C02-01), WRF4G (CGL2010-22158-C02-01), EXTREMBLES (CGL2010-21869), C3E (200800050084091), iMar21 (CTM201015009) and MARUCA (E17/08), and was partially funded by projects ‘MAREN’ (Atlantic Area Transnational Programme) and ‘CoCoNet’ (FP7-OCEAN-2011)

Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services.

Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better short-to-medium-term resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g., 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions, inferred using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for S2S climate forecasts to be incorporated in localised heat-health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe

Sensitivity of El Niño intensity and timing to preceding subsurface heat magnitude

Despite extensive ongoing efforts on improving the long-term prediction of El Niño-Southern Oscillation, the predictability in state-of-the-art operational schemes remains limited by factors such as the spring barrier and the influence of atmospheric winds. Recent research suggests that the 2014/15 El Niño (EN) event was stalled as a result of an unusually strong basin-wide easterly wind burst in June, which led to the discharge of a large fraction of the subsurface ocean heat. Here we use observational records and numerical experiments to explore the sensitivity of EN to the magnitude of the heat buildup occurring in the ocean subsurface 21 months in advance. Our simulations suggest that a large increase in heat content during this phase can lead to basin-wide uniform warm conditions in the equatorial Pacific the winter before the occurrence of a very strong EN event. In our model configuration, the system compensates any initial decrease in heat content and naturally evolves towards a new recharge, resulting in a delay of up to one year in the occurrence of an EN event. Both scenarios substantiate the non-linear dependency between the intensity of the subsurface heat buildup and the magnitude and timing of subsequent EN episodes

Climate services for health: From global observations to local interventions.

Despite the wealth of available climate data available, there is no consensus on the most appropriate product choice for health impact modelling and how this influences downstream climate-health decisions. We discuss challenges related to product choice, highlighting the importance of considering data biases and co-development of climate services between different sectors

WRF4G: WRF experiment management made simple

his work presents a framework, WRF4G, to manage the experiment workflow of the Weather Research and Forecasting (WRF) modelling system. WRF4G provides a flexible design, execution and monitoring for a general class of scientific experiments. It has been designed with the aim of facilitating the management and reproducibility of complex experiments. Furthermore, the concepts behind the design of this framework can be straightforwardly extended to other modelsThis work has been supported by the Spanish National R&D Plan under projects WRF4G (CGL2011-28864, co-funded by the European Regional Development Fund –ERDF–) and CORWES (CGL2010-22158-C02-01) and the IS-ENES2 project from the 7FP of the European Commission (grant agreement no. 312979). C. Blanco acknowledges financial support 5 from programa de Personal Investigador en Formación Predoctoral from Universidad de Cantabria, co-funded by the regional government of Cantabria. The authors are thankful to the developers of third party software (e.g. GridWay, WRFV3, python and NetCDF), which was intensively used in this work

Climadjust: an operational service to adjust biases in climate projections

Ponencia presentada en: XII Congreso de la Asociación Española de Climatología celebrado en Santiago de Compostela entre el 19 y el 21 de octubre de 2022.[ES]Las proyecciones climáticas de los modelos climáticos globales y regionales suelen presentar sesgos respecto a las observaciones. Habitualmente, ajustar estos sesgos es el primer paso para obtener información climática accionable, que pueda utilizarse en los estudios de impacto. Sin embargo, este proceso de ajuste de los sesgos es altamente técnico y demanda recursos especializados en términos de infraestructuras de computación y conocimientos científico-técnicos. Climadjust (https://climadjust.com/) es un servicio web desarrollado con la financiación y apoyo del Servicio de Cambio Climático de Copernicus (C3S), que implementa un servicio de ajuste de sesgos fácil de usar para los usuarios. El servicio ha sido desarrollado por Predictia -empresa centrada en el desarrollo de servicios climáticos- en colaboración con el Instituto de Física de Cantabria (IFCA-CSIC-UC). Climadjust proporciona recursos escalables en la nube para obtener proyecciones climáticas ajustadas para un área de interés, para datos de CMIP y CORDEX. En este proceso, los usuarios pueden (i) cargar sus propios datos de observaciones de referencia para ajustar las proyecciones climáticas, o elegir datos provenientes de ERA5-Land o WFDE-5, (ii) elegir entre seis técnicas de ajuste de sesgo de última generación, y (iii) validar los resultados a través del marco estándar desarrollado en la acción europea COST VALUE. El resultado es un archivo netCDF validado, listo para ser utilizado por los usuarios.[EN]Climate projections obtained from global and regional climate models usually exhibit biases: systematic deviations from observations. Adjusting these biases is typically the first step towards obtaining actionable climate information to be used in impact studies. However, this bias adjustment process is highly technical and resourcedemanding, in terms of data and computing infrastructures, and technical knowledge. Climadjust (https://climadjust.com/) is a web service developed with the support of the Copernicus Climate Change Service (C3S), that implements user-friendly bias adjustment for climate projections. The service was developed by Predictia —a company with a strong focus on climate services development and climate modelling— in collaboration with the Spanish Research Council (CSIC). Climadjust provides scalable cloud resources to compute bias-adjusted climate projections from the ensembles of CMIP and CORDEX datasets over customised areas of interest. In this process, the users are able to (i) upload their own dataset of observations to adjust the climate projections, or choose among reference datasets such as ERA5-Land or WFDE-5, (ii) choose among six state-of-the-art Bias Adjustment techniques, and (iii) validate the results through the standard framework developed in the European VALUE COST Action. The output is a validated netCDF file, ready to be used by the climate impact modellers

Regional climate hindcast simulations within EURO-CORDEX: evaluation of a WRF multi-physics ensemble

In the current work we present six hindcast WRF (Weather Research and Forecasting model) simulations for the EURO-CORDEX (European Coordinated Regional Climate Downscaling Experiment) domain with different configurations in microphysics, convection and radiation for the time period 1990?2008. All regional model simulations are forced by the ERA-Interim reanalysis and have the same spatial resolution (0.44°). These simulations are evaluated for surface temperature, precipitation, short- and longwave downward radiation at the surface and total cloud cover. The analysis of the WRF ensemble indicates systematic temperature and precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons. Overestimation of total cloud cover and underestimation of downward shortwave radiation at the surface, mostly linked to the Grell?Devenyi convection and CAM (Community Atmosphere Model) radiation schemes, intensifies the negative bias in summer temperatures over northern Europe (max ?2.5 °C). Conversely, a strong positive bias in downward shortwave radiation in summer over central (40?60%) and southern Europe mitigates the systematic cold bias over these regions, signifying a typical case of error compensation. Maximum winter cold biases are over northeastern Europe (?2.8 °C); this location suggests that land?atmosphere rather than cloud?radiation interactions are to blame. Precipitation is overestimated in summer by all model configurations, especially the higher quantiles which are associated with summertime deep cumulus convection. The largest precipitation biases are produced by the Kain?Fritsch convection scheme over the Mediterranean. Precipitation biases in winter are lower than those for summer in all model configurations (15?30%). The results of this study indicate the importance of evaluating not only the basic climatic parameters of interest for climate change applications (temperature and precipitation), but also other components of the energy and water cycle, in order to identify the sources of systematic biases, possible compensatory or masking mechanisms and suggest pathways for model improvement.The contribution from Universidad de Cantabria was funded by the Spanish R&D programme through projects CORWES (CGL2010-22158-C02-01) and WRF4G (CGL2011-28864), co-funded by the European Regional Development Fund. M. García-Díez acknowledges financial support from the EXTREMBLES (CGL2010-21869) project