ALBERT

Description: In the framework of the KlimAdat national project, the Hungarian Meteorological Service (OMSZ) is aiming to perform 10 km horizontal resolution simulations with the 2015 version of the REMO regional climate model over Central and Eastern Europe. The long-term simulations were preceded by a 10-year long sensitivity study on domain size, which is summarised in this paper. We selected three different domains embedded in each other, which contain the whole area of the Danube and Tisza river catchments. Lateral boundary conditions were obtained from the 50 km resolution REMO driven by the MPI-ESM-LR global climate model. Simulations were performed for the period of 1970–1980 including 1-year spin-up. Monthly and seasonal means of daily 2 m temperature, precipitation sum and several precipitation indices were evaluated. Reference datasets were E-OBS 19.0 and CarpatClim-HU. We can conclude, that the selection of domain size has a larger impact on the simulation of precipitation, and in the case of the seasonal mean of the precipitation indices, the differences amongst the results obtained on each model domain exceed 10 %. In general, the smallest biases occurred on the largest domain, therefore further long-term simulations are being produced on this domain.

Description: Convection-permitting weather forecasting models allow for prediction of rainfall events with increasing levels of detail. However, the high resolutions used can create problems and introduce the so-called “double penalty” problem when attempting to verify the forecast accuracy. Post-processing within an ensemble prediction system can help to overcome these issues. In this paper, two new up-scaling algorithms based on Machine Learning and Statistical approaches are proposed and tested. The aim of these tools is to enhance the skill and value of the forecasts and to provide a better tool for forecasters to predict severe weather.

Description: The Urban Heat Island (UHI) describes the increase of near surface temperatures within an urban area compared to its rural surrounding. While the concept of the UHI is in itself quite simple, it is more complex to apply it to gridded datasets. The main complication lies in the rural baseline definition. Therefore, we propose three approaches to calculate the spatial UHI representation for gridded datasets from (i) a single point baseline, (ii) an area averaged baseline, and (iii) a nearest neighbor-based baseline field. Based on these approaches, seven methods are tested as an example for a case study utilizing model simulations for three metropolitan areas in Central and Western Europe (Berlin, Paris and Rhine-Ruhr Metropolitan Area). The results show that all methods perform reasonable in absence of complex terrain, biases and large scale temperature gradients. However, with at least one of these features present, the UHI visualization is less prominent or nonexistent, except for the nearest-neighbor approach which consistently shows reasonable spatial characteristics of the UHI across all scenarios.

Description: Laboratory measurements of drop fall speeds by Gunn–Kinzer under still air conditions with pressure corrections of Beard are accepted as the “gold standard”. We present measured fall speeds of 2 and 3 mm raindrops falling in turbulent flow with 2D-video disdrometer (2DVD) and simultaneous measurements of wind velocity fluctuations using a 3D-sonic anemometer. The findings based on six rain events are, (i) the mean fall speed decreases (from the Gunn–Kinzer terminal velocity) with increasing turbulent intensity, and (ii) the standard deviation increases with increase in the rms of the air velocity fluctuations. These findings are compared with other observations reported in the literature.

Description: Climate change ought to be a natural part of the weather conversation on TV, radio and social media. Inspired by similar projects in other countries, the Norwegian Meteorological institute established a project in 2019 to develop their TV meteorologists as climate change communicators. The main objective in the project was to integrate research-based, localized climate content in the weather presentation, as to inform and engage the Norwegian public about climate change. Over a period of almost two years, the project has produced several climate stories on the national TV-news. The majority of the stories have also been shared through social media and through press releases to reach a wide range of audiences. In this paper, experiences from the project at the Norwegian Meteorological institute are shared along with recommendations for climate communication. We claim that TV meteorologists can have an important role in climate change communication, with a potential that is often not yet fully realized, and give our thoughts on how to further develop their role.

Description: Agricultural production is largely determined by weather conditions during the crop growing season. An important aspect of crop yield estimation concerns crop growth development. The occurrence of meteorological events such as frosts, droughts or heat stress during the crop life cycle or during certain phenological stages helps explain yield fluctuations of common arable crops. We developed a methodology and visualisation tool for risk assessment, and tested the workflow for drought and frost risk for winter wheat, winter barley and grain maize in Belgium. The methodology has the potential to be extended to other extreme weather events and their impacts on crop growth in different regions of the world.

Description: Accurate and reliable solid precipitation estimates for high mountain regions are crucial for many research applications. Yet, measuring snowfall at high elevation remains a major challenge. In consequence, observational coverage is typically sparse, and the validation of spatially distributed precipitation products is complicated. This study presents a novel approach using reliable daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glacier sites to assess the performance of various gridded precipitation products. The ground observations are available during two and four winter seasons. The performance of three readily-available precipitation data products based on different data sources (gauge-based, remotely-sensed, and re-analysed) is assessed in terms of their accuracy compared to the ground reference. Furthermore, we include a data set, which corresponds to the remotely-sensed product with a local adjustment to independent SWE measurements. We find a large bias of all precipitation products at a monthly and seasonal resolution, which also shows a seasonal trend. Moreover, the performance of the precipitation products largely depends on in situ wind direction during snowfall events. The varying performance of the three precipitation products can be partly explained with their compilation background and underlying data basis.

Description: Seasonal climate forecast products offer useful information for farmers supporting them in planning and making decisions in their management practices, such as crop choice, planting and harvesting time, and water management. Driven by the need of stakeholders for tailored seasonal forecast products, our goal was to assess the applicability of seasonal forecast outputs in agriculture and to develop and pilot with stakeholders a set of seasonal climate outlooks for this sector in Finland. Finnish end users were involved in both the design and testing of the outlooks during the first pilot season of 2019. The seasonal climate outlooks were developed using the SEAS5 seasonal forecast system provided by ECMWF. To improve the prediction skill of the seasonal forecast data, several bias adjustment approaches were evaluated. The tested methods increased the quality of temperature forecast, but no suitable approach was found for eliminating the biases from precipitation data. Besides the widely applied indices, such as mean temperature, growing degree days, cold spell duration, total precipitation and dry conditions, new sector-oriented indices (such as progress of growing season) have been implemented and issued for various lead times (up to 3 months). The first result of forecast evaluation, the development of seasonal forecast indices and the first pilot season of May–October 2019 are presented. We found that the temperature-based outlooks performed well, with better performance skills for short lead times, providing useful information for the farmers in activity management. Precipitation indices had poor skills for each forecasted month, and further research is needed for improving the quality of forecast for Finland. The farmers who have tested the seasonal climate outlooks considered those beneficial and valuable, helping them in planning their activities. Following the first pilot season, further research and implementation work took place to improve our understanding of the skill of seasonal forecasts and increase the quality of tailored seasonal climate services.

Description: The single-column version of the shared ALADIN-HIRLAM numerical weather prediction system, called MUSC, was developed by Météo-France in the 2000s and has a growing user-base in both HIRLAM and ALADIN countries. Tools to derive the required input, to run the experiments and to handle outputs of experiments carried out using MUSC have been developed within the HARMONIE-AROME canonical model configuration of the ALADIN-HIRLAM system are described within and constitute a large portion of this paper. The paper also illustrates the usefulness of the single-column approach for testing and developing HARMONIE-AROME physical parametrizations related to cloud microphysics and radiative transfer. Study cases concerning these physical parametrizations have been included for illustration purposes.

Description: We assess the sustainability of groundwater irrigation in the Euro-Mediterranean region. After analysing the available data on groundwater irrigation, we identify areas where irrigation causes groundwater depletion. To prevent the latter, we experiment with guidelines to restrict groundwater irrigation to sustainable levels, simulating beneficial and detrimental impacts in terms of improved environmental flow conditions and crop yield losses. To carry out these analyses, we apply the integrated model of water resources, irrigation and crop production LISFLOOD-EPIC. Crop growth is simulated accounting for atmospheric conditions and abiotic stress factors, including transpiration deficit. Four irrigation methods are modelled: drip, sprinkler, and intermittent and permanent flooding. Hydrologic and agricultural modules are dynamically coupled at the daily time scale through soil moisture, plant water uptake, and irrigation water abstraction and application. Water abstractions of other sectors are simulated based on requirement data. Water may be withdrawn from groundwater, rivers, lakes and reservoirs. As groundwater is abstracted to buffer the effects of drought, we use groundwater depletion to detect unsustainable water exploitation. We characterise reported data of annual groundwater abstractions for irrigation available at country and sub-national levels. Country data are the most complete, but their spatial resolution is often coarse. While the resolution of sub-national data is finer, their coverage is heterogeneous. Simulated and reported irrigation groundwater abstractions compare well in several areas, particularly in France, while some structural discrepancies emerge: simulated values tend to be larger than those reported, especially in southern Spain; and simulated inter-annual variability is significantly smaller than reported in some areas, most remarkably in Turkey. Potential causes of these discrepancies are simplified model assumptions influencing irrigation frequency and amounts; lack of high temporal and spatial resolution data on irrigated areas, and irrigation technologies and distribution; and possible unreported abstractions in areas where groundwater irrigation is significant. We identify areas undergoing groundwater depletion from model simulations. In the southern Iberian Peninsula, Greece, Middle East and northern Africa, most simulated depletion is caused by irrigation. In other Mediterranean areas, depletion is caused by all sectors combined. From well measurements of groundwater table depth in Spain, we find statistically significant decline rates affecting large areas of the south, thus in agreement with the model, but also areas in the north-eastern and central parts where model estimates detect no depletion. The comparison of model- and well-based depletion rates is limited by spatial scale differences and groundwater model assumptions, for which we suggest potential research directions. We design rules restricting irrigation groundwater abstraction to prevent groundwater depletion and minimise severe irrigation shortages. We optimise them and simulate their effects in the southern Iberian Peninsula. Irrigation restrictions cause crop yield reductions in groundwater-dependent irrigated areas, particularly in the Algarve and Segura river basin districts. At the same time, they positively impact environmental flows. This study shows the potential of integrated agro-hydrologic modelling for detecting water resources over-exploitation and exploring trade-offs between crop production, sustainable irrigation and ecosystem support.