FUME 2.0 – Flexible Universal processor for Modeling Emissions

This paper introduces FUME 2.0, an open-source emission processor for air quality modeling, and documents the software structure, capabilities, and sample usage. FUME provides a customizable framework for emission preparation tailored to user needs. It is designed to work with heterogeneous emission inventory data, unify them into a common structure, and generate model-ready emissions for various chemical transport models (CTMs). Key features include flexibility in input data formats, support for spatial and temporal disaggregation, chemical speciation, and integration of external models like MEGAN. FUME employs a modular Python interface and PostgreSQL/PostGIS backend for efficient data handling. The workflow comprises data import, geographical transformation, chemical and temporal disaggregation, and output generation steps. Outputs for mesoscale CTMs CMAQ, CAMx, and WRF-Chem and the large-eddy-simulation model PALM are implemented along with a generic NetCDF format. Benchmark runs are discussed on a typical configuration with cascading domains, with import and preprocessing times scaling near-linearly with grid size. FUME facilitates air quality modeling from continental to regional and urban scales by enabling effective processing of diverse inventory datasets.</p

PALM-USM v1.0: A new urban surface model integrated into the PALM large-eddy simulation model

Urban areas are an important part of the climate system and many aspects of urban climate have direct effects on human health and living conditions. This implies that reliable tools for local urban climate studies supporting sustainable urban planning are needed. However, a realistic implementation of urban canopy processes still poses a serious challenge for weather and climate modelling for the current generation of numerical models. To address this demand, a new urban surface model (USM), describing the surface energy processes for urban environments, was developed and integrated as a module into the PALM large-eddy simulation model. The development of the presented first version of the USM originated from modelling the urban heat island during summer heat wave episodes and thus implements primarily processes important in such conditions. The USM contains a multi-reflection radiation model for shortwave and longwave radiation with an integrated model of absorption of radiation by resolved plant canopy (i.e. trees, shrubs). Furthermore, it consists of an energy balance solver for horizontal and vertical impervious surfaces, and thermal diffusion in ground, wall, and roof materials, and it includes a simple model for the consideration of anthropogenic heat sources. The USM was parallelized using the standard Message Passing Interface and performance testing demonstrates that the computational costs of the USM are reasonable on typical clusters for the tested configurations. The module was fully integrated into PALM and is available via its online repository under the GNU General Public License (GPL). The USM was tested on a summer heat-wave episode for a selected Prague crossroads. The general representation of the urban boundary layer and patterns of surface temperatures of various surface types (walls, pavement) are in good agreement with in situ observations made in Prague. Additional simulations were performed in order to assess the sensitivity of the results to uncertainties in the material parameters, the domain size, and the general effect of the USM itself. The first version of the USM is limited to the processes most relevant to the study of summer heat waves and serves as a basis for ongoing development which will address additional processes of the urban environment and lead to improvements to extend the utilization of the USM to other environments and conditions

Sensitivity analysis of the PALM model system 6.0 in the urban environment

Sensitivity of the PALM model 6.0 with respect to land-surface and building properties is tested in a real urban environment in the vicinity of a typical crossroads in a densely built-up residential area in Prague, Czech Republic. The turbulence-resolving PALM is able to simulate the urban boundary layer flow for realistic setups. Besides an accurate representation of the relevant physical processes, the model performance also depends on the input data describing the urban setup, namely the building and land-surface properties. Two types of scenario are employed. The first one is the synthetic scenarios altering mainly surface and material parameters such as albedo, emissivity or wall conductivity, testing sensitivity of the model simulations to potentially erroneous input data. Second, urbanistic-type scenarios are analysed, in which commonly considered urban heat island mitigation measures such as greening of the streets or changing surface materials are applied in order to assess the limits of the effects of a particular type of scenario. For the synthetic scenarios, surface parameters used in radiation balance equations are found to be the most sensitive overall followed by the volumetric heat capacity and thermal conductivity of walls. Other parameters show a limited average effect; however, some can still be significant during some parts of the day, such as surface roughness in the morning hours. The second type, the urbanistic scenarios, shows urban vegetation to be the most effective measure, especially when considering both physical and biophysical temperature indicators. The influence of both types of scenario was also tested for air quality, specifically PM2.5 dispersion, which generally shows opposite behaviour to that of thermal indicators; i.e. improved thermal comfort brings deterioration of PM2.5 concentrations. © 2021 Michal Belda et al

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect

Seasonal snow cover plays a major role in the climate system of the Northern Hemisphere via its effect on land surface albedo and fluxes. In climate models the parameterization of interactions between snow and atmosphere remains a source of uncertainty and biases in the representation of local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled with different land surface models to simulate snow–atmosphere interactions over Europe in winter and spring. We use a previously defined index, the snow-albedo sensitivity index (SASI), to quantify the radiative forcing associated with snow cover anomalies. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over middle and high latitudes in Europe. The choice of parameterizations, and primarily the choice of the land surface model, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover accurately. The degree of agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that, during this time period, the choice of the land surface model is more critical for the representation of SASI than the atmospheric model

Regional Climate Modeling

Regional climate models are commonly used for downscaling global climate simulations to the regional scale using nested limited-area models. One of the main goals of this work was the application of regional model RegCM in very high resolution for the region with complex topography in the framework of EC FP6 project CECILIA. RegCM was employed to downscale climate change scenario simulations performed by ECHAM5 model according to the IPCC A1B emission scenario for Central and Eastern Europe in 10km resolution. Validation of model performance, assessed by nesting RegCM in ERA-40 reanalysis, shows improvement of regional climate patterns mainly in mountainous areas. Temperature is well represented with mostly cold bias around -1 žC. Precipitation is affected by large biases around 80 %, in mountainous areas up to 400 % overestimation in winter. Downscaled climate change signal shows average warming 0.5­1.5 žC in period 2021­2050 and 2­4 žC in period 2071­2100. Precipitation changes are mostly within ±0.5 mm/day. RegCM3­beta version with adjusted precipitation scheme parameters shows improvement of the precipitation bias, difference in climate change is rather negligible. Experiments with different convection schemes of RegCM in a case study for Africa performed in the framework of CORDEX project are..

Modelování klimatu na omezené oblasti

Regional climate models are commonly used for downscaling global climate simulations to the regional scale using nested limited-area models. One of the main goals of this work was the application of regional model RegCM in very high resolution for the region with complex topography in the framework of EC FP6 project CECILIA. RegCM was employed to downscale climate change scenario simulations performed by ECHAM5 model according to the IPCC A1B emission scenario for Central and Eastern Europe in 10km resolution. Validation of model performance, assessed by nesting RegCM in ERA-40 reanalysis, shows improvement of regional climate patterns mainly in mountainous areas. Temperature is well represented with mostly cold bias around -1 žC. Precipitation is affected by large biases around 80 %, in mountainous areas up to 400 % overestimation in winter. Downscaled climate change signal shows average warming 0.5­1.5 žC in period 2021­2050 and 2­4 žC in period 2071­2100. Precipitation changes are mostly within ±0.5 mm/day. RegCM3­beta version with adjusted precipitation scheme parameters shows improvement of the precipitation bias, difference in climate change is rather negligible. Experiments with different convection schemes of RegCM in a case study for Africa performed in the framework of CORDEX project are...Regionální klimatické modely slouží pro regionalizaci globálních simulací klimatu za použití metody vnořených modelů pracujících na omezené oblasti. Jedním z hlavních cílů této práce bylo využití regionálního modelu RegCM ve velmi vysokém rozlišení v oblasti s komplexní topografií v rámci projektu CECILIA šestého rámcového programu EK. Model RegCM byl aplikován pro regionalizaci globálních simulací scénářů klimatické změny v regionu střední a východní Evropy. Použit byl emisní scénář A1B podle IPCC a globální simulace modelu ECHAM5. Horizontální rozlišení regionálního modelu bylo 10 km. Pro validaci byly použity okrajové podmínky z reanalýzy ERA-40. Vysoké rozlišení regionálního modelu přináší vylepšení modelového klimatu především v horských oblastech. Průměrné teploty jsou dobře zachyceny, model je většinou chladnější než realita, systematická odchylka je přibližně -1 žC. Srážky jsou modelem silně nadhodnoceny, průměrně o 80 %, v horských oblastech až o 400 %. Regionální scénáře budoucího klimatu ukazují oteplování mezi 0.5 a 1.5 žC v období 2021­2050 a mezi 2 a 4 žC v období 2071­2100. Změny srážek jsou většinou v rozmezí ±0.5 mm/den. Možnost vylepšení simulace srážek v modelu je ukázana na verzi RegCM­beta. V práci jsou také ukázány experimenty se simulacemi modelu RegCM pro Afriku v rámci projektu...Katedra fyziky atmosféryDepartment of Atmospheric PhysicsFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

DataSheet1_Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6.docx

The multi-model ensembles like CMIP5 or CMIP6 provide a tool to analyze structural uncertainty of climate simulations. Currently developed regional and local climate change scenarios for the Czech Republic assess the uncertainty based on state-of-the-art Global Climate Model (GCM) and Regional Climate Model (RCM) ensembles. Present study focuses on multi-model spread of projected changes in long-term monthly means and inter-annual variability of monthly mean minimum, mean and maximum daily air temperature and monthly mean precipitation. We concentrate in more detail on the simulation of CNRM-ESM2-1, the driving GCM for the convection permitting ALADIN-Climate/CZ simulation contributing to the local scenarios in very high resolution. For this GCM, we also analyze a mini-ensemble with perturbed initial conditions to evaluate the range of internal climate variability. The results for the Czech Republic reveal minor differences in model performance in the reference period whereas quite substantial inter-generation shift in projected future change towards higher air temperature and lower summer precipitation in CMIP6 comparing to CMIP5. One of the prominent features across GCM generations is the pattern of summer precipitation decrease over central Europe. Further, projected air temperature increase is higher in summer and autumn than in winter and spring, implying increase of thermal continentality of climate. On the other hand, slight increase of winter precipitation and tendency towards decrease of summer precipitation lead to projected decrease of ombric continentality. The end of 21st century projections also imply higher probability of dry summer periods, higher precipitation amounts in the cold half of the year and extremely high temperature in summer. Regarding the CNRM-ESM2-1, it is often quite far from the multi-model median. Therefore, we strictly recommend to accompany any analysis based on the simulation of nested Aladin-CLIMATE/CZ with proper uncertainty estimate. The range of uncertainty connected to internal climate variability based on one GCM is often quite large in comparison to the range of whole CMIP6 ensemble. It implies that when constructing climate change scenarios for the Central Europe region, attention should be paid not only to structural uncertainty represented by inter-model differences and scenario uncertainty, but also to the influence of internal climate variability.</p