Researchers and end users using climate data face a challenge when they analyze the data they need. Data volumes are increasing very rapidly, and the ability to download all needed data is often no longer possible. Most of the climate analysis tools for research and application needs must use very large datasets, often distributed among several data centres and into a large quantity of files. This is especially true when they are stored in a federated architecture like the ESGF. One of these tools is icclim (https://github.com/cerfacs-globc/icclim ), a flexible python software package to calculate climate indices and indicators. This tool adhere as much as possible to metadata conventions such as CF, implementing also provenance information. It also aims at providing increasing support for all FAIR aspects. It is designed with performance and optimisation in mind, because the goal is to provide on-demand calculations for users. It provides the implementation of most of the international standard climate indices such as ECAD, ETCCDI, ET-SCI, including the correct methodology for calculating percentile indices using the bootstrapping method. It has been validated against R.Climdex as well (https://cran.r-project.org/web/packages/climdex.pcic/index.html ). The new 5.x version of icclim is now based on functions from the xclim python library, which was inspired by earlier versions of icclim, but using xarray and dask for data access and processing. icclim is also a candidate as the software to calculate climate indices for the C3S toolbox (https://cds.climate.copernicus.eu/cdsapp#!/toolbox ). icclim is integrated in the IS-ENES C4I 2.0 platform (https://climate4impact.eu/ ), using a Jupyter notebook collection in a SWIRRL environment (Software for Interactive Reproducible Research Labs https://gitlab.com/KNMI-OSS/swirrl ). Having access to this type of analysis tool is very useful, and seamless integration with front-ends like C4I enable the use of those tools by a larger number of researchers and end users. This project (IS-ENES3) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084.
Generation of 2D meshes with reduced number of elements while yielding accurate results is a major challenge in coastal numerical models. High-quality 2D unstructured meshes were generated using sizing functions, which were computed from Euclidean distances to coastal features at given spatial locations and assigned element sizes based on calculated distances. The coastal features consist of National Water Model (NWM) streamlines, National Hydrography Dataset (NHD), NOAA Medium Resolution Shoreline and bathymetric features from the United States Army Corps of Engineers (USACE). This approach allows improved integration of the hydrodynamic D-Flow Flexible Mesh (D-Flow FM) model into the hydrological NWM and results in an optimum number of computational points. The method grants the user flexibility to control element sizes and avoids manual iterative procedures by determining an optimal element-sizing function that defines small element scales in regions where geometrical and physical characteristics exist, with larger scales elsewhere. Newly created continental-scale meshes on the Atlantic Ocean, Gulf of Mexico and Pacific Ocean coastlines demonstrate the application of the proposed method for automatic generation of unstructured, high-quality 2D meshes.
Atmospheric ice-nucleating particles (INPs) from mineral dust and non-proteinaceous biological sources can influence cloud formation, precipitation, and Earth’s radiation budget due to their efficient freezing abilities. The ambient aerosol particles from these sources are abundant with ambient concentrations exceeding a few µg m^-3 for each type. Thus, the characterization of INPs and aerosol particles from these sources is important. We typically characterize their specific surface area (SSA), which is the primary variable to estimate their ice-nucleation active surface site density, using a sorbate gas, such as nitrogen. However, it is still uncertain how these particles interact with water vapor under subzero temperatures. To fill this gap, we used the 3Flex instrument (Micromeritics Instrument Corp.) with multiple sorbates to comprehensively characterize the nanoscale surface structure, pore size distribution, and accessibility to water molecules of a commercially available model proxy of mineral dust (illite NX) and cellulose materials. To date, we have completed more than 60 physisorption 3Flex experiments with various sorbates, such as CO2, H2O, Kr, and N2, for each sorbent. In particular, we examined SSA by water vapor sorption at temperatures relevant to atmospheric heterogeneous freezing (~ 0 to -20 °C). We will present our results as physisorption isotherms. In addition, our preliminary results of temperature-dependent SSA observed for micro- and nano-crystalline cellulose materials as well as illite NX will be discussed. Our preliminary result suggests that the SSA of illite NX is less temperature-dependent compared to the cellulose materials, which may be potentially swelling while interacting with water. Therefore, illite NX may be suitable for an INP test proxy.
The system of trade wind cumulus clouds observed during the RICO field project was simulated by an LES model over a 50x50 km2 domain size. Parameters of latent heat release were analyzed with the goal of parameterizing their effects on grids typical for NWP and large-scale models. Over 2000 clouds were examined focusing on relationship between parameters of latent heat release (phase transition rates) and dynamical/microphysical cloud characteristics. The phase transition rates (Tr), which in warm tropical clouds are represented by processes of condensation/evaporation, were analyzed by stratifying the clouds by their size/stage of maturity. The analyzed parameters included, among others, integral mass and buoyancy fluxes, cloud and rain water parameters. In our previous investigation we found that a remarkably strong correlation exists between Tr and upward mass flux (ℳ). The strong dependence of phase transition rates on ℳ, as well as linear relationship between Tr and ℳ, was explained by applying the condensation theory and the concept of “quasi-steady” supersaturation. The LES derived slope of the linear fit agreed with its theoretically predicted value with an error less than 5%. This result implies that supersaturation in clouds, on average, varies within a few percentage points of its quasi-steady value. The theory, as well as LES data, show that the Tr - ℳ linear fit is valid for local variables, and, therefore, may be integrated to obtain horizontal mean parameters. Expanding the Tr - ℳ relationship for vertically dependent horizontal mean variables, may provide the framework for development of sub-grid scale (SGS) latent heat release parameterization. It was also suggested that calculating the slope of the linear fit from concurrent measurements of temperature and vertical velocity, and comparing it with the theoretical slope based on the quasi-steady supersaturation assumption, may offer a method for estimating the supersaturation in clouds.