Search

The January 2022 NICEST2Newsletteris out and availablehere(HTML and pdf format). Below is a short summary: FAIR for Climate Science in the Nordics The NeIC-NICEST2 staff attended the Metadata for Machines workshop (M4M) organized by EOSC-Nordic for the Climate Community and it was a great success! Diagnostics for Earth System Modelling We are inviting researchers, Research Software Engineers that use or develop diagnostics tools for Earth System Models to fill out our survey:https://forms.gle/AxS7apMXKw6Po5iu5 Lumi and NICEST2: a joint interest for joint resources Our first experience with EuroHPC has been a success and we believe that having joint access to Lumi would benefit the NeIC-NICEST2 staff, and more generally the Nordic climate community. Upcoming seminar series on the use of GPUs in the field of climate Science Our next seminar series will be dedicated to GPUs and Climate science and we are gathering a list of potential presenters. (email jeani-at-uio-no). &nbsp

To reproduce eScience, several challenges need to be solved: scientific workflows need to be automated; the involved software versions need to be provided in an unambiguous way; input data needs to be easily accessible; High-Performance Computing (HPC) clusters are often involved and to achieve bit-to-bit reproducibility, it might be even necessary to execute the code on a particular cluster to avoid differences caused by different HPC platforms (and unless this is a scientist's local cluster, it needs to be accessed across (administrative) borders). Preferably, to allow even inexperienced users to (re-)produce results, all should be user-friendly. While some easy-to-use web-based scientific portals support already to access HPC resources, this typically only refers to computing and data resources that are local. By the example of two community-specific portals in the fields of biodiversity and climate research, we present a solution for accessing remote HPC (and cloud) compute and data resources from scientific portals across borders, involving rigorous container-based packaging of the software version and setup automation, thus enhancing reproducibility., Comment: Accepted at 2nd Workshop on Reproducible Workflows, Data Management, and Security. During eScience in Salt Lake City, Utah, USA. 11-14 October 2022

We describe here how to package and distribute Earth System Models, generate automatically Docker/Singularity containers, run containerized ESMs on various platforms (laptop, cloud, HPC), distribute workflow tasks (for inputs, execution, quality control, diagnostics) and publish workflows for FAIR (Findable, Accessible, Interoperable and Reusable) and open climate research. We demonstrate that containers provide native performance on HPCs. When added to Galaxy (https://galaxyproject.org), ESMs become available to all, regardless of their technical skills., This presentation was produced for the NICEST2 and EOSC-Nordic projects, that was supported respectively by the Nordic e-Infrastructure Collaboration (NeIC), which is part of NordForsk, and by the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652.

This report summarizes the first NICEST2 hackathon with FAIR experts and Earth System Model specialists to understand what needs to be done to make climate data FAIR. It will help us to define our roadmap for FAIR Climate in the Nordics., This report was produced for the NeIC NICEST2 project, that is supported by the Nordic e-Infrastructure Collaboration (NeIC), which is part of NordForsk.

Presentation given during the CSC GPU hackathon where NorESM/NICEST2 team worked together on the porting of BLOM on GPUs. The presentation shows the work done during the hackathon with the porting of a few routines on GPUs. The focus was not on performance and overall there was no gain (and even degradation of the overall performance because only a portion of the code has been ported to GPUs). Howeer, the hackathon was an excellent experience where the NorESM/NICEST2 team learned a lot about GPUs and discussed on possible methodology for migrating ESMs to GPUs. This work is of course only preliminary. Team Members: Martin L. Sætra (MET Norway) Yanchun He (NERSC) Mats Bentsen (NORCE) Alok Gupta (NORCE) Helge Avlesen (NORCE) Thomas Röblitz (UiB) Tomas Torsvik (UiB) Anne C. Fouilloux (UiO) Jørgen Nordmoen (UiO) Mentors: Mark Abraham (ENCCS) Nisha Agarwal (C-DAC) Gonzalo Gadeschi (NVIDIA) The NorESM/NICEST2 Team would like to thank the 3 mentors involved during the hackathon., This presentation was produced for the NICEST2 project, that was supported by the Nordic e-Infrastructure Collaboration (NeIC), which is part of NordForsk.

Presentation of EOSC-Nordic & NICEST2 projects at the INES (Infrastructure for Norwegian Earth System modelling) annual meeting, September 2020. Presentation given online. The goal was to coordinate with national initiative (INES) for Climate modelling., EOSC-Nordic project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652

The data management process for a research project covers the collection, storage, transformation, analysis and archiving or deletion of the data. Each of these stages can involve some complex and detailed steps. Projects in the same discipline may have very similar, or even the same, series of steps in a given stage. For example, more than one project may make use of the same multi-purpose detector. For each project the transformation from the raw data into more physical quantities (such as pulse signals from a detector into three-dimensional coordinates) could be common. If one project were able to define such a workflow and include it as part of their DMP, other projects would be able to reuse it. Capturing these workflows in the DMP would more accurately reflect the data management process and provide some value to follow-up, or related projects that have similar workflows and which would be able to reuse these workflows. The task we set ourselves for the hackathon was to take some specific workflows from a portion of the data management process for the Climate community in the EOSC-Nordic project and see how we could capture the workflow within the maDMP schema., This report is a summary of work done by the Something team at the RDA Hackathon on machine-actionable Data Management Plans that took place between 27th and 29th May 2020

The Lagrangian particle dispersion model FLEXPART was in its original version in the mid-1990s designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modelling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g. greenhouse gases, short-lived climate forcers like black carbon, or volcanic emissions, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to the global scale. In particular, inverse modelling based on source-receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.3, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecast System (IFS), and data from the United States' National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physico-chemical parametrizations, input/output formats and available pre- and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes. The deviation from 100 % efficiency is almost entirely due to remaining non-parallelized parts of the code, suggesting large potential for further speed-up. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g. to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option for running backward in time from atmospheric concentrations at receptor locations since many years, but this has now been extended to work also for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing of FLEXPART output data and briefly report on alternative FLEXPART versions.

By combining AVHRR data from the NOAA satellites with information from a database of in situ measurements, large-scale maps can be generated of the microphysical parameters most immediately significant for the modelling of global circulation and climate. From the satellite data, the clouds can be classified into cumuliform, stratiform and cirrus classes and then into further sub-classes by cloud top temperature. At the same time a database of in situ measurements made by research aircraft is classified into the same sub-classes and a statistical analysis is used to derive relationships between the sub-classes and the cloud microphysical properties. These two analyses are then linked to give estimates of the microphysical properties of the satellite observed clouds. Examples are given of the application of this technique to derive maps of the probability of occurrence of precipitating clouds and of precipitating water content derived from a case study within the International Cirrus Experiment (ICE) held in 1989 over the North Sea.

This contribution to the EUCREX mission 206 series aims at evaluating the performances of three methods considered for the retrieval of cloud microphysical properties (optical depth, droplet effective radius and liquid water path (LWP)) from Advanced Very High Resolution Radiometer (AVHRR) satellite images. These procedures, namely a set of empirical parameterizations, a physically based inversion code, and a neuronal approach, are tested with the mission 206 case study. The influence of horizontal averaging on the retrieved cloud properties is also investigated to assess the limitations of the comparison between satellite observations and aircraft measurements performed at a smaller scale.

The influence of two initialization schemes implemented for cirrus cloud simulation with a mesoscale model (RAMS) when run at two distinct horizontal resolutions (40 and 13.5 km) is investigated. The first method combines both first guess and analyzed data issued from the European Centre for Medium-Range Weather Forecasts (ECMWF). Indeed, the former provides a good estimation of the amplitude of relative humidity, but the field is phase shifted while the latter gives a correct phase but a bad amplitude (because of the difficulty of measuring relative humidity at pressure levels lower than 400 mb). The second scheme uses information at three scale levels: ECMWF three-dimensional fields, an atmospheric vertical profile, and a cloud cover mask derived from a National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite image. An interpolation algorithm that conserves the power spectrum slopes is used. Finally, several comparisons between the model outputs and observational data collected during the European Cloud and Radiation Experiment campaign are made. At a horizontal scale of 13.5 km, the authors’ new assimilation procedure allows a much better characterization of the whole three-dimensional cloud field (i.e., both horizontally and vertically) in terms of optical thickness and ice water content distributions, whereas the simulations are not improved at a larger horizontal scale (40 km).

As part of the European Cloud and Radiation Experiment campaign, we conducted studies with the regional atmospheric modeling system in order to simulate stratocumulus clouds observed on April 18, 1994, over Brittany (France). This three-dimensional model was used in its non hydrostatic mode, with bulk microphysical and radiative transfer parameterizations. The first initialization employed data provided by the European Center for Medium-range Weather Forecasts (ECMWF) and radiosoundings obtained at the Guipavas ground station. The model outputs were then compared with the corresponding NOAA-AVHRR (advanced very high resolution radiometer) satellite image and aircraft data collected in situ during the experiment. The relative differences between modeled and actual microphysical quantities (i.e., mixing ratio of water vapor, liquid water content, or liquid water path) were found to be less than 25% along the airplane trajectory; this disagreement was as large as 200% elsewhere in the experimental domain, principally because cloud cover was underestimated. The development of a simple time-to-space analysis made it possible to force the mesoscale model with satellite images combined with vertical profiles and tridimensional data of the ECMWF, leading to a more accurate spatial distribution of clouds over a large domain and diminishing these errors to 10%.

[1] This paper reports on the first experiences with Advanced Technology Microwave Sounder (ATMS) data at the European Centre for Medium-Range Weather Forecasts (ECMWF), both in terms of the contribution to the calibration/validation exercise and in terms of initial assimilation trials. Comparisons in antenna temperature space against short-term forecasts are used to establish the fidelity of the data. Monitoring of ATMS data against short-term forecasts shows that the data are generally of good quality, with a noise performance that is well within specification and after appropriate averaging, comparable to or better than that of the Advanced Microwave Sounding Unit-A (AMSU-A). Biases vary smoothly with scan positions, even before an appropriate antenna pattern correction has been established, and ATMS looks better than AMSU-A in this regard. Outer scan positions can be assimilated without restrictions due to biases, and together with the wider swath, this leads to a better coverage from one ATMS compared to one AMSU-A. There are indications of larger interchannel and spatial error correlations in ATMS data than for AMSU-A, possibly linked to a weak striping effect. The analysis and forecast impact in initial assimilation trials over two seasons are significantly positive in the short range over the Southern Hemisphere and in the long range over the Northern Hemisphere, with an otherwise overall neutral impact. Experiments in the context of a depleted observing system suggest that ATMS gives a comparable forecast impact to that from a single AMSU-A/Microwave Humidity Sounder combination.

International audience; In this conclusion paper, remote sensing retrievals of cloud optical thickness performed during the EUCREX mission 206 are analyzed. The comparison with estimates derived from in situ measurements demonstrates that the adiabatic model of cloud microphysics is more realistic than the vertically uniform plane parallel model (VUPPM) for parameterization of optical thickness. The analysis of the frequency distributions of optical thickness in the cloud layer then shows that the adiabatic model provides a good prediction when the cloud layer is thick and homogeneous, while it overestimates significantly the optical thickness when the layer is thin and broken. Finally, it is shown that the effective optical thickness over the whole sampled cloud is smaller than the adiabatic prediction based on the mean geometrical thickness of the cloud layer. The high sensitivity of the optical thickness on cloud geometrical thickness suggests that the effect of aerosol and droplet concentration on precipitation efficiency, and therefore on cloud extent and lifetime, is likely to be more significant than the Twomey effect.