Potential des Anbaus von Saflor (Carthamus tinctorius L.) unter den Bedingungen des Ökologischen Landbaus in Mitteleuropa

Under organic farming conditions, twenty safflower genotypes were tested for genotype-by-environment interaction (GxE) and stability across four locations in Germany and Switzerland for seed yield and oil content.. ANOVA showed highly significant differences among the genotypes, locations and GxL interactions for yield and oil content. None of the genotypes had a significant regression coefficient or a mean square deviation from the regression coefficient, thereby all genotypes are considered stable for seed yield, whereas, BS-62929 and PI-5724755 were relatively the most stable for oil content

Evaluierung von Saflor-Akzessionen für den Ökologischen Landbau

In Deutschland werden im Ökologischen Landbau trotz vorhandener Nachfrage nach Speiseöl nur sehr wenige Ölpflanzen angebaut. Saflor oder Färberdistel ist dabei als eine sinnvolle Alternative zu Raps bzw. Sonnenblumen anzusehen, es liegen jedoch bislang nur wenige Untersuchungen zur Anbauwürdigkeit vor. 741 Saflorherkünfte wurden 2002 auf ihre Anbaueignung unter hiesigen Klimabedingungen zweiortig geprüft. 2003 wurden 65 daraus ausgelesene, überlegene Herkünfte in einer dreiortigen Leistungsprüfung getestet. Es zeigte sich neben einer sehr großen Variabilität des verwendeten Materials, dass sich vorrangig europäische Formen durch eine gute Kornausbildung und höhere Samenerträge auszeichneten. Zwischen beiden Jahren bestand keine Beziehung in den Samenerträgen der 65 Herkünfte

ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP

A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology–climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user

xclim: xarray-based climate data analytics

xclim is a Python library that enables computation of climate indicators over large, hetero- geneous data sets. It is built using xarray objects and operations, can seamlessly benefit from the parallelization handling provided by dask, and relies on community conventions for data formatting and metadata attributes. xclim is meant as a tool to facilitate both climate science research and the delivery of operational climate services and products. In addition to climate indicator calculations, xclim also includes utilities for bias correction and statistical adjustment, ensemble analytics, model diagnostics, data quality assurance, and metadata standards compliance

Web processing service for climate impact and extreme weather event analyses. Flyingpigeon (Version 1.0)

Analyses of extreme weather events and their impacts often requires big data processing of ensembles of climate model simulations. Researchers generally proceed by downloading the data from the providers and processing the data files " at home " with their own analysis processes. However, the growing amount of available climate model and observation data makes this procedure quite awkward. In addition, data processing knowledge is kept local, instead of being consolidated into a common resource of reusable code. These drawbacks can be mitigated by using a web processing service (WPS). A WPS hosts services such as data analysis processes that are accessible over the web, and can be installed close to the data archives. We developed a WPS named 'flyingpigeon' that communicates over an HTTP network protocol based on standards defined by the Open Geospatial Consortium (OGC) [23], to be used by climatologists and impact modelers as a tool for analyzing large datasets remotely. Here, we present the current processes we developed in flyingpigeon relating to commonly-used processes (preprocessing steps, spatial subsets at continent, country or region level, and climate indices) as well as methods for specific climate data analysis (weather regimes, analogues of circulation, segetal flora distribution, and species distribution models). We also developed a novel, browser-based interactive data visualization for circulation analogues , illustrating the flexibility of WPS in designing custom outputs. Bringing the software to the data instead of transferring the data to the code is becoming increasingly necessary, especially with the upcoming massive climate datasets

Web processing service for climate impact and extreme weather event analyses. Flyingpigeon (Version 1.0)

International audienceAnalyses of extreme weather events and their impacts often requires big data processing of ensembles of climate model simulations. Researchers generally proceed by downloading the data from the providers and processing the data files " at home " with their own analysis processes. However, the growing amount of available climate model and observation data makes this procedure quite awkward. In addition, data processing knowledge is kept local, instead of being consolidated into a common resource of reusable code. These drawbacks can be mitigated by using a web processing service (WPS). A WPS hosts services such as data analysis processes that are accessible over the web, and can be installed close to the data archives. We developed a WPS named 'flyingpigeon' that communicates over an HTTP network protocol based on standards defined by the Open Geospatial Consortium (OGC) [23], to be used by climatologists and impact modelers as a tool for analyzing large datasets remotely. Here, we present the current processes we developed in flyingpigeon relating to commonly-used processes (preprocessing steps, spatial subsets at continent, country or region level, and climate indices) as well as methods for specific climate data analysis (weather regimes, analogues of circulation, segetal flora distribution, and species distribution models). We also developed a novel, browser-based interactive data visualization for circulation analogues , illustrating the flexibility of WPS in designing custom outputs. Bringing the software to the data instead of transferring the data to the code is becoming increasingly necessary, especially with the upcoming massive climate datasets

Ouranosinc/xclim: v0.42.0

Contributors to this version: Trevor James Smith (@Zeitsperre), Juliette Lavoie (@juliettelavoie), Éric Dupuis (@coxipi), Pascal Bourgault (@aulemahal). Announcements xclim now supports testing against tagged versions of Ouranosinc/xclim-testdata &lt;https://github.com/Ouranosinc/xclim-testdata&gt;_ in order to support older versions of xclim. For more information, see the Contributing Guide for more details. (PR/1339). xclim v0.42.0 will be the last version to explicitly support Python3.8. (GH/1268, PR/1344). New features and enhancements Two previously private functions for selecting a day of year in a time series when performing calendar conversions are now exposed. (GH/1305, PR/1317). New functions are: xclim.core.calendar.yearly_interpolated_doy xclim.core.calendar.yearly_random_doy scipy is no longer pinned below v1.9 and lmoments3&gt;=1.0.5 is now a core dependency and installed by default with pip. (GH/1142, PR/1171). Fix bug on number of bins in xclim.sdba.propeties.spatial_correlogram. (PR/1336) Add resample_before_rl argument to control when resampling happens in maximum_consecutive_{frost|frost_free|dry|tx}_days and in heat indices (in _threshold) (GH/1329, PR/1331) Add xclim.ensembles.make_criteria to help create inputs for the ensemble-reduction methods. (GH/1338, PR/1341). Bug fixes Warnings emitted from regular usage of some indices (snowfall_approximation with method="brown", effective_growing_degree_days) due to successive convert_units_to calls within their logic have been silenced. (PR/1319). Fixed a bug that prevented the use of the sdba_encode_cf option with xarray 2023.3.0 (PR/1333). Fixed bugs in xclim.core.missing and xclim.sdba.base.Grouper when using pandas 2.0. (PR/1344). Breaking changes The call signatures for xclim.ensembles.create_ensemble and xclim.ensembles._base._ens_align_dataset have been deprecated. Calls to these functions made with the original signature will emit warnings. Changes will become breaking in xclim&gt;=0.43.0.(GH/1305, PR/1317). Affected variable: mf_flag (bool) -&gt; multifile (bool) The indice and indicator for last_spring_frost has been modified to use tasmin by default, reflecting its docstring and literature definition (GH/1324, PR/1325). following indices now accept the op argument for modifying the threshold comparison operator (PR/1325): snw_season_length, snd_season_length, growing_season_length, frost_season_length, frost_free_season_length, rprcptot, daily_pr_intensity In order to support older environments, pandas is now conditionally pinned below v2.0 when installing xclim on systems running Python3.8. (PR/1344). Bug fixes xclim.indices.run_length.last_run nows works when freq is not None. (GH/1321, PR/1323). Internal changes Added xclim to the ouranos Zenodo community . (PR/1313). Significant documentation adjustments. (GH/1305, PR/1308): The CONTRIBUTING page has been moved to the top level of the repository. Information concerning the licensing of xclim is clearly indicated in README. sphinx-autodoc-typehints is now used to simplify call signatures generated in documentation. The SDBA module API is now found with the rest of the User API documentation. HISTORY.rst has been renamed CHANGES.rst, to follow dask-like conventions. Hyperlink targets for individual indices and indicators now point to their entries under API or Indices. Module-level docstrings have migrated from the library scripts directly into the documentation RestructuredText files. The documentation now includes a page explaining the reasons for developing xclim and a section briefly detailing similar and related projects. Markdown explanations in some Jupyter Notebooks have been edited for clarity Removed Mapping abstract base class types in call signatures (dict variables were always expected). (PR/1308). Changes in testing setup now prevent test_mean_radiant_temperature from sometimes causing a segmentation fault. (GH/1303, PR/1315). Addressed a formatting bug that caused Indicators with multiple variables returned to not be properly formatted in the documentation. (GH/1305, PR/1317). tox now include sbck and eofs flags for easier testing of dependencies. CI builds now test against sbck-python @ master. (PR/1328). upstream CI tests are now run on push to master, at midnight, and can also be triggered via workflow_dispatch. Failures from upstream build will open issues using xarray-contrib/issue-from-pytest-log. (PR/1327). Warnings from set _version_deprecated within Indicators now emit FutureWarning instead of DeprecationWarning for greater visibility. (PR/1319). The Graphics section of the Usage notebook has been expanded upon while grammar and spelling mistakes within the notebook-generated documentation have been reduced. (GH/1335, PR/1338, suggested from PyOpenSci Software Review). The Contributing guide now lists three separate subsections to help users understand the gains from optional dependencies. (GH/1335, PR/1338, suggested from PyOpenSci Software Review). </ul

Ouranosinc/xclim: v0.41.0

Contributors to this version: Trevor James Smith (@Zeitsperre), Pascal Bourgault (@aulemahal), Ludwig Lierhammer (@ludwiglierhammer), Éric Dupuis (@coxipi). New features and enhancements New properties xclim.sdba.properties.decorrelation_length and xclim.sdba.properties.transition_probability. (PR/1252) New indicators ensembles.change_significance now supports Mann-whitney U-test and flexible realization. (PR/1285). New indices and indicators for converting from snow water equivalent to snow depth (snw_to_snd) and snow depth to snow water equivalent (snd_to_snw) using snow density [kg / m^3]. (PR/1271). New indices and indicators for determining upwelling radiation (shortwave_upwelling_radiation_from_net_downwelling and longwave_upwelling_radiation_from_net_downwelling; CF variables rsus and rlus) from net and downwelling radiation (shortwave: rss and rsds; longwave: rls and rlds). (PR/1271). New indice and indicator {snd | snw}_season_{length | start | end} which generalize snow_cover_duration and continuous_snow_cover_{start | end} to allow using these functions with variable snw (PR/1275). New indice and indicator (dryness_index) for estimating soil humidity classifications for winegrowing regions (based on Riou et al. (1994)). (GH/355, PR/1235). Breaking changes xclim testing default behaviours have been changed (GH/1295, PR/1297): Running pytest will no longer use pytest-xdist distributed testing be default (can be set with -n auto|logical|#. Coverage is also no longer gathered/reported by default. Running tox will now set pytest-xdist to use -n logical processes (with a max of 10). Default behaviour for testing is to no longer always fetch xclim-testdata. If testdata is found in $HOME/.xclim_testing_data, files will be copied to individual processes, otherwise, will be fetched as needed. Environment variables evaluated when running pytest have been changed (GH/1295, PR/1297): For testing against specific branches of xclim-testdata: MAIN_TESTDATA_BRANCH -&gt; XCLIM_TESTDATA_BRANCH The option to skip fetching of testdata (SKIP_TEST_DATA) has been removed A new environment variable (XCLIM_PREFETCH_TESTING_DATA) is now available to gather xclim-testdata before running test ensemble (default: False). Environment variables are now passed to tox on execution. Bug fixes build_indicator_module_from_yaml now accepts a reload argument. When re-building a module that already exists, reload=True removes all previous indicator before creating the new ones. (GH/1192, PR/1284). The test for French translations of official indicators was fixed and translations for CFFWIS indices, FFDI, KDBI, DF and Jetstream metric woollings have been added or fixed. (PR/1271). use_ufunc in windowed_run_count is now supplied with argument freq to warn users that the 1d method does not support resampling after run length operations (GH/1279, PR/1291). {snd | snw}_max_doy now avoids an error due to xr.argmax when there are all-NaN slices. (PR/1277). Internal changes xclim has adopted PEP 517 and PEP 621 (pyproject.toml using the flit backend) to replace the legacy setup.py used to manage package organisation and building. Many tooling configurations that already supported the pyproject.toml standard have been migrated to this file. CI and development tooling documentation has been updated to reflect these changes. (PR/1278, suggested from PyOpenSci Software Review). Documentation source files have been moved around to remove some duplicated image files. (PR/1278). Coveralls GitHub Action removed as it did not support pyproject.toml-based configurations. (PR/1278). Add a remark about how xclim's CFFWIS is different from the original 1982 implementation. (GH/1104, PR/1284). Update CI runs to use Python3.9 when examining upstream dependencies. Replace setup-conda action with provision-with-micromamba action. (PR/1286). Update CI runs to always use tox~=4.0 and the latest virtual machine images (now ubuntu-22.04). (PR/1288, PR/1297). SBCK installation command now points to the official development repository. (PR/1288). Some references in the BibTeX were updated to point to better resources. (PR/1288). Add a GitHub CI workflow for performing dependency security review scanning. (PR/1287). Grammar and spelling corrections were applied to some docstrings. (PR/1271). Added [radiation] ([power] / [area]) to list of defined acceptable units. (PR/1271). Updated testing data used to generate the atmosds dataset to use more reproducibly-converted ERA5 data, generated with the miranda Python package. (PR/1269). Updated testing dependencies to use pytest-xdist&gt;=3.2, allowing for the new --dist=worksteal scheduler for distributing the pool of remaining tests across workers after individual workers have exhausted their own queues. (PR/1235). Adding infer context to the unit conversion in of the training of ExtremeValues. (PR/1299). Added sphinxcontrib-svg2pdfconverter for converting SVG graphics within documentation to PDF-compatible images. (PR/1296). README badges for supported Python versions and repository health have been added. (GH/1304, PR/1307). </ul

Earth System Model Evaluation Tool (ESMValTool) v2.0-an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of (1) an easy-to-install, well-documented Python package providing the core functionalities (ESMValCore) that performs common preprocessing operations and (2) a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability, the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top-down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klimarechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

ESMValTool

ESMValTool: A community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP