ESMValTool pre-processing functions for eWaterCycle

eWaterCycle is a framework in which hydrological modelers can work together in a collaborative environment. In this environment, they can, for example, compare and analyze the results of models that use different sources of (meteorological) forcing data. The final goal of eWaterCycle is to advance the state of FAIR (Findable, Accessible, Interoperable, and Reusable) and open science in hydrological modeling.Comparing hydrological models has always been a challenging task. Hydrological models exhibit great complexity and diversity in the exact methodologies applied, competing for hypotheses of hydrologic behavior, technology stacks, and programming languages used in those models. Pre-processing of forcing data is one of the roadblocks that was identified during the FAIR Hydrological Modelling workshop organized by the Lorentz Center in April 2019. Forcing data can be retrieved from a wide variety of sources with discrepant variable names and frequencies, and spatial and temporal resolutions. Moreover, some hydrological models make specific assumptions about the definition of the forcing variables. The pre-processing is often performed by various sets of scripts that may or may not be included with model source codes, making it hard to reproduce results. Generally, there are common steps in the data preparation among different models. Therefore, it would be a valuable asset to the hydrological community if the pre-processing of FAIR input data could also be done in a FAIR manner.Within the context of the eWaterCycle II project, a common pre-processing system has been created for hydrological modeling based on ESMValTool (Earth System Model Evaluation Tool). ESMValTool is a community diagnostic and performance metrics tool developed for the evaluation of Earth system models. The ESMValTool pre-processing functions cover a broad range of operations on data before diagnostics or metrics are applied; for example, vertical interpolation, land-sea masking, re-gridding, multi-model statistics, temporal and spatial manipulations, variable derivation and unit conversion. The pre-processor performs these operations in a centralized, documented and efficient way. The current pre-processing pipeline of the eWaterCycle using ESMValTool consists of hydrological model-specific recipes and supports ERA5 and ERA-Interim data provided by the ECMWF (European Centre for Medium-Range Weather Forecasts). The pipeline starts with the downloading and CMORization (Climate Model Output Rewriter) of input data. Then a recipe is prepared to find the data and run the preprocessors. When ESMValTool runs a recipe, it will also run the diagnostic script that contains model-specific analysis to derive required forcing variables, and it will store provenance information to ensure transparency and reproducibility. In the near future, the pipeline is extended to include Earth observation data, as these data are paramount to the data assimilation in eWaterCycle.In this presentation we will show how using the pre-processor from ESMValTool for Hydrological modeling leads to connecting Hydrology and Climate sciences, and increase the impact and sustainability of ESMValTool.

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Preprocessing of hydrological models’ input in eWaterCycle with ESMValTool

10.5194/egusphere-egu21-6051 ◽

2021 ◽

Author(s):

Fakhereh Alidoost ◽

Jerom Aerts ◽

Bouwe Andela ◽

Jaro Camphuijsen ◽

Nick van De Giesen ◽

...

Keyword(s):

Programming Languages ◽

Hydrological Model ◽

Performance Metrics ◽

Climate Model ◽

Meteorological Data ◽

Climate Science ◽

Hydrological Models ◽

Earth System ◽

Evaluation Tool ◽

Spatial Coverage

Hydrological models exhibit great complexity and diversity in the exact methodologies applied, competing for hypotheses of hydrologic behaviour, technology stacks, and programming languages used in those models. The preprocessing of forcing (meteorological) data is often performed by various sets of scripts that may or may not be included with model source codes, making it hard to reproduce results. Moreover, forcing data can be retrieved from a wide variety of forcing products with discrepant variable names and frequencies, spatial and temporal resolutions, and spatial coverage. Even though there is an infinite amount of preprocessing scripts for different models, these preprocessing scripts use only a limited set of operations, mainly re-gridding, temporal and spatial manipulations, variable derivation, and unit conversion. Also, these exact same preprocessing functions are used in analysis and evaluation of output from Earth system models in climate science.Within the context of the eWaterCycle II project (https://www.ewatercycle.org/), a common preprocessing system has been created for hydrological modelling based on ESMValTool (Earth System Model Evaluation Tool). ESMValTool is a community-driven diagnostic and performance metrics tool that supports a broad range of preprocessing functions. Using a YAML script called a recipe, instructions are provided to ESMValTool: the datasets which need to be analyzed, the preprocessors that need to be applied, and the model-specific analysis (i.e. diagnostic script) which need to be run on data. ESMValTool is modular and flexible so all preprocessing functions can also be used directly in a Python script and additional analyses can easily be added.The current preprocessing pipeline of the eWaterCycle using ESMValTool consists of hydrological model-specific scripts and supports ERA5 and ERA-Interim data provided by the ECMWF (European Centre for Medium-Range Weather Forecasts), as well as CMIP5 and CMIP6 climate model data. The pipeline starts with the downloading and CMORization (Climate Model Output Rewriter) of input data. Then a recipe is prepared to find the data and run the preprocessors. When ESMValTool runs a recipe, it produces preprocessed data that can be passed as input to a hydrological model. It will also store provenance and citation information to ensure transparency and reproducibility. This leads to less time spent on building custom preprocessing, more reproducible and comparable hydrological science.In this presentation, we will give an overview of the current preprocessing pipeline of the eWaterCycle, outline ESMValTool preprocessing functions, and introduce available hydrological recipes and diagnostic scripts for the PCRGLOB, WFLOW, HYPE, MARRMOT and LISFLOOD models.

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Recent developments on the Earth System Model Evaluation Tool

10.5194/egusphere-egu21-3476 ◽

2021 ◽

Author(s):

Bouwe Andela ◽

Fakhereh Alidoost ◽

Lukas Brunner ◽

Jaro Camphuijsen ◽

Bas Crezee ◽

...

Keyword(s):

Model Evaluation ◽

Performance Metrics ◽

Climate Model ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Evaluation Tool ◽

Scientific Review ◽

The Earth ◽

User Friendly

The Earth System Model Evaluation Tool (ESMValTool) is a free and open-source community diagnostic and performance metrics tool for the evaluation of Earth system models such as those participating in the Coupled Model Intercomparison Project (CMIP). Version 2 of the tool (Righi et al. 2020, www.esmvaltool.org) features a brand new design composed of a core that finds and processes data according to a &#8216;recipe&#8217; and an extensive collection of ready-to-use recipes and associated diagnostic codes for reproducing results from published papers. Development and discussion of the tool (mostly) takes place in public on https://github.com/esmvalgroup and anyone with an interest in climate model evaluation is welcome to join there.&#160;Since the initial release of version 2 in the summer of 2020, many improvements have been made to the tool. It is now more user friendly with extensive documentation available on docs.esmvaltool.org and a step by step online tutorial. Regular releases, currently planned three times a year, ensure that recent contributions become available quickly while still ensuring a high level of quality control. The tool can be installed from conda, but portable docker and singularity containers are also available.&#160;Recent new features include a more user-friendly command-line interface, citation information per figure including CMIP6 data citation using ES-DOC, more and faster preprocessor functions that require less memory, automatic corrections for a larger number of CMIP6 datasets, support for more observational and reanalysis datasets, and more recipes and diagnostics.&#160;The tool is now also more reliable, with improved automated testing through more unit tests for the core, as well as a recipe testing service running at DKRZ for testing the scientific recipes and diagnostics that are bundled into the tool. The community maintaining and developing the tool is growing, making the project less dependent on individual contributors. There are now technical and scientific review teams that review new contributions for technical quality and scientific correctness and relevance respectively, two new principal investigators for generating a larger support base in the community, and a newly created user engagement team that is taking care of improving the overall user experience.

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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

Geoscientific Model Development ◽

10.5194/gmd-13-3383-2020 ◽

2020 ◽

Vol 13 (7) ◽

pp. 3383-3438 ◽

Cited By ~ 10

Author(s):

Veronika Eyring ◽

Lisa Bock ◽

Axel Lauer ◽

Mattia Righi ◽

Manuel Schlund ◽

...

Keyword(s):

Model Evaluation ◽

Large Scale ◽

Performance Metrics ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Evaluation Tool ◽

The Earth ◽

And Performance ◽

Earth System Models

Abstract. 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.

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The EC-Earth3 Earth System Model for the Climate Model Intercomparison Project 6

10.5194/gmd-2020-446 ◽

2021 ◽

Cited By ~ 3

Author(s):

Ralf Döscher ◽

Mario Acosta ◽

Andrea Alessandri ◽

Peter Anthoni ◽

Almut Arneth ◽

...

Keyword(s):

Physical Performance ◽

Performance Metrics ◽

Climate Model ◽

Earth System Model ◽

System Model ◽

Historical Period ◽

Earth System ◽

Dynamic Features ◽

The Earth ◽

Intercomparison Project

Abstract. The Earth System Model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different HPC systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behaviour and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.

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Extending the Modular Earth Submodel System (MESSy v2.54) model hierarchy: the ECHAM/MESSy IdeaLized (EMIL) model setup

Geoscientific Model Development ◽

10.5194/gmd-13-5229-2020 ◽

2020 ◽

Vol 13 (11) ◽

pp. 5229-5257

Author(s):

Hella Garny ◽

Roland Walz ◽

Matthias Nützel ◽

Thomas Birner

Keyword(s):

Climate Model ◽

Polar Vortex ◽

Equilibrium Temperature ◽

Earth System ◽

Tracer Transport ◽

Dynamical Core ◽

Vortex Strength ◽

Model Hierarchy ◽

Model Setup ◽

The Impact

Abstract. As models of the Earth system grow in complexity, a need emerges to connect them with simplified systems through model hierarchies in order to improve process understanding. The Modular Earth Submodel System (MESSy) was developed to incorporate chemical processes into an Earth System model. It provides an environment to allow for model configurations and setups of varying complexity, and as of now the hierarchy ranges from a chemical box model to a fully coupled chemistry–climate model. Here, we present a newly implemented dry dynamical core model setup within the MESSy framework, denoted as ECHAM/MESSy IdeaLized (EMIL) model setup. EMIL is developed with the aim to provide an easily accessible idealized model setup that is consistently integrated in the MESSy model hierarchy. The implementation in MESSy further enables the utilization of diagnostic chemical tracers. The setup is achieved by the implementation of a new submodel for relaxation of temperature and horizontal winds to given background values, which replaces all other “physics” submodels in the EMIL setup. The submodel incorporates options to set the needed parameters (e.g., equilibrium temperature, relaxation time and damping coefficient) to functions used frequently in the past. This study consists of three parts. In the first part, test simulations with the EMIL model setup are shown to reproduce benchmarks provided by earlier dry dynamical core studies. In the second part, the sensitivity of the coupled troposphere–stratosphere dynamics to various modifications of the setup is studied. We find a non-linear response of the polar vortex strength to the prescribed meridional temperature gradient in the extratropical stratosphere that is indicative of a regime transition. In agreement with earlier studies, we find that the tropospheric jet moves poleward in response to the increase in the polar vortex strength but at a rate that strongly depends on the specifics of the setup. When replacing the idealized topography to generate planetary waves by mid-tropospheric wave-like heating, the response of the tropospheric jet to changes in the polar vortex is strongly damped in the free troposphere. However, near the surface, the jet shifts poleward at a higher rate than in the topographically forced simulations. Those results indicate that the wave-like heating might have to be used with care when studying troposphere–stratosphere coupling. In the third part, examples for possible applications of the model system are presented. The first example involves simulations with simplified chemistry to study the impact of dynamical variability and idealized changes on tracer transport, and the second example involves simulations of idealized monsoon circulations forced by localized heating. The ability to incorporate passive and chemically active tracers in the EMIL setup demonstrates the potential for future studies of tracer transport in the idealized dynamical model.

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Near-Real-Time Surface-Based CAPE from Merged Hyperspectral IR Satellite Sounder and Surface Meteorological Station Data

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0155.1 ◽

2019 ◽

Vol 58 (8) ◽

pp. 1613-1632 ◽

Cited By ~ 2

Author(s):

Callyn Bloch ◽

Robert O. Knuteson ◽

Antonia Gambacorta ◽

Nicholas R. Nalli ◽

Jessica Gartzke ◽

...

Keyword(s):

Real Time ◽

Convective Available Potential Energy ◽

Processing System ◽

Naval Research ◽

Observation Data ◽

Profile Data ◽

Surface Observation ◽

Polar Orbiting Satellite ◽

The Impact ◽

Surface Observations

AbstractNear-real-time satellite-derived temperature and moisture soundings provide information about the changing atmospheric vertical thermodynamic structure occurring between successive routine National Weather Service (NWS) radiosonde launches. In particular, polar-orbiting satellite soundings become critical to the computation of stability indices over the central United States in the midafternoon, when there are no operational NWS radiosonde launches. Accurate measurements of surface temperature and dewpoint temperature are key in the calculation of severe weather indices, including surface-based convective available potential energy (SBCAPE). This paper addresses a shortcoming of current operational infrared-based satellite soundings, which underestimate the surface parcel temperature and dewpoint when CAPE is nonzero. This leads to a systematic underestimate of SBCAPE. This paper demonstrates a merging of satellite-derived vertical profiles with surface observations to address this deficiency for near-real-time applications. The National Oceanic and Atmospheric Administration (NOAA) Center for Environmental Prediction (NCEP) Meteorological Assimilation Data Ingest System (MADIS) hourly surface observation data are blended with satellite soundings derived using the NOAA Unique Combined Atmospheric Processing System (NUCAPS) to create a greatly improved SBCAPE calculation. This study is not intended to validate NUCAPS or the combined NUCAPS + MADIS product, but to demonstrate the benefits of combining observational weather satellite profile data and surface observations. Two case studies, 18 June 2017 and 3 July 2017, are used in this study to illustrate the success of the combined NUCAPS + MADIS SBCAPE compared to the NUCAPS-only SBCAPE estimate. In addition, a 6-month period, April–September 2018, was analyzed to provide a comprehensive analysis of the impact of using surface observations in satellite SBCAPE calculations. To address the need for reduced data latency, a near-real-time merged satellite and surface observation product is demonstrated using NUCAPS products from the Community Satellite Processing Package (CSPP) applied to direct broadcast data received at the University of Wisconsin–Madison, Hampton University in Virginia, and the Naval Research Laboratory in Monterey, California. Through this study, it is found that the combination of the MADIS surface observation data and the NUCAPS satellite profile data improves the SBCAPE estimate relative to comparisons with the Storm Prediction Center (SPC) mesoscale analysis and the NAM analysis compared to the NUCAPS-only SBCAPE estimate. An assessment of the 6-month period between April and September 2018 determined the dry bias in NUCAPS at the surface is the primary cause of the underestimation of the NUCAPS-only SBCAPE estimate.

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Progress towards a probabilistic Earth system model: examining the impact of stochasticity in the atmosphere and land component of EC-Earth v3.2

Geoscientific Model Development ◽

10.5194/gmd-12-3099-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 3099-3118 ◽

Cited By ~ 2

Author(s):

Kristian Strommen ◽

Hannah M. Christensen ◽

Dave MacLeod ◽

Stephan Juricke ◽

Tim N. Palmer

Keyword(s):

Climate Model ◽

Weather Forecasting ◽

Southern Oscillation ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Beneficial Impact ◽

The Impact ◽

Mean State

Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

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Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2

10.5194/gmd-2018-337 ◽

2019 ◽

Author(s):

Kristian Strommen ◽

Hannah M. Christensen ◽

David MacLeod ◽

Stephan Juricke ◽

Tim N. Palmer

Keyword(s):

Climate Model ◽

Weather Forecasting ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Beneficial Impact ◽

The Mean ◽

The Impact ◽

Mean State

Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model, two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic earth-system model in atmosphere-only mode. Stochastic parametrisations have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as ENSO, North Atlantic weather regimes and the Indian monsoon. Stochasticity in the land-component has been shown to improve variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied, It is shown that the inclusion all three schemes notably change the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to large changes in the model's energy budget. This implies that in order to keep the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

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Model evaluation expectations of European ESM communities: results from a survey

10.5194/egusphere-egu21-15681 ◽

2021 ◽

Author(s):

Jerome Servonnat ◽

Eric Guilyardi ◽

Zofia Stott ◽

Kim Serradell ◽

Axel Lauer ◽

...

Keyword(s):

Model Evaluation ◽

Common Ground ◽

Performance Metrics ◽

Coupled Model ◽

System Modelling ◽

Earth System ◽

Evaluation Tool ◽

Technical Standards ◽

Community Effort ◽

And Performance

Developing an Earth system model evaluation tool for a broad user community is a real challenge, as the potential users do not necessarily have the same needs or expectations. While many evaluation tasks across user communities include common steps, significant differences are also apparent, not least the investment by institutions and individuals in bespoke tools. A key question is whether there is sufficient common ground to pursue a community tool with broad appeal and application.We present the main results of a survey carried out by Assimila for the H2020 IS-ENES3 project to review the model evaluation needs of European Earth System Modelling communities. Interviewing approximately 30 participants among several European institutions, the survey targeted a broad range of users, including model developers, model users, evaluation data providers, and infrastructure providers. The output of the study provides an analysis of&#160; requirements focusing on key technical, standards, and governance aspects.The study used ESMValTool as a&#160; current benchmark in terms of European evaluation tools. It is a community diagnostics and performance metrics tool for the evaluation of Earth System Models that allows for comparison of single or multiple models, either against predecessor versions or against observations. The tool is being developed in such a way that additional analyses can be added. As a community effort open to both users and developers, it encourages open exchange of diagnostic source code and evaluation results. It is currently used in Coupled Model Intercomparison Projects as well as for the development and testing of &#8220;new&#8221; models.A key result of the survey is the widespread support for ESMValTool amongst users, developers, and even those who have taken or promote other approaches. The results of the survey identify priorities and opportunities in the further development of the ESMValTool to ensure long-term adoption of the tool by a broad community.

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ESMValCore: analyzing CMIP data made easy

10.5194/egusphere-egu2020-17472 ◽

2020 ◽

Author(s):

Bouwe Andela ◽

Lisa Bock ◽

Björn Brötz ◽

Faruk Diblen ◽

Laura Dreyer ◽

...

Keyword(s):

Open Source ◽

Performance Metrics ◽

Modular Design ◽

Coupled Model ◽

Application Programming Interface ◽

Earth System ◽

Evaluation Tool ◽

System Models ◽

Scientific Analysis ◽

Earth System Models

The Earth System Model Evaluation Tool (ESMValTool) is a free and open-source community diagnostic and performance metrics tool for the evaluation of Earth system models participating in the Coupled Model Intercomparison Project (CMIP). Version 2 of the tool (Righi et al. 2019, www.esmvaltool.org) features a brand new design, consisting of ESMValCore (https://github.com/esmvalgroup/esmvalcore), a package for working with CMIP data and ESMValTool (https://github.com/esmvalgroup/esmvaltool), a package containing the scientific analysis scripts. This new version has been specifically developed to handle the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex Earth system models. The tool also supports CMIP5 and CMIP3 datasets, as well as a large number of re-analysis and observational datasets that can be formatted according to the same standards (CMOR) on-the-fly or through scripts currently included in the ESMValTool package.At the heart of this new version is the ESMValCore software package, which provides a configurable framework for finding CMIP files using a &#8220;data reference syntax&#8221;, applying commonly used pre-processing functions to them, running analysis scripts, and recording provenance. Numerous pre-processing functions, e.g. for data selection, regridding, and statistics are readily available and the modular design makes it easy to add more. The ESMValCore package is easy to install with relatively few dependencies, written in Python 3, based on state-of-the-art open-source libraries such as Iris and Dask, and widely used standards such as YAML, NetCDF, CF-Conventions, and W3C PROV. An extensive set of automated tests and code quality checks ensure the reliability of the package. Documentation is available at https://esmvaltool.readthedocs.io.The ESMValCore package uses human-readable recipes to define which variables and datasets to use, how to pre-process that data, and what scientific analysis scripts to run. The package provides convenient interfaces, based on the YAML and NetCDF/CF-convention file formats, for running diagnostic scripts written in any programming language. Because the ESMValCore framework takes care of running the workflow defined in the recipe in parallel, most analyses run much faster, with no additional programming effort required from the authors of the analysis scripts. For example, benchmarks show a factor of 30 speedup with respect to version 1 of the tool for a representative recipe on a 24 core machine. A large collection of standard recipes and associated analysis scripts is available in the ESMValTool package for reproducing selected peer-reviewed analyses. The ESMValCore package can also be used with any other script that implements it&#8217;s easy to use interface. All pre-processing functions of the ESMValCore can also be used directly from any Python program. These features allow for use by a wide community of scientific users and developers with different levels of programming skills and experience.Future plans involve extending the public Python API (application programming interface) from just preprocessor functions to include all functionality, including finding the data and running diagnostic scripts. This would make ESMValCore suitable for interactive data exploration from a Jupyter Notebook.

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