the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hourly historical and near-future weather and climate variables for energy system modelling
- 1Department of Meteorology, University of Reading, UK
- 2School of Geographical Sciences, University of Bristol, UK
- 3National Centre for Atmospheric Science, Reading, UK
- 4Newcastle University, Newcastle-upon-Tyne, UK
- 1Department of Meteorology, University of Reading, UK
- 2School of Geographical Sciences, University of Bristol, UK
- 3National Centre for Atmospheric Science, Reading, UK
- 4Newcastle University, Newcastle-upon-Tyne, UK
Abstract. Energy systems are becoming increasingly exposed to the impacts of weather and climate due to the uptake of renewable generation and the electrification of the heat and transport sectors. The need for high-quality meteorological data to manage present and near-future risks is urgent. This paper provides a comprehensive set of multi-decadal, time series of hourly meteorological variables and weather-dependent power systems components for use in the energy systems modelling community. Despite the growing interest in the impacts of climate variability and climate change on energy systems over the last decade, it remains rare for multi-decadal simulations of meteorological data to be used within detailed simulations. This is partly due to computational constraints, but also due to technical barriers limiting the use of meteorological data by non-specialists. This paper presents a new European level dataset which can be used to investigate the impacts of climate variability and climate change on multiple aspects of near-future energy systems. The datasets correspond to a suite of well-documented, easy-to-use, self-consistent hourly- nationally-aggregated and sub-national time series for 2 m temperature, 10 m wind speed, 100 m wind speed, surface solar irradiance, wind power capacity factor, solar power factor and degree days spanning over 30 European countries. This dataset is available for the historical period (1950–2020), and is accessible from https://researchdata.reading.ac.uk/id/eprint/321 with reserved DOI: http://dx.doi.org/10.17864/1947.000321 (Bloomfield and Brayshaw, 2021b).
As well as this a companion dataset is created where the ERA5 reanalysis is adjusted to represent the impacts of near-term climate change (centred on the year 2035) based on five high resolution climate model simulations. This data is available for a 70 year period for central and Northern Europe. The data is accessible from https://researchdata.reading.ac.uk/id/eprint/331 with reserved DOI: http://dx.doi.org/10.17864/1947.000331 (Bloomfield and Brayshaw, 2021a).
To the authors’ knowledge, this is the first time a comprehensive set of high quality hourly time series relating to future climate projections has been published, which is specifically designed to support the energy sector. The purpose of this paper is to detail the methods required for processing the climate model data and illustrate the importance of accounting for climate variability and climate change within energy system modelling from sub-national to European scale. While this study is therefore not intended to be an exhaustive analysis of climate impacts, it is hoped that publishing this data will promote greater use of climate data within energy system modelling.
Hannah C. Bloomfield et al.
Status: closed
-
RC1: 'Comment on essd-2021-436', Anonymous Referee #1, 18 Mar 2022
# General Comments
The data is accessible from the provided links and in good shape. I was able to download the data, load it into memory and inspect it. It would be helpful to mention that the climate projected data comprises "only" 3 years. The major drawback is the confinement of the sub-national datasets to the UK (and Ireland for offshore). However the data will be useful for studies focused on UK and potentially the North-Sea area where major investments in offshore wind power are to expect in the upcoming years.Â
# Specific comments
* Introduction: it was not mentioned that their exist automated tools that convert different kind of meteorological datasets into spatially resolved time-series for renewable technologies, like `atlite` (<https://doi.org/10.21105/joss.03294>) or `pvlib` (<https://doi.org/10.21105/joss.00884>). These may potentially allow for processing climate projected data.
* Introduction: There have been other approaches made to use climate projected data from EURO-CORDEX for energy system modelling, e.g. (<https://www.sciencedirect.com/science/article/abs/pii/S0306261918313953>)
* For now the geographical scope of the national and sub-national dataset are diverging. The sub-national dataset only includes GB (and Ireland for offshore). However, for European energy system models we need a sub-national resolution across multiple countries. For a future project it would be helpful to create datasets on NUTS1/NUTS2 resolution for same set of countries as included in the NUTS0 dataset.
* The section 4.2. "How could climate change impact past power system extremes?" is a bit poor. The authors set a strong focus on the extreme temperature periods. However it is rather the interplay between renewable power potential and the demand that is important here. Dark, cold periods with weak wind potential are the most challenging for the (renewable) energy supply. How and to what extent are these changing in the climate projections?
* The python scripts in <https://researchdata.reading.ac.uk/331/> rely on a long deprecated python package `mpl_toolkits.basemap` which was deprecated in the favor of Cartopy. When including such a package it would be helpful to provide a general conda `environment.yaml` file or a pip `requirements.txt`. Otherwise it is hard for users to run the scripts.
# Technical Corrections
* Equation (5) misses a closing parenthesisÂ
- AC1: 'Reply on RC1', Hannah Bloomfield, 11 Apr 2022
-
RC2: 'Comment on essd-2021-436', Anonymous Referee #2, 20 Mar 2022
## Comments
Overall, the manuscript at hand is of high quality. The motivations and methodology are clearly described and the text is easy to follow.
The main purpose of the paper is to introduce datasets of bias-corrected historical and projected time series of weather and energy system variables at the European level. Historical data are based on ERA5. Future projections are based on selected global climate models. The datasets are suitably licensed under CC-BY 4.0 and accompanied by explanations in a README. NetCDF is the file format of choice.I fully agree with the other reviewer (https://doi.org/10.5194/essd-2021-436-RC1). Rather than repeating their arguments, I would like to second all points raised to give them more weight.
In particular, I would encourage the authors to evaluate whether
- they could extend the datasets to NUTS1 and NUTS2 across all of Europe within a reasonable amount of work to maximise the dataset's impact,
- they could clear out deprecations in code and provide a dependencies file to execute the Python scripts in order to ease reproducibility,
- they could specify more prominently for which levels of radiative forcing the future time series are provided.
## Technical Corrections
- line 336: 70 yer -> 70 year?
- AC2: 'Reply on RC2', Hannah Bloomfield, 11 Apr 2022
Status: closed
-
RC1: 'Comment on essd-2021-436', Anonymous Referee #1, 18 Mar 2022
# General Comments
The data is accessible from the provided links and in good shape. I was able to download the data, load it into memory and inspect it. It would be helpful to mention that the climate projected data comprises "only" 3 years. The major drawback is the confinement of the sub-national datasets to the UK (and Ireland for offshore). However the data will be useful for studies focused on UK and potentially the North-Sea area where major investments in offshore wind power are to expect in the upcoming years.Â
# Specific comments
* Introduction: it was not mentioned that their exist automated tools that convert different kind of meteorological datasets into spatially resolved time-series for renewable technologies, like `atlite` (<https://doi.org/10.21105/joss.03294>) or `pvlib` (<https://doi.org/10.21105/joss.00884>). These may potentially allow for processing climate projected data.
* Introduction: There have been other approaches made to use climate projected data from EURO-CORDEX for energy system modelling, e.g. (<https://www.sciencedirect.com/science/article/abs/pii/S0306261918313953>)
* For now the geographical scope of the national and sub-national dataset are diverging. The sub-national dataset only includes GB (and Ireland for offshore). However, for European energy system models we need a sub-national resolution across multiple countries. For a future project it would be helpful to create datasets on NUTS1/NUTS2 resolution for same set of countries as included in the NUTS0 dataset.
* The section 4.2. "How could climate change impact past power system extremes?" is a bit poor. The authors set a strong focus on the extreme temperature periods. However it is rather the interplay between renewable power potential and the demand that is important here. Dark, cold periods with weak wind potential are the most challenging for the (renewable) energy supply. How and to what extent are these changing in the climate projections?
* The python scripts in <https://researchdata.reading.ac.uk/331/> rely on a long deprecated python package `mpl_toolkits.basemap` which was deprecated in the favor of Cartopy. When including such a package it would be helpful to provide a general conda `environment.yaml` file or a pip `requirements.txt`. Otherwise it is hard for users to run the scripts.
# Technical Corrections
* Equation (5) misses a closing parenthesisÂ
- AC1: 'Reply on RC1', Hannah Bloomfield, 11 Apr 2022
-
RC2: 'Comment on essd-2021-436', Anonymous Referee #2, 20 Mar 2022
## Comments
Overall, the manuscript at hand is of high quality. The motivations and methodology are clearly described and the text is easy to follow.
The main purpose of the paper is to introduce datasets of bias-corrected historical and projected time series of weather and energy system variables at the European level. Historical data are based on ERA5. Future projections are based on selected global climate models. The datasets are suitably licensed under CC-BY 4.0 and accompanied by explanations in a README. NetCDF is the file format of choice.I fully agree with the other reviewer (https://doi.org/10.5194/essd-2021-436-RC1). Rather than repeating their arguments, I would like to second all points raised to give them more weight.
In particular, I would encourage the authors to evaluate whether
- they could extend the datasets to NUTS1 and NUTS2 across all of Europe within a reasonable amount of work to maximise the dataset's impact,
- they could clear out deprecations in code and provide a dependencies file to execute the Python scripts in order to ease reproducibility,
- they could specify more prominently for which levels of radiative forcing the future time series are provided.
## Technical Corrections
- line 336: 70 yer -> 70 year?
- AC2: 'Reply on RC2', Hannah Bloomfield, 11 Apr 2022
Hannah C. Bloomfield et al.
Data sets
ERA5 derived time series of European aggregated surface weather variables, wind power, and solar power capacity factors: hourly data from 1950-2020. Bloomfield, H. C. and Brayshaw., D. J. https://researchdata.reading.ac.uk/id/eprint/321
Future climate projections of surface weather variables, wind power, and solar power capacity factors across North-West Europe Bloomfield., H. C., Brayshaw., D. J. https://researchdata.reading.ac.uk/id/eprint/331
Hannah C. Bloomfield et al.
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