Hourly historical and near-future weather and climate variables for energy system modelling

Bloomfield, Hannah C.; Brayshaw, David J.; Deakin, Matthew; Greenwood, David

doi:https://doi.org/10.5194/essd-14-2749-2022

Articles | Volume 14, issue 6

https://doi.org/10.5194/essd-14-2749-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-14-2749-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 6

Data description paper

|

15 Jun 2022

Data description paper |

| 15 Jun 2022

Hourly historical and near-future weather and climate variables for energy system modelling

Hannah C. Bloomfield, David J. Brayshaw, Matthew Deakin, and David Greenwood

Download

Final revised paper (published on 15 Jun 2022)
Preprint (discussion started on 28 Jan 2022)

Interactive discussion

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.

# 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 parenthesis

Citation: https://doi.org/10.5194/essd-2021-436-RC1
- AC1: 'Reply on RC1', Hannah Bloomfield, 11 Apr 2022
  
  Thanks for your useful feedback which has helped to improve the manuscript. A detailed response to all of the comments is given in the attached PDF. We will be sure to activate the DOI's on the datasets as soon as the reviewers are happy.
  
  Citation: https://doi.org/10.5194/essd-2021-436-AC1
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?

Citation: https://doi.org/10.5194/essd-2021-436-RC2
- AC2: 'Reply on RC2', Hannah Bloomfield, 11 Apr 2022
  
  Thanks for your useful feedback which has helped to improve the manuscript. A detailed response to all of the comments is given in the attached PDF. We will be sure to activate the DOI's on the datasets as soon as the reviewers are happy.
  
  Citation: https://doi.org/10.5194/essd-2021-436-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Hannah Bloomfield on behalf of the Authors (11 Apr 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (10 May 2022) by Martin Schultz

AR by Hannah Bloomfield on behalf of the Authors (12 May 2022)

Short summary

There is a global increase in renewable generation to meet carbon targets and reduce the impacts of climate change. Renewable generation and electricity demand depend on the weather. This means there is a need for high-quality weather data for energy system modelling. We present a new European-level, 70-year dataset which has been specifically designed to support the energy sector. We provide hourly, sub-national climate outputs and include the impacts of near-term climate change.