Journal cover Journal topic
Earth System Science Data The data publishing journal
Journal topic

Journal metrics

Journal metrics

  • IF value: 9.197 IF 9.197
  • IF 5-year value: 9.612 IF 5-year
    9.612
  • CiteScore value: 12.5 CiteScore
    12.5
  • SNIP value: 3.137 SNIP 3.137
  • IPP value: 9.49 IPP 9.49
  • SJR value: 4.532 SJR 4.532
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 48 Scimago H
    index 48
  • h5-index value: 35 h5-index 35
Preprints
https://doi.org/10.5194/essd-2020-52
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-52
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 28 May 2020

Submitted as: data description paper | 28 May 2020

Review status
This preprint is currently under review for the journal ESSD.

BAYWRF: a convection-resolving,present-day climatological atmospheric dataset for Bavaria

Emily Collier and Thomas Mölg Emily Collier and Thomas Mölg
  • Climate System Research Group, Institute of Geography, Friedrich-Alexander University Erlangen-Nürnberg(FAU), Erlangen, Germany

Abstract. Climate impact assessments require information about climate change at regional and ideally local scales. In dendroecological studies, this information has traditionally been obtained using statistical methods, which preclude the linkage of local climate changes to large-scale drivers in a process-based way. As part of recent efforts to investigate the impact of climate change on forest ecosystems in Bavaria, Germany, within the BayTreeNet project, we developed a high-resolution atmospheric modelling dataset, BAYWRF, for the region of Bavaria over the thirty-year period of September 1987 to August 2018. The atmospheric model employed in this study, WRF, was configured with two nested domains of 7.5- and 1.5-km grid spacing, centred over Bavaria and forced at the outer lateral boundaries by ERA5 reanalysis data. Based on a shorter evaluation period of September 2017 to August 2018, we evaluate two aspects of the simulations: (i) we assess model biases compared with an extensive network of observational data at both two-hourly and daily mean temporal resolutions, and (ii) we investigate the influence of using grid analysis nudging. The model represents variability in near-surface meteorological conditions well, with a clear improvement when nudging is used, although there are cold and warm biases in winter and summer, respectively. We also present a brief overview of the full dataset, which will provide a unique and valuable tool for investigating climate change in Bavaria with high interdisciplinary relevance. Data from the finest resolution WRF domain are available for download at daily temporal resolution from a public repository at the Open Science Framework (Collier, 2020; https://www.doi.org/10.17605/OSF.IO/AQ58B).

Emily Collier and Thomas Mölg

Interactive discussion

Status: open (until 12 Aug 2020)
Status: open (until 12 Aug 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Emily Collier and Thomas Mölg

Data sets

BAYWRF E. Collier https://doi.org/10.17605/OSF.IO/AQ58B

Emily Collier and Thomas Mölg

Viewed

Total article views: 217 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
141 69 7 217 3 3
  • HTML: 141
  • PDF: 69
  • XML: 7
  • Total: 217
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 28 May 2020)
Cumulative views and downloads (calculated since 28 May 2020)

Viewed (geographical distribution)

Total article views: 198 (including HTML, PDF, and XML) Thereof 198 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

Latest update: 09 Jul 2020
Download
Short summary
As part of the BayTreeNet project that aims to investigate the impact of climate change on forest ecosystems in Bavaria, we developed a high-resolution atmospheric dataset, BAYWRF, for this region that covers the period of September 1987 to August 2018. The data reproduce observed variability in recent meteorological conditions well and provide a useful tool for linking large-scale climate change to local impacts on economic, societal, ecological, and agricultural processes.
As part of the BayTreeNet project that aims to investigate the impact of climate change on...
Citation