GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses
Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide...
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ftcopernicus:oai:publications.copernicus.org:gmd76867 2023-05-15T17:58:00+02:00 GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses Cao, Bin Quan, Xiaojing Brown, Nicholas Stewart-Jones, Emilie Gruber, Stephan 2019-11-07 application/pdf https://doi.org/10.5194/gmd-12-4661-2019 https://gmd.copernicus.org/articles/12/4661/2019/ eng eng doi:10.5194/gmd-12-4661-2019 https://gmd.copernicus.org/articles/12/4661/2019/ eISSN: 1991-9603 Text 2019 ftcopernicus https://doi.org/10.5194/gmd-12-4661-2019 2020-07-20T16:22:35Z Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data. Text permafrost Tundra Copernicus Publications: E-Journals Canada Merra ENVELOPE(12.615,12.615,65.816,65.816) Geoscientific Model Development 12 11 4661 4679 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data. |
format |
Text |
author |
Cao, Bin Quan, Xiaojing Brown, Nicholas Stewart-Jones, Emilie Gruber, Stephan |
spellingShingle |
Cao, Bin Quan, Xiaojing Brown, Nicholas Stewart-Jones, Emilie Gruber, Stephan GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses |
author_facet |
Cao, Bin Quan, Xiaojing Brown, Nicholas Stewart-Jones, Emilie Gruber, Stephan |
author_sort |
Cao, Bin |
title |
GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses |
title_short |
GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses |
title_full |
GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses |
title_fullStr |
GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses |
title_full_unstemmed |
GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses |
title_sort |
globsim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses |
publishDate |
2019 |
url |
https://doi.org/10.5194/gmd-12-4661-2019 https://gmd.copernicus.org/articles/12/4661/2019/ |
long_lat |
ENVELOPE(12.615,12.615,65.816,65.816) |
geographic |
Canada Merra |
geographic_facet |
Canada Merra |
genre |
permafrost Tundra |
genre_facet |
permafrost Tundra |
op_source |
eISSN: 1991-9603 |
op_relation |
doi:10.5194/gmd-12-4661-2019 https://gmd.copernicus.org/articles/12/4661/2019/ |
op_doi |
https://doi.org/10.5194/gmd-12-4661-2019 |
container_title |
Geoscientific Model Development |
container_volume |
12 |
container_issue |
11 |
container_start_page |
4661 |
op_container_end_page |
4679 |
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1766166524266020864 |