Globsim v.3 – Improvements to an open-source software library for utilizing atmospheric reanalyses in point-scale land surface simulation

Abstract

The lack of long-term and consistent meteorological observations limits the application of land-surface simulators (e.g., of phenomena in hydrology, the cryosphere, ecology) at remote locations. For example, most permafrost areas are remote and lacking consistent meteorological time series, models that describe permafrost change over time cannot be driven for comparison with observations or for impact studies. Reanalysis-derived time series are valuable because they are available with global coverage, for a long time period, and for a broad set of physically consistent variables. Multiple reanalyses can be used to provide estimates of uncertainty. Practically, however, this data is difficult to use for several reasons: grid-scale reanalyses must be downscaled and interpolated horizontally (and vertically within the atmospheric column for mountains regions) to the site‑scale, differences in variables, units, and delivery between reanalyses must be reconciled, and large volumes of data need to be handled. Globsim is an open-source python library (available via GitHub) that was developed to handle these challenges and to facilitate a simulation workflow that takes advantage of the multiple reanalysis products available today. It outputs sub-daily meteorological time series that resemble meteorological stations for any location on the planet. Since the release of the first version of Globsim, we have improved usability, refactored code for maintainability and speed, and fixed a number of bugs. We also added support for ERA5 ensemble data, and added more sophisticated heuristic downscaling algorithms, including TOPOscale for elevation-adjusted radiative fluxes. We use Globsim as a core tool in a multi-model permafrost simulation workflow and, as a future step, we intend to use it as part of a debiasing routine to make predictions of permafrost using climate scenarios. We expect this tool to be broadly applicable to climate change impact modelers and other scientists using climate driven simulations working in (remote) locations that lack meteorological data of sufficient quality and duration for their application.