The Plumbing of Land Surface Models: Benchmarking Model Performance

Abstract

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori-before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs' performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

“…Hence, particularly in the northern part of the catchment, further observations such as ET measurements are desirable for further improving the land surface model. These additional observations could be used for future land surface model benchmarking (Best et al, 2015) or for more constrained parameter estimates (Shi et al, 2015).…”

Improving river flow generation over Great Britain in a land surface model required for coupled land-atmosphere interactions

“…Previous studies using JULES (e.g. Best et al, 2015;Schellekens et al, 2017;Ukkola et al, 2016) use a fixed 0 parameter within the PDM scheme. In this study we vary the values of 0 and are able to improve performance (% bias and NS) as a result.…”

FluxnetLSM R package (v1.0): a community tool for processing FLUXNET data for use in land surface modelling

“…For evaluation variables, longer gaps (by default up to 30 days, set by argument regfill) are gap-filled using a linear regression of each evaluation variable against one or several meteorological variables (adapted from Best et al, 2015). When incoming short-wave radiation, air temperature and humidity (relative humidity or vapour pressure deficit) are available, the code will perform a multiple linear regression against these variables.…”

“…Flux towers measure ecosystem-scale exchanges of carbon dioxide, water vapour fluxes and energy (Baldocchi, 2014) and have proven invaluable for LSM evaluation and benchmarking (Abramowitz et al, 2008;Best et al, 2015;Blyth et al, 2010;Haughton et al, 2016;Luo et al, 2012;Williams et al, 2009). Flux towers are particularly useful for modelling applications as they provide simultaneous observations of the meteorological data needed for forcing offline models as well as the key ecosystem variables against which models may be evaluated (e.g.…”

“…Past studies have relied on various (often ad hoc) gap-filling methods that are rarely fully documented in the literature. Worryingly, it would be virtually impossible to reproduce many existing LSM evaluation and benchmarking studies, although we note some exceptions (Best et al, 2015). The R package provides a community tool for creating LSM forcing datasets in a fully citeable and reproducible framework.…”