On the predictability of land surface fluxes

The land surface is a key component in the earth’s climate system, and is one of the main contributors to uncertainty in climate projections. Fluxes of energy, momentum, water and carbon between the land surface and the atmosphere provide important feedbacks in the climate system. Land surface models (LSMs) have been a key tool in understanding land surface fluxes, the processes that drive them, and their impacts on climate change. Recent work by Best et al. (2015) has demonstrated that the performance of modern LSMs is much worse than expected, and this has acted as a catalyst, forcing the land surface community to re-evaluate models and the methods used to evaluate them. This thesis contributes to this effort by attempting to address the question *how predictable are land surface fluxes* in two ways: by analysing the performance of LSMs, and by addressing the predictability of measured fluxes in the FLUXNET dataset directly using empirical modelling. I first explore the performance of LSMs and show that the Best et al. (2015) results are not due to poor methodology; and further, performance problems appear to be at least partly shared between models. Next, I assess the predictability of land surface fluxes from meteorological variables by using empirical models on a broader set of FLUXNET data. This work pinpoints a number of key variables that provide independent information suitable for predicting fluxes. It also describes a set of empirical models of staged complexity that are suitable as benchmarks for LSMs. Finally, I investigate the variability of predictability within the FLUXNET data, and the degree to which it is determined by site characteristics. Predictability is shown to be affected by site aridity, but is otherwise less determined by site characteristics than expected. This thesis shows that substantial performance gains can be made in land surface predictability, with further gains likely with better exploitation of newly available observational data. The methods described provide a basis for improving the standardisation and rigour of LSM evaluation, which will ultimately lead to improvements to land surface models, and the climate projections that are based on them.

Grantor dc:publisher

UNSW, Sydney

Year dc:date

2019

Author dc:creator

• Haughton, Ned

• Land surface modelling
• Land surface fluxes
• Land-atmosphere interactions
• Model evaluation
• Hydrological modelling

Statement dc:rights

• open access
• CC BY-NC-ND 3.0
• free_to_read

Licence

• https://purl.org/coar/access_right/c_abf2
• https://creativecommons.org/licenses/by-nc-nd/3.0/au/

Language dc:language

EN

Identifier

https://doi.org/10.26190/unsworks/21219

OAI identifier oai:identifier

oai:unsworks.library.unsw.edu.au:1959.4/61990