TY - JOUR
AU - 
AB - The Cryosphere, 11, 989–996, 2017 www.the-cryosphere.net/11/989/2017/ doi:10.5194/tc-11-989-2017 © Author(s) 2017. CC Attribution 3.0 License. 1,† 2 3 Andrew G. Slater , David M. Lawrence , and Charles D. Koven NSIDC/CIRES, University of Colorado, Boulder, CO 80303, USA National Center for Atmospheric Research, Boulder, CO 80305, USA Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA deceased, September 2016 Correspondence to: David Lawrence (dlawren@ucar.edu) Received: 8 November 2016 – Discussion started: 15 November 2016 Revised: 15 March 2017 – Accepted: 16 March 2017 – Published: 20 April 2017 Abstract. Land models require evaluation in order to un- models produce external and internal feedbacks that can op- derstand results and guide future development. Examining erate on various temporal and spatial scales. It is therefore functional relationships between model variables can pro- imperative that such models be rigorously evaluated in or- vide insight into the ability of models to capture fundamen- der to interpret their performance, as well as to guide future tal processes and aid in minimizing uncertainties or deficien- development. cies in model forcing. This study quantifies the proficiency Verifying a model result against observations using statis- of land models to appropriately transfer heat from the soil tics such as root 
TI - Process-level model evaluation: a snow and heat transfer metric
JF - The Cryosphere
DO - 10.5194/tc-11-989-2017
DA - 2017-04-20
UR - https://www.deepdyve.com/lp/unpaywall/process-level-model-evaluation-a-snow-and-heat-transfer-metric-c8V8YeN4FJ
DP - DeepDyve
ER -