Abstract
Abstract. This study compares the performance of the Community Land Models (CLM4.5 and CLM5) against tower and ground measurements from a tropical montane rainforest in Costa Rica. The study site receives over 4000 mm of mean annual precipitation and has high daily levels of relative humidity. The measurement tower is equipped with eddy-covariance and vertical profile systems able to measure various micrometeorological variables, particularly in wet and complex terrain. In this work, results from point-scale simulations for both CLM4.5 and its updated version (CLM5) are compared to observed canopy flux and micrometeorological data. Both models failed to capture the effects of frequent rainfall events and mountainous topography on the variables of interest (temperatures, leaf wetness, and fluxes). Overall, CLM5 alleviates some errors in CLM4.5, but CLM5 still cannot precisely simulate a number of canopy processes for this forest. Soil, air, and canopy temperatures, as well as leaf wetness, remain too sensitive to incoming solar radiation rates despite updates to the model. As a result, daytime vapor flux and carbon flux are overestimated, and modeled temperature differences between day and night are higher than those observed. Slope effects appear in the measured average diurnal variations of surface albedo and carbon flux, but CLM5 cannot simulate these features. This study suggests that both CLMs still require further improvements concerning energy partitioning processes, such as leaf wetness process, photosynthesis model, and aerodynamic resistance model for wet and mountainous regions.
Highlights
Tropical forests play a critical role in determining regional and global climate
Net radiation was underpredicted by an average of −20 W m−2 (Fig. 4a, b) in both CLM4.5 and CLM4.5 and its updated version (CLM5)
photosynthetically active radiation (PAR) profiles showed that radiation levels within the canopy had a skewed, or hysteretic, cycle (Fig. 4e, f), which was not captured by CLM
Summary
Tropical forests play a critical role in determining regional and global climate. Due to their significance for the global water (Zhang et al, 2010; Choudhury and DiGirolamo, 1998) and carbon cycles (Huntingford et al, 2013; Beer et al, 2010), accurate modeling of tropical regions is important for the prediction of future climate and climate change impacts. To improve land surface models addressing tropical ecosystem biosphere–atmosphere interactions, accurately partitioning net radiation (energy) and water is critical for these models, especially with respect to estimating latent heat flux. The modeling community has recently embraced additional components in order to represent more realistic processes and to resolve research questions related to soil carbon and nitrogen cycling (Thornton et al, 2007), multilayer plant canopies (Ryder et al, 2016; Launiainen et al, 2015; Bonan et al, 2018), and even more sophisticated systems (e.g., urban settings, heat stress effects) (Lawrence et al, 2018; Buzan et al, 2015) These changes have led to the development of a plethora of sub-models, making it difficult to identify a specific sub-model or set of sub-models from which model error arises. 4. to determine which canopy–atmosphere processes (i.e., sub-models) are most poorly represented in order to suggest priorities for future model improvements
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