Abstract
Abstract. Land surface heterogeneity has long been recognized as important to represent in the land surface models. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types, although subgrid topography also has major controls on surface processes. In this study, we developed a new subgrid classification method (SGC) that accounts for variability of both topography and vegetation cover. Each model grid cell was represented with a variable number of elevation classes and each elevation class was further described by a variable number of vegetation types optimized for each model grid given a predetermined total number of land response units (LRUs). The subgrid structure of the Community Land Model (CLM) was used to illustrate the newly developed method in this study. Although the new method increases the computational burden in the model simulation compared to the CLM subgrid vegetation representation, it greatly reduced the variations of elevation within each subgrid class and is able to explain at least 80% of the total subgrid plant functional types (PFTs). The new method was also evaluated against two other subgrid methods (SGC1 and SGC2) that assigned fixed numbers of elevation and vegetation classes for each model grid (SGC1: M elevation bands–N PFTs method; SGC2: N PFTs–M elevation bands method). Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0°and 2.0°) with three maximum-allowed total number of LRUs (i.e., NLRU of 24, 18 and 12) over North America (NA), the new method yielded more computationally efficient subgrid representation compared to SGC1 and SGC2, particularly at coarser model resolutions and moderate computational intensity (NLRU = 18). It also explained the most PFTs and elevation variability that is more homogeneously distributed spatially. The SGC method will be implemented in CLM over the NA continent to assess its impacts on simulating land surface processes.
Highlights
As the terrestrial component of earth system models, land surface models play important roles in representing the interactions between terrestrial biosphere and atmosphere, which is important for predicting future states of the earth system and assessing anthropogenic impacts on the climate system
When assigning one elevation band to each plant functional types (PFTs) within the model grid, the spatial distribution of σep clearly corresponds with topographic variations (Fig. 4c and d)
Using updated datasets of high-resolution digital elevation model (DEM) and PFTs, this study provides a systematic analysis and comparison of different ways to classify subgrid surface elevation and vegetation to provide an optimal approach that improves both accuracy and computational efficiency
Summary
As the terrestrial component of earth system models, land surface models play important roles in representing the interactions between terrestrial biosphere and atmosphere, which is important for predicting future states of the earth system and assessing anthropogenic impacts on the climate system. Using land surface parameters and meteorological forcing data as input, land surface models simulate key land processes such as photosynthesis, respiration, and evapotranspiration that regulate mass, energy, moisture, and momentum exchange between soil, vegetation and atmosphere. Realistic and high spatial resolution representation of land surface characteristics is important for accurate estimation of surface hydrology, heat fluxes, and surface CO2 exchanges in climate models for applications across global, regional, and sub-regional scales. Numerous efforts have improved the representation of land surface characteristics either by developing higher-resolution land cover datasets (e.g., Bonan et al, 2002a, b; Lawrence and Chase, 2007; Ke et al, 2012) or by representing subgrid spatial heterogeneity of land surface parameters (e.g., Koster and Suarez, 1992; Seth et al, 1994). Y. Ke et al.: Enhancing the representation of subgrid land surface characteristics
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