## 1 Introduction  
Plant functional traits are core attributes that reflect important ecological strategies in plants [1; 2]. As a leaf functional trait, specific leaf area (SLA) is an important indicator of resource trade-off strategies under environmental changes, which determines the ability of a plant to capture light [3; 4; 5]. Specific leaf area refers to the leaf area per unit of leaf biomass, which is of great significance for vegetation photosynthesis and ecosystem productivity [6; 7].  
Substantial variation in SLA exists among plant functional types (PFTs) (e.g., needleleaf evergreen tree and broadleaf deciduous tree) and species for a given PFT. Numerous surveys have shown that SLA varies with different plant functional types (PFTs), following a general pattern of herb > shrub > tree, broadleaved > coniferous, and deciduous > evergreen [8; 9; 10]. Moreover, plant SLA among different species also shows significant variability. Based on the TRY Global Plant Traits Database, the variation coefficients (CV)of SLA data within the same PFT reach up to 23%-78% [9]. Such a large interspecific variation in SLA for a given PFT has also been reported in China and other regions [11]. For example, [PERSON] et al. investigated plant SLA for 76 natural communities in China, the CV of different forest types ranged from 40% to 60%, and those of herb and shrub were 66% and 57%, respectively [12]. In the study by [PERSON] et al., the CV of SLA was 34.3% for the broadleaf deciduous tree and 44.7% for the needleleaf evergreen tree based on field data across sites in the USA [13]. However, such a leaf trait diversity has not been well described in state-of-the-art terrestrial biosphere models.  
In most terrestrial biosphere models, vegetation is grouped into different plant functional types with specific plant functional traits. This simple scheme is an effective way to characterize the functional diversity of ecosystems on the regional and global scales [14; 15]. As for the expression of SLA, each plant functional type has a corresponding mean SLA value that can be calculated from observations. Few models consider the vertical variability of SLA within the canopy as in the community land model (CLM) [16; 17]. Nevertheless, the default trait value settings for each PFT in different terrestrial biosphere models vary with each other and are different from the observations made at multiple spatial scales [9; 18]. The uncertainties in plant trait parameters would considerably impact the prediction of vegetation productivity [19; 20]. For example, the uncertainties in leaf longevity resulted in a greater than 30% change in vegetation biomass for temperate broadleaf evergreen tree in the Lund-Potsdam-Jena model [21]. However, how the SLA variation within each plant functional type influences the terrestrial gross primary productivity (GPP) simulation still remains unclear.  
The objectives of this study were to investigate (1) the mismatch between simulated SLA in the CLM (version 4.5) and observed data for 1056 species in China and the differences in default SLA values across seven terrestrial biosphere models and (2) how the variation in SLA values influences GPP modeling for different ecosystems. Here, we first compared the default SLA values in the CLM4.5 model with published plant SLA observation data collected in China from 2005 to 2022. The variation in observed SLA data for each PFT and the difference in default parameter values of SLA in seven terrestrial biosphere models (i.e., BEPS, JULES, Hybrid, BIOME-BGC, SiBCASA, CLM4.5, and IBIS) were then quantified. Taking three forest ecosystems and one grassland ecosystem as an example, we finally quantified the uncertainties in modeled GPP caused by the mismatch in SLA between the mean observed values and original default values in CLM4.5, and the SLA variations among species and across different models for a given PFT.