Earth System Models (ESMs) project that the terrestrial carbon sink will continue to grow as atmospheric CO$_2$ increases, but this projection is uncertain due to biases in the simulated climate and how ESMs represent ecosystem processes. In particular, the strength of the CO$_2$ fertilization effect, which is modulated by nutrient cycles, varies substantially across models. This study evaluates land carbon balance uncertainties for the Canadian Earth System Model (CanESM) by conducting simulations where the latest version of CanESM’s land surface component is driven offline with raw and bias-adjusted CanESM5 climate forcing data. To quantify the impact of nutrient limitation, we complete simulations where the nitrogen cycle is enabled or disabled. Results show that bias adjustment improves model performance across most ecosystem variables, primarily due to reduced biases in precipitation. Turning the nitrogen cycle on increases the global land carbon sink during the historical period (1995-2014) due to enhanced nitrogen deposition, placing it within the Global Carbon Budget uncertainty range. During the future period (2080-2099), the simulated land carbon sink increases in response to bias adjustment and decreases in response to the dynamic carbon-nitrogen interaction, leading to a net decrease when both factors are acting together. The dominating impact of the nitrogen cycle demonstrates the importance of representing nutrient limitation in ESMs. Such efforts may produce more robust carbon balance projections in support of global climate change mitigation policies such as the 2015 Paris Agreement.

Drought conditions caused by soil moisture stress and/or high vapour pressure deficit pose a challenge to many terrestrial ecosystem models (TEMs). The Canadian LAnd Surface Scheme Including biogeochemical Cycles (CLASSIC) employs an empirical approach to link soil moisture stress with stomatal conductance. Such soil moisture-based empirical approaches typically perform poorly during drought. Here, we implemented an explicit plant hydraulics parameterization, i.e., Stomatal Optimization based on Xylem hydraulics (SOX), in CLASSIC, thereby connecting the soil-plant-atmosphere continuum through plant hydraulic traits. Performance of the resulting CLASSIC$_{SOX}$ was evaluated against carbon and water fluxes measured with eddy covariance at eight boreal forest flux tower sites in North America. Compared to CLASSIC, CLASSIC$_{SOX}$ better simulated gross primary productivity (GPP) across all sites, i.e., coefficient of determination (R$^{2}$) increased (0.51 to 0.59), root mean square error (RMSE) and bias decreased (1.85 to 1.54 g C m$^{-2}$ d$^{-1}$) and (-0.99 to -0.58 g C m$^{-2}$ d$^{-1}$), respectively. Under drought conditions, identified using the Palmer drought severity index, GPP simulated with CLASSIC$_{SOX}$ improved compared to CLASSIC, i.e., R$^{2}$ increased (0.51 to 0.60), and RMSE and bias decreased (1.79 to 1.46 g C m$^{-2}$ d$^{-1}$) and (-0.97 to -0.53 g C m$^{-2}$ d$^{-1}$), respectively. In contrast, CLASSIC$_{SOX}$ simulated evapotranspiration worsened, i.e., R$^{2}$ decreased (0.61 to 0.42), RMSE increased (0.54 to 0.62 mm d$^{-1}$), and bias changed direction (0.09 to -0.09 mm d$^{-1}$). As evaporation is a highly parameterized process in CLASSIC, it likely needs to be re-parameterized to account for the SOX transpiration behaviour.

Process-based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH4 fluxes (FCH4) at different time scales. A Monte Carlo approach has been developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that 1) significant model-observation disagreements are mainly at short- to intermediate time scales (< 15 days); 2) most of the models can capture the CH4 variability at long time scales (> 32 days) for the boreal and Arctic tundra wetland sites but have limited performance for temperate and tropical/subtropical sites; 3) model error approximates pink noise patterns, indicating that biases at short time scales (< 5 days) could contribute to persistent systematic biases on longer time scales; and 4) differences in error pattern are related to model structure (e.g. proxy of CH4 production). Our evaluation suggests the need to accurately replicate FCH4 variability in future wetland CH4 model developments.

Canada’s boreal forests and tundra ecosystems are responding to unprecedented climate change with implications for the global carbon (C) cycle and global climate. However, our ability to model the response of Canada’s terrestrial ecosystems to climate change is limited and there has been no comprehensive, process-based assessment of Canada’s terrestrial C cycle. We tailor the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to Canada and evaluate its C cycling performance against independent reference data. We utilize skill scores to assess model performance against reference data alongside benchmark scores that quantify the level of agreement between the reference data sets to aid in interpretation. Our results demonstrate CLASSIC’s sensitivity to prescribed vegetation cover. They also show that the addition of five region-specific PFTs improves CLASSIC’s skill at simulating the Canadian C cycle. CLASSIC performs well when tailored to Canada, falls within the range of the reference data sets, and meets or exceeds the benchmark scores for most C cycling processes. New region-specific land cover products, well-informed plant functional type (PFT) parameterizations, and more detailed reference data sets will facilitate improvements to the representation of the terrestrial C cycle in regional and global land surface models (LSMs). Incorporating a parameterization for boreal disturbance processes and explicitly representing peatlands and permafrost soils will improve CLASSIC’s future performance in Canada and other boreal regions. This is an important step toward a comprehensive process-based assessment of Canada’s terrestrial C cycle and evaluating Canada’s net C balance under climate change.

Land surface/Earth System models depend upon accurate simulation of evapotranspiration (ET) to avoid excessive biases in simulated energy, water, and carbon cycles. The Canadian Land Surface Scheme including biogeochemical Cycles (CLASSIC), the land surface scheme of the Canadian Earth System Model (CanESM) shows reasonable ET fluxes globally, but CLASSIC’s partitioning into evaporation (E) and transpiration (T) can be improved. Specifically, CLASSIC exhibited a high soil evaporation (Es) bias in sparsely vegetated areas during wet periods, which can deplete soil water and decrease photosynthesis and T later in the year. A dry surface layer (DSL) parameterization was implemented to address biases in Es through an increased surface resistance to water vapour and heat fluxes. In arid/semi-arid regions, the DSL decreased Es, leading to improved seasonality of ET and increased gross primary productivity (GPP) due to an increase in soil moisture. The DSL simulations significantly (t-test, p<0.01) increased T/ET from 0.25 in baseline CLASSIC to 0.30 in the DSL simulations. T/ET was further increased to 0.41 (p<0.01), comparable to the CMIP5 model mean, by allowing T to occur from the dry canopy fraction while water evaporates from the wet fraction. This mainly affected densely vegetated areas, where T and ET increased significantly (p<0.01) and canopy E was reduced (p<0.01). In seasonally dry tropical forests, higher T and ET reduced GPP. Despite increases in arid/semi-arid regions, the reduced GPP in tropical forests resulted in ∼1.6% lower global GPP (p=0.018) than baseline CLASSIC. Including these modifications in CanESM might reduce biases in climate.