We present a representation of nitrogen and phosphorus cycling in the vegetation demography model the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), within the Energy Exascale Earth System (E3SM) land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. The model tracks nutrient uptake, losses via turnover from both live plants and mortality into soil decomposition, and allocation during tissue growth for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake is governed by fine root biomass, and plants vary in their fine root carbon allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth System Models (ESMs).

Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknow on what scales these estimates are robust. Here, we use the time-dependent Bayesian synthesis top-down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (URR) decrease significantly at decreasing spatial scales. On the continental-scale, the mean URR is about 60%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental-scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

Tropical cyclones dominate the disturbance regime experienced by forest ecosystems in many parts of the world. Interactions between cyclone disturbance regimes and nutrient availability strongly influence forest ecosystem dynamics. However, uncertainty exists over the importance of soil fertility properties (i.e., total soil phosphorus-P concentration) in mediating forest resistance and recovery from cyclone disturbance. We hypothesized that forests on soils with low total P (e.g., developed on limited-P parent material) have a higher resistance to but a slower recovery from cyclone disturbance than forests on high P soils. We investigated cyclone impacts on litterfall, an essential conduit for nutrient recycling in forest ecosystems. We compiled site-level forest litterfall data from 53 studies and datasets associated with 15 naturally-occurring one simulated tropical cyclone in 23 sites within five regions (Taiwan, Australia, Mexico, Hawaii, and the Caribbean)and four cyclone basins. We calculated the effect sizes of cyclone disturbance on the litterfall mass and nutrient (P and nitrogen-N) concentrations and fluxes during the first (< five) years post-disturbance across a total soil P gradient. We also assessed the effect of 20 covariates on the degree of cyclone impact on litterfall. Total litterfall mass flux increased by 4820%following cyclone disturbance. Such an initial increase in litterfall mass reflects the magnitude of cyclone-derived plant material input to the forest floor, which was highest in the Caribbean and lowest in Taiwan. Among 20 covariates, soil P and region were the best predictors of cyclone effects on total litterfall mass, explaining 80% of the variance. The effect sizes increased linearly with soil P and region, from significantly lower in Taiwan (low-P) to largest in the Caribbean (high-P). Total litterfall P and N fluxes increased significantly post-cyclone, whereas the increase in leaf P flux was twice as that in Nflux. Results highlight the importance of understanding the interactions between disturbance and nutrient gradients in forest ecosystems to understand forest responses to altered cyclone regimes expected under climate change.

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO2 emissions during the 1959-2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models collectively referred to as the TRENDY ensemble and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well TRENDY models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference datasets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.