The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Although plant photosynthetic capacity as determined by the maximum carboxylation rate (i.e., V_c, max25) and the maximum electron transport rate (i.e., J_max25) at a reference temperature (generally 25 °C) is known to vary considerably in space and time in response to environmental conditions, it is typically parameterized in Earth system models (ESMs) with tabulated values associated with plant functional types. In this study, we have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA) to predict photosynthetic capacity at the global scale under different environmental conditions. We adopt an optimality hypothesis to nitrogen allocation among light capture, electron transport, carboxylation and respiration. The LUNA model is able to reasonably capture the measured spatial and temporal patterns of photosynthetic capacity as it explains ∼ 55 % of the global variation in observed values of V_c, max25 and ∼ 65 % of the variation in the observed values of J_max25. Model simulations with LUNA under current and future climate conditions demonstrate that modeled values of V_c, max25 are most affected in high-latitude regions under future climates. ESMs that relate the values of V_c, max25 or J_max25 to plant functional types only are likely to substantially overestimate future global photosynthesis.

Photosynthetic capacity, determined by light harvesting and carboxylation reactions, is a key plant trait that determines the rate of photosynthesis; however, in Earth System Models (ESMs) at a reference temperature, it is either a fixed value for a given plant functional type or derived from a linear function of leaf nitrogen content. In this study, we conducted a comprehensive analysis that considered correlations of environmental factors with photosynthetic capacity as determined by maximum carboxylation (V_c,m) rate scaled to 25°C (i.e.,V_c,25; μmol CO₂·m⁻²·s⁻¹) and maximum electron transport rate (Jmax) scaled to 25°C (i.e.,J₂₅; μmol electron·m⁻²·s⁻¹) at the global scale. Our results showed that the percentage of variation in observedV_c,25andJ₂₅explained jointly by the environmental factors (i.e., day length, radiation, temperature, and humidity) were 2–2.5 times and 6–9 times of that explained by area‐based leaf nitrogen content, respectively. Environmental factors influenced photosynthetic capacity mainly through photosynthetic nitrogen use efficiency, rather than through leaf nitrogen content. The combination of leaf nitrogen content and environmental factors was able to explain ~56% and ~66% of the variation inV_c,25andJ₂₅at the global scale, respectively. Our analyses suggest that model projections of plant photosynthetic capacity and hence land–atmosphere exchange under changing climatic conditions could be substantially improved if environmental factors are incorporated into algorithms used to parameterize photosynthetic capacity in ESMs.

This study examines the impact of variation in root‐zone soil moisture (RZSM), a key component of the Earth's hydrologic cycle and climate system, on regional carbon fluxes across seven North American ecosystems. P‐band synthetic aperture radar‐derived RZSM estimates were incorporated into the ecosystem demography (ED2) terrestrial biosphere model through a model‐data blending approach. Analysis shows that the model qualitatively captures inter‐daily and seasonal variability of observed RZSM at seven flux tower sites (r = 0.59 ± 0.26 andr = 0.70 ± 0.22 for 0–10 and 10–40 cm of soil layers, respectively;P < 0.001). Incorporating the remotely sensed RSZM estimates increases the accuracy (root‐mean‐square deviations decrease from 0.10 ± 0.07 and 0.09 ± 0.06 m³·m⁻³to 0.08 ± 0.05 and 0.07 ± 0.03 m³·m⁻³for 0–10 and 10–40 cm of soil layers, respectively) of the model's RZSM predictions. The regional carbon fluxes predicted by the native and RZSM‐constrained model were used to quantify sensitivities of gross primary productivity, autotrophic respiration (R_a), heterotrophic respiration (R_h), and net ecosystem exchange to variation in RZSM. Gross primary productivity exhibited the largest sensitivity (6.6 ± 10.7 kg·cm⁻²·year·θ⁻¹) followed by R_a(2.9 ± 7.3 kg·cm⁻²·year⁻¹·θ⁻¹), R_h(2.6 ± 3.1 kg·cm⁻²·year⁻¹·θ⁻¹), and net ecosystem exchange (−1.7 ± 7.8 kg·cm⁻²·year⁻¹·θ⁻¹). Analysis shows that these carbon flux sensitivities varied considerably across regions, reflecting influences of canopy structure, soil properties, and the ecophysiological properties of different plant functional types. This study highlights (1) the importance of improved terrestrial biosphere model predictions of RZSM to improve predictions of terrestrial carbon fluxes, (2) a need for improved pedotransfer functions, and (3) improved understanding of how soil characteristics, climate, and vegetation composition interact to govern the responses of different ecosystems to changing hydrological conditions.

The El Niño–Southern Oscillation (ENSO) in 2015 was one of the strongest observed in almost 20 years and set the stage for a severe drought and the emergence of widespread fires and related smoke emission over large parts of Southeast Asia. In the tropical lowlands of Sumatra, which were heavily affected by the drought and haze, large areas of tropical rainforest have been converted into oil palm (Elaeis guineensis Jacq.) plantations during the past decades. In this study, we investigate the impact of drought and smoke haze on the net ecosystem CO2 exchange, evapotranspiration, yield and surface energy budget in a commercial oil palm plantation in Jambi province (Sumatra, Indonesia) by using micrometeorological measurements, the eddy covariance method, yield data and a multiple linear regression model (MLRM). With the MLRM we identify the contribution of meteorological and environmental parameters to the net ecosystem CO2 exchange. During the initial part of the drought, when incoming shortwave radiation was elevated, net CO2 uptake increased by 50 % despite a decrease in upper-layer soil moisture by 35 %, an increase in air temperature by 10 % and a tripling of atmospheric vapour pressure deficit. Emerging smoke haze decreased incoming solar radiation by 35 % compared to non-drought conditions and diffuse radiation almost became the sole shortwave radiation flux for 2 months, resulting in a strong decrease in net CO2 uptake by 86 %. Haze conditions resulted in a complete pause of oil palm net carbon accumulation for about 1.5 months and contributed to a decline in oil palm yield by 35 %. With respect to a projected pronounced drying trend over the western Pacific during a future El Niño, our model showed that an increase in drought may stimulate net CO2 uptake, while more severe smoke haze, in combination with drought, can lead to pronounced losses in productivity and net CO2 uptake, highlighting the importance of fire prevention.

Summary: Atmospheric carbon dioxide concentration (C_a) has a direct and measurable effect on plant growth. However, it does not affect all plant species equally, which could lead to shifts in competitive dominance of species in ecosystems. We used a dynamic plant carbon–nitrogen model to systematically examine how species traits affect the long‐term C_a responsiveness of C₃ plants when growing as established monocultures in the field. The model was tested against responses of 7 C₃ herbaceous species growing in a free‐air C_a enrichment (FACE) experiment (BioCON) in Minnesota, USA. Model simulations showed that several species traits affected the C_a response strongly, giving rise to a number of testable hypotheses about interspecific differences in responsiveness to C_a. The largest responses to rising C_a were obtained for species with low carbon‐use efficiency (net primary production: gross primary production ratio), low foliar carbon allocation, low stomatal conductance, low instantaneous photosynthetic nitrogen use efficiency and low specific leaf area. In general, our model predicted that, for established plants growing in resource‐limited field conditions, species with slow growth rates would be most responsive to elevated C_a. This prediction was supported by data from the BioCON experiment. Our model also predicts that, for young plants growing in non‐resource‐limited conditions, species with high growth rates will be most responsive to elevated C_a. This difference in species ranking under different resource availabilities is largely explained by the indirect effects of C_a on leaf area. Leaf‐area feedbacks favour fast‐growing species the most during leaf‐area expansion, but following stand maturation they favour slow‐growing species the most. These results imply that species that respond strongly to elevated C_a in short‐term (non‐resource‐limited) glasshouse experiments are unlikely to also be the most responsive in resource‐limited field conditions, and therefore that we cannot directly extrapolate from glasshouse experiments to predict which species will be most responsive to elevated C_a in the long term.