Predicted responses of transpiration to elevated atmospheric CO2 concentration (eCO2 ) are highly variable amongst process-based models. To better understand and constrain this variability amongst models, we conducted an intercomparison of 11 ecosystem models applied to data from two forest free-air CO2 enrichment (FACE) experiments at Duke University and Oak Ridge National Laboratory. We analysed model structures to identify the key underlying assumptions causing differences in model predictions of transpiration and canopy water use efficiency. We then compared the models against data to identify model assumptions that are incorrect or are large sources of uncertainty. We found that model-to-model and model-to-observations differences resulted from four key sets of assumptions, namely (i) the nature of the stomatal response to elevated CO2 (coupling between photosynthesis and stomata was supported by the data); (ii) the roles of the leaf and atmospheric boundary layer (models which assumed multiple conductance terms in series predicted more decoupled fluxes than observed at the broadleaf site); (iii) the treatment of canopy interception (large intermodel variability, 2-15%); and (iv) the impact of soil moisture stress (process uncertainty in how models limit carbon and water fluxes during moisture stress). Overall, model predictions of the CO2 effect on WUE were reasonable (intermodel μ = approximately 28% ± 10%) compared to the observations (μ = approximately 30% ± 13%) at the well-coupled coniferous site (Duke), but poor (intermodel μ = approximately 24% ± 6%; observations μ = approximately 38% ± 7%) at the broadleaf site (Oak Ridge). The study yields a framework for analysing and interpreting model predictions of transpiration responses to eCO2 , and highlights key improvements to these types of models.
Abstract. Biogenic volatile organic compounds (BVOCs) are emitted in large quantities from the terrestrial biosphere and play a significant role in atmospheric gaseous and aerosol compositions. Secondary organic aerosols (SOAs) resulting from BVOC oxidation affect the radiation budget both directly, through the scattering and absorption of sunlight, and indirectly, by modifying cloud properties. Human activities have extensively altered natural vegetation cover, primarily by converting forests into agricultural land. In this work, a global atmospheric chemistry–climate model, coupled with a dynamic global vegetation model, was employed to study the impacts of perturbing the biosphere through human-induced land use change, thereby exploring changes in BVOC emissions and the atmospheric aerosol burden. A land use scheme was implemented to constrain tree plant functional type (PFT) cover based on land transformation fraction maps from the year 2015. Two scenarios were evaluated: (1) one comparing present-day land cover, which includes areas deforested for crops and grazing land, with potential natural vegetation (PNV) cover simulated by the model, and (2) an extreme reforestation scenario in which present-day grazing land is restored to natural vegetation levels. We find that, compared to the PNV scenario, present-day deforestation results in a 26 % reduction in BVOC emissions, which decreases the global biogenic SOA (bSOA) burden by 0.16 Tg (a decrease of 29 %), while the total organic aerosol (OA) burden decreases by 0.17 Tg (a reduction of 9 %). On the other hand, the extreme reforestation scenario, compared to present-day land cover, suggests an increase in BVOC emissions of 22 %, which increases the bSOA burden by 0.11 Tg and the total OA burden by 0.12 Tg – increases of 26 % and 6 %, respectively. For the present-day deforestation scenario, we estimate a positive total radiative effect (aerosol + cloud) of 60.4 mW m−2 (warming) relative to the natural vegetation scenario, while for the extreme reforestation scenario, we report a negative (cooling) effect of 38.2 mW m−2 relative to current vegetation cover.
Summary Community trait assembly in highly diverse tropical rainforests is still poorly understood. Based on more than a decade of field measurements in a biodiversity hotspot of southern Ecuador, we implemented plant trait variation and improved soil organic matter dynamics in a widely used dynamic vegetation model (the Lund‐Potsdam‐Jena General Ecosystem Simulator, LPJ‐GUESS) to explore the main drivers of community assembly along an elevational gradient. In the model used here (LPJ‐GUESS‐NTD, where NTD stands for nutrient‐trait dynamics), each plant individual can possess different trait combinations, and the community trait composition emerges via ecological sorting. Further model developments include plant growth limitation by phosphorous (P) and mycorrhizal nutrient uptake. The new model version reproduced the main observed community trait shift and related vegetation processes along the elevational gradient, but only if nutrient limitations to plant growth were activated. In turn, when traits were fixed, low productivity communities emerged due to reduced nutrient‐use efficiency. Mycorrhizal nutrient uptake, when deactivated, reduced net primary production (NPP) by 61–72% along the gradient. Our results strongly suggest that the elevational temperature gradient drives community assembly and ecosystem functioning indirectly through its effect on soil nutrient dynamics and vegetation traits. This illustrates the importance of considering these processes to yield realistic model predictions.
Abstract. Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the "rock moisture" reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM, LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soils databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case-study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a mixed evergreen temperate broadleaf-needleleaf forest whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models.
Supplementary data from the DGVM simulations underlying the main figures presented in the publication. Naming convention: {variable}_{aggregation period}-{comparison period [only relevant for bsprob]}_{climate model}-{climate scenario}.tif Variables are: bsprob-Snell2013ed = biome shift probability (biomization adjusted from Snell et al. 2013) [%] nee = net ecosystem exchange [kgC/m2/year] Global climate models include GFDL = GFDL-ESM4 and IPSL= IPSL-CM6A-LR. Climate scenarios refer to the shared socioeconomic pathways (SSP) SSP126= SSP1-2.6 and SSP370= SSP3-7.0.
