Max Planck Institute for Biogeochemistry

Abstract. Although shrubs employ distinct water- and carbon-use strategies compared to trees and are increasingly expanding across warming tundra and grassland, they remain insufficiently represented in global land surface models. Here, we incorporated two shrub types, deciduous and evergreen, into the nutrient-enabled terrestrial biosphere model QUINCY, which features a state-of-the-art treatment of soil nutrient dynamics and carbon exchange. We investigate the change in carbon fluxes and storage due to shrub cover, it's response to climate and CO2 fertilization effect and the role of nitrogen availability. With this new implementation, shrubs showed reasonable seasonal cycle of gross primary production (GPP) at 50 % of the Arctic study sites. The model achieved mean R2 values of 0.5 and 0.6, when compared with in situ measurements and remote sensing products for modeled shrubs. However, at 50 % of the study sites the model underestimated observed GPP due to too strong simulated nitrogen limitation. Compared to needle leaved evergreen forest the modeled gross primary production of shrubs is similarly distributed with a non-significant difference in the median. Compared to graminoids the carbon fluxes of shrubs are 40 % higher. Shrubs produce a substantial, though lower, above-ground biomass than needle leaved trees and show phenological patterns that are distinct from those of trees. Although CO2 fertilization generally benefits all plant types, shrubs appear to maintain a particularly strong growth response under elevated CO2 concentrations. We also demonstrated that the modeled deciduous shrubs reduce their nitrogen sources substantially more than evergreen shrubs, generally resulting in a 50 % decrease in gross primary production. Providing the plants with unlimited nitrogen and thus doubling gross primary production at most sites improved the model-measurement agreement by 15 %. A similar effect occurred when initializing nitrogen and carbon contents best on permafrost profiles, resulting in partly alleviating nitrogen limitation in the model. These finding underlines the importance of including evergreen and deciduous shrub PFTs in global land surface models to accurately predict ongoing changes in the Arctic carbon cycle. However, the strong nitrogen limitation of Arctic shrub productivity when using the standard model parametrizations suggests that the Arctic contribution to global land carbon is underestimated by global models.

The capacity of nutrient-limited forests to enhance carbon (C) sequestration under elevated CO2 (eCO2) remains a critical uncertainty in C cycle modeling. While existing evidence suggests that low phosphorus (P) bioavailability may constrain CO2 fertilization effects on plant growth, the extent to which this limitation modulates ecosystem responses to eCO2 in forests adapted to P-deficient soils remains poorly understood. Here, using eight P-enabled models, we simulated the magnitudes and mechanisms through which P bioavailability interacts with eCO2, emulating an ecosystem-scale P enrichment experiment at a P-limited Eucalyptus forest undergoing long-term Free-Air CO2 Enrichment. While models predicted pronounced P effects on tree growth, P enrichment unexpectedly did not increase the CO2 effects on tree growth and ecosystem C sequestration. Models prioritized either CO2-driven or P-driven growth, but rarely both. This tradeoff emerged due to model-specific assumptions on 1) partitioning of the extra P in soil labile versus nonlabile pools; 2) plant photosynthetic acclimation to P deficiency; 3) C and nutrient use strategies regulating plant size and allocation; and 4) microbial-driven soil decomposition processes. By generating divergent yet biologically plausible outcomes, these predictions establish critical testable hypotheses for empirical research and highlight multiple P-related pathways that may influence the future land C sink.

The Amazon rainforest has a large area occupied by rivers and wetlands, responsible for the majority of biogenic methane (CH4) emission in the region. Biogenic emissions of CH4, which is a potent greenhouse gas, are a key source of uncertainty for the global budget. Atmospheric transport models can be useful to assess the spatial distribution of CH4 concentrations and the influence of land use and climate change. It is important to use computational models with updated emission data to obtain more accurate results in atmospheric gas transport simulations. Here we present an analysis performed with the Weather Research and Forecasting model coupled with the Greenhouse Gases module (WRF-GHG), using updated maps of wetland and fast carbon pool to calculate biogenic emissions of CH4 in the Amazon region. Three wetland maps (wetmaps) and two fast carbon pool (CPOOL) maps were used in the simulations. The resulting methane concentrations were compared to in situ observations at the ATTO tower (Amazon Tall Tower Observatory). The simulations were performed from 1st to 13th January 2023 (considering the first seven days as spin up), in a single domain with 6 km resolution and grid of 212 x 121 centered at ATTO. Boundary conditions were provided by ERA5 and CAMS. The simulation using the default emission model along with the minimum inundation wetmap and the updated CPOOL map showed results closer to observational data (bias of 15 ppb) than the other simulations (bias in the range 20-320 ppb). Using the default maps resulted in an overestimation of 4.1% in CH4 concentrations at ATTO. The modeled CH4 concentrations time series showed a pronounced diurnal variability, likely driven by boundary layer dynamics and advection. On the other hand, observations showed rather constant concentrations, suggesting that background regional emissions dominate the CH4 signal at ATTO. Considering a model grid cell over a section of the Amazon River near the ATTO site (150 km southeast), simulated emissions ranged between 42 (minimum inundation and updated CPOOL map) and 623 (maximum inundation and default CPOOL map) mg CH4 m^-2 day^-1, while WetCHARTs emission inventories are in the range 153-276 mg CH4 m^-2 day^-1 and the literature reports averages of 18-21 mg CH4 m^-2 day^-1 for the Amazon River, based on field measurements. Overall, the results show a high sensibility of the WRF-GHG model towards the choice of wetland and CPOOL maps in Amazonia. Also, the correct representation of CH4 background concentrations is key to improve the simulations of near surface concentrations in areas less impacted by local wetland emissions, like ATTO.

There is a growing need within the paleoclimate community for robust soil paleoproxies capable of reconstructing past terrestrial environments with high precision. Existing proxies for past mean annual air temperature (MAT), such as branched GDGTs (1) and chironomids (2), suffer from large uncertainties (i.e., ≥ 4°C error on these land temperature reconstructions), which limit their applicability.Bacteriohopanepolyols (BHPs) are pentacyclic triterpenoid membrane lipids produced by bacteria that are ubiquitous in terrestrial and aquatic environments (3). Functionalized BHPs have a large structural diversity both in the rings and head groups. They have been detected in sedimentary archives extending back 1.2 Myr (4), underscoring their considerable potential as tools for reconstructing past climatic conditions.BHPs with nucleoside (adenosyl and inosyl) head groups (Nu-BHPs) have been widely used as indicators of terrestrial organic matter input into marine systems (Rsoil) (5). Recently, a large range of previously unknown Nu-BHPs were identified thanks to a newly developed method using Ultra High Performance Liquid Chromatography – high resolution Orbitrap Mass Spectrometry (6). The relative abundances of several Nu-BHPs found in Alaskan soils were shown to correlate with pH and temperature and thus are potential paleotemperature proxies (7). To validate these correlations on a global scale, we present Nu-BHP abundances analyzed across 89 globally distributed surface soil samples. These include soils previously used to calibrate branched GDGTs (1), as well as soils from Northern Norway and Finland and Brazil, to complete coverage from the Arctic to the tropics. Complementary analyses included six soil environmental variables (pH, latitude, total organic carbon (TOC), C/N, δ¹³C, δ¹⁵N) and four climate parameters (mean annual and warmest quarter air temperature, obtained from CHELSA climatological data (8), annual and wettest quarter precipitation, retrieved from the Copernicus Climate Change Service (9)).Forty-eight Nu-BHPs were identified in soils with a pH range of 3.3-8.1 and total organic carbon (TOC) range of 0.2 and 48.4%. The most dominant compound in the dataset is adenosylhopane with 0 methylations. Of the forty-eight Nu-BHPs, thirty compounds were present in trace amounts (less than 1% of total relative abundances). The remaining eighteen Nu-BHPs were further used to investigate climatic controls on Nu-BHP abundances.This showed that only a few Nu-BHPs showed a good correlation with pH (R2 ~0.65), while temperature did not appear to influence Nu-BHP distributions. Non-metric multidimensional scaling analysis was conducted on relative abundance of these eighteen Nu-BHPs, along with the soil environmental variables and climate parameters (Fig. 1). This revealed that none of the measured parameters measured fully explains the variability in Nu-BHP distributions. We hypothesize that the main control factors instead are related to nutrient availability and/or bacterial community diversity. Future work includes investigating these variables using samples with strong nutrient and pH gradients; and known bacterial community abundances. ReferencesWeijers et al., 2007.Brooks et al., 2001. Cooke et al., 2009. Zhu et al., 2011.Talbot et al., 2014. Hopmans et al., 2021. O’Connor, 2025. Krager et al., 2017. Dorigo et al., Copernicus Climate Change Service (C3S) Climate Data Store (CDS).

Soil organic matter (SOM) forms the foundation of microbial life in the soil and its processes. However, what drives the organization of organic matter turnover and microbial communities into growth remains unclear. In particular, we ask whether physical conditions in the soil—such as the quantity or quality of litter inputs—exist to which soil microbial processes adapt in order to maximize microbial growth as a proxy for power. We address this question in the frame of the German priority program 2322 by building on the maximum power principle. The principle suggests that biological systems tend to maximize the flux of energy into useful power under given constraints. We study a minimal model of SOM dynamics at steady state. In the model, litter inputs add to the organic matter pool, which is decomposed by microbial enzymes into compounds available for microbial uptake. The flux of Gibbs free energy provided with litter is used to build biomass while dissipating it during cycling, and the microbial decay returns as dead microbial biomass to the soil pool. We explore how different model structures, feedbacks, and parameterizations might lead to a maximum in the flux of free energy to microbial biomass, thereby providing insights into the conditions under which microbial growth is energetically optimized in soils.

Reliable simulation of carbon and water fluxes in forest ecosystems is essential for understanding global energy, carbon, and water cycles, while it remains limited by the large number of poorly constrained parameters in land surface models, particularly in regions lacking flux observations. While model-data integration using satellite and eddy covariance data has improved performance, it does not resolve the fundamental problem of parameter identifiability.Here, we use SINDBAD (Koirala et al., 2025), a model-data integration framework, to evaluate whether eco evolutionary optimality (EEO) principles can act as effective constraints on a coupled carbon water land surface model when flux observations are unavailable. Using 37 forest sites worldwide spanning 1979-2017, we compare three experiments that differ in the type of constraints applied, i.e., vegetation structure only, vegetation structure plus flux observations, and vegetation structure plus EEO based constraints, to assess to what extent theoretical optimality principles can help even without direct flux information.We find that vegetation structure alone is insufficient to reproduce observed carbon and water fluxes, especially at water limited sites. Incorporating EEO constraints leads to clear improvements in simulations of gross primary productivity, ecosystem respiration, and evapotranspiration under water limitation, while effects are weaker at energy limited sites. EEO constrained simulations also show more realistic sensitivities of fluxes to precipitation and temperature, in some cases exceeding those obtained when flux observations are directly assimilated.These results suggest that eco evolutionary optimality principles can provide meaningful constraints on land surface models with high dimensional parameter spaces, reducing effective parameter uncertainty under data sparse conditions.

Drylands contain most of the global soil inorganic carbon (SIC), yet its sources and vulnerability are poorly constrained. We measured SIC content and its radiocarbon (14C) at 55 dryland sites across Asia and Europe to investigate the origins of SIC and its environmental factors. In the top 10 cm, SIC generally increased with aridity, but extremely dry sites with low soil organic carbon (SOC) often had low SIC, implying that low vegetation input limits both SOC accumulation and pedogenic carbonate formation. The Δ14C of topsoil SIC was positively related to the Δ14C of SOC and declined with aridity, consistent with reduced influence of modern C sources. SIC content was linked mainly to net primary productivity (NPP) and SOC, whereas Δ14C-SIC was controlled primarily by soil pH, which governs carbonate dissolution and precipitation reactions. Thus, a greater imprint of modern carbon is found in carbonates of less alkaline surface soils, either indicating a greater potential for forming new pedogenic carbonates or greater isotopic exchange. At a subset of sites on the Chinese Loess Plateau and the Inner Mongolia Plateau, soil samples were collected across multiple depth intervals. SIC contents remained relatively constant with depth, whereas Δ14C-SIC decreased, indicating reduced contributions of recent carbon to SIC. We estimate that carbonates reflecting the influence of modern C sources accounted for about 30% of topsoil SIC but only about 10% in subsoils. These results show that dryland subsoils retain older, more stable, and likely geogenic carbonates, whereas topsoils contain younger pedogenic carbonates that are more influenced by and potentially vulnerable to environmental change.

Droughts threaten ecosystems worldwide and are projected to occur more frequently and with greater intensity in the future. Accurately projecting ecosystem responses to these climate extremes relies on vegetation models. While process-based models are evolving fast and many models now represent plant hydraulic processes, including these mechanisms often comes at the cost of an increase in the number of difficult-to-constraint model parameters. Concurrently, recent experimental advances open a new avenue for parameter constraining, by providing high-temporal resolution data on plant hydraulic variables, for example continuous in situ measurements of water potential and sap flux. However, much of this novel data has not yet been considered by vegetation models.Here, we utilize a rare, comprehensive time-series of data obtained in the Hainich experimental forest, specifically high-temporal resolution datasets of (1) sap flux, (2) stem water potential, (3) Net Ecosystem Exchange (NEE), and (4) Evapotranspiration (ET). With this data spanning both the water and carbon axes of plant function, we constrain the terrestrial biosphere model QUINCY and the latest development of its plant hydraulic architecture. We find that integrating such complementary experimental data yields three key outcomes. First, it evaluates the physical representation of plant hydraulic theory within the model. Second, it results in tighter constraints on plant-hydraulic parameters. High-temporal resolution water potential and sap flow data are vital here, as they resolve the diurnal lags necessary to identify capacitance parameters that remain unidentifiable under daily or weekly sampling. By capturing these fast-response dynamics, the model not only narrows parameter uncertainty but also reveals critical functional interdependencies and correlations that define plant hydraulic strategy. Third, these constraints yield more robust projections by significantly reducing the variability in simulated stocks and fluxes under future climate scenarios. We conclude that the growing availability of continuous data from novel physiological sensors is essential to constrain and build trust in increasingly complex vegetation models, as demonstrated here for plant hydraulics.

The Amazon rainforest stores ~150–200 Pg of carbon and plays a central role in the global carbon cycle, yet it is highly vulnerable to land-use change, fire, and climate change, leaving its future carbon balance uncertain. Net Ecosystem Exchange (NEE) quantifies the balance between ecosystem carbon uptake and release, but long-term NEE assessments in Central Amazonia remain scarce due to limited observational coverage from eddy covariance towers and CO₂ profile measurements. The Amazon Tall Tower Observatory (ATTO; https://www.attoproject.org) helps overcome this limitation by providing more than a decade of continuous measurements and by capturing two extreme drought events (2015/2016 and 2023/2024). In this work, we analyzed 11 years of NEE estimates at ATTO (2014-2024) and we propose a methodology for selecting the friction velocity threshold (u*), based on the identification of a plateau in the NEE-u* relationship, where NEE becomes independent of increasing turbulence, and all periods with u below the threshold are filtered due to insufficient turbulence*. We estimate monthly u* thresholds ranging from 0.18 to 0.21 m s⁻¹. We found that, on average, the forest in the ATTO flux footprint generally acted as a net carbon sink. However, in years following severe droughts, such as 2016 and 2024, we detect a temporary reversal, with the ecosystem becoming a CO₂ source during the wet season. We also quantified the effect of environmental drivers modulating NEE across seasons. We find that higher air temperature reduces carbon uptake during the wet season. In contrast, soil moisture shows opposite relationships depending on season: during the dry season, increasing soil moisture (10 cm depth) reduces net carbon uptake, whereas during the wet season, increasing it enhances net carbon uptake. Our findings deliver critical observational evidence to refine model parameterizations of tropical carbon-water interactions and to reduce uncertainty in predictions of the Amazon carbon balance under future climate scenarios.