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Abstract Soil carbon (C) responses to environmental change represent a major source of uncertainty in the global C cycle. Feedbacks between soil C stocks and climate drivers could impact atmospheric CO₂levels, further altering the climate. Here, we assessed the reliability of Earth system model (ESM) predictions of soil C change using the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). ESMs predicted global soil C gains under the high emission scenario, with soils taking up 43.9 Pg (95% CI: 9.2–78.5 Pg) C on average during the 21st century. The variation in global soil C change declined significantly from CMIP5 (with average of 48.4 Pg [95% CI: 2.0–94.9 Pg] C) to CMIP6 models (with average of 39.3 Pg [95% CI: 23.9–54.7 Pg] C). For some models, a small C increase in all biomes contributed to this convergence. For other models, offsetting responses between cold and warm biomes contributed to convergence. Although soil C predictions appeared to converge in CMIP6, the dominant processes driving soil C change at global or biome scales differed among models and in many cases between earlier and later versions of the same model. Random Forest models, for soil carbon dynamics, accounted for more than 63% variation of the global soil C change predicted by CMIP5 ESMs, but only 36% for CMIP6 models. Although most CMIP6 models apparently agree on increased soil C storage during the 21st century, this consensus obscures substantial model disagreement on the mechanisms underlying soil C response, calling into question the reliability of model predictions. 

There is growing evidence that biological condensates, which are also referred to as membraneless organelles, and liquid-liquid phase separation play critical roles regulating many important cellular processes. Understanding the roles these condensates play in biology is predicated on understanding the material properties of these complex substances. Recently, micropipette aspiration (MPA) has been proposed as a tool to assay the viscosity and surface tension of condensates. This tool allows the measurement of both material properties in one relatively simple experiment, in contrast to many other techniques that only provide one or a ratio of parameters. While this technique has been commonly used in the literature to determine the material properties of membrane-bound objects dating back decades, the model describing the dynamics of MPA for objects with an external membrane does not correctly capture the hydrodynamics of unbounded fluids, leading to a calibration param- eter several orders of magnitude larger than predicted. In this work we derive a new model for MPA of biological condensates that does not require any calibration and is consistent with the hydrodynamics of the MPA geometry. We validate the predictions of this model by conducting MPA experiments on a standard silicone oil of known material properties and are able to predict the viscosity and surface tension using MPA. Finally, we reanalyze with this new model the MPA data presented in previous works for condensates formed from LAF-1 RGG domains. 

Abstract. Future global changes will impact carbon (C) fluxes andpools in most terrestrial ecosystems and the feedback of terrestrial carboncycling to atmospheric CO2. Determining the vulnerability of C in ecosystems to future environmental change is thus vital for targeted land management and policy. The C capacity of an ecosystem is a function of its C inputs(e.g., net primary productivity – NPP) and how long C remains in the systembefore being respired back to the atmosphere. The proportion of C capacitycurrently stored by an ecosystem (i.e., its C saturation) provides informationabout the potential for long-term C pools to be altered by environmental andland management regimes. We estimated C capacity, C saturation, NPP, andecosystem C residence time in six US grasslands spanning temperature andprecipitation gradients by integrating high temporal resolution C pool andflux data with a process-based C model. As expected, NPP across grasslandswas strongly correlated with mean annual precipitation (MAP), yet Cresidence time was not related to MAP or mean annual temperature (MAT). We linksoil temperature, soil moisture, and inherent C turnover rates (potentiallydue to microbial function and tissue quality) as determinants of carbon residence time. Overall, we found that intermediates between extremes in moisture andtemperature had low C saturation, indicating that C in these grasslands maytrend upwards and be buffered against global change impacts. Hot and drygrasslands had greatest C saturation due to both small C inputs through NPPand high C turnover rates during soil moisture conditions favorable formicrobial activity. Additionally, leaching of soil C during monsoon eventsmay lead to C loss. C saturation was also high in tallgrass prairie due tofrequent fire that reduced inputs of aboveground plant material.Accordingly, we suggest that both hot, dry ecosystems and those frequentlydisturbed should be subject to careful land management and policy decisionsto prevent losses of C stored in these systems. 

Abstract Soils store more carbon than other terrestrial ecosystems 1,2 . How soil organic carbon (SOC) forms and persists remains uncertain 1,3 , which makes it challenging to understand how it will respond to climatic change 3,4 . It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss 5–7 . Although microorganisms affect the accumulation and loss of soil organic matter through many pathways 4,6,8–11 , microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes 12,13 . Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved 7,14,15 . Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.