First Record of Fungal Diversity in the Tropical and Warm-Temperate Middle Miocene Climate Optimum Forests of Eurasia

The middle Miocene Climate Optimum (MMCO) was the warmest interval of the last 23 million years and is one of the best analogs for proposed future climate change scenarios. Fungi play a key role in the terrestrial carbon cycle as dominant decomposers of plant debris, and through their interactions with plants and other organisms as symbionts, parasites, and endobionts. Thus, their study in the fossil record, especially during the MMCO, is essential to better understand biodiversity changes and terrestrial carbon cycle dynamics in past analogous environments, as well as to model future ecological and climatic scenarios. The fossil record also offers a unique long-term, large-scale dataset to evaluate fungal assemblage dynamics across long temporal and spatial scales, providing a better understanding of how ecological factors influenced assemblage development through time. In this study, we assessed the fungal diversity and community composition recorded in two geological sections from the middle Miocene from the coal mines of Thailand and Slovakia. We used presence-absence data to quantify the fungal diversity of each locality. Spores and other fungal remains were identified to modern taxa whenever possible; laboratory codes and fossil names were used when this correlation was not possible. This study represents the first of its kind for Thailand, and it expands existing work from Slovakia. Our results indicate a total of 281 morphotaxa. This work will allow us to use modern ecological data to make inferences about ecosystem characteristics and community dynamics for the studied regions. It opens new horizons for the study of past fungal diversity based on modern fungal ecological analyses. It also sheds light on how global variations in fungal species richness and community composition were affected by different climatic conditions and under rapid increases of temperature in the past to make inferences for the near climatic future.

Hysteresis of terrestrial carbon cycle to CO2 ramp-up and -down forcing

To prevent excessive global warming, we have faced a situation to reduce net carbon dioxide (CO2) emissions. However, the behavior of Earth’s terrestrial biosphere under negative emissions is highly uncertain. Herein, we show strong hysteresis in the terrestrial carbon cycle in response to CO2 ramp-up and -down forcing. Owing to the strong hysteresis lag, the terrestrial biosphere stores more carbon at the end of simulations than at its initial state, lessening the burden on net-negative emissions. This hysteresis is latitudinally dependent, showing a longer timescale of reversibility in high latitudes. Particularly, carbon in boreal forests can be stored for a long time. However, the hysteresis of the carbon cycle in the pan-Arctic region depends on the presence of permafrost processes. That is, unexpected irreversible carbon emissions may occur in permafrost even after achieving net-zero emissions, indicating the importance of permafrost processes, which is highly uncertain based on our current knowledge.

Handling Several Sugars at a Time: a Case Study of Xyloglucan Utilization by Ruminiclostridium cellulolyticum

The study of the decomposition of recalcitrant plant biomass is of great interest as the limiting step of terrestrial carbon cycle and to produce plant-derived valuable chemicals and energy. While extracellular cellulose degradation and catabolism have been studied in detail, few publications describe the complete metabolism of hemicelluloses and, to date, the published models are limited to the extracellular degradation and sequential entry of simple sugars.

A novel model–data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2

The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model-data eco-informatics system, and its application for the development of a proof-of-concept carbon "reanalysis" product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower Net Ecosystem Exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit-Wishart Ensemble Filter (TWEnF) State Data Assimilation (SDA) framework, a generalization of the common Ensemble Kalman Filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ~500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and LAI (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced RMSE compared to the calibrated forecast. Ultimately, PEcAn's carbon cycle reanalysis provides a scalable framework for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) and accelerating terrestrial carbon cycle research.

Thermokarst Lagoons: A Core-Based Assessment of Depositional Characteristics and an Estimate of Carbon Pools on the Bykovsky Peninsula

Permafrost region subsurface organic carbon (OC) pools are a major component of the terrestrial carbon cycle and vulnerable to a warming climate. Thermokarst lagoons are an important transition stage with complex depositional histories during which permafrost and lacustrine carbon pools are transformed along eroding Arctic coasts. The effects of temperature and salinity changes during thermokarst lake to lagoon transitions on thaw history and lagoon deposits are understudied. We analyzed two 30-m-long sediment cores from two thermokarst lagoons on the Bykovsky Peninsula, Northeast Siberia, using sedimentological, geochronological, hydrochemical, and biogeochemical techniques. Using remote sensing we distinguished between a semi-closed and a nearly closed lagoon. We (1) characterized the depositional history, (2) studied the impact of marine inundation on ice-bearing permafrost and taliks, and (3) quantified the OC pools for different stages of thermokarst lagoons. Fluvial and former Yedoma deposits were found at depth between 30 and 8.5 m, while lake and lagoon deposits formed the upper layers. The electrical conductivity of the pore water indicated hypersaline conditions for the semi-closed lagoon (max: 108 mS/cm), while fresh to brackish conditions were observed beneath a 5 m-thick surface saline layer at the nearly closed lagoon. The deposits had a mean OC content of 15 ± 2 kg/m3, with higher values in the semi-closed lagoon. Based on the cores we estimated a total OC pool of 5.7 Mt-C for the first 30 m of sediment below five mapped lagoons on the Bykovsky Peninsula. Our results suggest that paleo river branches shaped the middle Pleistocene landscape followed by late Pleistocene Yedoma permafrost accumulation and early Holocene lake development. Afterward, lake drainage, marine flooding, and bedfast ice formation caused the saline enrichment of pore water, which led to cryotic talik development. We find that the OC-pool of Arctic lagoons may comprise a substantial inventory of partially thawed and partially refrozen OC, which is available for microbial degradation processes at the Arctic terrestrial-marine interface. Climate change in the Arctic leading to sea level rise, permafrost thaw, coastal erosion, and sea ice loss may increase the rate of thermokarst lagoon formation and thus increase the importance of lagoons as biogeochemical processors of former permafrost OC.

Response of Soil Respiration and Its Components to Precipitation Exclusion in Vitex negundo Var. Heterophylla Shrubland of the Middle Taihang Mountain in North China

Assessing the response of soil heterotrophic and autotrophic respiration to climate change is critical for forecasting terrestrial carbon cycle behavior in the future. In the present study, we conducted a drought experiment in Vitexnegundo var. heterophylla shrub ecosystem of the Middle Taihang Mountain. Three precipitation manipulation treatments (natural conditions/ambient precipitation (CK), reduced precipitation by 30% (PE30), and reduced precipitation by 60% (PE60)) were used to study the impact of different levels of precipitation exclusion on total soil respiration (Rs) and its heterotrophic (Rh) and autotrophic (Ra) components. Our results showed that the rates of Rs and its components were significantly decreased under the precipitation exclusion treatments. The proportion of Rh in Rs reduced from 72.6% for CK to 71.9% under PE60. The annual cumulative C fluxes of Rs decreased by 47.8 g C m−2 in PE30 and 106.0 g C m−2 in PE60, respectively. An exponential relationship was observed between the rate of each soil respiration component and soil temperature in all treatments ( p < 0.01). Moreover, each soil respiration component rate was better represented by a quadratic model which included soil moisture ( p < 0.01). However, including both of soil temperature and soil moisture did not explain more variation in soil respiration components compared than the regression model with soil moisture only. In addition, excluding precipitation increased the temperature sensitivity (Q10 values) of Rs and its Ra and Rh components compared to the control. Collectively, our findings suggest that increased drought will inhibit the release of carbon from the soil to the atmosphere, and will likely decrease the contribution of Rh to Rs in this semiarid shrubland ecosystem.

Spatial, Phenological, and Inter-Annual Variations of Gross Primary Productivity in the Arctic from 2001 to 2019

Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MOD17A2H GPP product using the FLUXNET 2015 dataset in the Arctic, then explored the spatial patterns, seasonal variations, and interannual trends of GPP, and investigated the dependence of the spatiotemporal variations in GPP on land cover types, latitude, and elevation from 2001 to 2019. The results showed that MOD17A2H was consistent with in situ measurements (R = 0.8, RMSE = 1.26 g C m−2 d−1). The functional phenology was also captured by the MOD17A2H product (R = 0.62, RMSE = 9 days) in the Arctic. The spatial variation of the seasonal magnitude of GPP and its interannual trends is partly related to land cover types, peaking in forests and lowest in grasslands. The interannual trend of GPP decreased as the latitude and elevation increased, except for the latitude between 62°~66° N and elevation below 700 m. Our study not only revealed the variation of GPP in the Arctic but also helped to understand the carbon cycle over this region.

Optimal model complexity for terrestrial carbon cycle prediction

The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.