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Agricultural management has recently drawn substantial attention as regenerative practices are showing their potential in enhancing soil quality and fertility, biodiversity, and carbon uptake from the atmosphere into the soil. In Nordic countries, there is a great number of farmers farming regeneratively. However, they are still a minority, and we are lacking scientific studies on the effects of various improved practices and their combination and use in practice in northern growing conditions.We conducted a long-term study of an agricultural grassland ecosystem in southern Finland where regenerative management practices were implemented from 2018 onwards. We monitored H2O and CO2 fluxes with the eddy covariance method, and additionally, CH4, N2O and CO2 with flux chambers. We also measured meteorological variables and several soil and vegetation parameters. We studied the impacts of different cutting heights for the silage grass on the ecosystem carbon sequestration. Furthermore, we utilised the data in model simulations with the grassland model BASGRA to study the carbon dynamics of the agricultural ecosystem in various weather conditions and with different management decisions. Our results indicate that the studied years, which were differing greatly from the long-term average weather, may have had a great impact on carbon dynamics on the grassland. Both empirical data and modelling demonstrated net carbon accumulation into the ecosystem under the selected improved practices.

Draining peatlands for forestry and agriculture has been a common practice in Nordic countries in the last century. In drained peatland forests, trees act as carbon sink, while well-aerated peat soil is a source of carbon. Traditionally in even aged forestry all the trees are removed in clear-cut harvesting, whereas the continous cover forestry, with partial removal of a stand in selection cuttings, have been suggested to serve as climate wise option for peatlands. A process-based model ‘Landscape De-Nitrification De-Composition’ (LDNDC) was used to simulate fluxes of matter and energy of a drained nutrient-rich peatland forest ecosystem in southern Finland. LDNDC utilizes several sub-models for physiology, biogeochemistry, hydrology and microclimate and it simulates ecosystem water, energy and carbon balances including methane balance along with vegetation structure development. Multiple species can be simulated simultaneously as a mixed forest cohort, and contributions of the ground vegetation can be included. Different management methods of the forestry industry, e.g clear cutting or selection cutting have been simulated successfully. Local meterological data from 2010-2018 was used to drive the model and this data was cycled through several times to start the simulation run from 1969. The amount of carbon storage in the soil was set according to the measurements at nutrient-rich peatlands. Three different simulation runs were made for a clear-cut or a selection cutting taking place in 2016 and a reference forest with no cutting. Pine was simulated as a dominant tree species prior to the management actions along with spruce and birch as a secondary canopy and alpine meadows as ground vegetation. Eddy covariance and chamber measurements from both management and reference sites were used to evaluate model performance. The model captured the net ecosystem exchange, gross primary production, terrestrial ecosystem respiration and methane fluxes well. The model also captured the changes in soil moisture and water-table level caused by the applied forest management methods. Leaf area index (LAI) of the combined vegetation cohort represented the measured LAI quite well along with the growth of the individual species. Successful implementation of the model resulted in extension of simulations until 2100 using different climate drivers to investigate effects of future management scenarios on various ecosystem balances. These model results can be utilized to provide recommendations for peatland forest management, which can ensure reduction in forestry related emissions and improve the possibilities for the peatland forest to act as a sink of carbon.

Soil organic carbon (SOC) models are important tools for assessing global SOC distributions and how carbon stocks are affected by climate change. Their performances, however, are affected by data and methods used to calibrate them. Here we study how a new version of the Yasso SOC model, here named Yasso20, performs if calibrated individually or with multiple datasets and how the chosen calibration method affects the parameter estimation. We also compare Yasso20 to the previous version of the Yasso model. We found that when calibrated with multiple datasets, the model showed a better global performance compared to a single-dataset calibration. Furthermore, our results show that more advanced calibration algorithms should be used for SOC models due to multiple local maxima in the likelihood space. The comparison showed that the resulting model performed better with the validation data than the previous version of Yasso.

Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. Using a single common set of parameters across all sites, the calibrated model was able to accurately reproduce surface reflectance for sites with highly varied forest composition and structure. However, the calibrated model's predictions of leaf area index (LAI) were less robust, capturing only 46 % of the variability in the observations. Comparing the ED2 radiative transfer model with another two-stream soil–leaf–canopy radiative transfer model commonly used in remote sensing studies (PRO4SAIL) illustrated structural errors in the ED2 representation of direct radiation backscatter that resulted in systematic underestimation of reflectance. In addition, we also highlight that, to directly compare with a two-stream radiative transfer model like EDR, we had to perform an additional processing step to convert the directional reflectance estimates of AVIRIS to hemispherical reflectance (also known as “albedo”). In future work, we recommend that vegetation models add the capability to predict directional reflectance, to allow them to more directly assimilate a wide range of airborne and satellite reflectance products. We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.

Publisher Copyright: © 2022 Northern forest soils are a major carbon (C) reservoir of global importance. To estimate how the C balance in these soils will change, the roles of tree roots and soil microbes in C balance should first be decoupled. This study determined how the activity of heterotrophs and tree roots together with root-associated microbes in the rhizosphere varies in coniferous forest soils in boreal, hemiboreal, and temperate climates along a latitudinal gradient using a trenching approach. We created experimental plots without living tree roots, measured soil respiration (CO2 efflux) from these and from unmanipulated plots using the chamber technique, and partitioned the efflux into root-rhizosphere (RR) and heterotrophic (RH) respiration. The share of RR in ecosystem gross primary production (GPP) decreased from north to south in the Scots pine (Pinus sylvestris L.) and the Norway spruce (Picea abies (L.) Karst.) forests, with the exception of a mixed site, where the share of RR in GPP varied strongly between the years. RR per ground area and per root biomass were mainly independent of climate within the gradient. RH per ground area increased from north to south with temperature, while RH per soil C did not change with temperature. Soil moisture did not significantly affect the respiration components in the northernmost site, whereas soil moisture was positively connected with RH and negatively with RR in other Scots pine sites and positively connected with RR in pure Norway spruce stands. The dynamic ecosystem model LPJ-GUESS was able to capture the seasonal dynamics of RH and RR at the sites, but overall accuracy varied markedly between the sites, as the model underestimated RH in the southern site and RR elsewhere. Our study provides knowledge about the nature of soil respiration components. The valuable insights can be used in more accurate land-ecosystem modelling of forest ecosystems.

Indirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates., Nature Communications, 12, ISSN:2041-1723

Soil Organic Carbon (SOC) models are important tools in determining global SOC distributions and how carbon stocks are affected by climate change. Their performances are, however, affected by data and methods used to calibrate them. Here we study how the Yasso SOC model performs if calibrated individually or with multiple datasets and how the chosen calibration method affected the parameter estimation. We found that when calibrated with multiple datasets, the model showed a better global performance compared to a single dataset calibration. Furthermore, our results show that more advanced calibration algorithms should be used for SOC models due to the multiple local maximas in the likelihood space.

Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON’s first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support.

Process-based ecosystem models help us understand and predict ecosystem processes, but using them has long involved a difficult choice between performing data- and labor-intensive site-level calibrations or relying on general parameters that may not reflect local conditions. Hierarchical Bayesian (HB) calibration provides a third option that frees modelers from assuming model parameters to be completely generic or completely site-specific and allows a formal distinction between prediction at known calibration sites and “out-of-sample” prediction to new sites. Here, we compare calibrations of a process-based dynamic vegetation model to eddy-covariance data across 12 temperate deciduous Ameriflux sites fit using either site-specific, joint cross-site, or HB approaches. To be able to apply HB to computationally demanding process-based models we introduce a novel emulator-based HB calibration tool, which we make available through the PEcAn community cyberinfrastructure. Using these calibrations to make predictions at held-out tower sites, we show that the joint cross-site calibration is falsely over-confident because it neglects parameter variability across sites and therefore underestimates variance in parameter distributions. By showing which parameters show high site-to-site variability, HB calibration also formally gives us a structure that can detect which process representations are missing from the models and prioritize errors based on the magnitude of the associated uncertainty. For example, in our case-study, we were able to identify large site-to-site variability in the parameters related to the temperature responses of respiration and photosynthesis, associated with a lack of thermal acclimation and adaptation in the model. Moving forward, HB approaches present important new opportunities for statistical modeling of the spatiotemporal variability in modeled parameters and processes that yields both new insights and improved predictions.

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century., Model‐data integration demands of the ecological community, managers, policymakers and the society are becoming increasingly diverse and complex. A new strategy is needed to approach challenges in reducing the informatics bottlenecks to process ecological information, involving the whole research community to play an active role in confronting models with data and advancing our ecological understanding. We argue that a major step towards achieving ecological model‐data integration in a way that is transparent, easily communicable, and scales up to the size and diversity of the ecological community lies in the development and support of community‐wide cyberinfrastructure.

Model-calculated forecasts of soil organic carbon (SOC) are important for approximating global terrestrial carbon pools and assessing their change. However, the lack of detailed observations limits the reliability and applicability of these SOC projections. Here, we studied if state data assimilation (SDA) can be used to continuously update the modeled state with available total carbon measurements in order to improve future SOC estimations. We chose six fallow test sites with measurements time series spanning 30 to 80 years for this initial test. In all cases, SDA improved future projections but to varying degrees. Furthermore, already including the first few measurements impacted the state enough to reduce the error in decades long projections in by at least 1 t C ha−1. Our results show the benefits of implementing SDA methods for forecasting SOC, but also highlight implementation aspects that need consideration and further research.

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks in our ability to process information have reduced our capacity to fully exploit the growing volume and variety of data. Here, we take a critical look at the information infrastructure that connects modeling and measurement efforts, and propose a roadmap that accelerates production of new knowledge. We propose that community cyberinfrastructure tools can help mend the divisions between empirical research and modeling, and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, not just a clique of ‘modelers’. This roadmap focuses on five key opportunities for community tools: the underlying backbone to community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is key to meeting the pressing needs of science and society in the 21st century.

In environmental science, we often deal with complex systems, many processes and interactions with relatively little mechanistic understanding which is translated into our models. It is also not uncommon that there are not enough measurements to inform the parameters of these models directly. However, we have increasingly more data from observatory networks and remote sensing missions that can be mapped to the properties that we are trying to predict with our models. In that case, such data can be used to indirectly inform model parameters through likelihood-based estimation methods. This chapter contrasts two likelihood-based approaches, namely maximum-likelihood estimation and Bayesian inference. The simple examples used here reveal important and scientifically relevant dissimilarities between the two approaches. Bayesian approach allows us to treat all terms in models as probability distributions while producing readily interpretable results. The power of Bayesian approach becomes more apparent as we increase the complexity of our models to reflect the complexity of our study systems. This chapter demonstrates how Bayesian inference comes to the fore as not only a way to make environmental science more relevant but also as one of the best ways of progressing it.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22025-2

East Africa hosts a striking diversity of terrestrial ecosystems, which vary both in space and time due to complex regional topography and a dynamic climate. The structure and functioning of these ecosystems under this environmental setting can be studied with dynamic vegetation models (DVMs) in a spatially explicit way. Yet, regional applications of DVMs to East Africa are rare and a comprehensive validation of such applications is missing. Here, we simulated the present-day and mid-Holocene vegetation of East Africa with the DVM, LPJ-GUESS and we conducted an exhaustive comparison of model outputs with maps of potential modern vegetation distribution, and with pollen records of local change through time. Overall, the model was able to reproduce the observed spatial patterns of East African vegetation. To see whether running the model at higher spatial resolutions (10′ × 10′) contribute to resolve the vegetation distribution better and have a better comparison scale with the observational data (i.e. pollen data), we run the model with coarser spatial resolution (0.5° × 0.5°) for the present-day as well. Both the area- and point-wise comparison showed that a higher spatial resolution allows to better describe spatial vegetation changes induced by the complex topography of East Africa. Our analysis of the difference between modelled mid-Holocene and modern-day vegetation showed that whether a biome shifts to another is best explained by both the amount of change in precipitation it experiences and the amount of precipitation it received originally. We also confirmed that tropical forest biomes were more sensitive to a decrease in precipitation compared to woodland and savanna biomes and that Holocene vegetation changes in East Africa were driven not only by changes in annual precipitation but also by changes in its seasonality.

The El Niño Southern Oscillation (ENSO), is the main driver for the interannual variability in East African rainfall with significant impact on vegetation and agriculture, and dire consequences for food and social security. In this study, we identify and quantify the ENSO contribution to the East African rainfall variability to forecast future East African vegetation response to rainfall variability related to a predicted intensified ENSO. To differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data using Empirical Orthogonal Teleconnections (EOT) analysis. Then, we simulated the vegetation under the historical climate without components related to ENSO teleconnections. We found ENSO driven patterns in vegetation response and confirmed that EOT analysis can successfully produce coupled tropical Pacific Sea Surface temperature-East African rainfall teleconnection from observed datasets. We further simulated East African vegetation response under future climate change as it is projected by climate models and under future climate change combined with a predicted increased ENSO intensity. Our EOT analysis highlight that climate simulations are still not good at capturing rainfall variability due to ENSO, and as we show here the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution. We simulated considerable differences in East African vegetation growth under the influence of an intensified ENSO regime which will bring further environmental stress to a region with a reduced capacity to adapt effects of global climate change and food security.

The present-day vegetation in the tropics is mainly characterized by forests worldwide except in tropical East Africa, where forests only occur as patches at the coast and in the uplands. These forest patches result from the peculiar aridity that is linked to the uplift of the region during the Late Cenozoic. The Late Cenozoic vegetation history of East Africa is of particular interest as it has set the scene for the contemporary events in mammal and hominin evolution. In this study, we investigate the conditions under which these forest patches could have been connected, and a previous continuous forest belt could have extended and fragmented. We apply a dynamic vegetation model with a set of climatic scenarios in which we systematically alter the present-day environmental conditions such that they would be more favourable for a continuous forest belt in tropical East Africa. We consider varying environmental factors, namely temperature, precipitation and atmospheric CO 2 concentrations. Our results show that all of these variables play a significant role in supporting the forest biomes and a continuous forest belt could have occurred under certain combinations of these settings. With our current knowledge of the palaeoenvironmental history of East Africa, it is likely that the region hosted these conditions during the Late Cenozoic. Recent improvements on environmental hypotheses of hominin evolution highlight the role of periods of short and extreme climate variability during the Late Cenozoic specific to East Africa in driving evolution. Our results elucidate how the forest biomes of East Africa can appear and disappear under fluctuating environmental conditions and demonstrate how this climate variability might be recognized on the biosphere level.