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Soils are the largest terrestrial reservoir of organic carbon, and so even small changes in soil carbon stocks can have significant effects on the atmosphere and climate. To select effective strategies to mitigate climate change, predictions of how soils will respond to future changes in climate and land use are needed. Achieving meaningful predictions requires a deep understanding of the highly complex, open, multicomponent soil organic matter system. One of the most effective methods for predicting the dynamics of soil organic matter is mathematical modeling. Process-oriented (physically based) models make it possible to present the basic concepts about the mechanisms that determine the behavior of this system in a mathematically formalized form and conduct a quantitative analysis. The uncertainty of the forecasts depends on the level of development of the theory explaining the dynamics of soil organic matter, the models representing it and their experimental support. This review examines the achievements of the last decade in modeling the role of microorganisms in the stabilization of soil organic matter, the concept of soil saturation with organic carbon, and temperature control, as well as the development of reactive transport models describing the dynamics of organic carbon in the soil profile and the representation of the dynamics of soil organic matter in global climate models. Unsolved problems associated with the high variability in the structure of new generation soil organic matter dynamics models are discussed. [ABSTRACT FROM AUTHOR]

Biogeochemical models for predicting carbon dynamics increasingly include microbial processes, reflecting the importance of microorganisms in regulating the movement of carbon between soils and the atmosphere. Soil viruses can redirect carbon among various chemical pools, indicating a need for quantification and development soil carbon models that explicitly represent viral dynamics. In this opinion, we derive a global estimate of carbon potentially released from microbial biomass by viral infections in soils and synthesize a quantitative soil carbon budget from existing literature that explicitly includes viral impacts. We then adapt known mechanisms by which viruses influence carbon cycles in marine ecosystems into a soil‐explicit framework. Finally, we explore the diversity of virus–host interactions during infection and conceptualize how infection mode may impact soil carbon fate. Our synthesis highlights key knowledge gaps hindering the incorporation of viruses into soil carbon cycling research and generates specific hypotheses to test in the pursuit of better quantifying microbial dynamics that explain ecosystem‐scale carbon fluxes. The importance of identifying critical drivers behind soil carbon dynamics, including these elusive but likely pervasive viral mechanisms of carbon redistribution, becomes more pressing with climate change. [ABSTRACT FROM AUTHOR]

Several biogeochemical models have been applied to understand the potential effects of management practices on soil organic carbon (SOC) sequestration, crop growth, and yield. In this study, the denitrification and decomposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice–wheat rotation under organic/inorganic fertilization with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC underpredicted harvestable grain C-biomass of rice where percent difference (PD) varied from 29.22% to 42.14%, and over-simulated grain C-biomass of wheat where PD was −55.01% with 50% nitrogen–phosphorus–potassium (NPK) and 50% animal manure applied under the CT treatment. However, after calibration by adjusting default values of soil and crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (e.g., average PD ranged from −2.81% to −6.17%). DNDC also predicted the effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha−1). DNDC simulated SOC trends for rice–wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice–wheat systems.

Abstract Biogeochemical models simulate soil nitrogen (N) turnover and are often used to assess N losses through denitrification. Though models simulate a complete N budget, often only a subset of N pools/fluxes (i.e., N2O, NO3−, NH3, NOx) are published since the full budget cannot be validated with measured data. Field studies rarely include full N balances, especially N2 fluxes, which are difficult to quantify. Limiting publication of modeling results based on available field data represents a missed opportunity to improve the understanding of modeled processes. We propose that the modeler community support publication of all simulated N pools and processes in future studies.

Several biogeochemical models have been applied to understand the potential effects of management practices on soil organic carbon (SOC) sequestration, crop growth, and yield. In this study, the denitrification and decomposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice–wheat rotation under organic/inorganic fertilization with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC underpredicted harvestable grain C-biomass of rice where percent difference (PD) varied from 29.22% to 42.14%, and over-simulated grain C-biomass of wheat where PD was −55.01% with 50% nitrogen–phosphorus–potassium (NPK) and 50% animal manure applied under the CT treatment. However, after calibration by adjusting default values of soil and crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (e.g., average PD ranged from −2.81% to −6.17%). DNDC also predicted the effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha−1). DNDC simulated SOC trends for rice–wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice–wheat systems. [ABSTRACT FROM AUTHOR]

Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. As a consequence, scientific literature contains various up-to-date, advanced oil spill predictive models, capable of simulating the trajectory and evolution of an oil slick generated by the accidental release from ships, hydrocarbon production, or other activities. To predict in near real time oil spill transport and fate with increased reliability, these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. The present study reviews the available different met-ocean forcings that have been used in oil-spill modeling, simulating hypothetical or real oil spill scenarios, worldwide. Seven state-of-the-art oil-spill models are critically examined in terms of the met-ocean data used as forcing inputs in the simulation of twenty-three case studies. The results illustrate that most oil spill models are coupled to different resolution, forecasting meteorological and hydrodynamic models, posing, however, limited consideration in the forecasted wave field (expressed as the significant wave height, the wave period, and the Stokes drift) that may affect oil transport, especially at the coastal areas. Moreover, the majority of oil spill models lack any linkage to the background biogeochemical conditions; hence, limited consideration is given to processes such as oil biodegradation, photo-oxidation, and sedimentation. Future advancements in oil-spill modeling should be directed towards the full operational coupling with high-resolution atmospheric, hydrodynamic, wave, and biogeochemical models, improving our understanding of the relative impact of each physical and oil weathering process. [ABSTRACT FROM AUTHOR]

Marine biogeochemical models are an effective tool for formulating hypothesis and gaining mechanistic understanding of how an ecosystem functions. This paper presents a comprehensive review of biogeochemical models and explores their applications in different marine ecosystems. It also assesses their performance in reproducing key biogeochemical components, such as chlorophyll-a, nutrients, carbon, and oxygen cycles. The study focuses on four distinct zones: tropical, temperate, polar/subpolar, and high nutrient low chlorophyll (HNLC). Each zone exhibits unique physical and biogeochemical characteristics, which are defined and used to evaluate the models’ performance. While biogeochemical models have demonstrated the ability to simulate various ecosystem components, limitations and assumptions persist. Thus, this review addresses these limitations and discusses the challenges and future developments of biogeochemical models. Key areas for improvement involve incorporating missing components such as viruses, archaea, mixotrophs, refining parameterizations for nitrogen transformations, detritus representation, and considering the interactions of fish and zooplankton within the models.

Arctic shelf seas receive greater quantities of river runoff than any other ocean region and are experiencing increased freshwater loads and associated terrestrial matter inputs since recent decades. Amplified terrestrial permafrost thaw and coastal erosion is exposing previously frozen organic matter, enhancing its mobilization and release to nearshore regions. Changing terrestrial dissolved organic matter (terr-DOM) loads and composition may alter shelf primary productivity and respiration, ultimately affecting net regional CO2 air–sea fluxes. However, the future evolution of Arctic Ocean climate feedbacks are highly dependent upon the biological degradability of terr-DOM in coastal waters, a factor often omitted in modelling studies. Here, we assess the sensitivity of CO2 air–sea fluxes from East Siberian Arctic Shelf (ESAS) waters to changing terr-DOM supply and degradability using a biogeochemical model explicitly accounting for bacteria dynamics and shifting terr-DOM composition. We find increasing terr-DOM loads and degradability trigger a series of biogeochemical and ecological processes shifting ESAS waters from a net sink to a net source of CO2, even after accounting for strengthening coastal productivity by additional land-derived nutrients. Our results suggest that future projected inputs of labile terr-DOM from peat and permafrost thaw may strongly increase the CO2 efflux from the Arctic shelf sea, causing currently unquantified positive feedback to climate change. [ABSTRACT FROM AUTHOR]

Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. As a consequence, scientific literature contains various up-to-date, advanced oil spill predictive models, capable of simulating the trajectory and evolution of an oil slick generated by the accidental release from ships, hydrocarbon production, or other activities. To predict in near real time oil spill transport and fate with increased reliability, these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. The present study reviews the available different met-ocean forcings that have been used in oil-spill modeling, simulating hypothetical or real oil spill scenarios, worldwide. Seven state-of-the-art oil-spill models are critically examined in terms of the met-ocean data used as forcing inputs in the simulation of twenty-three case studies. The results illustrate that most oil spill models are coupled to different resolution, forecasting meteorological and hydrodynamic models, posing, however, limited consideration in the forecasted wave field (expressed as the significant wave height, the wave period, and the Stokes drift) that may affect oil transport, especially at the coastal areas. Moreover, the majority of oil spill models lack any linkage to the background biogeochemical conditions; hence, limited consideration is given to processes such as oil biodegradation, photo-oxidation, and sedimentation. Future advancements in oil-spill modeling should be directed towards the full operational coupling with high-resolution atmospheric, hydrodynamic, wave, and biogeochemical models, improving our understanding of the relative impact of each physical and oil weathering process.

Deviations of surface ocean dissolved oxygen (O 2) from equilibrium with the atmosphere should be rectified about twenty times more quickly than deviations of dissolved carbon dioxide (CO 2). Therefore, persistent O 2 disequilibria in the Labrador Sea, while CO 2 is close to equilibrium, has been a matter of interest to many previous works. Here we investigate this phenomenon by using a novel analytical technique, the 'CORS (Carbon Dioxide and Oxygen Relative to Saturation) method', and also by using more data than was available previously. We compare observations to results from a model we developed for the Labrador Sea which combines plankton ecology with biogeochemical cycling of oxygen, carbon and nitrogen. In contrast to earlier works which mostly considered individual factors in isolation, here we used the model, together with data, to distinguish between the varying influences of several processes potentially contributing to the long-lasting O 2 undersaturation: mixed layer depth, duration of mixed layer deepening, convection, entrainment and bottom water O 2 content. Our model experiments confirm that, for the same gas exchange rate, the effects on surface O 2 concentration differ significantly among the identified drivers. Our results suggest that prolonged surface O 2 undersaturation is not always dependent on the extreme winter mixed layer depths, but rather that even moderately deep mixed layers (e.g. 300 m), when prolonged and in conjunction with continuous entrainment of oxygen-depleted deep water, can also drive persistent surface O 2 anomalies. An implication of our results is that regions in the North Atlantic with maximum winter mixed layer depths of only a few hundred metres should also show persistent surface O 2 undersaturation. We further reveal that convection in deep water formation regions produces trendlines that do not pass through the origin of a plot of CO 2 vs. O 2 deviations which have previously been thought to indicate erroneous data. • Extreme winter mixed layer depths in the Labrador Sea drives its surface O 2 anomaly because of continuous entrainment of O 2 -poor deep waters. • Moderately shallow maximum winter mixing depths (e.g. 300 m) can also drive long-duration surface O 2 undersaturation. • Questionable autonomous data previously assumed to be erroneous may in fact be real. [ABSTRACT FROM AUTHOR]

Marine ecosystems are complex and dynamic in nature and influenced by various environmental factors such as temperature, salinity, ocean currents, nutrient availability, light penetration, and anthropogenic activities. Macronutrients (nitrate, phosphate, and silicate) and dissolved oxygen (DO) are crucial properties for determining the health, function, and dynamics of marine ecosystems. There are known limitations with the in-situ measurements that emphasize the importance of satellite-based models for estimating these properties on the required space and time scales. In this study, we present a number of robust Gaussian Process Regression (GPR) models comprising of 16 DO models and 24 macronutrients models for estimating the concentrations of global-scale ocean surface DO and macronutrients. These models were rigorously trained and tested using the large in-situ datasets. Model performance was assessed using independent in-situ data and it was found that the proposed models yielded high accuracies (Root Mean Square Difference (RMSD) in μmol kg−1, Mean Absolute Difference (MAD) in μmol kg−1, and coefficient of determination (R 2 )): DO: 8.276, 3.802, and 0.984; Nitrate: 0.827, 0.329, and 0.987; Phosphate: 0.068, 0.034, and 0.983; and Silicate: 1.921, 0.757, and 0.982. The optimal input parameters and kernel combinations for GPR models were identified as (i) sea surface temperature (SST), sea surface salinity (SSS), and latitude/longitude for DO, and (ii) SST, SSS, DO, and latitude/longitude for macronutrients. The satellite estimates based on the exponential kernel functions showed good agreement with in-situ data (RMSD, MAD, R 2 , Slope, and Intercept: 9.794, 4.850, 0.948, 0.986, and 4.206 for the DO products, 1.711, 0.652, 0.824, 0.884, and 0.249 for the nitrate products, 0.127, 0.064, 0.805, 0.869, and 0.033 for the phosphate products, and 2.809, 1.067, 0.533, 0.622, and 1.117 for the silicate products). Further tests on World Ocean Atlas (WOA) 2018 SST and SSS data yielded similar results for the DO and macronutrients contents. To realize the importance of this study, we investigated the early and substantial spring bloom occurrences in the Gulf of Alaska in response to the DO and macronutrients contents as well as the monthly and interannual variations and anomalies of SST, SSS, DO, nitrate, phosphate, and silicate caused by the Pacific Decadal Oscillation (PDO) in the California Current System (CCS) and Oceanic Niño Index (ONI) in the Niño-3.4 region using climatological data (2002−2023). The proposed models will have important implications for remote sensing of regional and global biogeochemical properties and marine ecosystem dynamics. • Proposed models for ocean surface dissolved oxygen and macronutrients. • Rigorously assessed with both in-situ and satellite data. • Satellite estimates closely align with in-situ data. • Significant implications for remote sensing of dissolved oxygen and macronutrients [ABSTRACT FROM AUTHOR]

Estuaries make an important contribution to the global greenhouse gas budget. Yet modeling predictions of carbon dioxide (CO2) and methane (CH4) emissions from estuaries remain highly uncertain due to both simplified assumptions about the underpinning hydrologic and biologic processes and inadequate data availability to uniquely define parameters related to CO2 and CH4 processes. This study presents a modeling framework to quantify the sensitivity and uncertainty of predicted CO2 and CH4 concentrations and emissions, which is demonstrated through application to a subtropical urban estuary (Brisbane River, Australia). A 3D hydrodynamic‐biogeochemical model was constructed, and calibrated using the model‐independent Parameter ESTimation software (PEST) with field data sets that captured strong gradients of CO2 and CH4 concentrations and emissions along the estuary. The approach refined uncertainty in the estimation of whole‐estuary annual emissions, and enabled us to assess the sensitivity and uncertainty of CO2 and CH4 dynamics. Estuarine CO2 concentrations were most sensitive to uncertainty in riverine inputs, whereas estuarine CH4 concentrations were most sensitive to sediment production and pelagic oxidation. Over the modeled year, variance in the daily fluctuations in carbon emissions from this case‐study spanned the full range of emission rates reported for estuaries around the world, highlighting that spatially or temporally limited sampling regimes could significantly bias estuarine greenhouse gas emission estimates. The combination of targeted field campaigns with the modeling approach presented in this study can help to improve carbon budgeting in estuaries, reduce uncertainty in emission estimates, and support management strategies to reduce or offset estuary greenhouse gas emissions. Plain Language Summary: Global estuaries are a major source of CO2 emission and play a disproportionate role in global carbon cycling relative to their area. Estuaries are also a source of CH4 that has approximately 34 times the global‐warming potential of CO2 over a 100‐year time period. However, large uncertainties remain in estimating the CO2 and CH4 emissions from estuaries due to limited observations to cover their large spatiotemporal variations, and lack of knowledge of their dynamics in response to the external inputs and internal biogeochemical reactions. In this study, we developed a modeling framework to address the sensitivity and uncertainty of CO2 and CH4 dynamics in the Brisbane River Estuary to both the riverine and wastewater inputs and biogeochemical reaction parameters. We showed that CO2 concentrations were most sensitive to catchment inputs and CH4 concentrations were most sensitive to the rate of sediment production and oxidation in the water. The estimated annual‐total CO2 equivalent emission from Brisbane River Estuary in 2016 was 3,514 ± 445 tonnes. The combination of targeted field campaigns with the modeling approach can help to improve carbon budgeting in estuaries, reduce uncertainty in emission estimates, and support management strategies to reduce or offset estuary greenhouse gas emissions. Key Points: Estuarine CO2 is most sensitive to external riverine CO2 inputsEstuarine CH4 is most sensitive to sediment production and oxidation in the waterThe modeling approach constrained the uncertainty in the estimation of CO2 and CH4 outgassing [ABSTRACT FROM AUTHOR]

Mesozooplankton play a key role in marine ecosystems as they modulate the transfer of energy from phytoplankton to large marine organisms. In addition, they directly influence the oceanic cycles of carbon and nutrients through vertical migrations, fecal pellet production, respiration, and excretion. Mesozooplankton are mainly made up of metazoans, which undergo important size changes during their life cycle, resulting in significant variations in metabolic rates. However, most marine biogeochemical models represent mesozooplankton as protists-like organisms. Here, we study the potential caveats of this simplistic representation by using a chemostat-like zero-dimensional model with four different Nutrient-Phytoplankton-Zooplankton configurations in which the description of mesozooplankton ranges from protist-type organisms to using a size-based formulation including explicit reproduction and ontogenetic growth. We show that the size-based formulation strongly impacts mesozooplankton. First, it generates a delay of a few months in the response to an increase in food availability. Second, the increase in mesozooplankton biomass displays much larger temporal variations, in the form of successive cohorts, because of the dependency of the ingestion rate to body size. However, the size-based formulation does not affect smaller plankton or nutrient concentrations. A proper assessment of these top-down effects would require implementing our size-resolved approach in a 3-dimensional biogeochemical model. Furthermore, the bottom-up effects on higher trophic levels resulting from the significant changes in the temporal dynamics of mesozooplankton could be estimated in an end-to-end model coupling low and high trophic levels. [ABSTRACT FROM AUTHOR]

We use a Nitrogen-Phytoplankton-Zooplankton-Detritus (NPZD) biogeochemical model implemented in a time-dependent box model scheme to simulate the temporal dynamics of the pelagic ecosystem in the Southern California Current System (SCCS). The model was forced by winds, sea surface temperature and light. Nutrient inputs to the modeled box were driven by coastal upwelling or upwelling due to wind-stress curl in order to assess the importance of each process in the temporal dynamics of the SCCS ecosystem. Model results were compared to the CalCOFI dataset, both in terms of climatological annual cycles and actual values. This comparison led to modifications of the basic model structure to better represent the coastal ecosystem, particularly phytoplankton growth and zooplankton mortality terms. Wind-stress curl-induced upwelling was found to be significant only in the offshore regions while coastal upwelling better represented the dynamics of the inshore areas. The two upwelling mechanisms work in synchrony, however, to bring nutrients to surface waters during the same time periods. Finally, the effect of low-frequency perturbations, such as those associated with the ENSO and NPGO, were assessed by comparing model results and data. Since the NPGO cycle largely impacts the SCCS through modifications of upwelling-favorable winds, its effects were well represented in the model results. In contrast, ENSO responses were poorly captured in the simulations because such perturbations alter the system by changing surface water mass distributions via mechanisms that were not included in the model forcing. © 2011 Elsevier B.V.

Model uncertainty is rarely considered in the field of biogeochemical modeling. The standard biogeochemical modeling approach is to proceed based on one selected model with the “right” complexity level based on data availability. However, other plausible models can result in dissimilar answer to the scientific question in hand using the same set of data. Relying on a single model can lead to underestimation of uncertainty associated with the results and therefore lead to unreliable conclusions. Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models with multiple levels of complexity. The aim of this paper is two fold, first to explore the impact of a model’s complexity level on the accuracy of the end results and second to introduce a probabilistic multi-model strategy in the context of a process-based biogeochemical model. We developed three different versions of a biogeochemical model, TOUGHREACT-N, with various complexity levels. Each one of these models was calibrated against the observed data from a tomato field in Western Sacramento County, California, and considered two different weighting sets on the objective function. This way we created a set of six ensemble members. The Bayesian Model Averaging (BMA) approach was then used to combine these ensemble members by the likelihood that an individual model is correct given the observations. Our results demonstrated that none of the models regardless of their complexity level under both weighting schemes were capable of representing all the different processes within our study field. Later we found that it is also valuable to explore BMA to assess the structural inadequacy inherent in each model. The performance of BMA expected prediction is generally superior to the individual models included in the ensemble especially when it comes to predicting gas emissions. The BMA assessed 95% uncertainty bounds bracket 90–100% of the observations. The results clearly indicate the need to consider a multi-model ensemble strategy over a single model selection in biogeochemical modeling study.

The oceans play a fundamental role in the global carbon cycle, providing a sink for atmospheric carbon. Key to this role is the vertical transport of organic carbon from the surface to the deep ocean. This transport is a product of a diverse range of physical and biogeochemical processes that determine the formation and fate of this material, and in particular how much carbon is sequestered in the deep ocean. Models can be used to both diagnose biogeochemical processes and predict how the various processes will change in the future. Global biogeochemical models use simplified representations of food webs and processes but are converging on values for the export of organic carbon from the surface ocean. Other models concentrate on understanding specific processes and can be used to develop parameterizations for global models. Model development is continuing by adding representations and parameterizations of higher trophic levels and mesopelagic processes, and these are expected to improve model performance.

In the frame of a national, joint scientific appraisal, 45 scientific French-speaking experts have been mandated in 2015–2016 by the French ministries of Environment and Agriculture to perform a global review of scientific literature dealing with the eutrophication phenomenon, in freshwater as well as in marine waters. This paper summarizes the main results of this review restricted to a sub-domain, the modelling approach of the marine eutrophication. After recalling the different aims pursued, an overview is given on the historical time course of this modelling effort, its world distribution and the various tools used. Then, the main results obtained are examined, highlighting the specific strengths and weaknesses of the present models. Needs for future improvement are then listed. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) turnover increasingly is conceptualized as a tension between accessibility to microorganisms and protection from decomposition via physical and chemical association with minerals in emerging soil biogeochemical theory. Yet, these components are missing from the original mathematical models of belowground carbon dynamics and remain underrepresented in more recent compartmental models that separate SOM into discrete pools with differing turnover times. Thus, a gap currently exists between the emergent understanding of SOM dynamics and our ability to improve terrestrial biogeochemical projections that rely on the existing models. In this opinion paper, we portray the SOM paradigm as a triangle composed of three nodes: conceptual theory, analytical measurement, and numerical models. In successful approaches, we contend that the nodes are connected—models capture the essential features of dominant theories while measurement tools generate data adequate to parameterize and evaluate the models—and balanced—models can inspire new theories via emergent behaviors, pushing empiricists to devise new measurements. Many exciting advances recently pushed the boundaries on one or more nodes. However, newly integrated triangles have yet to coalesce. We conclude that our ability to incorporate mechanisms of microbial decomposition and physicochemical protection into predictions of SOM change is limited by current disconnections and imbalances among theory, measurement, and modeling. Opportunities to reintegrate the three components of the SOM paradigm exist by carefully considering their linkages and feedbacks at specific scales of observation. [ABSTRACT FROM AUTHOR]

Abstract: Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2O emissions. Yield‐scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed. [ABSTRACT FROM AUTHOR]

Sea ice is one the largest biomes on earth, yet it is poorly described by biogeochemical and climate models. In this paper, published and unpublished data on sympagic (ice-associated) algal biodiversity and productivity have been compiled from more than 300 sea-ice cores and organized into a systematic framework. Significant patterns in microalgal community structure emerged from this framework. Autotrophic flagellates characterize surface communities, interior communities consist of mixed microalgal populations and pennate diatoms dominate bottom communities. There is overlap between landfast and pack-ice communities, which supports the hypothesis that sympagic microalgae originate from the pelagic environment. Distribution in the Arctic is sometimes quite different compared to the Antarctic. This difference may be related to the time of sampling or lack of dedicated studies. Seasonality has a significant impact on species distribution, with a potentially greater role for flagellates and centric diatoms in early spring. The role of sea-ice algae in seeding pelagic blooms remains uncertain. Photosynthesis in sea ice is mainly controlled by environmental factors on a small scale and therefore cannot be linked to specific ice types. Overall, sea-ice communities show a high capacity for photoacclimation but low maximum productivity compared to pelagic phytoplankton. Low carbon assimilation rates probably result from adaptation to extreme conditions of reduced light and temperature in winter. We hypothesize that in the near future, bottom communities will develop earlier in the season and develop more biomass over a shorter period of time as light penetration increases due to the thinning of sea ice. The Arctic is already witnessing changes. The shift forward in time of the algal bloom can result in a mismatch in trophic relations, but the biogeochemical consequences are still hard to predict. With this paper we provide a number of parameters required to improve the reliability of sea-ice biogeochemical models.

We describe a new ocean-sea ice-biogeochemical model, apply it to the Bothnian Bay in the northern Baltic Sea for the time period 1991–2007 and provide the first long-term mesoscale estimates of modelled sea-ice primary production in the northern Baltic Sea. After comparing the available physical and biogeochemical observations within the study area and the time period investigated with the model results, we show the modelled spatial, intra- and interannual variability in sea-ice physical and biogeochemical properties and consider the main factors limiting ice algal primary production. Sea-ice permeability in the studied area was low compared with the polar oceans, which appeared to be a major reason for the generally low primary production rates. Although the sea ice was less saline in the northernmost parts of the basin, these parts were characterized by sea ice with a larger amount of habitable space, higher levels of photosynthetically active radiation and increased macronutrient availability near the coast, which favoured higher algal growth rates. Other parts of the southern central basin were mostly co-limited by less favourable light conditions (i.e., earlier ice breakups associated with fewer sunlight hours) and lower seawater macronutrient concentrations than in the coastal zones. Although a change towards milder winters (i.e., reduced ice cover, thickness and length of the ice season) was previously detected on a half-century timescale and could partly be seen here, analysis of the temporal evolution of sea-ice biogeochemical properties showed no significant trends over time, though these properties were characterized by large interannual variability.

Reservoir models play an important role in representing fluxes of matter and energy in ecological systems and are the basis of most models in biogeochemistry. These models are commonly used to study the effects of environmental change on the cycling of biogeochemical elements and to predict potential transitions of ecosystems to alternative states. To study critical regime changes of time-dependent, externally forced biogeochemical systems, we analyze the behavior of reservoir models typical for element cycling (e.g., terrestrial carbon cycle) with respect to time-varying signals by applying the mathematical concept of input to state stability (ISS). In particular, we discuss ISS as a generalization of preceding stability notions for non-autonomous, non-linear reservoir models represented by systems of ordinary differential equations explicitly dependent on time and a time-varying input signal. We also show how ISS enhances existing stability concepts, previously only available for linear time variant (LTV) systems, to a tool applicable also in the non-linear case. [ABSTRACT FROM AUTHOR]

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research. [ABSTRACT FROM AUTHOR]

We used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of four phytoplankton groups to the total primary production. First, we assessed the contribution of each phytoplankton groups to the total primary production at a global scale for the period 1998–2011. Globally, diatoms contributed the most to the total phytoplankton production (~50%, the equivalent of ~20 PgC∙y−1). Coccolithophores and chlorophytes each contributed ~20% (~7 PgC∙y−1) of the total primary production and cyanobacteria represented about 10% (~4 PgC∙y−1) of the total primary production. Primary production by diatoms was highest in the high latitudes (>40°) and in major upwelling systems (Equatorial Pacific and Benguela system). We then assessed interannual variability of this group-specific primary production over the period 1998–2011. Globally the annual relative contribution of each phytoplankton groups to the total primary production varied by maximum 4% (1–2 PgC∙y−1). We assessed the effects of climate variability on group-specific primary production using global (i.e., Multivariate El Niño Index, MEI) and “regional” climate indices (e.g., Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability as indicated by significant correlation (p < 0.05) between the MEI and the group-specific primary production from all groups except coccolithophores. In the Atlantic, climate variability as indicated by NAO was significantly correlated to the primary production of 2 out of the 4 groups in the North Central Atlantic (diatoms/cyanobacteria) and in the North Atlantic (chlorophytes and coccolithophores). We found that climate variability as indicated by SAM had only a limited effect on group-specific primary production in the Southern Ocean. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale.

Monitoring and predicting the biogeochemical state of the ocean and marine ecosystems is an important application of operational oceanography that needs to be expanded. The accurate depiction of the ocean’s physical environment enabled by Global Ocean Data Assimilation Experiment (GODAE) systems, in both real-time and reanalysis modes, is already valuable for various applications, such as the fishing industry and fisheries management. However, most of these applications require accurate estimates of both physical and biogeochemical ocean conditions over a wide range of spatial and temporal scales. In this paper, we discuss recent developments that enable the coupling new biogeochemical models and assimilation components with the existing GODAE systems, and we examine the potential of such systems in several areas of interest: phytoplankton biomass monitoring in open oceans, ocean carbon cycle monitoring and assessment, marine ecosystem management at seasonal and longer time scales, and downscaling in coastal areas. A number of key requirements and research priorities are then identified for the future. The GODAE systems will need to improve their representation of physical variables that currently are not yet considered essential, such as upper-ocean vertical fluxes that are critically important to biological activity. Further, the observing systems will need to be expanded in terms of in situ platforms (with intensified deployments of sensors for O2 and chlorophyll, and inclusion of new sensors for nutrients, zooplankton, micronekton biomass, and others), satellite missions (e.g., hyperspectral instruments for ocean color, light detection and ranging (LIDAR) systems for mixed-layer depths, wide-swath altimeters for coastal sea levels), and improved methods to assimilate these new measurements.

Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development. [Display omitted] • We investigate multi-model performance in simulating C and N fluxes in agriculture. • Correlated model residuals hinder reliable C–N flux estimates. • Residual correlation analysis is applied to ensemble crop and grassland models. • Partially calibrated models can be practical for implementing model ensembles. • Fully calibrated models are key to model development. [ABSTRACT FROM AUTHOR]

1. Litter decomposition is a biogeochemical process fundamental to element cycling within ecosystems, influencing plant productivity, species composition and carbon storage. 2. Climate has long been considered the primary broad-scale control on litter decomposition rates, yet recent work suggests that plant litter traits may predominate. Both decomposition paradigms, however, rely on inferences from cross-biome litter decomposition studies that analyse site-level means. 3. We re-analyse data from a classical cross-biome study to demonstrate that previous research may falsely inflate the regulatory role of climate on decomposition and mask the influence of unmeasured local-scale factors. 4. Using the re-analysis as a platform, we advocate experimental designs of litter decomposition studies that involve high within-site replication, measurements of regulatory factors and processes at the same local spatial grain, analysis of individual observations and biome-scale gradients. 5. Synthesis. We question the assumption that climate is the predominant regulator of decomposition rates at broad spatial scales. We propose a framework for a new generation of studies focused on factoring local-scale variation into the measurement and analysis of soil processes across broad scales. Such efforts may suggest a revised decomposition paradigm and ultimately improve confidence in the structure, parameter estimates and hence projections of biogeochemical models. [ABSTRACT FROM AUTHOR]

Catchment dissolved organic carbon (DOC) fluxes are governed by complex interactions, which control biogeochemical processes generating DOC and hydrological connectivity, facilitating transport through the landscape to streams. This paper presents the development of a coupled hydrological-biogeochemical model for a northern watershed with organic-rich soils, to simulate daily DOC concentrations. The parsimonious model design allows the relative importance of DOC fluxes from the major landscape units (e.g. hillslopes, groundwater and riparian saturation area) to be determined. The dynamic extent of the saturated riparian zone, which at maximum wetness comprised 40 % of the drainage area, contributed 84 % of DOC to the stream, of which 16 % was derived from the hillslope soils. This shows the disproportional riparian influence on stream water chemistry and the importance of the non-linearity in hydrological connectivity. The temporal connectivity of each of the landscape units was dependent on antecedent moisture conditions, with highly transient connections between the hillslope and valley bottom saturated area, which were entirely disconnected during the driest periods. The groundwater contribution remained constant, but its relative importance increased during the driest periods. The study emphasises the importance of conceptualising hydrological connectivity and its relation to hydroclimatic factors, as well soil biogeochemical processes, when modelling stream water DOC. [ABSTRACT FROM AUTHOR]

This systematic review considers how water quality and aquatic ecology models represent the phosphorus cycle. Although the focus is on phosphorus, many of the observations and discussion points here relate to aquatic ecosystem models in general. The review considers how models compare across domains of application, the degree to which current models are fit for purpose, how to choose between multiple alternative formulations, and how models might be improved. Lake and marine models have been gradually increasing in complexity, with increasing emphasis on inorganic processes and ecosystems. River models have remained simpler, but have been more rigorously assessed. Processes important in less eutrophic systems have often been neglected: these include the biogeochemistry of organic phosphorus, transformations associated with fluxes through soils and sediments, transfer rate-limited phosphorus uptake, and responses of plants to pulsed nutrient inputs. Arguments for and against increasing model complexity, physical and physiological realism are reviewed. [ABSTRACT FROM AUTHOR]

We used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of four phytoplankton groups to the total primary production. First, we assessed the contribution of each phytoplankton groups to the total primary production at a global scale for the period 1998-2011. Globally, diatoms contributed the most to the total phytoplankton production (~50%, the equivalent of ~20 PgC·y-1). Coccolithophores and chlorophytes each contributed ~20% (~7 PgC·y-1) of the total primary production and cyanobacteria represented about 10% (~4 PgC·y-1) of the total primary production. Primary production by diatoms was highest in the high latitudes (>40°) and in major upwelling systems (Equatorial Pacific and Benguela system). We then assessed interannual variability of this group-specific primary production over the period 1998-2011. Globally the annual relative contribution of each phytoplankton groups to the total primary production varied by maximum 4% (1-2 PgC·y-1). We assessed the effects of climate variability on group-specific primary production using global (i.e., Multivariate El Niño Index, MEI) and "regional"climate indices (e.g., Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability as indicated by significant correlation (p < 0.05) between the MEI and the group-specific primary production from all groups except coccolithophores. In the Atlantic, climate variability as indicated by NAO was significantly correlated to the primary production of 2 out of the 4 groups in the North Central Atlantic (diatoms/cyanobacteria) and in the North Atlantic (chlorophytes and coccolithophores). We found that climate variability as indicated by SAM had only a limited effect on group-specific primary production in the Southern Ocean. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale. [ABSTRACT FROM AUTHOR]

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Abstract: We use a Nitrogen-Phytoplankton-Zooplankton-Detritus (NPZD) biogeochemical model implemented in a time-dependent box model scheme to simulate the temporal dynamics of the pelagic ecosystem in the Southern California Current System (SCCS). The model was forced by winds, sea surface temperature and light. Nutrient inputs to the modeled box were driven by coastal upwelling or upwelling due to wind-stress curl in order to assess the importance of each process in the temporal dynamics of the SCCS ecosystem. Model results were compared to the CalCOFI dataset, both in terms of climatological annual cycles and actual values. This comparison led to modifications of the basic model structure to better represent the coastal ecosystem, particularly phytoplankton growth and zooplankton mortality terms. Wind-stress curl-induced upwelling was found to be significant only in the offshore regions while coastal upwelling better represented the dynamics of the inshore areas. The two upwelling mechanisms work in synchrony, however, to bring nutrients to surface waters during the same time periods. Finally, the effect of low-frequency perturbations, such as those associated with the ENSO and NPGO, were assessed by comparing model results and data. Since the NPGO cycle largely impacts the SCCS through modifications of upwelling-favorable winds, its effects were well represented in the model results. In contrast, ENSO responses were poorly captured in the simulations because such perturbations alter the system by changing surface water mass distributions via mechanisms that were not included in the model forcing. [Copyright &y& Elsevier]

Abstract: A numerical solution for an idealized double-gyre is used to investigate the sensitivity of ocean dynamics and passive tracer advection to horizontal resolution (Δx) in a mesoscale eddy rich regime. In agreement with previous studies, we find that ocean dynamical solutions are strongly sensitive to grid resolution. With mesoscale resolution , eddies are marginally resolved and their impact on tracer transport is not well represented. At submesoscale resolution , the number of mesoscale eddies and their energy is increased, due to the resolved submesoscales. The changes are mostly seen in the vorticity and vertical velocity fields, and are less obvious in the temperature field. In contrast, we demonstrate that the offline transport of passive tracer is not altered when the finest scales present in the dynamical solutions are discarded. We do so by showing that dynamical solutions obtained with can be degraded (following a flux preserving procedure) down to resolutions without significantly impacting passive tracer solutions. The reason for this stems from the level of dissipation/diffusion required during the integration of the dynamical model which smoothes variance at wavelength smaller than at least 5–10 Δx. This result is used to derive a method which alleviates data storage needs and accelerates tracer advection simulations, with a gain of the order of 103 in computing time. The method involves three steps: (1) on-line resolution of the dynamics with , (2) degradation of the 3D velocity field on a grid and (3) off-line tracer transport with the degraded velocity on the ΔX grid. It opens promising perspectives for submesoscale bio-physical modelling at reduced numerical cost. [Copyright &y& Elsevier]

Abstract: In this work, we develop a lake eutrophication model to determine restoration policies for water quality improvement. This hybrid biogeochemical model has been formulated within a simultaneous dynamic optimization framework as an optimal control problem, whose solution provides limiting nutrient inflow profiles to the lake, as well as in-lake biomanipulation profiles. The water quality model comprises a set of partial differential algebraic equations in time and space, which result from dynamic mass balances on main phytoplankton groups, nutrients, dissolved oxygen and biochemical demand of oxygen. Spatial discretization has been performed in two layers. The simultaneous approach proceeds by discretizing control and state variables by collocation over finite elements and solving the large scale nonlinear program with an interior point method with successive quadratic programming techniques. [Copyright &y& Elsevier]

The influence of the coastal ocean on global net annual air-sea CO2 fluxes remains uncertain. However, it is well known that air-sea pCO2 disequilibria can be large (ocean pCO2 ranging from ∼400 µatm above atmospheric saturation to ∼250 µatm below) in eastern boundary currents, and it has been hypothesized that these regions may be an appreciable net carbon sink. In addition it has been shown that the high productivity in these regions (responsible for the exceptionally low surface pCO2) can cause nutrients and inorganic carbon to become more concentrated in the lower layer of the water column over the shelf relative to adjacent open ocean waters of the same density. This paper explores the potential role of the winter season in determining the net annual CO2 flux in temperate zone eastern boundary currents, using the results from a box model. The model is parameterized and forced to represent the northernmost part of the upwelling region on the North American Pacific coast. Model results are compared to the few summer data that exist in that region. The model is also used to determine the effect that upwelling and downwelling strength have on the net annual CO2 flux. Results show that downwelling may play an important role in limiting the amount of CO2 outgassing that occurs during winter. Finally data from three distinct regions on the Pacific coast are compared to highlight the importance of upwelling and downwelling strength in determining carbon fluxes in eastern boundary currents and to suggest that other features, such as shelf width, are likely to be important. [ABSTRACT FROM AUTHOR]

The article discusses a study which investigated the application of nonlinear variants of the Kalman filter (KF) to sequential parameter estimation in biogeochemical models. The study focused on two components of the statistical model, which include Q, the covariance of the stochastic forcing used to represent model error and R, the observation error covariance matrix. Sensitivity of parameter estimates from the extended and ensemble Kalman filters to the choice of Q, R, initial parameters and ensemble size using pseudo data from a simple yet highly nonlinear test model.

In a recent study, Magnani et al. report how atmospheric nitrogen deposition drives stand-lifetime net ecosystem productivity (NEPav) for midlatitude forests, with an extremely high C to N response (725 kg C kg−1 wet-deposited N for their European sites). We present here a re-analysis of these data, which suggests a much smaller C : N response for total N inputs. Accounting for dry, as well as wet N deposition reduces the C : N response to 177 : 1. However, if covariance with intersite climatological differences is accounted for, the actual C : N response in this dataset may be <70 : 1. We then use a model analysis of 22 European forest stands to simulate the findings of Magnani et al. Multisite regression of simulated NEPav vs. total N deposition reproduces a high C : N response (149 : 1). However, once the effects of intersite climatological differences are accounted for, the value is again found to be much smaller, pointing to a real C : N response of about 50–75 : 1. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a theoretical approach to formulate a model which comprises the full food web. The lower part of the food web is represented by a biogeochemical model which interacts explicitly with a fish-production model. The fish-production model component builds on existing theories but was substantially reformulated in order to facilitate the model coupling. The dynamics of the fish-production model is basically driven by the predator–prey interaction. We use the example of the Baltic Sea, which has a relatively simple foodweb structure. The fish biomass is dominated by three groups, sprat, herring and cod, which represent about 80% of fish biomass in the Baltic. The zooplanktivors sprat and herring are eaten by cod. In this paper we start the construction of the model as a simple box system, which can be considered as an isolated water column of 10×10 km2 times the water depth in the central Bornholm basin of the Baltic Sea. The stepwise building up of the model is illustrated by example simulations, which allow to assess the consistence of the theoretical approach and the choices of parameters. As last step we introduce a simple biogeochemical model and link it with the fish model. The resulting model system is strictly mass conserving without unspecified sources of food or so. We conduct experiments with the model system and show that it can reproduce features such as interannual variation in fish catches and trophic cascades. [Copyright &y& Elsevier]