Simulated nitrous oxide emissions from multiple agroecosystems in the U.S. Corn Belt using the modified SWAT-C model

Agriculture is a major source of nitrous oxide (N2O) emissions into the atmosphere. However, assessing the impacts of agricultural conservation practices, land use change, and climate adaptation measures on N2O emissions at a large scale is a challenge for process-based model applications. Here, we integrated six N2O emission algorithms for the nitrification processes and seven N2O emission algorithms for the denitrification process into the Soil and Water Assessment Tool-Carbon (SWAT-C). We evaluated the different combinations of methods in simulating N2O emissions under corn (Zea mays L.) production systems with various conservation practices, including fertilization, tillage, and crop rotation (represented by 14 experimental treatments and 83 treatment-years) at five experimental sites across the U.S. Midwest. The SWAT-C model exhibited wide variability in simulating daily average N2O emissions across treatment-years with different method configurations, as indicated by the ranges of R2, NSE, and BIAS (0.04-0.68, -1.78-0.60, and -0.94-0.001, respectively). Our results indicate that the denitrification process has a stronger impact on N2O emissions than the nitrification process. The best performing N2O emission algorithms are those rooted in the CENTURY model, which considers soil pH and respiration effects that were overlooked by other algorithms. The optimal N2O emission algorithm explained about 63% of the variability of annual average N2O emissions, with NSE and BIAS of 0.60 and -0.033, respectively. The model can reasonably represent the impacts of agricultural conservation practices on N2O emissions. We anticipate that the improved SWAT-C model, with its flexible configurations and robust modeling and assessment capabilities, will provide a valuable tool for studying and managing N2O emissions from agroecosystems.

Collaborative Use of Sensor Networks and Cyberinfrastructure to Understand Complex Ecosystem Interactions in a Tropical Rainforest: Challenges and Lessons Learned

Collaborations between ecosystem ecologists and engineers have led to impressive progress in developing complex models of biogeochemical fluxes in response to global climate change. Ecology and engineering iteratively inform and transform each other in these efforts. Nested data streams from local sources, adjacent networks, and remote sensing sources together magnify the capacity of ecosystem ecologists to observe systems in near real-time and address questions at temporal and spatial scales that were previously unobtainable. We describe our research experiences working in a Costa Rican rainforest ecosystem with the challenges presented by constant high humidity, 4300 mm of annual rainfall, flooding, small invertebrates entering the tiniest openings, stinging insects, and venomous snakes. Over the past two decades, we faced multiple challenges and learned from our mistakes to develop a broad program of ecosystem research at multiple levels of integration. This program involved integrated networks of diverse sensors on a series of canopy towers linked to multiple belowground soil sensor arrays that could transport sensor data streams from the forest directly to an off-site location via a fiber optic cable. In our commentary, we highlight three components of our work: (1) the eddy flux measurements using canopy towers; (2) the soil sensor arrays for measuring the spatial and temporal patterns of CO2 and O2 fluxes at the soil-atmosphere interface; and (3) focused investigations of the ecosystem impact of leaf-cutter ants as "ecosystem engineers" on carbon fluxes.

Estimating canopy stomatal conductance and photosynthesis in apple trees by upscaling parameters from the leaf scale to the canopy scale in Jinzhong Basin on Loess Plateau

The estimations of stomatal conductance and photosynthesis performed by upscaling the parameters from the leaf scale to the canopy scale are key points in the fields of forest ecohydrology and physiology. The foundation for solving this scientific problem is determining the optimal models for calculating the leaf stomatal conductance (gl) and photosynthetic rate (Pl). In this study, we used the Jarvis model combined with modification factors, including leaf-air temperature (ΔT) and CO2 concentration inside and outside the stomata (ΔC), to estimate gl and the new Ye light-response model to estimate the Pl of apple trees in Jinzhong Basin on Loess Plateau. The results show that the modified Jarvis (JarvisΔT-ΔC) model and the new Ye light-response model could estimate gl and Pl, respectively, with very high accuracy, with R2 values of 0.926 and 0.959 for the former, and 0.987 and 0.983 for the latter in 2019 and 2021, respectively. Then, we estimated the canopy stomatal conductance (gc) and photosynthetic rate (Pc) by first dividing the apple tree canopy into sunlit and shaded leaves and then summing the contribution of sunlit and shaded gl, Pl and leaf area index. Our efforts will be a valid reference for estimating the gc and Pc of other tree or crop species in the future.

Biome-scale temperature sensitivity of ecosystem respiration revealed by atmospheric CO2 observations

The temperature sensitivity of ecosystem respiration regulates how the terrestrial carbon sink responds to a warming climate but has been difficult to constrain observationally beyond the plot scale. Here we use observations of atmospheric CO2 concentrations from a network of towers together with carbon flux estimates from state-of-the-art terrestrial biosphere models to characterize the temperature sensitivity of ecosystem respiration, as represented by the Arrhenius activation energy, over various North American biomes. We infer activation energies of 0.43 eV for North America and 0.38 eV to 0.53 eV for major biomes therein, which are substantially below those reported for plot-scale studies (approximately 0.65 eV). This discrepancy suggests that sparse plot-scale observations do not capture the spatial-scale dependence and biome specificity of the temperature sensitivity. We further show that adjusting the apparent temperature sensitivity in model estimates markedly improves their ability to represent observed atmospheric CO2 variability. This study provides observationally constrained estimates of the temperature sensitivity of ecosystem respiration directly at the biome scale and reveals that temperature sensitivities at this scale are lower than those based on earlier plot-scale studies. These findings call for additional work to assess the resilience of large-scale carbon sinks to warming.

Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

Ecosystem respiration (RE) plays a critical role in terrestrial carbon cycles, and quantification of RE is important for understanding the interaction between climate change and carbon dynamics. We used a multi-level attention network, Geoman, to identify the relative importance of environmental factors and to simulate spatiotemporal changes in RE in northern China’s grasslands during 2001–2015, based on 18 flux sites and multi-source spatial data. Results indicate that Geoman performed well (R2 = 0.87, RMSE = 0.39 g C m−2 d−1, MAE = 0.28 g C m−2 d−1), and that grassland type and soil texture are the two most important environmental variables for RE estimation. RE in alpine grasslands showed a decreasing gradient from southeast to northwest, and that of temperate grasslands showed a decreasing gradient from northeast to southwest. This can be explained by the enhanced vegetation index (EVI), and soil factors including soil organic carbon density and soil texture. RE in northern China’s grasslands showed a significant increase (1.81 g C m−2 yr−1) during 2001–2015. The increase rate of RE in alpine grassland (2.36 g C m−2 yr−1) was greater than that in temperate grassland (1.28 g C m−2 yr−1). Temperature and EVI contributed to the interannual change of RE in alpine grassland, and precipitation and EVI were the main contributors in temperate grassland. This study provides a key reference for the application of advanced deep learning models in carbon cycle simulation, to reduce uncertainties and improve understanding of the effects of biotic and climatic factors on spatiotemporal changes in RE.

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05° spatial resolution for the period 2001–2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R2) = 0.58, root mean square error (RMSE) = 2.74 g C·m−2, bias = –0.34 g C·m−2) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future.

Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests

Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra- and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate-driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non-structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf-growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%-210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38 Pg C/year from forest ecosystems during 1985-2016. The results showed an increasing trend of GPP in North America (1.0 Pg C/decade). The inclusion of climate-driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis.

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink

Satellite observations show that leaf area index (LAI) has increased globally since 1981, but the impact of this vegetation structural change on the global terrestrial carbon cycle has not been systematically evaluated. Through process-based diagnostic ecosystem modeling, we find that the increase in LAI alone was responsible for 12.4% of the accumulated terrestrial carbon sink (95 ± 5 Pg C) from 1981 to 2016, whereas other drivers of CO₂ fertilization, nitrogen deposition, and climate change (temperature, radiation, and precipitation) contributed to 47.0%, 1.1%, and −28.6% of the sink, respectively. The legacy effects of past changes in these drivers prior to 1981 are responsible for the remaining 65.5% of the accumulated sink from 1981 to 2016. These results refine the attribution of the land sink to the various drivers and would help constrain prognostic models that often have large uncertainties in simulating changes in vegetation and their impacts on the global carbon cycle.

There lacks systematic analysis on the importance of vegetation structural change in the global terrestrial carbon cycle. Here the authors conducted a multi-model comparison analysis and find that the increase in leaf area index has been responsible for 12.4% of the accumulated terrestrial carbon sink from 1981 to 2016.

Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest

Tropical South America plays a central role in global climate. Bowen ratio teleconnects to circulation and precipitation processes far afield, and the global CO2 growth rate is strongly influenced by carbon cycle processes in South America. However, quantification of basin-wide seasonality of flux partitioning between latent and sensible heat, the response to anomalies around climatic norms, and understanding of the processes and mechanisms that control the carbon cycle remains elusive. Here, we investigate simulated surface-atmosphere interaction at a single site in Brazil, using models with different representations of precipitation and cloud processes, as well as differences in scale of coupling between the surface and atmosphere. We find that the model with parameterized clouds/precipitation has a tendency toward unrealistic perpetual light precipitation, while models with explicit treatment of clouds produce more intense and less frequent rain. Models that couple the surface to the atmosphere on the scale of kilometers, as opposed to tens or hundreds of kilometers, produce even more realistic distributions of rainfall. Rainfall intensity has direct consequences for the "fate of water," or the pathway that a hydrometeor follows once it interacts with the surface. We find that the model with explicit treatment of cloud processes, coupled to the surface at small scales, is the most realistic when compared to observations. These results have implications for simulations of global climate, as the use of models with explicit (as opposed to parameterized) cloud representations becomes more widespread.

Modelling carbon sources and sinks in terrestrial vegetation

Contents Summary 652 I. Introduction 652 II. Discrepancy in predicting the effects of rising [CO₂ ] on the terrestrial C sink 655 III. Carbon and nutrient storage in plants and its modelling 656 IV. Modelling the source and the sink: a plant perspective 657 V. Plant-scale water and Carbon flux models 660 VI. Challenges for the future 662 Acknowledgements 663 Authors contributions 663 References 663 SUMMARY: The increase in atmospheric CO₂ in the future is one of the most certain projections in environmental sciences. Understanding whether vegetation carbon assimilation, growth, and changes in vegetation carbon stocks are affected by higher atmospheric CO₂ and translating this understanding in mechanistic vegetation models is of utmost importance. This is highlighted by inconsistencies between global-scale studies that attribute terrestrial carbon sinks to CO₂ stimulation of gross and net primary production on the one hand, and forest inventories, tree-scale studies, and plant physiological evidence showing a much less pronounced CO₂ fertilization effect on the other hand. Here, we review how plant carbon sources and sinks are currently described in terrestrial biosphere models. We highlight an uneven representation of complexity between the modelling of photosynthesis and other processes, such as plant respiration, direct carbon sinks, and carbon allocation, largely driven by available observations. Despite a general lack of data on carbon sink dynamics to drive model improvements, ways forward toward a mechanistic representation of plant carbon sinks are discussed, leveraging on results obtained from plant-scale models and on observations geared toward model developments.

Disentangling the complexity of permafrost soil by using high resolution profiling of microbial community composition, key functions and respiration rates

Thawing permafrost can stimulate microbial activity, leading to faster decomposition of formerly preserved organic matter and CO₂ release. Detailed knowledge about the vertical distribution of the responsible microbial community that is changing with increasing soil depth is limited. In this study, we determined the microbial community composition from cores sampled in a high Arctic heath at Svalbard, Norway; spanning from the active layer (AL) into the permafrost layer (PL). A special aim has been on identifying a layer of recently thawed soil, the transition zone (TZ), which might provide new insights into the fate of thawing permafrost. A unique sampling strategy allowed us to observe a diverse and gradually shifting microbial community in the AL, a Bacteroidetes dominated community in the TZ and throughout the PL, a community strongly dominated by a single Actinobacteria family (Intrasporangiaceae). The contrasting abundances of these two taxa caused a community difference of about 60%, just within 3 cm from TZ to PL. We incubated subsamples at about 5°C and measured highest CO₂ production rates under aerobic incubations, yet contrasting for five different layers and correlating to the microbial community composition. This high resolution strategy provides new insights on how microbial communities are structured in permafrost and a better understanding of how they respond to thaw.

Improving SWAT for simulating water and carbon fluxes of forest ecosystems

As a widely used watershed model for assessing impacts of anthropogenic and natural disturbances on water quantity and quality, the Soil and Water Assessment Tool (SWAT) has not been extensively tested in simulating water and carbon fluxes of forest ecosystems. Here, we examine SWAT simulations of evapotranspiration (ET), net primary productivity (NPP), net ecosystem exchange (NEE), and plant biomass at ten AmeriFlux forest sites across the U.S. We identify unrealistic radiation use efficiency (Bio_E), large leaf to biomass fraction (Bio_LEAF), and missing phosphorus supply from parent material weathering as the primary causes for the inadequate performance of the default SWAT model in simulating forest dynamics. By further revising the relevant parameters and processes, SWAT's performance is substantially improved. Based on the comparison between the improved SWAT simulations and flux tower observations, we discuss future research directions for further enhancing model parameterization and representation of water and carbon cycling for forests.

Circadian rhythms have significant effects on leaf-to-canopy scale gas exchange under field conditions

BACKGROUND

Molecular clocks drive oscillations in leaf photosynthesis, stomatal conductance, and other cell and leaf-level processes over ~24 h under controlled laboratory conditions. The influence of such circadian regulation over whole-canopy fluxes remains uncertain; diurnal CO2 and H2O vapor flux dynamics in the field are currently interpreted as resulting almost exclusively from direct physiological responses to variations in light, temperature and other environmental factors. We tested whether circadian regulation would affect plant and canopy gas exchange at the Montpellier European Ecotron. Canopy and leaf-level fluxes were constantly monitored under field-like environmental conditions, and under constant environmental conditions (no variation in temperature, radiation, or other environmental cues).

RESULTS

We show direct experimental evidence at canopy scales of the circadian regulation of daytime gas exchange: 20-79 % of the daily variation range in CO2 and H2O fluxes occurred under circadian entrainment in canopies of an annual herb (bean) and of a perennial shrub (cotton). We also observed that considering circadian regulation improved performance by 8-17 % in commonly used stomatal conductance models.

CONCLUSIONS

Our results show that circadian controls affect diurnal CO2 and H2O flux patterns in entire canopies in field-like conditions, and its consideration significantly improves model performance. Circadian controls act as a 'memory' of the past conditions experienced by the plant, which synchronizes metabolism across entire plant canopies.

The global impact factors of net primary production in different land cover types from 2005 to 2011

With the seriously polluted environment due to social development, the sustainability of net primary production (NPP), which is used to feed most lives on the earth, has become one of the biggest concerns that we have to consider for the sake of food shortage. There have been many researches analyzing one or two potential impact factors of NPP based on field observation data, which brings about many uncertainties for further calculation. Moreover, the frequently used process-based models heavily depend on the understandings of researchers about the NPP process. The premises of such models hinder the impact factor analysis from being objective and confident. To overcome such shortages, we collected 27 potential impact factors of global NPP in terms of eight land cover types. The feature variables include atmosphere, biosphere, anthroposphere and lithosphere parameters, which can be obtained from public available remote sensed products. The experiment shows that latitude, irradiance ultraviolet and normalized difference vegetation index are dominant factors impacting global NPP. Anthropogenic activities, precipitation and surface emissivity are influencing NPP calculation largely. However, some commonly used biosphere parameters in process-based models are actually not playing that important roles in NPP estimation. This work provides a new insight in analyzing NPP impact factors, being more objective and comprehensive compared with frequently used process-based models.

Forest biogeochemistry in response to drought

Trees alter their use and allocation of nutrients in response to drought, and changes in soil nutrient cycling and trace gas flux (N2 O and CH4 ) are observed when experimental drought is imposed on forests. In extreme droughts, trees are increasingly susceptible to attack by pests and pathogens, which can lead to major changes in nutrient flux to the soil. Extreme droughts often lead to more common and more intense forest fires, causing dramatic changes in the nutrient storage and loss from forest ecosystems. Changes in the future manifestation of drought will affect carbon uptake and storage in forests, leading to feedbacks to the Earth's climate system. We must improve the recognition of drought in nature, our ability to manage our forests in the face of drought, and the parameterization of drought in earth system models for improved predictions of carbon uptake and storage in the world's forests.

Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db)

Tropical forests play a critical role in the global carbon (C) cycle, storing ~45% of terrestrial C and constituting the largest component of the terrestrial C sink. Despite their central importance to the global C cycle, their ecosystem-level C cycles are not as well-characterized as those of extra-tropical forests, and knowledge gaps hamper efforts to quantify C budgets across the tropics and to model tropical forest-climate interactions. To advance understanding of C dynamics of pantropical forests, we compiled a new database, the Tropical Forest C database (TropForC-db), which contains data on ground-based measurements of ecosystem-level C stocks and annual fluxes along with disturbance history. This database currently contains 3568 records from 845 plots in 178 geographically distinct areas, making it the largest and most comprehensive database of its type. Using TropForC-db, we characterized C stocks and fluxes for young, intermediate-aged, and mature forests. Relative to existing C budgets of extra-tropical forests, mature tropical broadleaf evergreen forests had substantially higher gross primary productivity (GPP) and ecosystem respiration (Reco), their autotropic respiration (Ra) consumed a larger proportion (~67%) of GPP, and their woody stem growth (ANPPstem) represented a smaller proportion of net primary productivity (NPP, ~32%) or GPP (~9%). In regrowth stands, aboveground biomass increased rapidly during the first 20 years following stand-clearing disturbance, with slower accumulation following agriculture and in deciduous forests, and continued to accumulate at a slower pace in forests aged 20-100 years. Most other C stocks likewise increased with stand age, while potential to describe age trends in C fluxes was generally data-limited. We expect that TropForC-db will prove useful for model evaluation and for quantifying the contribution of forests to the global C cycle. The database version associated with this publication is archived in Dryad (DOI: 10.5061/dryad.t516f) and a dynamic version is maintained at https://github.com/forc-db.

Water use efficiency of China's terrestrial ecosystems and responses to drought

Water use efficiency (WUE) measures the trade-off between carbon gain and water loss of terrestrial ecosystems, and better understanding its dynamics and controlling factors is essential for predicting ecosystem responses to climate change. We assessed the magnitude, spatial patterns, and trends of WUE of China’s terrestrial ecosystems and its responses to drought using a process-based ecosystem model. During the period from 2000 to 2011, the national average annual WUE (net primary productivity (NPP)/evapotranspiration (ET)) of China was 0.79 g C kg⁻¹ H₂O. Annual WUE decreased in the southern regions because of the decrease in NPP and the increase in ET and increased in most northern regions mainly because of the increase in NPP. Droughts usually increased annual WUE in Northeast China and central Inner Mongolia but decreased annual WUE in central China. “Turning-points” were observed for southern China where moderate and extreme droughts reduced annual WUE and severe drought slightly increased annual WUE. The cumulative lagged effect of drought on monthly WUE varied by region. Our findings have implications for ecosystem management and climate policy making. WUE is expected to continue to change under future climate change particularly as drought is projected to increase in both frequency and severity.

Predictability of the terrestrial carbon cycle

Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool-dominant transfers, and the first-order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.

Global variability in leaf respiration in relation to climate, plant functional types and leaf traits

Leaf dark respiration (Rdark ) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of Rdark and associated leaf traits. Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in Rdark . Area-based Rdark at the prevailing average daily growth temperature (T) of each site increased only twofold from the Arctic to the tropics, despite a 20°C increase in growing T (8-28°C). By contrast, Rdark at a standard T (25°C, Rdark (25) ) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher Rdark (25) at a given photosynthetic capacity (Vcmax (25) ) or leaf nitrogen concentration ([N]) than species at warmer sites. Rdark (25) values at any given Vcmax (25) or [N] were higher in herbs than in woody plants. The results highlight variation in Rdark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of Rdark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs).

North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget

The terrestrial ecosystems of North America have been identified as a sink of atmospheric CO₂ though there is no consensus on the magnitude. However, the emissions of non-CO₂ greenhouse gases (CH₄ and N₂O) may offset or even overturn the climate cooling effect induced by the CO₂ sink. Using a coupled biogeochemical model, in this study, we have estimated the combined global warming potentials (GWP) of CO₂, CH₄ and N₂O fluxes in North American terrestrial ecosystems and quantified the relative contributions of environmental factors to the GWP changes during 1979-2010. The uncertainty range for contemporary global warming potential has been quantified by synthesizing the existing estimates from inventory, forward modeling, and inverse modeling approaches. Our "best estimate" of net GWP for CO₂, CH₄ and N₂O fluxes was -0.50 ± 0.27 Pg CO₂ eq/year (1 Pg = 10¹⁵ g) in North American terrestrial ecosystems during 2001-2010. The emissions of CH₄ and N₂O from terrestrial ecosystems had offset about two thirds (73 %±14 %) of the land CO₂ sink in the North American continent, showing large differences across the three countries, with offset ratios of 57 % ± 8 % in US, 83 % ± 17 % in Canada and 329 % ± 119 % in Mexico. Climate change and elevated tropospheric ozone concentration have contributed the most to GWP increase, while elevated atmospheric CO₂ concentration have contributed the most to GWP reduction. Extreme drought events over certain periods could result in a positive GWP. By integrating the existing estimates, we have found a wide range of uncertainty for the combined GWP. From both climate change science and policy perspectives, it is necessary to integrate ground and satellite observations with models for a more accurate accounting of these three greenhouse gases in North America.

Scale criticality in estimating ecosystem carbon dynamics

Scaling is central to ecology and Earth system sciences. However, the importance of scale (i.e. resolution and extent) for understanding carbon dynamics across scales is poorly understood and quantified. We simulated carbon dynamics under a wide range of combinations of resolution (nine spatial resolutions of 250 m, 500 m, 1 km, 2 km, 5 km, 10 km, 20 km, 50 km, and 100 km) and extent (57 geospatial extents ranging from 108 to 1 247 034 km(2) ) in the southeastern United States to explore the existence of scale dependence of the simulated regional carbon balance. Results clearly show the existence of a critical threshold resolution for estimating carbon sequestration within a given extent and an error limit. Furthermore, an invariant power law scaling relationship was found between the critical resolution and the spatial extent as the critical resolution is proportional to A(n) (n is a constant, and A is the extent). Scale criticality and the power law relationship might be driven by the power law probability distributions of land surface and ecological quantities including disturbances at landscape to regional scales. The current overwhelming practices without considering scale criticality might have largely contributed to difficulties in balancing carbon budgets at regional and global scales.

The microbial ecology of permafrost

Permafrost constitutes a major portion of the terrestrial cryosphere of the Earth and is a unique ecological niche for cold-adapted microorganisms. There is a relatively high microbial diversity in permafrost, although there is some variation in community composition across different permafrost features and between sites. Some microorganisms are even active at subzero temperatures in permafrost. An emerging concern is the impact of climate change and the possibility of subsequent permafrost thaw promoting microbial activity in permafrost, resulting in increased potential for greenhouse-gas emissions. This Review describes new data on the microbial ecology of permafrost and provides a platform for understanding microbial life strategies in frozen soil as well as the impact of climate change on permafrost microorganisms and their functional roles.

Climate-driven uncertainties in modeling terrestrial gross primary production: a site level to global-scale analysis

We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m(-2)  yr(-1) across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.

Multi-scale geospatial agroecosystem modeling: a case study on the influence of soil data resolution on carbon budget estimates

The development of effective measures to stabilize atmospheric CO2 concentration and mitigate negative impacts of climate change requires accurate quantification of the spatial variation and magnitude of the terrestrial carbon (C) flux. However, the spatial pattern and strength of terrestrial C sinks and sources remain uncertain. In this study, we designed a spatially-explicit agroecosystem modeling system by integrating the Environmental Policy Integrated Climate (EPIC) model with multiple sources of geospatial and surveyed datasets (including crop type map, elevation, climate forcing, fertilizer application, tillage type and distribution, and crop planting and harvesting date), and applied it to examine the sensitivity of cropland C flux simulations to two widely used soil databases (i.e. State Soil Geographic-STATSGO of a scale of 1:250,000 and Soil Survey Geographic-SSURGO of a scale of 1:24,000) in Iowa, USA. To efficiently execute numerous EPIC runs resulting from the use of high resolution spatial data (56m), we developed a parallelized version of EPIC. Both STATSGO and SSURGO led to similar simulations of crop yields and Net Ecosystem Production (NEP) estimates at the State level. However, substantial differences were observed at the county and sub-county (grid) levels. In general, the fine resolution SSURGO data outperformed the coarse resolution STATSGO data for county-scale crop-yield simulation, and within STATSGO, the area-weighted approach provided more accurate results. Further analysis showed that spatial distribution and magnitude of simulated NEP were more sensitive to the resolution difference between SSURGO and STATSGO at the county or grid scale. For over 60% of the cropland areas in Iowa, the deviations between STATSGO- and SSURGO-derived NEP were larger than 1MgCha(-1)yr(-1), or about half of the average cropland NEP, highlighting the significant uncertainty in spatial distribution and magnitude of simulated C fluxes resulting from differences in soil data resolution.

Toward verifying fossil fuel CO2 emissions with the CMAQ model: motivation, model description and initial simulation

Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2 spatiotemporal variability and verify CO2 fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2 simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere-atmosphere exchange, and meteorology in regulating the spatial distribution of CO2 near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2 concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver CO, the CMAQ model using hourly varying, high-resolution CO2 fossil-fuel emissions from the Vulcan inventory and Carbon Tracker optimized NEE reproduced the observed diurnal profile of CO2 reasonably well but with a low bias in the early morning. The spatial distribution of CO2 was found to correlate with NO(x), SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2 observations and understand CO2 variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2 source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2 spatiotemporal variability and various uncertainties in the future.

IMPLICATIONS

Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant.

Gaps in knowledge and data driving uncertainty in models of photosynthesis

Regional and global models of the terrestrial biosphere depend critically on models of photosynthesis when predicting impacts of global change. This paper focuses on identifying the primary data needs of these models, what scales drive uncertainty, and how to improve measurements. Overall, there is a need for an open, cross-discipline database on leaf-level photosynthesis in general, and response curves in particular. The parameters in photosynthetic models are not constant through time, space, or canopy position but there is a need for a better understanding of whether relationships with drivers, such as leaf nitrogen, are themselves scale dependent. Across time scales, as ecosystem models become more sophisticated in their representations of succession they needs to be able to approximate sunfleck responses to capture understory growth and survival. At both high and low latitudes, photosynthetic data are inadequate in general and there is a particular need to better understand thermal acclimation. Simple models of acclimation suggest that shifts in optimal temperature are important. However, there is little advantage to synoptic-scale responses and circadian rhythms may be more beneficial than acclimation over shorter timescales. At high latitudes, there is a need for a better understanding of low-temperature photosynthetic limits, while at low latitudes the need is for a better understanding of phosphorus limitations on photosynthesis. In terms of sampling, measuring multivariate photosynthetic response surfaces are potentially more efficient and more accurate than traditional univariate response curves. Finally, there is a need for greater community involvement in model validation and model-data synthesis.

Disturbance legacies and climate jointly drive tree growth and mortality in an intensively studied boreal forest

Most North American forests are at some stage of post-disturbance regrowth, subject to a changing climate, and exhibit growth and mortality patterns that may not be closely coupled to annual environmental conditions. Distinguishing the possibly interacting effects of these processes is necessary to put short-term studies in a longer term context, and particularly important for the carbon-dense, fire-prone boreal forest. The goals of this study were to combine dendrochronological sampling, inventory records, and machine-learning algorithms to understand how tree growth and death have changed at one highly studied site (Northern Old Black Spruce, NOBS) in the central Canadian boreal forest. Over the 1999-2012 inventory period, mean tree diameter increased even as stand density and basal area declined significantly. Tree mortality averaged 1.4 ± 0.6% yr-(1), with most mortality occurring in medium-sized trees; new recruitment was minimal. There have been at least two, and probably three, significant influxes of new trees since stand initiation, but none in recent decades. A combined tree ring chronology constructed from sampling in 2001, 2004, and 2012 showed several periods of extreme growth depression, with increased mortality lagging depressed growth by ~5 years. Higher minimum and maximum air temperatures exerted a negative influence on tree growth, while precipitation and climate moisture index had a positive effect; both current- and previous-year data exerted significant effects. Models based on these variables explained 23-44% of the ring-width variability. We suggest that past climate extremes led to significant mortality still visible in the current forest structure, with decadal dynamics superimposed on slower patterns of fire and succession. These results have significant implications for our understanding of previous work at NOBS, the carbon sequestration capability of old-growth stands in a disturbance-prone landscape, and the sustainable management of regional forests in a changing climate.

Predictive systems ecology

Human societies, and their well-being, depend to a significant extent on the state of the ecosystems that surround them. These ecosystems are changing rapidly usually in response to anthropogenic changes in the environment. To determine the likely impact of environmental change on ecosystems and the best ways to manage them, it would be desirable to be able to predict their future states. We present a proposal to develop the paradigm of predictive systems ecology, explicitly to understand and predict the properties and behaviour of ecological systems. We discuss the necessary and desirable features of predictive systems ecology models. There are places where predictive systems ecology is already being practised and we summarize a range of terrestrial and marine examples. Significant challenges remain but we suggest that ecology would benefit both as a scientific discipline and increase its impact in society if it were to embrace the need to become more predictive.

Modifying the Soil and Water Assessment Tool to simulate cropland carbon flux: model development and initial evaluation

Climate change is one of the most compelling modern issues and has important implications for almost every aspect of natural and human systems. The Soil and Water Assessment Tool (SWAT) model has been applied worldwide to support sustainable land and water management in a changing climate. However, the inadequacies of the existing carbon algorithm in SWAT limit its application in assessing impacts of human activities on CO2 emission, one important source of greenhouse gasses (GHGs) that traps heat in the earth system and results in global warming. In this research, we incorporate a revised version of the CENTURY carbon model into SWAT to describe dynamics of soil organic matter (SOM)-residue and simulate land-atmosphere carbon exchange. We test this new SWAT-C model with daily eddy covariance (EC) observations of net ecosystem exchange (NEE) and evapotranspiration (ET) and annual crop yield at six sites across the U.S. Midwest. Results show that SWAT-C simulates well multi-year average NEE and ET across the spatially distributed sites and capture the majority of temporal variation of these two variables at a daily time scale at each site. Our analyses also reveal that performance of SWAT-C is influenced by multiple factors, such as crop management practices (irrigated vs. rainfed), completeness and accuracy of input data, crop species, and initialization of state variables. Overall, the new SWAT-C demonstrates favorable performance for simulating land-atmosphere carbon exchange across agricultural sites with different soils, climate, and management practices. SWAT-C is expected to serve as a useful tool for including carbon flux into consideration in sustainable watershed management under a changing climate. We also note that extensive assessment of SWAT-C with field observations is required for further improving the model and understanding potential uncertainties of applying it across large regions with complex landscapes.

On improving the communication between models and data

The potential for model-data synthesis is growing in importance as we enter an era of 'big data', greater connectivity and faster computation. Realizing this potential requires that the research community broaden its perspective about how and why they interact with models. Models can be viewed as scaffolds that allow data at different scales to inform each other through our understanding of underlying processes. Perceptions of relevance, accessibility and informatics are presented as the primary barriers to broader adoption of models by the community, while an inability to fully utilize the breadth of expertise and data from the community is a primary barrier to model improvement. Overall, we promote a community-based paradigm to model-data synthesis and highlight some of the tools and techniques that facilitate this approach. Scientific workflows address critical informatics issues in transparency, repeatability and automation, while intuitive, flexible web-based interfaces make running and visualizing models more accessible. Bayesian statistics provides powerful tools for assimilating a diversity of data types and for the analysis of uncertainty. Uncertainty analyses enable new measurements to target those processes most limiting our predictive ability. Moving forward, tools for information management and data assimilation need to be improved and made more accessible.