Multiscale Synthesis Research Articles

A new global time-series GPP production: DFRF-GPP

Understanding the global ecosystem carbon cycle requires a quantitative estimation of terrestrial gross primary production (GPP). Although most existing GPP estimates can accurately reflect the spatial distribution, interannual trends and even seasonal cycles of GPP under average conditions to some extent, the GPP estimates of different models under extreme climate conditions are still unsatisfactory. In this study, based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. The quality of DFRF-GPP was evaluated by comparison with current commonly used GPP datasets and GPP observations at FLUXNET flux sites. The results showed that DFRF-GPP is in great agreement with other GPP products and can reproduce the spatial and temporal patterns and interannual trends of global GPP, with optimal performance in regions such as those north of 30° in the Northern Hemisphere where there are more FLUXNET flux sites. DFRF-GPP has higher accuracy and excellent performance on drought and high-temperature samples, with higher sensitivity to drought and high temperature. This study not only provides a set of time-series products of global gross primary production but also provides a convenient and practical solution for enhancing the applicability of GPP estimation products in large-scale extreme climate studies.

Mid-sized felids threatened by habitat degradation in Southeast Asia

Deforestation and poaching in Southeast Asia have driven a stark decline in the region's apex predators, including large felids like tigers and leopards. Meanwhile, some small felids thrive in the region's human-modified landscapes. The extent to which medium-sized felids cope with anthropogenic disturbances remains poorly understood, but this information is crucial for the conservation of threatened felids and key trophic interactions that maintain high-diversity food webs. Here, we use the largest camera-trap dataset from Southeast Asia to conduct a multi-scale synthesis of the habitat associations of two cryptic felids, the Near-Threatened Asiatic golden cat (Catopuma temminckii) and the Endangered bay cat (Catopuma badia). Unlike many mesopredators, both species exhibited poor tolerance to habitat degradation (i.e. selective logging, edges or fragmentation). The golden cat was positively associated with forest patch size and elevation, and negatively associated with degraded forests, and the bay cat was negatively associated with human population density. Our habitat suitability model suggests that ongoing forest fragmentation and degradation have critically reduced suitable habitat for the golden cat, giving reason to suspect a population decline that calls for a revision of the species' IUCN Red List status to Vulnerable. There is also evidence that the bay cat may be more widely distributed in Borneo than previously thought, including in areas currently threatened by deforestation. Our results indicate both species face a high risk of becoming extirpated from many of the region's remaining forests. In areas where apex predators have been extirpated, these charismatic mid-sized felids can become umbrella species to protect forests with high biodiversity value.

CT synthesis from MRI with an improved multi-scale learning network

Introduction: Using MRI to synthesize CT and substitute its function in radiation therapy has drawn wide research interests. Currently, deep learning models have become the first choice for MRI—CT synthesis because of their ability to study complex non-linear relations. However, existing studies still lack the ability to learn complex local and global MRI–CT relations in the same time , which influences the intensity and structural performance of synthetic images.Methods: This study proposes a hybrid multi-scale model to explore rich local and global MRI—CT relations, relations, namely, the hybrid multi-scale synthesis network (HMSS-Net). It includes two modules modelling different resolution inputs. In the low-resolution module, the Transformer method is applied to build its bottleneck part to expand the receptive field and explore long-range MRI—CT relations to estimate the coarse distribution of widely spread tissues and large organs. In the high-resolution module, residual and dense connections are applied to explore complex local MRI—CT relations under multiple step sizes. Then, the feature spaces of two modules are combined together and utilized to provide synthetic CT. HMSS-Net also introduces the multi-scale structural similarity index measure loss to provide multi-scale supervision during training.Results: The experimental results on head and neck regions of 78 patients showed that HMSS–Net reduced the average of 7.6/3.13 HU on the mean absolute error and increased the average of 2.1/1.8% on the dice coefficient of bone compared with competing image–to–image synthesis methods.Conclusion: The results imply that HMSS–Net could effectively improve the intensity and structural performances of synthetic CT.

Towards a whole-system framework for wildfire monitoring using Earth observations.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole-system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre-fire vegetation inventories, (2) active-fire monitoring, (3) post-fire assessment, and (4) multi-scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co-development of information products using cloud computing that benefits from open-access Earth observation and other geospatial data layers.

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.

Prior biosphere model impact on global terrestrial CO<sub>2</sub> fluxes estimated from OCO-2 retrievals

Abstract. This study assesses the impact of different state of the art global biospheric CO2 flux models, when applied as prior information, on inverse model “top-down” estimates of terrestrial CO2 fluxes obtained when assimilating Orbiting Carbon Observatory 2 (OCO-2) observations. This is done with a series of observing system simulation experiments (OSSEs) using synthetic CO2 column-average dry air mole fraction (XCO2) retrievals sampled at the OCO-2 satellite spatiotemporal frequency. The OSSEs utilized a 4-D variational (4D-Var) assimilation system with the GEOS-Chem global chemical transport model (CTM) to estimate CO2 net ecosystem exchange (NEE) fluxes using synthetic OCO-2 observations. The impact of biosphere models in inverse model estimates of NEE is quantified by conducting OSSEs using the NASA-CASA, CASA-GFED, SiB-4, and LPJ models as prior estimates and using NEE from the multi-model ensemble mean of the Multiscale Synthesis and Terrestrial Model Intercomparison Project as the “truth”. Results show that the assimilation of simulated XCO2 retrievals at OCO-2 observing modes over land results in posterior NEE estimates which generally reproduce “true” NEE globally and over terrestrial TransCom-3 regions that are well-sampled. However, we find larger spread among posterior NEE estimates, when using different prior NEE fluxes, in regions and seasons that have limited OCO-2 observational coverage and a large range in “bottom-up” NEE fluxes. Seasonally averaged posterior NEE estimates had standard deviations (SD) of ∼10 % to ∼50 % of the multi-model-mean NEE for different TransCom-3 land regions with significant NEE fluxes (regions/seasons with a NEE flux ≥0.5 PgC yr−1). On a global average, the seasonally averaged residual impact of the prior model NEE assumption on the posterior NEE spread is ∼10 %–20 % of the posterior NEE mean. Additional OCO-2 OSSE simulations demonstrate that posterior NEE estimates are also sensitive to the assumed prior NEE flux uncertainty statistics, with spread in posterior NEE estimates similar to those when using variable prior model NEE fluxes. In fact, the sensitivity of posterior NEE estimates to prior error statistics was larger than prior flux values in some regions/times in the tropics and Southern Hemisphere where sufficient OCO-2 data were available and large differences between the prior and truth were evident. Overall, even with the availability of spatiotemporally dense OCO-2 data, noticeable residual differences (up to ∼20 %–30 % globally and 50 % regionally) in posterior NEE flux estimates remain that were caused by the choice of prior model flux values and the specification of prior flux uncertainties.

Divergence in land surface modeling: linking spread to structure

Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. We also quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. We also find that initial conditions explain 90% of variation in global satellite-era values—initial conditions largely predetermine transient endpoints, historical environmental change notwithstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement current community best practices in model development.

Multi-scale synthesis of historical fire regimes along the south-central US prairie\u2013forest border

BackgroundAlong the prairie–forest border in the south-central USA exists one of the most extensive areas of uncut forest in the nation (>323 750 hectares), providing unique potential for developing multi-century records of environmental changes through dendrochronological analyses. Twentieth century changes in vegetation, increased prescribed fire management, and recent years of elevated wildfire activity have increased interest in understanding the region’s long-term fire regime characteristics. To address this need, we analyzed and compared fire intervals, seasonality, severity, and extent based on fire-scar history datasets from three new and ten existing study sites.ResultsAt the study sites, mean fire intervals ranged from 3 to 10 years prior to Euro-American settlement and generally became more frequent after. The majority of fires occurred in the dormant season and resulted in low percentages of trees scarred. Coinciding with Euro-American settlement (EAS), fire frequencies appear to have varied by geography. At the regional scale, fire regimes have trended towards decreased fire severity and slightly decreased fire intervals over the past 300 years. Further, fires appear to have increased in extent from circa 1770 to the mid to late 1800s, after which it began to decline, circa 1920.ConclusionsAlthough frequent, descriptions of fire regimes since the eighteenth century should be characterized as time-dependent and spatially variable, likely depending on local socio-ecological influences. Similar influences may explain fire frequency increases following EAS, while fire severity trended lower.

Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange

Terrestrial ecosystems contribute most of the interannual variability (IAV) in atmospheric carbon dioxide (CO2 ) concentrations, but processes driving the IAV of net ecosystem CO2 exchange (NEE) remain elusive. For a predictive understanding of the global C cycle, it is imperative to identify indicators associated with ecological processes that determine the IAV of NEE. Here, we decompose the annual NEE of global terrestrial ecosystems into their phenological and physiological components, namely maximum carbon uptake (MCU) and release (MCR), the carbon uptake period (CUP), and two parameters, α and β, that describe the ratio between actual versus hypothetical maximum C sink and source, respectively. Using long-term observed NEE from 66 eddy covariance sites and global products derived from FLUXNET observations, we found that the IAV of NEE is determined predominately by MCU at the global scale, which explains 48% of the IAV of NEE on average while α, CUP, β, and MCR explain 14%, 25%, 2%, and 8%, respectively. These patterns differ in water-limited ecosystems versus temperature- and radiation-limited ecosystems; 31% of the IAV of NEE is determined by the IAV of CUP in water-limited ecosystems, and 60% of the IAV of NEE is determined by the IAV of MCU in temperature- and radiation-limited ecosystems. The Lund-Potsdam-Jena (LPJ) model and the Multi-scale Synthesis and Terrestrial Model Inter-comparison Project (MsTMIP) models underestimate the contribution of MCU to the IAV of NEE by about 18% on average, and overestimate the contribution of CUP by about 25%. This study provides a new perspective on the proximate causes of the IAV of NEE, which suggest that capturing the variability of MCU is critical for modeling the IAV of NEE across most of the global land surface.

A multiscale synthesis: characterizing acute cartilage failure under an aggregate tibiofemoral joint loading.

Knee articular cartilage is characterized by a complex mechanical behavior, posing a challenge to develop an efficient and precise model. We argue that the cartilage damage, in general, can be traced to the fibril level as a plastic deformation, defined as micro-defects. To investigate these micro-defects, we have developed a detailed finite element model of the entire healthy tibiofemoral joint (TF) including a multiscale constitutive model which considers the structural hierarchies of the articular cartilage. The net model was simulated under physiological loading conditions to predict joint response under 2000N axial compression and damage initiation under high axial loading (max 7 KN) when the TF joint flexed to 30°. Computed results sufficiently agreed with earlier experimental and numerical studies. Further, initiation and propagation of damage in fibrils were computed at the tibial cartilage located mainly in the superficial and middle layers. Our simulation results also indicated that the stiffer the fibril is (higher cross-link densities), the higher the contact stress required to elicit a fibril yield and the higher the rate of yielding as a function of increased contact stress. To the best of our knowledge, this is the first model that combines macro-continuum joint mechanics and micromechanics at the tissue level. The computational construct presented here serves as a simulation platform to explore the interplay between acute cartilage damage and micromechanics characteristics at the tropocollagen level.

CO2 footprint reduction via the optimal design of Carbon-Hydrogen-Oxygen SYmbiosis Networks (CHOSYNs)

Carbon dioxide (CO2) has significant potential as a chemical feedstock as it is an abundant source of carbon atoms, entails zero or negative acquisition costs and utilization reduces environmental impact. However, significant challenges remain as the use of this feedstock involves unfavorable thermodynamics in reaction and separation, material transportation and end use application. Opportunities exist to overcome these limitations especially within eco-industrial parks which involve a group of facilities cooperating by exchanging materials and energy. An important class of EIPs, Carbon-Hydrogen-Oxygen SYmbiosis Networks (CHOSYN), has been recently introduced to integrate hydrocarbon streams from multiple plants while allowing for chemical conversion (in addition to the conventional exchange, separation, and allocation) through a multiscale targeting approach. The objective of this work is to address the problem of designing a CHOSYN with the conversion of CO2 into value-added chemicals while reducing the carbon footprint for the whole system. A multi-scale synthesis and optimization approach is proposed. First, atomic-level information coupled with economic data are used to establish benchmarks for the utilization of process streams, the purchase of external sources, and the discharge of CO2. A pre-synthesis approach is adopted to develop optimal policies for the CO2-separation technologies. Candidate CO2-utilization reactions are integrated with the existing plants to produce value-added chemicals while addressing economic and environmental objectives. Carbon-footprint constraints are added based on life-cycle analysis of the used sources and energy associated with the various reaction and separation steps. This systematic procedure captures significant interactions between surrounding CO2 sources and CO2 sinks while reducing the difficulty of selection and design of the CO2 capture process. A case study is developed and solved to demonstrate the application of this new method.

Land carbon models underestimate the severity and duration of drought\u2019s impact on plant productivity

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0–6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO2.

Новая теория освоения (пространства) Арктики и Севера: полимасштабный междисциплинарный синтез

Новая теория освоения (пространства) Арктики и Севера: полимасштабный междисциплинарный синтез

Contribution of environmental forcings to US runoff changes for the period 1950–2010

Runoff in the United States is changing, and this study finds that the measured change is dependent on the geographic region and varies seasonally. Specifically, observed annual total runoff had an insignificant increasing trend in the US between 1950 and 2010, but this insignificance was due to regional heterogeneity with both significant and insignificant increases in the eastern, northern, and southern US, and a greater significant decrease in the western US. Trends for seasonal mean runoff also differed across regions. By region, the season with the largest observed trend was autumn for the east (positive), spring for the north (positive), winter for the south (positive), winter for the west (negative), and autumn for the US as a whole (positive). Based on the detection and attribution analysis using gridded WaterWatch runoff observations along with semi-factorial land surface model simulations from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we found that while the roles of CO2 concentration, nitrogen deposition, and land use and land cover were inconsistent regionally and seasonally, the effect of climatic variations was detected for all regions and seasons, and the change in runoff could be attributed to climate change in summer and autumn in the south and in autumn in the west. We also found that the climate-only and historical transient simulations consistently underestimated the runoff trends, possibly due to precipitation bias in the MsTMIP driver or within the models themselves.

Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques

Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land–Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.

Increased light‐use efficiency in northern terrestrial ecosystems indicated by CO2 and greening observations

AbstractObservations show an increasing amplitude in the seasonal cycle of CO2 (ASC) north of 45°N of 56 ± 9.8% over the last 50 years and an increase in vegetation greenness of 7.5–15% in high northern latitudes since the 1980s. However, the causes of these changes remain uncertain. Historical simulations from terrestrial biosphere models in the Multiscale Synthesis and Terrestrial Model Intercomparison Project are compared to the ASC and greenness observations, using the TM3 atmospheric transport model to translate surface fluxes into CO2 concentrations. We find that the modeled change in ASC is too small but the mean greening trend is generally captured. Modeled increases in greenness are primarily driven by warming, whereas ASC changes are primarily driven by increasing CO2. We suggest that increases in ecosystem‐scale light use efficiency (LUE) have contributed to the observed ASC increase but are underestimated by current models. We highlight potential mechanisms that could increase modeled LUE.

Technical note: 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange

Abstract. The land surface provides a boundary condition to atmospheric forward and flux inversion models. These models require prior estimates of CO2 fluxes at relatively high temporal resolutions (e.g., 3-hourly) because of the high frequency of atmospheric mixing and wind heterogeneity. However, land surface model CO2 fluxes are often provided at monthly time steps, typically because the land surface modeling community focuses more on time steps associated with plant phenology (e.g., seasonal) than on sub-daily phenomena. Here, we describe a new dataset created from 15 global land surface models and 4 ensemble products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), temporally downscaled from monthly to 3-hourly output. We provide 3-hourly output for each individual model over 7 years (2004–2010), as well as an ensemble mean, a weighted ensemble mean, and the multi-model standard deviation. Output is provided in three different spatial resolutions for user preferences: 0.5° × 0.5°, 2.0° × 2.5°, and 4.0° × 5.0° (latitude × longitude). These data are publicly available from doi:10.3334/ORNLDAAC/1315.

An analysis of global terrestrial carbon, water and energy dynamics using the carbon–nitrogen coupled CLASS-CTEMN+ model

A carbon–nitrogen (C–N) coupled terrestrial ecosystem model, CLASS-CTEMN+, was used to simulate global terrestrial carbon, water, and energy cycles at 0.5-degree spatial resolution from 1901 to 2010. Forcing and initializing data sets from the North American Carbon Program (NACP)—Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) were used. Two versions of the model (the C–N coupled and the C-only version) were compared. The C–N (C) version of the model simulated global annual total gross ecosystem productivity (GEP) of 122.7 (128.2) PgCyr−1, ecosystem respiration (Re) of 119.1 (122.4) PgCyr−1, net ecosystem productivity (NEP) of 3.64 (5.82) PgCyr−1, net primary productivity (NPP) of 62.7 (67.4) PgCyr−1, latent heat (LE) of 146.2 (167.4) ZJyr−1, sensible heat (H) of 194.0 (162) ZJyr−1, soil organic carbon (SOC) of 1230 (1328) PgC, and total vegetation biomass (Tvg) of 608 (629) PgC, over the 1980–2010 period were comparable to scaled observations and modelling studies in the literature. Our results suggest that altough inclusion of N cycling has not had a large effect on the simulated total global terrestrial ecosystem C budget over this period; however the temporal evolution and spatial distribution of the simulated C budget revealed important influences of the N cycle. Analysis of long-term annual variations in response to evolving climate, CO2 concentration levels, and N deposition over the 1901–2010 period showed largely similar increasing trends in global GEP, Re, NEP and NPP until about 1970, after which the rate of increase was suppressed in the C–N coupled version of the model relative to the C-only version. Spatial plots showed that GEP and Re were reduced in the C–N coupled version of the model over the temperate and boreal regions and in high latitudes, where N limitation is well documented. The strongest response in NEP was in the boreal forests of North America, Northern Europe and Northwestern Asia, which showed decreases in response to the inclusion of a N cycle. Commensurate with the reductions in GEP and NEP, leaf area index (LAI) and total vegetation biomass (Tvg) also showed decreases in the boreal region, while SOC showed wider decreases in temperate, boreal and northern regions.This study helps to identify and diagnose the impact of N controls on the simulated spatiotemporal patterns of terrestrial C sinks and sources, and to evaluate and quantify the variability of simulated N cycle effects on different plant functional types (PFTs). These results have important implications for future climate change scenarios because the response to increasing CO2, N deposition, and climate patterns shows diverging model behavior as compared with the C-only version, when a N cycle is incorporated in the model.

A Conditional Multiscale Locally Gaussian Texture Synthesis Algorithm

Exemplar-based texture synthesis is defined as the process of generating, from an input texture sample, new texture images that are perceptually equivalent to the input. In the present work, we model texture self-similarity with conditional Gaussian distributions in the patch space in order to extend the use of stitching techniques. Then, a multiscale texture synthesis algorithm is introduced, where texture patches are modeled at each scale as spatially variable Gaussian vectors in the patch space. The Gaussian distribution for each patch is inferred from the set of its nearest neighbors in the patch space obtained from the input sample. This approach is tested over several real and synthetic texture images, and its results show the effectiveness of the proposed technique for a wide range of textures.

Toward “optimal” integration of terrestrial biosphere models

AbstractMultimodel ensembles (MME) are commonplace in Earth system modeling. Here we perform MME integration using a 10‐member ensemble of terrestrial biosphere models (TBMs) from the Multiscale synthesis and Terrestrial Model Intercomparison Project (MsTMIP). We contrast optimal (skill based for present‐day carbon cycling) versus naïve (“one model‐one vote”) integration. MsTMIP optimal and naïve mean land sink strength estimates (−1.16 versus −1.15 Pg C per annum respectively) are statistically indistinguishable. This holds also for grid cell values and extends to gross uptake, biomass, and net ecosystem productivity. TBM skill is similarly indistinguishable. The added complexity of skill‐based integration does not materially change MME values. This suggests that carbon metabolism has predictability limits and/or that all models and references are misspecified. Resolving this issue requires addressing specific uncertainty types (initial conditions, structure, and references) and a change in model development paradigms currently dominant in the TBM community.