Articles | Volume 8, issue 6
Research article 21 Jun 2011
Research article | 21 Jun 2011
A systematic approach for comparing modeled biospheric carbon fluxes across regional scales
D. N. Huntzinger et al.
Related subject area
Biogeochemistry: Modelling, TerrestrialExtending a land-surface model with Sphagnum moss to simulate responses of a northern temperate bog to whole ecosystem warming and elevated CO2Improving the representation of high-latitude vegetation distribution in dynamic global vegetation modelsRobust processing of airborne laser scans to plant area density profilesInvestigating the sensitivity of soil heterotrophic respiration to recent snow cover changes in Alaska using a satellite-based permafrost carbon modelHysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activityModelling the habitat preference of two key Sphagnum species in a poor fen as controlled by capitulum water contentEvaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO2Assessing impacts of selective logging on water, energy, and carbon budgets and ecosystem dynamics in Amazon forests using the Functionally Assembled Terrestrial Ecosystem SimulatorMicrobial dormancy and its impacts on northern temperate and boreal terrestrial ecosystem carbon budgetImpacts of fertilization on grassland productivity and water quality across the European Alps: insights from a mechanistic modelHistorical CO2 emissions from land use and land cover change and their uncertaintyA Bayesian approach to evaluation of soil biogeochemical modelsRainfall intensification increases the contribution of rewetting pulses to soil heterotrophic respirationWide discrepancies in the magnitude and direction of modeled solar-induced chlorophyll fluorescence in response to light conditionsModeling biological nitrogen fixation in global natural terrestrial ecosystemsThe impact of a simple representation of non-structural carbohydrates on the simulated response of tropical forests to droughtBenchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, PanamaThe Climate Benefit of Carbon SequestrationModelling nitrification inhibitor effects on N2O emissions after fall- and spring-applied slurry by reducing nitrifier NH4+ oxidation rateDRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter modelsWintertime grassland dynamics may influence belowground biomass under climate change: a model analysisUnderstanding the effect of fire on vegetation composition and gross primary production in a semi-arid shrubland ecosystem using the Ecosystem Demography (EDv2.2) modelLow sensitivity of gross primary production to elevated CO2 in a mature eucalypt woodlandMetabolic tradeoffs and heterogeneity in microbial responses to temperature determine the fate of litter carbon in simulations of a warmer worldCompetition alters predicted forest carbon cycle responses to nitrogen availability and elevated CO2: simulations using an explicitly competitive, game-theoretic vegetation demographic modelThe importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forestsModelling the response of net primary productivity of the Zambezi teak forests to climate change along a rainfall gradient in ZambiaThree decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observationsUsing a modified DNDC biogeochemical model to optimize field management of a multi-crop (cotton, wheat, and maize) system: a site-scale case study in northern ChinaDecadal fates and impacts of nitrogen additions on temperate forest carbon storage: a data–model comparisonGlobal NO and HONO emissions of biological soil crusts estimated by a process-based non-vascular vegetation modelEstimating the soil N2O emission intensity of croplands in northwest EuropeUnifying soil organic matter formation and persistence frameworks: the MEMS modelEvaluating the simulated mean soil carbon transit times by Earth system models using observationsModeling anaerobic soil organic carbon decomposition in Arctic polygon tundra: insights into soil geochemical influences on carbon mineralizationNeglecting plant–microbe symbioses leads to underestimation of modeled climate impactsA simple time-stepping scheme to simulate leaf area index, phenology, and gross primary production across deciduous broadleaf forests in the eastern United StatesQuantifying global N2O emissions from natural ecosystem soils using trait-based biogeochemistry modelsOptimal inverse estimation of ecosystem parameters from observations of carbon and energy fluxesEmergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation modelsEvaluation of simulated ozone effects in forest ecosystems against biomass damage estimates from fumigation experimentsLeaf area index identified as a major source of variability in modeled CO2 fertilizationAn improved parameterization of leaf area index (LAI) seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) modelling frameworkEcosystem carbon transit versus turnover times in response to climate warming and rising atmospheric CO2 concentrationLinking big models to big data: efficient ecosystem model calibration through Bayesian model emulationControls of terrestrial ecosystem nitrogen loss on simulated productivity responses to elevated CO2The impact of spatiotemporal variability in atmospheric CO2 concentration on global terrestrial carbon fluxesMicrobial decomposition processes and vulnerable arctic soil organic carbon in the 21st centuryDiffusion limitations and Michaelis–Menten kinetics as drivers of combined temperature and moisture effects on carbon fluxes of mineral soilsAn evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa
Xiaoying Shi, Daniel M. Ricciuto, Peter E. Thornton, Xiaofeng Xu, Fengming Yuan, Richard J. Norby, Anthony P. Walker, Jeffrey M. Warren, Jiafu Mao, Paul J. Hanson, Lin Meng, David Weston, and Natalie A. Griffiths
Biogeosciences, 18, 467–486,Short summary
The Sphagnum mosses are the important species of a wetland ecosystem. To better represent the peatland ecosystem, we introduced the moss species to the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. We tested the model against field observations and used the model to make projections of the site's carbon cycle under warming and atmospheric CO2 concentration scenarios.
Peter Horvath, Hui Tang, Rune Halvorsen, Frode Stordal, Lena Merete Tallaksen, Terje Koren Berntsen, and Anders Bryn
Biogeosciences, 18, 95–112,Short summary
We evaluated the performance of three methods for representing vegetation cover. Remote sensing provided the best match to a reference dataset, closely followed by distribution modelling (DM), whereas the dynamic global vegetation model (DGVM) in CLM4.5BGCDV deviated strongly from the reference. Sensitivity tests show that use of threshold values for predictors identified by DM may improve DGVM performance. The results highlight the potential of using DM in the development of DGVMs.
Johan Arnqvist, Julia Freier, and Ebba Dellwik
Biogeosciences, 17, 5939–5952,Short summary
Data generated by airborne laser scans enable the characterization of surface vegetation for any application that might need it, such as forest management, modeling for numerical weather prediction, or wind energy estimation. In this work we present a new algorithm for calculating the vegetation density using data from airborne laser scans. The new routine is more robust than earlier methods, and an implementation in popular programming languages accompanies the article to support new users.
Yonghong Yi, John S. Kimball, Jennifer D. Watts, Susan M. Natali, Donatella Zona, Junjie Liu, Masahito Ueyama, Hideki Kobayashi, Walter Oechel, and Charles E. Miller
Biogeosciences, 17, 5861–5882,Short summary
We developed a 1 km satellite-data-driven permafrost carbon model to evaluate soil respiration sensitivity to recent snow cover changes in Alaska. Results show earlier snowmelt enhances growing-season soil respiration and reduces annual carbon uptake, while early cold-season soil respiration is linked to the number of snow-free days after the land surface freezes. Our results also show nonnegligible influences of subgrid variability in surface conditions on model-simulated CO2 seasonal cycles.
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, and Scott R. Saleska
Biogeosciences, 17, 5849–5860,Short summary
Methane (CH4) is a strong greenhouse gas that can accelerate climate change and offset mitigation efforts. A key assumption embedded in many large-scale climate models is that ecosystem CH4 emissions can be estimated by fixed temperature relations. Here, we demonstrate that CH4 emissions cannot be parameterized by emergent temperature response alone due to variability driven by microbial and abiotic interactions. We also provide mechanistic understanding for observed CH4 emission hysteresis.
Jinnan Gong, Nigel Roulet, Steve Frolking, Heli Peltola, Anna M. Laine, Nicola Kokkonen, and Eeva-Stiina Tuittila
Biogeosciences, 17, 5693–5719,Short summary
In this study, which combined a field and lab experiment with modelling, we developed a process-based model for simulating dynamics within peatland moss communities. The model is useful because Sphagnum mosses are key engineers in peatlands; their response to changes in climate via altered hydrology controls the feedback of peatland biogeochemistry to climate. Our work showed that moss capitulum traits related to water retention are the mechanism controlling moss layer dynamics in peatlands.
Tea Thum, Julia E. M. S. Nabel, Aki Tsuruta, Tuula Aalto, Edward J. Dlugokencky, Jari Liski, Ingrid T. Luijkx, Tiina Markkanen, Julia Pongratz, Yukio Yoshida, and Sönke Zaehle
Biogeosciences, 17, 5721–5743,Short summary
Global vegetation models are important tools in estimating the impacts of global climate change. The fate of soil carbon is of the upmost importance as its emissions will enhance the atmospheric carbon dioxide concentration. To evaluate the skill of global vegetation models to model the soil carbon and its responses to environmental factors, it is important to use different data sources. We evaluated two different soil carbon models by using atmospheric carbon dioxide concentrations.
Maoyi Huang, Yi Xu, Marcos Longo, Michael Keller, Ryan G. Knox, Charles D. Koven, and Rosie A. Fisher
Biogeosciences, 17, 4999–5023,Short summary
The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is enhanced to mimic the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events; determine the survivorship of cohorts in the disturbed forest; and modifying the biomass, coarse woody debris, and litter pools. This study lays the foundation to simulate land use change and forest degradation in FATES as part of an Earth system model.
Junrong Zha and Qianla Zhuang
Biogeosciences, 17, 4591–4610,Short summary
This study incorporated microbial dormancy into a detailed microbe-based biogeochemistry model to examine the fate of Arctic carbon budgets under changing climate conditions. Compared with the model without microbial dormancy, the new model estimated a much higher carbon accumulation in the region during the last and current century. This study highlights the importance of the representation of microbial dormancy in earth system models to adequately quantify the carbon dynamics in the Arctic.
Martina Botter, Matthias Zeeman, Paolo Burlando, and Simone Fatichi
Revised manuscript accepted for BG
Thomas Gasser, Léa Crepin, Yann Quilcaille, Richard A. Houghton, Philippe Ciais, and Michael Obersteiner
Biogeosciences, 17, 4075–4101,Short summary
We combine several lines of evidence to provide a robust estimate of historical CO2 emissions from land use change. Our novel approach leads to reduced uncertainty and identifies key remaining sources of uncertainty and discrepancy. We also quantify the carbon removal by natural ecosystems that would have occurred if these ecosystems had not been destroyed (mostly via deforestation). Over the last decade, this foregone carbon sink amounted to about 50 % of the actual emissions.
Hua W. Xie, Adriana L. Romero-Olivares, Michele Guindani, and Steven D. Allison
Biogeosciences, 17, 4043–4057,Short summary
Soil biogeochemical models (SBMs) are needed to predict future soil CO2 emissions levels, but we presently lack statistically rigorous frameworks for assessing the predictive utility of SBMs. In this study, we demonstrate one possible approach to evaluating SBMs by comparing the fits of two models to soil CO2 respiration data with recently developed Bayesian statistical goodness-of-fit metrics. Our results demonstrate that our approach is a viable one for continued development and refinement.
Stefano Manzoni, Arjun Chakrawal, Thomas Fischer, Joshua P. Schimel, Amilcare Porporato, and Giulia Vico
Biogeosciences, 17, 4007–4023,Short summary
Carbon dioxide is produced by soil microbes through respiration, which is particularly fast when soils are moistened by rain. Will respiration increase with future more intense rains and longer dry spells? With a mathematical model, we show that wetter conditions increase respiration. In contrast, if rainfall totals stay the same, but rain comes all at once after long dry spells, the average respiration will not change, but the contribution of the respiration bursts after rain will increase.
Nicholas C. Parazoo, Troy Magney, Alex Norton, Brett Raczka, Cédric Bacour, Fabienne Maignan, Ian Baker, Yongguang Zhang, Bo Qiu, Mingjie Shi, Natasha MacBean, Dave R. Bowling, Sean P. Burns, Peter D. Blanken, Jochen Stutz, Katja Grossmann, and Christian Frankenberg
Biogeosciences, 17, 3733–3755,Short summary
Satellite measurements of solar-induced chlorophyll fluorescence (SIF) provide a global measure of photosynthetic change. This enables scientists to better track carbon cycle responses to environmental change and tune biochemical processes in vegetation models for an improved simulation of future change. We use tower-instrumented SIF measurements and controlled model experiments to assess the state of the art in terrestrial biosphere SIF modeling and find a wide range of sensitivities to light.
Tong Yu and Qianlai Zhuang
Biogeosciences, 17, 3643–3657,Short summary
Biological nitrogen fixation (BNF) plays an important role in the global nitrogen cycle. However, the fixation rate has usually been measured or estimated at a particular observational site. This study develops a BNF model considering the symbiotic relationship between legume plants and bacteria. The model is extensively calibrated with site-level observational data and then extrapolated to the global terrestrial ecosystems to quantify the fixation rate in the 1990s.
Simon Jones, Lucy Rowland, Peter Cox, Deborah Hemming, Andy Wiltshire, Karina Williams, Nicholas C. Parazoo, Junjie Liu, Antonio C. L. da Costa, Patrick Meir, Maurizio Mencuccini, and Anna B. Harper
Biogeosciences, 17, 3589–3612,Short summary
Non-structural carbohydrates (NSCs) are an important set of molecules that help plants to grow and respire when photosynthesis is restricted by extreme climate events. In this paper we present a simple model of NSC storage and assess the effect that it has on simulations of vegetation at the ecosystem scale. Our model has the potential to significantly change predictions of plant behaviour in global vegetation models, which would have large implications for predictions of the future climate.
Charles D. Koven, Ryan G. Knox, Rosie A. Fisher, Jeffrey Q. Chambers, Bradley O. Christoffersen, Stuart J. Davies, Matteo Detto, Michael C. Dietze, Boris Faybishenko, Jennifer Holm, Maoyi Huang, Marlies Kovenock, Lara M. Kueppers, Gregory Lemieux, Elias Massoud, Nathan G. McDowell, Helene C. Muller-Landau, Jessica F. Needham, Richard J. Norby, Thomas Powell, Alistair Rogers, Shawn P. Serbin, Jacquelyn K. Shuman, Abigail L. S. Swann, Charuleka Varadharajan, Anthony P. Walker, S. Joseph Wright, and Chonggang Xu
Biogeosciences, 17, 3017–3044,Short summary
Tropical forests play a crucial role in governing climate feedbacks, and are incredibly diverse ecosystems, yet most Earth system models do not take into account the diversity of plant traits in these forests and how this diversity may govern feedbacks. We present an approach to represent diverse competing plant types within Earth system models, test this approach at a tropical forest site, and explore how the representation of disturbance and competition governs traits of the forest community.
Carlos A. Sierra, Susan E. Crow, Martin Heimann, Holger Metzler, and Ernst-Detleft Schulze
Revised manuscript accepted for BGShort summary
The Climate Benefit of carbon Sequestration (CBS) is a metric developed to quantify avoided warming by two separate processes: the amount of carbon drawdown from the atmosphere, and the time this carbon is stored in a reservoir. This metric can be useful for quantifying the role of forests and soils for climate change mitigation, and to better quantify the benefits of carbon removals by sinks.
Robert F. Grant, Sisi Lin, and Guillermo Hernandez-Ramirez
Biogeosciences, 17, 2021–2039,Short summary
Nitrification inhibitors (NI) have been shown to reduce emissions of nitrous oxide (N20), a potent greenhouse gas, from fertilizer and manure applied to agricultural fields. However difficulties in measuring N20 emissions limit our ability to estimate these reductions. Here we propose and test a mathematical model that may allow us to estimate these reductions under diverse site conditions. These estimates will be useful in determining emission factors for NI-amended fertilizer and manure.
Moritz Laub, Michael Scott Demyan, Yvonne Funkuin Nkwain, Sergey Blagodatsky, Thomas Kätterer, Hans-Peter Piepho, and Georg Cadisch
Biogeosciences, 17, 1393–1413,Short summary
Loss of soil carbon to the atmosphere represents a global challenge. We tested an innovative way to reduce the high uncertainty related to turnover of carbon stored in soils. With the use of infrared spectra of soils from model bare fallow systems, we were able to better assess the current state of soil carbon and predict its behavior in overdecadal time spans. In agreement with recent studies, carbon turnover seems faster than earlier assumed, with potential for high loss under mismanagement.
Genki Katata, Rüdiger Grote, Matthias Mauder, Matthias J. Zeeman, and Masakazu Ota
Biogeosciences, 17, 1071–1085,Short summary
In this paper, we demonstrate that high physiological activity levels during the extremely warm winter are allocated into the below-ground biomass and only to a minor extent used for additional plant growth during early spring. This process is so far largely unaccounted for in scenario analysis using global terrestrial biosphere models, and it may lead to carbon accumulation in the soil and/or carbon loss from the soil as a response to global warming.
Karun Pandit, Hamid Dashti, Andrew T. Hudak, Nancy F. Glenn, Alejandro N. Flores, and Douglas J. Shinneman
Revised manuscript accepted for BGShort summary
Our study explores the application of a dynamic global vegetation model, Ecosystem Demography (EDv2.2) to understand temporal dynamics of ecosystem under alternate fire regimes and the spatial behavior of post-fire restoration. Point-based simulations suggested dominance of shrub in a non-fire scenario and contrasting phases of shrub and C3 grass growth for a fire scenario. Regional simulations showed a decline in GPP for fire affected areas for initial couple of years before showing recovery.
Jinyan Yang, Belinda E. Medlyn, Martin G. De Kauwe, Remko A. Duursma, Mingkai Jiang, Dushan Kumarathunge, Kristine Y. Crous, Teresa E. Gimeno, Agnieszka Wujeska-Klause, and David S. Ellsworth
Biogeosciences, 17, 265–279,Short summary
This study addressed a major knowledge gap in the response of forest productivity to elevated CO2. We first quantified forest productivity of an evergreen forest under both ambient and elevated CO2, using a model constrained by in situ measurements. The simulation showed the canopy productivity response to elevated CO2 to be smaller than that at the leaf scale due to different limiting processes. This finding provides a key reference for the understanding of CO2 impacts on forest ecosystems.
Grace Pold, Seeta A. Sistla, and Kristen M. DeAngelis
Biogeosciences, 16, 4875–4888,Short summary
The litter decomposition model DEMENT was run under ambient temperatures and with 5 °C; of warming. We found that the loss of litter carbon to the atmosphere as CO2 was exacerbated by warming when the microbes in the model differed in their temperature responses, compared to when all microbes responded identically to warming. Our results therefore indicate that predicted changes in litter carbon stocks are sensitive to heterogeneity in key parameters of soil decomposer physiology.
Ensheng Weng, Ray Dybzinski, Caroline E. Farrior, and Stephen W. Pacala
Biogeosciences, 16, 4577–4599,Short summary
Our study illustrates that the competition processes for light and soil resources in a game-theoretic vegetation demographic model can substantially change the prediction of the contribution of ecosystems to the global carbon cycle. The model that tracks the competitive allocation strategies can generate significantly different ecosystem-level predictions than those with fixed allocation strategies.
Sophie Flack-Prain, Patrick Meir, Yadvinder Malhi, Thomas Luke Smallman, and Mathew Williams
Biogeosciences, 16, 4463–4484,Short summary
Across the Amazon rainforest, trees take in carbon through photosynthesis. However, photosynthesis across the basin is threatened by predicted shifts in rainfall patterns. To unpick how changes in rainfall affect photosynthesis, we use a model which combines climate data with our knowledge of photosynthesis and other plant processes. We find that stomatal constraints are less important, and instead shifts in leaf surface area and leaf properties drive changes in photosynthesis with rainfall.
Justine Ngoma, Maarten C. Braakhekke, Bart Kruijt, Eddy Moors, Iwan Supit, James H. Speer, Royd Vinya, and Rik Leemans
Biogeosciences, 16, 3853–3867,Short summary
The Zambezi teak forests are a source of raw material for the timber industry. Through application of the LPJ-GUESS vegetation model, we determined the forests' response to climate change at the wetter Kabompo, drier Sesheke, and intermediate Namwala sites in Zambia. While increased CO2 concentration enhances forests' productivity at Kabompo and Namwala, the decreased rainfall will reduce forests' productivity at Sesheke by the year 2099, resulting in reduced raw material for saw millers.
Karel Castro-Morales, Gregor Schürmann, Christoph Köstler, Christian Rödenbeck, Martin Heimann, and Sönke Zaehle
Biogeosciences, 16, 3009–3032,Short summary
To obtain nearly 30 years of global terrestrial carbon fluxes, we simultaneously incorporated in a land surface model three different time periods of two observational data sets: absorbed photosynthetic active radiation and atmospheric CO2 concentrations. One decade of data is enough to improve the modeled long-term trends and seasonal amplitudes of the assimilated variables, particularly in boreal regions. This model has the potential to provide short-term predictions of land carbon fluxes.
Wei Zhang, Chunyan Liu, Xunhua Zheng, Kai Wang, Feng Cui, Rui Wang, Siqi Li, Zhisheng Yao, and Jiang Zhu
Biogeosciences, 16, 2905–2922,Short summary
A biogeochemical process model-based approach for screening the best management practices (BMPs) of a three-crop system was proposed. The BMPs are the management alternatives with the lowest negative impact potentials that still satisfy all given constraints. Three BMP alternatives with overlapping uncertainties of simulated NIPs were screened from 6000 scenarios using the modified DNDC95 model, which could sustain crop yields, enlarge SOC stock, mitigate GHG, and reduce other nitrogen losses.
Susan J. Cheng, Peter G. Hess, William R. Wieder, R. Quinn Thomas, Knute J. Nadelhoffer, Julius Vira, Danica L. Lombardozzi, Per Gundersen, Ivan J. Fernandez, Patrick Schleppi, Marie-Cécile Gruselle, Filip Moldan, and Christine L. Goodale
Biogeosciences, 16, 2771–2793,Short summary
Nitrogen deposition and fertilizer can change how much carbon is stored in plants and soils. Understanding how much added nitrogen is recovered in plants or soils is critical to estimating the size of the future land carbon sink. We compared how nitrogen additions are recovered in modeled soil and plant stocks against data from long-term nitrogen addition experiments. We found that the model simulates recovery of added nitrogen into soils through a different process than found in the field.
Philipp Porada, Alexandra Tamm, Jose Raggio, Yafang Cheng, Axel Kleidon, Ulrich Pöschl, and Bettina Weber
Biogeosciences, 16, 2003–2031,Short summary
The trace gases NO and HONO are crucial for atmospheric chemistry. It has been suggested that biological soil crusts in drylands contribute substantially to global NO and HONO emissions, based on empirical upscaling of laboratory and field observations. Here we apply an alternative, process-based modeling approach to predict these emissions. We find that biological soil crusts emit globally significant amounts of NO and HONO, which also vary depending on the type of biological soil crust.
Vasileios Myrgiotis, Mathew Williams, Robert M. Rees, and Cairistiona F. E. Topp
Biogeosciences, 16, 1641–1655,Short summary
This study focuses on a northwestern European cropland region and shows that the type of crop growing on a soil has notable effects on the emission of nitrous oxide (N2O – a greenhouse gas) from that soil. It was found that N2O emissions from soils under oilseed cultivation are significantly higher than soils under cereal cultivation. This variation is mostly explained by the fact that oilseeds require more nitrogen (fertiliser) than cereals, especially at early crop growth stages.
Andy D. Robertson, Keith Paustian, Stephen Ogle, Matthew D. Wallenstein, Emanuele Lugato, and M. Francesca Cotrufo
Biogeosciences, 16, 1225–1248,Short summary
Predicting how soils respond to varying environmental conditions or land-use change is essential if we aim to promote sustainable management practices and help mitigate climate change. Here, we present a new ecosystem-scale soil model (MEMS v1) that is built upon recent, novel findings and can be run using very few inputs. The model accurately predicted soil carbon stocks for more than 8000 sites across Europe, ranging from cold, wet forests in sandy soils to hot, dry grasslands in clays.
Jing Wang, Jianyang Xia, Xuhui Zhou, Kun Huang, Jian Zhou, Yuanyuan Huang, Lifen Jiang, Xia Xu, Junyi Liang, Ying-Ping Wang, Xiaoli Cheng, and Yiqi Luo
Biogeosciences, 16, 917–926,Short summary
Soil is critical in mitigating climate change mainly because soil carbon turns over much slower in soils than vegetation and the atmosphere. However, Earth system models (ESMs) have large uncertainty in simulating carbon dynamics due to their biased estimation of soil carbon transit time (τsoil). Here, the τsoil estimates from 12 ESMs that participated in CMIP5 were evaluated by a database of measured τsoil. We detected a large spatial variation in measured τsoil across the globe.
Jianqiu Zheng, Peter E. Thornton, Scott L. Painter, Baohua Gu, Stan D. Wullschleger, and David E. Graham
Biogeosciences, 16, 663–680,Short summary
Arctic warming exposes soil carbon to increased degradation, increasing CO2 and CH4 emissions. Models underrepresent anaerobic decomposition that predominates wet soils. We simulated microbial growth, pH regulation, and anaerobic carbon decomposition in a new model, parameterized and validated with prior soil incubation data. The model accurately simulated CO2 production and strong influences of water content, pH, methanogen biomass, and competing electron acceptors on CH4 production.
Mingjie Shi, Joshua B. Fisher, Richard P. Phillips, and Edward R. Brzostek
Biogeosciences, 16, 457–465,Short summary
The ability of plants to slow climate change by taking up carbon hinges in part on there being ample soil nitrogen. We used a model that accounts for the carbon cost to plants of supporting nitrogen-acquiring microbes to explore how nitrogen limitation affects climate. Our model predicted that nitrogen limitation will enhance temperature and decrease precipitation; thus, our results suggest that carbon spent to support nitrogen-acquiring microbes is a critical component of the Earth's climate.
Qinchuan Xin, Yongjiu Dai, and Xiaoping Liu
Biogeosciences, 16, 467–484,Short summary
Terrestrial biosphere models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. A time-stepping scheme is proposed to simulate leaf area index, phenology, and gross primary production via climate variables. The method performs well on simulating deciduous broadleaf forests across the eastern United States; it provides a simplified and improved version of the growing production day model for use in land surface modeling.
Tong Yu and Qianlai Zhuang
Biogeosciences, 16, 207–222,
Debsunder Dutta, David S. Schimel, Ying Sun, Christiaan van der Tol, and Christian Frankenberg
Biogeosciences, 16, 77–103,Short summary
Canopy structural and leaf photosynthesis parameterizations are often fixed over time in Earth system models and represent large sources of uncertainty in predictions of carbon and water fluxes. We develop a moving window nonlinear optimal parameter inversion framework using constraining flux and satellite reflectance observations. The results demonstrate the applicability of the approach for error reduction and capturing the seasonal variability of key ecosystem parameters.
Matthias Forkel, Niels Andela, Sandy P. Harrison, Gitta Lasslop, Margreet van Marle, Emilio Chuvieco, Wouter Dorigo, Matthew Forrest, Stijn Hantson, Angelika Heil, Fang Li, Joe Melton, Stephen Sitch, Chao Yue, and Almut Arneth
Biogeosciences, 16, 57–76,Short summary
Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
Martina Franz, Rocio Alonso, Almut Arneth, Patrick Büker, Susana Elvira, Giacomo Gerosa, Lisa Emberson, Zhaozhong Feng, Didier Le Thiec, Riccardo Marzuoli, Elina Oksanen, Johan Uddling, Matthew Wilkinson, and Sönke Zaehle
Biogeosciences, 15, 6941–6957,Short summary
Four published ozone damage functions previously used in terrestrial biosphere models were evaluated regarding their ability to simulate observed biomass dose–response relationships using the O-CN model. Neither damage function was able to reproduce the observed ozone-induced biomass reductions. Calibrating a plant-functional-type-specific relationship between accumulated ozone uptake and leaf-level photosynthesis did lead to a good agreement between observed and modelled ozone damage.
Qianyu Li, Xingjie Lu, Yingping Wang, Xin Huang, Peter M. Cox, and Yiqi Luo
Biogeosciences, 15, 6909–6925,Short summary
Land-surface models have been widely used to predict the responses of terrestrial ecosystems to climate change. A better understanding of model mechanisms that govern terrestrial ecosystem responses to rising atmosphere [CO2] is needed. Our study for the first time shows that the expansion of leaf area under rising [CO2] is the most important response for the stimulation of land carbon accumulation by a land-surface model: CABLE. Processes related to leaf area should be better calibrated.
Ali Asaadi, Vivek K. Arora, Joe R. Melton, and Paul Bartlett
Biogeosciences, 15, 6885–6907,Short summary
Non-structural carbohydrates (NSCs), which play a central role in a plant's life processes and its response to environmental conditions, are typically not included in terrestrial biogeochemistry models used in Earth system models (ESMs). In this study, we include NSC pools in the framework of the land component of the Canadian ESM and show how they help address the long-standing problem of delayed leaf phenology.
Xingjie Lu, Ying-Ping Wang, Yiqi Luo, and Lifen Jiang
Biogeosciences, 15, 6559–6572,Short summary
How long does C cycle through terrestrial ecosystems is a critical question for understanding land C sequestration capacity under future rising atmosphere [CO2] and climate warming. Under climate change, previous conventional concepts with a steady-state assumption will no longer be suitable for a non-steady state. Our results using the new concept, C transit time, suggest more significant responses in terrestrial C cycle under rising [CO2] and climate warming.
Istem Fer, Ryan Kelly, Paul R. Moorcroft, Andrew D. Richardson, Elizabeth M. Cowdery, and Michael C. Dietze
Biogeosciences, 15, 5801–5830,Short summary
The computer models we use to understand and forecast the ecosystem changes have multiple components that determine their outcomes. Due to our limited observation capacities, these components bear uncertainties that in return affect our predictions. While there are techniques for reducing these uncertainties, they are not applicable to every model due to computational and statistical barriers. This research presents a method that lowers those barriers and allows us to improve model predictions.
Johannes Meyerholt and Sönke Zaehle
Biogeosciences, 15, 5677–5698,Short summary
Terrestrial biosphere models employ various representations of ecosystem nitrogen loss, some based on soil N availability, some based on net N mineralization. We show in local and global simulations that this variety leads to pronounced uncertainty in the predicted magnitude and sign of ecosystem N loss change under elevated CO2. Suprisingly, this uncertainty barely affects predicted carbon storage responses to elevated CO2, illustrating the need for new benchmarks especially in the boreal zone.
Eunjee Lee, Fan-Wei Zeng, Randal D. Koster, Brad Weir, Lesley E. Ott, and Benjamin Poulter
Biogeosciences, 15, 5635–5652,Short summary
Land carbon fluxes are controlled in part by the responses of terrestrial ecosystems to atmospheric conditions near the Earth's surface. This study offers a comprehensive evaluation of the consequences of multiple facets of spatiotemporal variability in atmospheric CO2 for carbon cycle dynamics. Globally, consideration of the diurnal CO2 variability reduces the gross primary production and net land carbon uptake. The relative contributions of other variability vary regionally and seasonally.
Junrong Zha and Qianlai Zhuang
Biogeosciences, 15, 5621–5634,Short summary
This study used a detailed microbial-based soil decomposition biogeochemistry model to examine the fate of much arctic soil carbon under changing climate conditions. We found that the detailed microbial decomposition biogeochemistry model estimated a much lower carbon accumulation in the region during this century. The amount of soil carbon considered in the 21st-century simulations determines the regional carbon sink and source strengths, regardless of the complexity of models used.
Fernando Esteban Moyano, Nadezda Vasilyeva, and Lorenzo Menichetti
Biogeosciences, 15, 5031–5045,Short summary
Soils are complex systems storing large quantities of carbon in the form of organic matter. Understanding how climatic drivers such as temperature and moisture influence the decomposition and thus the turnover of this carbon is crucial for predicting feedbacks between climate and soils. This study aims at improving our mechanistic understanding of how these factors interact to drive decomposition and thus modify the capacity of soils to emit or capture atmospheric CO2.
Nemesio J. Rodríguez-Fernández, Arnaud Mialon, Stephane Mermoz, Alexandre Bouvet, Philippe Richaume, Ahmad Al Bitar, Amen Al-Yaari, Martin Brandt, Thomas Kaminski, Thuy Le Toan, Yann H. Kerr, and Jean-Pierre Wigneron
Biogeosciences, 15, 4627–4645,Short summary
Existing global scale above-ground biomass (AGB) maps are made at very high spatial resolution collecting data during several years. In this paper we discuss the use of a new data set from the SMOS satellite: the vegetation optical depth estimated from low microwave frequencies. It is shown that this new data set is highly sensitive to AGB. The spacial resolution of SMOS is coarse (40 km) but the new data set can be used to monitor AGB variations with time due to its high revisit frequency.
Alkhaled, A. A., Michalak, A. M., Kawa, S. R., Olsen, S. C., and Wang, J. W.: A global evaluation of the regional spatial variability of column integrated CO2 distributions, J. Geophys. Res.-Atmos., 113, D20303, https://doi.org/10.1029/2007jd009693, 2008.
Baker, I., Denning, A. S., Hanan, N., Prihodko, L., Uliasz, M., Vidale, P. L., Davis, K., and Bakwin, P.: Simulated and observed fluxes of sensible and latent heat and CO2 at the WLEF-TV tower using SiB2.5, Glob. Change Biol., 9, 1262–1277, 2003. Baker, D. F., Law, R. M., Gurney, K. R., Rayner, P., Peylin, P., Denning, A. S., Bousquet, P., Bruhwiler, L., Chen, Y. H., Ciais, P., Fung, I. Y., Heimann, I. M., John, J., Maki, T., Maksyutov, S., Masarie, K., Prather, M., Pak, B., Taguchi, S., and Zhu, Z.: TransCom 3 inversion intercomparison: Impact of transport model errors on the interannual variability of regional CO2 fluxes, 1988–2003, Global Biogeochem. Cy., 20(1), https://doi.org/10.1029/2004gb002439, 2006.
Baker, I. T., Prihodko, L., Denning, A. S., Goulden, M., Miller, S., and da Rocha, H. R.: Seasonal drought stress in the Amazon: Reconciling models and observations, J. Geophys. Res.-Biogeo., 113, G00b01, https://doi.org/10.1029/2007jg000644, 2008.
Baldocchi, D., Falge, E., Gu, L. H., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X. H., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, U. K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Fluxnet: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, 2001.
Batjes, N. H.: Total carbon and nitrogen in the soils of the world, Eur. J. Soil Sci., 47, 151–163, 1996.
Bondeau, A., Kicklighter, D. W., Kaduk, J., and Participants Potsdam, N. P. P. M. I.: Comparing global models of terrestrial net primary productivity (NPP): Importance of vegetation structure on seasonal NPP estimates, Glob. Change Biol., 5, 35–45, 1999.
Burnham, K. P. and Anderson, D. R.: Model selection and multimodel inference: A practical information-theoretical approach, 2nd edn., Springer Science, New York, 2002.
Chiles, J. P. and Delfiner, P.: Geostatistics: Modeling spatial uncertainty, John Wiley & Sons, New York, 695 pp., 1999.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and environmental-regulation of stomatal conductance, photosynthesis and transpiration - a model that includes a laminar boundary-layer, Agr. Forest Meteorol., 54, 107–136, 1991.
Cramer, W. and Field, C. B.: Comparing global models of terrestrial net primary productivity (NPP): Introduction, Glob. Change Biol., 5, III–IV, 1999.
Cramer, W., Kicklighter, D. W., Bondeau, A., Moore, B., Churkina, C., Nemry, B., Ruimy, A., and Schloss, A. L.: Comparing global models of terrestrial net primary productivity (NPP): Overview and key results, Glob. Change Biol., 5, 1–15, 1999.
Cressie, N. A. C.: Statistics for Spatial Data, John Wiley, New York, 990 pp., 1993.
DeFries, R. S. and Townshend, J. R. G.: 1 Degree Global Land Cover Data Set Derived from AVHRR, available at: http://glcf.umiacs.umd.edu/data/landcover/index.shtml, from the Global Land Cover Facility, University of Maryland Institute for Advanced Computer Studies, College Park, Maryland, USA, 1994.
Denning, A. S., Collatz, G. J., Zhang, C. G., Randall, D. A., Berry, J. A., Sellers, P. J., Colello, G. D., and Dazlich, D. A.: Simulations of terrestrial carbon metabolism and atmospheric CO2 in a general circulation model: 1. Surface carbon fluxes, Tellus B, 48, 521–542, 1996.
Denning, A. S., Nicholls, M., Prihodko, L., Baker, I., Vidale, P. L., Davis, K., and Bakwin, P.: Simulated variations in atmospheric CO2 over a Wisconsin forest using a coupled ecosystem-atmosphere model, Glob. Change Biol., 9, 1241–1250, 2003.
Erickson, T. A., Williams, M. W., and Winstral, A.: Persistence of topographic controls on the spatial distribution of snow in rugged mountain terrain, Colorado, united states, Water Resour. Res., 41, W04014, https://doi.org/10.1029/2003wr002973, 2005.
Food and Agriculture Organization of the United Nations (FAO): Digital Soil Map of the World, Version 3.5. FAO, Rome, Italy, 2005
Forster, M. R.: Key concepts in model selection: Performance and generalizability, J. Math. Psychol., 44, 205–231, 2000.
Gourdji, S. M., Mueller, K. L., Schaefer, K., and Michalak, A. M.: Global monthly averaged CO2 fluxes recovered using a geostatistical inverse modeling approach: 2. Results including auxiliary environmental data, J. Geophys. Res.-Atmos., 113, D21115, https://doi.org/10.1029/2007jd009733, 2008.
Gourdji, S. M., Hirsch, A. I., Mueller, K. L., Yadav, V., Andrews, A. E., and Michalak, A. M.: Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study, Atmos. Chem. Phys., 10, 6151–6167, https://doi.org/10.5194/acp-10-6151-2010, 2010.
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Pak, B. C., Baker, D., Bousquet, P., Bruhwiler, L., Chen, Y. H., Ciais, P., Fung, I. Y., Heimann, M., John, J., Maki, T., Maksyutov, S., Peylin, P., Prather, M., and Taguchi, S.: Transcom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks, Global Biogeochem. Cy. , 18(1), 21, https://doi.org/10.1029/2003GB002111, 2004.
Hansen, J., Ruedy, R., Glascoe, J., and Sato, M.: GISS analysis of surface temperature change, J. Geophys. Res., 104, 30997–31022, https://doi.org/10.1029/1999JD900835, 1999.
Heimann, M., Esser, G., Haxeltine, A., Kaduk, J., Kicklighter, D. W., Knorr, W., Kohlmaier, G. H., McGuire, A. D., Melillo, J., Moore, B., Otto, R. D., Prentice, I. C., Sauf, W., Schloss, A., Sitch, S., Wittenberg, U., and Wurth, G.: Evaluation of terrestrial carbon cycle models through simulations of the seasonal cycle of atmospheric CO2: First results of a model intercomparison study, Global Biogeochem. Cy., 12, 1–24, 1998.
Hoeting, J. A., Davis, R. A., Merton, A. A., and Thompson, S. E.: Model selection for geostatistical models, Ecol. Appl., 16, 87–98, 2006.
House, J. I., Prentice, I. C., Ramankutty, N., Houghton, R. A., and Heimann, M.: Reconciling apparent inconsistencies in estimates of terrestrial CO2 sources and sinks, Tellus B, 55, 345–363, 2003.
Isaaks, E. H. and Srivastava, R. M.: An introduction to applied geostatistics, Oxford University Press, New York, 561 pp., 1989.
Kitanidis, P. K.: Introduction to geostatistics: Applications in hydrogeology, Cambridge University Press, New York, 249 pp., 1997.
Knorr, W.: Annual and interannual CO2 exchanges of the terrestrial biosphere: Process-based simulations and uncertainties, Global Ecol. Biogeogr., 9, 225–252, 2000.
Law, B. E., Falge, E., Gu, L., Baldocchi, D. D., Bakwin, P., Berbigier, P., Davis, K., Dolman, A. J., Falk, M., Fuentes, J. D., Goldstein, A., Granier, A., Grelle, A., Hollinger, D., Janssens, I. A., Jarvis, P., Jensen, N. O., Katul, G., Mahli, Y., Matteucci, G., Meyers, T., Monson, R., Munger, W., Oechel, W., Olson, R., Pilegaard, K., Paw, K. T., Thorgeirsson, H., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation, Agr. Forest Meteorol., 113, 97–120, 2002.
Leemans, R. and Cramer, W.: The IIASA database for mean monthly values of temperature, precipitation and cloudiness of a global terrestrial grid, International Institute for Applied Systems Analysis (IIASA), RR-91-18, 1991.
Lokupitiya, R. S., Zupanski, D., Denning, A. S., Kawa, S. R., Gurney, K. R., and Zupanski, M.: Estimation of global CO2 fluxes at regional scale using the maximum likelihood ensemble filter, J. Geophys. Res.-Atmos., 113(19), D20110, https://doi.org/10.1029/2007jd009679, 2008.
Melillo, J. M., Borchers, J., Chaney, J., Fisher, H., Fox, S., Haxeltine, A., Janetos, A., Kicklighter, D. W., Kittel, T. G. F., McGuire, A. D., McKeown, R., Neilson, R., Nemani, R., Ojima, D. S., Painter, T., Pan, Y., Parton, W. J., Pierce, L., Pitelka, L., Prentice, C., Rizzo, B., Rosenbloom, N. A., Running, S., Schimel, D. S., Sitch, S., Smith, T., and Woodward, I.: Vegetation ecosystem modeling and analysis project – comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate-change and CO2 doubling, Global Biogeochem. Cy., 9, 407–437, 1995.
Michalak, A. M., Bruhwiler, L., and Tans, P. P.: A geostatistical approach to surface flux estimation of atmospheric trace gases, J. Geophys. Res.-Atmos., 109, D14109, https://doi.org/10.1029/2003jd004422, 2004.
Mueller, K. L., Gourdji, S. M., and Michalak, A. M.: Global monthly averaged CO2 fluxes recovered using a geostatistical inverse modeling approach: 1. Results using atmospheric measurements, J. Geophys. Res.-Atmos., 113, D21114, https://doi.org/10.1029/2007jd009734, 2008.
Mueller, K. L., Yadav, V., Curtis, P. S., Vogel, C. S., and Michalak, A. M.: Attributing the variability of eddy-covariance CO2 flux measurements at various temporal scales using environmental data for a mixed northern hardwood forest, Global Biogeochem. Cy., GB3023, https://doi.org/10.1029/2009GB003642, 2010.
Nicholls, M. E., Denning, A. S., Prihodko, L., Vidale, P. L., Baker, I., Davis, K., and Bakwin, P.: A multiple-scale simulation of variations in atmospheric carbon dioxide using a coupled biosphere-atmospheric model, J. Geophys. Res.-Atmos., 109, D18117, https://doi.org/10.1029/2003jd004482, 2004.
Olsen, S. C. and Randerson, J. T.: Differences between surface and column atmospheric CO2 and implications for carbon cycle research, J. Geophys. Res.-Atmos., 109(D2), https://doi.org/10.1029/2003jd003968, 2004.
Pan, Y. D., Melillo, J. M., McGuire, A. D., Kicklighter, D. W., Pitelka, L. F., Hibbard, K., Pierce, L. L., Running, S. W., Ojima, D. S., Parton, W. J., Schimel, D. S., and Members, V.: Modeled responses of terrestrial ecosystems to elevated atmospheric CO2: A comparison of simulations by the biogeochemistry models of the vegetation/ecosystem modeling and analysis project (vemap), Oecologia, 114, 389–404, 1998.
Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J., Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Petron, G., Hirsch, A. I., Worthy, D. E. J., van der Werf, G. R., Randerson, J. T., Wennberg, P. O., Krol, M. C., and Tans, P. P.: An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, P. Natl. Acad. Sci. USA, 104, 18925–18930, 2007.
Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A., and Klooster, S. A.: Terrestrial ecosystem production – a process model-based on global satellite and surface data, Global Biogeochem. Cy., 7, 811–841, 1993.
Randerson, J. T., Thompson, M. V., Conway, T. J., Fung, I. Y., and Field, C. B.: The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide, Global Biogeochem. Cy., 11, 535–560, 1997.
Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: CO2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmos. Chem. Phys., 3, 1919–1964, https://doi.org/10.5194/acp-3-1919-2003, 2003.
Ruimy, A., Kergoat, L., Bondeau, A., and Participants Potsdam, N. P. P. M. I.: Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency, Glob. Change Biol., 5, 56–64, 1999.
Schaefer, K., Collatz, G. J., Tans, P., Denning, A. S., Baker, I., Berry, J., Prihodko, L., Suits, N., and Philpott, A.: Combined simple biosphere/carnegie-ames-stanford approach terrestrial carbon cycle model, J. Geophys. Res.-Biogeo., 113, G03034, https://doi.org/10.1029/2007jg000603, 2008.
Schimel, D. S., Emanuel, W., Rizzo, B., Smith, T., Woodward, F. I., Fisher, H., Kittel, T. G. F., McKeown, R., Painter, T., Rosenbloom, N., Ojima, D. S., Parton, W. J., Kicklighter, D. W., McGuire, A. D., Melillo, J. M., Pan, Y., Haxeltine, A., Prentice, C., Sitch, S., Hibbard, K., Nemani, R., Pierce, L., Running, S., Borchers, J., Chaney, J., Neilson, R., and Braswell, B. H.: Continental scale variability in ecosystem processes: Models, data, and the role of disturbance, Ecol. Monogr., 67, 251–271, 1997.
Schloss, A. L., Kicklighter, D. W., Kaduk, J., Wittenberg, U., and Participants Potsdam, N. P. P. M. I.: Comparing global models of terrestrial net primary productivity (NPP): Comparison of NPP to climate and the normalized difference vegetation index (NDVI), Glob. Change Biol., 5, 25–34, 1999.
Schwarz, G.: Estimating dimension of a model, Ann. Stat., 6, 461–464, 1978.
Sellers, P. J., Mintz, Y., Sud, Y. C., and Dalcher, A.: A simple biosphere model (SiB) for use within general-circulation models, J. Atmos. Sci., 43, 505–531, 1986.
Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B., Dazlich, D. A., Zhang, C., Collelo, G. D., and Bounoua, L.: A revised land surface parameterization (SiB2) for atmospheric GCMs: 1. Model formulation, J. Climate, 9, 676–705, 1996a.
Sellers, P. J., Los, S. O., Tucker, C. J., Justice, C. O., Dazlich, D. A., Collatz, G. J., and Randall, D. A.: A revised land surface parameterization (SiB2) for atmospheric GCMs: 2. The generation of global fields of terrestrial biophysical parameters from satellite data, J. Climate, 9, 706–737, 1996b.
Stoy, P. C., Katul, G. G., Siqueira, M. B. S., Juang, J. Y., McCarthy, H. R., Kim, H. S., Oishi, A. C., and Oren, R.: Variability in net ecosystem exchange from hourly to inter-annual time scales at adjacent pine and hardwood forests: A wavelet analysis, Tree Physiol., 25, 887–902, 2005.
Stoy, P. C., Richardson, A. D., Baldocchi, D. D., Katul, G. G., Stanovick, J., Mahecha, M. D., Reichstein, M., Detto, M., Law, B. E., Wohlfahrt, G., Arriga, N., Campos, J., McCaughey, J. H., Montagnani, L., Paw U, K. T., Sevanto, S., and Williams, M.: Biosphere-atmosphere exchange of CO2 in relation to climate: a cross-biome analysis across multiple time scales, Biogeosciences, 6, 2297–2312, https://doi.org/10.5194/bg-6-2297-2009, 2009.
Tucker, C. J., Pinzon,J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E. F., and El Saleous, N.: An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26(20), 4485–4498, 2005.
Urbanski, S., Barford, C., Wofsy, S., Kucharik, C., Pyle, E., Budney, J., McKain, K., Fitzjarrald, D., Czikowsky, M., and Munger, J. W.: Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard forest, J. Geophys. Res.-Biogeo., 112(G2), G02020, https://doi.org/10.1029/2006jg000293, 2007.
van der Werf, G. R., Randerson, J. T., Collatz , G. J., and Giglio, L.: Carbon emissions from fires in tropical and sub-tropical ecosystems, Glob. Change Biol., 9, 547–562, 2003.
van der Werf, G. R., Randerson, J. T., Collatz, G. J., Giglio, L., Kasibhatla, P. S., Arellano, A. F., Olsen, S. C., and Kasischke, E. S.: Continental-scale partitioning of fire emissions during the 1997 to 2001 El Nino/La Nina period, Science, 303, 73–76, 2004.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., and Arellano Jr., A. F.: Interannual variability in global biomass burning emissions from 1997 to 2004, Atmos. Chem. Phys., 6, 3423–3441, https://doi.org/10.5194/acp-6-3423-2006, 2006.
Wang, J. W., Denning, A. S., Lu, L. X., Baker, I. T., Corbin, K. D., and Davis, K. J.: Observations and simulations of synoptic, regional, and local variations in atmospheric CO2, J. Geophys. Res.-Atmos., 112, D04108, https://doi.org/10.1029/2006jd007410, 2007.
Webster, R. and Oliver, M. A.: Geostatistics for environmental scientists, 2nd edn., John Wiley & Sons, West Sussex, England, 2007.
Wulder, M. A., White, J. C., Coops, N. C., Nelson, Y., and Boots, B.: Using local spatial autocorrelation to compare outputs from a forest growth model, Ecol. Model., 209, 264–276, https://doi.org/10.1016/j.ecolmodel.2007.06.033, 2007.
Yadav, V., Mueller, K. L., Dragoni, D., and Michalak, A. M.: A geostatistical synthesis study of factors affecting gross primary productivity in various ecosystems of North America, Biogeosciences, 7, 2655–2671, https://doi.org/10.5194/bg-7-2655-2010, 2010.
Zhou, T., Shi, P. J., Hui, D. F., and Luo, Y. Q.: Global pattern of temperature sensitivity of soil heterotrophic respiration (Q(10)) and its implications for carbon-climate feedback, J. Geophys. Res.-Biogeo., 114, G02016, https://doi.org/10.1029/2008jg000850, 2009.
Zobler, L.: A world soil file for global climate modeling, Technical Memorandum 87802, NASA Goddard Institute for Space Studies (GISS), New York, NY, 1986.
Zupanski, D., Denning, A. S., Uliasz, M., Zupanski, M., Schuh, A. E., Rayner, P. J., Peters, W., and Corbin, K. D.: Carbon flux bias estimation employing maximum likelihood ensemble filter (MLEF), J. Geophys. Res.-Atmos., 112, D17107, https://doi.org/10.1029/2006JD008371, 2007.