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KATO, Tomomichi. "Data assimilation for terrestrial biosphere model." Climate in Biosphere 13 (2013): 1–7. http://dx.doi.org/10.2480/cib.13.1.
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Ichii, K., T. Suzuki, T. Kato, A. Ito, T. Hajima, M. Ueyama, T. Sasai, et al. "Multi-model analysis of terrestrial carbon cycles in Japan: reducing uncertainties in model outputs among different terrestrial biosphere models using flux observations." Biogeosciences Discussions 6, no. 4 (August 27, 2009): 8455–502. http://dx.doi.org/10.5194/bgd-6-8455-2009.
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Abstract. Terrestrial biosphere models show large uncertainties when simulating carbon and water cycles, and reducing these uncertainties is a priority for developing more accurate estimates of both terrestrial ecosystem statuses and future climate changes. To reduce uncertainties and improve the understanding of these carbon budgets, we investigated the ability of flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine-based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and an improved model (based on calibration using flux observations). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using flux observations (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs, and model calibration using flux observations significantly improved the model outputs. These results show that to reduce uncertainties among terrestrial biosphere models, we need to conduct careful validation and calibration with available flux observations. Flux observation data significantly improved terrestrial biosphere models, not only on a point scale but also on spatial scales.
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PENG, Shu-Shi, Chao YUE, and Jin-Feng CHANG. "Developments and applications of terrestrial biosphere model." Chinese Journal of Plant Ecology 44, no. 4 (2020): 436–48. http://dx.doi.org/10.17521/cjpe.2019.0315.
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Ichii, K., T. Suzuki, T. Kato, A. Ito, T. Hajima, M. Ueyama, T. Sasai, et al. "Multi-model analysis of terrestrial carbon cycles in Japan: limitations and implications of model calibration using eddy flux observations." Biogeosciences 7, no. 7 (July 2, 2010): 2061–80. http://dx.doi.org/10.5194/bg-7-2061-2010.
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Abstract. Terrestrial biosphere models show large differences when simulating carbon and water cycles, and reducing these differences is a priority for developing more accurate estimates of the condition of terrestrial ecosystems and future climate change. To reduce uncertainties and improve the understanding of their carbon budgets, we investigated the utility of the eddy flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine – based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four eddy flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and a modified model (based on model parameter tuning using eddy flux data). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using eddy flux data (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs. This study demonstrated that careful validation and calibration of models with available eddy flux data reduced model-by-model differences. Yet, site history, analysis of model structure changes, and more objective procedure of model calibration should be included in the further analysis.
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Medvigy, David, and Paul R. Moorcroft. "Predicting ecosystem dynamics at regional scales: an evaluation of a terrestrial biosphere model for the forests of northeastern North America." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1586 (January 19, 2012): 222–35. http://dx.doi.org/10.1098/rstb.2011.0253.
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Terrestrial biosphere models are important tools for diagnosing both the current state of the terrestrial carbon cycle and forecasting terrestrial ecosystem responses to global change. While there are a number of ongoing assessments of the short-term predictive capabilities of terrestrial biosphere models using flux-tower measurements, to date there have been relatively few assessments of their ability to predict longer term, decadal-scale biomass dynamics. Here, we present the results of a regional-scale evaluation of the Ecosystem Demography version 2 (ED2)-structured terrestrial biosphere model, evaluating the model's predictions against forest inventory measurements for the northeast USA and Quebec from 1985 to 1995. Simulations were conducted using a default parametrization, which used parameter values from the literature, and a constrained model parametrization, which had been developed by constraining the model's predictions against 2 years of measurements from a single site, Harvard Forest (42.5° N, 72.1° W). The analysis shows that the constrained model parametrization offered marked improvements over the default model formulation, capturing large-scale variation in patterns of biomass dynamics despite marked differences in climate forcing, land-use history and species-composition across the region. These results imply that data-constrained parametrizations of structured biosphere models such as ED2 can be successfully used for regional-scale ecosystem prediction and forecasting. We also assess the model's ability to capture sub-grid scale heterogeneity in the dynamics of biomass growth and mortality of different sizes and types of trees, and then discuss the implications of these analyses for further reducing the remaining biases in the model's predictions.
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O'Malley-James, Jack T., Charles S. Cockell, Jane S. Greaves, and John A. Raven. "Swansong biospheres II: the final signs of life on terrestrial planets near the end of their habitable lifetimes." International Journal of Astrobiology 13, no. 3 (January 14, 2014): 229–43. http://dx.doi.org/10.1017/s1473550413000426.
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AbstractThe biosignatures of life on Earth do not remain static, but change considerably over the planet's habitable lifetime. Earth's future biosphere, much like that of the early Earth, will consist of predominantly unicellular microorganisms due to the increased hostility of environmental conditions caused by the Sun as it enters the late stage of its main sequence evolution. Building on previous work, the productivity of the biosphere is evaluated during different stages of biosphere decline between 1 and 2.8 Gyr from present. A simple atmosphere–biosphere interaction model is used to estimate the atmospheric biomarker gas abundances at each stage and to assess the likelihood of remotely detecting the presence of life in low-productivity, microbial biospheres, putting an upper limit on the lifetime of Earth's remotely detectable biosignatures. Other potential biosignatures such as leaf reflectance and cloud cover are discussed.
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Lei, Yadong, Xu Yue, Hong Liao, Cheng Gong, and Lin Zhang. "Implementation of Yale Interactive terrestrial Biosphere model v1.0 into GEOS-Chem v12.0.0: a tool for biosphere–chemistry interactions." Geoscientific Model Development 13, no. 3 (March 12, 2020): 1137–53. http://dx.doi.org/10.5194/gmd-13-1137-2020.
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Abstract. The terrestrial biosphere and atmospheric chemistry interact through multiple feedbacks, but the models of vegetation and chemistry are developed separately. In this study, the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic vegetation model with biogeochemical processes, is implemented into the Chemical Transport Model GEOS-Chem (GC) version 12.0.0. Within this GC-YIBs framework, leaf area index (LAI) and canopy stomatal conductance dynamically predicted by YIBs are used for dry deposition calculation in GEOS-Chem. In turn, the simulated surface ozone (O3) by GEOS-Chem affect plant photosynthesis and biophysics in YIBs. The updated stomatal conductance and LAI improve the simulated O3 dry deposition velocity and its temporal variability for major tree species. For daytime dry deposition velocities, the model-to-observation correlation increases from 0.69 to 0.76, while the normalized mean error (NME) decreases from 30.5 % to 26.9 % using the GC-YIBs model. For the diurnal cycle, the NMEs decrease by 9.1 % for Amazon forests, 6.8 % for coniferous forests, and 7.9 % for deciduous forests using the GC-YIBs model. Furthermore, we quantify the damaging effects of O3 on vegetation and find a global reduction of annual gross primary productivity by 1.5 %–3.6 %, with regional extremes of 10.9 %–14.1 % in the eastern USA and eastern China. The online GC-YIBs model provides a useful tool for discerning the complex feedbacks between atmospheric chemistry and the terrestrial biosphere under global change.
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Pereira, Fabio F., Fabio Farinosi, Mauricio E. Arias, Eunjee Lee, John Briscoe, and Paul R. Moorcroft. "Technical note: A hydrological routing scheme for the Ecosystem Demography model (ED2+R) tested in the Tapajós River basin in the Brazilian Amazon." Hydrology and Earth System Sciences 21, no. 9 (September 14, 2017): 4629–48. http://dx.doi.org/10.5194/hess-21-4629-2017.
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Abstract. Land surface models are excellent tools for studying how climate change and land use affect surface hydrology. However, in order to assess the impacts of Earth processes on river flows, simulated changes in runoff need to be routed through the landscape. In this technical note, we describe the integration of the Ecosystem Demography (ED2) model with a hydrological routing scheme. The purpose of the study was to create a tool capable of incorporating to hydrological predictions the terrestrial ecosystem responses to climate, carbon dioxide, and land-use change, as simulated with terrestrial biosphere models. The resulting ED2+R model calculates the lateral routing of surface and subsurface runoff resulting from the terrestrial biosphere models' vertical water balance in order to determine spatiotemporal patterns of river flows within the simulated region. We evaluated the ED2+R model in the Tapajós, a 476 674 km2 river basin in the southeastern Amazon, Brazil. The results showed that the integration of ED2 with the lateral routing scheme results in an adequate representation (Nash–Sutcliffe efficiency up to 0.76, Kling–Gupta efficiency up to 0.86, Pearson's R up to 0.88, and volume ratio up to 1.06) of daily to decadal river flow dynamics in the Tapajós. These results are a consistent step forward with respect to the no river representation common among terrestrial biosphere models, such as the initial version of ED2.
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Wu, Mousong, Marko Scholze, Michael Voßbeck, Thomas Kaminski, and Georg Hoffmann. "Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS." Remote Sensing 11, no. 1 (December 25, 2018): 27. http://dx.doi.org/10.3390/rs11010027.
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The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO2 fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre—Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO2. This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands.
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Hoogakker, B. A. A., R. S. Smith, J. S. Singarayer, R. Marchant, I. C. Prentice, J. R. M. Allen, R. S. Anderson, et al. "Terrestrial biosphere changes over the last 120 kyr." Climate of the Past 12, no. 1 (January 18, 2016): 51–73. http://dx.doi.org/10.5194/cp-12-51-2016.
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Abstract. A new global synthesis and biomization of long (> 40 kyr) pollen-data records is presented and used with simulations from the HadCM3 and FAMOUS climate models and the BIOME4 vegetation model to analyse the dynamics of the global terrestrial biosphere and carbon storage over the last glacial–interglacial cycle. Simulated biome distributions using BIOME4 driven by HadCM3 and FAMOUS at the global scale over time generally agree well with those inferred from pollen data. Global average areas of grassland and dry shrubland, desert, and tundra biomes show large-scale increases during the Last Glacial Maximum, between ca. 64 and 74 ka BP and cool substages of Marine Isotope Stage 5, at the expense of the tropical forest, warm-temperate forest, and temperate forest biomes. These changes are reflected in BIOME4 simulations of global net primary productivity, showing good agreement between the two models. Such changes are likely to affect terrestrial carbon storage, which in turn influences the stable carbon isotopic composition of seawater as terrestrial carbon is depleted in 13C.
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Claussen, Martin, Victor Brovkin, Andrey Ganopolski, Claudia Kubatzki, and Vladimir Petoukhov. "Modelling global terrestrial vegetation–climate interaction." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1365 (January 29, 1998): 53–63. http://dx.doi.org/10.1098/rstb.1998.0190.
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By coupling an atmospheric general circulation model asynchronously with an equilibrium vegetation model, manifold equilibrium solutions of the atmosphere–biosphere system have been explored. It is found that under present–day conditions of the Earth's orbital parameters and sea–surface temperatures, two stable equilibria of vegetation patterns are possible: one corresponding to present–day sparse vegetation in the Sahel, the second solution yielding savannah which extends far into the south–western part of the Sahara. A similar picture is obtained for conditions during the last glacial maximum (21 000 years before present (BP)). For the mid–Holocene (6000 years BP), however, the model finds only one solution: the green Sahara. We suggest that this intransitive behaviour of the atmosphere–biosphere is related to a westward shift of the Hadley–Walker circulation. A conceptual model of atmosphere–vegetation dynamics is used to interpret the bifurcation as well as its change in terms of stability theory.
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Tian, H. "Modeling Primary Productivity of the Terrestrial Biosphere in Changing Environments: Toward a Dynamic Biosphere Model." Critical Reviews in Plant Sciences 17, no. 5 (September 1998): 541–57. http://dx.doi.org/10.1016/s0735-2689(98)00364-5.
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Davies-Barnard, Taraka, Andy Ridgwell, Joy Singarayer, and Paul Valdes. "Quantifying the influence of the terrestrial biosphere on glacial–interglacial climate dynamics." Climate of the Past 13, no. 10 (October 26, 2017): 1381–401. http://dx.doi.org/10.5194/cp-13-1381-2017.
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Abstract. The terrestrial biosphere is thought to be a key component in the climatic variability seen in the palaeo-record. It has a direct impact on surface temperature through changes in surface albedo and evapotranspiration (so-called biogeophysical effects) and, in addition, has an important indirect effect through changes in vegetation and soil carbon storage (biogeochemical effects) and hence modulates the concentrations of greenhouse gases in the atmosphere. The biogeochemical and biogeophysical effects generally have opposite signs, meaning that the terrestrial biosphere could potentially have played only a very minor role in the dynamics of the glacial–interglacial cycles of the late Quaternary. Here we use a fully coupled dynamic atmosphere–ocean–vegetation general circulation model (GCM) to generate a set of 62 equilibrium simulations spanning the last 120 kyr. The analysis of these simulations elucidates the relative importance of the biogeophysical versus biogeochemical terrestrial biosphere interactions with climate. We find that the biogeophysical effects of vegetation account for up to an additional −0.91 °C global mean cooling, with regional cooling as large as −5 °C, but with considerable variability across the glacial–interglacial cycle. By comparison, while opposite in sign, our model estimates of the biogeochemical impacts are substantially smaller in magnitude. Offline simulations show a maximum of +0.33 °C warming due to an increase of 25 ppm above our (pre-industrial) baseline atmospheric CO2 mixing ratio. In contrast to shorter (century) timescale projections of future terrestrial biosphere response where direct and indirect responses may at times cancel out, we find that the biogeophysical effects consistently and strongly dominate the biogeochemical effect over the inter-glacial cycle. On average across the period, the terrestrial biosphere has a −0.26 °C effect on temperature, with −0.58 °C at the Last Glacial Maximum. Depending on assumptions made about the destination of terrestrial carbon under ice sheets and where sea level has changed, the average terrestrial biosphere contribution over the last 120 kyr could be as much as −50 °C and −0.83 °C at the Last Glacial Maximum.
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Gourdji, S. M., K. L. Mueller, V. Yadav, D. N. Huntzinger, A. E. Andrews, M. Trudeau, G. Petron, et al. "North American CO<sub>2</sub> exchange: intercomparison of modeled estimates with results from a fine-scale atmospheric inversion." Biogeosciences Discussions 8, no. 4 (July 11, 2011): 6775–832. http://dx.doi.org/10.5194/bgd-8-6775-2011.
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Abstract. Robust estimates of regional-scale terrestrial CO2 exchange are needed to support carbon management policies and to improve the predictive ability of models representing carbon-climate feedbacks. Large discrepancies remain, however, both among and between CO2 flux estimates from atmospheric inverse models and terrestrial biosphere models. Improved atmospheric inverse models that provide robust estimates at sufficiently fine spatial scales could prove especially useful for monitoring efforts, while also serving as a validation tool for process-based assumptions in terrestrial biosphere models. A growing network of continental sites collecting continuous CO2 measurements provides the information needed to drive such models. This study presents results from a regional geostatistical inversion over North America for 2004, taking advantage of continuous data from the nine sites operational in that year, as well as available flask and aircraft observations. The approach does not require explicit prior flux estimates, resolves fluxes at finer spatiotemporal scales than previous North American inversion studies, and uses a Lagrangian transport model coupled with high-resolution winds (i.e. WRF-STILT) to resolve near-field influences around measurement locations. The estimated fluxes are used in an inter-comparison with other inversion studies and a suite of terrestrial biosphere model estimates collected through the North American Carbon Program Regional and Continental Interim Synthesis. Differences among inversions are found to be smallest in areas of the continent best-constrained by the atmospheric data, pointing to the value of an expanded measurement network. Aggregation errors in previous coarser-scale inversion studies are likely to explain a portion of the remaining spread. The spatial patterns from a geostatistical inversion that includes auxiliary environmental variables from the North American Regional Reanalysis were similar to those from the median of the biospheric model estimates during the growing season, but diverged more strongly in the dormant season. This could be due to a lack of sensitivity in the inversion during the dormant season, but may also point to a lack of skill in the biospheric models outside of the growing season, particularly in agricultural areas. For the annual continental budget, the boundary conditions used as an input into the inversions were seen to have a substantial impact on the estimated net flux, with a difference of ~0.8 PgC yr−1 associated with results using two different plausible sets of boundary conditions.
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Kwon, O.-Yul, and Jerald L. Schnoor. "Simple Global Carbon Model: The atmosphere-terrestrial biosphere-ocean interaction." Global Biogeochemical Cycles 8, no. 3 (September 1994): 295–305. http://dx.doi.org/10.1029/94gb00768.
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Polglase, PJ, and YP Wang. "Potential CO2-Enhanced Carbon Storage by the Terrestrial Biosphere." Australian Journal of Botany 40, no. 5 (1992): 641. http://dx.doi.org/10.1071/bt9920641.
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Geochemical models that deduce latitudinal source/sink relationships of atmospheric CO2 suggest that, in tropical regions, there is almost zero net exchange of CO2 between the atmosphere and the terrestrial biosphere. The implication is that CO2-enhanced carbon storage (CO2-ECS) by tropical biomes is negating the output of CO2 from deforestation. We describe here a 10-biome model for CO2-ECS, in which carbon accumulation in living vegetation is coupled to the Rothamsted soil carbon model. A biotic growth factor (β) was used to describe the relationship between literature estimates of net primary production (NPP) and atmospheric CO2 concentration. Using β = 0.3 as a reference state, CO2-ECS by the global biosphere in 1990 was 1.1 Gt. When more appropriate values of β were used (derived from a theoretical response of vegetation to increasing temperature and CO2), CO2-ECS was 1.3 Gt, of which tropical biomes accounted for 0.7 Gt. There are many uncertainties in this (and other) models; total CO2-ECS is particularly sensitive to changes in NPP. Unless published surveys have underestimated tropical NPP by a factor of about 2, then it is unlikely that CO2-ECS could have negated the 1.5-3.0 Gt of carbon that are estimated to have been emitted by tropical deforestation in 1990.
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Kindermann, J., M. K. B. L�deke, F. W. Badeck, R. D. Otto, A. Klaudius, Ch H�ger, G. W�rth, et al. "Structure of a global and seasonal carbon exchange model for the terrestrial biosphere the frankfurt biosphere model (FBM)." Water, Air, & Soil Pollution 70, no. 1-4 (October 1993): 675–84. http://dx.doi.org/10.1007/bf01105029.
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Famiglietti, Caroline A., T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, et al. "Optimal model complexity for terrestrial carbon cycle prediction." Biogeosciences 18, no. 8 (April 30, 2021): 2727–54. http://dx.doi.org/10.5194/bg-18-2727-2021.
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Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.
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Raupach, M. R. "Dynamics of resource production and utilisation in two-component biosphere-human and terrestrial carbon systems." Hydrology and Earth System Sciences 11, no. 2 (February 21, 2007): 875–89. http://dx.doi.org/10.5194/hess-11-875-2007.
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Abstract. This paper analyses simple models for "production-utilisation" systems, reduced to two state variables for producers and utilisers, respectively. Two modes are distinguished: in "harvester" systems the resource utilisation involves active seeking on the part of the utilisers, while in "processor" systems, utilisers function as passive material processors. An idealised model of biosphere-human interactions provides an example of a harvester system, and a model of plant and soil carbon dynamics exemplifies a processor system. The biosphere-human interaction model exhibits a number of features in accord with experience, including a tendency towards oscillatory behaviour which in some circumstances results in limit cycles. The plant-soil carbon model is used to study the effect of random forcing of production (for example by weather and climate fluctuations), showing that with appropriate parameter choices the model can flip between active-biosphere and dormant-biosphere equilibria under the influence of random forcing. This externally-driven transition between locally stable states is fundamentally different from Lorenzian chaos. A behavioural difference between two-component processor and harvester systems is that harvester systems have a capacity for oscillatory behaviour while processor systems do not.
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Tian, Hanqin, Jia Yang, Chaoqun Lu, Rongting Xu, Josep G. Canadell, Robert B. Jackson, Almut Arneth, et al. "The Global N2O Model Intercomparison Project." Bulletin of the American Meteorological Society 99, no. 6 (June 2018): 1231–51. http://dx.doi.org/10.1175/bams-d-17-0212.1.
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AbstractNitrous oxide (N2O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and spatiotemporal patterns of N2O fluxes and the key drivers of N2O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N2O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N2O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N2O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N2O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N2O dynamics; and 3) provide a benchmarking estimate of N2O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N2O estimation derived from the NMIP is a key component of the global N2O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.
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Graven, Heather, Colin E. Allison, David M. Etheridge, Samuel Hammer, Ralph F. Keeling, Ingeborg Levin, Harro A. J. Meijer, et al. "Compiled records of carbon isotopes in atmospheric CO<sub>2</sub> for historical simulations in CMIP6." Geoscientific Model Development 10, no. 12 (December 5, 2017): 4405–17. http://dx.doi.org/10.5194/gmd-10-4405-2017.
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Abstract. The isotopic composition of carbon (Δ14C and δ13C) in atmospheric CO2 and in oceanic and terrestrial carbon reservoirs is influenced by anthropogenic emissions and by natural carbon exchanges, which can respond to and drive changes in climate. Simulations of 14C and 13C in the ocean and terrestrial components of Earth system models (ESMs) present opportunities for model evaluation and for investigation of carbon cycling, including anthropogenic CO2 emissions and uptake. The use of carbon isotopes in novel evaluation of the ESMs' component ocean and terrestrial biosphere models and in new analyses of historical changes may improve predictions of future changes in the carbon cycle and climate system. We compile existing data to produce records of Δ14C and δ13C in atmospheric CO2 for the historical period 1850–2015. The primary motivation for this compilation is to provide the atmospheric boundary condition for historical simulations in the Coupled Model Intercomparison Project 6 (CMIP6) for models simulating carbon isotopes in the ocean or terrestrial biosphere. The data may also be useful for other carbon cycle modelling activities.
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Wang, H., I. C. Prentice, and T. W. Davis. "Biophsyical constraints on gross primary production by the terrestrial biosphere." Biogeosciences 11, no. 20 (October 31, 2014): 5987–6001. http://dx.doi.org/10.5194/bg-11-5987-2014.
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Abstract. Persistent divergences among the predictions of complex carbon-cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. Such problems with current models indicate an urgent need to reassess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green-vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, Δ E) and elevation, based on an optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high, or higher, in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.
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Wang, H., I. C. Prentice, and T. W. Davis. "Biophysical constraints on gross primary production by the terrestrial biosphere." Biogeosciences Discussions 11, no. 2 (February 25, 2014): 3209–40. http://dx.doi.org/10.5194/bgd-11-3209-2014.
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Abstract. Persistent divergences among the predictions of complex carbon cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. This and other problems with current models indicate an urgent need to re-assess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, ΔE) and elevation, based on optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high or higher in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.
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Wang, Y. P., R. M. Law, and B. Pak. "A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere." Biogeosciences Discussions 6, no. 5 (October 14, 2009): 9891–944. http://dx.doi.org/10.5194/bgd-6-9891-2009.
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Abstract. Carbon storage by many terrestrial ecosystems can be limited by nutrients, predominantly nitrogen (N) and phosphorous (P), in additional to other environmental constraints, water, light and temperature. However the spatial distribution and the extent of both N and P limitation at global scale have not been quantified. Here we have developed a global model of carbon (C), nitrogen (N) and phosphorus (P) cycles for the terrestrial biosphere. Model estimates of steady state C and N pool sizes and major fluxes between plant, litter and soil pools, under present climate conditions, agree well with various independent estimates. The total amount of C in the terrestrial biosphere is 2526 Gt C, and the C fractions in plant, litter and soil organic matter are 21, 6 and 73%. The total amount of N is 124 Gt N, with about 94% stored in the soil, 5% in the plant live biomass, and 1% in litter. We found that the estimates of total soil P and its partitioning into different pools in soil are quite sensitive to biochemical P mineralization that has not been included in any other global models previously. The total amount of P is 26 Gt P in the terrestrial biosphere, 17% of which is stored in the soil organic matter if biochemical P mineralization is modelled, or 40 Gt P, with 60% in soil organic matter, otherwise. This model was used to derive the global distribution of N or P limitation on the productivity of terrestrial ecosystems. Our model predicts that the net primary productivity of most tropical evergreen broadleaf forests and tropical savannahs is reduced by about 20% on average by P limitation, and most of the remaining biomes are N limited; N limitation is strongest in high latitude deciduous needle leaf forests, and reduces its net primary productivity by up to 40% under present conditions.
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Raupach, M. R. "Dynamics of resource production and utilisation in two-component biosphere-human and terrestrial carbon systems." Hydrology and Earth System Sciences Discussions 3, no. 4 (August 17, 2006): 2279–322. http://dx.doi.org/10.5194/hessd-3-2279-2006.
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Abstract. This paper analyses simple models for "production-utilisation" systems, reduced to two state variables for producers and utilisers, respectively. Two modes are distinguished: in "harvester" systems the resource utilisation involves active seeking on the part of the utilisers, while in "processor" systems, utilisers function as passive material processors. An idealised model of biosphere-human interactions provides an example of a harvester system, and a model of plant and soil carbon dynamics exemplifies a processor system. The biosphere-human interaction model exhibits a number of features in accord with experience, including a tendency towards oscillatory behaviour which in some circumstances results in limit cycles. The plant-soil carbon model is used to study the effect of random forcing of production (for example by weather and climate fluctuations), showing that with appropriate parameter choices the model can flip between active-biosphere and dormant-biosphere equilibria under the influence of random forcing. This externally-driven transition between locally stable states is fundamentally different from Lorenzian chaos. A basic behavioural difference between processor and harvester forms of producer-utiliser system is that harvester systems tend toward oscillatory behaviour (though they do not always do so), while processor systems do not have this tendency.
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Meyerholt, Johannes, and Sönke Zaehle. "Controls of terrestrial ecosystem nitrogen loss on simulated productivity responses to elevated CO<sub>2</sub>." Biogeosciences 15, no. 18 (September 24, 2018): 5677–98. http://dx.doi.org/10.5194/bg-15-5677-2018.
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Abstract. The availability of nitrogen is one of the primary controls on plant growth. Terrestrial ecosystem nitrogen availability is not only determined by inputs from fixation, deposition, or weathering, but is also regulated by the rates with which nitrogen is lost through various pathways. Estimates of large-scale nitrogen loss rates have been associated with considerable uncertainty, as process rates and controlling factors of the different loss pathways have been difficult to characterize in the field. Therefore, the nitrogen loss representations in terrestrial biosphere models vary substantially, adding to nitrogen cycle-related uncertainty and resulting in varying predictions of how the biospheric carbon sink will evolve under future scenarios of elevated atmospheric CO2. Here, we test three commonly applied approaches to represent ecosystem-level nitrogen loss in a common carbon–nitrogen terrestrial biosphere model with respect to their impact on projections of the effect of elevated CO2. We find that despite differences in predicted responses of nitrogen loss rates to elevated CO2 and climate forcing, the variety of nitrogen loss representation between models only leads to small variety in carbon sink predictions. The nitrogen loss responses are particularly uncertain in the boreal and tropical regions, where plant growth is strongly nitrogen-limited or nitrogen turnover rates are usually high, respectively. This highlights the need for better representation of nitrogen loss fluxes through global measurements to inform models.
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Kaminski, T., M. Scholze, M. Vossbeck, W. Knorr, M. Buchwitz, and M. Reuter. "Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide." Remote Sensing of Environment 203 (December 2017): 109–24. http://dx.doi.org/10.1016/j.rse.2017.08.017.
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Friend, A. D., A. K. Stevens, R. G. Knox, and M. G. R. Cannell. "A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0)." Ecological Modelling 95, no. 2-3 (February 1997): 249–87. http://dx.doi.org/10.1016/s0304-3800(96)00034-8.
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Kaminski, T., W. Knorr, M. Scholze, N. Gobron, B. Pinty, R. Giering, and P. P. Mathieu. "Consistent assimilation of MERIS FAPAR and atmospheric CO<sub>2</sub> into a terrestrial vegetation model and interactive mission benefit analysis." Biogeosciences Discussions 8, no. 6 (November 2, 2011): 10761–95. http://dx.doi.org/10.5194/bgd-8-10761-2011.
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Abstract. The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2 the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty is reflecting uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR provided by the MERIS sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial biosphere model BETHY. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation for two different set-ups: first at site-scale, where MERIS FAPAR observations at a range of sites are used as simultaneous constraints, and second at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. On both scales the assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.
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Kaminski, T., W. Knorr, M. Scholze, N. Gobron, B. Pinty, R. Giering, and P. P. Mathieu. "Consistent assimilation of MERIS FAPAR and atmospheric CO<sub>2</sub> into a terrestrial vegetation model and interactive mission benefit analysis." Biogeosciences 9, no. 8 (August 16, 2012): 3173–84. http://dx.doi.org/10.5194/bg-9-3173-2012.
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Abstract. The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2, the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty reflects uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR (fraction of absorbed photosynthetically active radiation) provided by the MERIS (ESA's Medium Resolution Imaging Spectrometer) sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial BETHY (Biosphere Energy Transfer Hydrology) model. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. The assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.
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Wang, Y. P., R. M. Law, and B. Pak. "A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere." Biogeosciences 7, no. 7 (July 23, 2010): 2261–82. http://dx.doi.org/10.5194/bg-7-2261-2010.
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Abstract. Carbon storage by many terrestrial ecosystems can be limited by nutrients, predominantly nitrogen (N) and phosphorus (P), in addition to other environmental constraints, water, light and temperature. However the spatial distribution and the extent of both N and P limitation at the global scale have not been quantified. Here we have developed a global model of carbon (C), nitrogen (N) and phosphorus (P) cycles for the terrestrial biosphere. Model estimates of steady state C and N pool sizes and major fluxes between plant, litter and soil pools, under present climate conditions, agree well with various independent estimates. The total amount of C in the terrestrial biosphere is 2767 Gt C, and the C fractions in plant, litter and soil organic matter are 19%, 4% and 77%. The total amount of N is 135 Gt N, with about 94% stored in the soil, 5% in the plant live biomass, and 1% in litter. We found that the estimates of total soil P and its partitioning into different pools in soil are quite sensitive to biochemical P mineralization. The total amount of P (plant biomass, litter and soil) excluding occluded P in soil is 17 Gt P in the terrestrial biosphere, 33% of which is stored in the soil organic matter if biochemical P mineralization is modelled, or 31 Gt P with 67% in soil organic matter otherwise. This model was used to derive the global distribution and uncertainty of N or P limitation on the productivity of terrestrial ecosystems at steady state under present conditions. Our model estimates that the net primary productivity of most tropical evergreen broadleaf forests and tropical savannahs is reduced by about 20% on average by P limitation, and most of the remaining biomes are N limited; N limitation is strongest in high latitude deciduous needle leaf forests, and reduces its net primary productivity by up to 40% under present conditions.
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Glassmeier, Karl-Heinz, Otto Richter, Joachim Vogt, Petra Möbus, and Antje Schwalb. "The Sun, geomagnetic polarity transitions, and possible biospheric effects: review and illustrating model." International Journal of Astrobiology 8, no. 3 (July 2009): 147–59. http://dx.doi.org/10.1017/s1473550409990073.
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AbstractThe Earth is embedded in the solar wind, this ever-streaming extremely tenuous ionized gas emanating from the Sun. It is the geomagnetic field which inhibits the solar wind plasma to directly impinge upon the terrestrial atmosphere. It is also the geomagnetic field which moderates and controls the entry of energetic particles of cosmic and solar origin into the atmosphere. During geomagnetic polarity transitions the terrestrial magnetic field decays down to about 10% of its current value. Also, the magnetic field topology changes from a dipole dominated structure to a multipole dominated topology. What happens to the Earth system during such a polarity transition, that is, during episodes of a weak transition field? Which modifications of the configuration of the terrestrial magnetosphere can be expected? Is there any influence on the atmosphere from the intensified particle bombardment? What are the possible effects on the biosphere? Is a polarity transition another example of a cosmic cataclysm? A review is provided on the current understanding of the problem. A first, illustrating model is also discussed to outline the complexity of any biospheric reaction on polarity transitions.
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Berg, Alexis, Benjamin Sultan, and Nathalie de Noblet-Ducoudré. "Including tropical croplands in a terrestrial biosphere model: application to West Africa." Climatic Change 104, no. 3-4 (June 26, 2010): 755–82. http://dx.doi.org/10.1007/s10584-010-9874-x.
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Sasai, Takahiro, Kazuhito Ichii, Yasushi Yamaguchi, and Ramakrishna Nemani. "Simulating terrestrial carbon fluxes using the new biosphere model “biosphere model integrating eco-physiological and mechanistic approaches using satellite data” (BEAMS)." Journal of Geophysical Research: Biogeosciences 110, G2 (December 2005): n/a. http://dx.doi.org/10.1029/2005jg000045.
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Saito, M., A. Ito, and S. Maksyutov. "Optimization of a prognostic biosphere model in atmospheric CO<sub>2</sub> variability and terrestrial biomass." Geoscientific Model Development Discussions 6, no. 3 (August 6, 2013): 4243–80. http://dx.doi.org/10.5194/gmdd-6-4243-2013.
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Abstract. This study investigated the capacity of a prognostic biosphere model to simulate global vegetation carbon dynamics and the variability in atmospheric CO2 concentrations under the current environmental conditions. Global data sets of atmospheric CO2 concentrations and terrestrial vegetation compositions of the aboveground biomass and net primary productivity (NPP) were assimilated into the biosphere model using an inverse modeling method combined with an atmospheric transport model. In this process, the optimal physiological parameters of the biosphere model were estimated by minimizing the misfit between the observed and modeled values, and acceptable parameters with various values were generated among the biome types. The model with the optimized parameters corresponded to the observed seasonal variations in the CO2 concentration, especially in the Northern Hemisphere where there are abundant observation stations, although the annual amplitudes were overestimated at a few stations. In simulating the mean annual aboveground biomass and NPP, the model also produced moderate estimates of the mean magnitudes and probability distributions for each biome. However, the model worked less efficiently in simulating the terrestrial vegetation compositions in some grids. These misfits suggest that some additional information about the disturbance and seasonal variability of the physiological parameters is required to improve the performance of the simulation model.
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Nayak, Rabindra K., N. R. Patel, and V. K. Dadhwal. "Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model." Environmental Monitoring and Assessment 170, no. 1-4 (November 12, 2009): 195–213. http://dx.doi.org/10.1007/s10661-009-1226-9.
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Sippel, S., F. E. L. Otto, M. Forkel, M. R. Allen, B. P. Guillod, M. Heimann, M. Reichstein, S. I. Seneviratne, K. Thonicke, and M. D. Mahecha. "A novel bias correction methodology for climate impact simulations." Earth System Dynamics 7, no. 1 (February 2, 2016): 71–88. http://dx.doi.org/10.5194/esd-7-71-2016.
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Abstract. Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and the nonpreservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias-corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of carefully considering statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying changes in past, current and future extremes.
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Sippel, S., F. E. L. Otto, M. Forkel, M. R. Allen, B. P. Guillod, M. Heimann, M. Reichstein, S. I. Seneviratne, K. Thonicke, and M. D. Mahecha. "A novel bias correction methodology for climate impact simulations." Earth System Dynamics Discussions 6, no. 2 (October 19, 2015): 1999–2042. http://dx.doi.org/10.5194/esdd-6-1999-2015.
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Abstract. Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance to carefully consider statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying past, current and future extremes.
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Schaphoff, Sibyll, Werner von Bloh, Anja Rammig, Kirsten Thonicke, Hester Biemans, Matthias Forkel, Dieter Gerten, et al. "LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description." Geoscientific Model Development 11, no. 4 (April 12, 2018): 1343–75. http://dx.doi.org/10.5194/gmd-11-1343-2018.
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Abstract. This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process.
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Schaphoff, Sibyll, Matthias Forkel, Christoph Müller, Jürgen Knauer, Werner von Bloh, Dieter Gerten, Jonas Jägermeyr, et al. "LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation." Geoscientific Model Development 11, no. 4 (April 12, 2018): 1377–403. http://dx.doi.org/10.5194/gmd-11-1377-2018.
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Abstract. The dynamic global vegetation model LPJmL4 is a process-based model that simulates climate and land use change impacts on the terrestrial biosphere, agricultural production, and the water and carbon cycle. Different versions of the model have been developed and applied to evaluate the role of natural and managed ecosystems in the Earth system and the potential impacts of global environmental change. A comprehensive model description of the new model version, LPJmL4, is provided in a companion paper (Schaphoff et al., 2018c). Here, we provide a full picture of the model performance, going beyond standard benchmark procedures and give hints on the strengths and shortcomings of the model to identify the need for further model improvement. Specifically, we evaluate LPJmL4 against various datasets from in situ measurement sites, satellite observations, and agricultural yield statistics. We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO2 concentration, while the permafrost scheme improves estimates of carbon stocks. The full LPJmL4 code including the new developments will be supplied open source through https://gitlab.pik-potsdam.de/lpjml/LPJmL. We hope that this will lead to new model developments and applications that improve the model performance and possibly build up a new understanding of the terrestrial biosphere.
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Byrne, Brendan, Dylan B. A. Jones, Kimberly Strong, Saroja M. Polavarapu, Anna B. Harper, David F. Baker, and Shamil Maksyutov. "On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?" Atmospheric Chemistry and Physics 19, no. 20 (October 22, 2019): 13017–35. http://dx.doi.org/10.5194/acp-19-13017-2019.
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Abstract. Interannual variations in temperature and precipitation impact the carbon balance of terrestrial ecosystems, leaving an imprint in atmospheric CO2. Quantifying the impact of climate anomalies on the net ecosystem exchange (NEE) of terrestrial ecosystems can provide a constraint to evaluate terrestrial biosphere models against and may provide an emergent constraint on the response of terrestrial ecosystems to climate change. We investigate the spatial scales over which interannual variability in NEE can be constrained using atmospheric CO2 observations from the Greenhouse Gases Observing Satellite (GOSAT). NEE anomalies are calculated by performing a series of inversion analyses using the GEOS-Chem adjoint model to assimilate GOSAT observations. Monthly NEE anomalies are compared to “proxies”, variables that are associated with anomalies in the terrestrial carbon cycle, and to upscaled NEE estimates from FLUXCOM. Statistically significant correlations (P<0.05) are obtained between posterior NEE anomalies and anomalies in soil temperature and FLUXCOM NEE on continental and larger scales in the tropics, as well as in the northern extratropics on subcontinental scales during the summer (R2≥0.49), suggesting that GOSAT measurements provide a constraint on NEE interannual variability (IAV) on these spatial scales. Furthermore, we show that GOSAT flux inversions are generally better correlated with the environmental proxies and FLUXCOM NEE than NEE anomalies produced by a set of terrestrial biosphere models (TBMs), suggesting that GOSAT flux inversions could be used to evaluate TBM NEE fluxes.
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Harvey, L. D. Danny. "Effect of model structure on the response of terrestrial biosphere models to CO2and temperature increases." Global Biogeochemical Cycles 3, no. 2 (June 1989): 137–53. http://dx.doi.org/10.1029/gb003i002p00137.
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Ito, Akihiko, and Motoko Inatomi. "Water-Use Efficiency of the Terrestrial Biosphere: A Model Analysis Focusing on Interactions between the Global Carbon and Water Cycles." Journal of Hydrometeorology 13, no. 2 (April 1, 2012): 681–94. http://dx.doi.org/10.1175/jhm-d-10-05034.1.
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Abstract Carbon and water cycles are intimately coupled in terrestrial ecosystems, and water-use efficiency (WUE; carbon gain at the expense of unit water loss) is one of the key parameters of ecohydrology and ecosystem management. In this study, the carbon cycle and water budget of terrestrial ecosystems were simulated using a process-based ecosystem model called Vegetation Integrative Simulator for Trace Gases (VISIT), and WUE was evaluated: WUEC, defined as gross primary production (GPP) divided by transpiration; and WUES, defined as net primary production (NPP) divided by actual evapotranspiration. Total annual WUEC and WUES of the terrestrial biosphere were estimated as 8.0 and 0.92 g C kg−1 H2O, respectively, for the period 1995–2004. Spatially, WUEC and WUES were only weakly correlated. WUES ranged from <0.2 g C kg−1 H2O in arid ecosystems to >1.5 g C kg−1 H2O in boreal and alpine ecosystems. The historical simulation implied that biospheric WUE increased from 1901 to 2005 (WUEC, +7%; WUES, +12%) mainly as a result of the augmentation of productivity in parallel with the atmospheric carbon dioxide increase. Country-based analyses indicated that total NPP is largely determined by water availability, and human appropriation of NPP is also related to water resources to a considerable extent. These results have implications for 1) responses of the carbon cycle to the anticipated global hydrological changes, 2) responses of the water budget to changes in the terrestrial carbon cycle, and 3) ecosystem management based on optimized resource use.
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MacBean, N., F. Maignan, P. Peylin, C. Bacour, F. M. Bréon, and P. Ciais. "Using satellite data to improve the leaf phenology of a global terrestrial biosphere model." Biogeosciences 12, no. 23 (December 10, 2015): 7185–208. http://dx.doi.org/10.5194/bg-12-7185-2015.
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Abstract. Correct representation of seasonal leaf dynamics is crucial for terrestrial biosphere models (TBMs), but many such models cannot accurately reproduce observations of leaf onset and senescence. Here we optimised the phenology-related parameters of the ORCHIDEE TBM using satellite-derived Normalized Difference Vegetation Index data (MODIS NDVI v5) that are linearly related to the model fAPAR. We found the misfit between the observations and the model decreased after optimisation for all boreal and temperate deciduous plant functional types, primarily due to an earlier onset of leaf senescence. The model bias was only partially reduced for tropical deciduous trees and no improvement was seen for natural C4 grasses. Spatial validation demonstrated the generality of the posterior parameters for use in global simulations, with an increase in global median correlation of 0.56 to 0.67. The simulated global mean annual gross primary productivity (GPP) decreased by ~ 10 PgC yr−1 over the 1990–2010 period due to the substantially shortened growing season length (GSL – by up to 30 days in the Northern Hemisphere), thus reducing the positive bias and improving the seasonal dynamics of ORCHIDEE compared to independent data-based estimates. Finally, the optimisations led to changes in the strength and location of the trends in the simulated vegetation productivity as represented by the GSL and mean annual fraction of absorbed photosynthetically active radiation (fAPAR), suggesting care should be taken when using un-calibrated models in attribution studies. We suggest that the framework presented here can be applied for improving the phenology of all global TBMs.
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Pan, Shufen, Hanqin Tian, Shree R. S. Dangal, Zhiyun Ouyang, Bo Tao, Wei Ren, Chaoqun Lu, and Steven Running. "Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation." Advances in Meteorology 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/965936.
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There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment.
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Foley, Jonathan A. "Net primary productivity in the terrestrial biosphere: The application of a global model." Journal of Geophysical Research 99, no. D10 (1994): 20773. http://dx.doi.org/10.1029/94jd01832.
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Keenan, T. F., Ian Baker, Alan Barr, Philippe Ciais, Ken Davis, Michael Dietze, Danillo Dragoni, et al. "Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange." Global Change Biology 18, no. 6 (March 30, 2012): 1971–87. http://dx.doi.org/10.1111/j.1365-2486.2012.02678.x.
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Thum, Tea, Silvia Caldararu, Jan Engel, Melanie Kern, Marleen Pallandt, Reiner Schnur, Lin Yu, and Sönke Zaehle. "A new model of the coupled carbon, nitrogen, and phosphorus cycles in the terrestrial biosphere (QUINCY v1.0; revision 1996)." Geoscientific Model Development 12, no. 11 (November 20, 2019): 4781–802. http://dx.doi.org/10.5194/gmd-12-4781-2019.
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Abstract. The dynamics of terrestrial ecosystems are shaped by the coupled cycles of carbon, nitrogen, and phosphorus, and these cycles are strongly dependent on the availability of water and energy. These interactions shape future terrestrial biosphere responses to global change. Here, we present a new terrestrial ecosystem model, QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), which has been designed from scratch to allow for a seamless integration of the fully coupled carbon, nitrogen, and phosphorus cycles with each other and also with processes affecting the energy and water balances in terrestrial ecosystems. This new model includes (i) a representation of plant growth which separates source (e.g. photosynthesis) and sink (growth rate of individual tissues, constrained by temperature and the availability of water and nutrients) processes; (ii) the acclimation of many ecophysiological processes to meteorological conditions and/or nutrient availability; (iii) an explicit representation of vertical soil processes to separate litter and soil organic matter dynamics; (iv) a range of new diagnostics (leaf chlorophyll content; 13C, 14C, and 15N isotope tracers) to allow for a more in-depth model evaluation. In this paper, we present the model structure and provide an assessment of its performance against a range of observations from global-scale ecosystem monitoring networks. We demonstrate that QUINCY v1.0 is capable of simulating ecosystem dynamics across a wide climate gradient, as well as across different plant functional types. We further provide an assessment of the sensitivity of key model predictions to the model's parameterisation. This work lays the ground for future studies to test individual process hypotheses using the QUINCY v1.0 framework in the light of ecosystem manipulation observations, as well as global applications to investigate the large-scale consequences of nutrient-cycle interactions for projections of terrestrial biosphere dynamics.
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Buendía, C., A. Kleidon, and A. Porporato. "The role of tectonic uplift, climate, and vegetation in the long-term terrestrial phosphorous cycle." Biogeosciences 7, no. 6 (June 25, 2010): 2025–38. http://dx.doi.org/10.5194/bg-7-2025-2010.
Full textAbstract:
Abstract. Phosphorus (P) is a crucial element for life and therefore for maintaining ecosystem productivity. Its local availability to the terrestrial biosphere results from the interaction between climate, tectonic uplift, atmospheric transport, and biotic cycling. Here we present a mathematical model that describes the terrestrial P-cycle in a simple but comprehensive way. The resulting dynamical system can be solved analytically for steady-state conditions, allowing us to test the sensitivity of the P-availability to the key parameters and processes. Given constant inputs, we find that humid ecosystems exhibit lower P availability due to higher runoff and losses, and that tectonic uplift is a fundamental constraint. In particular, we find that in humid ecosystems the biotic cycling seem essential to maintain long-term P-availability. The time-dependent P dynamics for the Franz Josef and Hawaii chronosequences show how tectonic uplift is an important constraint on ecosystem productivity, while hydroclimatic conditions control the P-losses and speed towards steady-state. The model also helps describe how, with limited uplift and atmospheric input, as in the case of the Amazon Basin, ecosystems must rely on mechanisms that enhance P-availability and retention. Our novel model has a limited number of parameters and can be easily integrated into global climate models to provide a representation of the response of the terrestrial biosphere to global change.
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MacBean, N., F. Maignan, P. Peylin, C. Bacour, F. M. Bréon, and P. Ciais. "Using satellite data to improve the leaf phenology of a global Terrestrial Biosphere Model." Biogeosciences Discussions 12, no. 16 (August 19, 2015): 13311–73. http://dx.doi.org/10.5194/bgd-12-13311-2015.
Full textAbstract:
Abstract. Correct representation of seasonal leaf dynamics is crucial for Terrestrial Biosphere Models (TBMs), but many such models cannot accurately reproduce observations of leaf onset and senescence. Here we optimized the phenology-related parameters of the ORCHIDEE TBM using satellite-derived Normalized Difference Vegetation Index data (MODIS NDVI v5). We found the misfit between the observations and the model decreased after optimisation for all boreal and temperate deciduous Plant Functional Types, primarily due to an earlier onset of leaf senescence. The model bias was only partially reduced for tropical deciduous trees and no improvement was seen for natural C4 grasses. Spatial validation demonstrated the generality of the posterior parameters for use in global simulations, with an increase in global median correlation of 0.56 to 0.67. The simulated global mean annual GPP decreased by ~10 Pg C yr−1 over the 1990–2010 period due to the substantially shortened Growing Season Length (up to 30 days in the Northern Hemisphere), thus reducing the positive bias and improving the seasonal dynamics of ORCHIDEE compared to independent data-based estimates. Finally, the optimisations led to changes in the strength and location of the trends in the simulated GSL and mean annual fAPAR, suggesting care should be taken when using un-calibrated models in attribution studies. We suggest that the framework presented here can be applied for improving the phenology of all global TBMs.