Normative productivity of the global vegetation
© Alexandrov and Matsunaga; licensee BioMed Central Ltd. 2008
Received: 16 October 2008
Accepted: 24 December 2008
Published: 24 December 2008
The biosphere models of terrestrial productivity are essential for projecting climate change and assessing mitigation and adaptation options. Many of them have been developed in connection to the International Geosphere-Biosphere Program (IGBP) that backs the work of the Intergovernmental Panel on Climate Change (IPCC). In the end of 1990s, IGBP sponsored release of a data set summarizing the model outputs and setting certain norms for estimates of terrestrial productivity. Since a number of new models and new versions of old models were developed during the past decade, these normative data require updating.
Here, we provide the series of updates that reflects evolution of biosphere models and demonstrates evolutional stability of the global and regional estimates of terrestrial productivity. Most of them fit well the long-living Miami model. At the same time we call attention to the emerging alternative: the global potential for net primary production of biomass may be as high as 70 PgC y-1, the productivity of larch forest zone may be comparable to the productivity of taiga zone, and the productivity of rain-green forest zone may be comparable to the productivity of tropical rainforest zone.
The departure from Miami model's worldview mentioned above cannot be simply ignored. It requires thorough examination using modern observational tools and techniques for model-data fusion. Stability of normative knowledge is not its ultimate goal – the norms for estimates of terrestrial productivity must be evidence-based.
The amount of plant organic matter produced on annual basis, so called net primary production or NPP, is the basic characteristic of the biosphere. It shows biosphere potential to supply primary food energy source for non-autotrophic species including humans. Human appropriation of terrestrial net primary production stems not only from the demand for food but also for fuel, construction materials, and paper. It is estimated to be from 8 to 15 PgC y-1 in total (including 3–6 PgC y-1 associated with food supply) .
NPP also shows biosphere potential to steer the Earth system by absorbing CO2, a gas whose atmospheric concentration affects global climate. NPP characterizes the "gross" terrestrial carbon sink – the amount of CO2 annually sequestered by vegetation. The net land-to-atmosphere flux is much smaller because the "gross" sink is compensated for by various carbon sources. Its magnitude is estimated to be from 0.3 to 1.5 PgC y-1 . The coupled carbon-cycle-climate models show the wide range of projections for the magnitude of the terrestrial uptake in the middle of this century: from 0 to 8 PgC y-1 .
Appropriation (or re-direction) of NPP is also one method of climate change mitigation. Protecting non-living organic matter from decomposition and burning , reducing deforestation rates [5–7], and increasing the forest harvest age  will "re-direct" NPP to carbon pools with longer turnover times. Implementation of these measures may partly compensate for emissions from fossil fuel burning.
The total terrestrial NPP is generally assumed to be about 60 PgC y-1 . Biosphere models differ on this value. Comparison of global NPP models carried out more than a decade ago revealed that estimates ranged from 44.4 to 66.3 PgC y-1 . One of the major results of that comparative study was releasing average estimates of NPP over a geographic grid with a half-degree resolution . These were the first normative data on global NPP created by summarizing modelling efforts. ("Normative data" means the data that result from a model ensemble, not from a single model, and therefore may be accepted as norms.)
The data have not been updated since then, although a number of new models and new versions of old models were developed during the last decade. Here we present the series of updates reflecting the evolution of biosphere models.
Evolutional stability of normative data
Normative productivity of major vegetation zones
Normative NPP version
Emergent alternative data
In modelling terrestrial productivity we are facing the problem of structural uncertainty. Field observations hardly allow us to make a reasonable choice between competing conceptual frameworks, to form an agreement on the best model structure, or even to discriminate between adequate descriptions of significant processes from inadequate ones . Therefore, we approach this problem through retrospection of modelling efforts.
Terrestrial productivity has been a focus of biosphere studies over the last three decades. First, the global pattern of NPP was characterized by data collected during the International Biological Program (1964–1974). Then, the data was turned into empirical models that relate gradations in NPP to environmental factors of known geographic distribution. Later, a number of process-based models were developed in connection to the IGBP activities. This is definitely a field of science that hardly may be referred to as immature.
Nevertheless, the range of estimates remains roughly constant over this period. Early estimates of terrestrial NPP range from 10 to 100 PgC y-1 . Starting in the 1970s, they fall between 40 and 80 PgC y-1. The estimates of empirical models  vary from 50 to 65 PgC y-1, and the estimates of process-based models are expected to vary in the same range . Re-analysis of the NPP measurements stored in the Osnabrück NPP database show that a 90% confidence interval for the expected value is 50–70 PgC y-1 . It seems that it may be difficult to reduce this 20% level of uncertainty in the commonly accepted estimate of terrestrial NPP while leaving research methods unchanged.
Therefore, we are focusing here on the stability of normative estimates – that is, estimates acceptable for use in policy relevant assessments. The diversity of research results does not matter until a viable alternative to the commonly accepted norms emerges. This study confirms that 60 PgC y-1 remains to be the best candidate for further use in policy relevant assessments.
The major output of this study, gridded normative data on terrestrial productivity, may find use in benchmarking NPP models employed in coupled carbon-cycle-climate models. Could the wide range of projections for the future magnitude of the terrestrial uptake be attributed to the diversity of NPP models employed? Which projections correspond to well-established beliefs, and which do not?
Recent IPCC guidelines focus on objective reporting of uncertainty stemming from climate model pluralism . However, epistemological pluralism [23, 24] is no more a topical issue in "a world that is aware of its responsibility for planetary change and will demand globally concerted actions" [25, 26]. One of the things that the world community is likely to expect from scientists is evaluating effectiveness of these actions in an objective and unambiguous manner . Hence, it seems a time for moving the focus of attention to objective reporting of well-established beliefs.
Objective reporting of well-established beliefs suggests drawing distinctions between normative knowledge (or text-book knowledge) and alternative knowledge (or frontier knowledge). The former is the solid knowledge that has stood the test of time and is well confirmed by a number of independent research studies. Frontier knowledge is something new, and something really new cannot be turned into solid knowledge immediately. Since each model may be considered as normative for some regions and as alternative for other regions, we are not drawing distinctions between models. Instead, we are sorting model outputs and doing what is called "knowledge engineering" .
The stability of well-established beliefs stems from the stability of research methods, and therefore it can be temporal. Rapid development of meteorological methods for measuring CO2 fluxes offers some benefits over traditional methods of measuring productivity. The observation network for measurements of gas, water and energy exchange between terrestrial ecosystems and the atmosphere, so-called FLUXNET , produced a large collection of data. This calls for re-calibration of existing models [30, 31], and, hypothetically, may lead to changes in our judgement on typical values of productivity. Similar effects may have measurements coming from new satellite sensors.
The evolution of scientific theories is often considered a Darwinian process of natural selection that determines which theory survives and drifts them toward consensus . The scheme of building normative data  employed in this study simulates the process of data selection by setting transparent criteria of fitness.
This algorithm works against new estimates that do not fall within the range implied by the initial ensemble: Miami NPP model, Montreal NPP model, TGER-NPP model and the outputs of the Potsdam NPP model intercomparison. Moreover, it works against new estimates that may increase uncertainty in the mean value, which is measured as the width of the confidence interval: δ = 2 c·s·n-1/2, where n is the number of estimates, s is standard deviation, c is 95th percentile of Student's t distribution with n-1 degree of freedom. All this is filtering out erroneous estimates as well as correct estimates when they are dramatically contradicting normative knowledge formed by the initial ensemble.
Since no well-agreed-upon method exists at the moment for distinguishing between erroneous and correct estimates of NPP, every estimate is included either into the normative ensemble or into the alternative ensemble. The former represents the current state of knowledge, whereas the latter represents emerging alternatives to current knowledge.
Noteworthy also are discrepancies in estimates that may result from different spatial resolution of models and/or input data. They were reduced by filtering out models of low spatial resolution.
Normative NPP, version 1.5.0
This data file (Additional file 1) was formed by averaging the outputs of the Miami NPP model, Montreal NPP model, TGER-NPP model and the outputs of the Potsdam NPP model intercomparison (PotsdamNPP).
Normative NPP, version 1.6.0
Compare the value of TsuBiMo NPP for a given cell (x,y) of the geographic grid, u(x,y) with the normative ensemble of estimates for this cell, w(x,y).
If u(x,y) > wmax(x,y) or u(x,y) < wmin(x,y), normative NPP, v(x,y), remains unchanged; otherwise go to step 3.
Append u(x,y) to w(x,y), calculate mean value, μ, of thus formed list of estimates and the width of its confidence interval, δ.
If δ is greater than the width of confidence interval for the mean value of w(x,y), v(x,y) remains unchanged, otherwise v(x,y) = μ.
NB. Exclude the numbers denoting missing values (-9999) from calculations of mean values and their confidence intervals.
Normative NPP, versions 1.7.0–1.13.0
Each of the data files (Additional files 3, 4, 5, 6, 7, 8, 9) was formed, using the algorithm described above, from the normative ensemble of estimates and outputs of the model mentioned in the data file description.
Alternative NPP, version 1.6.0
Compare the value of TsuBiMo NPP for a given cell (x,y) of the geographic grid, u(x,y) with the normative ensemble of estimates (version 1.5.0) for this cell, w(x,y).
If wmin(x,y) < u(x,y) < wmax(x,y), alternative NPP, v(x,y), remains unchanged; otherwise go to step 3.
Append u(x,y) to w(x,y), calculate mean value of thus formed list of estimates and the width of its confidence interval, δ.
If δ is less than the width of confidence interval for the mean value of w(x,y), v(x,y) remains unchanged, otherwise go to the step 5.
Append u(x,y) to the list of alternative estimates, s(x,y); calculate the mean value, μ, of thus formed list; set v(x,y) = μ.
NB. Exclude the numbers denoting missing values (-9999) from calculations of mean values and their confidence intervals.
Alternative NPP, versions 1.7.0–1.13.0
Each of the data file (Additional files 11, 12, 13, 14, 15, 16, 17) was formed, using the algorithm described above, from the alternative ensemble of estimates and outputs of the model mentioned in the data file description.
The results of this study should be interpreted in the same manner as the results of Potsdam NPP Model Intercomparison , and should not be taken out of context. For example, normative productivity of crops under specific crop management system may be either higher or lower than Normative NPP.
Appendix: a brief overview of modelling efforts
Efforts to model terrestrial productivity may be categorized into three types: (type 1) developing an empirical model interpolating and extrapolating measured NPP values, (type 2) developing an empirical model interpolating and extrapolating parameters of a process-based model of productivity using measured NPP values, (type 3) developing an empirical model interpolating and extrapolating parameters of a process-based model of productivity using measured values of this parameters.
The typical representative of the first type, which is referred to as empirical models, is the Miami NPP model. The observed gradations in the observed data are attributed to factors of known global distribution using mathematical functions that have no biological meaning. Then, these functions are used to produce a global pattern of productivity from given global patterns of mean annual temperature and precipitations.
TsuBiMo represents the second type, which is referred to as semi-empirical process-based models. The measured values of NPP are considered as indirect measurements of light-saturated rates of photosynthesis . The values of this parameter are restored from the NPP measurements using technique known as model-data fusion . Then, gradations in the light-saturated rate of photosynthesis are attributed to gradations in the temperature and precipitation during the growing season. The temperature dependence is modelled with a generalized Arrhenius function , whereas the humidity factor is modelled with a function that has no biophysical meaning.
A chronology of modelling efforts
1973 ... 1984
HRBM 3.0 
GLO-PEM updated version *,** 
TsuBiMo 1.2 
TsuBiMo 1.3 
Most process-based models require species-specific parameterization that becomes problematic on a global scale. Since the global distribution of species-specific parameters is not well known, they are normally set at some ad hoc values. The recent development of techniques for model-data fusion  opens up possibilities for transforming process-based models into semi-empirical, process-based models.
New techniques for data-fusion (such as neural networks) together with growing databases of NPP measurements offer new opportunities for empirical modelling. The NCEAS model  may be a first sign of the new boom in empirical modelling.
This work was conducted in connection to the CGER/NIES research program on forest carbon sinks modelling and databasing. The idea of this work was conceived following discussions with Pep Canadell, Takehisa Oikawa and Seita Emori on the subjects related to model development, comparison and evaluation. Galina Churkina, Steve Running, Maosheng Zhao, Takahiro Sasai, Akihito Ito, David Zaks, Ning Zeng, Jinho Yoon, Stephen Prince and Scott Goetz helped us in data acquisition. Wolfgang Knorr, Ning Zeng, Martin Heimann, David McGuire, Ian Woodward, Kazuo Mabuchi, Colin Prentice, Rik Leemans, Vladimir Krapivin, Leonid Goloubyatnikov, Alexander Tarko, Kazuhito Ichii, Nicolas Viovy, Galina Churkina and Steve Running gave advice regarding the chronology of modelling efforts. Colin Prentice, Rik Leemans, Kazuhito Ichii and Galina Churkina helped us to clean manuscript from sad misprints, omissions and logical gaps. The usage of English was corrected by Benjamin Brown.
- Imhoff ML, Bounoua L, Ricketts T, Loucks C, Harriss R, Lawrence WT: Global patterns in human consumption of net primary production. Nature 2004, 429: 870–873. 10.1038/nature02619View ArticleGoogle Scholar
- Denman KL, Brasseur G, Chidthaisong A, Ciais P, Cox PM, Dickinson RE, Hauglustaine D, Henize C, Holland E, Jacob D, Lohmann U, Ramachandran S, da Silva Dias PL, Wofsy SC, Zhang X: Couplings Between Changes in the Climate System and Biogeochemistry. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment. Edited by: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL. Cambridge: Cambridge University Press; 2007:500–587.Google Scholar
- Heimann M, Reichstein M: Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 2008, 451: 289–292. 10.1038/nature06591View ArticleGoogle Scholar
- Zeng N: Carbon sequestration via wood burial. Carbon Balance and Management 2008, 3: 1. 10.1186/1750-0680-3-1View ArticleGoogle Scholar
- Gurney K, Raymond L: Targeting deforestation rates in climate change policy: a 'Preservation Pathway' approach. Carbon Balance and Management 2008, 3: 2. 10.1186/1750-0680-3-2View ArticleGoogle Scholar
- Kindermann G, Obersteiner M, Rametsteiner E, McCallum I: Predicting the deforestation-trend under different carbon-prices. Carbon Balance and Management 2006, 1: 15. 10.1186/1750-0680-1-15View ArticleGoogle Scholar
- van Minnen J, Strengers B, Eickhout B, Swart R, Leemans R: Quantifying the effectiveness of climate change mitigation through forest plantations and carbon sequestration with an integrated land-use model. Carbon Balance and Management 2008, 3: 3. 10.1186/1750-0680-3-3View ArticleGoogle Scholar
- Alexandrov G: Carbon stock growth in a forest stand: the power of age. Carbon Balance and Management 2007, 2: 4. 10.1186/1750-0680-2-4View ArticleGoogle Scholar
- Steffen W, Noble I, Canadell J, Apps M, Schulze ED, Jarvis PG, Baldocchi D, Ciais P, Cramer W, Ehleringer J, Farquhar G, Field CB, Ghazi A, Gifford R, Heimann M, Houghton R, Kabat P, Korner C, Lambin E, Linder S, Mooney HA, Murdiyarso D, Post WM, Prentice IC, Raupach MR, Schimel DS, Shvidenko A, Valentini R: The terrestrial carbon cycle: Implications for the Kyoto Protocol. Science 1998, 280: 1393–1394. 10.1126/science.280.5368.1393View ArticleGoogle Scholar
- Cramer W, Kicklighter DW, Bondeau A, Moore B, Churkina C, Nemry B, Ruimy A, Schloss AL: Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Global Change Biology 1999, 5: 1–15. 10.1046/j.1365-2486.1999.00009.xView ArticleGoogle Scholar
- ISLSCP Initiative II, Hall FG, Collatz G, Los S, Brown de Colstoun E, Landis D:NASA. 2005. [http://islscp2.sesda.com/ISLSCP2_1/html_pages/groups/carbon/model_npp_xdeg.html]Google Scholar
- Lieth H: Modeling the primary productivity of the world. Primary Productivity of the Biosphere 1975, 237–263.View ArticleGoogle Scholar
- Esser G: Sensitivity of global carbon pools and fluxes to human and potential climatic impacts. Tellus 1987, 39B: 245–260.View ArticleGoogle Scholar
- Post WM, King AW, Wullschleger SD: Historical variations in terrestrial biospheric carbon storage. Global Biogeochemical Cycles 1997, 11: 99–109. 10.1029/96GB03942View ArticleGoogle Scholar
- Grace PR, Post WM, Hennessy K: The potential impact of climate change on Australia's soil organic carbon resources. Carbon Balance Manage 2006.,1(14):
- Friedlingstein P, Delire C, Muller JP, Gerard JC: The climate induced variation of the continental biosphere – a model simulation of the Last glacial maximum. Geophys Res Lett 1992, 19: 897–900. 10.1029/92GL00546View ArticleGoogle Scholar
- Brovkin V, Bendtsen J, Claussen M, Ganopolski A, Kubatzki C, Petoukhov V, Andreev A: Carbon cycle, vegetation and climate dynamics in the Holocene: Experiments with the CLIMBER-2 model. Global Biogeochemical Cycles 2002, 16: 1139. doi: 10.1029/2001GB001662 doi: 10.1029/2001GB001662 10.1029/2001GB001662View ArticleGoogle Scholar
- Alexandrov GA, Oikawa T, Yamagata Y: The scheme for globalization of a process-based model explaining gradations in terrestrial NPP and its application. Ecological Modelling 2002, 148: 293–306. 10.1016/S0304-3800(01)00456-2View ArticleGoogle Scholar
- Ajtay GL, Ketner P, Duvigneaud P: Terrestrial primary production and phytomass. The Global Carbon Cycle 1979, 129–181.Google Scholar
- Box EO, Dye D, Kajiwara K, Tateishi R, Bai X: Global Environmental Data Sets from the Toyota Crown Laboratory Global Engineering Research Project (1991–1994) on CD-ROM. 1994.Google Scholar
- Alexandrov GA, Oikawa T, Esser G: Estimating terrestrial NPP: what the data say and how they may be interpreted? Ecological Modelling 1999, 117: 361–369. 10.1016/S0304-3800(99)00019-8View ArticleGoogle Scholar
- Parker W: Understanding Pluralism in Climate Modeling. Foundations of Science 2006, 11: 349–368. 10.1007/s10699-005-3196-xView ArticleGoogle Scholar
- van Asselt MBA, Rotmans J: Uncertainty in integrated assessment modelling: From positivism to pluralism. Climatic Change 2002, 54: 75–105. 10.1023/A:1015783803445View ArticleGoogle Scholar
- Healy S: Epistemological pluralism and the 'politics of choice'. Futures 2003, 35: 689–701. 10.1016/S0016-3287(03)00022-3View ArticleGoogle Scholar
- Socolow R: Climate change mitigation under strong carbon constraints. Proceedings of 50th Anniversary of the Global Carbon Dioxide Record Symposium: 28–30 November 2007; Kona (Hawaii)Google Scholar
- Cicerone RJ: Climate Change: It is not just scientists. Proceedings of 50th Anniversary of the Global Carbon Dioxide Record Symposium: 28–30 November 2007; Kona (Hawaii)Google Scholar
- Alexandrov G, Heimann M, Jones C, Tans P: On 50th Anniversary of the Global Carbon Dioxide Record. Carbon Balance and Management 2007, 2: 11. 10.1186/1750-0680-2-11View ArticleGoogle Scholar
- Kendal S, Creen M: An Introduction to Knowledge Engineering. Springer; 2007.Google Scholar
- Valentini R, Baldocchi D, Olson R: FLUXNET: A challenge that is becoming reality. Global Change Newslett 1999, 37: 15–17.Google Scholar
- Friend AD, Arneth A, Kiang NY, Lomas M, Ogee J, Rodenbeckk C, Running SW, Santaren JD, Sitch S, Viovy N, Woodward FI, Zaehle S: FLUXNET and modelling the global carbon cycle. Global Change Biology 2007, 13: 610–633. 10.1111/j.1365-2486.2006.01223.xView ArticleGoogle Scholar
- Alexandrov GA, Yamagata Y, Saigusa N, Oikawa T: Re-calibrating TsuBiMo with eddy-covariance measurements at Takayama. Agricultural and Forest Meteorology 2005, 134: 135–142. 10.1016/j.agrformet.2005.08.012View ArticleGoogle Scholar
- Prentice IC, Heimann M, Sitch S: The carbon balance of the terrestrial biosphere: Ecosystem models and atmospheric observations. Ecological Applications 2000, 10: 1553–1573. 10.1890/1051-0761(2000)010[1553:TCBOTT]2.0.CO;2View ArticleGoogle Scholar
- Prentice IC: The carbon cycle and atmospheric carbon dioxide. Climate Change 2001: The Scientific Basis 2001, 183–237.Google Scholar
- Peters W, Jacobson AR, Sweeney C, Andrews AE, Conway TJ, Masarie K, Miller JB, Bruhwiler LMP, Potron G, Hirsch AI, Worthy DEJ, Werf GR, Randerson JT, Wennberg PO, Krol MC, Tans PP: An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker. Proceedings of the National Academy of Sciences 2007, 104: 18925–18930. 10.1073/pnas.0708986104View ArticleGoogle Scholar
- Canadell JG, Dickinson R, Hibbard K, Raupach M, Young O: Global Carbon Project (2003) Science Framework and Implementation. 2003, 1: 69.Google Scholar
- Alexandrov GA, Matsunaga T: Evaluating consistency of biosphere models: software tools for a web-based service. Proceedings of the iEMSs Fourth Biennial Meeting: International Congress on Environmental Modelling and Software (iEMSs 2008); Barcelona 2008. [http://www.iemss.org/iemss2008/uploads/Main/S12–02-Alexandrov_et_al-IEMSS2008.pdf]Google Scholar
- Bradie M: Epistemiology from an Evolutionary Point of View. In Conceptual Issues in Evolutionary Biology. Edited by: Sober E. MIT Press; 1994:453–476.Google Scholar
- Alexandrov G, Oikawa T: TsuBiMo: a biosphere model of the CO2-fertilization effect. Climate Research 2002, 19: 265–270. 10.3354/cr019265View ArticleGoogle Scholar
- Alexandrov GA, Chan D, Chen M, Gurney K, Higuchi K, Ito A, Jones CD, Komarov A, Mabuchi K, Matross DM, Veroustraete F, Verstraeten WW: Model-data fusion in studies of the terrestrial carbon sink. In Environmental Modelling, software and decision support. Edited by: Jakeman AJ, Voinov AA, Rizzoli AE, Chen SH. Amsterdam: Elsevier; 2009:329–344.Google Scholar
- Alexandrov GA, Yamagata Y: A peaked function for modeling temperature dependence of plant productivity. Ecological Modelling 2007, 200: 189–192. 10.1016/j.ecolmodel.2006.07.012View ArticleGoogle Scholar
- Leemans R, Cramer W: The IIASA database for mean monthly values of temperature, precipitation and cloudiness on a global terrestrial grid. The IIASA Database for Mean Monthly Values of Temperature, Precipitation and Cloudiness on a Global Terrestrial Grid 1991.Google Scholar
- NOAA-EPA Global Ecosystems Database Project: Global Ecosystem Database Version 1.0. User's Guide, Documentation, Reprints and Digital Data on CD-ROM. Boulder: USDOC/NOAA National Geophysical Data Center; 1992.Google Scholar
- Running SW: Estimating terrestrial primary productivity by combining remote sensing and ecosystem simulation. In Ecological Studies: Remote Sensing of Biosphere Functioning. Edited by: Hobbs RJ, Mooney HA. New York: Springer-Verlag; 1990:65–86.View ArticleGoogle Scholar
- Running SW, Hunt ER: Generalization of a forest ecosystem process model for other biomes, Biome-BGC, and an application for global-scale models. In Scaling Physological Processes: Leaf to Globe. Edited by: Ehleringer JR, Field CB. San Diego: Academic Press; 1993:141–158.View ArticleGoogle Scholar
- Del Grosso S, Parton W, Stohlgren T, Zheng D, Bachelet D, Prince S, Hibbard K, Olson R: Global potential net primary production predicted from vegetation class, precipitation, and temperature. Ecology 2008, 2117–2126. 10.1890/07-0850.1Google Scholar
- Krapivin VF: Mathematical model for global ecological investigations. Ecological Modelling 1993, 67: 103–127. 10.1016/0304-3800(93)90001-9View ArticleGoogle Scholar
- Tarko AM: Anthropogenic Changes of the Global Biosphere Processes. Mathematical Modeling. Moscow: Fizmatlit; 2005.Google Scholar
- Golubyatnikov LL, Denisenko EA: Modeling the Values of Net Primary Production for the Zonal Vegetation of European Russia. Biology Bulletin 2001, 28: 293–300. 10.1023/A:1016648722322View ArticleGoogle Scholar
- Bolin B: Carbon Cycle Modelling. New York: J. Wiley; 1981.Google Scholar
- Goudriaan J, Ketner P: A simulation study for the global carbon cycle, including man's impact on the biosphere. Climatic Change 1984, 6: 167–192. 10.1007/BF00144611View ArticleGoogle Scholar
- Sitch S, Huntingford C, Gedeny N, Levy PE, Lomas M, Piao SL, Betts R, Ciais P, Cox P, Friedlingstein P, Jones CD, Prentice IC, Woodward FI: Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs). Global Change Biology 2008, 14: 2015–2039. 10.1111/j.1365-2486.2008.01626.xView ArticleGoogle Scholar
- Sato H, Itoh A, Kohyama T: SEIB-DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach. Ecological Modelling 2007, 200: 279–307. 10.1016/j.ecolmodel.2006.09.006View ArticleGoogle Scholar
- OGED: Carbon Sink Archives.2008. [http://www-cger.nies.go.jp/cger-e/db/enterprise/csa/index.html]Google Scholar
- Lieth H, Box EO: Evapotranspiration and primary productivity. Publications in Climatology 1972, 25: 37–46.Google Scholar
- Uchijima Z, Seino H: Agroclimate evaluation of net primary productivity of natural vegetation (1) Chikugo model for evaluating net primary productivity. J Agr Meteorol 1985, 40: 343–352.View ArticleGoogle Scholar
- Seino H, Uchijima Z: Global distribution of net primary productivity of terrestrial vegetation. Journal of Agricultural Meteorology 1992, 48: 39–48.View ArticleGoogle Scholar
- Box EO: Estimating the seasonal carbon source-sink geography of a natural, steady-state terrestrial biosphere. J Appl Meteorol 1988, 27: 1109–1124. Publisher Full Text 10.1175/1520-0450(1988)027<1109:ETSCSS>2.0.CO;2View ArticleGoogle Scholar
- Box EO, Holben BN, Kalb V: Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux. Vegetatio 1989, 80: 71–89. 10.1007/BF00048034View ArticleGoogle Scholar
- Potter CS, Randerson JT, Field CD, Matson PA, Vitousek PM, Mooney HA, Klooster SA: Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem Cycles 1993, 7: 811–841. 10.1029/93GB02725View ArticleGoogle Scholar
- Kindermann J, Luedeke MKB, Badeck F-W, Otto RD, Klaudius A, Haeger C, Wuerth G, Lang T, Daenges S, Habermehl S, Kohlmaier GH: Structure of a global and seasonal carbon exchange model for the terrestrial biosphere the frankfurt biosphere model (FBM). Water, Air, & Soil Pollution 1993, 70: 675–684. 10.1007/BF01105029View ArticleGoogle Scholar
- Parton WJ, Scurlock JMO, Ojima DS, Gilmanov TG, Scholes RJ, Schimel DS, Kirchner T, Menaut J-C, Seastedt T, Moya EG, Kamnalrut A, Kinyamario JI: Observations and Modeling of Biomass and Soil Organic Matter Dynamics for the Grassland Biome Worldwide. Global Biogeochemical Cycles 1993, 7: 785–809. 10.1029/93GB02042View ArticleGoogle Scholar
- Melillo JM, McGuire AD, Kicklighter DW, Moore B, Vorosmarty CJ, Schloss AL: Global Climate-Change and Terrestrial Net Primary Production. Nature 1993, 363: 234–240. 10.1038/363234a0View ArticleGoogle Scholar
- Esser G, Hoffstadt J, Mack F, Wittenberg U: High Resolution Biosphere Model: Documentation. Gessen, Justus Liebig University; 1994.Google Scholar
- Warnant P, Francois L, Strivay D, Gerard JC: Caraib – A Global-Model of Terrestrial Biological Productivity. Global Biogeochemical Cycles 1994, 8: 255–270. 10.1029/94GB00850View ArticleGoogle Scholar
- Foley JA: Net Primary Productivity in the Terrestrial Biosphere – the Application of A Global-Model. Journal of Geophysical Research-Atmospheres 1994, 99: 20773–20783. 10.1029/94JD01832View ArticleGoogle Scholar
- Goldewijk K, Leemans R: The terrestrial biosphere and global change. In Carbon balance of world's forested ecosystems: towards an global assessment. Edited by: Kanninen M. Helsinki: Painatuskeskus; 1994:163–175.Google Scholar
- Prince SD, Goward S: Global net primary production: a remote sensing approach. Journal of Biogeography 1995, 22: 815–835. 10.2307/2845983View ArticleGoogle Scholar
- Knorr W, Heimann M: Impact of drought stress and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model. Tellus, Series B 1995, 47B: 471–489. 10.1034/j.1600-0889.47.issue4.7.xView ArticleGoogle Scholar
- McGuire AD, Melillo JM, Kicklighter DW, Joyce LA: Equilibrium responses of soil carbon to climate change: Empirical and process-based estimates. Journal of Biogeography 1995, 22: 785–796. 10.2307/2845980View ArticleGoogle Scholar
- Woodward FI, Smith TM, Emanuel WR: A Global Land Primary Productivity and Phytogeography Model. Global Biogeochemical Cycles 1995, 9: 471–490. 10.1029/95GB02432View ArticleGoogle Scholar
- Sellers PJ, Randall DA, Collatz GJ, Berry JA, Field CB, Dazlich DA, Zhang C, Collelo GD, Bounoua L: A revised land surface parameterization (SiB2) for atmospheric GCMs 1. Model formulation. Journal of Climate 1996, 9: 676–705. Publisher Full Text 10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2View ArticleGoogle Scholar
- Kaduk J, Heimann M: A prognostic phenology scheme for global terrestrial carbon cycle models. Climate Research 1996, 6: 1–19. 10.3354/cr006001View ArticleGoogle Scholar
- Ruimy A, Dedieu G, Saugier B: TURC: A diagnostic model of continental gross primary productivity and net primary productivity. Global Biogeochemical Cycles 1996, 10: 269–285. 10.1029/96GB00349View ArticleGoogle Scholar
- Haxeltine A, Prentice IC: BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Global Biogeochemical Cycles 1996, 10: 693–709. 10.1029/96GB02344View ArticleGoogle Scholar
- Friend AD, Stevens AK, Knox RG, Cannell MGR: A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0). Ecological Modelling 1997, 95: 249–287. 10.1016/S0304-3800(96)00034-8View ArticleGoogle Scholar
- Mabuchi K, Sato Y, Kida H: Numerical study of the relationships between climate and the carbon dioxide cycle on a regional scale. Journal of the Meteorological Society of Japan 2000, 78: 25–46.Google Scholar
- Mabuchi K, Sato Y, Kida H, Saigusa N, Oikawa T: A Biosphere-Atmosphere Interaction Model (BAIM) and its primary verifications using grassland data. Papers in Meteorology and Geophysics 1997, 47: 115–140. 10.2467/mripapers.47.115View ArticleGoogle Scholar
- Alexandrov GA, Yamagata Y, Oikawa T: Towards a model for projecting Net Ecosystem Production of the world forests. Ecological Modelling 1999, 123: 183–191. 10.1016/S0304-3800(99)00128-3View ArticleGoogle Scholar
- Goetz SJ, Prince SD, Goward SN, Thawley MM, Small J: Satellite remote sensing of primary production: an improved production efficiency modeling approach. Ecological Modelling 1999, 122: 239–255. 10.1016/S0304-3800(99)00140-4View ArticleGoogle Scholar
- Ito A, Oikawa T: A simulation model of the carbon cycle in land ecosystems (Sim-CYCLE): a description based on dry-matter production theory and plot-scale validation. Ecological Modelling 2002, 151: 143–176. 10.1016/S0304-3800(01)00473-2View ArticleGoogle Scholar
- Knorr W: Annual and interannual CO2 exchanges of the terrestrial biosphere: process-based simulations and uncertainties. Global Ecology and Biogeography 2000, 9: 225–252. 10.1046/j.1365-2699.2000.00159.xView ArticleGoogle Scholar
- Knorr W, Gobron N, Scholze M, Kaminski T, Schnur R, Pinty B: Impact of terrestrial biosphere carbon exchanges on the anomalous CO2 increase in 2002–2003. Geophysical Research Letters 2007., 34:Google Scholar
- Kaminski T, Knorr W, Rayner PJ, Heimann M: Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle. Global Biogeochemical Cycles 2002., 16:Google Scholar
- Alexandrov G, Yamagata Y: Verification of carbon sink assessment: Can we exclude natural sinks? Climatic Change 2004, 67: 437–447. 10.1007/s10584-004-2801-2View ArticleGoogle Scholar
- Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H: A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. BioScience 2004, 54: 547–560. 10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2View ArticleGoogle Scholar
- Zhao M, Heinsch FA, Nemani RR, Running SW: Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment 2005, 95: 164–176. 10.1016/j.rse.2004.12.011View ArticleGoogle Scholar
- Ito A, Oikawa T: Global Mapping of Terrestrial Primary Productivity and Light-Use Efficiency with a Process-Based Model. In Global Environmental Change in the Ocean and on Land. Edited by: Shiyomi M. Tokyo: Terrapub; 2004:343–358.Google Scholar
- Alexandrov GA, Yamagata Y: Apparent Trends in Productivity of Monsoon Asia from 1982 to 2002. EOA Trans AGU 2005., 86:Google Scholar
- Sasai T, Ichii K, Yamaguchi Y, Nemani R: 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 2005., 110:Google Scholar
- Zeng N, Mariotti A, Wetzel P: Terrestrial mechanisms of interannual CO2 variability. Global Biogeochemical Cycles 2005., 19:Google Scholar
- Zeng N: Glacial-interglacial atmospheric CO2 change – The glacial burial hypothesis. Advances in Atmospheric Sciences 2003, 20: 677–693. 10.1007/BF02915395View ArticleGoogle Scholar
- Mabuchi K, Kida H: On-line climate model simulation of the global carbon cycle and verification using the in situ observation data.2006. [http://www.iemss.org/iemss2006/papers/w16/64_MABUCHI_1.pdf]Google Scholar
- Krinner G, Viovy N, de Noblet-Ducoudre N, Ogee J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice IC: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles 2005., 19:Google Scholar
- Rayner PJ, Scholze M, Knorr W, Kaminski T, Giering R, Widmann H: Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS). Global Biogeochemical Cycles 2005., 19:Google Scholar
- Scholze M, Kaminski T, Rayner P, Knorr W, Giering R: Propagating uncertainty through prognostic carbon cycle data assimilation system simulations. Journal of Geophysical Research-Atmospheres 2007., 112:Google Scholar
- Churkina G, Trusilova K, Vetter M, Dentener F: Contributions of nitrogen deposition and forest regrowth to terrestrial carbon uptake. Carbon Balance and Management 2007, 2: 5. 10.1186/1750-0680-2-5View ArticleGoogle Scholar
- Raddatz TJ, Reick CH, Knorr W, Kattge J, Roeckner E, Schnur R, Schnitzler KG, Wetzel P, Jungclaus J: Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty-first century? Climate Dynamics 2007, 29: 565–574. 10.1007/s00382-007-0247-8View ArticleGoogle Scholar
- Zaks DPM, Ramankutty N, Barford CC, Foley JA: From Miami to Madison: Investigating the relationship between climate and terrestrial net primary production. Global Biogeochemical Cycles 2007, 21: GB3004. 10.1029/2006GB002705View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.