Limits...
Normative productivity of the global vegetation.

Alexandrov GA, Matsunaga T - Carbon Balance Manag (2008)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Office for Global Environmental Database, Center for Global Environmental Research, National Institute for Environmental Studies, Onogawa 16-2, Tsukuba, Japan. g.alexandrov@nies.go.jp.

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

No MeSH data available.


Related in: MedlinePlus

Normative NPP (version 1.13.0) of major vegetation zones plotted against mean annual temperature (left pane) and annual precipitation (right pane). Points mark mean values, ellipses delineate standard deviations from the mean values, and lines represent temperature curve and humidity curve of the Miami NPP model, respectively. Legend: 42 – tundra, 14 – larch forests, 36 – needle-leaf forests, 13 – summer-green broad-leaved forests, 4 – evergreen broad-leaved forests, 8 – tropical rainforests, 6 – deserts, 27 – semi-desert scrubs, 7 – shrublands, 15 – grasslands, 10 – subhumid woodlands, 3 – raingreen forests.
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Figure 1: Normative NPP (version 1.13.0) of major vegetation zones plotted against mean annual temperature (left pane) and annual precipitation (right pane). Points mark mean values, ellipses delineate standard deviations from the mean values, and lines represent temperature curve and humidity curve of the Miami NPP model, respectively. Legend: 42 – tundra, 14 – larch forests, 36 – needle-leaf forests, 13 – summer-green broad-leaved forests, 4 – evergreen broad-leaved forests, 8 – tropical rainforests, 6 – deserts, 27 – semi-desert scrubs, 7 – shrublands, 15 – grasslands, 10 – subhumid woodlands, 3 – raingreen forests.

Mentions: Most of sub-totals, in fact, fit well the "long-living" Miami NPP model (Figure 1). The Miami NPP model [12] is still used as a benchmark for NPP models and in global carbon cycle modelling [13-17]. Relating biome productivity to the mean annual temperature, this model implicitly presumes a certain correlation between the climatic conditions of the growing season and those of the whole year. Therefore, it may underestimate or overestimate productivity wherein the presumed correlation breaks down. For example, tundra (42) and the vegetation zone of larch forests (14) are equally cold in terms of mean annual temperature (Figure 2), but summer is warmer in the vegetation zone of larch forests. Therefore, process-based models, which are more sensitive to the seasonality of climatic conditions, normally estimate the productivity of larch forests to be higher than that of tundra. Similarly, they give higher estimate for the vegetation zone of needle-leaf evergreen forests (36). The lower estimate for tropical rainforests (8) may manifest the sensitivity of process-based models to limiting factors other than heat and water supply (e.g., nitrogen limitation).


Normative productivity of the global vegetation.

Alexandrov GA, Matsunaga T - Carbon Balance Manag (2008)

Normative NPP (version 1.13.0) of major vegetation zones plotted against mean annual temperature (left pane) and annual precipitation (right pane). Points mark mean values, ellipses delineate standard deviations from the mean values, and lines represent temperature curve and humidity curve of the Miami NPP model, respectively. Legend: 42 – tundra, 14 – larch forests, 36 – needle-leaf forests, 13 – summer-green broad-leaved forests, 4 – evergreen broad-leaved forests, 8 – tropical rainforests, 6 – deserts, 27 – semi-desert scrubs, 7 – shrublands, 15 – grasslands, 10 – subhumid woodlands, 3 – raingreen forests.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654439&req=5

Figure 1: Normative NPP (version 1.13.0) of major vegetation zones plotted against mean annual temperature (left pane) and annual precipitation (right pane). Points mark mean values, ellipses delineate standard deviations from the mean values, and lines represent temperature curve and humidity curve of the Miami NPP model, respectively. Legend: 42 – tundra, 14 – larch forests, 36 – needle-leaf forests, 13 – summer-green broad-leaved forests, 4 – evergreen broad-leaved forests, 8 – tropical rainforests, 6 – deserts, 27 – semi-desert scrubs, 7 – shrublands, 15 – grasslands, 10 – subhumid woodlands, 3 – raingreen forests.
Mentions: Most of sub-totals, in fact, fit well the "long-living" Miami NPP model (Figure 1). The Miami NPP model [12] is still used as a benchmark for NPP models and in global carbon cycle modelling [13-17]. Relating biome productivity to the mean annual temperature, this model implicitly presumes a certain correlation between the climatic conditions of the growing season and those of the whole year. Therefore, it may underestimate or overestimate productivity wherein the presumed correlation breaks down. For example, tundra (42) and the vegetation zone of larch forests (14) are equally cold in terms of mean annual temperature (Figure 2), but summer is warmer in the vegetation zone of larch forests. Therefore, process-based models, which are more sensitive to the seasonality of climatic conditions, normally estimate the productivity of larch forests to be higher than that of tundra. Similarly, they give higher estimate for the vegetation zone of needle-leaf evergreen forests (36). The lower estimate for tropical rainforests (8) may manifest the sensitivity of process-based models to limiting factors other than heat and water supply (e.g., nitrogen limitation).

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Office for Global Environmental Database, Center for Global Environmental Research, National Institute for Environmental Studies, Onogawa 16-2, Tsukuba, Japan. g.alexandrov@nies.go.jp.

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

No MeSH data available.


Related in: MedlinePlus