| 1. |
- Leuzinger, Sebastian, et al.
(författare)
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A sink-limited growth model improves biomass estimation along boreal and alpine tree lines
- 2013
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Ingår i: Global Ecology and Biogeography. - HOBOKEN 07030-5774, NJ USA : John Wiley & Sons. - 1466-822X .- 1466-8238. ; 22:8, s. 924-932
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Tidskriftsartikel (refereegranskat)abstract
- Aim Despite increasing evidence for plant growth often being limited by sink (meristem) activity rather than source (photosynthesis) activity, all currently available dynamic global vegetation models (DGVMs) simulate plant growth via source-limited processes. For a given climatic region, this may lead to an overestimation of carbon stock per unit surface area, particularly if a model fails to correctly predict forest cover. Our aim is to improve the Lund-Potsdam-Jena (LPJ) DGVM by replacing the source-limited (SoL) tree growth algorithm by a sink-limited (SiL) one. Location Our analysis focuses on the cold tree line at high latitudes and altitudes. We study two altitudinal transects in the Swiss Alps and the northern tree line. Methods We limit annual net primary productivity of the LPJ DGVM by an algorithm based on the annual sum of growing degree-days (GDD), assuming that maximum plant growth is reached asymptotically with increasing GDD. Results Comparing simulation results with observational data, we show that the locations of both the northern and the alpine tree line are estimated more accurately when using a SiL algorithm than when using the commonly employed SoL algorithm. Also, simulated carbon stocks decrease in a more realistic manner towards the tree line when the SiL algorithm is used. This has far-reaching implications for estimating and projecting present and future carbon stocks in temperature-limited ecosystems. Main conclusions In the range of 60-80 degrees N over Europe and Asia, carbon stored in vegetation is estimated to be c. 50% higher in the LPJ standard version (LPJ-SoL) compared with LPJ-SiL, resulting in a global difference in estimated biomass of 25 Pg (c. 5% of the global terrestrial standing biomass). Similarly, the simulated elevation of the upper tree line in the European Alps differs by c. 400 m between the two model versions, thus implying an additional overestimation of carbon stored in mountain forests around the world.
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| 2. |
- Pappas, Christoforos, et al.
(författare)
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Sensitivity analysis of a process-based ecosystem model : Pinpointing parameterization and structural issues
- 2013
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Ingår i: Journal of Geophysical Research - Biogeosciences. - : American Geophysical Union. - 2169-8953 .- 2169-8961. ; 118:2, s. 505-528
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Tidskriftsartikel (refereegranskat)abstract
- Dynamic vegetation models have been widely used for analyzing ecosystem dynamics and their interactions with climate. Their performance has been tested extensively against observations and by model intercomparison studies. In the present analysis, Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), a state-of-the-art ecosystem model, was evaluated by performing a global sensitivity analysis. The study aims at examining potential model limitations, particularly with regard to long-term applications. A detailed sensitivity analysis based on variance decomposition is presented to investigate structural model assumptions and to highlight processes and parameters that cause the highest variability in the output. First- and total-order sensitivity indices were calculated for selected parameters using Sobol's methodology. In order to elucidate the role of climate on model sensitivity, different climate forcings were used based on observations from Switzerland. The results clearly indicate a very high sensitivity of LPJ-GUESS to photosynthetic parameters. Intrinsic quantum efficiency alone is able to explain about 60% of the variability in vegetation carbon fluxes and pools for a wide range of climate forcings. Processes related to light harvesting were also found to be important together with parameters affecting forest structure (growth, establishment, and mortality). The model shows minor sensitivity to hydrological and soil texture parameters, questioning its skills in representing spatial vegetation heterogeneity at regional or watershed scales. In the light of these results, we discuss the deficiencies of LPJ-GUESS and possibly that of other, structurally similar, dynamic vegetation models and we highlight potential directions for further model improvements.
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| 3. |
- Reyer, Christopher P. O., et al.
(författare)
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A plant's perspective of extremes : terrestrial plant responses to changing climatic variability
- 2013
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Ingår i: Global Change Biology. - HOBOKEN 07030-5774, NJ USA : John Wiley & Sons. - 1354-1013 .- 1365-2486. ; 19:1, s. 75-89
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Forskningsöversikt (refereegranskat)abstract
- We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches.
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