Nonlinear instability and sensitivity of a theoretical grassland ecosystem to finite-amplitude perturbations

Mu, M.; Wang, Biao

doi:10.5194/npg-14-409-2007

Cited by 24 publications

(10 citation statements)

References 23 publications

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“…Their variations may be the important factors causing abrupt change of the grassland equilibrium state. The findings also validate the conclusions about the shading effects of the wilted biomass on the grassland equilibrium state proposed by Mu and Wang (2007).…”

Section: Further Analysessupporting

confidence: 77%

Nonlinearly combined impacts of initial perturbation from human activities and parameter perturbation from climate change on the grassland ecosystem

Sun

2011

Nonlin. Processes Geophys.

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Abstract. Human activities and climate change are important factors that affect grassland ecosystems. A new optimization approach, the approach of conditional nonlinear optimal perturbation (CNOP) related to initial and parameter perturbations, is employed to explore the nonlinearly combined impacts of human activities and climate change on a grassland ecosystem using a theoretical grassland model. In our study, it is assumed that the initial perturbations and parameter perturbations are regarded as human activities and climate change, respectively. Numerical results indicate that the climate changes causing the maximum effect in the grassland ecosystem are different under disparate intensities of human activities. This implies the pattern of climate change is very critical to the maintenance or degradation of grassland ecosystem in light of high intensity of human activities and that the grassland ecosystem should be rationally managed when the moisture index decreases. The grassland ecosystem influenced by the nonlinear combination of human activities and climate change undergoes abrupt change, while the grassland ecosystem affected by other types of human activities and climate change fails to show the abrupt change under a certain range of perturbations with the theoretical model. The further numerical analyses also indicate that the growth of living biomass and the evaporation from soil surface shaded by the wilted biomass may be crucial factors contributing to the abrupt change of the grassland equilibrium state within the theoretical model.

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Section: Further Analysessupporting

confidence: 77%

Nonlinearly combined impacts of initial perturbation from human activities and parameter perturbation from climate change on the grassland ecosystem

Sun

2011

Nonlin. Processes Geophys.

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“…Then what is the effect of nonlinearity on the sensitivity of grassland ecosystem? This question motivated the work of Mu and Wang [58] .…”

Section: Applications Of Cnop In Sensitivity Analysis Of Ecosystemmentioning

confidence: 97%

“…Recently, Mu and Wang [58] has further extended the application of CNOP to the sensitivity analysis of grassland ecosystem, in attempt to reveal the effect of nonlinearity on the transition between grassland and desert states.…”

Section: Applications Of Cnop In Sensitivity Analysis Of Ecosystemmentioning

confidence: 99%

“…Mu and Wang [58] used this three-variable theoretical model to attack the nonlinear instability and sensitivity of ecosystem. The results showed that the moisture index μ plays an essential role in the grassland ecosystem illustrated by the adopted model.…”

Section: Applications Of Cnop In Sensitivity Analysis Of Ecosystemmentioning

confidence: 99%

“…To explore the nonlinear feature of the ecosystem, Mu and Wang [58] also calculated the LSVs of GESs and DESs. Comparisons between their nonlinear evolutions demonstrated that for the same magnitude of CNOP and LSV, CNOPs tend to be more likely to yield a transition than LSVs do ( Figure 14).…”

Section: Applications Of Cnop In Sensitivity Analysis Of Ecosystemmentioning

confidence: 99%

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Conditional nonlinear optimal perturbation: Applications to stability, sensitivity, and predictability

Duan

2009

Sci. China Ser. D-Earth Sci.

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Conditional nonlinear optimal perturbation (CNOP) is a nonlinear generalization of linear singular vector (LSV) and features the largest nonlinear evolution at prediction time for the initial perturbations in a given constraint. It was proposed initially for predicting the limitation of predictability of weather or climate. Then CNOP has been applied to the studies of the problems related to predictability for weather and climate. In this paper, we focus on reviewing the recent advances of CNOP's applications， which involves the ones of CNOP in problems of ENSO amplitude asymmetry, block onset, and the sensitivity analysis of ecosystem and ocean's circulations, etc. Especially, CNOP has been primarily used to construct the initial perturbation fields of ensemble forecasting, and to determine the sensitive area of target observation for precipitations. These works extend CNOP's applications to investigating the nonlinear dynamical behaviors of atmospheric or oceanic systems, even a coupled system, and studying the problem of the transition between the equilibrium states. These contributions not only attack the particular physical problems, but also show the superiority of CNOP to LSV in revealing the effect of nonlinear physical processes. Consequently, CNOP represents the optimal precursors for a weather or climate event; in predictability studies, CNOP stands for the initial error that has the largest negative effect on prediction; and in sensitivity analysis, CNOP is the most unstable (sensitive) mode. In multi-equilibrium state regime, CNOP is the initial perturbation that induces the transition between equilibriums most probably. Furthermore, CNOP has been used to construct ensemble perturbation fields in ensemble forecast studies and to identify sensitive area of target observation. CNOP theory has become more and more substantial. It is expected that CNOP also serves to improve the predictability of the realistic predictions for weather and climate events plays an increasingly important role in exploring the nonlinear dynamics of atmospheric, oceanic and coupled atmosphere-ocean system. optimal perturbation, predictability, stability, sensitivityOne of the central problems in atmospheric and oceanic sciences is the predictability for weather and climate, in which estimating the prediction uncertainty is very important. Tennekes [1] proclaimed that no forecast was complete without an estimate of the prediction error. This perspective can be traced back to Thompson [2] . Since then, operational weather forecasting has progressed to the point of explicit attempts to quantifying the evolution of initial uncertainty during each forecast [3][4][5] , which, furthermore, has been permeated through coupled model forecasts. Some understandings of the predictability of the tropical ocean-atmosphere system have been gained by studying the growth of errors and uncertainties during forecasts [6][7][8][9][10] .Despite the consensus on the best approach for predicting large dynamical systems like the Earth's atmos-

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A flexible method to determine the sensitive physical parameter combination for soil carbon under five plant types

Sun

2017

Ecosphere

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Citation: Sun, G., and M. Mu. 2017. A flexible method to determine the sensitive physical parameter combination for soil carbon under five plant types. Ecosphere 8(8):e01920. 10. 1002/ecs2.1920 Abstract. Model parameter errors are a critical source of uncertainty in simulations of soil carbon. It is important to determine which parameter errors should be reduced to improve the simulation ability of soil carbon. It is essential to determine the most sensitive parameter combination. In this study, a new flexible approach is employed to identify the most sensitive parameter combination based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) using the Lund-Potsdam-Jena model. The CNOP-P is a type of parameter error that triggers the upper bound of uncertainty in a numerical simulation and prediction, for which a nonlinear effect can be reported. To compare the sensitivity of parameter combinations, the one-at-a-time (OAT) approach is also applied to determine the sensitivity of each parameter. Using the CNOP-P approach, parameter errors can really cause large simulation errors for soil carbon throughout an entire study region (2674.69 g C/m 2 ). Results show the most sensitive parameter combinations of soil carbon are different for five plant functional types (PFTs). For all cases, f air (fraction of decomposed litter emitted as CO 2 to the atmosphere) and f inter (fraction of soil-bound decomposed litter entering intermediate soil carbon pool) comprise the most sensitive parameter combination for boreal needle-leaved evergreen trees, boreal needle-leaved summergreen trees, temperate broadleaved summergreen trees, temperate broadleaved evergreen trees, and C3 perennial grass. For C3 perennial grass under semi-arid conditions, the hydrology process is also critical in the uncertainty of simulated soil carbon. The most sensitive parameter combination using the new flexible approach is different to that of the top rank of sensitivity of each parameter when employing the CNOP-P and OAT methods, which implies that a nonlinear effect of the parameter combination is crucial for determining the sensitive parameter combination. The sensitivity of the parameter combination is dependent on the PFTs and climate condition. Among factors causing the uncertainty of simulated soil carbon, the fast-decomposing soil carbon pool plays the main contribution. It is considered that the physical process involved in the fast-decomposing soil carbon pool needs to be improved as a priority compared to other physical processes to enhance the simulation ability of soil carbon.

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Nonlinear instability and sensitivity of a theoretical grassland ecosystem to finite-amplitude perturbations

Cited by 24 publications

References 23 publications

Nonlinearly combined impacts of initial perturbation from human activities and parameter perturbation from climate change on the grassland ecosystem

Nonlinearly combined impacts of initial perturbation from human activities and parameter perturbation from climate change on the grassland ecosystem

Conditional nonlinear optimal perturbation: Applications to stability, sensitivity, and predictability

A flexible method to determine the sensitive physical parameter combination for soil carbon under five plant types

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