Evaluation of the New Dynamic Global Vegetation Model in CAS-ESM

Zhu, Jiang; Zeng, Xiaodong; Zhang, Minghua; Dai, Yongjiu; Ji, Duoying; Li, Fang; Zhang, Qian; Zhang, He; Song, Xiaolan

doi:10.1007/s00376-017-7154-7

Cited by 23 publications

(13 citation statements)

References 43 publications

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“…Accordingly, DGVM predict dominance by trees in the most productive regions, by grasses in less productive regions, and by shrubs in the least productive non-desert regions (Zeng et al, 2008). The underrepresentation of C3 grasses by DGVM across the 20 study plots in our study accords with the results of Zhu et al (2018), who found that C3 grasses are underpredicted on a global level in an earlier version of DGVM.…”

Section: Dgvm Performancesupporting

confidence: 88%

“…Remote sensing (RS) is often used for evaluation, benchmarking and improvement of parameters in of DGVMs (Zhu et al, 2018). RS products are commonly used to describe vegetation cover using vegetation classes derived from multispectral images based on vegetation indices such as the normalized difference vegetation index (NDVI) (Xie et al, 2008;Franklin and Wulder, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models

Horvath

Tang

Halvorsen

et al. 2020

Preprint

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Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

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Section: Dgvm Performancesupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models

Horvath

Tang

Halvorsen

et al. 2020

Preprint

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“…To examine the overall pattern across the study area and to assess the models' ability to produce overall predictions of PFTs that accord with the PFTs' overall frequency (as given by the reference), aggregated PFT profiles obtained by each of the DGVM, RS and DM methods were compared with the aggregated PFT profile of the AR reference dataset by a chi-square test (Zuur et al, 2007). To identify strongly deviating modelling results at a plot scale, the dissimilarity between PFTs profiles obtained by each of the DGVM, RS and DM methods and the PFT profile of the AR dataset for each plot was calculated by using proportional dissimilarity (Czekanowski, 1909):…”

Section: Comparison Of Pft Profilesmentioning

confidence: 99%

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

et al. 2021

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Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

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“…To represent the vegetation dynamics at the global scale, the dynamic global vegetation models (DGVMs) have been developed [101]. Although these DGVMs are able to roughly simulate the distribution of global vegetation types and the equilibrium of ecosystems, it is hard for them to predict the evolution of terrestrial vegetation under the current global climate change situation [102,103]. Therefore, statistical climate predictions based on satellite-based vegetation data, applying local weather and climate predictors, have been developed [40][41][42].…”

Section: Discussionmentioning

confidence: 99%

Climate Prediction of Satellite-Based Spring Eurasian Vegetation Index (NDVI) using Coupled Singular Value Decomposition (SVD) Patterns

Fan

2019

Remote Sensing

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Satellite-based normalized difference vegetation index (NDVI) data are widely used for estimating vegetation greenness. Seasonal climate predictions of spring (April–May–June) NDVI over Eurasia are explored by applying the year-to-year increment approach. The prediction models were developed based on the coupled modes of singular value decomposition (SVD) analyses between Eurasian NDVI and climate factors. One synchronous predictor, the spring surface air temperature from the NCEP’s Climate Forecast System (SAT-CFS), and three previous-season predictors (winter (December–January–February) sea-ice cover over the Barents Sea (SICBS), winter sea surface temperature over the equatorial Pacific (SSTP), and winter North Atlantic Oscillation (NAO) were chosen to develop four single-predictor schemes: the SAT-CFS scheme, SICBS scheme, SSTP scheme, and NAO scheme. Meanwhile, a statistical scheme that involves the three previous-season predictors (i.e., SICBS, SSTP, and NAO) and a hybrid scheme that includes all four predictors are also proposed. To evaluate the prediction skills of the schemes, one-year-out cross-validation and independent hindcast results are analyzed, revealing the hybrid scheme as having the best prediction skill. The results indicate that the temporal correlation coefficients at 92% of grid points over Eurasia are significant at the 5% significance level in the hybrid scheme, which is the best among all the schemes. Furthermore, spatial correlation coefficients (SCCs) of the six schemes are significant at the 1% significance level in most years during 1983–2015, with the averaged SCC of the hybrid scheme being the highest (0.60). The grid-averaged root-mean-square-error of the hybrid scheme is 0.04. By comparing the satellite-based NDVI value with the independent hindcast results during 2010–2015, it can be concluded that the hybrid scheme shows high prediction skill in terms of both the spatial pattern and the temporal variability of spring Eurasian NDVI.

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Evaluation of the New Dynamic Global Vegetation Model in CAS-ESM

Cited by 23 publications

References 43 publications

Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models

Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Climate Prediction of Satellite-Based Spring Eurasian Vegetation Index (NDVI) using Coupled Singular Value Decomposition (SVD) Patterns

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