Attribution of seasonal leaf area index trends in the northern latitudes with “optimally” integrated ecosystem models

Abstract

Significant increases in remotely sensed vegetation indices in the northern latitudes since the 1980s have been detected and attributed at annual and growing season scales. However, we presently lack a systematic understanding of how vegetation responds to asymmetric seasonal environmental changes. In this study, we first investigated trends in the seasonal mean leaf area index (LAI) at northern latitudes (north of 30°N) between 1982 and 2009 using three remotely sensed long-term LAI data sets. The most significant LAI increases occurred in summer (0.009 m m year , p < .01), followed by autumn (0.005 m m year , p < .01) and spring (0.003 m m year , p < .01). We then quantified the contribution of elevating atmospheric CO concentration (eCO ), climate change, nitrogen deposition, and land cover change to seasonal LAI increases based on factorial simulations from 10 state-of-the-art ecosystem models. Unlike previous studies that used multimodel ensemble mean (MME), we used the Bayesian model averaging (BMA) to optimize the integration of model ensemble. The optimally integrated ensemble LAI changes are significantly closer to the observed seasonal LAI changes than the traditional MME results. The BMA factorial simulations suggest that eCO provides the greatest contribution to increasing LAI trends in all seasons (0.003-0.007 m m year ), and is the main factor driving asymmetric seasonal LAI trends. Climate change controls the spatial pattern of seasonal LAI trends and dominates the increase in seasonal LAI in the northern high latitudes. The effects of nitrogen deposition and land use change are relatively small in all seasons (around 0.0002 m m year and 0.0001-0.001 m m year , respectively). Our analysis of the seasonal LAI responses to the interactions between seasonal changes in environmental factors offers a new perspective on the response of global vegetation to environmental changes.

Satellite Leaf Area Index: Global Scale Analysis of the Tendencies Per Vegetation Type Over the Last 17 Years

“…Zhu, Z. et al [66] used ten ecosystem models and suggested that the positive trends during summer, autumn and spring in the Northern latitudes (> 30 • N) are mainly due to elevating atmospheric CO 2 concentration. The effects of nitrogen deposition and land use change would be relatively small.…”

Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region

“…There are large model‐to‐model differences in both the direction and magnitude of the effects induced by climate forcing and land‐use change. Furthermore, the trends in GPP driven by climate forcing, atmospheric CO 2 , land‐use change and nitrogen deposition were computed by using segmented linear regression breaking at the sixtieth (Zhu et al, ). The model factorial simulations suggested that the dominant environmental drivers were consistent during 1901–2010 (Figure b).…”

“…are often used to evaluate the models. However, a relatively large discrepancy among these long-term LAI products has also been reported in some recent studies (Xiao et al, 2017;Zhu et al, 2017). Therefore, the uncertainty of LAI data itself should be considered when evaluating model performances.…”

Global vegetation variability and its response to elevated CO&lt;sub&gt;2&lt;/sub&gt;, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data

“…In addition, large discrepancies between models were found when predicting ecosystem variability and trends Zhu et al, 2017). Unsurprisingly, the dominant factors obtained from different models are often significantly different (Beer et al, 2010;Huntzinger et al, 2017).…”

“…Ichii et al, 2013;Piao et al, 2013;Zhang et al, 2015). However, performance in predicting LAI and GPP trends and variability has been found to vary among models (Murray-Tortarolo et al, 2013;Piao et al, 2013;Anav et al, 2015;Zhu et al, 2017). To better assess model performance in this regard, we selected 13 sub-regions associated with 5 different regional climate and land cover conditions ( Table 1) This improvement may be due to SSiB4/TRIFFID better capturing the interannual variability in semi-arid areas which dominate interannual variability (Ahlstrom et al, 2015).…”