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

Abstract

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.

Variability in frost occurrence under climate change and consequent risk of damage to trees of western Quebec, Canada

“…Still, sustainable forest management should consider the potential negative impacts of late frosts on trees, even under climate warming. Importantly, the risk of frost exposure we calculated over the latitudinal transect is theoretical and aimed to show the wide variation in expected risk of frost exposure that is currently unaccounted for in dynamic global vegetation models 14 , 15 .…”

“…In addition to the direct damage to plants, leafless trees impact surface albedo. Therefore, the plant functional types used in dynamic vegetation models to group plant species bearing similar eco-physiological characteristics such as leaf longevity, photosynthetic pathway, deciduous or evergreen and herbaceous or woody 14 , 15 should integrate leaf phenology and the impact of frost on trees. This may improve the accurateness of Earth system models (ESMs) in predicting the response of ecosystems to climate change.…”

Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice

“…Our ambition is that this synthesis will be useful for both modelers and end users of distribution modeling results, such as planning authorities, nature managers and industry stakeholders. The focus of this synthesis is limited to correlative distribution models and will neither address dynamic (e.g., Horvath et al, 2021) nor mechanistic (e.g., Dormann et al, 2012), i.e., process-based, models.…”

“…When specific ecological hypotheses are to be tested, deductive studies are recommended. Linking the correlative approach of distribution modeling to mechanistic and dynamic modeling (Dormann et al, 2012), may improve both methods and increases the credibility if they support the same ecological story (Horvath et al, 2021).…”

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes

“…Several recent studies have started the task of improving these hard‐coded thresholds, from different perspectives (e.g. Horvath et al, 2021; Liu et al, 2018a). However, the data sources vary in resolution and quality, and only average climate thresholds are often employed in model test beds.…”