Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations

Matthias Forkel,Wouter Dorigo,Markus Drüke,Nuno Carvalhais,Werner Von Bloh,Martin Thurner,Sibyll Schaphoff,Kirsten Thonicke

doi:10.1038/s41598-019-55187-7

Abstract

The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations.

Highlights

The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback
We found that the bias in herbaceous cover was especially related to a parameter that controls the phenology of the tropical herbaceous plant functional types (PFTs) at high temperatures (i.e. TMAX_BASE_TrH) and www.nature.com/scientificreports to the leaf longevity and light extinction coefficient parameters of TrH as well as one parameter that controls the sun-induced fluorescence (SIF)-gross primary production (GPP) relationship in the broadleaved rain-green PFT (Supplementary Fig. S4)
Our results demonstrate that parameters for productivity, phenology and vegetation dynamics within a dynamic global vegetation models (DGVMs) can be jointly estimated from the multitude of satellite observations

Summary

Introduction

The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. We use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. We aim to explore how the combined information from satellite data on FAPAR, SIF, above-ground biomass of trees, and tree cover distribution can be used to constrain parameters of the LPJmL (version 4.0)[33] DGVM and to improve simulations of regional to global vegetation distribution and carbon turnover (Fig. 1a) Based on these satellite datasets, we compute a multivariate cost function (i.e. model-data error, see Methods) to optimize model parameters that regulate the simulated phenology, photosynthesis, vegetation carbon turnover, establishment, mortality and bioclimatic limits of plant functional types (PFTs) (see Supplementary Table S1).

Objectives

Methods

Results

Conclusion