Integrated remote sensing and model approach for impact assessment of future climate change on the carbon budget of global forest ecosystems

Highlights

• We quantitatively evaluated the role of global forest in terrestrial carbon cycle.

• We explored the spatial-temporal dynamics of future global forest carbon budget.

• We simulated the responses of global forest carbon budget to future climate change.

• Individual-based carbon budget model FORCCHN and remote sensing outputs were used.

Abstract

At present, global warming is an indisputable fact, and more and more attention has been paid to the impacts of climate warming on global ecological environments. Forests play increasing significant roles in regulating global carbon balance and mitigating climate change. Therefore, to understand the response mechanisms of the carbon budget of global forest ecosystems to future climate change, an improved version of the FORest ecosystem Carbon budget model for CHiNa (FORCCHN) and future Representative Concentration Pathway (RCP) scenario RCP4.5 and RCP8.5 were applied in this study. The results demonstrated that the global forest ecosystems will play a major role in the carbon sink under the future two climate change scenarios. In particular, the average carbon budget of global forest ecosystems under RCP4.5 scenario was estimated to be 0.017 kg(C)·m⁻²·yr⁻¹ from 2007 to 2100. The future carbon sink areas of global forest ecosystems will increase significantly. Under RCP4.5 and RCP8.5 climate scenarios, the carbon sink areas of global forest ecosystems during 2026–2100 would be significantly been expanded than those in 2007–2025, with increases of 83.16–87.26% and 23.53–29.70%, respectively. The impacts of future climate change on carbon budget of global forest ecosystems will significantly vary between different regions. The carbon budget of forests will be enhanced in the northern hemisphere and significantly weakened in the southern hemisphere under the future two climate change scenarios. The carbon sink regions of global forests will be mainly distributed in the middle and high latitudes of the northern hemisphere. In particular, the forests' carbon budget in northeastern and central Asia, northern Europe and western North America will increase by 40% ~ 80%. However, the carbon budget of forests will decrease by 20% ~ 40% in the most regions of the southern hemisphere. In northern South America and central Africa, the forests' carbon budget will be reduced by more than 40%. In the future, in some areas of southern hemisphere, where the forests' carbon budget was predicted to be reduced, some measures for improving forest carbon sink, such as strengthening forest tending, enforcing prohibiting deforestation laws and scientific forest management, and so on, should be implemented to ensure immediate mitigation and adaptation to climate change.

Introduction

As one of the main manifestations of global change, climate warming effect on global terrestrial carbon cycles, and this effect has important guiding significance for the development of accurate understanding of the carbon cycle process and related policies (IPCC, 2014; Baldocchi and Penuelas, 2019; Cook-Patton et al., 2020). The carbon budget, that is the net ecosystem productivity (NEP), was first proposed by Woodwell et al. (1978) when analysing the sources and sinks of the terrestrial biosphere. It is used to express the net storage of carbon in large-scale ecosystems, and is the difference between the net primary productivity of vegetation and the heterotrophic respiration of soil. On a global scale, the NEP can indicate the carbon dioxide exchange between the terrestrial ecosystem and atmospheric system. As the main component of terrestrial ecosystems, forests have important roles in the global carbon cycle and are valued globally for the services they provided to society (Pan et al., 2011; Zhao et al., 2020), slowing down the increases in the contents of CO₂ and other greenhouse gases in the atmosphere, and maintaining the global climate. Photosynthesis and respiration lead to the exchange of substantial amounts of carbon with the atmosphere, and approximately 50% of the global terrestrial carbon is stored in forest ecosystems (Watson et al., 2000; Rajashekara et al., 2018). Therefore, with global climate change becoming increasingly significant, the assessment on carbon budget of forest ecosystems has also attracted attentions from the scientific and social communities (IPCC, 2013; Korkanç, 2014; Cao et al., 2018; Zhao et al., 2019; Cook-Patton et al., 2020).

Methods of assessing the forests' carbon budget mainly include forest inventory, vorticity related flux observation, isotope tracing, and model simulation (Ma et al., 2017; Zhao et al., 2019). Changes in the forests' carbon budget span seasons, years, and even decades, and vary spatially according to the regional, environmental, and climatic conditions and the local vegetation types. Therefore, traditional sampling, fixed-point observations, and other methods of researching dynamic changes in the carbon fluxes of large-scale (regional or global) forest ecosystems are often affected and limited by survey methods, number of observation stations and findings. With developments of remote sensing, geographic information systems and computer technology, the carbon cycle model describing the process of ecosystem carbon cycle and its relationship with global change is being developed rapidly and has become an important and irreplaceable method in the researches of forests' carbon budget with great prospects.

In recent years, with the occurrence of global change, model simulators in China and overseas have conducted a large number of meaningful researches on forests' NEP and their responses to global change and obtained useful conclusions (Zhao et al., 2012; Wang, 2014; Batjes, 2016；Devaraju et al., 2016; Sun and Mu, 2017; Sun and Mu, 2018; Zhao et al., 2019；Zhao et al., 2020; Fang et al., 2020). For example, Wang (2014) used three different vegetation models and climate change prediction data to evaluate the impact of increases in temperature and CO₂ on ecosystem productivity in China; and they found that they positively affected vegetation productivity. Devaraju et al. (2016) analysed the simulation results from CESM model and found that the effects of CO₂ fertilisation, climatic warming and nitrogen deposition during 1850–2005 increased the net primary productivity (NPP), with increases of 2.3 Pg(C)·yr⁻¹, 0.35 Pg(C)·yr⁻¹, and 2.0 Pg(C)·yr⁻¹, respectively. However, owing to the poor understanding of various processes in forest ecosystems and the limitations of some methods and technologies used on the global scale, these ecosystem carbon cycle models have many key problems in simulating the carbon cycle of global forest ecosystems, which still need to be solved in model structure, parameters, boundary field and initial field. For this purpose, an individual tree species FORCCHN model has been established, which can replace the growth table model based on the growth process by the photosynthesis and respiration model based on the physiological mechanism (Yan and Zhao, 2007). The FORCCHN model can flexibly use the inventory data as the initial field (more accurately), or use the remote sensing information to inverse initial field. Thus, it can be used to estimate the carbon budget of global forest ecosystems in the future changing environment, significantly enhancing the estimation ability of future forests' carbon budget.

In previous study, we have investigated the spatial–temporal dynamics of future carbon fixed in forest vegetation and soil (Zhao et al., 2020). However, there has been little robust research on the comprehensive impacts of future climate change on the carbon budget of global forest ecosystems up to now. In this study, we continued to explore the spatial-temporal dynamics of carbon budget of global forest ecosystems under future climate change scenarios based on the improved FORCCHN model and remote sensing, and predict the responses of the carbon budget of global forest ecosystems to future climate change using long-term datasets based on the results from Zhao et al. (2020).