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Forestation can contribute to climate change mitigation. However, increasing frequency and intensity of climate extremes are posed to have profound impact on forests and consequently on the mitigation potential of forestation efforts. In this perspective, we critically assess forestation-reliant climate mitigation scenarios from five different integrated assessment models (IAMs) by showcasing the spatially explicit exposure of forests to fire weather and the simulated increase in global annual burned area. We provide a detailed description of the feedback from climate change to forest carbon uptake in IAMs. Few IAMs are currently accounting for feedback mechanisms like loss from fire disturbance. Consequently, many forestation areas proposed by IAM scenarios will be exposed to fire-promoting weather conditions and without costly prevention measures might be object to frequent burning. We conclude that the actual climate mitigation portfolio in IAM scenarios is subject to substantial uncertainty and that the risk of overly optimistic estimates of negative emission potential of forestation should be avoided. As a way forward we propose how to integrate more detailed climate information when modeling climate mitigation pathways heavily relying on forestation.

Out of 1150 Mha (million hectares) of forest designated primarily for production purposes in 2020, plantations accounted for 11 % (131 Mha) of this area and fulfilled more than 33 % of the global industrial roundwood demand. However, adding additional timber plantations to meet increasing timber demand intensifies competition for scarce land resources between different land uses such as food, feed, livestock and timber production. Despite the significance of plantations with respect to roundwood production, their importance in meeting the long-term timber demand and the implications of plantation expansion for overall land-use dynamics have not been studied in detail, in particular regarding the competition for land between agriculture and forestry in existing land-use models. This paper describes the extension of the modular, open-source land system Model of Agricultural Production and its Impact on the Environment (MAgPIE) using a detailed representation of forest land, timber production and timber demand dynamics. These extensions allow for a better understanding of the land-use dynamics (including competition for land) and the associated land-use change emissions of timber production. We show that the spatial cropland patterns differ when timber production is accounted for, indicating that timber plantations compete with cropland for the same scarce land resources. When plantations are established on cropland, it causes cropland expansion and deforestation elsewhere. Using the exogenous extrapolation of historical roundwood production from plantations, future timber demand and plantation rotation lengths, we model the future spatial expansion of forest plantations. As a result of increasing timber demand, we show a 177 % increase in plantation area by the end of the century (+171 Mha in 1995–2100). We also observe (in our model results) that the increasing demand for timber amplifies the scarcity of land, which is indicated by shifting agricultural land-use patterns and increasing yields from cropland compared with a case without forestry. Through the inclusion of new forest plantation and natural forest dynamics, our estimates of land-related CO2 emissions better match with observed data, in particular the gross land-use change emissions and carbon uptake (via regrowth), reflecting higher deforestation with the expansion of managed land and timber production as well as higher regrowth in natural forests and plantations.

Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

Most scenarios from integrated assessment models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieve negative emissions (together with CCS – carbon capture and storage). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random-forest (RF) algorithm is used to upscale a bioenergy yield dataset of 3963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0.5∘×0.5∘ spatial resolution. We also provide a combined “best bioenergy crop” yield map by selecting one of the five crop types with the highest yield in each of the grid cells, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha−1 yr−1 (DM – dry matter). High yields mainly occur in the Amazon region and southeastern Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are > 50 % higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models or to identify the most suitable lands for future bioenergy crop plantations. The 0.5∘×0.5∘ global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from this study can be download from https://doi.org/10.5281/zenodo.3274254 (Li, 2019).

The open-source modeling framework MAgPIE (Model of Agricultural Production and its Impact on the Environment) combines economic and biophysical approaches to simulate spatially explicit global scenarios of land use within the 21st century and the respective interactions with the environment. Besides various other projects, it was used to simulate marker scenarios of the Shared Socioeconomic Pathways (SSPs) and contributed substantially to multiple IPCC assessments. However, with growing scope and detail, the non-linear model has become increasingly complex, computationally intensive and non-transparent, requiring structured approaches to improve the development and evaluation of the model.Here, we provide an overview on version 4 of MAgPIE and how it addresses these issues of increasing complexity using new technical features: modular structure with exchangeable module implementations, flexible spatial resolution, in-code documentation, automatized code checking, model/output evaluation and open accessibility. Application examples provide insights into model evaluation, modular flexibility and region-specific analysis approaches. While this paper is focused on the general framework as such, the publication is accompanied by a detailed model documentation describing contents and equations, and by model evaluation documents giving insights into model performance for a broad range of variables.With the open-source release of the MAgPIE 4 framework, we hope to contribute to more transparent, reproducible and collaborative research in the field. Due to its modularity and spatial flexibility, it should provide a basis for a broad range of land-related research with economic or biophysical, global or regional focus.

Land management for carbon storage is discussed as being indispensable for climate change mitigation because of its large potential to remove carbon dioxide from the atmosphere, and to avoid further emissions from deforestation. However, the acceptance and feasibility of land-based mitigation projects depends on potential side effects on other important ecosystem functions and their services. Here, we use projections of future land use and land cover for different land-based mitigation options from two land-use models (IMAGE and MAgPIE) and evaluate their effects with a global dynamic vegetation model (LPJ-GUESS). In the land-use models, carbon removal was achieved either via growth of bioenergy crops combined with carbon capture and storage, via avoided deforestation and afforestation, or via a combination of both. We compare these scenarios to a reference scenario without land-based mitigation and analyse the LPJ-GUESS simulations with the aim of assessing synergies and trade-offs across a range of ecosystem service indicators: carbon storage, surface albedo, evapotranspiration, water runoff, crop production, nitrogen loss, and emissions of biogenic volatile organic compounds. In our mitigation simulations cumulative carbon storage by year 2099 ranged between 55 and 89 GtC. Other ecosystem service indicators were influenced heterogeneously both positively and negatively, with large variability across regions and land-use scenarios. Avoided deforestation and afforestation led to an increase in evapotranspiration and enhanced emissions of biogenic volatile organic compounds, and to a decrease in albedo, runoff, and nitrogen loss. Crop production could also decrease in the afforestation scenarios as a result of reduced crop area, especially for MAgPIE land-use patterns, if assumed increases in crop yields cannot be realized. Bioenergy-based climate change mitigation was projected to affect less area globally than in the forest expansion scenarios, and resulted in less pronounced changes in most ecosystem service indicators than forest-based mitigation, but included a possible decrease in nitrogen loss, crop production, and biogenic volatile organic compounds emissions.