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Modeling forest plantations for carbon uptake with the LPJmL dynamic global vegetation model
We present an extension of the dynamic global vegetation model, Lund-Potsdam-Jena Managed Land (LPJmL), to simulate planted forests intended for carbon (C) sequestration. We implemented three functional types to simulate plantation trees in temperate, tropical, and boreal climates. The parameters of these functional types were optimized to fit target growth curves (TGCs). These curves represent the evolution of stemwood C over time in typical productive plantations and were derived by combining field observations and LPJmL estimates for equivalent natural forests. While the calibrated model underestimates stemwood C growth rates compared to the TGCs, it represents substantial improvement over using natural forests to represent afforestation. Based on a simulation experiment in which we compared global natural forest versus global forest plantation, we found that forest plantations allow for much larger C uptake rates on the timescale of 100 years, with a maximum difference of a factor of 1.9, around 54 years. In subsequent simulations for an ambitious but realistic scenario in which 650Mha (14% of global managed land, 4.5% of global land surface) are converted to forest over 85 years, we found that natural forests take up 37PgC versus 48PgC for forest plantations. Comparing these results to estimations of C sequestration required to achieve the 2°C climate target, we conclude that afforestation can offer a substantial contribution to climate mitigation. Full evaluation of afforestation as a climate change mitigation strategy requires an integrated assessment which considers all relevant aspects, including costs, biodiversity, and trade-offs with other land-use types. Our extended version of LPJmL can contribute to such an assessment by providing improved estimates of C uptake rates by forest plantations. © 2019 American Institute of Physics Inc.. All rights reserved.
Three centuries of dual pressure from land use and climate change on the biosphere
Human land use and anthropogenic climate change (CC) are placing mounting pressure on natural ecosystems worldwide, with impacts on biodiversity, water resources, nutrient and carbon cycles. Here, we present a quantitative macro-scale comparative analysis of the separate and joint dual impacts of land use and land cover change (LULCC) and CC on the terrestrial biosphere during the last ca. 300 years, based on simulations with a dynamic global vegetation model and an aggregated metric of simultaneous biogeochemical, hydrological and vegetation-structural shifts. We find that by the beginning of the 21st century LULCC and CC have jointly caused major shifts on more than 90% of all areas now cultivated, corresponding to 26% of the land area. CC has exposed another 26% of natural ecosystems to moderate or major shifts. Within three centuries, the impact of LULCC on landscapes has increased 13-fold. Within just one century, CC effects have caught up with LULCC effects.
A multi-model analysis of risk of ecosystem shifts under climate change
Climate change may pose a high risk of change to Earth's ecosystems: shifting climatic boundaries may induce changes in the biogeochemical functioning and structures of ecosystems that render it difficult for endemic plant and animal species to survive in their current habitats. Here we aggregate changes in the biogeochemical ecosystem state as a proxy for the risk of these shifts at different levels of global warming. Estimates are based on simulations from seven global vegetation models (GVMs) driven by future climate scenarios, allowing for a quantification of the related uncertainties. 5–19% of the naturally vegetated land surface is projected to be at risk of severe ecosystem change at 2 ° C of global warming (ΔGMT) above 1980–2010 levels. However, there is limited agreement across the models about which geographical regions face the highest risk of change. The extent of regions at risk of severe ecosystem change is projected to rise with ΔGMT, approximately doubling between ΔGMT = 2 and 3 ° C, and reaching a median value of 35% of the naturally vegetated land surface for ΔGMT = 4 °C. The regions projected to face the highest risk of severe ecosystem changes above ΔGMT = 4 °C or earlier include the tundra and shrublands of the Tibetan Plateau, grasslands of eastern India, the boreal forests of northern Canada and Russia, the savanna region in the Horn of Africa, and the Amazon rainforest.
First process-based simulations of climate change impacts on global tea production indicate large effects in the World’s major producer countries
Modeling of climate change impacts have mainly been focused on a small number of annual staple crops that provide most of the world's calories. Crop models typically do not represent perennial crops despite their high economic, nutritional, or cultural value. Here we assess climate change impacts on global tea production, chosen because of its high importance in culture and livelihoods of people around the world. We extended the dynamic global vegetation model with managed land, LPJmL4, global crop model to simulate the cultivation of tea plants. Simulated tea yields were validated and found in good agreement with historical observations as well as experiments on the effects of increasing CO2 concentrations. We then projected yields into the future under a range of climate scenarios from the Inter-Sectoral Impact Model Intercomparison Project. Under current irrigation levels and lowest climate change scenarios, tea yields are expected to decrease in major producing countries. In most climate scenarios, we project that tea yields are set to increase in China, India, and Vietnam. However, yield losses are expected to affect Kenya, Indonesia, and Sri Lanka. If abundant water supply and full irrigation is assumed for all tea cultivation areas, yields are projected to increase in all regions.
Benchmarking carbon fluxes of the ISIMIP2a biome models
The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions (E LUC) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al (2015), and 2) the land CO2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F Jena and F CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models (p < 0.05), which is consistent with the R + L estimate (p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F Jena and F CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.