2019, Braakhekke, Maarten C., Doelman, Jonathan C., Baas, Peter, Müller, Christoph, Schaphoff, Sibyll, Stehfest, Elke, van Vuuren, Detlef P.

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.

2018, Huang, Shan, Wu, Zhijun, Poulain, Laurent, van Pinxteren, Manuela, Merkel, Maik, Assmann, Denise, Herrmann, Hartmut, Wiedensohler, Alfred

Marine aerosol particles are an important part of the natural aerosol systems and might have a significant impact on the global climate and biological cycle. It is widely accepted that truly pristine marine conditions are difficult to find over the ocean. However, the influence of continental and anthropogenic emissions on the marine boundary layer (MBL) aerosol is still less understood and non-quantitative, causing uncertainties in the estimation of the climate effect of marine aerosols. This study presents a detailed chemical characterization of the MBL aerosol as well as the source apportionment of the organic aerosol (OA) composition. The data set covers the Atlantic Ocean from 53∘ N to 53∘ S, based on four open-ocean cruises in 2011 and 2012. The aerosol particle composition was measured with a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), which indicated that sub-micrometer aerosol particles over the Atlantic Ocean are mainly composed of sulfates (50 % of the particle mass concentration), organics (21 %) and sea salt (12 %). OA has been apportioned into five factors, including three factors linked to marine sources and two with continental and/or anthropogenic origins. The marine oxygenated OA (MOOA, 16 % of the total OA mass) and marine nitrogen-containing OA (MNOA, 16 %) are identified as marine secondary products with gaseous biogenic precursors dimethyl sulfide (DMS) or amines. Marine hydrocarbon-like OA (MHOA, 19 %) was attributed to the primary emissions from the Atlantic Ocean. The factor for the anthropogenic oxygenated OA (Anth-OOA, 19 %) is related to continental long-range transport. Represented by the combustion oxygenated OA (Comb-OOA), aged combustion emissions from maritime traffic and wild fires in Africa contributed, on average, a large fraction to the total OA mass (30 %). This study provides the important finding that long-range transport was found to contribute averagely 49 % of the submicron OA mass over the Atlantic Ocean. This is almost equal to that from marine sources (51 %). Furthermore, a detailed latitudinal distribution of OA source contributions showed that DMS oxidation contributed markedly to the OA over the South Atlantic during spring, while continental-related long-range transport largely influenced the marine atmosphere near Europe and western and central Africa (15∘ N to 15∘ S). In addition, supported by a solid correlation between marine tracer methanesulfonic acid (MSA) and the DMS-oxidation OA (MOOA, R2>0.85), this study suggests that the DMS-related secondary organic aerosol (SOA) over the Atlantic Ocean could be estimated by MSA and a scaling factor of 1.79, especially in spring.

2019, Zscheischler, Jakob, Fischer, Erich M., Lange, Stefan

Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure. © 2019 Copernicus GmbH. All rights reserved.

2018, Tittensor, D.P., Eddy, T.D., Lotze, H.K., Galbraith, E.D., Cheung, W., Barange, M., Blanchard, J.L., Bopp, L., Bryndum-Buchholz, A., Büchner, M., Bulman, C., Carozza, D.A., Christensen, V., Coll, M., Dunne, J.P., Fernandes, J.A., Fulton, E.A., Hobday, A.J., Huber, V., Jennings, S., Jones, M., Lehodey, P., Link, J.S., MacKinson, S., Maury, O., Niiranen, S., Oliveros-Ramos, R., Roy, T., Schewe, J., Shin, Y.-J., Silva, T., Stock, C.A., Steenbeek, J., Underwood, P.J., Volkholz, J., Watson, J.R., Walker, N.D.

Model intercomparison studies in the climate and Earth sciences communities have been crucial to building credibility and coherence for future projections. They have quantified variability among models, spurred model development, contrasted within-and among-model uncertainty, assessed model fits to historical data, and provided ensemble projections of future change under specified scenarios. Given the speed and magnitude of anthropogenic change in the marine environment and the consequent effects on food security, biodiversity, marine industries, and society, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. Here, we describe the Fisheries and Marine Ecosystem Model Intercomparison Project protocol version 1.0 (Fish-MIP v1.0), part of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is a cross-sectoral network of climate impact modellers. Given the complexity of the marine ecosystem, this class of models has substantial heterogeneity of purpose, scope, theoretical underpinning, processes considered, parameterizations, resolution (grain size), and spatial extent. This heterogeneity reflects the lack of a unified understanding of the marine ecosystem and implies that the assemblage of all models is more likely to include a greater number of relevant processes than any single model. The current Fish-MIP protocol is designed to allow these heterogeneous models to be forced with common Earth System Model (ESM) Coupled Model Intercomparison Project Phase 5 (CMIP5) outputs under prescribed scenarios for historic (from the 1950s) and future (to 2100) time periods; it will be adapted to CMIP phase 6 (CMIP6) in future iterations. It also describes a standardized set of outputs for each participating Fish-MIP model to produce. This enables the broad characterization of differences between and uncertainties within models and projections when assessing climate and fisheries impacts on marine ecosystems and the services they provide. The systematic generation, collation, and comparison of results from Fish-MIP will inform an understanding of the range of plausible changes in marine ecosystems and improve our capacity to define and convey the strengths and weaknesses of model-based advice on future states of marine ecosystems and fisheries. Ultimately, Fish-MIP represents a step towards bringing together the marine ecosystem modelling community to produce consistent ensemble medium-and long-term projections of marine ecosystems.