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Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; van Oijen, M.; Zhao, G.; Constantin, J.; Raynal, H.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Sosa, C.; Kersebaum, K.-C.; Nendel, C.; Grosz, B.; Dechow, R.; Kiese, R.; Haas, E.; Klatt, S.; Teixeira, E.; Zhao, Z.; Wang, E.; Weihermüller, L.; Gaiser, T.; Ewert, F. |
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Impact of climate aggregation over different scales on regional NPP modelling |
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2016 |
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CropM; |
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Vienna (Austria) |
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European Geosciences Union General Assembly 2016, 2016-04-17 to 2016-04-22, Vienna |
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2578 |
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Sándor, R.; Barcza, Z.; Acutis, M.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Ma, S.; Perego, A.; Rolinski, S.; Ruget, F.; Sanna, M.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
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Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance |
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2016 |
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European Journal of Agronomy |
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European Journal of Agronomy |
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Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes |
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• We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems. |
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1161-0301 |
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Holman, I. |
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How do models treat climate change adaptation? |
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2016 |
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Presentation SC 8.4 Impact indicators & models. How do models treat climate change adaptation?, Ian Holman, Cranfield University, United Kingdom (2016). Presented at the international conference Adaptation Futures 2016, Rotterdam, the Netherlands. No Label |
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Rotterdam (Netherlands) |
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AdaptationFutures 2016, 10-13 May 2016, Rotterdam |
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2490 |
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Roggero, P.P. |
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Managing Agricultural Greenhouse Gases Network (MAGGnet): Exploring Greenhouse Gas Mitigation Potential of Cropland Management Practices |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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Sp9-8 |
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Global Research Alliance on Agricultural Greenhouse Gases Established: December 2009, United Nations Climate Change Conference, Copenhagen, Denmark•Purpose: Facilitate research, development and extension of technologies and practices that will help deliver ways to grow more food (and more climate-resilient food systems) without growing greenhouse gas emissions.•Current Membership: 46 countries (Europe, Americas, Asia Pacific, Africa) |
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4840 |
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Matthews, A. |
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Is agriculture off the hook in the EU’s 2030 Climate Policy |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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EU climate policy and AFOLU•Overall 2030 level of ambition agreed by European Council October 2014•Commission ESR proposal July 2016 – sharing of effort in NETS across MS plus trading mechanisms•Commission LULUCF proposal – integration of LULUCF into climate policy•AFOLU mitigation pursued through CAP as well as flanking environmental policies•No specific EU targets for agricultural mitigation in NETS•Ultimately, how AFOLU mitigation is pursued will depend on MS decisions2Implications of EU bubble•Commission has put in place trading mechanisms in NETS sectors to ensure least-cost fulfilment of overall EU targets•Challenge of MS ESR targets also depends on use MS make of trading mechanisms•MS have not to date made use of these mechanisms and prefer to meet targets domestically•A number of MS have domestic targets in addition to EU targets•ESR IA looked at adding central information site, central market place for AEA transfers or mandatory auctioning•Links with annual monitoring and 5-year legal compliance checks (2027 and 2032) |
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4843 |
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