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- ItemThe GGCMI Phase 2 emulators: Global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)(Katlenburg-Lindau : Copernicus, 2020) Franke, James A.; Müller, Christoph; Elliott, Joshua; Ruane, Alex C.; Jägermeyr, Jonas; Snyder, Abigail; Dury, Marie; Falloon, Pete D.; Folberth, Christian; François, Louis; Hank, Tobias; Izaurralde, R. Cesar; Jacquemin, Ingrid; Jones, Curtis; Li, Michelle; Liu, Wenfeng; Olin, Stefan; Phillips, Meridel; Pugh, Thomas A. M.; Reddy, Ashwan; Williams, Karina; Wang, Ziwei; Zabel, Florian; Moyer, Elisabeth J.Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: Atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: That growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts. © 2020 EDP Sciences. All rights reserved.
- ItemImplications of potentially lower climate sensitivity on climate projections and policy(Bristol : IOP Publishing, 2014) Rogelj, Joeri; Meinshausen, Malte; Sedláček, Jan; Knutti, RetoClimate sensitivity, the long-term temperature response to CO2, has been notoriously difficult to constrain until today. Estimates based on the observed warming trends favor lower values, while the skill with which comprehensive climate models are able to simulate present day climate implies higher values to be more plausible. We find that much lower values would postpone crossing the 2 °C temperature threshold by about a decade for emissions near current levels, or alternatively would imply that limiting warming to below 1.5 °C would require about the same emission reductions as are now assumed for 2 °C. It is just as plausible, however, for climate sensitivity to be at the upper end of the consensus range. To stabilize global-mean temperature at levels of 2 °C or lower, strong reductions of greenhouse gas emissions in order to stay within the allowed carbon budget seem therefore unavoidable over the 21st century. Early reductions and the required phase-out of unabated fossil fuel emissions would be an important societal challenge. However, erring on the side of caution reduces the risk that future generations will face either the need for even larger emission reductions or very high climate change impacts.
- ItemComplementing thermosteric sea level rise estimates(München : European Geopyhsical Union, 2015) Lorbacher, K.; Nauels, A.; Meinshausen, M.Thermal expansion of seawater has been one of the most important contributors to global sea level rise (SLR) over the past 100 years. Yet, observational estimates of this volumetric response of the world's oceans to temperature changes are sparse and mostly limited to the ocean's upper 700 m. Furthermore, only a part of the available climate model data is sufficiently diagnosed to complete our quantitative understanding of thermosteric SLR (thSLR). Here, we extend the available set of thSLR diagnostics from the Coupled Model Intercomparison Project Phase 5 (CMIP5), analyze those model results in order to complement upper-ocean observations and enable the development of surrogate techniques to project thSLR using vertical temperature profile and ocean heat uptake time series. Specifically, based on CMIP5 temperature and salinity data, we provide a compilation of thermal expansion time series that comprise 30 % more simulations than currently published within CMIP5. We find that 21st century thSLR estimates derived solely based on observational estimates from the upper 700 m (2000 m) would have to be multiplied by a factor of 1.39 (1.17) with 90 % uncertainty ranges of 1.24 to 1.58 (1.05 to 1.31) in order to account for thSLR contributions from deeper levels. Half (50 %) of the multi-model total expansion originates from depths below 490 ± 90 m, with the range indicating scenario-to-scenario variations. To support the development of surrogate methods to project thermal expansion, we calibrate two simplified parameterizations against CMIP5 estimates of thSLR: one parameterization is suitable for scenarios where hemispheric ocean temperature profiles are available, the other, where only the total ocean heat uptake is known (goodness of fit: ±5 and ±9 %, respectively).
- ItemPALADYN v1.0, a comprehensive land surface-vegetation-carbon cycle model of intermediate complexity(München : European Geopyhsical Union, 2016) Willeit, Matteo; Ganopolski, AndreyPALADYN is presented; it is a new comprehensive and computationally efficient land surface–vegetation–carbon cycle model designed to be used in Earth system models of intermediate complexity for long-term simulations and paleoclimate studies. The model treats in a consistent manner the interaction between atmosphere, terrestrial vegetation and soil through the fluxes of energy, water and carbon. Energy, water and carbon are conserved. PALADYN explicitly treats permafrost, both in physical processes and as an important carbon pool. It distinguishes nine surface types: five different vegetation types, bare soil, land ice, lake and ocean shelf. Including the ocean shelf allows the treatment of continuous changes in sea level and shelf area associated with glacial cycles. Over each surface type, the model solves the surface energy balance and computes the fluxes of sensible, latent and ground heat and upward shortwave and longwave radiation. The model includes a single snow layer. Vegetation and bare soil share a single soil column. The soil is vertically discretized into five layers where prognostic equations for temperature, water and carbon are consistently solved. Phase changes of water in the soil are explicitly considered. A surface hydrology module computes precipitation interception by vegetation, surface runoff and soil infiltration. The soil water equation is based on Darcy's law. Given soil water content, the wetland fraction is computed based on a topographic index. The temperature profile is also computed in the upper part of ice sheets and in the ocean shelf soil. Photosynthesis is computed using a light use efficiency model. Carbon assimilation by vegetation is coupled to the transpiration of water through stomatal conductance. PALADYN includes a dynamic vegetation module with five plant functional types competing for the grid cell share with their respective net primary productivity. PALADYN distinguishes between mineral soil carbon, peat carbon, buried carbon and shelf carbon. Each soil carbon type has its own soil carbon pools generally represented by a litter, a fast and a slow carbon pool in each soil layer. Carbon can be redistributed between the layers by vertical diffusion and advection. For the vegetated macro surface type, decomposition is a function of soil temperature and soil moisture. Carbon in permanently frozen layers is assigned a long turnover time which effectively locks carbon in permafrost. Carbon buried below ice sheets and on flooded ocean shelves is treated differently. The model also includes a dynamic peat module. PALADYN includes carbon isotopes 13C and 14C, which are tracked through all carbon pools. Isotopic discrimination is modelled only during photosynthesis. A simple methane module is implemented to represent methane emissions from anaerobic carbon decomposition in wetlands (including peatlands) and flooded ocean shelf. The model description is accompanied by a thorough model evaluation in offline mode for the present day and the historical period.
- ItemCorrelations between climate network and relief data(Göttingen : Copernicus GmbH, 2014) Peron, T.K.D.; Comin, C.H.; Amancio, D.R.; Da F. Costa, L.; Rodrigues, F.A.; Kurths, J.In the last few years, the scientific community has witnessed an ongoing trend of using ideas developed in the study of complex networks to analyze climate dynamics. This powerful combination, usually called climate networks, can be used to uncover non-trivial patterns of weather changes throughout the years. Here we investigate the temperature network of the North American region and show that two network characteristics, namely degree and clustering, have marked differences between the eastern and western regions. We show that such differences are a reflection of the presence of a large network community on the western side of the continent. Moreover, we provide evidence that this large community is a consequence of the peculiar characteristics of the western relief of North America.
- ItemChanges of temperature-related agroclimatic indices in Poland(Heidelberg : Springer Verlag, 2016) Graczyk, D.; Kundzewicz, Z.W.