Articles | Volume 15, issue 2
Model description paper
01 Feb 2022
Model description paper | 01 Feb 2022
C-LLAMA 1.0: a traceable model for food, agriculture, and land use
Thomas S. Ball et al.
No articles found.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam. Discuss.,
Revised manuscript accepted for ESDShort summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well-captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Garry D. Hayman, Edward Comyn-Platt, Chris Huntingford, Anna B. Harper, Tom Powell, Peter M. Cox, William Collins, Christopher Webber, Jason Lowe, Stephen Sitch, Joanna I. House, Jonathan C. Doelman, Detlef P. van Vuuren, Sarah E. Chadburn, Eleanor Burke, and Nicola Gedney
Earth Syst. Dynam., 12, 513–544,Short summary
We model greenhouse gas emission scenarios consistent with limiting global warming to either 1.5 or 2 °C above pre-industrial levels. We quantify the effectiveness of methane emission control and land-based mitigation options regionally. Our results highlight the importance of reducing methane emissions for realistic emission pathways that meet the global warming targets. For land-based mitigation, growing bioenergy crops on existing agricultural land is preferable to replacing forests.
Elisa Lovecchio and Timothy M. Lenton
Geosci. Model Dev., 13, 1865–1883,Short summary
We present here the newly developed BPOP box model. BPOP is aimed at studying the impact of large-scale changes in the biological pump, i.e. the cycle of production, export and remineralization of the marine organic matter, on the nutrient and oxygen concentrations in the shelf and open ocean. This model has been developed to investigate the global consequences of the evolution of larger and heavier phytoplankton cells but can be applied to a variety of past and future case studies.
Emma W. Littleton, Anna B. Harper, Naomi E. Vaughan, Rebecca J. Oliver, Maria Carolina Duran-Rojas, and Timothy M. Lenton
Geosci. Model Dev., 13, 1123–1136,Short summary
This study presents new functionality to represent bioenergy crops and harvests in JULES, a land surface model. Such processes must be explicitly represented before the environmental effects of large-scale bioenergy production can be fully evaluated, using Earth system modelling. This new functionality allows for many types of bioenergy plants and harvesting regimes to be simulated, such as perennial grasses, short rotation coppicing, and forestry rotations.
David I. Armstrong McKay and Timothy M. Lenton
Clim. Past, 14, 1515–1527,Short summary
This study uses statistical analyses to look for signs of declining resilience (i.e. greater sensitivity to small shocks) in the global carbon cycle and climate system across the Palaeocene–Eocene Thermal Maximum (PETM), a global warming event 56 Myr ago driven by rapid carbon release. Our main finding is that carbon cycle resilience declined in the 1.5 Myr beforehand (a time of significant volcanic emissions), which is consistent with but not proof of a carbon release tipping point at the PETM.
Sebastian Bathiany, Bregje van der Bolt, Mark S. Williamson, Timothy M. Lenton, Marten Scheffer, Egbert H. van Nes, and Dirk Notz
The Cryosphere, 10, 1631–1645,Short summary
We examine if a potential "tipping point" in Arctic sea ice, causing abrupt and irreversible sea-ice loss, could be foreseen with statistical early warning signals. We assess this idea by using several models of different complexity. We find robust and consistent trends in variability that are not specific to the existence of a tipping point. While this makes an early warning impossible, it allows to estimate sea-ice variability from only short observational records or reconstructions.
Timothy M. Lenton, Peter-Paul Pichler, and Helga Weisz
Earth Syst. Dynam., 7, 353–370,Short summary
We identify six past revolutions in energy input and material cycling in Earth and human history. We find that human energy use has now reached a magnitude comparable to the biosphere, and conclude that a prospective sustainability revolution will require scaling up new solar energy technologies and the development of much more efficient material recycling systems. Our work was inspired by recognising the connections between Earth system science and industrial ecology at the "LOOPS" workshop.
Mark S. Williamson, Sebastian Bathiany, and Timothy M. Lenton
Earth Syst. Dynam., 7, 313–326,Short summary
We find early warnings of abrupt changes in complex dynamical systems such as the climate where the usual early warning indicators do not work. In particular, these are systems that are periodically forced, for example by the annual cycle of solar insolation. We show these indicators are good theoretically in a general setting then apply them to a specific system, that of the Arctic sea ice, which has been conjectured to be close to such a tipping point. We do not find evidence of it.
Z. A. Thomas, F. Kwasniok, C. A. Boulton, P. M. Cox, R. T. Jones, T. M. Lenton, and C. S. M. Turney
Clim. Past, 11, 1621–1633,Short summary
Using a combination of speleothem records and model simulations of the East Asian Monsoon over the penultimate glacial cycle, we search for early warning signals of past tipping points. We detect a characteristic slower response to perturbations prior to an abrupt monsoon shift at the glacial termination; however, we do not detect these signals in the preceding shifts. Our results have important implications for detecting tipping points in palaeoclimate records outside glacial terminations.
G. Colbourn, A. Ridgwell, and T. M. Lenton
Geosci. Model Dev., 6, 1543–1573,
V. N. Livina and T. M. Lenton
The Cryosphere, 7, 275–286,
Related subject area
Climate and Earth system modelingICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scalesOcean Modeling with Adaptive REsolution (OMARE; version 1.0) – refactoring the NEMO model (version 4.0.1) with the parallel computing framework of JASMIN – Part 1: Adaptive grid refinement in an idealized double-gyre caseMonthly-scale extended predictions using the atmospheric model coupled with a slab oceanstoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impactsURANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental ResearchCombining regional mesh refinement with vertically enhanced physics to target marine stratocumulus biases as demonstrated in the Energy Exascale Earth System Model version 1Evaluation of native Earth system model output with ESMValTool v2.6.0WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transferThe Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction systemClimate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2Accelerated photosynthesis routine in LPJmL4Improving scalability of Earth system models through coarse-grained component concurrency – a case study with the ICON v2.6.5 modelling systemTemperature forecasting by deep learning methodsPathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenariosInclusion of a cold hardening scheme to represent frost tolerance is essential to model realistic plant hydraulics in the Arctic–boreal zone in CLM5.0-FATES-HydroImplementation and evaluation of the GEOS-Chem chemistry module version 13.1.2 within the Community Earth System Model v2.1Assessment of JSBACHv4.30 as a land component of ICON-ESM-V1 in comparison to its predecessor JSBACHv3.2 of MPI-ESM1.2Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED)Impact of increased resolution on the representation of the Canary upwelling system in climate modelsAssessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI): protocol and initial results from the first simulationsIntroducing the VIIRS-based Fire Emission Inventory version 0 (VFEIv0)Impact of physical parameterizations on wind simulation with WRF V18.104.22.168 under stable conditions at planetary boundary layer gray-zone resolution: a case study over the coastal regions of North ChinaAdvancing precipitation prediction using a new-generation storm-resolving model framework – SIMA-MPAS (V1.0): a case study over the western United StatesSURFER v2.0: a flexible and simple model linking anthropogenic CO2 emissions and solar radiation modification to ocean acidification and sea level riseAnalysis of Systematic Biases in Tropospheric Hydrostatic Delay Models and Construction of Correction ModelA new bootstrap technique to quantify uncertainty in estimates of ground surface temperature and ground heat flux histories from geothermal dataModeling the topographic influence on aboveground biomass using a coupled model of hillslope hydrology and ecosystem dynamicsImpacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)A modeling framework to understand historical and projected ocean climate change in large coupled ensemblesTriCCo v1.1.0 – a cubulation-based method for computing connected components on triangular gridsEstimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0.1The Moist Quasi-Geostrophic Coupled Model: MQ-GCM 2.0Cell tracking of convective rainfall: sensitivity of climate-change signal to tracking algorithm and cell definition (Cell-TAO v1.0)Transport parameterization of the Polar SWIFT model (version 2)Analog data assimilation for the selection of suitable general circulation modelsUncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0Grid refinement in ICON v2.6.4Simulating marine neodymium isotope distributions using ND v1.0 coupled to the ocean component of the FAMOUS-MOSES1 climate model: sensitivities to reversible scavenging efficiency and benthic source distributionsClassification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning datasetThe ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effectsHORAYZON v1.2: an efficient and flexible ray-tracing algorithm to compute horizon and sky view factorLPJ-GUESS/LSMv1.0: a next-generation land surface model with high ecological realismDownscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44Intercomparison of four algorithms for detecting tropical cyclones using ERA5Inland lake temperature initialization via coupled cycling with atmospheric data assimilationwavetrisk-2.1: an adaptive dynamical core for ocean modellingRepresenting surface heterogeneity in land–atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertimeAWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 modelThe Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2
Cathy Hohenegger, Peter Korn, Leonidas Linardakis, René Redler, Reiner Schnur, Panagiotis Adamidis, Jiawei Bao, Swantje Bastin, Milad Behravesh, Martin Bergemann, Joachim Biercamp, Hendryk Bockelmann, Renate Brokopf, Nils Brüggemann, Lucas Casaroli, Fatemeh Chegini, George Datseris, Monika Esch, Geet George, Marco Giorgetta, Oliver Gutjahr, Helmuth Haak, Moritz Hanke, Tatiana Ilyina, Thomas Jahns, Johann Jungclaus, Marcel Kern, Daniel Klocke, Lukas Kluft, Tobias Kölling, Luis Kornblueh, Sergey Kosukhin, Clarissa Kroll, Junhong Lee, Thorsten Mauritsen, Carolin Mehlmann, Theresa Mieslinger, Ann Kristin Naumann, Laura Paccini, Angel Peinado, Divya Sri Praturi, Dian Putrasahan, Sebastian Rast, Thomas Riddick, Niklas Roeber, Hauke Schmidt, Uwe Schulzweida, Florian Schütte, Hans Segura, Radomyra Shevchenko, Vikram Singh, Mia Specht, Claudia Christine Stephan, Jin-Song von Storch, Raphaela Vogel, Christian Wengel, Marius Winkler, Florian Ziemen, Jochem Marotzke, and Bjorn Stevens
Geosci. Model Dev., 16, 779–811,Short summary
Models of the Earth system used to understand climate and predict its change typically employ a grid spacing of about 100 km. Yet, many atmospheric and oceanic processes occur on much smaller scales. In this study, we present a new model configuration designed for the simulation of the components of the Earth system and their interactions at kilometer and smaller scales, allowing an explicit representation of the main drivers of the flow of energy and matter by solving the underlying equations.
Yan Zhang, Xuantong Wang, Yuhao Sun, Chenhui Ning, Shiming Xu, Hengbin An, Dehong Tang, Hong Guo, Hao Yang, Ye Pu, Bo Jiang, and Bin Wang
Geosci. Model Dev., 16, 679–704,Short summary
We construct a new ocean model, OMARE, that can carry out multi-scale ocean simulation with adaptive mesh refinement. OMARE is based on the refactorization of NEMO with a third-party, high-performance piece of middleware. We report the porting process and experiments of an idealized western-boundary current system. The new model simulates turbulent and temporally varying mesoscale and submesoscale processes via adaptive refinement. Related topics and future work with OMARE are also discussed.
Zhenming Wang, Shaoqing Zhang, Yishuai Jin, Yinglai Jia, Yangyang Yu, Yang Gao, Xiaolin Yu, Mingkui Li, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 16, 705–717,Short summary
To improve the numerical model predictability of monthly extended-range scales, we use the simplified slab ocean model (SOM) to restrict the complicated sea surface temperature (SST) bias from a 3-D dynamical ocean model. As for SST prediction, whether in space or time, the WRF-SOM is verified to have better performance than the WRF-ROMS, which has a significant impact on the atmosphere. For extreme weather events such as typhoons, the predictions of WRF-SOM are in good agreement with WRF-ROMS.
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571,Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Markus Köhli, Martin Schrön, Steffen Zacharias, and Ulrich Schmidt
Geosci. Model Dev., 16, 449–477,Short summary
In the last decades, Monte Carlo codes were often consulted to study neutrons near the surface. As an alternative for the growing community of CRNS, we developed URANOS. The main model features are tracking of particle histories from creation to detection, detector representations as layers or geometric shapes, a voxel-based geometry model, and material setup based on color codes in ASCII matrices or bitmap images. The entire software is developed in C++ and features a graphical user interface.
Peter A. Bogenschutz, Hsiang-He Lee, Qi Tang, and Takanobu Yamaguchi
Geosci. Model Dev., 16, 335–352,Short summary
Models that are used to simulate and predict climate often have trouble representing specific cloud types, such as stratocumulus, that are particularly thin in the vertical direction. It has been found that increasing the model resolution can help improve this problem. In this paper, we develop a novel framework that increases the horizontal and vertical resolutions only for areas of the globe that contain stratocumulus, hence reducing the model runtime while providing better results.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333,Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang
Geosci. Model Dev., 16, 199–209,Short summary
More and more researchers use deep learning models to replace physics-based parameterizations to accelerate weather simulations. However, embedding the ML models within the weather models is difficult as they are implemented in different languages. This work proposes a coupling framework to allow ML-based parameterizations to be coupled with the Weather Research and Forecasting (WRF) model. We also demonstrate using the coupler to couple the ML-based radiation schemes with the WRF model.
Dario Nicolì, Alessio Bellucci, Paolo Ruggieri, Panos J. Athanasiadis, Stefano Materia, Daniele Peano, Giusy Fedele, Riccardo Hénin, and Silvio Gualdi
Geosci. Model Dev., 16, 179–197,Short summary
Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of the near-term climate and are useful for informing decision-makers on future climate-related risks. The predictive skill for key variables is assessed from the operational decadal prediction system compared with non-initialized historical simulations so as to quantify the added value of initialization.
Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156,Short summary
All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the atmosphere, and thus their horizontal variations within the grid cell are averaged out. In this regard, a novel scheme considering the variation and partitioning of the surface heat fluxes within the grid cell is developed. The scheme reduces the long-standing rainfall biases on the southern and eastern margins of the Tibetan Plateau. The performance of key variables at the global scale is also evaluated.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33,Short summary
The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Leonidas Linardakis, Irene Stemmler, Moritz Hanke, Lennart Ramme, Fatemeh Chegini, Tatiana Ilyina, and Peter Korn
Geosci. Model Dev., 15, 9157–9176,Short summary
In Earth system modelling, we are facing the challenge of making efficient use of very large machines, with millions of cores. To meet this challenge we will need to employ multi-level and multi-dimensional parallelism. Component concurrency, being a function parallel technique, offers an additional dimension to the traditional data-parallel approaches. In this paper we examine the behaviour of component concurrency and identify the conditions for its optimal application.
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956,Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Thomas Bossy, Thomas Gasser, and Philippe Ciais
Geosci. Model Dev., 15, 8831–8868,Short summary
We developed a new simple climate model designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: calibration using Bayesian inference, the possibility of coupling with integrated assessment models, and the capacity to explore climate scenarios compatible with limiting climate impacts. Here, we describe the model and its calibration using the latest data from complex CMIP6 models and the IPCC AR6, and we assess its performance.
Marius S. A. Lambert, Hui Tang, Kjetil S. Aas, Frode Stordal, Rosie A. Fisher, Yilin Fang, Junyan Ding, and Frans-Jan W. Parmentier
Geosci. Model Dev., 15, 8809–8829,Short summary
In this study, we implement a hardening mortality scheme into CTSM5.0-FATES-Hydro and evaluate how it impacts plant hydraulics and vegetation growth. Our work shows that the hydraulic modifications prescribed by the hardening scheme are necessary to model realistic vegetation growth in cold climates, in contrast to the default model that simulates almost nonexistent and declining vegetation due to abnormally large water loss through the roots.
Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Haipeng Lin, Elizabeth W. Lundgren, Steve Goldhaber, Steven R. H. Barrett, and Daniel J. Jacob
Geosci. Model Dev., 15, 8669–8704,Short summary
We bring the state-of-the-science chemistry module GEOS-Chem into the Community Earth System Model (CESM). We show that some known differences between results from GEOS-Chem and CESM's CAM-chem chemistry module may be due to the configuration of model meteorology rather than inherent differences in the model chemistry. This is a significant step towards a truly modular Earth system model and allows two strong but currently separate research communities to benefit from each other's advances.
Rainer Schneck, Veronika Gayler, Julia E. M. S. Nabel, Thomas Raddatz, Christian H. Reick, and Reiner Schnur
Geosci. Model Dev., 15, 8581–8611,Short summary
The versions of ICON-A and ICON-Land/JSBACHv4 used for this study constitute the first milestone in the development of the new ICON Earth System Model ICON-ESM. JSBACHv4 is the successor of JSBACHv3, and most of the parameterizations of JSBACHv4 are re-implementations from JSBACHv3. We assess and compare the performance of JSBACHv4 and JSBACHv3. Overall, the JSBACHv4 results are as good as JSBACHv3, but both models reveal the same main shortcomings, e.g. the depiction of the leaf area index.
Dave van Wees, Guido R. van der Werf, James T. Randerson, Brendan M. Rogers, Yang Chen, Sander Veraverbeke, Louis Giglio, and Douglas C. Morton
Geosci. Model Dev., 15, 8411–8437,Short summary
We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial.
Adama Sylla, Emilia Sanchez Gomez, Juliette Mignot, and Jorge López-Parages
Geosci. Model Dev., 15, 8245–8267,Short summary
Increasing model resolution depends on the subdomain of the Canary upwelling considered. In the Iberian Peninsula, the high-resolution (HR) models do not seem to better simulate the upwelling indices, while in Morocco to the Senegalese coast, the HR models show a clear improvement. Thus increasing the resolution of a global climate model does not necessarily have to be the only way to better represent the climate system. There is still much work to be done in terms of physical parameterizations.
Jadwiga H. Richter, Daniele Visioni, Douglas G. MacMartin, David A. Bailey, Nan Rosenbloom, Brian Dobbins, Walker R. Lee, Mari Tye, and Jean-Francois Lamarque
Geosci. Model Dev., 15, 8221–8243,Short summary
Solar climate intervention using stratospheric aerosol injection is a proposed method of reducing global mean temperatures to reduce the worst consequences of climate change. We present a new modeling protocol aimed at simulating a plausible deployment of stratospheric aerosol injection and reproducibility of simulations using other Earth system models: Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI).
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109,Short summary
The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Entao Yu, Rui Bai, Xia Chen, and Lifang Shao
Geosci. Model Dev., 15, 8111–8134,Short summary
A large number of simulations are conducted to investigate how different physical parameterization schemes impact surface wind simulations under stable weather conditions over the coastal regions of North China using the Weather Research and Forecasting model with a horizontal grid spacing of 0.5 km. Results indicate that the simulated wind speed is most sensitive to the planetary boundary layer schemes, followed by short-wave/long-wave radiation schemes and microphysics schemes.
Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, and Michael Duda
Geosci. Model Dev., 15, 8135–8151,Short summary
We focus on the recent development of a state-of-the-art storm-resolving global climate model and investigate how this next-generation model performs for precipitation prediction over the western USA. Results show realistic representations of precipitation with significantly enhanced snowpack over complex terrains. The model evaluation advances the unified modeling of large-scale forcing constraints and realistic fine-scale features to advance multi-scale climate predictions and changes.
Marina Martínez Montero, Michel Crucifix, Victor Couplet, Nuria Brede, and Nicola Botta
Geosci. Model Dev., 15, 8059–8084,Short summary
We present SURFER, a lightweight model that links CO2 emissions and geoengineering to ocean acidification and sea level rise from glaciers, ocean thermal expansion and Greenland and Antarctic ice sheets. The ice sheet module adequately describes the tipping points of both Greenland and Antarctica. SURFER is understandable, fast, accurate up to several thousands of years, capable of emulating results obtained by state of the art models and well suited for policy analyses.
Haopeng Fan, Siran Li, Zhongmiao Sun, Guorui Xiao, Xinxing Li, and Xiaogang Liu
The bias of traditional tropospheric zenith hydrostatic delay (ZHD) model is usually thought negligible, yet it still reaches 10 mm sometimes and would lead to mm-level position errors for space geodetic observations. Therefore, We analyzed the bias’ characteristics and present a grid model to correct the traditional ZHD formula. When we verified the efficiency based on data from ECMWF (European Centre for Medium-Range Weather Forecasts), it turned out that ZHD biases were rectified by ~50 %.
Francisco José Cuesta-Valero, Hugo Beltrami, Stephan Gruber, Almudena García-García, and J. Fidel González-Rouco
Geosci. Model Dev., 15, 7913–7932,Short summary
Inversions of subsurface temperature profiles provide past long-term estimates of ground surface temperature histories and ground heat flux histories at timescales of decades to millennia. Theses estimates complement high-frequency proxy temperature reconstructions and are the basis for studying continental heat storage. We develop and release a new bootstrap method to derive meaningful confidence intervals for the average surface temperature and heat flux histories from any number of profiles.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901,Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Wentao Zhang, Xiangjun Shi, and Chunsong Lu
Geosci. Model Dev., 15, 7751–7766,Short summary
The two-moment bulk cloud microphysics scheme used in CAM6 was modified to consider the impacts of the ice-crystal size distribution shape parameter (μi). After that, how the μi impacts cloud microphysical processes and then climate simulations is clearly illustrated by offline tests and CAM6 model experiments. Our results and findings are useful for the further development of μi-related parameterizations.
Yona Silvy, Clément Rousset, Eric Guilyardi, Jean-Baptiste Sallée, Juliette Mignot, Christian Ethé, and Gurvan Madec
Geosci. Model Dev., 15, 7683–7713,Short summary
A modeling framework is introduced to understand and decompose the mechanisms causing the ocean temperature, salinity and circulation to change since the pre-industrial period and into 21st century scenarios of global warming. This framework aims to look at the response to changes in the winds and in heat and freshwater exchanges at the ocean interface in global climate models, throughout the 1850–2100 period, to unravel their individual effects on the changing physical structure of the ocean.
Aiko Voigt, Petra Schwer, Noam von Rotberg, and Nicole Knopf
Geosci. Model Dev., 15, 7489–7504,Short summary
In climate science, it is helpful to identify coherent objects, for example, those formed by clouds. However, many models now use unstructured grids, which makes it harder to identify coherent objects. We present a new method that solves this problem by moving model data from an unstructured triangular grid to a structured cubical grid. We implement the method in an open-source Python package and show that the method is ready to be applied to climate model data.
Jérémy Bernard, Erwan Bocher, Elisabeth Le Saux Wiederhold, François Leconte, and Valéry Masson
Geosci. Model Dev., 15, 7505–7532,Short summary
OpenStreetMap is a collaborative project aimed at creaing a free dataset containing topographical information. Since these data are available worldwide, they can be used as standard data for geoscience studies. However, most buildings miss the height information that constitutes key data for numerous fields (urban climate, noise propagation, air pollution). In this work, the building height is estimated using statistical modeling using indicators that characterize the building's environment.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469,Short summary
Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
Cell tracking algorithms allow the properties of a convective cell to be studied across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm’s criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Ingo Wohltmann, Daniel Kreyling, and Ralph Lehmann
Geosci. Model Dev., 15, 7243–7255,Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Juan Ruiz, Pierre Ailliot, Thi Tuyet Trang Chau, Pierre Le Bras, Valérie Monbet, Florian Sévellec, and Pierre Tandeo
Geosci. Model Dev., 15, 7203–7220,Short summary
We present a new approach to validate numerical simulations of the current climate. The method can take advantage of existing climate simulations produced by different centers combining an analog forecasting approach with data assimilation to quantify how well a particular model reproduces a sequence of observed values. The method can be applied with different observations types and is implemented locally in space and time significantly reducing the associated computational cost.
Chahan M. Kropf, Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch
Geosci. Model Dev., 15, 7177–7201,Short summary
Mathematical models are approximations, and modellers need to understand and ideally quantify the arising uncertainties. Here, we describe and showcase the first, simple-to-use, uncertainty and sensitivity analysis module of the open-source and open-access climate-risk modelling platform CLIMADA. This may help to enhance transparency and intercomparison of studies among climate-risk modellers, help focus future research, and lead to better-informed decisions on climate adaptation.
Günther Zängl, Daniel Reinert, and Florian Prill
Geosci. Model Dev., 15, 7153–7176,Short summary
This article describes the implementation of grid refinement in the ICOsahedral Nonhydrostatic (ICON) model, which has been jointly developed at several German institutions and constitutes a unified modeling system for global and regional numerical weather prediction and climate applications. The grid refinement allows using a higher resolution in regional domains and transferring the information back to the global domain by means of a feedback mechanism.
Suzanne Robinson, Ruza Ivanovic, Lauren Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul Valdes
We present the implementation of neodymium (Nd) isotopes into the ocean model of FAMOUS (ND v1.0). Nd fluxes from seafloor sediment alongside incorporation of Nd onto sinking particles represent the major global sources and sinks. However, model-data mismatch in the North Pacific and northern North Atlantic suggest that certain reactive components of the sediment interact the most with seawater. Our results are important for interpreting Nd isotopes in terms of ocean circulation.
Sébastien Gardoll and Olivier Boucher
Geosci. Model Dev., 15, 7051–7073,Short summary
Tropical cyclones (TCs) are one of the most devastating natural disasters, which justifies monitoring and prediction in the context of a changing climate. In this study, we have adapted and tested a convolutional neural network (CNN) for the classification of reanalysis outputs (ERA5 and MERRA-2 labeled by HURDAT2) according to the presence or absence of TCs. We tested the impact of interpolation and of "mixing and matching" the training and test sets on the performance of the CNN.
Marco A. Giorgetta, William Sawyer, Xavier Lapillonne, Panagiotis Adamidis, Dmitry Alexeev, Valentin Clément, Remo Dietlicher, Jan Frederik Engels, Monika Esch, Henning Franke, Claudia Frauen, Walter M. Hannah, Benjamin R. Hillman, Luis Kornblueh, Philippe Marti, Matthew R. Norman, Robert Pincus, Sebastian Rast, Daniel Reinert, Reiner Schnur, Uwe Schulzweida, and Bjorn Stevens
Geosci. Model Dev., 15, 6985–7016,Short summary
This work presents a first version of the ICON atmosphere model that works not only on CPUs, but also on GPUs. This GPU-enabled ICON version is benchmarked on two GPU machines and a CPU machine. While the weak scaling is very good on CPUs and GPUs, the strong scaling is poor on GPUs. But the high performance of GPU machines allowed for first simulations of a short period of the quasi-biennial oscillation at very high resolution with explicit convection and gravity wave forcing.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816,Short summary
This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
Christian R. Steger, Benjamin Steger, and Christoph Schär
Geosci. Model Dev., 15, 6817–6840,Short summary
Terrain horizon and sky view factor are crucial quantities for many geoscientific applications; e.g. they are used to account for effects of terrain on surface radiation in climate and land surface models. Because typical terrain horizon algorithms are inefficient for high-resolution (< 30 m) elevation data, we developed a new algorithm based on a ray-tracing library. A comparison with two conventional methods revealed both its high performance and its accuracy for complex terrain.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745,Short summary
We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
Jorge Baño-Medina, Rodrigo Manzanas, Ezequiel Cimadevilla, Jesús Fernández, Jose González-Abad, Antonio S. Cofiño, and José Manuel Gutiérrez
Geosci. Model Dev., 15, 6747–6758,Short summary
Deep neural networks are used to produce downscaled regional climate change projections over Europe for temperature and precipitation for the first time. The resulting dataset, DeepESD, is analyzed against state-of-the-art downscaling methodologies, reproducing more accurately the observed climate in the historical period and showing plausible future climate change signals with low computational requirements.
Stella Bourdin, Sébastien Fromang, William Dulac, Julien Cattiaux, and Fabrice Chauvin
Geosci. Model Dev., 15, 6759–6786,Short summary
When studying tropical cyclones in a large dataset, one needs objective and automatic procedures to detect their specific pattern. Applying four different such algorithms to a reconstruction of the climate, we show that the choice of the algorithm is crucial to the climatology obtained. Mainly, the algorithms differ in their sensitivity to weak storms so that they provide different frequencies and durations. We review the different options to consider for the choice of the tracking methodology.
Stanley G. Benjamin, Tatiana G. Smirnova, Eric P. James, Eric J. Anderson, Ayumi Fujisaki-Manome, John G. W. Kelley, Greg E. Mann, Andrew D. Gronewold, Philip Chu, and Sean G. T. Kelley
Geosci. Model Dev., 15, 6659–6676,Short summary
Application of 1-D lake models coupled within earth-system prediction models will improve accuracy but requires accurate initialization of lake temperatures. Here, we describe a lake initialization method by cycling within a weather prediction model to constrain lake temperature evolution. We compared these lake temperature values with other estimates and found much reduced errors (down to 1-2 K). The lake cycling initialization is now applied to two operational US NOAA weather models.
Nicholas K.-R. Kevlahan and Florian Lemarié
Geosci. Model Dev., 15, 6521–6539,Short summary
WAVETRISK-2.1 is an innovative climate model for the world's oceans. It uses state-of-the-art techniques to change the model's resolution locally, from O(100 km) to O(5 km), as the ocean changes. This dynamic adaptivity makes optimal use of available supercomputer resources, and allows two-dimensional global scales and three-dimensional submesoscales to be captured in the same simulation. WAVETRISK-2.1 is designed to be coupled its companion global atmosphere model, WAVETRISK-1.x.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384,Short summary
The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427,Short summary
We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493,Short summary
The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
Alexander, P., Brown, C., Arneth, A., Finnigan, J., and Rounsevell, M. D. A.: Human appropriation of land for food: The role of diet, Global Environ. Chang., 41, 88–98, https://doi.org/10.1016/j.gloenvcha.2016.09.005, 2016.
Alexander, P., Brown, C., Arneth, A., Finnigan, J., Moran, D., and Rounsevell, M. D. A.: Losses, inefficiencies and waste in the global food system, Agr. Syst., 153, 190–200, https://doi.org/10.1016/j.agsy.2017.01.014, 2017.
Allen, M. R., Dube, O. P., Solecki, W., Aragón-Durand, F., Cramer, W., Humphreys, S., Kainuma, M., Kala, J., Mahowald, N., Mulugetta, Y., Perez, R., Wairiu, M., and Zickfeld, K.: Framing and Context. Global Warming of 1.5 ∘C, in: IPCC Special Report on the impacts of global warming of 1.5 ∘C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, IPCC, 2022.
Arneth, A., Denton, F., Agus, F., Elbehri, A., Erb, K., Osman Elasha, B., Rahimi, M., Rounsevell, M., Spence, M., and Valentini, R.: Framing and Context. Climate Change and Land, in: IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, IPCC, 2022.
Ball, T. S.: C-LLAMA v1.0: a traceable model for food, agriculture and land-use, Zenodo [code], https://doi.org/10.5281/zenodo.5083000, 2021.
Bijl, D. L., Bogaart, P. W., Dekker, S. C., Stehfest, E., de Vries, B. J. M., and van Vuuren, D. P.: A physically-based model of long-term food demand, Global Environ. Chang., 45, 47–62, https://doi.org/10.1016/j.gloenvcha.2017.04.003, 2017.
Bordonal, R. de O., Carvalho, J. L. N., Lal, R., de Figueiredo, E. B., de Oliveira, B. G., and La Scala, N.: Sustainability of sugarcane production in Brazil. A review, Agron. Sustain. Dev., 38, 13, https://doi.org/10.1007/s13593-018-0490-x, 2018.
Calvin, K., Wise, M., Clarke, L., Edmonds, J., Kyle, P., Luckow, P., and Thomson, A.: Implications of simultaneously mitigating and adapting to climate change: Initial experiments using GCAM, Climatic Change, 117, 545–560, https://doi.org/10.1007/s10584-012-0650-y, 2013.
Davis, K. F. and D'Odorico, P.: Livestock intensification and the influence of dietary change: A calorie-based assessment of competition for crop production, Sci. Total Environ., 538, 817–823, https://doi.org/10.1016/j.scitotenv.2015.08.126, 2015.
De Miranda, E. E. and Fonseca, M. F.: Chapter 4 – Sugarcane: Food production, energy, and environment, in: Sugarcane Biorefinery, Technology and Perspectives, Elsevier Inc., 67–88, ISBN: 978-0-12-814236-3, 2019.
Duro, J. A., Lauk, C., Kastner, T., Erb, K. H., and Haberl, H.: Global inequalities in food consumption, cropland demand and land-use efficiency: A decomposition analysis, Global Environ. Chang., 64, 102124, https://doi.org/10.1016/j.gloenvcha.2020.102124, 2020.
Eitelberg, D. A., van Vliet, J., and Verburg, P. H.: A review of global potentially available cropland estimates and their consequences for model-based assessments, Global Change Biol., 21, 1236–1248, https://doi.org/10.1111/gcb.12733, 2015.
Erb, K. H., Luyssaert, S., Meyfroidt, P., Pongratz, J., Don, A., Kloster, S., Kuemmerle, T., Fetzel, T., Fuchs, R., Herold, M., Haberl, H., Jones, C. D., Marín-Spiotta, E., McCallum, I., Robertson, E., Seufert, V., Fritz, S., Valade, A., Wiltshire, A., and Dolman, A. J.: Land management: data availability and process understanding for global change studies, Global Change Biol., 23, 512–533, https://doi.org/10.1111/gcb.13443, 2017.
FAOSTAT (Food and Agriculture Organization Corporate Statistical Database): https://doi.org/10.25504/FAIRsharing.z0rqUk, 2012.
FAOSTAT: Food and Agriculture Organization Corporate Statistical Database, https://doi.org/10.25504/FAIRsharing.z0rqUk, 2012.
FAOSTAT: Crops and livestock products, FAOSTAT [data set], https://www.fao.org/faostat/en/#data/QCL (last access: 13 January 2022), 2021a.
FAOSTAT: Food Balances (-2013, old methodology and population), FAOSTAT [data set], available at: https://www.fao.org/faostat/en/#data/FBSH (last access: 13 January 2022), 2021b.
FAOSTAT: Inputs – Land Use, FAOSTAT [data set], available at: https://www.fao.org/faostat/en/#data/RL (last access: 13 January 2022), 2021c.
FAOSTAT: Agriculture – Total, FAOSTAT [data set], available at: https://www.fao.org/faostat/en/#data, last access: 13 January 2022.
Fricko, O., Havlik, P., Rogelj, J., Klimont, Z., Gusti, M., Johnson, N., Kolp, P., Strubegger, M., Valin, H., Amann, M., Ermolieva, T., Forsell, N., Herrero, M., Heyes, C., Kindermann, G., Krey, V., McCollum, D. L., Obersteiner, M., Pachauri, S., Rao, S., Schmid, E., Schoepp, W., and Riahi, K.: The marker quantification of the Shared Socioeconomic Pathway 2: A middle-of-the-road scenario for the 21st century, Global Environ. Chang., 42, 251–267, https://doi.org/10.1016/j.gloenvcha.2016.06.004, 2017.
Frieler, K., Schauberger, B., Arneth, A., Balkovič, J., Chryssanthacopoulos, J., Deryng, D., Elliott, J., Folberth, C., Khabarov, N., Müller, C., Olin, S., Pugh, T. A. M., Schaphoff, S., Schewe, J., Schmid, E., Warszawski, L., and Levermann, A.: Understanding the weather signal in national crop-yield variability, Earths Future, 5, 605–616, https://doi.org/10.1002/2016EF000525, 2017.
Fritz, S., See, L., Mccallum, I., You, L., Bun, A., Moltchanova, E., Duerauer, M., Albrecht, F., Schill, C., Perger, C., Havlik, P., Mosnier, A., Thornton, P., Wood-Sichra, U., Herrero, M., Becker-Reshef, I., Justice, C., Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G., Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F., Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner, S., Rajak, D. R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov, A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., van der Velde, M., Dunwoody, A., Kraxner, F., and Obersteiner, M.: Mapping global cropland and field size, Global Change Biol., 21, 1980–1992, https://doi.org/10.1111/gcb.12838, 2015.
Fujimori, S., Masui, T., and Matsuoka, Y.: AIM/CGE [basic] manual, Center for Social and Environmental Systems Research, NIES: Tsukuba, Japan, 2012.
Gough, C., Garcia-Freites, S., Jones, C., Mander, S., Moore, B., Pereira, C., Röder, M., Vaughan, N., and Welfle, A.: Challenges to the use of BECCS as a keystone technology in pursuit of 1.5 ∘C, Global Sustainability, 1, E5, https://doi.org/10.1017/sus.2018.3, 2018.
Gustavsson, J. and Cederberg, C.: Global Food losses and Food waste, in: Save Food Congress, Düsseldorf, Germany, 16 May 2011.
Haberl, H., Erb, K. H., Krausmann, F., Gaube, V., Bondeau, A., Plutzar, C., Gingrich, S., Lucht, W., and Fischer-Kowalski, M.: Quantifying and mapping the human appropriation of net primary production in earth's terrestrial ecosystems, P. Natl. Acad. Sci. USA, 104, 12942–12947, https://doi.org/10.1073/pnas.0704243104, 2007.
Havlík, P., Valin, H., Herrero, M., Obersteiner, M., Schmid, E., Rufino, M. C., Mosnier, A., Thornton, P. K., Böttcher, H., Conant, R. T., Frank, S., Fritz, S., Fuss, S., Kraxner, F., and Notenbaert, A.: Climate change mitigation through livestock system transitions, P. Natl. Acad. Sci. USA, 111, 3709–3714, https://doi.org/10.1073/pnas.1308044111, 2014.
Herrero, M., Henderson, B., Havlík, P., Thornton, P. K., Conant, R. T., Smith, P., Wirsenius, S., Hristov, A. N., Gerber, P., Gill, M., Butterbach-Bahl, K., Valin, H., Garnett, T., and Stehfest, E.: Greenhouse gas mitigation potentials in the livestock sector, Nat. Clim. Chang., 6, 452–461, https://doi.org/10.1038/nclimate2925, 2016.
Holman, B. W. B. and Malau-Aduli, A. E. O.: Spirulina as a livestock supplement and animal feed, J. Anim. Physiol. An. N., 97, 615–623, https://doi.org/10.1111/j.1439-0396.2012.01328.x, 2013.
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., and Fischer, G.: Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands, Climatic Change, 109, 117–161, https://doi.org/10.1007/s10584-011-0153-2, 2011.
Iizumi, T., Furuya, J., Shen, Z., Kim, W., Okada, M., Fujimori, S., Hasegawa, T., and Nishimori, M.: Responses of crop yield growth to global temperature and socioeconomic changes, Sci. Rep., 7, 1–10, https://doi.org/10.1038/s41598-017-08214-4, 2017.
KC, S. and Lutz, W.: The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100, Global Environ. Chang., 42, 181–192, https://doi.org/10.1016/j.gloenvcha.2014.06.004, 2017.
Kearney, J.: Food consumption trends and drivers, Philos. T. Roy. Soc. B, 365, 2793–2807, https://doi.org/10.1098/rstb.2010.0149, 2010.
Kitinoja, L.: Use of cold chains for reducing food losses in developing countries, PEF White Pap., 6, 1–16, 2013.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Krausmann, F., Erb, K. H., Gingrich, S., Lauk, C., and Haberl, H.: Global patterns of socioeconomic biomass flows in the year 2000: A comprehensive assessment of supply, consumption and constraints, Ecol. Econ., 65, 471–487, https://doi.org/10.1016/j.ecolecon.2007.07.012, 2008.
Lambin, E. F. and Meyfroidt, P.: Global land use change, economic globalization, and the looming land scarcity, P. Natl. Acad. Sci. USA, 108, 3465–3472, https://doi.org/10.1073/pnas.1100480108, 2011.
Lambin, E. F., Geist, H. J., and Lepers, E.: Dynamics of land-use and land-cover change in tropical regions, Annu. Rev. Env. Resour., 28, 205–241, https://doi.org/10.1146/annurev.energy.28.050302.105459, 2003.
Lipinski, B., Hanson, C., Lomax, J., Kitinoja, L., Waite, R., and Searchinger, T.: Reducing Food Loss and Waste, working paper, World Resources Institute, available at: https://www.wri.org/research/reducing-food-loss-and-waste (last access: 7 December 2021), 2013.
Liu, C., Hotta, Y., Santo, A., Hengesbaugh, M., Watabe, A., Totoki, Y., Allen, D., and Bengtsson, M.: Food waste in Japan: Trends, current practices and key challenges, J. Clean. Prod., 133, 557–564, https://doi.org/10.1016/j.jclepro.2016.06.026, 2016.
Mohanty, S. K. and Swain, M. R.: Chapter 3 – Bioethanol Production From Corn and Wheat: Food, Fuel, and Future, in: Bioethanol Production from Food Crops, Elsevier Inc., 45-59, ISBN: 978-0-12-813766-6, 2019.
Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., and Foley, J. A.: Closing yield gaps through nutrient and water management, Nature, 490, 254–257, https://doi.org/10.1038/nature11420, 2012.
Nicholls, Z. R. J., Meinshausen, M., Lewis, J., Gieseke, R., Dommenget, D., Dorheim, K., Fan, C.-S., Fuglestvedt, J. S., Gasser, T., Golüke, U., Goodwin, P., Hartin, C., Hope, A. P., Kriegler, E., Leach, N. J., Marchegiani, D., McBride, L. A., Quilcaille, Y., Rogelj, J., Salawitch, R. J., Samset, B. H., Sandstad, M., Shiklomanov, A. N., Skeie, R. B., Smith, C. J., Smith, S., Tanaka, K., Tsutsui, J., and Xie, Z.: Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response, Geosci. Model Dev., 13, 5175–5190, https://doi.org/10.5194/gmd-13-5175-2020, 2020.
Oerke, E. C. and Dehne, H. W.: Safeguarding production – Losses in major crops and the role of crop protection, Crop Prot., 23, 275–285, https://doi.org/10.1016/j.cropro.2003.10.001, 2004.
Oliveira Vieira, E., Venturoso, O. J., Reinicke, F., Cézar, C., Silva, D., Oliveira Porto, M., Cavali, J., Vieira, N. T., and Ferreira, E.: Production, Conservation and Health Assessment of Acid Silage Vicera of Freshwater Fish as a Component of Animal Feed, International Journal of Agriculture and Forestry, 5, 177–181, https://doi.org/10.5923/j.ijaf.20150503.01, 2015.
Parfitt, J., Barthel, M., and MacNaughton, S.: Food waste within food supply chains: Quantification and potential for change to 2050, Philos. T. R. Soc. B, 365, 3065–3081, https://doi.org/10.1098/rstb.2010.0126, 2010.
Pikaar, I., Matassa, S., Bodirsky, B. L., Weindl, I., Der, F. H., Rabaey, K., Boon, N., Bruschi, M., Yuan, Z., Van Zanten, H., Herrero, M., Verstraete, W., and Popp, A.: Decoupling Livestock from Land Use through Industrial Feed Production Pathways, Environ. Sci. Technol., 52, 7351–7359, https://doi.org/10.1021/acs.est.8b00216, 2018.
Popp, A., Rose, S. K., Calvin, K., Van Vuuren, D. P., Dietrich, J. P., Wise, M., Stehfest, E., Humpenöder, F., Kyle, P., Van Vliet, J., Bauer, N., Lotze-Campen, H., Klein, D., and Kriegler, E.: Land-use transition for bioenergy and climate stabilization: model comparison of drivers, impacts and interactions with other land use based mitigation options, Clim. Change, 123, 495–509, https://doi.org/10.1007/s10584-013-0926-x, 2014.
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest, E., Bodirsky, B. L., Dietrich, J. P., Doelmann, J. C., Gusti, M., Hasegawa, T., Kyle, P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin, H., Waldhoff, S., Weindl, I., Wise, M., Kriegler, E., Lotze-Campen, H., Fricko, O., Riahi, K., and Van Vuuren, D. P.: Land-use futures in the shared socio-economic pathways, Global Environ. Chang., 42, 331–345, https://doi.org/10.1016/j.gloenvcha.2016.10.002, 2017.
Powell, T.: Closing loops to rebalance the global carbon cycle: Biomass flows modelling of global agricultural carbon fluxes, PhD Thesis, Univ. Exet., 2015.
Powell, T. W. R. and Lenton, T. M.: Future carbon dioxide removal via biomass energy constrained by agricultural efficiency and dietary trends, Energy Environ. Sci., 5, 8116–8133, https://doi.org/10.1039/c2ee21592f, 2012.
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C., and Foley, J. A.: Recent patterns of crop yield growth and stagnation, Nat. Commun., 3, 1293–1297, https://doi.org/10.1038/ncomms2296, 2012.
Ray, D. K., Gerber, J. S., Macdonald, G. K., and West, P. C.: Climate variation explains a third of global crop yield variability, Nat. Commun., 6, 1–9, https://doi.org/10.1038/ncomms6989, 2015.
Roe, S., Streck, C., Obersteiner, M., Frank, S., Griscom, B., Drouet, L., Fricko, O., Gusti, M., Harris, N., Hasegawa, T., Hausfather, Z., Havlík, P., House, J., Nabuurs, G., Popp, A., José, M., Sánchez, S., Sanderman, J., Smith, P., Stehfest, E., and Lawrence, D.: Contribution of the land sector to a 1.5 ∘C world, Nat. Clim. Chang., 9, 817–828, https://doi.org/10.1038/s41558-019-0591-9, 2019.
Rogelj, J., Shindell, D., Jiang, K., Fifita, S., Forster, P., Ginzburg, V., Handa, C., Kheshgi, H., Kobayashi, S., Kriegler, E., Mundaca, L., Séférian, R. and Vilariño, M. V.: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development, in: Global Warming of 1.5 ∘C, in: IPCC Special Report on the impacts of global warming of 1.5 ∘C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, IPCC, 2022.
Röös, E., Bajželj, B., Smith, P., Patel, M., Little, D., and Garnett, T.: Greedy or needy? Land use and climate impacts of food in 2050 under different livestock futures, Global Environ. Chang., 47, 1–12, https://doi.org/10.1016/j.gloenvcha.2017.09.001, 2017.
Sarofim, M. C., Smith, J. B., St. Juliana, A., and Hartin, C.: Improving reduced complexity model assessment and usability, Nat. Clim. Chang., 11, 9–11, https://doi.org/10.1038/s41558-020-00973-9, 2021.
Savary, S., Ficke, A., Aubertot, J. N., and Hollier, C.: Crop losses due to diseases and their implications for global food production losses and food security, Food Secur., 4, 519–537, https://doi.org/10.1007/s12571-012-0200-5, 2012.
Shepon, A., Eshel, G., Noor, E., and Milo, R.: Energy and protein feed-to-food conversion efficiencies in the US and potential food security gains from dietary changes, Environ. Res. Lett., 11, 105002, https://doi.org/10.1088/1748-9326/11/10/105002, 2016.
Singh, I. D. and Stoskopf, N. C.: Harvest Index in Cereals1, Agron. J., 63, 224–226, https://doi.org/10.2134/agronj1971.00021962006300020008x, 1971.
Stancu, V., Haugaard, P., and Lähteenmäki, L.: Determinants of consumer food waste behaviour: Two routes to food waste, Appetite, 96, 7–17, https://doi.org/10.1016/j.appet.2015.08.025, 2016.
Swain, M., Blomqvist, L., McNamara, J., and Ripple, W. J.: Reducing the environmental impact of global diets, Sci. Total Environ., 610–611, 1207–1209, https://doi.org/10.1016/j.scitotenv.2017.08.125, 2018.
Willett, W., Rockström, J., Loken, B., et al.: Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems, The Lancet, 393, 10170, 447–492, https://doi.org/10.1016/S0140-6736(18)31788-4, 2019.
Thornton, P. K.: Livestock production: Recent trends, future prospects, Philos. T. R. Soc. B, 365, 2853–2867, https://doi.org/10.1098/rstb.2010.0134, 2010.
Tilman, D. and Clark, M.: Global diets link environmental sustainability and human health, Nature, 515, 518–522, https://doi.org/10.1038/nature13959, 2014.
Tsugane, S. and Sawada, N.: The JPHC study: Design and some findings on the typical Japanese diet, Jpn. J. Clin. Oncol., 44, 777–782, https://doi.org/10.1093/jjco/hyu096, 2014.
Tufarelli, V., Ragni, M., and Laudadio, V.: Feeding forage in poultry: A promising alternative for the future of production systems, Agriculture, 8, 1–10, https://doi.org/10.3390/agriculture8060081, 2018.
UNFCCC: Conference of the Parties, Adoption of the Paris Agreement, U.N. Doc. FCCC/CP/2015/L.9/Rev/1, available at: https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf (last access: 7 December 2021), 2015.
United Nations Environment Programme: Food Waste Index – Report 2021, Report, ISBN: 978-92-807-3851-3, 2021.
Van Dyk, J. S., Gama, R., Morrison, D., Swart, S., and Pletschke, B. I.: Food processing waste: Problems, current management and prospects for utilisation of the lignocellulose component through enzyme synergistic degradation, Renew. Sust. Energ. Rev., 26, 521–531, https://doi.org/10.1016/j.rser.2013.06.016, 2013.
Van Rossum, G. and Drake Jr., F. L.: Python reference manual, Centrum voor Wiskunde en Informatica, Amsterdam, 1995.
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative concentration pathways: an overview, Clim. Change, 109, 5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011.
Van Zanten, H. H. E., Herrero, M., Van Hal, O., Röös, E., Muller, A., Garnett, T., Gerber, P. J., Schader, C., and De Boer, I. J. M.: Defining a land boundary for sustainable livestock consumption, Global Change Biol., 24, 4185–4194, https://doi.org/10.1111/gcb.14321, 2018.
Vaughan, N. E., Gough, C., Mander, S., Littleton, E. W., Welfle, A., Gernaat, D. E. H. J., and Van Vuuren, D. P.: Evaluating the use of biomass energy with carbon capture and storage in low emission scenarios, Environ. Res. Lett., 13, 044014, https://doi.org/10.1088/1748-9326/aaaa02, 2018.
Weindl, I., Popp, A., Bodirsky, B. L., Rolinski, S., Lotze-Campen, H., Biewald, A., Humpenöder, F., Dietrich, J. P., and Stevanović, M.: Livestock and human use of land: Productivity trends and dietary choices as drivers of future land and carbon dynamics, Global Planet. Change, 159, 1–10, https://doi.org/10.1016/j.gloplacha.2017.10.002, 2017.
Winkler, K., Fuchs, R., Rounsevell, M., and Herold, M.: Global land use changes are four times greater than previously estimated, Nat. Commun., 12, 1–10, https://doi.org/10.1038/s41467-021-22702-2, 2021.
Wirsenius, S., Azar, C., and Berndes, G.: How much land is needed for global food production under scenarios of dietary changes and livestock productivity increases in 2030?, Agr. Syst., 103, 621–638, https://doi.org/10.1016/j.agsy.2010.07.005, 2010.
World Bank: Worldwide Governance IndicatorsWorldwide Governance Indicators, available at: https://databank.worldbank.org/source/worldwide-governance-indicators/ (last access: 13 January 2022), 2020.
C-LLAMA is a simple model of the global food system operating at a country level from 2013 to 2050. The model begins with projections of diet composition and populations for each country, producing a demand for each food commodity and finally an agricultural land use in each country. The model can be used to explore the sensitivity of agricultural land use to various drivers within the food system at country, regional, and continental spatial aggregations.
C-LLAMA is a simple model of the global food system operating at a country level from 2013 to...