Articles | Volume 11, issue 6
https://doi.org/10.5194/gmd-11-2353-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-2353-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
19 Jun 2018
Model description paper |
| 19 Jun 2018
| 19 Jun 2018
TAMSAT-ALERT v1: a new framework for agricultural decision support
Dagmawi Asfaw
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, UK
Emily Black
Department of Meteorology, University of Reading, Reading, UK
Matthew Brown
Department of Atmospheric, Oceanic and Planetary Physics, University
of Oxford, Oxford, UK
Kathryn Jane Nicklin
School of Earth and Environment, University of Leeds, Leeds, UK
Frederick Otu-Larbi
Ghana Meteorological Agency, Accra, Ghana
Ewan Pinnington
Department of Meteorology, University of Reading, Reading, UK
Andrew Challinor
School of Earth and Environment, University of Leeds, Leeds, UK
Ross Maidment
Department of Meteorology, University of Reading, Reading, UK
Tristan Quaife
Department of Meteorology, University of Reading, Reading, UK
Related authors
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Bethan L. Harris, Tristan Quaife, Christopher M. Taylor, and Phil P. Harris
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-2, https://doi.org/10.5194/esd-2024-2, 2024
Preprint under review for ESD
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The response of vegetation productivity to rainfall is a crucial process linking the water and carbon cycles and influencing the evolution of the climate system. However, there are many uncertainties in its representation in Earth System Models. This work uses a range of Earth Observation products to show that these models produce very different vegetation productivity responses to short-term rainfall events due to their differing sensitivities to processes driving water availability.
Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool
Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023, https://doi.org/10.5194/esd-14-1295-2023, 2023
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We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.
Samantha Petch, Bo Dong, Tristan Quaife, Robert P. King, and Keith Haines
Hydrol. Earth Syst. Sci., 27, 1723–1744, https://doi.org/10.5194/hess-27-1723-2023, https://doi.org/10.5194/hess-27-1723-2023, 2023
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Gravitational measurements of water storage from GRACE (Gravity Recovery and Climate Experiment) can improve understanding of the water budget. We produce flux estimates over large river catchments based on observations that close the monthly water budget and ensure consistency with GRACE on short and long timescales. We use energy data to provide additional constraints and balance the long-term energy budget. These flux estimates are important for evaluating climate models.
Caitlyn A. Hall, Sam Illingworth, Solmaz Mohadjer, Mathew Koll Roxy, Craig Poku, Frederick Otu-Larbi, Darryl Reano, Mara Freilich, Maria-Luisa Veisaga, Miguel Valencia, and Joey Morales
Geosci. Commun., 5, 275–280, https://doi.org/10.5194/gc-5-275-2022, https://doi.org/10.5194/gc-5-275-2022, 2022
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In this manifesto, we offer six points of reflection that higher education geoscience educators can act upon to recognise and unlearn their biases and diversify the geosciences in higher education, complementing current calls for institutional and organisational change. This serves as a starting point to gather momentum to establish community-built opportunities for implementing and strengthening diversity, equity, inclusion, and justice holistically in geoscience education.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
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We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife
Hydrol. Earth Syst. Sci., 25, 1617–1641, https://doi.org/10.5194/hess-25-1617-2021, https://doi.org/10.5194/hess-25-1617-2021, 2021
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Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth's surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in situ soil moisture sensors after combining model predictions with satellite observations.
Camilla Mathison, Andrew J. Challinor, Chetan Deva, Pete Falloon, Sébastien Garrigues, Sophie Moulin, Karina Williams, and Andy Wiltshire
Geosci. Model Dev., 14, 437–471, https://doi.org/10.5194/gmd-14-437-2021, https://doi.org/10.5194/gmd-14-437-2021, 2021
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Sequential cropping (also known as multiple or double cropping) is a common cropping system, particularly in tropical regions. Typically, land surface models only simulate a single crop per year. To understand how sequential crops influence surface fluxes, we implement sequential cropping in JULES to simulate all the crops grown within a year at a given location in a seamless way. We demonstrate the method using a site in Avignon, four locations in India and a regional run for two Indian states.
Ewan Pinnington, Tristan Quaife, Amos Lawless, Karina Williams, Tim Arkebauer, and Dave Scoby
Geosci. Model Dev., 13, 55–69, https://doi.org/10.5194/gmd-13-55-2020, https://doi.org/10.5194/gmd-13-55-2020, 2020
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We present LAVENDAR, a mathematical method for combining observations with models of the terrestrial environment. Here we use it to improve estimates of crop growth in the UK Met Office land surface model. However, the method is model agnostic, requires no modification to the underlying code and can be applied to any part of the model. In the example application we improve estimates of maize yield by 74 % by assimilating observations of leaf area, crop height and photosynthesis.
Camilla Mathison, Chetan Deva, Pete Falloon, and Andrew J. Challinor
Earth Syst. Dynam., 9, 563–592, https://doi.org/10.5194/esd-9-563-2018, https://doi.org/10.5194/esd-9-563-2018, 2018
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Sowing and harvest dates are a significant source of uncertainty within crop models. South Asia is one region with a large uncertainty. We aim to provide more accurate sowing and harvest dates than currently available and that are relevant for climate impact assessments. This method reproduces the present day sowing and harvest dates for most parts of India and when applied to two future periods provides a useful way of modelling potential growing season adaptations to changes in future climate.
Ewan Pinnington, Tristan Quaife, and Emily Black
Hydrol. Earth Syst. Sci., 22, 2575–2588, https://doi.org/10.5194/hess-22-2575-2018, https://doi.org/10.5194/hess-22-2575-2018, 2018
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This paper combines satellite observations of precipitation and soil moisture to understand what key information they offer to improve land surface model estimates of soil moisture over Ghana. When both observations are combined with the chosen land surface model we reduce the unbiased root-mean-squared error in a 5-year model hindcast by 27 %; this bodes well for the production of improved soil moisture estimates over sub-Saharan Africa where subsistence farming remains prevalent.
Robin J. Hogan, Tristan Quaife, and Renato Braghiere
Geosci. Model Dev., 11, 339–350, https://doi.org/10.5194/gmd-11-339-2018, https://doi.org/10.5194/gmd-11-339-2018, 2018
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This paper describes a fast new method for calculating how much sunlight is absorbed and reflected by forests and other types of vegetation, rigorously taking account of the complex 3-D structure. Careful evaluation shows it to perform well even in difficult scenes with snow on the ground. The method is suitable for use within the computer models used to make weather and climate forecasts, where it has the potential to improve predictions of near-surface temperature and photosynthesis rates.
Karina Williams, Jemma Gornall, Anna Harper, Andy Wiltshire, Debbie Hemming, Tristan Quaife, Tim Arkebauer, and David Scoby
Geosci. Model Dev., 10, 1291–1320, https://doi.org/10.5194/gmd-10-1291-2017, https://doi.org/10.5194/gmd-10-1291-2017, 2017
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This study looks in detail at how well the crop model within the Joint UK Land Environment Simulator (JULES), a community land-surface model, is able to simulate irrigated maize in Nebraska. We use the results to point to future priorities for model development and describe how our methodology can be adapted to set up model runs for other sites and crop varieties.
A. Loew, P. M. van Bodegom, J.-L. Widlowski, J. Otto, T. Quaife, B. Pinty, and T. Raddatz
Biogeosciences, 11, 1873–1897, https://doi.org/10.5194/bg-11-1873-2014, https://doi.org/10.5194/bg-11-1873-2014, 2014
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Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
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We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
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This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Jiachen Lu, Negin Nazarian, Melissa Hart, Scott Krayenhoff, and Alberto Martilli
EGUsphere, https://doi.org/10.5194/egusphere-2023-2811, https://doi.org/10.5194/egusphere-2023-2811, 2023
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based "mass flux" term. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
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We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion by either uniform erosion processes where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea level history, material properties, and the relative influence of different erosional processes.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
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In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
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The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
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Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
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Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
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We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-34, https://doi.org/10.5194/gmd-2022-34, 2023
Revised manuscript accepted for GMD
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Especially over the mid-latitudes precipiation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-GCM, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-GCM. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to a cloud ice loss and to an improvement of the process rate bias.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
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To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Cited articles
Asfaw, D., Black, E., Brown, M., Nicklin, K. J., Otu-Larbi, F., Pinnington, E., Challinor, A., Maidment, R., and
Quaife, T.: TAMSAT-ALERT v1: A new framework for agricultural decision support,
https://doi.org/10.5281/zenodo.1164603, 2018.
Bannayan, M., Crout, N. M., and Hoogenboom, G.: Application of the
CERES-Wheat model for within-season prediction of winter wheat yield in the
United Kingdom, Agron. J., 95, 114–125, https://doi.org/10.2134/agronj2003.0114,
2003.
Barnston, A. G. and Tippett, M. K.: Climate information, outlooks, and
understanding-where does the IRI stand?, Earth Perspectives, 1, 20,
https://doi.org/10.1186/2194-6434-1-20, 2014.
Black, E., Greatrex, H., Young, M., and Maidment, R.: Incorporating
satellite data into weather index insurance, B. Am.
Meteorol. Soc., 97, ES203–ES206, https://doi.org/10.1175/BAMS-D-16-0148.1,
2016.
Boyd, E., Cornforth, R. J., Lamb, P. J., Tarhule, A., Lélé, M. I., and Brouder, A.: Building resilience to face
recurring environmental crisis in African Sahel, Nat. Clim. Change, 3,
631–638, https://doi.org/10.1038/NCLIMATE1856, 2013.
Brown, M., Black, E., Asfaw, D., and Otu-larbi, F.: Monitoring drought in
Ghana using TAMSAT-ALERT: a new decision support system, Weather-Royal
Meteorology Society, 72, 201–205, https://doi.org/10.1002/wea.3033, 2017.
Canal, N., Deudon, O., Le Bris, X., Gate, P., Pigeon, G., Regimbeau, M., and
Calvet, J.-C.: Anticipation of the winter wheat growth based on seasonal
weather forecasts over France, Meteorol. Appl., 24, 432–443,
https://doi.org/10.1002/met.1642, 2017.
Challinor, A. J. and Wheeler, T. R.: Crop yield reduction in the tropics
under climate change: Processes and uncertainties, Agr. Forest
Meteorol., 148, 343–356, https://doi.org/10.1016/j.agrformet.2007.09.015, 2008.
Challinor, A., Wheeler, T. R., Osborne, T. M., and Slingo, J. M.: Assessing the vulnerability of
crop productivity to climate change thresholds using an integrated crop-climate model, in:
Avoiding Dangerous Climate Change, edited by: Schellnhuber, J., Cramer, W., Nakicenovic, N.,
Yohe, G., and Wigley, T., Cambridge University Press, United Kingdom, 187–194, doi10.2277/0521864712,
2006.
Challinor, A. J., Wheeler, T. R., Craufurd, P. Q., Slingo, J. M., and
Grimes, D. I. F.: Design and optimisation of a large-area process-based
model for annual crops, Agr. Forest Meteorol., 124, 99–120,
https://doi.org/10.1016/j.agrformet.2004.01.002, 2004.
Challinor, A. J., Slingo, J. M., Wheeler, T. R., and Doblas-Reyes, F. J.:
Probabilistic simulations of crop yield over western India using the DEMETER
seasonal hindcast ensembles, Tellus A, 57, 498–512, https://doi.org/10.3402/tellusa.v57i3.14670, 2005.
Challinor, A. J., Wheeler, T. R., Craufurd, P. Q., Ferro, C. A. T., and
Stephenson, D. B.: Adaptation of crops to climate change through genotypic
responses to mean and extreme temperatures, Agr. Ecosyst.
Environ., 119, 190–204, https://doi.org/10.1016/j.agee.2006.07.009, 2007.
Challinor, A. J., Simelton, E. S., Fraser, E. D. G., Hemming, D., and
Collins, M.: Increased crop failure due to climate change: assessing
adaptation options using models and socio-economic data for wheat in China,
Environ. Res. Lett., 5, 034012,
https://doi.org/10.1088/1748-9326/5/3/034012, 2010.
Dzotsi, K., Agboh-Noameshie, A., Struif Bontkes, T., Singh, U., and Dejean,
P.: Using DSSAT to derive optimum combinations of cultivar and sowing date
for maize in southern Togo, in: Decision Support Tools for Smallholder
Agriculture in Sub-Saharan Africa; A Practical Guide, edited by: Bontkes, T.
and Wopereis, M., IFDC Muscle Shoals, USA, and CTA,
Wageningen, 100–112, 2003.
FAO/WFP-Global Information and Early Warning System on food and agriculture
(FAO/WFP-GIEWS): Special report FAO/WFP crop and food supply assessment
mission to northern Ghana, available at:
http://www.fao.org/docrep/005/y6325e/y6325e00.htm (last access: June 2018), 2002.
Hansen, J. W. and Indeje, M.: Linking dynamic seasonal climate forecasts with
crop simulation for maize yield prediction in semi-arid Kenya, Agr. Forest Meteorol., 125, 143–157, https://doi.org/10.1016/j.agrformet.2004.02.006,
2004.
Hansen, J. W., Challinor, A., Ines, A., Wheeler, T., and Moron, V.:
Translating climate forecasts into agricultural terms: Advances and
challenges, Clim. Res., 33, 27–41, https://doi.org/10.3354/cr033027, 2006.
Kassie, B. T., Van Ittersum, M. K., Hengsdijk, H., Asseng, S., Wolf, J., and
Rötter, R. P.: Climate-induced yield variability and
yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia,
Field Crop. Re., 160, 41–53, https://doi.org/10.1016/j.fcr.2014.02.010, 2014.
Kassie, B. T., Asseng, S., Rotter, R. P., Hengsdijk, H., Ruane, A. C., and
Van Ittersum, M. K.: Exploring climate change impacts and adaptation options
for maize production in the Central Rift Valley of Ethiopia using different
climate change scenarios and crop models, Clim. Change, 129, 145–158,
https://doi.org/10.1007/s10584-014-1322-x, 2015.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A.,
Zhang, Q., Van Den Dool, H., Saha, S., Mendez, M. P., Becker, E., Peng, P.,
Tripp, P., Huang, J., Dewitt, D. G., Tippett, M. K., Barnston, A. G., Li,
S., Rosati, A., Schubert, S. D., Rienecker, M., Suarez, M., Li, Z. E.,
Marshak, J., Lim, Y. K., Tribbia, J., Pegion, K., Merryfield, W. J., Denis,
B., and Wood, E. F.: The North American multimodel ensemble: Phase-1
seasonal-to-interannual prediction; phase-2 toward developing intraseasonal
prediction, B. Am. Meteorol. Soc., 95, 585–601,
https://doi.org/10.1175/BAMS-D-12-00050.1, 2014.
MacCarthy, D. S., Adiku, S. G. K., Freduah, B. S., and Gbefo, F.: Using
CERES-Maize and ENSO as Decision Support Tools to Evaluate Climate-Sensitive
Farm Management Practices for Maize Production in the Northern Regions of
Ghana, Front. Plant Sci., 8, 1–13, https://doi.org/10.3389/fpls.2017.00031,
2017.
MacCarthy, D. S., Kihara, J., Masikati, P., and Adiku, S. G.
K.: Decision support tools for site-specific fertilizer recommendations and agricultural planning in selected countries
in sub-Sahara Africa, Nutr. Cycl. Agroecosys., 110, 343–359,
https://doi.org/10.1007/s10705-017-9877-3, 2018.
Maidment, R. I., Grimes, D., Black, E., Tarnavsky, E., Young, M., Greatrex,
H., Allan, R. P., Stein, T., Nkonde, E.,
Senkunda, S., and Alcantara, E. M. U.: A new, long-term daily
satellite-based rainfall dataset for operational monitoring
in Africa, Scientific Data, 4, 1–19, https://doi.org/10.1038/sdata.2017.63, 2017.
Martey, E., Wiredu, A. N., Etwire, P. M., Buah, S. S. J., Fosu, M.,
Bidzakin, J., Ahiabor, B. D. K., and Kusi, F.: Fertilizer Adoption and Use
Intensity Among Smallholder Farmers in Northern Ghana: A Case Study of the
AGRA Soil Health Project, Sustainable Agriculture Research, 3, 24–36,
https://doi.org/10.5539/sar.v3n1p24, 2014.
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet. Part 2:
Geographic distribution of crop areas, yields, physiological types, and net
primary production in the year 2000, Glob. Biogeochem. Cy., 22,
GB1022, https://doi.org/10.1029/2007GB002947, 2008.
Muller, C., Cramer, W., Hare, W. L., and Lotze-Campen, H.: Climate change risks
for African agriculture, P. Natl. Acad. Sci. USA, 108, 4313–4315,
https://doi.org/10.1073/pnas.1015078108, 2011.
Muller, W. A., Appenzeller, C., Doblas-Reyes, F. J., and Liniger, M. A.: A
debiased ranked probability skill score to evaluate probabilistic ensemble
forecasts with small ensemble sizes, J. Climate, 18, 1513–1523,
https://doi.org/10.1175/JCLI3361.1, 2005.
Obeng-Antwi, K., Manfred Ewool, A. H., Abate, T., Menkir, A., Badu-Apraku,
B., and Abdoulaye, T.: New Drought Tolerant Maize Varieties for Ghana, A
Quarterly Bulletin of the Drought Tolerant Maize for Africa Project, available at: http://dtma.cimmyt.org (last access: June 2018), 2,
1–4, 2013.
Osborne, T., Rose, G., and Wheeler, T.: Variation in the global-scale
impacts of climate change on crop productivity due to climate model
uncertainty and adaptation, Agr. Forest Meteorol., 170,
183–194, https://doi.org/10.1016/j.agrformet.2012.07.006, 2013.
Osborne, T. M., Lawrence, D. M., Challinor, A. J., Slingo, J. M., and
Wheeler, T. R.: Development and assessment of a coupled crop-climate model,
Glob. Change Biol., 13, 169–183, 2007.
Owusu, K. and Waylen, P.: Trends in spatio-temporal variability in annual
rainfall in Ghana (1951–2000), Weather, 64, 115–120, https://doi.org/10.1002/wea.255,
2009.
Owusu, K. and Waylen, P. R.: The changing rainy season climatology of
mid-Ghana, Theor. Appl. Climatol., 112, 419–430,
https://doi.org/10.1007/s00704-012-0736-5, 2013.
PARI: Potentials and Possibilities for German Collaboration in Agriculture,
Program of Accompanying Research for Agricultural Innovation, available at:
http://research4agrinnovation.org/wp-content/uploads/2016/03/Ghana.pdf (last access: June 2018),
2015.
Parkes, B., Challinor, A., and Nicklin, K.: Crop failure rates in a
geoengineered climate: impact of climate change and marine cloud
brightening, Environ. Res. Lett., 10, 084003,
https://doi.org/10.1088/1748-9326/10/8/084003, 2015.
Ragasa, C., Dankyi, A., Acheampong, P., Wiredu, A. N., Chapoto, A., Asamoah,
M., and Tripp, R.: Patterns of adoption of improved rice technologies in
Ghana, Ghana Strategy Support Program (GSSP) Working Paper, 36, 1–33,
https://doi.org/10.13140/2.1.5093.4727, 2013.
Ramirez-Villegas, J. and Challinor, A. J.: Towards a genotypic adaptation
strategy for Indian groundnut cultivation using an ensemble of crop
simulations, Clim. Change, 138, 223–238, https://doi.org/10.1007/s10584-016-1717-y,
2016.
Ramirez-Villegas, J., Koehler, A. K., and Challinor, A. J.: Assessing
uncertainty and complexity in regional-scale crop model simulations,
Eur. J. Agron., 88, 84–95, https://doi.org/10.1016/j.eja.2015.11.021,
2015a.
Ramirez-Villegas, J., Watson, J., and Challinor, A. J.: Identifying traits
for genotypic adaptation using crop models, J. Exp. Bot.,
66, 3451–3462, https://doi.org/10.1093/jxb/erv014, 2015b.
Semenov, M. A. and Doblas-Reyes, F. J.: Utility of dynamical seasonal
forecasts in predicting crop yield, Clim. Res., 34, 71–81,
https://doi.org/10.3354/Cr034071, 2007.
Sheffield, J., Wood, E. F., Chaney, N., Guan, K., Sadri, S., Yuan, X., Olang,
L., Amani, A., Ali, A., Demuth, S., and Ogallo, L.: A drought monitoring and
forecasting system for sub-Sahara African water resources and food security,
B. Am. Meteorol. Soc., 95, 861–882,
https://doi.org/10.1175/BAMS-D-12-00124.1, 2014.
Sidibe, Y., Williams, T. O., and Kolavalli, S.: Flood recession agriculture
for food security in Northern Ghana: Literature review on extent,
challenges, and opportunities, Ghana Strategy Support Program working paper,
42, 1–18, https://doi.org/10.13140/RG.2.1.3250.8405, 2016.
Tarhule, A., Saley-Bana, Z., and Lamb, P. J.: RainWatch: A prototype GIS for
rainfall monitoring in West Africa, B. Am. Meteorol.
Soc., 90, 1607–1614, https://doi.org/10.1175/2009BAMS2697.1, 2009.
Watson, J. and Challinor, A.: The relative importance of rainfall,
temperature and yield data for a regional-scale crop
model, Agr. Forest Meteorol., 170, 47–57, https://doi.org/10.1016/j.agrformet.2012.08.001,
2013.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo,
P.: The WFDEI meteorological forcing data set: WATCH Forcing data
methodology applied to ERA-Interim reanalysis data, Water Resour.
Res., 50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014.
Weigel, A. P., Liniger, M. A., and Appenzeller, C.: Generalization of the
Discrete Brier and Ranked Probability Skill Scores for Weighted Multimodel
Ensemble Forecasts, Mon. Weather Rev., 135, 2778–2785,
https://doi.org/10.1175/MWR3428.1, 2007.
Short summary
TAMSAT-ALERT is a framework for combining observational and forecast information into continually updated assessments of the likelihood of user-defined adverse events like low cumulative rainfall or lower than average crop yield. It is easy to use and flexible to accommodate any impact model that uses meteorological data. The results show that it can be used to monitor the meteorological impact on yield within a growing season and to test the value of routinely issued seasonal forecasts.
TAMSAT-ALERT is a framework for combining observational and forecast information into...
