Articles | Volume 8, issue 11
https://doi.org/10.5194/gmd-8-3639-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-3639-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
06 Nov 2015
Model description paper |
| 06 Nov 2015
S4CAST v2.0: sea surface temperature based statistical seasonal forecast model
R. Suárez-Moreno
CORRESPONDING AUTHOR
Departamento de Geofísica y Meteorología, Facultad de Físicas, Universidad Complutense de Madrid, Plaza de las Ciencias 1, 28040 Madrid, Spain
Instituto de Geociencias (IGEO), Facultad de Ciencias Geológicas, Universidad Complutense de Madrid – CSIC, C/José Antonio Novais 12, 28040 Madrid, Spain
B. Rodríguez-Fonseca
Departamento de Geofísica y Meteorología, Facultad de Físicas, Universidad Complutense de Madrid, Plaza de las Ciencias 1, 28040 Madrid, Spain
Instituto de Geociencias (IGEO), Facultad de Ciencias Geológicas, Universidad Complutense de Madrid – CSIC, C/José Antonio Novais 12, 28040 Madrid, Spain
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Geosci. Model Dev., 16, 1427–1444, https://doi.org/10.5194/gmd-16-1427-2023, https://doi.org/10.5194/gmd-16-1427-2023, 2023
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Haopeng Fan, Siran Li, Zhongmiao Sun, Guorui Xiao, Xinxing Li, and Xiaogang Liu
Geosci. Model Dev., 16, 1345–1358, https://doi.org/10.5194/gmd-16-1345-2023, https://doi.org/10.5194/gmd-16-1345-2023, 2023
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Gang Liu, Shushi Peng, Chris Huntingford, and Yi Xi
Geosci. Model Dev., 16, 1277–1296, https://doi.org/10.5194/gmd-16-1277-2023, https://doi.org/10.5194/gmd-16-1277-2023, 2023
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Suzanne Robinson, Ruza F. Ivanovic, Lauren J. Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul J. Valdes
Geosci. Model Dev., 16, 1231–1264, https://doi.org/10.5194/gmd-16-1231-2023, https://doi.org/10.5194/gmd-16-1231-2023, 2023
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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, https://doi.org/10.5194/gmd-16-779-2023, https://doi.org/10.5194/gmd-16-779-2023, 2023
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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, https://doi.org/10.5194/gmd-16-679-2023, https://doi.org/10.5194/gmd-16-679-2023, 2023
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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, https://doi.org/10.5194/gmd-16-705-2023, https://doi.org/10.5194/gmd-16-705-2023, 2023
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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, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
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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, https://doi.org/10.5194/gmd-16-449-2023, https://doi.org/10.5194/gmd-16-449-2023, 2023
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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, https://doi.org/10.5194/gmd-16-335-2023, https://doi.org/10.5194/gmd-16-335-2023, 2023
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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, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
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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, https://doi.org/10.5194/gmd-16-199-2023, https://doi.org/10.5194/gmd-16-199-2023, 2023
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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, https://doi.org/10.5194/gmd-16-179-2023, https://doi.org/10.5194/gmd-16-179-2023, 2023
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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, https://doi.org/10.5194/gmd-16-135-2023, https://doi.org/10.5194/gmd-16-135-2023, 2023
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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, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
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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, https://doi.org/10.5194/gmd-15-9157-2022, https://doi.org/10.5194/gmd-15-9157-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8831-2022, https://doi.org/10.5194/gmd-15-8831-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8809-2022, https://doi.org/10.5194/gmd-15-8809-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8669-2022, https://doi.org/10.5194/gmd-15-8669-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8581-2022, https://doi.org/10.5194/gmd-15-8581-2022, 2022
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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.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
EGUsphere, https://doi.org/10.5194/egusphere-2022-980, https://doi.org/10.5194/egusphere-2022-980, 2022
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Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth System Models. These updates include the ability to run the scheme on Graphics Processing Units (GPUs) and changes to the numerical description of precipitation, as well as a correction to ice number. There are big improvements in computational performance that can be achieved with GPU acceleration.
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, https://doi.org/10.5194/gmd-15-8411-2022, https://doi.org/10.5194/gmd-15-8411-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8245-2022, https://doi.org/10.5194/gmd-15-8245-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8221-2022, https://doi.org/10.5194/gmd-15-8221-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8111-2022, https://doi.org/10.5194/gmd-15-8111-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8135-2022, https://doi.org/10.5194/gmd-15-8135-2022, 2022
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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, https://doi.org/10.5194/gmd-15-8059-2022, https://doi.org/10.5194/gmd-15-8059-2022, 2022
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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.
Francisco José Cuesta-Valero, Hugo Beltrami, Stephan Gruber, Almudena García-García, and J. Fidel González-Rouco
Geosci. Model Dev., 15, 7913–7932, https://doi.org/10.5194/gmd-15-7913-2022, https://doi.org/10.5194/gmd-15-7913-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7751-2022, https://doi.org/10.5194/gmd-15-7751-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7683-2022, https://doi.org/10.5194/gmd-15-7683-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7489-2022, https://doi.org/10.5194/gmd-15-7489-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7505-2022, https://doi.org/10.5194/gmd-15-7505-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
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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.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
EGUsphere, https://doi.org/10.5194/egusphere-2022-943, https://doi.org/10.5194/egusphere-2022-943, 2022
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It is hard for scientists to write efficient code which runs fast on all kinds of supercomputers. They like writing Python because it is easier to read and use. We re-wrote a Fortran code that simulates weather and climate into Python. The Python code re-writes itself to a much faster language to run on either normal processors or graphics cards. On one big computer system, our code is 3.5–4x faster on its graphics cards than the original code is on its processors.
Ingo Wohltmann, Daniel Kreyling, and Ralph Lehmann
Geosci. Model Dev., 15, 7243–7255, https://doi.org/10.5194/gmd-15-7243-2022, https://doi.org/10.5194/gmd-15-7243-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7203-2022, https://doi.org/10.5194/gmd-15-7203-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7177-2022, https://doi.org/10.5194/gmd-15-7177-2022, 2022
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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, https://doi.org/10.5194/gmd-15-7153-2022, https://doi.org/10.5194/gmd-15-7153-2022, 2022
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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.
Sébastien Gardoll and Olivier Boucher
Geosci. Model Dev., 15, 7051–7073, https://doi.org/10.5194/gmd-15-7051-2022, https://doi.org/10.5194/gmd-15-7051-2022, 2022
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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, https://doi.org/10.5194/gmd-15-6985-2022, https://doi.org/10.5194/gmd-15-6985-2022, 2022
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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, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
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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, https://doi.org/10.5194/gmd-15-6817-2022, https://doi.org/10.5194/gmd-15-6817-2022, 2022
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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.
Cited articles
Adams, R. M., Chen, C. C., McCarl, B. A., and Weiher, R. F.: The economic consequences of ENSO events for agriculture, Clim. Res., 13, 165–172, 1999.
Ault, T. R., Cole, J. E., and St George, S.: The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models, Geophys. Res. Lett., 39, L21705, https://doi.org/10.1029/2012GL053424, 2012.
Baboo, S. S. and Shereef, I. K.: An efficient weather forecasting system using artificial neural network, International Journal of Environmental Science and Development, 1, 2010–0264, 2010.
Barnett, T. P.: Monte Carlo climate forecasting, J. Climate, 8, 1005–1022, 1995.
Barnett, T. P. and Preisendorfer, R.: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis, Mon. Weather Rev., 115, 1825–1850, 1987.
Barnett, T. P., Graham, N., Pazan, S., White, W., Latif, M., and Flügel, M.: ENSO and ENSO-related predictability. Part I: Prediction of equatorial Pacific sea surface temperature with a hybrid coupled ocean-atmosphere model, J. Climate, 6, 1545–1566, 1993.
Barnston, A. G.: Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score, Weather Forecast., 7, 699–709, 1992.
Barnston, A. G. and Ropelewski, C. F.: Prediction of ENSO episodes using canonical correlation analysis, J. Climate, 5, 1316–1345, 1992.
Barnston, A. G. and Smith, T. M.: Specification and prediction of global surface temperature and precipitation from global SST using CCA, J. Climate, 9, 2660–2697, 1996.
Barnston, A. G. and Tippett, M. K.: Climate information, outlooks, and understanding – where does the IRI stand?, Earth Perspectives, 1, 1–17, 2014.
Barnston, A. G. and van den Dool, H. M.: A degeneracy in cross-validated skill in regression-based forecasts, J. Climate, 6, 963–977, 1993.
Barnston, A. G., van den Dool, H. M., Rodenhuis, D. R., Ropelewski, C. R., Kousky, V. E., O'Lenic, E. A., and Leetmaa, A.: Long-lead seasonal forecasts-Where do we stand?, B. Am. Meteorol. Soc., 75, 2097–2114, 1994.
Barnston, A. G., He, Y., and Glantz, M. H.: Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset, B. Am. Meteorol. Soc., 80, 217–243, 1999.
Barnston, A. G., Tippet, M. K., van den Dool, H. M., and Unger, D. A.: Toward an Improved Multi-model ENSO Prediction, J. Appl. Meteorol. Clim., 54, 1579–1595, https://doi.org/10.1175/JAMC-D-14-0188.1, 2015.
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013.
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: from CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018, 2013.
Biasutti, M., Sobel, A. H., and Kushnir, Y.: AGCM precipitation biases in the tropical Atlantic, J. Climate, 19, 935–958, 2006.
Bjerknes, J.: Atmospheric teleconnections from the equatorial pacific 1, Mon. Weather Rev., 97, 163–172, 1969.
Bretherton, C. S., Smith, C., and Wallace, J. M.: An intercomparison of methods for finding coupled patterns in climate data, J. Climate, 5, 541–560, 1992.
Brown, J. N., Gupta, A. S., Brown, J. R., Muir, L. C., Risbey, J. S., Whetton, P., and Wijffels, S. E.: Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific, Climatic Change, 119, 147–161, 2013.
Bulić, I. H. and Kucharski, F.: Delayed ENSO impact on spring precipitation over North/Atlantic European region, Clim. Dynam., 382, 2593–2612, 2012.
Camberlin, P., Janicot, S., and Poccard, I.: Seasonality and atmospheric dynamics of the teleconnection between African rainfall and tropical sea-surface temperature: Atlantic vs. ENSO, Int. J. Climatol., 21, 973–1005, 2001.
Cane, M. A., Zebiak, S. E., and Dolan, S. C.: Experimental forecasts of EL Nino, Nature, 321, 827–832, 1986.
Chang, P., Fang, Y., Saravanan, R., Ji, L., and Seidel, H.: The cause of the fragile relationship between the Pacific El Nino and the Atlantic Nino, Nature, 443, 324–328, 2006.
Cherry, S.: Singular value decomposition analysis and canonical correlation analysis, J. Climate, 9, 2003–2009, 1996.
Cherry, S.: Some comments on singular value decomposition analysis, J. Climate, 10, 1759–1761, 1997.
Chung, C. E. and Ramanathan, V.: Weakening of North Indian SST gradients and the monsoon rainfall in India and the Sahel, J. Climate, 19, 2036–2045, 2006.
Coelho, C. A. S., Stephenson, D. B., Balmaseda, M., Doblas-Reyes, F. J., and van Oldenborgh, G. J.: Toward an integrated seasonal forecasting system for South America, J. Climate, 19, 3704–3721, 2006.
Dayan, H., Vialard, J., Izumo, T., and Lengaigne, M.: Does sea surface temperature outside the tropical Pacific contribute to enhanced ENSO predictability?, Clim. Dynam., 43, 1311–1325, 2014.
Deng, X., Huang, J., Qiao, F., Naylor, R. L., Falcon, W. P., Burke, M., and Battisti, D.: Impacts of El Nino-Southern Oscillation events on China's rice production, J. Geogr. Sci., 20, 3–16, 2010.
Diatta, S. and Fink, A. H.: Statistical relationship between remote climate indices and West African monsoon variability, Int. J. Climatol., 34, 3348–3367, https://doi.org/10.1002/joc.3912, 2014.
Ding, H., Keenlyside, N. S., and Latif, M.: Impact of the equatorial Atlantic on the El Nino southern oscillation, Clim. Dynam., 38, 1965–1972, 2012.
Doi, T., Vecchi, G. A., Rosati, A. J., and Delworth, T. L.: Biases in the Atlantic ITCZ in seasonal-interannual variations for a coarse-and a high-resolution coupled climate model, J. Climate, 25, 5494–5511, 2012.
Drosdowsky, W. and Chambers, L. E.: Near-global sea surface temperature anomalies as predictors of Australian seasonal rainfall, J. Climate, 14, 1677–1687, 2001.
Elsner, J. B. and Schmertmann, C. P.: Assessing forecast skill through cross validation, Weather Forecast., 9, 619–624, 1994.
Enfield, D. B. and Cid-Serrano, L.: Projecting the risk of future climate shifts, Int. J. Climatol., 26, 885–895, 2006.
Folland, C. K., Palmer, T. N., and Parker, D. E.: Sahel rainfall and worldwide sea temperatures, 1901–85, Nature, 320, 602–607, 1986.
Fontaine, B. and Janicot, S.: Sea surface temperature fields associated with West African rainfall anomaly types, J. Climate, 9, 2935–2940, 1996.
Fontaine, B., Trzaska, S., and Janicot, S.: Evolution of the relationship between near global and Atlantic SST modes and the rainy season in West Africa: statistical analyses and sensitivity experiments, Clim. Dynam., 14, 353–368, 1998.
Fontaine, B., Philippon, N., and Camberlin, P.: An improvement of June–September rainfall forecasting in the Sahel based upon region April–May moist static energy content (1968–1997), Geophys. Res. Lett., 26, 2041–2044, 1999.
Fontaine, B., Monerie, P. A., Gaetani, M., and Roucou, P.: Climate adjustments over the African-Indian monsoon regions accompanying Mediterranean Sea thermal variability, J. Geophys. Res.-Atmos., 116, D23122, https://doi.org/10.1029/2011JD016273, 2011.
Frankignoul, C. and Hasselmann, K.: Stochastic climate models, part II application to sea-surface temperature anomalies and thermocline variability, Tellus, 29, 289–305, 1977.
Gaetani, M., Fontaine, B., Roucou, P., and Baldi, M.: Influence of the Mediterranean Sea on the West African monsoon: Intraseasonal variability in numerical simulations, J. Geophys. Res.-Atmos., 115, D24115, https://doi.org/10.1029/2010JD014436, 2010.
Gardner, M. W. and Dorling, S. R.: Artificial neural networks (the multilayer perceptron)–a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627–2636, 1998.
Garric, G., Douville, H., and Déqué, M.: Prospects for improved seasonal predictions of monsoon precipitation over Sahel, Int. J. Climatol., 22, 331–345, 2002.
Giannini, A., Chiang, J. C., Cane, M. A., Kushnir, Y., and Seager, R.: The ENSO teleconnection to the tropical Atlantic Ocean: contributions of the remote and local SSTs to rainfall variability in the tropical Americas, J. Climate, 14, 4530–4544, 2001.
Giannini, A., Saravanan, R., and Chang, P.: Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales, Science, 302, 1027–1030, 2003.
Gill, A.: Some simple solutions for heat-induced tropical circulation, Q. J. Roy. Meteor. Soc., 106, 447–462, 1980.
Glahn, H. R. and Lowry, D. A.: The use of model output statistics (MOS) in objective weather forecasting, J. Appl. Meteorol., 11, 1203–1211, 1972.
Hansen, J. W., Hodges, A. W., and Jones, J. W.: ENSO Influences on Agriculture in the Southeastern United States, J. Climate, 11, 404–411, 1998.
Ham, Y. G., Kug, J. S., Park, J. Y., and Jin, F. F.: Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events, Nat. Geosci., 6, 112–116, 2013a.
Ham, Y. G., Sung, M. K., An, S. I., Schubert, S. D., and Kug, J. S.: Role of tropical Atlantic SST variability as a modulator of El Niño teleconnections, Asia-Pac. J. Atmos. Sci., 1–15, 2013b.
Harrison, D. E. and Larkin, N. K.: El Niño-Southern Oscillation sea surface temperature and wind anomalies, 1946–1993, Rev. Geophys., 36, 353–399, 1998.
Hasselmann, K.: Stochastic climate models part I. Theory, Tellus, 28, 473–485, 1976.
Haylock, M. R., Peterson, T. C., Alves, L. M., Ambrizzi, T., Anunciação, Y. M. T., Baez, J., and Vincent, L. A.: Trends in total and extreme South American rainfall in 1960–2000 and links with sea surface temperature, J. Climate, 19, 1490–1512, 2006.
Hsieh, W. W.: Nonlinear canonical correlation analysis of the tropical Pacific climate variability using a neural network approach, J. Climate, 14, 2528–2539, 2001.
Hsieh, W. W. and Tang, B.: Applying neural network models to prediction and data analysis in meteorology and oceanography, B. Am. Meteorol. Soc., 79, 1855–1870, 1998.
Janicot, S.: Spatiotemporal variability of West African rainfall. Part I: Regionalizations and typings, J. Climate, 5, 489–497, 1992.
Janicot, S., Moron, V., and Fontaine, B.: Sahel droughts and ENSO dynamics, Geophys. Res. Lett., 23, 515–518, 1996.
Janicot, S., Harzallah, A., Fontaine, B., and Moron, V.: West African monsoon dynamics and eastern equatorial Atlantic and Pacific SST anomalies (1970–88), J. Climate, 11, 1874–1882, 1998.
Janicot, S., Trzaska, S., and Poccard, I.: Summer Sahel-ENSO teleconnection and decadal time scale SST variations, Clim. Dynam., 18, 303–320, 2001.
Janowiak, J. E.: An investigation of interannual rainfall variability in Africa, J. Climate, 1, 240–255, 1988.
Ji, M., Kumar, A., and Leetmaa, A.: A multiseason climate forecast system at the National Meteorological Center, B. Am. Meteorol. Soc., 75, 569–577, 1994a.
Ji, M., Kumar, A., and Leetmaa, A.: An experimental coupled forecast system at the National Meteorological Center, Tellus A, 46, 398–418, 1994b.
Joly, M. and Voldoire, A.: Influence of ENSO on the West African monsoon: temporal aspects and atmospheric processes, J. Climate, 22, 3193–3210, 2009.
Keenlyside, N. S., Ding, H., and Latif, M.: Potential of equatorial Atlantic variability to enhance El Niño prediction, Geophys. Res. Lett., 40, 2278–2283, 2013.
Klein, S. A., Soden, B. J., and Lau, N. C.: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge, J. Climate, 12, 917–932, 1999.
Klein, W. H. and Glahn, H. R.: Forecasting local weather by means of model output statistics, B. Am. Meteorol. Soc., 55, 1217–1227, 1974.
Knutti, R., Stocker, T. F., Joos, F., and Plattner, G. K.: Probabilistic climate change projections using neural networks, Clim. Dynam., 21, 257–272, 2003.
Korecha, D. and Barnston, A. G.: Predictability of June–September rainfall in Ethiopia, Mon. Weather Rev., 135, 628–650, 2007.
Kovats, R. S.: El Niño and human health, B. World Health Organ., 78, 1127–1135, 2000.
Kovats, R. S., Bouma, M. J., Hajat, S., Worrall, E., and Haines, A.: El Niño and health, The Lancet, 362, 1481–1489, 2003.
Latif, M. and Barnett, T. P.: Interactions of the tropical oceans, J. Climate, 8, 952–964, 1995.
Legates, D. R. and Willmott, C. J.: Mean seasonal and spatial variability in gauge-corrected, global precipitation, Int. J. Climatol., 10, 111–127, 1990.
Legler, D. M., Bryant, K. J., and O'Brien, J. J.: Impact of ENSO-related climate anomalies on crop yields in the US, Climatic Change, 42, 351–375, 1999.
Li, G. and Xie, S. P.: Origins of tropical-wide SST biases in CMIP multi-model ensembles, Geophys. Res. Lett., 39, L22703, https://doi.org/10.1029/2012GL053777, 2012.
Li, G. and Xie, S. P.: Tropical Biases in CMIP5 Multimodel Ensemble: The Excessive Equatorial Pacific Cold Tongue and Double ITCZ Problems, J. Climate, 27, 1765–1780, 2014.
Li, Z. and Kafatos, M.: Interannual variability of vegetation in the United States and its relation to El Nino/Southern Oscillation, Remote Sens. Environ., 71, 239–247, 2000.
Lin, J. L.: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean-atmosphere feedback analysis, J. Climate, 20, 4497–4525, 2007.
Linthicum, K. J., Anyamba, A., Chretien, J. P., Small, J., Tucker, C. J., and Britch, S. C.: The role of global climate patterns in the spatial and temporal distribution of vector-borne disease, in: Vector Biology, Ecology and Control, 3–13, Springer, the Netherlands, 2010.
Livezey, R. E. and Chen, W. Y.: Statistical field significance and its determination by Monte Carlo techniques, Mon. Weather Rev., 111, 46–59, 1983.
López-Parages, J. and Rodríguez-Fonseca, B.: Multidecadal modulation of El Niño influence on the Euro-Mediterranean rainfall, Geophys. Res. Lett., 39, L02704, https://doi.org/10.1029/2011GL050049, 2012.
López-Parages, J., Rodrígez-Fonseca, B., and Terray, L.: A mechanism for the multidecadal modulation of ENSO teleconnections with Europe, Clim. Dynam., 45, 867–880, 2014.
Losada, T., Rodríguez-Fonseca, B., Polo, I., Janicot, S., Gervois, S., Chauvin, F., and Ruti, P.: Tropical response to the Atlantic Equatorial mode: AGCM multimodel approach, Clim. Dynam., 35, 45–52, 2010a.
Losada, T., Rodríguez-Fonseca, B., Janicot, S., Gervois, S., Chauvin, F., and Ruti, P.: A multi-model approach to the Atlantic Equatorial mode: impact on the West African monsoon, Clim. Dynam., 35, 29–43, 2010b.
Losada, T., Rodríguez-Fonseca, B., Mohino, E., Bader, J., Janicot, S., and Mechoso, C. R.: Tropical SST and Sahel rainfall: A non-stationary relationship, Geophys. Res. Lett., 39, L12705, https://doi.org/10.1029/2012GL052423, 2012.
Lu, J.: The dynamics of the Indian Ocean sea surface temperature forcing of Sahel drought, Clim. Dynam., 33, 445–460, 2009.
Maia, A. H., Meinke, H., Lennox, S., and Stone, R.: Inferential, nonparametric statistics to assess the quality of probabilistic forecast systems, Mon. Weather Rev., 135, 351–362, 2007.
Majda, A. J., Timofeyev, I., and Eijnden, E. V.: Models for stochastic climate prediction, P. Natl. Acad. Sci., 96, 14687–14691, 1999.
Martín-Rey, M., Polo, I., Rodríguez-Fonseca, B., and Kucharski, F.: Changes in the interannual variability of the tropical Pacific as a response to an equatorial Atlantic forcing, Sci. Mar., 76, 105–116, 2012.
Martín-Rey, M., Rodríguez-Fonseca, B., Polo, I., and Kucharski, F.: On the Atlantic–Pacific Niños connection: a multidecadal modulated mode, Clim. Dynam., 43, 3163–3178, 2014.
Martín-Rey, M., Rodríguez-Fonseca, B., and Polo, I.: Atlantic opportunities for ENSO prediction, Geophys. Res. Lett., 42, 6802–6810, https://doi.org/10.1002/2015GL065062, 2015.
Mason, S. J., Goddard, L., Graham, N. E., Yulaeva, E., Sun, L., and Arkin, P. A.: The IRI seasonal climate prediction system and the 1997/98 El Niño event, B. Am. Meteorol. Soc., 80, 1853–1873, 1999.
McMichael, A. J., Woodruff, R. E., and Hales, S.: Climate change and human health: present and future risks, The Lancet, 367, 859–869, 2006.
Michaelsen, J.: Cross-validation in statistical climate forecast models, J. Clim. Appl. Meteorol., 26, 1589–1600, 1987.
Mohino, E., Janicot, S., and Bader, J.: Sahel rainfall and decadal to multi-decadal sea surface temperature variability, Clim. Dynam., 37, 419–440, 2011.
Mokhov, I. I. and Smirnov, D. A.: El Niño–Southern Oscillation drives North Atlantic Oscillation as revealed with nonlinear techniques from climatic indices, Geophys. Res. Lett., 33, L03708, https://doi.org/10.1029/2005GL024557, 2006.
Naylor, R. L., Falcon, W. P., Rochberg, D., and Wada, N.: Using El Nino/Southern Oscillation climate data to predict rice production in Indonesia, Climatic Change, 50, 255–265, 2001.
Newman, M. and Sardeshmukh, P. D.: A caveat concerning singular value decomposition, J. Climate, 8, 352–360, 1995.
Nnamchi, H. C. and Li, J.: Influence of the South Atlantic Ocean dipole on West African summer precipitation, J. Climate, 24, 1184–1197, 2011.
Nnamchi, H. C., Li, J., and Anyadike, R. N.: Does a dipole mode really exist in the South Atlantic Ocean?, J. Geophys. Res.-Atmos., 116, 2011.
Palmer, T. N.: Influence of the Atlantic, Pacific and Indian oceans on Sahel rainfall, Nature, 322, 251–253, https://doi.org/10.1038/322251a0, 1986.
Patz, J. A.: A human disease indicator for the effects of recent global climate change, P. Natl. Acad. Sci., 99, 12506–12508, 2002.
Patz, J. A., Campbell-Lendrum, D., Holloway, T., and Foley, J. A.: Impact of regional climate change on human health, Nature, 438, 310–317, 2005.
Penland, C. and Matrosova, L.: Prediction of tropical Atlantic sea surface temperatures using linear inverse modeling, J. Climate, 11, 483–496, 1998.
Penland, C. and Sardeshmukh, P. D.: The optimal growth of tropical sea surface temperature anomalies, J. Climate, 8, 1999–2024, 1995.
Phillips, J. G., Cane, M. A., and Rosenzweig, C.: ENSO, seasonal rainfall patterns and simulated maize yield variability in Zimbabwe, Agr. Forest Meteorol., 90, 39–50, 1998.
Podestá, G. P., Messina, C. D., Grondona, M. O., and Magrin, G. O.: Associations between grain crop yields in central-eastern Argentina and El Niño-Southern Oscillation, J. Appl. Meteorol., 38, 1488–1498, 1999.
Polo, I., Rodríguez-Fonseca, B., Losada, T., and García-Serrano, J.: Tropical Atlantic Variability modes (1979–2002). Part I: time-evolving SST modes related to West African rainfall, J. Climate, 21, 6457–6475, 2008.
Polo , I., Martin-Rey, M., Rodriguez-Fonseca, B., Kucharski, F., and Mechoso, C. R.: Processes in the Pacific La Niña onset triggered by the Atlantic Niño, Clim. Dynam., 44, 115–131, 2015.
Rasmusson, E. M. and Carpenter, T. H.: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño, Mon. Weather Rev., 110, 354–384, 1982.
Recalde-Coronel, G. C., Barnston, A. G., and Muñoz, Á. G.: Predictability of December-April Rainfall in Coastal and Andean Ecuador, J. Appl. Meteorol. Clim., 53, 1471–1493, https://doi.org/10.1175/JAMC-D-13-0133.1, 2014.
Richter, I. and Xie, S. P.: On the origin of equatorial Atlantic biases in coupled general circulation models, Clim. Dynam., 31, 587–598, 2008.
Richter, I., Xie, S. P., Wittenberg, A. T., and Masumoto, Y.: Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation, Clim. Dynam., 38, 985–1001, 2012.
Rimbu, N., Lohmann, G., Felis, T., and Pätzold, J.: Shift in ENSO teleconnections recorded by a northern Red Sea coral, J. Climate, 16, 1414–1422, 2003.
Rodríguez-Fonseca, B., Polo, I., García-Serrano, J., Losada, T., Mohino, E., Mechoso, C. R., and Kucharski, F.: Are Atlantic Niños enhancing Pacific ENSO events in recent decades?, Geophys. Res. Lett., 36, L20705, https://doi.org/10.1029/2009GL040048, 2009.
Rodríguez-Fonseca, B., Janicot, S., Mohino, E., Losada, T., Bader, J., Caminade, C., and Voldoire, A.: Interannual and decadal SST-forced responses of the West African monsoon, Atmos. Sci. Lett., 12, 67–74, 2011.
Rodríguez-Fonseca, B., Mohino, E., Mechoso, C. R., Caminade, C., Biasutti, M., Gaetani, M., García-Serrano, J., Vizy, E. K., Cook, K., Xue, Y., Polo, I., Losada, L., Druyan, L., Fontaine, B., Bader, J., Doblas-Reyes, F. J., Goddard, L., Janicot, S., Arribas, A., Lau, W., Colman, A., Vellinga, M., Rowell, D. P., Kucharski, F., and Voldoire, A.: Variability and Predictability of West African Droughts. A review on the role of Sea Surface Temperature Anomalies, J. Climate, 8, 4034–4060, https://doi.org/10.1175/JCLI-D-14-00130.1, 2015.
Roe, G. H. and Steig, E. J.: Characterization of millennial-scale climate variability, J. Climate, 17, 1929–1944, 2004.
Rowell, D. P.: Teleconnections between the tropical Pacific and the Sahel, Q. J. Roy. Meteor. Soc., 127, 1683–1706, 2001.
Rowell, D. P.: The impact of Mediterranean SSTs on the Sahelian rainfall season, J. Climate, 16, 849–862, 2003.
Rudolf, B., Becker, A., Schneider, U., Meyer-Christoffer, A., and Ziese, M.: The new "GPCC Full Data Reanalysis Version 5" providing high-quality gridded monthly precipitation data for the global land-surface is public available since December 2010, GPCC status report December, 2010.
Saravanan, R. and Chang, P.: Interaction between tropical Atlantic variability and El Nino-southern oscillation, J. Climate, 13, 2177–2194, 2000.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., and Rudolf, B.: GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle, Theor. Appl. Climatol., 115, 15–40, 2014.
Schurer, A. P., Hegerl, G. C., Mann, M. E., Tett, S. F., and Phipps, S. J.: Separating forced from chaotic climate variability over the past millennium, J. Climate, 26, 6954–6973, 2013.
Shin, S. I., Sardeshmukh, P. D., and Webb, R. S.: Optimal tropical sea surface temperature forcing of North American drought, J. Climate, 23, 3907–3917, 2010.
Smith, T. M. and Reynolds, R. W.: Extended reconstruction of global sea surface temperatures based on COADS data (1854–1997), J. Climate, 16, 1495–1510, 2003.
Smith, T. M. and Reynolds, R. W.: Improved extended reconstruction of SST (1854–1997), J. Climate, 17, 2466–2477, 2004.
Smith, T. M., Reynolds, R. W., Peterson, T. C., and Lawrimore, J.: Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880–2006), J. Climate, 21, 2283–2296, 2008.
Shukla, R. P., Tripathi, K. C., Pandey, A. C., and Das, I. M. L.: Prediction of Indian summer monsoon rainfall using Niño indices: a neural network approach, Atmos. Res., 102, 99–109, 2011.
Tang, B., Hsieh, W. W., Monahan, A. H., Tangang, F. T.: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures, J. Climate, 13, 287–293, 2000.
Tao, F., Yokozawa, M., Zhang, Z., Hayashi, Y., Grassl, H., and Fu, C.: Variability in climatology and agricultural production in China in association with the East Asian summer monsoon and El Niño Southern Oscillation, Clim. Res., 28, 23–30, 2004.
Toniazzo, T. and Woolnough, S.: Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts, Clim. Dynam., 43, 2889–2913, 2013.
Travasso, M. I., Magrin, G. O., Grondona, M. O., and Rodríguez, G. R.: The use of SST and SOI anomalies as indicators of crop yield variability, Int. J Climatol., 29, 23–29, 2009.
Trenberth, K. E., Caron, J. M., Stepaniak, D. P., and Worley, S.: Evolution of El Niño–Southern Oscillation and global atmospheric surface temperatures, J. Geophys. Res.-Atmos., 107, AAC5.1–AAC5.17, https://doi.org/10.1029/2000JD000298, 2002.
Van den Dool, H. M.: Searching for analogues, how long must we wait?, Tellus A, 46, 314–324, 1994.
Van Oldenborgh, G. J. and Burgers, G.: Searching for decadal variations in ENSO precipitation teleconnections, Geophys. Res. Lett., 32, L15701, https://doi.org/10.1029/2005GL023110, 2005.
Vannière, B., Guilyardi, E., Madec, G., Doblas-Reyes, F. J., and Woolnough, S.: Using seasonal hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on ENSO, Clim. Dynam., 40, 963–981, 2013.
Verdin, J., Funk, C., Klaver, R., and Roberts, D.: Exploring the correlation between Southern Africa NDVI and Pacific sea surface temperatures: results for the 1998 maize growing season, Int. J. Remote Sens., 20, 2117–2124, 1999.
Vimont, D. J.: Analysis of the Atlantic meridional mode using linear inverse modeling: Seasonality and regional influences, J. Climate, 25, 1194–1212, 2012.
Vislocky, R. L. and Fritsch, J. M.: Improved model output statistics forecasts through model consensus, B. Am. Meteorol. Soc., 76, 1157–1164, 1995.
Wahl, S., Latif, M., Park, W., and Keenlyside, N.: On the tropical Atlantic SST warm bias in the Kiel Climate Model, Clim. Dynam., 36, 891–906, 2011.
Wallace, J. M., Smith, C., and Bretherton, C. S.: Singular value decomposition of wintertime sea surface temperature and 500-mb height anomalies, J. Climate, 5, 561–576, 1992.
Wang, S. Y., L'Heureux, M., and Chia, H. H.: ENSO prediction one year in advance using western North Pacific sea surface temperatures, Geophys. Res. Lett., 39, L05702, https://doi.org/10.1029/2012GL050909, 2012.
Ward, M. N.: Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa at interannual and multidecadal timescales, J. Climate, 11, 3167–3191, 1998.
Widmann, M.: One-dimensional CCA and SVD, and their relationship to regression maps, J. Climate, 18, 2785–2792, 2005.
Xue, Y., Chen, M., Kumar, A., Hu, Z. Z., and Wang, W.: Prediction skill and bias of tropical Pacific sea surface temperatures in the NCEP Climate Forecast System version 2, J. Climate, 26, 5358–5378, 2013.
Zebiak, S. E. and Cane, M. A.: A Model El Niño-Southern Oscillation, Mon. Weather Rev., 115, 2262–2278, 1987.
Short summary
The non-stationary links between sea surface temperature and global atmospheric circulation have served to create the S⁴CAST model. Here we describe the model, based on a statistical tool to be focused on the study of teleconnections and predictability of any climate-related variable that keeps a link with sea surface temperature. Due to its intuitive operation and free availability of the code, the model can be used both to supplement general circulation models and in a purely academic context.
The non-stationary links between sea surface temperature and global atmospheric circulation have...
