Articles | Volume 12, issue 3
https://doi.org/10.5194/gmd-12-1087-2019
© Author(s) 2019. 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-12-1087-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Mar 2019
Model description paper |
| 22 Mar 2019
| 22 Mar 2019
SEAS5: the new ECMWF seasonal forecast system
Stephanie J. Johnson
CORRESPONDING AUTHOR
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Timothy N. Stockdale
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Laura Ferranti
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Magdalena A. Balmaseda
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Franco Molteni
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Linus Magnusson
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Steffen Tietsche
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Damien Decremer
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Antje Weisheimer
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Gianpaolo Balsamo
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Sarah P. E. Keeley
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Kristian Mogensen
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Hao Zuo
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Beatriz M. Monge-Sanz
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
Related authors
No articles found.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Alexei Koldunov, Tobias Kölling, Josh Kousal, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Domokos Sármány, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
EGUsphere, https://doi.org/10.5194/egusphere-2024-913, https://doi.org/10.5194/egusphere-2024-913, 2024
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale"), and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Hannah L. Croad, John Methven, Ben Harvey, Sarah P. E. Keeley, and Ambrogio Volonté
Weather Clim. Dynam., 4, 617–638, https://doi.org/10.5194/wcd-4-617-2023, https://doi.org/10.5194/wcd-4-617-2023, 2023
Short summary
Short summary
The interaction between Arctic cyclones and the sea ice surface in summer is investigated by analysing the friction and sensible heat flux processes acting in two cyclones with contrasting evolution. The major finding is that the effects of friction on cyclone strength are dependent on a particular feature of cyclone structure: whether they have a warm or cold core during growth. Friction leads to cooling within both cyclone types in the lower atmosphere, which may contribute to their longevity.
Jonathan J. Day, Sarah Keeley, Gabriele Arduini, Linus Magnusson, Kristian Mogensen, Mark Rodwell, Irina Sandu, and Steffen Tietsche
Weather Clim. Dynam., 3, 713–731, https://doi.org/10.5194/wcd-3-713-2022, https://doi.org/10.5194/wcd-3-713-2022, 2022
Short summary
Short summary
A recent drive to develop seamless forecasting systems has culminated in the development of weather forecasting systems that include a coupled representation of the atmosphere, ocean and sea ice. Before this, sea ice and sea surface temperature anomalies were typically fixed throughout a given forecast. We show that the dynamic coupling is most beneficial during periods of rapid ice advance, where persistence is a poor forecast of the sea ice and leads to large errors in the uncoupled system.
Steve Delhaye, Thierry Fichefet, François Massonnet, David Docquier, Rym Msadek, Svenya Chripko, Christopher Roberts, Sarah Keeley, and Retish Senan
Weather Clim. Dynam., 3, 555–573, https://doi.org/10.5194/wcd-3-555-2022, https://doi.org/10.5194/wcd-3-555-2022, 2022
Short summary
Short summary
It is unclear how the atmosphere will respond to a retreat of summer Arctic sea ice. Much attention has been paid so far to weather extremes at mid-latitude and in winter. Here we focus on the changes in extremes in surface air temperature and precipitation over the Arctic regions in summer during and following abrupt sea ice retreats. We find that Arctic sea ice loss clearly shifts the extremes in surface air temperature and precipitation over terrestrial regions surrounding the Arctic Ocean.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, https://doi.org/10.5194/tc-15-325-2021, 2021
Short summary
Short summary
Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
Christine Pohl, Larysa Istomina, Steffen Tietsche, Evelyn Jäkel, Johannes Stapf, Gunnar Spreen, and Georg Heygster
The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, https://doi.org/10.5194/tc-14-165-2020, 2020
Short summary
Short summary
A spectral to broadband conversion is developed empirically that can be used in combination with the Melt Pond Detector algorithm to derive broadband albedo (300–3000 nm) of Arctic sea ice from MERIS data. It is validated and shows better performance compared to existing conversion methods. A comparison of MERIS broadband albedo with respective values from ERA5 reanalysis suggests a revision of the albedo values used in ERA5. MERIS albedo might be useful for improving albedo representation.
Joula Siponen, Petteri Uotila, Eero Rinne, and Steffen Tietsche
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-272, https://doi.org/10.5194/tc-2019-272, 2019
Manuscript not accepted for further review
Short summary
Short summary
Long sea-ice thickness time series are needed to better understand the Arctic climate and improve its forecasts. In this study 2002–2017 satellite observations are compared with reanalysis output, which is used as initial conditions for long forecasts. The reanalysis agrees well with satellite observations, with differences typically below 1 m when averaged in time, although seasonally and in certain years the differences are large. This is caused by uncertainties in reanalysis and observations.
Hao Zuo, Magdalena Alonso Balmaseda, Steffen Tietsche, Kristian Mogensen, and Michael Mayer
Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, https://doi.org/10.5194/os-15-779-2019, 2019
Short summary
Short summary
OCEAN5 is the fifth generation of the ocean and sea-ice analysis system at ECMWF. It was used for production of historical ocean and sea-ice states from 1979 onwards and is also used for generating real-time ocean and sea-ice states responsible for initializing the operational ECMWF weather forecasting system. This is a valuable data set with broad applications. A description of the OCEAN5 system and an assessment of the historical data set have been documented in this reference paper.
Christopher D. Roberts, Retish Senan, Franco Molteni, Souhail Boussetta, Michael Mayer, and Sarah P. E. Keeley
Geosci. Model Dev., 11, 3681–3712, https://doi.org/10.5194/gmd-11-3681-2018, https://doi.org/10.5194/gmd-11-3681-2018, 2018
Short summary
Short summary
This paper presents climate model configurations of the European Centre for Medium-Range Weather Forecasts Integrated Forecast System (ECMWF-IFS) for different combinations of ocean and atmosphere resolution. These configurations are used to perform multi-decadal experiments following the protocols of the High Resolution Model Intercomparison Project (HighResMIP) and phase 6 of the Coupled Model Intercomparison Project (CMIP6).
Steffen Tietsche, Magdalena Alonso-Balmaseda, Patricia Rosnay, Hao Zuo, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, https://doi.org/10.5194/tc-12-2051-2018, 2018
Short summary
Short summary
We compare Arctic sea-ice thickness from L-band microwave satellite observations and an ocean–sea ice reanalysis. There is good agreement for some regions and times but systematic discrepancy in others. Errors in both the reanalysis and observational products contribute to these discrepancies. Thus, we recommend proceeding with caution when using these observations for model validation or data assimilation. At the same time we emphasise their unique value for improving sea-ice forecast models.
Jonathan J. Day, Steffen Tietsche, Mat Collins, Helge F. Goessling, Virginie Guemas, Anabelle Guillory, William J. Hurlin, Masayoshi Ishii, Sarah P. E. Keeley, Daniela Matei, Rym Msadek, Michael Sigmond, Hiroaki Tatebe, and Ed Hawkins
Geosci. Model Dev., 9, 2255–2270, https://doi.org/10.5194/gmd-9-2255-2016, https://doi.org/10.5194/gmd-9-2255-2016, 2016
Short summary
Short summary
Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable.
Related subject area
Climate and Earth system modeling
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0
Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption
Architectural Insights and Training Methodology Optimization of Pangu-Weather
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Robust handling of extremes in quantile mapping – "Murder your darlings"
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
Evaluation of global fire simulations in CMIP6 Earth system models
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Design, evaluation and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
Introducing the MESMER-M-TPv0.1.0 module: Spatially Explicit Earth System Model Emulation for Monthly Precipitation and Temperature
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
A computationally light-weight model for ensemble forecasting of environmental hazard: General TAMSAT-ALERT v1.2.1
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
Short summary
Short summary
We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
Short summary
Short summary
Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
Short summary
Short summary
The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
Short summary
Short summary
We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
Short summary
Short summary
Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
Short summary
Short summary
Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
Short summary
Short summary
Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Deifilia Aurora To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
EGUsphere, https://doi.org/10.5194/egusphere-2024-1714, https://doi.org/10.5194/egusphere-2024-1714, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers three-dimensional atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20–30%. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases accessibility of training and working with the model.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-98, https://doi.org/10.5194/gmd-2024-98, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger range of data is likely encountered outside the calibration period. The end result is highly dependent on the method used, and we show that one needs to exclude data in the calibration range to activate the extrapolation functionality also in that time period, else there will be discontinuities in the timeseries.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
Short summary
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1456, https://doi.org/10.5194/egusphere-2024-1456, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant covariances during convective and frontal precipitation events. Common statistical downscaling techniques preserve expected covariances during convective precipitation. However, they dampen future intensification of frontal precipitation captured in global climate models and dynamical downscaling. This suggests statistical downscaling may not fully resolve non-stationary hydrologic processes as compared to dynamical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-97, https://doi.org/10.5194/gmd-2024-97, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Research software is crucial for scientific progress but is often developed by scientists with limited training, time, and funding, leading to software that is hard to understand, (re)use, modify, and maintain. Our study across 10 research sectors highlights strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. Recommendations include workshops, code quality metrics, funding, and adherence to FAIR standards.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
Short summary
Short summary
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-85, https://doi.org/10.5194/gmd-2024-85, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 CMIP6 ESMs. Most models reproduce global total, spatial pattern, seasonality, and regional historical changes well, but fail to simulate the recent decline in global burned area and underestimate the fire sensitivity to wet-dry conditions. They addressed three critical issues in CMIP5. We present targeted guidance for fire scheme development and methodologies to generate reliable fire projections.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
Short summary
Short summary
All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Giovanni Di Virgilio, Jason Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew Riley, and Jyothi Lingala
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-87, https://doi.org/10.5194/gmd-2024-87, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models, and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleußner
EGUsphere, https://doi.org/10.5194/egusphere-2024-278, https://doi.org/10.5194/egusphere-2024-278, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Their joint distribution largely determines the division into climate regimes. Yet, projecting precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows to generate monthly means of local precipitation and temperature at low computational costs.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
Short summary
Short summary
We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
Short summary
Short summary
In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Emily Black, John Ellis, and Ross Maidment
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-75, https://doi.org/10.5194/gmd-2024-75, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We present General TAMSAT-ALERT: a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and forecasting data into probabilistic hazard assessments. As such, it complements existing systems and enhances their utility for actionable hazard assessment.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
Short summary
Short summary
Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
Short summary
Short summary
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 driven by either uniform erosion 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.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation
Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present),
J. Hydrometeorol., 4, 1147–1167,
https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003. a
Ahlgrimm, M. and Forbes, R.: Improving the representation of low clouds and
drizzle in the ECMWF model based on ARM observations from the Azores, Mon.
Weather Rev., 142, 668–685, 2014. a
Balmaseda, M. A., Mogensen, K., and Weaver, A. T.: Evaluation of the ECMWF
ocean reanalysis system ORAS4, Q. J. Roy. Meteor.
Soc., 139, 1132–1161, https://doi.org/10.1002/qj.2063, 2013. a, b
Balsamo, G., Salgado, R., Dutra, E., Boussetta, S., Stockdale, T., and Potes,
M.: On the contribution of lakes in predicting near-surface temperature in a
global weather forecasting model, Tellus A, 64, 15829,
https://doi.org/10.3402/tellusa.v64i0.15829, 2012. a
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H.,
Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P.,
Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface
reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407,
https://doi.org/10.5194/hess-19-389-2015, 2015. a
Barnston, A. G., Tippett, M. K., L'Heureux, M. L., Li, S., and Dewitt, D. G.:
Skill of real-time seasonal ENSO model predictions during 2002–11: Is our
capability increasing?, B. Am. Meteorol. Soc., 93, 631–651,
https://doi.org/10.1175/BAMS-D-11-00111.1, 2012. a
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M.,
Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating
atmospheric variability with the ECMWF model: From synoptic to decadal
time-scales, Q. J. Roy. Meteor. Soc., 134,
1337–1351, 2008. a
Bechtold, P., Semane, N., Lopez, P., Chaboureau, J.-P., Beljaars, A., and
Bormann, N.: Representing Equilibrium and Nonequilibrium Convection in
Large-Scale Models, J. Atmos. Sci., 71, 734–753,
https://doi.org/10.1175/JAS-D-13-0163.1, 2014. a, b
Beljaars, A., Brown, A. R., and Wood, N.: A new parametrization of turbulent
orographic form drag, Q. J. Roy. Meteor. Soc.,
130, 1327–1347, 2004. a
Bell, C. J., Gray, L. J., Charlton-Perez, A. J., Joshi, M. M., and Scaife,
A. A.: Stratospheric communication of El Niño teleconnections to
European winter, J. Climate, 22, 4083–4096,
https://doi.org/10.1175/2009JCLI2717.1, 2009. a
Boussetta, S., Balsamo, G., Beljaars, A., Kral, T., and Jarlan, L.: Impact of
a satellite-derived leaf area index monthly climatology in a global numerical
weather prediction model, Int. J. Remote Sens., 34,
3520–3542, https://doi.org/10.1080/01431161.2012.716543, 2013. a
Breivik, Ø., Mogensen, K., Bidlot, J.-R., Balmaseda, A., and Janssen, P. A.
E. M.: Surface wave effects in the NEMO ocean model: Forced and coupled
experiments, J. Geophys. Res.-Oceans, 120, 2973–2992,
https://doi.org/10.1002/2014JC010565, 2015. a
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.:
Unified modeling and prediction of weather and climate: A 25-year journey,
B. Am. Meteorol. Soc., 93, 1865–1877,
https://doi.org/10.1175/BAMS-D-12-00018.1, 2012. a
Buckley, M. W. and Marshall, J.: Observations, inferences, and mechanisms of
the Atlantic Meridional Overturning Circulation: A review, Rev.
Geophys., 54, 5–63, https://doi.org/10.1002/2015RG000493, 2016. a
Buizza, R., Hollingsworth, A., Lalaurette, F., and Ghelli, A.: Probabilistic
Predictions of Precipitation Using the ECMWF Ensemble Prediction System,
Weather Forecast., 14, 168–189,
https://doi.org/10.1175/1520-0434(2000)015<0365:COPPOP>2.0.CO;2, 1999. a
Cariolle, D. and Déqué, M.: Southern hemisphere medium-scale
waves and total ozone disturbances in a spectral general circulation model,
J. Geophys. Res.-Atmos., 91, 10825–10846, 1986. a
Cariolle, D. and Teyssèdre, H.: A revised linear ozone photochemistry
parameterization for use in transport and general circulation models:
multi-annual simulations, Atmos. Chem. Phys., 7, 2183-2196,
https://doi.org/10.5194/acp-7-2183-2007, 2007. a
Cassou, C.: Intraseasonal interaction between the Madden Julian Oscillation
and the North Atlantic Oscillation, Nature, 455, 523–527, 2008. a
Charney, J. and Shukla, J.: Predictability of monsoons, in: Monsoon Dynamics,
edited by: Lighthill, J. and Pearce, R. P., chap. 6, 99–110, Cambridge University
Press, Cambridge, https://doi.org/10.1017/CBO9780511897580.009, 1981. a
Chassignet, E. P. and Marshall, D. P.: Gulf Stream Separation in Numerical
Ocean Models, in: Ocean Modeling in an Eddying Regime, edited by: Hecht, M. W. and Hasumi, H., Geophysical Monograph
Series, 177, 39–61,
https://doi.org/10.1029/177GM05, 2008. a
Craig, P. D. and Banner, M. L.: Modeling Wave-Enhanced Turbulence in the Ocean
Surface Layer, 24, 2546–2559, https://doi.org/10.1175/1520-0485(1994)024<2546:MWETIT>2.0.CO;2, 1994. a
de Rosnay, P., Balsamo, G., Albergel, C., Muñoz-Sabater, J., and Isaksen,
L.: Initialisation of land surface variables for Numerical Weather
Prediction, Surv. Geophys., 35, 607–621,
https://doi.org/10.1007/s10712-012-9207-x, 2014. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., Berg, L. V. D., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Dee, D. P., van de Berg, L., Bidlot,
J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J.,
Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L.,
Kållberg, P., Köhler, M., Matricardi, M., Mcnally, A. P.,
Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P.,
Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim
reanalysis: configuration and performance of the data assimilation system,
Q. J. Roy. Meteor. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011. a, b, c
Deser, C., Alexander, M. A., Xie, S.-P., and Phillips, A. S.: Sea Surface
Temperature Variability: Patterns and Mechanisms, Ann. Rev. Mar.
Sci., 2, 115–143, https://doi.org/10.1146/annurev-marine-120408-151453, 2010. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
Dunstone, N., Smith, D., Scaife, A., Hermanson, L., Eade, R., Robinson, N.,
Andrews, M., and Knight, J.: Skilful predictions of the winter North
Atlantic Oscillation one year ahead, Nat. Geosci., 9, 809–814,
https://doi.org/10.1038/ngeo2824, 2016. a
Dutra, E., Stepanenko, V. M., Balsamo, G., Viterbo, P., Miranda, P., Mironov,
D., and Schär, C.: An offline study of the impact of lakes in the
performance of the ECMWF surface scheme, Boreal Environ. Res., 15,
100–112, 2010b. a
Ebdon, R. A.: The quasibiennial oscillation and its association with
tropospheric circulation patterns, Meteorol. Mag., 104, 282–297,
1975. a
Fichefet, T. and Maqueda, M. A.: Sensitivity of a global sea ice model to the
treatment of ice thermodynamics and dynamics, J. Geophys.
Res.-Oceans, 102, 12609–12646, https://doi.org/10.1029/97JC00480, 1997. a
Folland, C. K., Scaife, A. A., Lindesay, J., and Stephenson, D. B.: How
potentially predictable is northern European winter climate a season ahead?,
Int. J. Climatol., 32, 801–818, https://doi.org/10.1002/joc.2314,
2012. a
Forbes, R. M. and Ahlgrimm, M.: On the Representation of High-Latitude
Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model, Mon. Weather
Rev., 142, 3425–3445, https://doi.org/10.1175/MWR-D-13-00325.1,
2014. a
Forbes, R. M. and Tompkins, A.: An improved representation of cloud and
precipitation, ECMWF Newsletter, 129, 13–18, doi:10.21957/nfgulzhe, 2011. a
Forbes, R. M., Tompkins, A. M., and Untch, A.: A new prognastic bulk
microphysics scheme for the IFS, ECMWF Technical Memorandam, 649, doi:10.21957/bf6vjvxk, 2011. a
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res.-Oceans, 118,
6704–6716, https://doi.org/10.1002/2013JC009067, 2013. a
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for
Ensemble Prediction Systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
Hogan, R. J. and Bozzo, A.: Mitigating errors in surface temperature forecasts
using approximate radiation updates, J. Adv. Model. Earth
Sy., 7, 836–853, 2015. a
Hogan, R. J. and Hirahara, S.: Effect of solar zenith angle specification in
models on mean shortwave fluxes and stratospheric temperatures, Geophys.
Res. Lett., 43, 482–488, 2016. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys.
Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Ineson, S. and Scaife, A.: The role of the stratosphere in the European
climate response to El Niño, Nat. Geosci., 2, 32–36, 2009. a
Inness, A., Baier, F., Benedetti, A., Bouarar, I., Chabrillat, S., Clark, H.,
Clerbaux, C., Coheur, P., Engelen, R. J., Errera, Q., Flemming, J., George,
M., Granier, C., Hadji-Lazaro, J., Huijnen, V., Hurtmans, D., Jones, L.,
Kaiser, J. W., Kapsomenakis, J., Lefever, K., Leitão, J., Razinger, M.,
Richter, A., Schultz, M. G., Simmons, A. J., Suttie, M., Stein, O.,
Thépaut, J.-N., Thouret, V., Vrekoussis, M., Zerefos, C., and the MACC
team: The MACC reanalysis: an 8 yr data set of atmospheric composition,
Atmos. Chem. Phys., 13, 4073–4109, https://doi.org/10.5194/acp-13-4073-2013,
2013. a
Janssen, P. A. E. M., Breivik, Ø., Mogensen, K., Vitart, F., Balmaseda,
M., Bidlot, J.-r., Keeley, S., Leutbecher, M., Magnusson, L., and Molteni,
F.: Air-Sea Interaction and Surface Waves, ECMWF Technical Memorandum, 712,
36 pp., 2013. a
Kim, H. M., Webster, P. J., and Curry, J. A.: Seasonal prediction skill of
ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern
Hemisphere Winter, Clim. Dynam., 39, 2957–2973,
https://doi.org/10.1007/s00382-012-1364-6, 2012. a
Köhler, M., Ahlgrimm, M., and Beljaars, A.: Unified treatment of dry
convective and stratocumulus-topped boundary layers in the ECMWF model,
Q. J. Roy. Meteor. Soc., 137, 43–57, 2011. a
Leutbecher, M., Lock, S. J., Ollinaho, P., Lang, S. T., Balsamo, G., Bechtold,
P., Bonavita, M., Christensen, H. M., Diamantakis, M., Dutra, E., English,
S., Fisher, M., Forbes, R. M., Goddard, J., Haiden, T., Hogan, R. J.,
Juricke, S., Lawrence, H., MacLeod, D., Magnusson, L., Malardel, S., Massart,
S., Sandu, I., Smolarkiewicz, P. K., Subramanian, A., Vitart, F., Wedi, N.,
and Weisheimer, A.: Stochastic representations of model uncertainties at
ECMWF: state of the art and future vision,
Q. J. Roy. Meteor. Soc., 143, 2315–2339, https://doi.org/10.1002/qj.3094, 2017. a, b
Lin, H., Brunet, G., and Derome, J.: An observed connection between the North
Atlantic oscillation and the Madden-Julian oscillation, J. Climate,
22, 364–380, https://doi.org/10.1175/2008JCLI2515.1, 2009. a
Lott, F. and Miller, M. J.: A new subgrid-scale orographic drag
parametrization: Its formulation and testing, Q. J. Roy. Meteor. Soc., 123, 101–127, 1997. a
Maclachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife,
A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J.,
Xavier, P., and Madec, G.: Global Seasonal forecast system version 5
(GloSea5): A high-resolution seasonal forecast system,
Q. J. Roy. Meteor. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396,
2015. a
Madec, G. and the NEMO team: NEMO ocean engine, available at:
https://www.nemo-ocean.eu/wp-content/uploads/NEMO_book.pdf (last access: 21 February 2019), 2016. a
Matheson, J. E. and Winkler, R. L.: Scoring Rules for Continuous Probability
Distributions, Manage. Sci., 22, 1087–1096, 1976. a
Maycock, A. C., Keeley, S. P., Charlton-Perez, A. J., and Doblas-Reyes, F. J.:
Stratospheric circulation in seasonal forecasting models: implications for
seasonal prediction, Clim. Dynam., 36, 309–321,
https://doi.org/10.1007/s00382-009-0665-x, 2011. a
McPhaden, M. J., Zebiak, S. E., Glantz, M. H., and Mcphaden, M.: ENSO as an
Concept Integrating in Earth Science, Science, 314, 1740–1745, 2006. a
Mironov, D., Heise, E., Kourzeneva, E., Ritter, B., Schneider, N., and
Terzhevik, A.: Implementation of the lake parameterisation scheme FLake into
the numerical weather prediction model COSMO, Boreal Environ. Res.,
15, 218–230, 2010. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J.. Geophys. Res.- Atmos., 102, 16663–16682, 1997. a
Mogensen, K., Alonso Balmaseda, M., and Weaver, A.: The NEMOVAR ocean data
assimilation system as implemented in the ECMWF ocean analysis for System 4,
ECMWF Technical Memorandum, 668, https://doi.org/10.21957/x5y9yrtm, 2012a. a
Mogensen, K., Keeley, S., and Towers, P.: Coupling of the NEMO and IFS models
in a single executable, ECMWF Technical Memorandum, 673, https://doi.org/10.21957/rfplwzuol, 2012b. a
Molteni, F., Stockdale, T. N., and Vitart, F.: Understanding and modelling
extra-tropical teleconnections with the Indo-Pacific region during the
northern winter, Clim. Dynam., 45, 3119–3140,
https://doi.org/10.1007/s00382-015-2528-y, 2015. a, b, c
Monge-Sanz, B. M., Chipperfield, M. P., Cariolle, D., and Feng, W.: Results
from a new linear O3 scheme with embedded heterogeneous chemistry compared
with the parent full-chemistry 3-D CTM, Atmos. Chem. Phys., 11, 1227–1242,
https://doi.org/10.5194/acp-11-1227-2011, 2011. a, b
Morcrette, J. J., Barker, H. W., Cole, J. N. S., Iacono, M. J., and Pincus, R.:
Impact of a new radiation package, McRad, in the ECMWF Integrated
Forecasting System, Mon. Weather Rev., 136, 4773–4798, 2008. a
Orr, A., Bechtold, P., Scinocca, J., Ern, M., and Janiskova, M.: Improved
middle atmosphere climate and forecasts in the ECMWF model through a
nonorographic gravity wave drag parameterization, J. Climate, 23,
5905–5926, 2010. a
Palmer, T. and Anderson, D. L. T.: The prospects for seasonal forecasting - A
review paper, Q. J. Roy. Meteor. Soc., 120,
755–793, https://doi.org/10.1002/qj.49712051802, 1994. a
Palmer, T. N.: Towards the probabilistic Earth-system simulator: A vision
for the future of climate and weather prediction, Q. J. Roy. Meteor. Soc.,
138, 841–861, https://doi.org/10.1002/qj.1923, 2012. a
Polichtchouk, I., Hogan, R. J., Shepherd, T. G., Bechtold, P., Stockdale, T.,
Malardel, S., Lock, S.-J., and Magnusson, L.: What influences the middle
atmosphere circulation in the IFS?, ECMWF Technical Memorandum, 809,
https://doi.org/10.21957/mfsnfv15o, 2017. a
Pujol, M.-I., Faugère, Y., Taburet, G., Dupuy, S., Pelloquin, C., Ablain,
M., and Picot, N.: DUACS DT2014: the new multi-mission altimeter data set
reprocessed over 20 years, Ocean Sci., 12, 1067–1090,
https://doi.org/10.5194/os-12-1067-2016, 2016. a
Raoult, B., Bergeron, C., López Alós, A., Thépaut, J.-N., and Dee, D.:
Climate service develops user-friendly data store, ECMWF newsletter, 151, 22–27,
https://doi.org/10.21957/p3c285, 2017. a
Reed, R. J., Campbell, W. J., Rasmussen, L. A., and Rogers, D. G.: Evidence
of a downward propagating, annual wind reversal in the equatorial
stratosphere, J. Geophys. Res., 66, 813–818, 1961. a
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An
improved in situ and satellite SST analysis for climate, J. Climate,
15, 1609–1625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2, 2002. a
Roberts, C. D., Senan, R., Molteni, F., Boussetta, S., Mayer, M., and Keeley,
S. P. E.: Climate model configurations of the ECMWF Integrated Forecasting
System (ECMWF-IFS cycle 43r1) for HighResMIP, Geosci. Model Dev., 11,
3681–3712, https://doi.org/10.5194/gmd-11-3681-2018, 2018. a
Saji, N. H., Goswami, B. N., Vinayachandran, P. N., and Yamagata, T.: A
dipole mode in the tropical Indian ocean, Nature, 401, 360–363,
https://doi.org/10.1038/43855, 1999. a
Sandu, I., Beljaars, A., Balsamo, G., and Ghelli, A.: Revision of the
surface roughness length table, ECMWF Newsletter, 130, 8–10, 2011. a
Sandu, I., Beljaars, A., and Balsamo, G.: Improving the representation of
stable boundary layers, ECMWF Newsletter, 138, 24–29, 2014. a
Sardeshmukh, P. D. and Hoskins, B. J.: The Generation of Global Rotational
Flow by Steady Idealized Tropical Divergence, J. Atmos. Sci., 45,
1228–1251, https://doi.org/10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2, 1988. a
Scaife, A. A., Athanassiadou, M., Andrews, M., Arribas, A., Baldwin, M.,
Dunstone, N., Knight, J., Maclachlan, C., Manzini, E., Müller, W.,
Pohlmann, H., Smith, D., Stockdale, T., and Williams, A.: Predictability of
the quasi-biennial oscillation and its northern winter teleconnection on
seasonal to decadal timescales, Geophys. Res. Lett., 41, 1752–1758,
https://doi.org/10.1002/2013GL059160.Received, 2014. a, b
Scaife, A. A., Comer, R. E., Dunstone, N. J., Knight, J. R., Smith, D. M.,
MacLachlan, C., Martin, N., Peterson, K. A., Rowlands, D., Carroll, E. B.,
Belcher, S., and Slingo, J.: Tropical rainfall, Rossby waves and regional
winter climate predictions, Q. J. Roy. Meteor. Soc., 143, 1–11,
https://doi.org/10.1002/qj.2910, 2017. a
Scaife, A. A., Ferranti, L., Alves, O., Athanasiadis, P., Baehr, J., Deque',
M., Dippe, T., Dunstone, N., Fereday, D., Gudgel, R. G., Greatbatch, R. J.,
Hermanson, L., Imada, Y., Jain, S., Kumar, A., MacLachlan, C., Merryfield,
W., Müller, W. A., Ren, H.-L., Smith, D., Takaya, Y., Vecchi, G., and
Yang, X.: Tropical Rainfall Predictions from Multiple Seasonal Forecast
Systems, Int. J. Climatol., https://doi.org/10.1002/joc.5855, online first, 2018. a
Shepherd, T. G., Polichtchouk, I., Hogan, R. J., and Simmons, A. J.: Report on
Stratosphere Task Force, ECMWF Technical Memorandam, 824, https://doi.org/10.21957/0vkp0t1xx, 2018. a
Shutts, G.: A kinetic energy backscatter algorithm for use in ensemble
prediction systems, Q. J. Roy. Meteor. Soc.,
131, 3079–3102, https://doi.org/10.1256/qj.04.106, 2005. a
Stockdale, T., Anderson, D. L. T., Alves, J. O. S., and Balmaseda, M. A.:
Global seasonal rainfall forecasts using a coupled ocean-atmosphere model,
Nature, 392, 370–373, 1998. a
Stockdale, T. N., Molteni, F., and Ferranti, L.: Atmospheric initial
conditions and the predictability of the Arctic Oscillation, Geophys.
Res. Lett., 42, 1173–1179, https://doi.org/10.1002/2014GL062681, 2015. a
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in
Large-Scale Models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a
Tiedtke, M.: Representation of clouds in large-scale models, Mon. Weather
Rev., 121, 3040–3061, 1993. a
Tietsche, S., Balmaseda, M. A., Zuo, H., and Mogensen, K.: Arctic sea ice in
the global eddy-permitting ocean reanalysis ORAP5, Clim. Dynam., 49,
775–789, https://doi.org/10.1007/s00382-015-2673-3, 2017.
a
Titchner, H. A. and Rayner, N. A.: The Met Office Hadley Centre sea ice and
sea surface temperature data set, version 2: 1. Sea ice concentrations,
J. Geophys. Res.-Atmos., 119, 2864–2889,
https://doi.org/10.1002/2014JD021914, 2014. a
Troccoli, A.: Seasonal climate forecasting, Meteorol. Appl., 17, 251–268,
https://doi.org/10.1002/met.184, 2010. a
Valcke, S.: The OASIS3 coupler: a European climate modelling community
software, Geosci. Model Dev., 6, 373–388,
https://doi.org/10.5194/gmd-6-373-2013, 2013. a
Van den Hurk, B., Viterbo, P., Beljaars, A. C. M., and Betts, A. K.: Offline
validation of the ERA40 surface scheme, ECMWF Technical Memorandum, 295,
https://doi.org/10.21957/9aoaspz8,
2000. a
Viterbo, P. and Beljaars, A. C. M.: An improved land surface parameterization
scheme in the ECMWF model and its validation, J. Climate, 8,
2716–2748, 1995. a
Wallace, J. M. and Gutzler, D. S.: Teleconnections in the Geopotential
Height Field during the Northern Hemisphere Winter, 109, 784–812,
https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2, 1981. a
Webster, P. J., Moore, A. M., Loschnigg, J. P., and Leben, R. R.: Coupled
ocean-atmosphere dynamics in the Indian Ocean during 1997–98, Nature, 401,
356–360, https://doi.org/10.1038/43848, 1999. a
Weisheimer, A. and Palmer, T. N.: On the reliability of seasonal climate
forecasts, J. R. Soc. Interface, 11, 20131162, https://doi.org/10.1098/rsif.2013.1162,
2014. a, b
Weisheimer, A., Corti, S., Palmer, T., and Vitart, F.: Addressing model
error through atmospheric stochastic physical parametrizations: impact on the
coupled ECMWF seasonal forecasting system, Philos. T. R. Soc. A, 372,
20130290, https://doi.org/10.1098/rsta.2013.0290, 2014. a, b
Zebiak, S. E.: Air–Sea Interaction in the Equatorial Atlantic Region, J.
Climate, 6, 1567–1586,
https://doi.org/10.1175/1520-0442(1993)006<1567:AIITEA>2.0.CO;2, 1993. a, b
Zeng, X. and Beljaars, A.: A prognostic scheme of sea surface skin
temperature for modeling and data assimilation, Geophys. Res. Lett., 32,
1–4, https://doi.org/10.1029/2005GL023030, 2005. a
Zuo, H., Balmaseda, M. A., Boisseson, E. D., Hirahara, S., Chrust, M., and
de Rosnay, P.: A generic ensemble generation scheme for data assimilation
and ocean analysis, ECMWF technical memorandum, 795, https://doi.org/10.21957/cub7mq0i4, 2017a. a, b, c
Zuo, H., Balmaseda, M. A., and Mogensen, K.: The new eddy-permitting ORAP5
ocean reanalysis: description, evaluation and uncertainties in climate
signals, Clim. Dynam., 49, 791–811, https://doi.org/10.1007/s00382-015-2675-1,
2017b. a
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The
ECMWF operational ensemble reanalysis-analysis system for ocean and sea-ice:
a description of the system and assessment, Ocean Sci. Discuss.,
https://doi.org/10.5194/os-2018-154, in review, 2019. a, b, c
Short summary
In this article, we describe the new ECMWF seasonal forecast system, SEAS5, which replaced its predecessor in November 2017. We describe the forecast methodology used in SEAS5 and compare results from SEAS5 to results from the previous seasonal forecast system, highlighting the strengths and weaknesses of SEAS5. SEAS5 data are publicly available through the Copernicus Climate Change Service's multi-system seasonal forecast.
In this article, we describe the new ECMWF seasonal forecast system, SEAS5, which replaced its...
