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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
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Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
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Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
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Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
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A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
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Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
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This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
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We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
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Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334, https://doi.org/10.5194/gmd-16-3313-2023, https://doi.org/10.5194/gmd-16-3313-2023, 2023
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This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
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Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
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Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
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This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
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Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
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Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
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This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
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A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
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A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
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C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
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FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
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
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
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It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
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The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-77, https://doi.org/10.5194/gmd-2023-77, 2023
Revised manuscript accepted for GMD
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Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth System model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses, and long-range predictions).
Magnus Hieronymus
Geosci. Model Dev., 16, 2343–2354, https://doi.org/10.5194/gmd-16-2343-2023, https://doi.org/10.5194/gmd-16-2343-2023, 2023
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A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023, https://doi.org/10.5194/gmd-16-2215-2023, 2023
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This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Nadine Goris, Klaus Johannsen, and Jerry Tjiputra
Geosci. Model Dev., 16, 2095–2117, https://doi.org/10.5194/gmd-16-2095-2023, https://doi.org/10.5194/gmd-16-2095-2023, 2023
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Climate projections of a high-CO2 future are highly uncertain. A new study provides a novel approach to identifying key regions that dynamically explain the model uncertainty. To yield an accurate estimate of the future North Atlantic carbon uptake, we find that a correct simulation of the upper- and interior-ocean volume transport at 25–30° N is key. However, results indicate that models rarely perform well for both indicators and point towards inconsistencies within the model ensemble.
Pyry Pentikäinen, Ewan J. O'Connor, and Pablo Ortiz-Amezcua
Geosci. Model Dev., 16, 2077–2094, https://doi.org/10.5194/gmd-16-2077-2023, https://doi.org/10.5194/gmd-16-2077-2023, 2023
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We used Doppler lidar to evaluate the wind profiles generated by a weather forecast model. We first compared the Doppler lidar observations with co-located radiosonde profiles, and they agree well. The model performs best over marine and coastal locations. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.
Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi
Geosci. Model Dev., 16, 2055–2076, https://doi.org/10.5194/gmd-16-2055-2023, https://doi.org/10.5194/gmd-16-2055-2023, 2023
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Bias correction (BC) has become indispensable to climate model output as a post-processing step to render output more useful for impact assessment studies. The current work presents a comparison of different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) for climate model simulations from three initiatives (CMIP6, CORDEX, and CORDEX-CORE) for a multivariate drought index (i.e., standardized precipitation evapotranspiration index).
Maria Chara Karypidou, Stefan Pieter Sobolowski, Lorenzo Sangelantoni, Grigory Nikulin, and Eleni Katragkou
Geosci. Model Dev., 16, 1887–1908, https://doi.org/10.5194/gmd-16-1887-2023, https://doi.org/10.5194/gmd-16-1887-2023, 2023
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Southern Africa is listed among the climate change hotspots; hence, accurate climate change information is vital for the optimal preparedness of local communities. In this work we assess the degree to which regional climate models (RCMs) are influenced by the global climate models (GCMs) from which they receive their lateral boundary forcing. We find that although GCMs exert a strong impact on RCMs, RCMs are still able to display substantial improvement relative to the driving GCMs.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
Geosci. Model Dev., 16, 1937–1960, https://doi.org/10.5194/gmd-16-1937-2023, https://doi.org/10.5194/gmd-16-1937-2023, 2023
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In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Laura C. Jackson, Eduardo Alastrué de Asenjo, Katinka Bellomo, Gokhan Danabasoglu, Helmuth Haak, Aixue Hu, Johann Jungclaus, Warren Lee, Virna L. Meccia, Oleg Saenko, Andrew Shao, and Didier Swingedouw
Geosci. Model Dev., 16, 1975–1995, https://doi.org/10.5194/gmd-16-1975-2023, https://doi.org/10.5194/gmd-16-1975-2023, 2023
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The Atlantic meridional overturning circulation (AMOC) has an important impact on the climate. There are theories that freshening of the ocean might cause the AMOC to cross a tipping point (TP) beyond which recovery is difficult; however, it is unclear whether TPs exist in global climate models. Here, we outline a set of experiments designed to explore AMOC tipping points and sensitivity to additional freshwater input as part of the North Atlantic Hosing Model Intercomparison Project (NAHosMIP).
Heather S. Rumbold, Richard J. J. Gilham, and Martin J. Best
Geosci. Model Dev., 16, 1875–1886, https://doi.org/10.5194/gmd-16-1875-2023, https://doi.org/10.5194/gmd-16-1875-2023, 2023
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The Joint UK Land Environment Simulator (JULES) uses a tiled representation of land cover but can only model a single dominant soil type within a grid box; hence there is no representation of sub-grid soil heterogeneity. This paper evaluates a new surface–soil tiling scheme in JULES and demonstrates the impacts of the scheme using several soil tiling approaches. Results show that soil tiling has an impact on the water and energy exchanges due to the way vegetation accesses the soil moisture.
Felix Pithan, Marylou Athanase, Sandro Dahlke, Antonio Sánchez-Benítez, Matthew D. Shupe, Anne Sledd, Jan Streffing, Gunilla Svensson, and Thomas Jung
Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, https://doi.org/10.5194/gmd-16-1857-2023, 2023
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Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, https://doi.org/10.5194/gmd-16-1735-2023, 2023
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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), changes to the numerical description of precipitation, and a correction to the ice number. There are big improvements in the computational performance that can be achieved with GPU acceleration.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shi-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-41, https://doi.org/10.5194/gmd-2023-41, 2023
Revised manuscript accepted for GMD
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This study examines how well the Taiwan Earth System Model version 1 (TaiESM1) simulates the El Niño Southern Oscillation (ENSO), which is an important tropical climate pattern that affects climate around the world. We found TaiESM1 can replicate the key features of ENSO, including its seasonal changes and how it affects the remote regions, but has a much stronger ENSO than observations. This bias is further examined to provide insights into how to improve ENSO in future climate models.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
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Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
EGUsphere, https://doi.org/10.5194/egusphere-2023-337, https://doi.org/10.5194/egusphere-2023-337, 2023
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Traditional Kalman smoothers are expensive to be applied in large global ocean operational forecast and reanalysis system. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Andrew Gettelman
EGUsphere, https://doi.org/10.5194/egusphere-2023-227, https://doi.org/10.5194/egusphere-2023-227, 2023
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A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Chen Zhang and Tianyu Fu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-303, https://doi.org/10.5194/gmd-2022-303, 2023
Revised manuscript accepted for GMD
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A new automatic calibration toolkit was developed and implemented into the recalibration of a three-dimensional water quality model with observations in a wider range of hydrological variability. Compared to the original model, the recalibrated model performed significantly better in modeled TP, Chla, and DO. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Olawale James Ikuyajolu, Luke Van Roekel, Steven R. Brus, Erin E. Thomas, Yi Deng, and Sarat Sreepathi
Geosci. Model Dev., 16, 1445–1458, https://doi.org/10.5194/gmd-16-1445-2023, https://doi.org/10.5194/gmd-16-1445-2023, 2023
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Wind-generated waves play an important role in modifying physical processes at the air–sea interface, but they have been traditionally excluded from climate models due to the high computational cost of running spectral wave models for climate simulations. To address this, our work identified and accelerated the computationally intensive section of WAVEWATCH III on GPU using OpenACC. This allows for high-resolution modeling of atmosphere–wave–ocean feedbacks in century-scale climate integrations.
Edward C. Chan, Joana Leitão, Andreas Kerschbaumer, and Timothy M. Butler
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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Yeti is a Handbook Emission Factors for Road Transport-based traffic emission inventory written in the Python 3 scripting language, which adopts a generalized treatment for activity data using traffic information of varying levels of detail introduced in a systematic and consistent manner, with the ability to maximize reusability. Thus, Yeti has been conceived and implemented with a high degree of data and process symmetry, allowing scalable and flexible execution while affording ease of use.
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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The traditional tropospheric zenith hydrostatic delay (ZHD) model's bias is usually thought negligible, yet it still reaches 10 mm sometimes and would lead to millimeter-level position errors for space geodetic observations. Therefore, we analyzed the bias’ characteristics and present a grid model to correct the traditional ZHD formula. When verifying the efficiency based on data from the ECMWF (European Centre for Medium-Range Weather Forecasts), ZHD biases were rectified by ~50 %.
Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
EGUsphere, https://doi.org/10.5194/egusphere-2022-1520, https://doi.org/10.5194/egusphere-2022-1520, 2023
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Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously for the first time. We show how sets of 3–5 models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
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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Due to computational limits, lower-complexity models (LCMs) were developed as a complementary tool for accelerating comprehensive Earth system models (ESMs) but still lack a good precipitation emulator for LCMs. Here, we developed a data-calibrated precipitation emulator (PREMU), a computationally effective way to better estimate historical and simulated precipitation by current ESMs. PREMU has potential applications related to land surface processes and their interactions with climate change.
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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We present the implementation of neodymium (Nd) isotopes into the ocean model of FAMOUS (Nd v1.0). Nd fluxes from seafloor sediment and incorporation of Nd onto sinking particles represent the major global sources and sinks, respectively. However, model–data mismatch in the North Pacific and northern North Atlantic suggest that certain reactive components of the sediment interact the most with seawater. Our results are important for interpreting Nd isotopes in terms of ocean circulation.
Yann Quilcaille, Thomas Gasser, Philippe Ciais, and Olivier Boucher
Geosci. Model Dev., 16, 1129–1161, https://doi.org/10.5194/gmd-16-1129-2023, https://doi.org/10.5194/gmd-16-1129-2023, 2023
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The model OSCAR is a simple climate model, meaning its representation of the Earth system is simplified but calibrated on models of higher complexity. Here, we diagnose its latest version using a total of 99 experiments in a probabilistic framework and under observational constraints. OSCAR v3.1 shows good agreement with observations, complex Earth system models and emerging properties. Some points for improvements are identified, such as the ocean carbon cycle.
Sandra L. LeGrand, Theodore W. Letcher, Gregory S. Okin, Nicholas P. Webb, Alex R. Gallagher, Saroj Dhital, Taylor S. Hodgdon, Nancy P. Ziegler, and Michelle L. Michaels
Geosci. Model Dev., 16, 1009–1038, https://doi.org/10.5194/gmd-16-1009-2023, https://doi.org/10.5194/gmd-16-1009-2023, 2023
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Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Roman Brogli, Christoph Heim, Jonas Mensch, Silje Lund Sørland, and Christoph Schär
Geosci. Model Dev., 16, 907–926, https://doi.org/10.5194/gmd-16-907-2023, https://doi.org/10.5194/gmd-16-907-2023, 2023
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The pseudo-global-warming (PGW) approach is a downscaling methodology that imposes the large-scale GCM-based climate change signal on the boundary conditions of a regional climate simulation. It offers several benefits in comparison to conventional downscaling. We present a detailed description of the methodology, provide companion software to facilitate the preparation of PGW simulations, and present validation and sensitivity studies.
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...