Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8245-2022
© Author(s) 2022. 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-15-8245-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
17 Nov 2022
Model evaluation paper |
| 17 Nov 2022
| 17 Nov 2022
Impact of increased resolution on the representation of the Canary upwelling system in climate models
Adama Sylla
CORRESPONDING AUTHOR
CECI, CNRS, CERFACS, Université de Toulouse, Toulouse, France
Laboratoire de Physique de l'Atmosphère et de l'Ocean Simeon Fongang (LPAO-SF/ESP/UCAD), Dakar, Senegal
Emilia Sanchez Gomez
CECI, CNRS, CERFACS, Université de Toulouse, Toulouse, France
Juliette Mignot
CNRS/IRD/MNHN, LOCEAN Laboratory, Sorbonne University, Paris, France
Jorge López-Parages
CECI, CNRS, CERFACS, Université de Toulouse, Toulouse, France
Physical Oceanography Group, University of Málaga, Spain
Related authors
No articles found.
Elsa Mohino, Paul-Arthur Monerie, Juliette Mignot, Moussa Diakhaté, Markus Donat, Christopher David Roberts, and Francisco Doblas-Reyes
Earth Syst. Dynam., 15, 15–40, https://doi.org/10.5194/esd-15-15-2024, https://doi.org/10.5194/esd-15-15-2024, 2024
Short summary
Short summary
The impact of the Atlantic multidecadal variability (AMV) on the rainfall distribution and timing of the West African monsoon is not well known. Analysing model output, we find that a positive AMV enhances the number of wet days, daily rainfall intensity, and extremes over the Sahel and tends to prolong the monsoon length through later demise. Heavy rainfall events increase all over the Sahel, while moderate ones only occur in the north. Model biases affect the skill in simulating AMV impact.
Yona Silvy, Clément Rousset, Eric Guilyardi, Jean-Baptiste Sallée, Juliette Mignot, Christian Ethé, and Gurvan Madec
Geosci. Model Dev., 15, 7683–7713, https://doi.org/10.5194/gmd-15-7683-2022, https://doi.org/10.5194/gmd-15-7683-2022, 2022
Short summary
Short summary
A modeling framework is introduced to understand and decompose the mechanisms causing the ocean temperature, salinity and circulation to change since the pre-industrial period and into 21st century scenarios of global warming. This framework aims to look at the response to changes in the winds and in heat and freshwater exchanges at the ocean interface in global climate models, throughout the 1850–2100 period, to unravel their individual effects on the changing physical structure of the ocean.
Antoine Colmet-Daage, Emilia Sanchez-Gomez, Sophie Ricci, Cécile Llovel, Valérie Borrell Estupina, Pere Quintana-Seguí, Maria Carmen Llasat, and Eric Servat
Hydrol. Earth Syst. Sci., 22, 673–687, https://doi.org/10.5194/hess-22-673-2018, https://doi.org/10.5194/hess-22-673-2018, 2018
Short summary
Short summary
Here, the first assessment of future changes in extreme precipitation in small Mediterranean watersheds is done through three watersheds frequently subjected to flash floods. Collaboration between Spanish and French laboratories enabled us to conclude that the intensity of high precipitation will increase at the end of the century. A high degree of confidence results from the multi-model approach used here with eight regional climate models (RCMs) developed in the Med and Euro-CORDEX project.
Julien Jouanno, Olga Hernandez, and Emilia Sanchez-Gomez
Earth Syst. Dynam., 8, 1061–1069, https://doi.org/10.5194/esd-8-1061-2017, https://doi.org/10.5194/esd-8-1061-2017, 2017
Related subject area
Climate and Earth system modeling
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields
An emulation-based approach for interrogating reactive transport models
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0
Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
URock 2023a: an open-source GIS-based wind model for complex urban settings
DASH: a MATLAB toolbox for paleoclimate data assimilation
Accurate Assessment of Land-Atmosphere Coupling in Climate Models Requires High Frequency Data Output
Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)
All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0
Energy conserving physics for nonhydrostatic dynamics in mass coordinate models
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Assessing the Sensitivity of Aerosol Mass Budget and Effective Radiative Forcing to Horizontal Grid Spacing in E3SMv1 Using A Regional Refinement Approach
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
Exchange-grid coupling approach for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Disentangling the Hydrological and Hydraulic Controls on Streamflow Variability in E3SM V2 – A Case Study in the Pantanal Region
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
Short summary
Short summary
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
Short summary
Short summary
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
Short summary
Short summary
Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
Short summary
Short summary
We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
Short summary
Short summary
This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
Short summary
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Xinzhu Yu, Li Liu, Chao Sun, Qingu Jiang, Biao Zhao, Zhiyuan Zhang, Hao Yu, and Bin Wang
Geosci. Model Dev., 16, 6285–6308, https://doi.org/10.5194/gmd-16-6285-2023, https://doi.org/10.5194/gmd-16-6285-2023, 2023
Short summary
Short summary
In this paper we propose a new common, flexible, and efficient parallel I/O framework for earth system modeling based on C-Coupler2.0. CIOFC1.0 can handle data I/O in parallel and provides a configuration file format that enables users to conveniently change the I/O configurations. It can automatically make grid and time interpolation, output data with an aperiodic time series, and accelerate data I/O when the field size is large.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
Short summary
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
Short summary
Short summary
The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
Short summary
Short summary
The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
Short summary
Short summary
Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
EGUsphere, https://doi.org/10.5194/egusphere-2023-2048, https://doi.org/10.5194/egusphere-2023-2048, 2023
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant time scales, enable demonstration of inter-model spread in land-atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
Short summary
Short summary
The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-184, https://doi.org/10.5194/gmd-2023-184, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We want to reduce errors in the moist energy budget in atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with underlying equations; other may be more practical. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
Short summary
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
Short summary
Short summary
Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-156, https://doi.org/10.5194/gmd-2023-156, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
1. The BARPA-R modelling configuration has been developed to produce high resolution climate hazard projections within the Australian Region. 2. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. 3. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-73, https://doi.org/10.5194/gmd-2023-73, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol-cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
Short summary
Short summary
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.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-166, https://doi.org/10.5194/gmd-2023-166, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean-atmosphere interactions.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and Lai-Yung Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1879, https://doi.org/10.5194/egusphere-2023-1879, 2023
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled Earth System Model. We found that calibrate only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
Short summary
Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
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).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
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. We show how sets of three to five 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.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
EGUsphere, https://doi.org/10.5194/egusphere-2023-1481, https://doi.org/10.5194/egusphere-2023-1481, 2023
Short summary
Short summary
Bias adjustment is commonly applied to climate models before using them to study the impacts of climate change to ensure the correspondence of models with observations at a local scale. However, this can introduce undesirable distortions in the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods to facilitate their transparent and rigorous application.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
EGUsphere, https://doi.org/10.5194/egusphere-2023-1718, https://doi.org/10.5194/egusphere-2023-1718, 2023
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period but also also exhibit some discrepancies.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
Short summary
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
Short summary
Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
Short summary
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govidaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-129, https://doi.org/10.5194/gmd-2023-129, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System, recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART 2nd Generation (GOCART-2G) are documented and we establish a benchmark simulation that is to be used for future development of the system. The four-year benchmark simulation was evaluated using in situ and space borne measurements to develop a baseline and prioritize future development.
Cited articles
Alvarez, I., deCastro, M., Gomez-Gesteira, M., and Prego, R.: Inter-and
intra-annual analysis of the salinity and temperature evolution in the
Galician Rias Baixas-ocean boundary (northwest Spain), J. Geophys. Res., 110,
C04008, https://doi.org/10.1029/2004JC002504, 2005. a, b
Alvarez, I., Gomez-Gesteira, M., deCastro, M., and Dias, J. M.: Spatiotemporal
evolution of upwelling regime along the western coast of the Iberian
Peninsula, J. Geophys. Res., 113, C07020, https://doi.org/10.1029/2008jc004744,
2008. a, b, c
Arístegui, J., Barton, E. D., Tett, P., Montero, M. F.,
García-Muñoz, M., Basterretxea, G., Cussatlegras, A.-S., Ojeda, A.,
and de Armas, D.: Variability in plankton community structure, metabolism,
and vertical carbon fluxes along an upwelling filament (Cape Juby, NW
Africa), Prog. Oceanogr., 62, 95–113,
https://doi.org/10.1016/j.pocean.2004.07.004, 2004. a
Arístegui, J., Barton, E. D., Álvarez-Salgado, X. A., Santos, A.
M. P., Figueiras, F. G., Kifani, S., Hernández-León, S., Mason, E.,
Machú, E., and Demarcq, H.: Sub-regional ecosystem variability in the
Canary Current upwelling, Prog. Oceanogr., 83, 33–48,
https://doi.org/10.1016/j.pocean.2009.07.031, 2009. a, b, c
Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith,
D. K., and Gombos, D.: A Cross-calibrated, Multiplatform Ocean Surface Wind
Velocity Product for Meteorological and Oceanographic Applications, B. Am. Meteorol. Soc., 92, 157–174,
https://doi.org/10.1175/2010bams2946.1, 2011. a
Bador, M., Boe, J., Terray, L., Alexander, L., Baker, A., Koenigk, T.,
Putrasahan, D., Roberts, M., Scoccimarro, E., Schiemann, R., Seddon, J.,
Senan, R., and Valcke, S.: Impact of higher spatial atmospheric resolution on
precipitation extremes over land in global climate models, J. Geophys. Res.,
125, e2019JD032184, https://doi.org/10.1029/2019JD032184, 2020. a, b
Bakun, A.: Coastal upwelling indices, west coast of North America, 1946–71,
U.S. Dep. Commer., NOAA Tech. Rep. NMFS SSRF-671, 103 pp., 1973. a
Bakun, A.: Coastal Ocean Upwelling, Science, 247, 198–201,
https://doi.org/10.1126/science.247.4939.198, 1990. a, b
Bakun, A. and Nelson, C. S.: The Seasonal Cycle of Wind-Stress Curl in
Subtropical Eastern Boundary Current Regions, J. Phys.
Oceanogr., 21, 1815–1834,
https://doi.org/10.1175/1520-0485(1991)021<1815:tscows>2.0.co;2, 1991. a
Balaguru, K., Van Roekel, L. P., Leung, L. R., and Veneziani, M.: Subtropical
Eastern North Pacific SST bias in earth system models, J. Geophys.
Res.-Oceans, 126, e2021JC017359, https://doi.org/10.1029/2021JC017359, 2021. a
Barton, E.D, Arístegui, J., Tett, P., Cantón, M., García-Braun, J., Hernández-León, S., Nykjaer, L., Almeida, C., Almunia, J., Ballesteros, S., Basterretxea, G., Escánez, J., Garcı́a-Weill, L., Hernández-Guerra, A., López-Laatzen, F., Molina, R., Montero, M.F., Navarro-Pérez, E., Rodrı́guez, J.M., Van Lenning, K., Vélez, H., and Wild, K.: The transition zone of the Canary current upwelling region, Prog.
Oceanogr., 41, 455–504, 1998. a
Barton, E., Field, D., and Roy, C.: Canary current upwelling: More or less?,
Prog. Oceanogr., 116, 167–178, https://doi.org/10.1016/j.pocean.2013.07.007,
2013. a
Bellucci, A., Athanasiadis, P. J., Scoccimarro, E., Ruggieri, P., Gualdi, S.,
Fedele, G., Haarsma, R. J., Garcia-Serrano, J., Castrillo, M., Putrahasan,
D., Sanchez-Gomez, E., Moine, M.-P., Roberts, C. D., Roberts, M. J., Seddon,
J., and Vidale, P. L.: Air-Sea interaction over the Gulf Stream in an
ensemble of HighResMIP present climate simulations, Clim. Dynam., 56,
2093–2111, https://doi.org/10.1007/s00382-020-05573-z, 2021. a
Benazzouz, A., Mordane, S., Orbi, A., Chagdali, M., Hilmi, K., Atillah, A.,
Lluís Pelegrí, J., and Hervé, D.: An improved coastal upwelling
index from sea surface temperature using satellite-based approach – The case
of the Canary Current upwelling system, Cont. Shelf Res., 81,
38–54, https://doi.org/10.1016/j.csr.2014.03.012, 2014. a, b
Blanton, J. O., Atkinson, L. P., Castillejo, F., and Montero, A. L.: Coastal
upwelling of the Rias Bajas, Galicia, northwest Spain, I; hydrographic
studies, Rapp. P. V. Reun. Cons. Int. Explor. Mer., 183, 179–190, 1984. a
Blanton, J. O., Tenore, K. R., Castillejo, F., Atkinson, L. P., Schwing, F. B.,
and Lavin, A.: The relationship of upwelling to mussel production in the rias
on the western coast of Spain, J. Mar. Res., 45, 497–511,
https://doi.org/10.1357/002224087788401115, 1987. a
Bravo, L., Ramos, M., Astudillo, O., Dewitte, B., and Goubanova, K.: Seasonal variability of the Ekman transport and pumping in the upwelling system off central-northern Chile (∼ 30∘ S) based on a high-resolution atmospheric regional model (WRF), Ocean Sci., 12, 1049–1065, https://doi.org/10.5194/os-12-1049-2016, 2016. a
Bryan, F. O., Tomas, R., Dennis, J. M., Chelton, D. B., Loeb, N. G., and
McClean, J. L.: Frontal Scale Air–Sea Interaction in
High-Resolution Coupled Climate Models, J. Climate, 23, 6277–6291,
https://doi.org/10.1175/2010jcli3665.1, 2010. a
Capet, X. J., Marchesiello, P., and McWilliams, J. C.: Upwelling response to
coastal wind profiles, Geophys. Res. Lett., 31, L13311,
https://doi.org/10.1029/2004gl020123, 2004. a, b
Cardone, V. J., Greenwood, J. G., and Cane, M. A.: On trends in historical
marine wind data, J. Clim., 3, 113–127, 1990. a
Castaño Tierno, A.: Tropical ocean-atmosphere interactions in
coupled models, The case of of the Equatorial thermocline and the northwest
African upwelling, PhD thesis, https://core.ac.uk/download/pdf/288477594.pdf (last access: 12 July 2022), 2020. a
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.: Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model, J. Adv. Model. Earth Sy., 11, 185–209, https://doi.org/10.1029/2018MS001369, 2019. a
Cropper, T. E., Hanna, E., and Bigg, G. R.: Spatial and temporal seasonal
trends in coastal upwelling off Northwest Africa, 1981–2012, Deep-Sea
Res. Pt. I, 86, 94–111,
https://doi.org/10.1016/j.dsr.2014.01.007, 2014. a, b
Czaja, A., Frankignoul, C., Minobe, S., and Vannière, B.: Simulating the
midlatitude atmospheric circulation: what might we gain from high-resolution
modeling of air-sea interactions?, Current Climate Change reports, 5,
390–406, 2019. a
Davey, M., Huddleston, M., Sperber, K., Braconnot, P., Bryan, F., Chen, D.,
Colman, R., Cooper, C., Cubasch, U., Delecluse, P., DeWitt, D., Fairhead, L.,
Flato, G., Gordon, C., Hogan, T., Ji, M., Kimoto, M., Kitoh, A., Knutson, T.,
Latif, M., Treut, H. L., Li, T., Manabe, S., Mechoso, C., Power, S.,
Roeckner, E., Terray, L., Vintzileos, A., Voss, R., Wang, B., Washington, W.,
Yoshikawa, I., Yu, J., Yukimoto, S., Zebiak, S., and Meehl, G.: STOIC: a
study of coupled model climatology and variability in tropical ocean regions,
Clim. Dynam., 18, 403–420, https://doi.org/10.1007/s00382-001-0188-6, 2002. a
de Boyer Montégut, C.: Mixed layer depth over the global ocean: An
examination of profile data and a profile-based climatology, J.
Geophys. Res., 109, C12003, https://doi.org/10.1029/2004jc002378, 2004. a, b, c
deCastro, M., Gomez-Gesteira, M., Alvarez, I., Cabanas, J. M., and Prego, R.:
Characterization of fall-winter upwelling recurrence along the Galician
western coast (NW Spain) from 2000 to 2005: Dependence on atmospheric
forcing, J. Mar. Syst., 72, 145–158, https://doi.org/10.1016/j.jmarsys.2007.04.005,
2008a. a
deCastro, M., Gómez-Gesteira, M., Lorenzo, M., Alvarez, I., and Crespo,
A.: Influence of atmospheric modes on coastal upwelling along the western
coast of the Iberian Peninsula, 1985 to 2005, Clim. Res., 36, 169–179,
https://doi.org/10.3354/cr00742, 2008b. a
Delworth, T. L., Rosati, A., Anderson, W., Adcroft, A. J., Balaji, V., Benson,
R., Dixon, K., Griffies, S. M., Lee, H.-C., Pacanowski, R. C., Vecchi, G. A.,
Wittenberg, A. T., Zeng, F., and Zhang, R.: Simulated Climate and Climate
Change in the GFDL CM2.5 High-Resolution Coupled Climate Model, J.
Climate, 25, 2755–2781, https://doi.org/10.1175/jcli-d-11-00316.1, 2012. a
Doi, T., Vecchi, G. A., Rosati, A. J., and Delworth, T. L.: Biases in the
Atlantic ITCZ in seasonal–interannual variations for a coarse-and a
high-resolution coupled climate model, J. Climate, 25, 5494–5511,
2012. a
Ducet, N., Le Traon, P. Y., and Reverdin, G.: Global high-resolution mapping of
ocean circulation from TOPEX/Poseidon and ERS-1 and -2, J.
Geophys. Res.-Oceans, 105, 19477–19498,
https://doi.org/10.1029/2000jc900063, 2000. a
Exarchou, E., Prodhomme, C., Brodeau, L., Guemas, V., and Doblas-Reyes, F.:
Origin of the warm eastern tropical Atlantic SST bias in a climate model,
Clim. Dynam., 51, 1819–1840, 2018. a
Faye, S., Lazar, A., Sow, B. A., and Gaye, A. T.: A model study of the
seasonality of sea surface temperature and circulation in the Atlantic
North-eastern Tropical Upwelling System, Frontiers in Physics, 3, 1–20,
https://doi.org/10.3389/fphy.2015.00076, 2015. a
Fraga, F.: Upwelling off the Galician Coast, Northwest Spain, in: Coastal
Upwelling, Coastal Estuarine, Stud., vol. 1, edited by: Richards, F. A.,
176–182, AGU, Washington, D. C., 1981. a
Freilich, M. H., Long, D. G., and Spencer, M. W.: SeaWinds: A scanning
scatterometer for ADEOS II – Science overview, Proc. Int. Geoscience and
Remote Sensing Symp., Pasadena, CA, IEEE, 960–963, 1994. a
Gomez-Gesteira, M., Moreira, C., Alvarez, I., and deCastro, M.: Ekman transport
along the Galician coast (northwest Spain) calculated from forecasted winds,
J. Geophys. Res., 111, C10005, https://doi.org/10.1029/2005jc003331, 2006. a, b, c
Gutjahr, O., Putrasahan, D., Lohmann, K., Jungclaus, J. H., von Storch, J.-S., Brüggemann, N., Haak, H., and Stössel, A.: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 12, 3241–3281, https://doi.org/10.5194/gmd-12-3241-2019, 2019. a, b, c, d, e
Haarsma, R., Acosta, M., Bakhshi, R., Bretonnière, P.-A., Caron, L.-P., Castrillo, M., Corti, S., Davini, P., Exarchou, E., Fabiano, F., Fladrich, U., Fuentes Franco, R., García-Serrano, J., von Hardenberg, J., Koenigk, T., Levine, X., Meccia, V. L., van Noije, T., van den Oord, G., Palmeiro, F. M., Rodrigo, M., Ruprich-Robert, Y., Le Sager, P., Tourigny, E., Wang, S., van Weele, M., and Wyser, K.: HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR – description, model computational performance and basic validation, Geosci. Model Dev., 13, 3507–3527, https://doi.org/10.5194/gmd-13-3507-2020, 2020. a, b
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. a, b, c
Harlass, J., Latif, M., and Park, W.: Improving climate model simulation of
tropical Atlantic sea surface temperature: The importance of enhanced
vertical atmosphere model resolution, Geophys. Res. Lett., 42,
2401–2408, 2015. a
Harlaß, J., Latif, M., and Park, W.: Alleviating tropical Atlantic sector
biases in the Kiel climate model by enhancing horizontal and vertical
atmosphere model resolution: climatology and interannual variability, Clim.
Dynam., 50, 2605–2635, https://doi.org/10.1007/s00382-017-3760-4, 2018. a
Herbland, A. and Voituriez, B.: La production primaire dans l'upwelling mauritanien en mars 1973, Cah. O.R.ST.OM., Sér. Océanogr., 12, 187–201, 1974. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hewitt, H. T., Roberts, M., Mathiot, P., Biastoch, A., Blockley, E.,
Chassignet, E. P., Fox-Kemper, B., Hyder, P., Marshall, D. P., Popova, E.,
Treguier, A.-M., Zanna, L., Yool, A., Yu, Y., Beadling, R., Bell, M.,
Kuhlbrodt, T., Arsouze, T., Bellucci, A., Castruccio, F., Gan, B.,
Putrasahan, D., Roberts, C. D., Roekel, L. V., and Zhang, Q.: Resolving and
Parameterising the Ocean Mesoscale in Earth System Models, Current Climate
Change Reports, 6, 137–152, https://doi.org/10.1007/s40641-020-00164-w, 2020. a
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T. P., Chepurin, G. A.,
Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.:
Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5):
Upgrades, Validations, and Intercomparisons, J. Climate, 30, 8179–8205. https://doi.org/10.1175/JCLI-D-16-0836.1, 2017. a
Huyer, A.: Coastal upwelling in the California current system, Prog.
Oceanogr., 12, 259–284,
https://doi.org/10.1016/0079-6611(83)90010-1, 1983. a
Jacox, M., Moore, A., Edwards, C., and Fiechter, J.: Spatially resolved
upwelling in the California Current System and its connections to climate
variability, Geophys. Res. Lett., 41, 3189–3196, 2014. a
Jacox, M. G., Edwards, C. A., Hazen, E. L., and Bograd, S. J.: Coastal
Upwelling Revisited: Ekman, Bakun, and Improved Upwelling Indices for the
U.S. West Coast, J. Geophys. Res.-Oceans, 123, 7332–7350,
https://doi.org/10.1029/2018jc014187, 2018. a
Lathuilière, C., Echevin, V., and Lévy, M.: Seasonal and intraseasonal
surface chlorophyll-a variability along the northwest African coast, J. Geophys. Res., 113, C05007, https://doi.org/10.1029/2007jc004433, 2008. a, b
Ma, J., Xu, S., and Wang, B.: Warm bias of sea surface temperature in Eastern
boundary current regions – a study of effects of horizontal
resolution in CESM, Ocean Dynam., 69, 939–954,
https://doi.org/10.1007/s10236-019-01280-4, 2019. a, b, c
Manabe, S., Stouffer, R. J., Spelman, M. J., and Bryan, K.: Transient Responses
of a Coupled Ocean–Atmosphere Model to Gradual Changes of
Atmospheric CO2. Part I. Annual Mean Response, J. Climate, 4,
785–818, https://doi.org/10.1175/1520-0442(1991)004<0785:troaco>2.0.co;2, 1991. a
Marcello, J., Hernández-Guerra, A., Eugenio, F., and Fonte, A.: Seasonal
and temporal study of the northwest African upwelling system, Int.
J. Remote Sens., 32, 1843–1859, https://doi.org/10.1080/01431161003631576,
2011. a
Marchesiello, P. and Estrade, P.: Upwelling limitation by onshore geostrophic
flow, J. Mar. Res., 68, 37–62,
https://doi.org/10.1357/002224010793079004, 2010. a
McGregor, H. V., Dima, M., Fischer, H. W., and Mulitza, S.: Rapid 20th-Century
Increase in Coastal Upwelling off Northwest Africa, Science, 315, 637–639,
https://doi.org/10.1126/science.1134839, 2007. a
Minas, H. J., Codispoti, L. A., and Dugdale, R. C.: Nutrients and primary production in the upwelling regions off northwest Africa, Rapports et Proces-Verbaux des Reunions, Conseil International pour l'Exploraion de la Mer, 180, 148–183, 1982. a
Mittelstaedt, E.: The ocean boundary along the northwest African coast:
Circulation and oceanographic properties at the sea surface, Prog.
Oceanogr., 26, 307–355, https://doi.org/10.1016/0079-6611(91)90011-a, 1991. a, b
Moreno-Chamarro, E., Caron, L.-P., Loosveldt Tomas, S., Vegas-Regidor, J., Gutjahr, O., Moine, M.-P., Putrasahan, D., Roberts, C. D., Roberts, M. J., Senan, R., Terray, L., Tourigny, E., and Vidale, P. L.: Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models, Geosci. Model Dev., 15, 269–289, https://doi.org/10.5194/gmd-15-269-2022, 2022. a
Ndoye, S., Capet, X., Estrade, P., Sow, B., Machu, E., Brochier, T.,
Döring, J., and Brehmer, P.: Dynamics of a “low-enrichment
high-retention” upwelling center over the southern Senegal shelf,
Geophys. Res. Lett., 44, 5034–5043, https://doi.org/10.1002/2017gl072789,
2017. a
Nykjær, L. and Van Camp, L.: Seasonal and interannual variability of
coastal upwelling along northwest Africa and Portugal from 1981 to 1991,
J. Geophys. Res., 99, 14197, https://doi.org/10.1029/94jc00814, 1994. a, b
Oerder, V., Colas, F., Echevin, V., Codron, F., Tam, J., and Belmadani, A.:
Peru-Chile upwelling dynamics under climate change, J. Geophys.
Res.-Oceans, 120, 1152–1172, https://doi.org/10.1002/2014jc010299, 2015. a
Parfitt, R., Czaja, A., and Kwon, Y.-O.: The impact of SST resolution change in
the ERA-Interim reanalysis on wintertime Gulf Stream frontal air-sea
interaction, Geophys. Res. Lett., 44, 3246–3254, 2017. a
Patricola, C. M., Li, M., Xu, Z., Chang, P., Saravanan, R., and Hsieh, J.-S.:
An investigation of tropical Atlantic bias in a high-resolution coupled
regional climate model, Clim. Dynam., 39, 2443–2463,
https://doi.org/10.1007/s00382-012-1320-5, 2012. a
Pauly, D. and Christensen, V.: Primary production required to sustain global
fisheries, Nature, 374, 255–257, https://doi.org/10.1038/374255a0, 1995. a
Pennington, J., Mahoney, K., Kuwahara, V., Kolber, D., Calienes, R., and
Chavez, F.: Primary production in the easterntropical Pacific, Prog.
Oceanogr., 69, 285–317, 2006. a
Pickett, M. H.: Ekman transport and pumping in the California Current based on
the U.S. Navy's high-resolution atmospheric model (COAMPS), J.
Geophys. Res., 108, 3327, https://doi.org/10.1029/2003jc001902, 2003. a, b
Pires, A. C., Nolasco, R., Rocha, A., and Dubert, J.: Assessing future climate
change in the Iberian Upwelling System, J. Coast. Res., 165,
1909–1914, https://doi.org/10.2112/si65-323.1, 2013. a
Putrasahan, D., Lohmann, K., von Storch, J.-S., Jungclaus, J. H., Gutjahr, O.,
and Haak, H.: Surface flux drivers for the slowdown of the Atlantic
meridional overturning circulation in a high-resolution global coupled
climate model, J. Adv. Model. Earth Sy., 11, 1349–1363,
2019. a
Putrasahan, D. A., Miller, A. J., and Seo, H.: Isolating mesoscale coupled
ocean–atmosphere interactions in the Kuroshio Extension region, Dyn.
Atmos. Oceans, 63, 60–78,
https://doi.org/10.1016/j.dynatmoce.2013.04.001, 2013. a
Raj, J., Bangalath, H. K., and Stenchikov, G.: West African Monsoon: current
state and future projections in a high-resolution AGCM, Clim. Dynam.,
52, 6441–6461, https://doi.org/10.1007/s00382-018-4522-7, 2019. a
Renault, L., Deutsch, C., McWilliams, J. C., Frenzel, H., Liang, J.-H., and
Colas, F.: Partial decoupling of primary productivity from upwelling in the
California Current system, Nat. Geosci., 9, 505–508,
https://doi.org/10.1038/ngeo2722, 2016. a
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and
Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface
Temperature, J. Climate, 20, 5473–5496,
https://doi.org/10.1175/2007jcli1824.1, 2007. a
Richter, I.: Climate model biases in the eastern tropical oceans: causes,
impacts and ways forward, Wiley Interdisciplinary Reviews: Climate Change, 6,
345–358, https://doi.org/10.1002/wcc.338, 2015. a, b, c
Richter, I. and Xie, S.-P.: On the origin of equatorial Atlantic biases in
coupled general circulation models, Clim. Dynam., 31, 587–598,
https://doi.org/10.1007/s00382-008-0364-z, 2008. a
Richter, I., Xie, S.-P., Wittenberg, A. T., and Masumoto, Y.: Tropical Atlantic
biases and their relation to surface wind stress and terrestrial
precipitation, Clim. Dynam., 38, 985–1001,
https://doi.org/10.1007/s00382-011-1038-9, 2012. 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
Roberts, M. J., Hewitt, H. T., Hyder, P., Ferreira, D., Josey, S. A.,
Mizielinski, M., and Shelly, A.: Impact of ocean resolution on coupled
air-sea fluxes and large-scale climate, Geophys. Res. Lett., 43,
10430–10438, https://doi.org/10.1002/2016GL070559, 2016. a
Roberts, M. J., Baker, A., Blockley, E. W., Calvert, D., Coward, A., Hewitt, H. T., Jackson, L. C., Kuhlbrodt, T., Mathiot, P., Roberts, C. D., Schiemann, R., Seddon, J., Vannière, B., and Vidale, P. L.: Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments, Geosci. Model Dev., 12, 4999–5028, https://doi.org/10.5194/gmd-12-4999-2019, 2019. a, b, c
Rossi, V., Feng, M., Pattiaratchi, C., Roughan, M., and Waite, A. M.: On the
factors influencing the development of sporadic upwelling in the Leeuwin
Current system, J. Geophys. Res.-Oceans, 118, 3608–3621,
https://doi.org/10.1002/jgrc.20242, 2013. a
Rykaczewski, R. R., Dunne, J. P., Sydeman, W. J., García-Reyes, M., Black,
B. A., and Bograd, S. J.: Poleward displacement of coastal
upwelling-favorable winds in the ocean's eastern boundary currents through
the 21st century, Geophys. Res. Lett., 42, 6424–6431,
https://doi.org/10.1002/2015gl064694, 2015. a, b
Santos, F., deCastro, M., Gómez-Gesteira, M., and Álvarez, I.:
Differences in coastal and oceanic SST warming rates along the Canary
upwelling ecosystem from 1982 to 2010, Cont. Shelf Res., 47, 1–6,
https://doi.org/10.1016/j.csr.2012.07.023, 2012. a, b
Schwing, F. B., O'Farrell, M., Steger, J. M., and Baltz, K.: Coastal upwelling
indices, west coast of North America, 1946–1995, Tech. Memo.
NOAA-TM-NMFS-SWFSC-231, 144 pp., NOAA, La Jolla, Calif., 1996. a
Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese,
B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J.,
Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D.,
Cornes, R., Cram, T. A., Crouthamel, R., Domínguez-Castro, F.,
Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan,
A., Kubota, H., Blancq, F. L., Lee, T.-C., Lorrey, A., Luterbacher, J.,
Maugeri, M., Mock, C. J., Moore, G. K., Przybylak, R., Pudmenzky, C., Reason,
C., Slonosky, V. C., Smith, C. A., Tinz, B., Trewin, B., Valente, M. A.,
Wang, X. L., Wilkinson, C., Wood, K., and Wyszyński, P.: Towards a more
reliable historical reanalysis: Improvements for version 3 of the Twentieth
Century Reanalysis system, Q. J. Roy. Meteorol.
Soc., 145, 2876–2908, https://doi.org/10.1002/qj.3598, 2019. a
Small, R. J., Bacmeister, J., Bailey, D., Baker, A., Bishop, S., Bryan, F., Caron, J., Dennis, J., Gent, P., Hsu, H.-m., Jochum, M., Lawrence, D., Muñoz, E., diNezio, P., Scheitlin, T., Tomas, R., Tribbia, J., Tseng, Y.-h., and Vertenstein, M.: A new synoptic scale
resolving global climate simulation using the Community Earth System Model,
J. Adv. Model. Earth Syst., 6, 1065–1094, 2014. a
Small, R. J., Curchitser, E., Hedstrom, K., Kauffman, B., and Large, W. G.: The
Benguela Upwelling System: Quantifying the Sensitivity to Resolution and
Coastal Wind Representation in a Global Climate Model*, J. Climate,
28, 9409–9432, https://doi.org/10.1175/jcli-d-15-0192.1, 2015. a, b, c, d
Smith, R. L.: Upwelling, Oceanogr. Mar. Biol. Ann. Rev., 6, 11–46, 1968. a
Sverdrup, H. U., Johnson, M. W., and Fleming, R. H.: The Oceans, Their Physics,
Chemistry and General Biology, 1060 pp., Prentice-Hall, Englewood Cliffs New
Jersey, 1942. a
Sylla, A.: Observations and Reanalyses products, Zenodo [data set], https://doi.org/10.5281/zenodo.7309969, 2022a. a, b, c, d
Sylla, A.: Matlab scripts, in: Geoscientific Model Development, Zenodo [code], https://doi.org/10.5281/zenodo.7309780, 2022b. 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., 119, 2864–2889, https://doi.org/10.1002/2013JD020316, 2014. a
Torres, R.: Spatial patterns of wind and sea surface temperature in the
Galician upwelling region, J. Geophys. Res., 108, 3130,
https://doi.org/10.1029/2002jc001361, 2003. a
Tretkoff, E.: Research Spotlight: Coastal cooling and marine productivity
increasing off Peru, Eos, Transactions American Geophysical Union, 92,
184–184, 2011. a
Van Camp, L., Nykjaer, L., Mittelstaedt, E., and Schlittenhardt, P.: Upwelling
and boundary circulation off Northwest Africa as depicted by infrared and
visible satellite observations, Prog. Oceanogr., 26, 357–402, 1991. a
Vanniere, B., Demory, M. E., Vidale, P. L., Schiemann, R., Roberts, M. J.,
Roberts, C. D., and Senan, R.: Multi-model evaluation of the sensitivity
of the global energy budget and hydrological cycle to resolution, Clim.
Dynam., 52, 6817-6846, https://doi.org/10.1007/s00382-018-4547-y, 2019. a
Vazquez, R., Parras-Berrocal, I., Cabos, W., Sein, D. V., Mañanes, R.,
Perez, J. I., and Izquierdo, A.: Climate Evaluation of a High-Resolution
Regional Model over the Canary Current Upwelling System, in: Lecture Notes in
Computer Science, 240–252, Springer International Publishing,
https://doi.org/10.1007/978-3-030-22747-0_19, 2019. a
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A.,
Chevallier, M., C. J., Guérémy, J. F., Michou, M., Moine, M. P.,
Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke,
S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes,
J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O.,
Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A.,
Sanchez-Gomez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK
experiments with CNRM-CM6-1., J. Adv. Model. Earth Sys.,
11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019. a, b
Wang, D., Gouhier, T. C., Menge, B. A., and Ganguly, A. R.: Intensification and
spatial homogenization of coastal upwelling under climate change, Nature,
518, 390–394, https://doi.org/10.1038/nature14235, 2015.
a
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D.,
Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson,
S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J.
G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L.,
Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and
Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and
GC3.1) Configurations, J. Adv. Model. Earth Sys., 10,
357–380, https://doi.org/10.1002/2017ms001115, 2018. a, b
Worley, S. J., Woodruff, S. D., Reynolds, R. W., Lubker, S. J., and Lott, N.:
ICOADS release 2.1 data and products, Int. J. Climatol.,
25, 823–842, https://doi.org/10.1002/joc.1166, 2005. a
Yoshida, K.: Coastal Upwellig off the California coast, Rec. Oceanogr. Works
Japan, 2, 8–20, 1995. a
Zuidema, P., Chang, P., Medeiros, B., Kirtman, B. P., Mechoso, R., Schneider, E. K., Toniazzo, T., Richter, I., Small, R. J, Bellomo, K., Brandt, P., de Szoeke, S., Farrar, J. T., Jung, E., Kato, S., Li, M., PatriCola, C., Wang, Z., Wood, R., and Xu, Z.:
Challenges and prospects for reducing coupled climate model SST biases in the
eastern tropical Atlantic and Pacific Oceans: The US CLIVAR Eastern Tropical
Oceans Synthesis Working Group, B. Am. Meteorol.
Soc., 97, 2305–2328, 2016. a, b
Short summary
Increasing model resolution depends on the subdomain of the Canary upwelling considered. In the Iberian Peninsula, the high-resolution (HR) models do not seem to better simulate the upwelling indices, while in Morocco to the Senegalese coast, the HR models show a clear improvement. Thus increasing the resolution of a global climate model does not necessarily have to be the only way to better represent the climate system. There is still much work to be done in terms of physical parameterizations.
Increasing model resolution depends on the subdomain of the Canary upwelling considered. In the...
