Articles | Volume 12, issue 10
https://doi.org/10.5194/gmd-12-4497-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-12-4497-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
28 Oct 2019
Model description paper |
| 28 Oct 2019
Description and evaluation of the Diat-HadOCC model v1.0: the ocean biogeochemical component of HadGEM2-ES
Ian J. Totterdell
CORRESPONDING AUTHOR
Met Office, Fitzroy Road, Exeter, EX1 3PB, UK
Related authors
Claudie Beaulieu, Harriet Cole, Stephanie Henson, Andrew Yool, Thomas R. Anderson, Lee de Mora, Erik T. Buitenhuis, Momme Butenschön, Ian J. Totterdell, and J. Icarus Allen
Biogeosciences, 13, 4533–4553, https://doi.org/10.5194/bg-13-4533-2016, https://doi.org/10.5194/bg-13-4533-2016, 2016
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Regime shifts have been suggested in the late 1970s and late 1980s in the Gulf of Alaska with important consequences for fisheries. Here we investigate the ability of a suite of ocean biogeochemical models of varying complexity to simulate these regime shifts. Our results demonstrate that ocean models can successfully simulate regime shifts in the Gulf of Alaska region, thereby improving our understanding of how changes in physical conditions are propagated from lower to upper trophic levels.
Claudie Beaulieu, Harriet Cole, Stephanie Henson, Andrew Yool, Thomas R. Anderson, Lee de Mora, Erik T. Buitenhuis, Momme Butenschön, Ian J. Totterdell, and J. Icarus Allen
Biogeosciences, 13, 4533–4553, https://doi.org/10.5194/bg-13-4533-2016, https://doi.org/10.5194/bg-13-4533-2016, 2016
Short summary
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Regime shifts have been suggested in the late 1970s and late 1980s in the Gulf of Alaska with important consequences for fisheries. Here we investigate the ability of a suite of ocean biogeochemical models of varying complexity to simulate these regime shifts. Our results demonstrate that ocean models can successfully simulate regime shifts in the Gulf of Alaska region, thereby improving our understanding of how changes in physical conditions are propagated from lower to upper trophic levels.
Related subject area
Climate and Earth system modeling
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
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
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
Truly Conserving with Conservative Remapping Methods
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
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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)
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
Earth system modeling on Modular Supercomputing Architectures: coupled atmosphere-ocean simulations with ICON 2.6.6-rc
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
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The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
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Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
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Understanding Changes in Cloud Simulations from E3SM Version 1 to Version 2
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
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We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
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This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
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We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
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In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
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The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
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Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
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Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
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We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
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To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
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
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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
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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
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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
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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
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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.
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
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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
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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.
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
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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
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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
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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
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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
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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
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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.
Karl E. Taylor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-177, https://doi.org/10.5194/gmd-2023-177, 2023
Revised manuscript accepted for GMD
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for 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.
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
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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.
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
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled Earth System Model. We found that 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
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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
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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).
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
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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. 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
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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.
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
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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.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
EGUsphere, https://doi.org/10.5194/egusphere-2023-1476, https://doi.org/10.5194/egusphere-2023-1476, 2023
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere-ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 59 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
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
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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
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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
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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.
Skyler Graap and Colin M. Zarzycki
EGUsphere, https://doi.org/10.5194/egusphere-2023-1450, https://doi.org/10.5194/egusphere-2023-1450, 2023
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations by balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Genevieve Clow, Nicole Lovenduski, Michael Levy, Keith Lindsay, and Jennifer Kay
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-143, https://doi.org/10.5194/gmd-2023-143, 2023
Revised manuscript accepted for GMD
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Satellite observations of chlorophyll allow us to study marine algae on a global scale – yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite chlorophyll simulator for an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can therefore be used to improve model validation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
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Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
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This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
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Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
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A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
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This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
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Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-100, https://doi.org/10.5194/gmd-2023-100, 2023
Revised manuscript accepted for GMD
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We outline a roadmap for preparation of a solar forcing dataset for the upcoming 7th round of the Coupled Model Intercomparison Project (CMIP6), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response, while also addressing the issues that were raised during CMIP6.
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
EGUsphere, https://doi.org/10.5194/egusphere-2023-1263, https://doi.org/10.5194/egusphere-2023-1263, 2023
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We performed systematic evaluation of clouds simulated in the E3SMv2 to document model performance on clouds and understand what updates in E3SMv2 have caused the 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 re-tuning of cloud macrophysics parameters. This study offers additional insights about clouds simulated in E3SMv2 and will benefit the future E3SM developments.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
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In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
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Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Cited articles
Anderson, T. R.: A spectrally averaged model of light penetration and photosynthesis, Limnol. Oceanogr., 38, 1403–1419, 1993. a
Antonov, J. I., Seidov, D., Boyer, T. P., Locarnini, R. A., Mishonov, A. V., Garcia, H. E., Baranova, O. K., Zweng, M. M., and Johnson, D. R.: World Ocean Atlas 2009, Volume 2: Salinity, in: NOAA Atlas NESDIS 68, edited by: Levitus, S., U.S. Gov. Printing Office, Wash., DC, 2010. a
Aranami, K. and Tsunogai, S.: Seasonal and regional comparison of oceanic and atmospheric dimethylsulfide in the northern North Pacific: Dilution effects on its concentration during winter, J. Geophys. Res., 109, D12303, https://doi.org/10.1029/2003JD004288, 2004. a
Benson, B. B. and Krause Jr., D.: The concentration and isotopic fractionation of oxygen dissolved in freshwater and seawater in equilibrium with the atmosphere, Limnol. Oceanogr., 29, 620–632, 1984. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bopp, L., Monfray, P., Aumont, O., Dufresne, J.-L., Le Treut, H., Madec, G., Terray, L., and Orr, J. C.: Potential impact of climate change on marine export production, Global Biogeochem. Cy., 15, 81–99, 2001. a
Bopp, L., Aumont, O., Cadule, P., Alvain, S., and Gehlen, M.: Response of diatoms diatribution to global warming and potential implications: A global model study, Geophys. Res. Lett., 32, L19606, https://doi.org/10.1029/2005GL023653, 2005. a
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013. a
Boyd, P. W., Ellwood, M. J., Tagliabue, A., and Twining, B. S.: Biotic and abiotic retention, recycling and remineralization of metals in the ocean, Nat. Geosci., 10, 167–173, https://doi.org/10.1038/NGEO2876, 2017. a, b
Boyd, P. W., Claustre, H., Levy, M., Siegel, D. A., and Weber, T.: Multi-faceted particle pumps drive carbon sequestration in the ocean, Nature, 568, 327–335, https://doi.org/10.1038/s41586-019-1098-2, 2019. a
Bryan, K.: A numerical method for the study of the circulation of the world ocean, J. Comput. Phys., 4, 347–376, 1969. a
Carr, M.-E., Friedrichs, M. A. M., Schmeltz, M., Aita, M. N., Antoine, D., Arrigo, K. R., Asanuma, I., Aumont, O., Barber, R., Behrenfeld, M., Bidigare, R., Buitenhuis, E. T., Campbell, J., Ciotti, A., Dierssen, H., Dowell, M., Dunne, J., Esaias, W., Gentili, B., Gregg, W., Groom, S., Hoepffner, N., Ishizaka, J., Kameda, T., Le Quéré, C., Lohrenz, S., Marra, J., Mélin, F., Moore, K., Morel, A., Reddy, T. E., Ryan, J., Scardi, M., Smyth, T., Turpie, K., Tilstone, G., Waters, K., and Yamanaka, Y.: A comparison of global estimates of marine primary production from ocean color, Deep-Sea Res. Pt. II, 53, 741–770, 2006. a
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011. a
Coleman, K. and Jenkinson, D. S.: RothC-26.3: a model for the turnover of carbon in soil, in: Evaluation of soil organic matter models using existing, long-term datasets, edited by: Powlson, D. S., Smith, P., and Smith, J. U., NATO ASI Series I, Springer-Verlag, Heidelberg, 38, 237–246, 1996.
Coleman, K. and Jenkinson, D. S.: RothC-26.3, A model for the turnover of carbon in soil: Model description and Windows User's guide: November 1999 issue, Harpenden, UK, available at: http://www.rothamsted.ac.uk/sustainable-soils-and-grassland-systems/rothamsted-carbon-model-rothc, https://repository.rothamsted.ac.uk/item/8829q/rothc-26-3-a-model-for-the-turnover-of-carbon-in-soil-model-description-and-windows-users-guide-november-1999-issue (last access: 18 October, 2019), 1999.
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011. a, b
Cox, M. D.: A primitive equation, three dimensional model of the ocean, Ocean Group Tech. Rep. 1, GFDL, Princeton, NJ, USA, 143 pp., 1984. a
Cox, P. M.: Description of the TRIFFID Dynamic Global Vegetation Model, Hadley Centre Technical Note HCTN 24, Met Office, FitzRoy Road, Exeter EX1 3PB, UK, available at: http://www.metoffice.gov.uk/media/pdf/9/h/HCTN_24.pdf and https://digital.nmla.metoffice.gov.uk/ (last access: 18 October 2019), 2001. a
Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J.: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 184–188, 2000. a
Dickson, A. G.: Thermodynamics of the dissociation of boric acid in synthetic seawater from 273.15 to 298.15 K, Deep-Sea Res., 37, 755–766, 1990.
Dukowicz, J. K. and Smith, R. D.: Implicit free surface method for the Bryan-Cox-Semtner ocean model, J. Geophys. Res., 99, 7991–8014, 1994. a
Eppley, R. W.: Temperature and phytoplankton growth in the sea, Fish. B-NOAA, 70, 1063–1085, 1972. a
Essery, R. L. H., Best, M. J., Betts, R. A., Cox, P. M., and Taylor, C. M.: Explicit representation of sub-grid heterogeneity in a GCM land-surface scheme, J. Hydrometeorol., 4, 530–543, 2003. a
Fanton d'Andon, O. H., Antoine, D., Mangin, A., Maritorena, S., Durand, D., Pradhan, Y., Lavender, S., Morel, A., Demaria, J., and Barrot, G.: Ocean colour sensors characterisation and expected error estimates of ocean colour merged products from GlobColour, Ocean Optics XIX, Barga, Italy, 51 pp., 6–10 October 2008.
Fasham, M. J. R., Ducklow, H. W., and McKelvie, S. M.: A nitrogen-based model of plankton dynamics in the oceanic mixed layer, J. Mar. Res., 48, 591–639, 1990.
Fasham, M. J. R., Flynn, K. J., Pondaven, P., Anderson, T. R., and Boyd, P. W.: Development of a robust marine ecosystem model to predict the role of iron in biogeochemical cycles: A comparison of results for iron-replete and iron-limited areas, and the SOIREE iron-enrichment experiment, Deep-Sea Res. Pt. I, 53, 333–366, https://doi.org/10.1016/j.dsr.2005.09.011, 2006. a
Ford, D. A., Edwards, K. P., Lea, D., Barciela, R. M., Martin, M. J., and Demaria, J.: Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model, Ocean Sci., 8, 751–771, https://doi.org/10.5194/os-8-751-2012, 2012. a, b, c
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Baranova, O. K., Zweng, M. M., and Johnson, D. R.: World Ocean Atlas 2009. Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation, in: NOAA Atlas NESDIS 69, edited by: Levitus, S., U.S. Gov. Printing Office, Washington, DC, 2010a. a
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Baranova, O. K., Zweng, M. M., and Johnson, D. R.: World Ocean Atlas 2009, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation, in: NOAA Atlas NESDIS 69, edited by: Levitus, S., U.S. Gov. Printing Office, Washington, DC, 2010b. a
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: Dynamic model of phytoplankton growth and acclimation: responses of the balanced growth rate and the chlorophyll a:carbon ratio to light, nutrient limitation and temperature, Mar. Ecol. Prog. Ser., 148, 187–200, 1997.
Gent, P. R., and McWilliams, J. C.: Isopycnal mixing in ocean circulation models, J. Phys. Oceanogr., 20, 150–155, 1990. a
Gentleman, W., Leising, A., Frost, B., Strom, S., and Murray, J.: Functional responses for zooplankton feeding on multiple resources: a review of assumptions and biological dynamics, Deep-Sea Res. Pt. II, 50, 2847–2875, https://doi.org/10.1016/j.dsr2.2003.07.001, 2003. a
Goldman, J. C. and Brewer, P. G.: Effect of nitrogen source and growth rate on phytoplankton-mediated changes in alkalinity, Limnol. Oceanogr., 25, 352–357, 1980. a
Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns, T. C., Mitchell, J. F. B., and Wood, R. A.: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clim. Dynam., 16, 147–168, 2000. a
Griffies, S. M.: The Gent-McWilliams skew flux, J. Phys. Oceanogr., 28, 831–841, 1998. a
Halloran, P. R., Bell, T. G., and Totterdell, I. J.: Can we trust empirical marine DMS parameterisations within projections of future climate?, Biogeosciences, 7, 1645–1656, https://doi.org/10.5194/bg-7-1645-2010, 2010. a
Halloran, P. R., Booth, B. B. B., Jones, C. D., Lambert, F. H., McNeall, D. J., Totterdell, I. J., and Völker, C.: The mechanisms of North Atlantic CO2 uptake in a large Earth System Model ensemble, Biogeosciences, 12, 4497–4508, https://doi.org/10.5194/bg-12-4497-2015, 2015. a
Hauck, J. and Völker, C.: Rising atmospheric CO2 leads to large impact of biology on Southern Ocean CO2 uptake via changes in the Revelle factor, Geophys. Res. Lett., 42, 1459–1464, https://doi.org/10.1002/2015GL063070, 2015
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997. a
Hunke, E. C. and Lipscomb, W. H.: CICE: The Los Alamos Sea Ice Model, documentation and software, Version 3.1, LA-CC-98-16, Los Alamos National Laboratory, Los Alamos, NM, 56 pp., 2004. a
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovern-mental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., 2013. a, b
Johns, T. C., Durman, C. F., Banks, H. T., Roberts, M. J., McLaren, A. J., Ridley, J. K., Senior, C. A., Williams, K. D., Jones, A., Rickard, G. J., Cusack, S., Ingram, W. J., Crucifix, M., Sexton, D. M. H., Joshi, M. M., Dong, B.-W., Spencer, H., Hill, R. S. R., Gregory, J. M., Keen, A. B., Pardaens, A. K., Lowe, J. A., Bodas-Salcedo, A., Stark, S., and Searl, Y.: The new Hadley Centre climate model HadGEM1: Evaluation of coupled simulations, J. Climate, 19, 1327–1353, 2006. a
Jones, C. D., Hughes, J. K., Bellouin, N., Hardiman, S. C., Jones, G. S., Knight, J., Liddicoat, S., O'Connor, F. M., Andres, R. J., Bell, C., Boo, K.-O., Bozzo, A., Butchart, N., Cadule, P., Corbin, K. D., Doutriaux-Boucher, M., Friedlingstein, P., Gornall, J., Gray, L., Halloran, P. R., Hurtt, G., Ingram, W. J., Lamarque, J.-F., Law, R. M., Meinshausen, M., Osprey, S., Palin, E. J., Parsons Chini, L., Raddatz, T., Sanderson, M. G., Sellar, A. A., Schurer, A., Valdes, P., Wood, N., Woodward, S., Yoshioka, M., and Zerroukat, M.: The HadGEM2-ES implementation of CMIP5 centennial simulations, Geosci. Model Dev., 4, 543–570, https://doi.org/10.5194/gmd-4-543-2011, 2011. a
Keeling, R. F., Stephens, B. B., Najjar, R. G., Doney, S. C., Archer D., and Heimann, M.: Seasonal variations in the atmospheric O2∕N2 ratio in relation to the kinetics of air-sea gas exchange, Global Biogeochem. Cy., 12, 141–163, https://doi.org/10.1029/97GB02339, 1998. a
Key, R. M., Kozyr, A., Sabine, C. L., Lee, K., Wanninkhof, R., Bullister, J., Feely, R. A., Millero, F., Mordy, C., and Peng, T.-H.: A global ocean carbon climatology: Results from GLODAP, Global Biogeochem. Cy., 18, GB4031, https://doi.org/10.1029/2004GB002247, 2004, a
Key, R. M., Olsen, A., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., and Suzuki, T.: Global Ocean Data Analysis Project, Version 2 (GLODAPv2), ORNL/CDIAC-162, ND-P093, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee, https://doi.org/10.3334/CDIAC/OTG.NDP093_GLODAPv2, 2015. a
Kraus, E. B. and Turner, J. S.: A one dimensional model of the seasonal thermocline II. The general theory and its consequences, Tellus, 19, 98–105, 1967 a
Kwiatkowski, L., Yool, A., Allen, J. I., Anderson, T. R., Barciela, R., Buitenhuis, E. T., Butenschön, M., Enright, C., Halloran, P. R., Le Quéré, C., de Mora, L., Racault, M.-F., Sinha, B., Totterdell, I. J., and Cox, P. M.: iMarNet: an ocean biogeochemistry model intercomparison project within a common physical ocean modelling framework, Biogeosciences, 11, 7291–7304, https://doi.org/10.5194/bg-11-7291-2014, 2014. a, b
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the
Leonard, B. P., MacVean, M. K., and Lock, A. P.: Positivity-Preserving Numerical Schemes for Multidimensional Advection, NASA Technical memorandum 106055, 64 pp., available at: http://ntrs.nasa.gov (last access: 18 October 2019), 1993. a
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., and Johnson, D. R.: World Ocean Atlas 2009, Volume 1: Temperature, in: NOAA Atlas NESDIS 67, edited by: Levitus, S., U.S. Gov. Printing Office, Washington, DC, 2010. a
Maritorena, S., Fanton d'Andon, O. H., Mangin, A., and Siegel, D. A.: Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues, Remote Sens. Environ., 114, 1791–1804, 2010.
Martin, J. H.: Iron as a Limiting Factor in Oceanic Productivity, in: Primary Productivity and Biogeochemical Cycles in the Sea, edited by: Falkowski, P. G., Woodhead, A. D., and Vivirito, K., Environ. Sci. Res., 43, Springer, Boston, MA, 1992. a
Martin, J. H., Knauer, G. A., Karl, D. M., and Broenkow, W. W.: VERTEX: carbon cycling in the northeast Pacific, Deep-Sea Res., 34, 267–285, 1987. a
Millero, F. J.: Thermodynamics of the carbon dioxide system in the ocean, Geochim. Cosmochim. Ac., 59, 661–677, 1995.
Morel, A.: Optical modelling of the upper ocean in relation to its biogenous matter content (case 1 waters), J. Geophys. Res., 93, 10749–10768, 1988.
Morel, A.: Light and marine photosynthesis: A spectral model with geochemical and climatological implications, Prog. Oceanogr., 26, 263–306, 1991.
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010.
O'Connor, F. M., Johnson, C. E., Morgenstern, O., Abraham, N. L., Braesicke, P., Dalvi, M., Folberth, G. A., Sanderson, M. G., Telford, P. J., Voulgarakis, A., Young, P. J., Zeng, G., Collins, W. J., and Pyle, J. A.: Evaluation of the new UKCA climate-composition model – Part 2: The Troposphere, Geosci. Model Dev., 7, 41–91, https://doi.org/10.5194/gmd-7-41-2014, 2014. a
Oki, T. and Sud, Y. C.: Design of total runoff integrating pathways (TRIP) – a global river channel network, Earth Interact., 2, 1–37, 1998. a
Pacanowski, R. C. and Griffies, S. M.: MOM 3.0 manual, NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, 692 pp., available from NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08542, 1998. a
Palmer, J. R. and Totterdell, I. J.: Production and export in a global ocean ecosystem model, Deep-Sea Res. Pt. I, 48, 1169–1198, 2001. a
Parekh, P., Follows, M. J., and Boyle, E.: Modeling the global ocean iron cycle, Global Biogeochem. Cy., 18, GB1002, https://doi.org/10.1029/2003GB002061, 2004. a
Platt, T., Sathyendranath, S., and Ravindran, P.: Primary production by phytoplankton: Analytic solutions for daily rates per unit area of water surface, Proc. R. Soc. Lond. B Bio, 241, 101–111, 1990.
Roy, R. N., Roy, L. N., Vogel, K. M., Moore, C. P., Pearson, T., Good, C. E., Millero, F. J., and Cambell, D. J.: Determination of the ionization constants of carbonic acid in seawater in salinities 5 to 45 and temperatures 0 to 45 ∘C, Mar. Chem., 44, 249–267, 1993.
Sabine, C. L., Key, R. M., Kozyr, A., Feely, R. A., Wanninkhof, R., Millero, F.J., Peng, T.-H., Bullister, J. L., and Lee, K.: Global Ocean Data Analysis Project: Results and Data. ORNL/CDIAC-145, NDP-083, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee, 110 pp., 2005. a
Schlitzer, R.: eGEOTRACES – Electronic Atlas of GEOTRACES Sections and Animated 3D Scenes, available at: https://www.egeotraces.org, last access: 18 October 2019. a
Siegel, D. A. Buessler, K. O., Doney, S. C., Sailley, S. F., Behrenfeld, M. J., and Boyd, P. W.: Global assessment of ocean carbon export by combining satellite observations and food-web models, Global Biogeochem. Cy., 28, 181–196, https://doi.org/10.1002/2013GB004743, 2014. a
Simo, R. and Dachs, J.: Global ocean emission of dimethylsulfide predicted from biogeophysical data, Global Biogeochem. Cy., 16, 1078, https://doi.org/10.1029/2001GB001829, 2002. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a, b
The HadGEM2 Development Team: Martin, G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., Hinton, T. J., Jones, C. D., McDonald, R. E., McLaren, A. J., O'Connor, F. M., Roberts, M. J., Rodriguez, J. M., Woodward, S., Best, M. J., Brooks, M. E., Brown, A. R., Butchart, N., Dearden, C., Derbyshire, S. H., Dharssi, I., Doutriaux-Boucher, M., Edwards, J. M., Falloon, P. D., Gedney, N., Gray, L. J., Hewitt, H. T., Hobson, M., Huddleston, M. R., Hughes, J., Ineson, S., Ingram, W. J., James, P. M., Johns, T. C., Johnson, C. E., Jones, A., Jones, C. P., Joshi, M. M., Keen, A. B., Liddicoat, S., Lock, A. P., Maidens, A. V., Manners, J. C., Milton, S. F., Rae, J. G. L., Ridley, J. K., Sellar, A., Senior, C. A., Totterdell, I. J., Verhoef, A., Vidale, P. L., and Wiltshire, A.: The HadGEM2 family of Met Office Unified Model climate configurations, Geosci. Model Dev., 4, 723–757, https://doi.org/10.5194/gmd-4-723-2011, 2011.
a, b, c
Visbeck, M., Marshall, J., Haine T., and Spall, M.: Specification of eddy transfer coefficients in coarse-resolution ocean circulation models, J. Phys. Oceanogr., 27, 381–402, 1997. a
Weiss, R. F.: Carbon dioxide in water and seawater: the solubility of a non-ideal gas, Mar. Chem., 2, 203–215, 1974. a
Woodward, S.: Modelling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model, J. Geophys. Res., 106, 18155–18166, 2001. a
Woodward, S.: Mineral dust in HadGEM2, Hadley Centre Technical Note HCTN 87, Met Office, FitzRoy Road, Exeter EX1 3PB, UK, available at: https://digital.nmla.metoffice.gov.uk/ (last access: 18 October 2019), 2011. a
Short summary
The paper presents a complete description of version 1.0 of the Diat-HadOCC ocean biogeochemical model. A full set of equations is included, as are the parameterisations used and the rationale for choosing them. The model was developed to be a sub-model of the HadGEM2-ES Earth system model, which has been used to run a range of simulations for the 5th Climate Model Intercomparison Project (CMIP5). Results from those simulations are shown and compared to data (where available).
The paper presents a complete description of version 1.0 of the Diat-HadOCC ocean biogeochemical...
