Articles | Volume 8, issue 7
https://doi.org/10.5194/gmd-8-2079-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-2079-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| Highlight paper
| 
16 Jul 2015
Methods for assessment of models | Highlight paper |
| 16 Jul 2015
14C-age tracers in global ocean circulation models
GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany
H. Wagner
GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany
P. Kähler
GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany
A. Oschlies
GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany
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Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-9, https://doi.org/10.5194/bg-2023-9, 2023
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The Peruvian system supports 10 % of the world's fishing yield. In the Peruvian system, wind and earth’s rotation bring cold, nutrient-rich water to the surface and allow phytoplankton to grow. But observations show that it grows worse at high upwelling. Using a model, we find that high upwelling happens when air mixes the water the most. Then phytoplankton is diluted and grows slowly due to low light and cool upwelled water. This study helps to estimate how it might change in a warming climate.
Karin Kvale, David P. Keller, Wolfgang Koeve, Katrin J. Meissner, Christopher J. Somes, Wanxuan Yao, and Andreas Oschlies
Geosci. Model Dev., 14, 7255–7285, https://doi.org/10.5194/gmd-14-7255-2021, https://doi.org/10.5194/gmd-14-7255-2021, 2021
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We present a new model of biological marine silicate cycling for the University of Victoria Earth System Climate Model (UVic ESCM). This new model adds diatoms, which are a key aspect of the biological carbon pump, to an existing ecosystem model. Our modifications change how the model responds to warming, with net primary production declining more strongly than in previous versions. Diatoms in particular are simulated to decline with climate warming due to their high nutrient requirements.
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Mariana Hill Cruz, Iris Kriest, Yonss Saranga José, Rainer Kiko, Helena Hauss, and Andreas Oschlies
Biogeosciences, 18, 2891–2916, https://doi.org/10.5194/bg-18-2891-2021, https://doi.org/10.5194/bg-18-2891-2021, 2021
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Markus Pahlow, Chia-Te Chien, Lionel A. Arteaga, and Andreas Oschlies
Geosci. Model Dev., 13, 4663–4690, https://doi.org/10.5194/gmd-13-4663-2020, https://doi.org/10.5194/gmd-13-4663-2020, 2020
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The stoichiometry of marine biotic processes is important for the regulation of atmospheric CO2 and hence the global climate. We replace a simplistic, fixed-stoichiometry plankton module in an Earth system model with an optimal-regulation model with variable stoichiometry. Our model compares better to the observed carbon transfer from the surface to depth and surface nutrient distributions. This work could aid our ability to describe and project the role of marine ecosystems in the Earth system.
Chia-Te Chien, Markus Pahlow, Markus Schartau, and Andreas Oschlies
Geosci. Model Dev., 13, 4691–4712, https://doi.org/10.5194/gmd-13-4691-2020, https://doi.org/10.5194/gmd-13-4691-2020, 2020
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We demonstrate sensitivities of tracers to parameters of a new optimality-based plankton–ecosystem model (OPEM) in the UVic-ESCM. We find that changes in phytoplankton subsistence nitrogen quota strongly impact the nitrogen inventory, nitrogen fixation, and elemental stoichiometry of ordinary phytoplankton and diazotrophs. We introduce a new likelihood-based metric for model calibration, and it shows the capability of constraining globally averaged oxygen, nitrate, and DIC concentrations.
Nadine Mengis, David P. Keller, Andrew H. MacDougall, Michael Eby, Nesha Wright, Katrin J. Meissner, Andreas Oschlies, Andreas Schmittner, Alexander J. MacIsaac, H. Damon Matthews, and Kirsten Zickfeld
Geosci. Model Dev., 13, 4183–4204, https://doi.org/10.5194/gmd-13-4183-2020, https://doi.org/10.5194/gmd-13-4183-2020, 2020
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In this paper, we evaluate the newest version of the University of Victoria Earth System Climate Model (UVic ESCM 2.10). Combining recent model developments as a joint effort, this version is to be used in the next phase of model intercomparison and climate change studies. The UVic ESCM 2.10 is capable of reproducing changes in historical temperature and carbon fluxes well. Additionally, the model is able to reproduce the three-dimensional distribution of many ocean tracers.
Sabine Mathesius, Julia Getzlaff, Heiner Dietze, Andreas Oschlies, and Markus Schartau
Earth Syst. Sci. Data, 12, 1775–1787, https://doi.org/10.5194/essd-12-1775-2020, https://doi.org/10.5194/essd-12-1775-2020, 2020
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Controlled manipulation of environmental conditions within large enclosures in the ocean, pelagic mesocosms, has become a standard method to explore responses of marine plankton communities to anthropogenic change. Among the challenges of interpreting mesocosm data is the often uncertain role of vertical mixing. This study introduces a mesocosm mixing model that is able to estimate vertical diffusivities and thus provides a tool for future mesocosm data analyses that account for mixing.
Iris Kriest, Paul Kähler, Wolfgang Koeve, Karin Kvale, Volkmar Sauerland, and Andreas Oschlies
Biogeosciences, 17, 3057–3082, https://doi.org/10.5194/bg-17-3057-2020, https://doi.org/10.5194/bg-17-3057-2020, 2020
Short summary
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Constants of global biogeochemical ocean models are often tuned
by handto match observations of nutrients or oxygen. We investigate the effect of this tuning by optimising six constants of a global biogeochemical model, simulated in five different offline circulations. Optimal values for three constants adjust to distinct features of the circulation applied and can afterwards be swapped among the circulations, without losing too much of the model's fit to observed quantities.
Fabian Reith, Wolfgang Koeve, David P. Keller, Julia Getzlaff, and Andreas Oschlies
Earth Syst. Dynam., 10, 711–727, https://doi.org/10.5194/esd-10-711-2019, https://doi.org/10.5194/esd-10-711-2019, 2019
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Short summary
This modeling study is the first one to look at the suitability and collateral effects of direct CO2 injection into the deep ocean as a means to bridge the gap between CO2 emissions and climate impacts of an intermediate CO2 emission scenario and a temperature target on a millennium timescale, such as the 1.5 °C climate target of the Paris Agreement.
Tronje P. Kemena, Angela Landolfi, Andreas Oschlies, Klaus Wallmann, and Andrew W. Dale
Earth Syst. Dynam., 10, 539–553, https://doi.org/10.5194/esd-10-539-2019, https://doi.org/10.5194/esd-10-539-2019, 2019
Short summary
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Oceanic deoxygenation is driven by climate change in several areas of the global ocean. Measurements indicate that ocean volumes with very low oxygen levels expand, with consequences for marine organisms and fishery. We found climate-change-driven phosphorus (P) input in the ocean is hereby an important driver for deoxygenation on longer timescales with effects in the next millennia.
Daniela Niemeyer, Iris Kriest, and Andreas Oschlies
Biogeosciences, 16, 3095–3111, https://doi.org/10.5194/bg-16-3095-2019, https://doi.org/10.5194/bg-16-3095-2019, 2019
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Recent studies suggest spatial variations of the marine particle flux length scale. Using a global biogeochemical ocean model, we investigate whether changes in particle size and size-dependent sinking can explain this variation. We address uncertainties by varying aggregate properties and circulation. Both aspects have an impact on the representation of nutrients, oxygen and oxygen minimum zones. The formation and sinking of large aggregates in productive areas lead to deeper flux penetration.
Yonss Saranga José, Lothar Stramma, Sunke Schmidtko, and Andreas Oschlies
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-155, https://doi.org/10.5194/bg-2019-155, 2019
Revised manuscript accepted for BG
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In situ observations along the Peruvian and Chilean coasts have exhibited variability in the water column oxygen concentration. This variability, which is attributed to the El Niño Southern Oscillation (ENSO), might have implication on the vertical extension of the Eastern Tropical South Pacific (ETSP) oxygen minimum zone. Here using a coupled physical-biogeochemical model, we provide new insights into how ENSO variability affects the vertical extension of the oxygen-poor waters of the ETSP.
Olaf Duteil, Andreas Oschlies, and Claus W. Böning
Biogeosciences, 15, 7111–7126, https://doi.org/10.5194/bg-15-7111-2018, https://doi.org/10.5194/bg-15-7111-2018, 2018
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Oxygen-depleted regions of the Pacific Ocean are currently expanding, which is threatening marine habitats. Based on numerical simulations, we show that the decrease in the intensity of the trade winds and the subsequent slowdown of the oceanic currents lead to a reduction in oxygen supply. Our study suggests that the prevailing positive conditions of the Pacific Decadal Oscillation since 1975, a major source of natural variability, may explain a significant part of the current deoxygenation.
Marine Bretagnon, Aurélien Paulmier, Véronique Garçon, Boris Dewitte, Séréna Illig, Nathalie Leblond, Laurent Coppola, Fernando Campos, Federico Velazco, Christos Panagiotopoulos, Andreas Oschlies, J. Martin Hernandez-Ayon, Helmut Maske, Oscar Vergara, Ivonne Montes, Philippe Martinez, Edgardo Carrasco, Jacques Grelet, Olivier Desprez-De-Gesincourt, Christophe Maes, and Lionel Scouarnec
Biogeosciences, 15, 5093–5111, https://doi.org/10.5194/bg-15-5093-2018, https://doi.org/10.5194/bg-15-5093-2018, 2018
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In oxygen minimum zone, the fate of the organic matter is a key question as the low oxygen condition would preserve the OM and thus enhance the biological carbon pump while the high microbial activity would foster the remineralisation and the greenhouse gases emission. To investigate this paradigm, sediment traps were deployed off Peru. We pointed out the influence of the oxygenation as well as the organic matter quantity and quality on the carbon transfer efficiency in the oxygen minimum zone.
Volkmar Sauerland, Ulrike Löptien, Claudine Leonhard, Andreas Oschlies, and Anand Srivastav
Geosci. Model Dev., 11, 1181–1198, https://doi.org/10.5194/gmd-11-1181-2018, https://doi.org/10.5194/gmd-11-1181-2018, 2018
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We present a concept to prove that a parametric model is well calibrated, i.e., that changes of its free parameters cannot lead to a much better model–data misfit anymore. The intention is motivated by the fact that calibrating global biogeochemical ocean models is important for assessment and inter-model comparison but computationally expensive.
Nadine Mengis, David P. Keller, and Andreas Oschlies
Earth Syst. Dynam., 9, 15–31, https://doi.org/10.5194/esd-9-15-2018, https://doi.org/10.5194/esd-9-15-2018, 2018
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The Systematic Correlation Matrix Evaluation (SCoMaE) method applies statistical information to systematically select, transparent, nonredundant indicators for a comprehensive assessment of the Earth system state. We show that due to changing climate forcing, such as anthropogenic climate change, the ad hoc assessment indicators might need to be reevaluated. Within an iterative process, this method would allow us to select scientifically consistent and societally relevant assessment indicators.
Karin F. Kvale, Samar Khatiwala, Heiner Dietze, Iris Kriest, and Andreas Oschlies
Geosci. Model Dev., 10, 2425–2445, https://doi.org/10.5194/gmd-10-2425-2017, https://doi.org/10.5194/gmd-10-2425-2017, 2017
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Computer models of ocean biology and chemistry are becoming increasingly complex, and thus more expensive, to run. One solution is to approximate the behaviour of the ocean physics and store that information outside of the model. That
offlineinformation can then be used to calculate a steady-state solution from the model's biology and chemistry, without waiting for a traditional
onlineintegration to complete. We show this offline method reproduces online results and is 100 times faster.
James C. Orr, Raymond G. Najjar, Olivier Aumont, Laurent Bopp, John L. Bullister, Gokhan Danabasoglu, Scott C. Doney, John P. Dunne, Jean-Claude Dutay, Heather Graven, Stephen M. Griffies, Jasmin G. John, Fortunat Joos, Ingeborg Levin, Keith Lindsay, Richard J. Matear, Galen A. McKinley, Anne Mouchet, Andreas Oschlies, Anastasia Romanou, Reiner Schlitzer, Alessandro Tagliabue, Toste Tanhua, and Andrew Yool
Geosci. Model Dev., 10, 2169–2199, https://doi.org/10.5194/gmd-10-2169-2017, https://doi.org/10.5194/gmd-10-2169-2017, 2017
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The Ocean Model Intercomparison Project (OMIP) is a model comparison effort under Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its physical component is described elsewhere in this special issue. Here we describe its ocean biogeochemical component (OMIP-BGC), detailing simulation protocols and analysis diagnostics. Simulations focus on ocean carbon, other biogeochemical tracers, air-sea exchange of CO2 and related gases, and chemical tracers used to evaluate modeled circulation.
Daniela Niemeyer, Tronje P. Kemena, Katrin J. Meissner, and Andreas Oschlies
Earth Syst. Dynam., 8, 357–367, https://doi.org/10.5194/esd-8-357-2017, https://doi.org/10.5194/esd-8-357-2017, 2017
Anja Engel, Hannes Wagner, Frédéric A. C. Le Moigne, and Samuel T. Wilson
Biogeosciences, 14, 1825–1838, https://doi.org/10.5194/bg-14-1825-2017, https://doi.org/10.5194/bg-14-1825-2017, 2017
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To better understand the role of oxygen for the biological carbon pump, we studied particle fluxes through hypoxic waters in the eastern tropical North Atlantic. Attenuation of organic carbon fluxes over depth was lower than expected from seawater temperatures, indicating co-effects of oxygen concentration. Differences were observed for individual organic components, suggesting that future carbon export fluxes may depend on changes in surface ocean organic matter quality under global change.
Markus Schartau, Philip Wallhead, John Hemmings, Ulrike Löptien, Iris Kriest, Shubham Krishna, Ben A. Ward, Thomas Slawig, and Andreas Oschlies
Biogeosciences, 14, 1647–1701, https://doi.org/10.5194/bg-14-1647-2017, https://doi.org/10.5194/bg-14-1647-2017, 2017
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Plankton models have become an integral part in marine ecosystem and biogeochemical research. These models differ in complexity and in their number of parameters. How values are assigned to parameters is essential. An overview of major methodologies of parameter estimation is provided. Aspects of parameter identification in the literature are diverse. Individual findings could be better synthesized if notation and expertise of the different scientific communities would be reasonably merged.
Yonss Saranga José, Heiner Dietze, and Andreas Oschlies
Biogeosciences, 14, 1349–1364, https://doi.org/10.5194/bg-14-1349-2017, https://doi.org/10.5194/bg-14-1349-2017, 2017
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This study aims to investigate the diverse subsurface nutrient patterns observed within anticyclonic eddies in the upwelling system off Peru. Two simulated anticyclonic eddies with opposing subsurface nitrate concentrations were analysed. The results show that diverse nutrient patterns within anticyclonic eddies are related to the presence of water mass from different origins at different depths, responding to variations in depth of the circulation strength at the edge of the eddy.
Iris Kriest, Volkmar Sauerland, Samar Khatiwala, Anand Srivastav, and Andreas Oschlies
Geosci. Model Dev., 10, 127–154, https://doi.org/10.5194/gmd-10-127-2017, https://doi.org/10.5194/gmd-10-127-2017, 2017
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Global biogeochemical ocean models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing features of the present ocean and their sensitivity to possible environmental changes. We present the first results from a framework that combines an offline biogeochemical tracer transport model with an estimation of distribution algorithm, calibrating six biogeochemical model parameters against observed oxygen and nutrients.
Fabian Reith, David P. Keller, and Andreas Oschlies
Earth Syst. Dynam., 7, 797–812, https://doi.org/10.5194/esd-7-797-2016, https://doi.org/10.5194/esd-7-797-2016, 2016
Björn Fiedler, Damian S. Grundle, Florian Schütte, Johannes Karstensen, Carolin R. Löscher, Helena Hauss, Hannes Wagner, Alexandra Loginova, Rainer Kiko, Péricles Silva, Toste Tanhua, and Arne Körtzinger
Biogeosciences, 13, 5633–5647, https://doi.org/10.5194/bg-13-5633-2016, https://doi.org/10.5194/bg-13-5633-2016, 2016
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Oxygen-depleted mesoscale features in the open eastern tropical North Atlantic, which are formed in the Mauritanian upwelling region, were discovered recently. This study examines biogeochemical structure and magnitudes of related processes within these isolated water masses. We found very low oxygen concentrations and strongly enhanced acidity at near-surface depth. Oxygen utilization and downward carbon export were found to exceed known values for this ocean region.
Bei Su, Markus Pahlow, and Andreas Oschlies
Biogeosciences, 13, 4985–5001, https://doi.org/10.5194/bg-13-4985-2016, https://doi.org/10.5194/bg-13-4985-2016, 2016
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Previously identified positive feedbacks within the nitrogen cycle in the eastern tropical South Pacific (ETSP) have challenged our understanding of the observed dynamics and stability of the nitrogen inventory. We present a box model analysis of the biological and biogeochemical relations in the ETSP among nitrogen deposition, benthic denitrification and phosphate regeneration. Our results suggest dominant stabilizing feedbacks tending to keep a balanced nitrogen inventory in the ETSP.
Carolin R. Löscher, Hermann W. Bange, Ruth A. Schmitz, Cameron M. Callbeck, Anja Engel, Helena Hauss, Torsten Kanzow, Rainer Kiko, Gaute Lavik, Alexandra Loginova, Frank Melzner, Judith Meyer, Sven C. Neulinger, Markus Pahlow, Ulf Riebesell, Harald Schunck, Sören Thomsen, and Hannes Wagner
Biogeosciences, 13, 3585–3606, https://doi.org/10.5194/bg-13-3585-2016, https://doi.org/10.5194/bg-13-3585-2016, 2016
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The ocean loses oxygen due to climate change. Addressing this issue in tropical ocean regions (off Peru and Mauritania), we aimed to understand the effects of oxygen depletion on various aspects of marine biogeochemistry, including primary production and export production, the nitrogen cycle, greenhouse gas production, organic matter fluxes and remineralization, and the role of zooplankton and viruses.
I. Kriest and A. Oschlies
Geosci. Model Dev., 8, 2929–2957, https://doi.org/10.5194/gmd-8-2929-2015, https://doi.org/10.5194/gmd-8-2929-2015, 2015
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We use a global model of oceanic P, N, and O2 cycles to investigate consequences of uncertainties in description of organic matter sinking, remineralization, denitrification, and N2-Fixation. After all biogeochemical and physical processes have been spun-up into a dynamically consistent steady-state, particle sinking and oxidant affinities of aerobic and anaerobic remineralization determine the extent of oxygen minimum zones, global nitrogen fluxes, and the oceanic nitrogen inventory.
L. Nickelsen, D. P. Keller, and A. Oschlies
Geosci. Model Dev., 8, 1357–1381, https://doi.org/10.5194/gmd-8-1357-2015, https://doi.org/10.5194/gmd-8-1357-2015, 2015
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In this paper we find that including the marine cycle of the phytoplankton nutrient iron in a global climate model improves the agreement between observed and simulated nutrient concentrations in the ocean and that a better description of the source of iron from the sediment to the ocean is more important than that of iron-containing dust deposition. Finally, we find that the response of the iron cycle to climate warming affects the phytoplankton growth and nutrient cycles.
B. Su, M. Pahlow, H. Wagner, and A. Oschlies
Biogeosciences, 12, 1113–1130, https://doi.org/10.5194/bg-12-1113-2015, https://doi.org/10.5194/bg-12-1113-2015, 2015
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A box model of the eastern tropical South Pacific oxygen minimum zone suggests that anaerobic water-column remineralization rates have to be slower than aerobic remineralization in order to explain the relatively high values of observed nitrate concentrations. Lateral oxygen supply sufficient to oxidize about one-fifth of the export production is required to prevent an anoxic deep ocean. Under these circumstances, the region can be a net source of fixed nitrogen to the surrounding ocean.
W. Koeve, O. Duteil, A. Oschlies, P. Kähler, and J. Segschneider
Geosci. Model Dev., 7, 2393–2408, https://doi.org/10.5194/gmd-7-2393-2014, https://doi.org/10.5194/gmd-7-2393-2014, 2014
A. E. F. Prowe, M. Pahlow, S. Dutkiewicz, and A. Oschlies
Biogeosciences, 11, 3397–3407, https://doi.org/10.5194/bg-11-3397-2014, https://doi.org/10.5194/bg-11-3397-2014, 2014
I. Kriest and A. Oschlies
Biogeosciences, 10, 8401–8422, https://doi.org/10.5194/bg-10-8401-2013, https://doi.org/10.5194/bg-10-8401-2013, 2013
O. Duteil, W. Koeve, A. Oschlies, D. Bianchi, E. Galbraith, I. Kriest, and R. Matear
Biogeosciences, 10, 7723–7738, https://doi.org/10.5194/bg-10-7723-2013, https://doi.org/10.5194/bg-10-7723-2013, 2013
C. J. Somes, A. Oschlies, and A. Schmittner
Biogeosciences, 10, 5889–5910, https://doi.org/10.5194/bg-10-5889-2013, https://doi.org/10.5194/bg-10-5889-2013, 2013
V. Cocco, F. Joos, M. Steinacher, T. L. Frölicher, L. Bopp, J. Dunne, M. Gehlen, C. Heinze, J. Orr, A. Oschlies, B. Schneider, J. Segschneider, and J. Tjiputra
Biogeosciences, 10, 1849–1868, https://doi.org/10.5194/bg-10-1849-2013, https://doi.org/10.5194/bg-10-1849-2013, 2013
A. Landolfi, H. Dietze, W. Koeve, and A. Oschlies
Biogeosciences, 10, 1351–1363, https://doi.org/10.5194/bg-10-1351-2013, https://doi.org/10.5194/bg-10-1351-2013, 2013
M. El Jarbi, J. Rückelt, T. Slawig, and A. Oschlies
Biogeosciences, 10, 1169–1182, https://doi.org/10.5194/bg-10-1169-2013, https://doi.org/10.5194/bg-10-1169-2013, 2013
L. M. Zamora, A. Oschlies, H. W. Bange, K. B. Huebert, J. D. Craig, A. Kock, and C. R. Löscher
Biogeosciences, 9, 5007–5022, https://doi.org/10.5194/bg-9-5007-2012, https://doi.org/10.5194/bg-9-5007-2012, 2012
Related subject area
Climate and Earth system modeling
Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2
Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning
An improved method of the Globally Resolved Energy Balance model by the Bayesian networks
Assessing predicted cirrus ice properties between two deterministic ice formation parameterizations
Various ways of using empirical orthogonal functions for climate model evaluation
C-Coupler3.0: an integrated coupler infrastructure for Earth system modelling
FEOTS v0.0.0: a new offline code for the fast equilibration of tracers in the ocean
Pace v0.2: a Python-based performance-portable atmospheric model
Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water
The sea level simulator v1.0: a model for integration of mean sea level change and sea level extremes into a joint probabilistic framework
Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data
The emergence of the Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic carbon uptake
Evaluating wind profiles in a numerical weather prediction model with Doppler lidar
Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin
The impact of lateral boundary forcing in the CORDEX-Africa ensemble over southern Africa
Effects of complex terrain on the shortwave radiative balance: a sub-grid-scale parameterization for the GFDL Earth System Model version 4.1
Understanding AMOC stability: the North Atlantic Hosing Model Intercomparison Project
Assessing methods for representing soil heterogeneity through a flexible approach within the Joint UK Land Environment Simulator (JULES) at version 3.4.1
Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition
Importance of ice nucleation and precipitation on climate with the Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1)
UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model
Porting the WAVEWATCH III (v6.07) wave action source terms to GPU
Yeti 1.0: a generalized framework for constructing bottom-up emission inventories from traffic sources at road-link resolutions
The Mixed Layer Depth in the Ocean Model Intercomparison Project (OMIP): Impact of Resolving Mesoscale Eddies
Analysis of systematic biases in tropospheric hydrostatic delay models and construction of a correction model
A new precipitation emulator (PREMU v1.0) for lower-complexity models
Simulating marine neodymium isotope distributions using Nd v1.0 coupled to the ocean component of the FAMOUS–MOSES1 climate model: sensitivities to reversible scavenging efficiency and benthic source distributions
CMIP6 simulations with the compact Earth system model OSCAR v3.1
Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1
The pseudo-global-warming (PGW) approach: methodology, software package PGW4ERA5 v1.1, validation, and sensitivity analyses
AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics
Cell tracking of convective rainfall: sensitivity of climate-change signal to tracking algorithm and cell definition (Cell-TAO v1.0)
ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales
Ocean Modeling with Adaptive REsolution (OMARE; version 1.0) – refactoring the NEMO model (version 4.0.1) with the parallel computing framework of JASMIN – Part 1: Adaptive grid refinement in an idealized double-gyre case
Monthly-scale extended predictions using the atmospheric model coupled with a slab ocean
Developing Spring Wheat in the Noah-MP LSM (v4.4) for Growing Season Dynamics and Responses to Temperature Stress
stoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impacts
URANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research
Combining regional mesh refinement with vertically enhanced physics to target marine stratocumulus biases as demonstrated in the Energy Exascale Earth System Model version 1
Evaluation of native Earth system model output with ESMValTool v2.6.0
WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system
LandInG 1.0: A toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources
Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
Accelerated photosynthesis routine in LPJmL4
Improving scalability of Earth system models through coarse-grained component concurrency – a case study with the ICON v2.6.5 modelling system
Temperature forecasting by deep learning methods
Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios
Inclusion of a cold hardening scheme to represent frost tolerance is essential to model realistic plant hydraulics in the Arctic–boreal zone in CLM5.0-FATES-Hydro
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
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Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
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Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
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This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
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A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
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A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
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C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
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FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
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It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
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The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Magnus Hieronymus
Geosci. Model Dev., 16, 2343–2354, https://doi.org/10.5194/gmd-16-2343-2023, https://doi.org/10.5194/gmd-16-2343-2023, 2023
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A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023, https://doi.org/10.5194/gmd-16-2215-2023, 2023
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This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Nadine Goris, Klaus Johannsen, and Jerry Tjiputra
Geosci. Model Dev., 16, 2095–2117, https://doi.org/10.5194/gmd-16-2095-2023, https://doi.org/10.5194/gmd-16-2095-2023, 2023
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Climate projections of a high-CO2 future are highly uncertain. A new study provides a novel approach to identifying key regions that dynamically explain the model uncertainty. To yield an accurate estimate of the future North Atlantic carbon uptake, we find that a correct simulation of the upper- and interior-ocean volume transport at 25–30° N is key. However, results indicate that models rarely perform well for both indicators and point towards inconsistencies within the model ensemble.
Pyry Pentikäinen, Ewan J. O'Connor, and Pablo Ortiz-Amezcua
Geosci. Model Dev., 16, 2077–2094, https://doi.org/10.5194/gmd-16-2077-2023, https://doi.org/10.5194/gmd-16-2077-2023, 2023
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We used Doppler lidar to evaluate the wind profiles generated by a weather forecast model. We first compared the Doppler lidar observations with co-located radiosonde profiles, and they agree well. The model performs best over marine and coastal locations. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.
Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi
Geosci. Model Dev., 16, 2055–2076, https://doi.org/10.5194/gmd-16-2055-2023, https://doi.org/10.5194/gmd-16-2055-2023, 2023
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Bias correction (BC) has become indispensable to climate model output as a post-processing step to render output more useful for impact assessment studies. The current work presents a comparison of different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) for climate model simulations from three initiatives (CMIP6, CORDEX, and CORDEX-CORE) for a multivariate drought index (i.e., standardized precipitation evapotranspiration index).
Maria Chara Karypidou, Stefan Pieter Sobolowski, Lorenzo Sangelantoni, Grigory Nikulin, and Eleni Katragkou
Geosci. Model Dev., 16, 1887–1908, https://doi.org/10.5194/gmd-16-1887-2023, https://doi.org/10.5194/gmd-16-1887-2023, 2023
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Southern Africa is listed among the climate change hotspots; hence, accurate climate change information is vital for the optimal preparedness of local communities. In this work we assess the degree to which regional climate models (RCMs) are influenced by the global climate models (GCMs) from which they receive their lateral boundary forcing. We find that although GCMs exert a strong impact on RCMs, RCMs are still able to display substantial improvement relative to the driving GCMs.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
Geosci. Model Dev., 16, 1937–1960, https://doi.org/10.5194/gmd-16-1937-2023, https://doi.org/10.5194/gmd-16-1937-2023, 2023
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In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Laura C. Jackson, Eduardo Alastrué de Asenjo, Katinka Bellomo, Gokhan Danabasoglu, Helmuth Haak, Aixue Hu, Johann Jungclaus, Warren Lee, Virna L. Meccia, Oleg Saenko, Andrew Shao, and Didier Swingedouw
Geosci. Model Dev., 16, 1975–1995, https://doi.org/10.5194/gmd-16-1975-2023, https://doi.org/10.5194/gmd-16-1975-2023, 2023
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The Atlantic meridional overturning circulation (AMOC) has an important impact on the climate. There are theories that freshening of the ocean might cause the AMOC to cross a tipping point (TP) beyond which recovery is difficult; however, it is unclear whether TPs exist in global climate models. Here, we outline a set of experiments designed to explore AMOC tipping points and sensitivity to additional freshwater input as part of the North Atlantic Hosing Model Intercomparison Project (NAHosMIP).
Heather S. Rumbold, Richard J. J. Gilham, and Martin J. Best
Geosci. Model Dev., 16, 1875–1886, https://doi.org/10.5194/gmd-16-1875-2023, https://doi.org/10.5194/gmd-16-1875-2023, 2023
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The Joint UK Land Environment Simulator (JULES) uses a tiled representation of land cover but can only model a single dominant soil type within a grid box; hence there is no representation of sub-grid soil heterogeneity. This paper evaluates a new surface–soil tiling scheme in JULES and demonstrates the impacts of the scheme using several soil tiling approaches. Results show that soil tiling has an impact on the water and energy exchanges due to the way vegetation accesses the soil moisture.
Felix Pithan, Marylou Athanase, Sandro Dahlke, Antonio Sánchez-Benítez, Matthew D. Shupe, Anne Sledd, Jan Streffing, Gunilla Svensson, and Thomas Jung
Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, https://doi.org/10.5194/gmd-16-1857-2023, 2023
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Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, https://doi.org/10.5194/gmd-16-1735-2023, 2023
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Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth system models. These updates include the ability to run the scheme on graphics processing units (GPUs), changes to the numerical description of precipitation, and a correction to the ice number. There are big improvements in the computational performance that can be achieved with GPU acceleration.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
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Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Olawale James Ikuyajolu, Luke Van Roekel, Steven R. Brus, Erin E. Thomas, Yi Deng, and Sarat Sreepathi
Geosci. Model Dev., 16, 1445–1458, https://doi.org/10.5194/gmd-16-1445-2023, https://doi.org/10.5194/gmd-16-1445-2023, 2023
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Wind-generated waves play an important role in modifying physical processes at the air–sea interface, but they have been traditionally excluded from climate models due to the high computational cost of running spectral wave models for climate simulations. To address this, our work identified and accelerated the computationally intensive section of WAVEWATCH III on GPU using OpenACC. This allows for high-resolution modeling of atmosphere–wave–ocean feedbacks in century-scale climate integrations.
Edward C. Chan, Joana Leitão, Andreas Kerschbaumer, and Timothy M. Butler
Geosci. Model Dev., 16, 1427–1444, https://doi.org/10.5194/gmd-16-1427-2023, https://doi.org/10.5194/gmd-16-1427-2023, 2023
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Yeti is a Handbook Emission Factors for Road Transport-based traffic emission inventory written in the Python 3 scripting language, which adopts a generalized treatment for activity data using traffic information of varying levels of detail introduced in a systematic and consistent manner, with the ability to maximize reusability. Thus, Yeti has been conceived and implemented with a high degree of data and process symmetry, allowing scalable and flexible execution while affording ease of use.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteacino Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
EGUsphere, https://doi.org/10.5194/egusphere-2023-310, https://doi.org/10.5194/egusphere-2023-310, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere, and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve "ocean eddies", which are the largest source of ocean variability and modulate the mixed layer properties. We find that the mixed layer depth is better represented in eddy-rich models, but unfortunately, not uniformly across the globe and not in all models.
Haopeng Fan, Siran Li, Zhongmiao Sun, Guorui Xiao, Xinxing Li, and Xiaogang Liu
Geosci. Model Dev., 16, 1345–1358, https://doi.org/10.5194/gmd-16-1345-2023, https://doi.org/10.5194/gmd-16-1345-2023, 2023
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The traditional tropospheric zenith hydrostatic delay (ZHD) model's bias is usually thought negligible, yet it still reaches 10 mm sometimes and would lead to millimeter-level position errors for space geodetic observations. Therefore, we analyzed the bias’ characteristics and present a grid model to correct the traditional ZHD formula. When verifying the efficiency based on data from the ECMWF (European Centre for Medium-Range Weather Forecasts), ZHD biases were rectified by ~50 %.
Gang Liu, Shushi Peng, Chris Huntingford, and Yi Xi
Geosci. Model Dev., 16, 1277–1296, https://doi.org/10.5194/gmd-16-1277-2023, https://doi.org/10.5194/gmd-16-1277-2023, 2023
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Due to computational limits, lower-complexity models (LCMs) were developed as a complementary tool for accelerating comprehensive Earth system models (ESMs) but still lack a good precipitation emulator for LCMs. Here, we developed a data-calibrated precipitation emulator (PREMU), a computationally effective way to better estimate historical and simulated precipitation by current ESMs. PREMU has potential applications related to land surface processes and their interactions with climate change.
Suzanne Robinson, Ruza F. Ivanovic, Lauren J. Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul J. Valdes
Geosci. Model Dev., 16, 1231–1264, https://doi.org/10.5194/gmd-16-1231-2023, https://doi.org/10.5194/gmd-16-1231-2023, 2023
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We present the implementation of neodymium (Nd) isotopes into the ocean model of FAMOUS (Nd v1.0). Nd fluxes from seafloor sediment and incorporation of Nd onto sinking particles represent the major global sources and sinks, respectively. However, model–data mismatch in the North Pacific and northern North Atlantic suggest that certain reactive components of the sediment interact the most with seawater. Our results are important for interpreting Nd isotopes in terms of ocean circulation.
Yann Quilcaille, Thomas Gasser, Philippe Ciais, and Olivier Boucher
Geosci. Model Dev., 16, 1129–1161, https://doi.org/10.5194/gmd-16-1129-2023, https://doi.org/10.5194/gmd-16-1129-2023, 2023
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The model OSCAR is a simple climate model, meaning its representation of the Earth system is simplified but calibrated on models of higher complexity. Here, we diagnose its latest version using a total of 99 experiments in a probabilistic framework and under observational constraints. OSCAR v3.1 shows good agreement with observations, complex Earth system models and emerging properties. Some points for improvements are identified, such as the ocean carbon cycle.
Sandra L. LeGrand, Theodore W. Letcher, Gregory S. Okin, Nicholas P. Webb, Alex R. Gallagher, Saroj Dhital, Taylor S. Hodgdon, Nancy P. Ziegler, and Michelle L. Michaels
Geosci. Model Dev., 16, 1009–1038, https://doi.org/10.5194/gmd-16-1009-2023, https://doi.org/10.5194/gmd-16-1009-2023, 2023
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Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Roman Brogli, Christoph Heim, Jonas Mensch, Silje Lund Sørland, and Christoph Schär
Geosci. Model Dev., 16, 907–926, https://doi.org/10.5194/gmd-16-907-2023, https://doi.org/10.5194/gmd-16-907-2023, 2023
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The pseudo-global-warming (PGW) approach is a downscaling methodology that imposes the large-scale GCM-based climate change signal on the boundary conditions of a regional climate simulation. It offers several benefits in comparison to conventional downscaling. We present a detailed description of the methodology, provide companion software to facilitate the preparation of PGW simulations, and present validation and sensitivity studies.
Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson
Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, https://doi.org/10.5194/gmd-16-869-2023, 2023
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We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev., 16, 851–867, https://doi.org/10.5194/gmd-16-851-2023, https://doi.org/10.5194/gmd-16-851-2023, 2023
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Cell-tracking algorithms allow for the study of properties of a convective cell across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm's criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Cathy Hohenegger, Peter Korn, Leonidas Linardakis, René Redler, Reiner Schnur, Panagiotis Adamidis, Jiawei Bao, Swantje Bastin, Milad Behravesh, Martin Bergemann, Joachim Biercamp, Hendryk Bockelmann, Renate Brokopf, Nils Brüggemann, Lucas Casaroli, Fatemeh Chegini, George Datseris, Monika Esch, Geet George, Marco Giorgetta, Oliver Gutjahr, Helmuth Haak, Moritz Hanke, Tatiana Ilyina, Thomas Jahns, Johann Jungclaus, Marcel Kern, Daniel Klocke, Lukas Kluft, Tobias Kölling, Luis Kornblueh, Sergey Kosukhin, Clarissa Kroll, Junhong Lee, Thorsten Mauritsen, Carolin Mehlmann, Theresa Mieslinger, Ann Kristin Naumann, Laura Paccini, Angel Peinado, Divya Sri Praturi, Dian Putrasahan, Sebastian Rast, Thomas Riddick, Niklas Roeber, Hauke Schmidt, Uwe Schulzweida, Florian Schütte, Hans Segura, Radomyra Shevchenko, Vikram Singh, Mia Specht, Claudia Christine Stephan, Jin-Song von Storch, Raphaela Vogel, Christian Wengel, Marius Winkler, Florian Ziemen, Jochem Marotzke, and Bjorn Stevens
Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, https://doi.org/10.5194/gmd-16-779-2023, 2023
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Models of the Earth system used to understand climate and predict its change typically employ a grid spacing of about 100 km. Yet, many atmospheric and oceanic processes occur on much smaller scales. In this study, we present a new model configuration designed for the simulation of the components of the Earth system and their interactions at kilometer and smaller scales, allowing an explicit representation of the main drivers of the flow of energy and matter by solving the underlying equations.
Yan Zhang, Xuantong Wang, Yuhao Sun, Chenhui Ning, Shiming Xu, Hengbin An, Dehong Tang, Hong Guo, Hao Yang, Ye Pu, Bo Jiang, and Bin Wang
Geosci. Model Dev., 16, 679–704, https://doi.org/10.5194/gmd-16-679-2023, https://doi.org/10.5194/gmd-16-679-2023, 2023
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We construct a new ocean model, OMARE, that can carry out multi-scale ocean simulation with adaptive mesh refinement. OMARE is based on the refactorization of NEMO with a third-party, high-performance piece of middleware. We report the porting process and experiments of an idealized western-boundary current system. The new model simulates turbulent and temporally varying mesoscale and submesoscale processes via adaptive refinement. Related topics and future work with OMARE are also discussed.
Zhenming Wang, Shaoqing Zhang, Yishuai Jin, Yinglai Jia, Yangyang Yu, Yang Gao, Xiaolin Yu, Mingkui Li, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 16, 705–717, https://doi.org/10.5194/gmd-16-705-2023, https://doi.org/10.5194/gmd-16-705-2023, 2023
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To improve the numerical model predictability of monthly extended-range scales, we use the simplified slab ocean model (SOM) to restrict the complicated sea surface temperature (SST) bias from a 3-D dynamical ocean model. As for SST prediction, whether in space or time, the WRF-SOM is verified to have better performance than the WRF-ROMS, which has a significant impact on the atmosphere. For extreme weather events such as typhoons, the predictions of WRF-SOM are in good agreement with WRF-ROMS.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-311, https://doi.org/10.5194/gmd-2022-311, 2023
Revised manuscript accepted for GMD
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Crop models incorporated in earth system models are essential to accurately simulate crop growth processes on Earth’s surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at point-scale; (2) apply dynamic planting/harvest schedules; (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
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stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Markus Köhli, Martin Schrön, Steffen Zacharias, and Ulrich Schmidt
Geosci. Model Dev., 16, 449–477, https://doi.org/10.5194/gmd-16-449-2023, https://doi.org/10.5194/gmd-16-449-2023, 2023
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In the last decades, Monte Carlo codes were often consulted to study neutrons near the surface. As an alternative for the growing community of CRNS, we developed URANOS. The main model features are tracking of particle histories from creation to detection, detector representations as layers or geometric shapes, a voxel-based geometry model, and material setup based on color codes in ASCII matrices or bitmap images. The entire software is developed in C++ and features a graphical user interface.
Peter A. Bogenschutz, Hsiang-He Lee, Qi Tang, and Takanobu Yamaguchi
Geosci. Model Dev., 16, 335–352, https://doi.org/10.5194/gmd-16-335-2023, https://doi.org/10.5194/gmd-16-335-2023, 2023
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Models that are used to simulate and predict climate often have trouble representing specific cloud types, such as stratocumulus, that are particularly thin in the vertical direction. It has been found that increasing the model resolution can help improve this problem. In this paper, we develop a novel framework that increases the horizontal and vertical resolutions only for areas of the globe that contain stratocumulus, hence reducing the model runtime while providing better results.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang
Geosci. Model Dev., 16, 199–209, https://doi.org/10.5194/gmd-16-199-2023, https://doi.org/10.5194/gmd-16-199-2023, 2023
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More and more researchers use deep learning models to replace physics-based parameterizations to accelerate weather simulations. However, embedding the ML models within the weather models is difficult as they are implemented in different languages. This work proposes a coupling framework to allow ML-based parameterizations to be coupled with the Weather Research and Forecasting (WRF) model. We also demonstrate using the coupler to couple the ML-based radiation schemes with the WRF model.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-307, https://doi.org/10.5194/gmd-2022-307, 2023
Revised manuscript accepted for GMD
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to >100,000 years. CLIMBER-X is available as open-source code and is expected to be a useful tool for studying past climate-carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Dario Nicolì, Alessio Bellucci, Paolo Ruggieri, Panos J. Athanasiadis, Stefano Materia, Daniele Peano, Giusy Fedele, Riccardo Hénin, and Silvio Gualdi
Geosci. Model Dev., 16, 179–197, https://doi.org/10.5194/gmd-16-179-2023, https://doi.org/10.5194/gmd-16-179-2023, 2023
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Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of the near-term climate and are useful for informing decision-makers on future climate-related risks. The predictive skill for key variables is assessed from the operational decadal prediction system compared with non-initialized historical simulations so as to quantify the added value of initialization.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-291, https://doi.org/10.5194/gmd-2022-291, 2023
Revised manuscript accepted for GMD
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We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent, so that users can make their own decisions on how to resolve these, should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156, https://doi.org/10.5194/gmd-16-135-2023, https://doi.org/10.5194/gmd-16-135-2023, 2023
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All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the atmosphere, and thus their horizontal variations within the grid cell are averaged out. In this regard, a novel scheme considering the variation and partitioning of the surface heat fluxes within the grid cell is developed. The scheme reduces the long-standing rainfall biases on the southern and eastern margins of the Tibetan Plateau. The performance of key variables at the global scale is also evaluated.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
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The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Leonidas Linardakis, Irene Stemmler, Moritz Hanke, Lennart Ramme, Fatemeh Chegini, Tatiana Ilyina, and Peter Korn
Geosci. Model Dev., 15, 9157–9176, https://doi.org/10.5194/gmd-15-9157-2022, https://doi.org/10.5194/gmd-15-9157-2022, 2022
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In Earth system modelling, we are facing the challenge of making efficient use of very large machines, with millions of cores. To meet this challenge we will need to employ multi-level and multi-dimensional parallelism. Component concurrency, being a function parallel technique, offers an additional dimension to the traditional data-parallel approaches. In this paper we examine the behaviour of component concurrency and identify the conditions for its optimal application.
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
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Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Thomas Bossy, Thomas Gasser, and Philippe Ciais
Geosci. Model Dev., 15, 8831–8868, https://doi.org/10.5194/gmd-15-8831-2022, https://doi.org/10.5194/gmd-15-8831-2022, 2022
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We developed a new simple climate model designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: calibration using Bayesian inference, the possibility of coupling with integrated assessment models, and the capacity to explore climate scenarios compatible with limiting climate impacts. Here, we describe the model and its calibration using the latest data from complex CMIP6 models and the IPCC AR6, and we assess its performance.
Marius S. A. Lambert, Hui Tang, Kjetil S. Aas, Frode Stordal, Rosie A. Fisher, Yilin Fang, Junyan Ding, and Frans-Jan W. Parmentier
Geosci. Model Dev., 15, 8809–8829, https://doi.org/10.5194/gmd-15-8809-2022, https://doi.org/10.5194/gmd-15-8809-2022, 2022
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In this study, we implement a hardening mortality scheme into CTSM5.0-FATES-Hydro and evaluate how it impacts plant hydraulics and vegetation growth. Our work shows that the hydraulic modifications prescribed by the hardening scheme are necessary to model realistic vegetation growth in cold climates, in contrast to the default model that simulates almost nonexistent and declining vegetation due to abnormally large water loss through the roots.
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Short summary
The natural abundance of 14C in CO2 dissolved in seawater is often used to evaluate circulation and age in the ocean and in ocean models. We study limitations of using natural 14C to determine the time elapsed since water had contact with the atmosphere. We find that, globally, bulk 14C age is dominated by two equally important components, (1) the time component of circulation and (2) the “preformed 14C-age”. Considering preformed 14C-age is critical for an assessment of circulation in models.
The natural abundance of 14C in CO2 dissolved in seawater is often used to evaluate circulation...
