Articles | Volume 15, issue 6
Geosci. Model Dev., 15, 2365–2393, 2022
https://doi.org/10.5194/gmd-15-2365-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 15, 2365–2393, 2022
https://doi.org/10.5194/gmd-15-2365-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
21 Mar 2022
Development and technical paper
| 21 Mar 2022
Impacts of a revised surface roughness parameterization in the Community Land Model 5.1
Ronny Meier et al.
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Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
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Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
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We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
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Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Quentin Lejeune, Edouard L. Davin, Grégory Duveiller, Bas Crezee, Ronny Meier, Alessandro Cescatti, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 1209–1232, https://doi.org/10.5194/esd-11-1209-2020, https://doi.org/10.5194/esd-11-1209-2020, 2020
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Trees are darker than crops or grasses; hence, they absorb more solar radiation. Therefore, land cover changes modify the fraction of solar radiation reflected by the land surface (its albedo), with consequences for the climate. We apply a new statistical method to simulations conducted with 15 recent climate models and find that albedo variations due to land cover changes since 1860 have led to a decrease in the net amount of energy entering the atmosphere by −0.09 W m2 on average.
Ronny Meier, Edouard L. Davin, Quentin Lejeune, Mathias Hauser, Yan Li, Brecht Martens, Natalie M. Schultz, Shannon Sterling, and Wim Thiery
Biogeosciences, 15, 4731–4757, https://doi.org/10.5194/bg-15-4731-2018, https://doi.org/10.5194/bg-15-4731-2018, 2018
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Deforestation not only releases carbon dioxide to the atmosphere but also affects local climatic conditions by altering energy fluxes at the land surface and thereby the local temperature. Here, we evaluate the local impact of deforestation in a widely used land surface model. We find that the model reproduces the daytime warming effect of deforestation well. On the other hand, the warmer temperatures observed during night in forests are not present in this model.
Wenfu Tang, Simone Tilmes, David M. Lawrence, Fang Li, Cenlin He, Louisa K. Emmons, Rebecca R. Buchholz, and Lili Xia
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-834, https://doi.org/10.5194/acp-2022-834, 2023
Preprint under review for ACP
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Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. Geoengineering reduces fire through decreasing surface temperature and wind speed and increasing relative humidity and soil water. However, geoengineering also yields reductions in precipitation, which offsets some of the fire reduction.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-431, https://doi.org/10.5194/essd-2022-431, 2022
Preprint under review for ESSD
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This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset for Europe (Version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Sjoukje Y. Philip, Sarah F. Kew, Geert Jan van Oldenborgh, Faron S. Anslow, Sonia I. Seneviratne, Robert Vautard, Dim Coumou, Kristie L. Ebi, Julie Arrighi, Roop Singh, Maarten van Aalst, Carolina Pereira Marghidan, Michael Wehner, Wenchang Yang, Sihan Li, Dominik L. Schumacher, Mathias Hauser, Rémy Bonnet, Linh N. Luu, Flavio Lehner, Nathan Gillett, Jordis S. Tradowsky, Gabriel A. Vecchi, Chris Rodell, Roland B. Stull, Rosie Howard, and Friederike E. L. Otto
Earth Syst. Dynam., 13, 1689–1713, https://doi.org/10.5194/esd-13-1689-2022, https://doi.org/10.5194/esd-13-1689-2022, 2022
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In June 2021, the Pacific Northwest of the US and Canada saw record temperatures far exceeding those previously observed. This attribution study found such a severe heat wave would have been virtually impossible without human-induced climate change. Assuming no nonlinear interactions, such events have become at least 150 times more common, are about 2 °C hotter and will become even more common as warming continues. Therefore, adaptation and mitigation are urgently needed to prepare society.
Ryan S. Padrón, Lukas Gudmundsson, Laibao Liu, Vincent Humphrey, and Sonia I. Seneviratne
Biogeosciences, 19, 5435–5448, https://doi.org/10.5194/bg-19-5435-2022, https://doi.org/10.5194/bg-19-5435-2022, 2022
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The answer to how much carbon land ecosystems are projected to remove from the atmosphere until 2100 is different for each Earth system model. We find that differences across models are primarily explained by the annual land carbon sink dependence on temperature and soil moisture, followed by the dependence on CO2 air concentration, and by average climate conditions. Our insights on why each model projects a relatively high or low land carbon sink can help to reduce the underlying uncertainty.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-413, https://doi.org/10.5194/essd-2022-413, 2022
Preprint under review for ESSD
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We present a new database of four annual fire weather indicators over 1850–2100 and over all land area. Our first analysis shows that in a 3 °C warmer world with respect to preindustrial times, the mean fire weather would on average double in duration and intensity. The produced dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as low as possible.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-216, https://doi.org/10.5194/gmd-2022-216, 2022
Preprint under review for GMD
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Some of our best tools to describe the state of the land system, including the intensity of heatwaves, have a problem. The model behind currently assumes that the amount of leaves in ecosystems always follow the same cycle. By using satellite observations of when the leaves are present, we show that getting the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show this has strong implications on our capacity to describe heatwaves across Europe.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven Johan De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia Isabelle Seneviratne, Carl Friedrich Schleussner, Wim Thiery, and Quentin Lejeune
EGUsphere, https://doi.org/10.5194/egusphere-2022-1024, https://doi.org/10.5194/egusphere-2022-1024, 2022
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The COP26 saw significant pledges made to to halt and reverse deforestation. The regional impacts of this are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards agilely exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model output representing the maximal impacts of aff/re/deforestation from which it explores plausible in-between outcomes itself.
Danny M. Leung, Jasper F. Kok, Longlei Li, Gregory S. Okin, Catherine Prigent, Martina Klose, Carlos Pérez Garcia-Pando, Laurent Menut, Natalie M. Mahowald, David M. Lawrence, and Marcelo Chamecki
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-719, https://doi.org/10.5194/acp-2022-719, 2022
Preprint under review for ACP
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Desert dust modeling is important for understanding climate change, as dust regulates the atmosphere's greenhouse effect and radiation. This study formulates and proposes a more physical and realistic desert dust emission scheme for global and regional climate models. By considering more aeolian processes in our emission scheme, our simulations match better against dust observations than existing schemes. We believe this work is vital in improving dust representation in climate models.
Mark Pickering, Alessandro Cescatti, and Gregory Duveiller
Biogeosciences, 19, 4833–4864, https://doi.org/10.5194/bg-19-4833-2022, https://doi.org/10.5194/bg-19-4833-2022, 2022
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This study explores two of the most recent products in carbon productivity estimation, FLUXCOM gross primary productivity (GPP), calculated by upscaling local measurements of CO2 exchange, and remotely sensed sun-induced chlorophyll a fluorescence (SIF). High-resolution SIF data are valuable in demonstrating similarity in the SIF–GPP relationship between vegetation covers, provide an independent probe of the FLUXCOM GPP model and demonstrate the response of SIF to meteorological fluctuations.
Steven J. De Hertog, Felix Havermann, Inne Vanderkelen, Suqi Guo, Fei Luo, Iris Manola, Dim Coumou, Edouard L. Davin, Gregory Duveiller, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
Earth Syst. Dynam., 13, 1305–1350, https://doi.org/10.5194/esd-13-1305-2022, https://doi.org/10.5194/esd-13-1305-2022, 2022
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Land cover and land management changes are important strategies for future land-based mitigation. We investigate the climate effects of cropland expansion, afforestation, irrigation, and wood harvesting using three Earth system models. Results show that these have important implications for surface temperature where the land cover and/or management change occurs and in remote areas. Idealized afforestation causes global warming, which might offset the cooling effect from enhanced carbon uptake.
Kathrin Wehrli, Fei Luo, Mathias Hauser, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, Dim Coumou, Wilhelm May, Philippe Le Sager, Frank Selten, Olivia Martius, Robert Vautard, and Sonia I. Seneviratne
Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, https://doi.org/10.5194/esd-13-1167-2022, 2022
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The ExtremeX experiment was designed to unravel the contribution of processes leading to the occurrence of recent weather and climate extremes. Global climate simulations are carried out with three models. The results show that in constrained experiments, temperature anomalies during heatwaves are well represented, although climatological model biases remain. Further, a substantial contribution of both atmospheric circulation and soil moisture to heat extremes is identified.
Fei Luo, Frank Selten, Kathrin Wehrli, Kai Kornhuber, Philippe Le Sager, Wilhelm May, Thomas Reerink, Sonia I. Seneviratne, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, and Dim Coumou
Weather Clim. Dynam., 3, 905–935, https://doi.org/10.5194/wcd-3-905-2022, https://doi.org/10.5194/wcd-3-905-2022, 2022
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Recent studies have identified the weather systems in observational data, where wave patterns with high-magnitude values that circle around the whole globe in either wavenumber 5 or wavenumber 7 are responsible for the extreme events. In conclusion, we find that the climate models are able to reproduce the large-scale atmospheric circulation patterns as well as their associated surface variables such as temperature, precipitation, and sea level pressure.
Francisco José Cuesta-Valero, Hugo Beltrami, Almudena García-García, Gerhard Krinner, Moritz Langer, Andrew H. MacDougall, Jan Nitzbon, Jian Peng, Karina von Schuckmann, Sonia I. Seneviratne, Noah Smith, Wim Thiery, Inne Vanderkelen, and Tonghua Wu
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2022-32, https://doi.org/10.5194/esd-2022-32, 2022
Preprint under review for ESD
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Climate change is caused by the accumulated heat in the Earth system, with the land storing the second largest amount of this extra heat. Here, new estimates of continental heat storage are obtained, including changes in inland water heat storage and permafrost heat storage in addition to changes in ground heat storage. We also argue that heat gains in all three components should be monitored independently of their magnitude due to heat-dependent processes affecting society and ecosystems.
Karina von Schuckmann, Audrey Minère, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Richard Allan, Paul M. Barker, Hugo Beltrami, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nikolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Noah Smith, Andrea Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-239, https://doi.org/10.5194/essd-2022-239, 2022
Revised manuscript under review for ESSD
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Earth climate is out of energy balance and this study quantifies how much heat has consequently accumulated over the past decades (89 %: ocean, 6 %: land, 4 %: cryosphere, 1 %: atmosphere). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris agreement’s global stocktake.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
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Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
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Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
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Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Fulden Batibeniz, Mathias Hauser, and Sonia Isabelle Seneviratne
EGUsphere, https://doi.org/10.5194/egusphere-2022-321, https://doi.org/10.5194/egusphere-2022-321, 2022
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We study single and concurrent extreme heatwaves, droughts, maximum precipitation, and strong wind events. Globally, these extremes become more frequent and affect more land areas under future warming. Only a few years would go by without any extremes. Heatwaves-droughts are projected to increase the most in subtropical countries, whereas this is the case for high precipitation-wind in the tropics. More people will be exposed to these extreme events unless mitigation actions are urgently taken.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
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Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
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We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
Shruti Nath, Quentin Lejeune, Lea Beusch, Sonia I. Seneviratne, and Carl-Friedrich Schleussner
Earth Syst. Dynam., 13, 851–877, https://doi.org/10.5194/esd-13-851-2022, https://doi.org/10.5194/esd-13-851-2022, 2022
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Uncertainty within climate model projections on inter-annual timescales is largely affected by natural climate variability. Emulators are valuable tools for approximating climate model runs, allowing for easy exploration of such uncertainty spaces. This study takes a first step at building a spatially resolved, monthly temperature emulator that takes local yearly temperatures as the sole input, thus providing monthly temperature distributions which are of critical value to impact assessments.
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
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We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Stephane Calmant, Ayan Santos Fleischmann, Frederic Frappart, Melanie Becker, Mohammad J. Tourian, Catherine Prigent, and Johary Andriambeloson
Hydrol. Earth Syst. Sci., 26, 1857–1882, https://doi.org/10.5194/hess-26-1857-2022, https://doi.org/10.5194/hess-26-1857-2022, 2022
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This study presents a better characterization of surface hydrology variability in the Congo River basin, the second largest river system in the world. We jointly use a large record of in situ and satellite-derived observations to monitor the spatial distribution and different timings of the Congo River basin's annual flood dynamic, including its peculiar bimodal pattern.
Lea Beusch, Zebedee Nicholls, Lukas Gudmundsson, Mathias Hauser, Malte Meinshausen, and Sonia I. Seneviratne
Geosci. Model Dev., 15, 2085–2103, https://doi.org/10.5194/gmd-15-2085-2022, https://doi.org/10.5194/gmd-15-2085-2022, 2022
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We introduce the first chain of computationally efficient Earth system model (ESM) emulators to translate user-defined greenhouse gas emission pathways into regional temperature change time series accounting for all major sources of climate change projection uncertainty. By combining the global mean emulator MAGICC with the spatially resolved emulator MESMER, we can derive ESM-specific and constrained probabilistic emulations to rapidly provide targeted climate information at the local scale.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
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Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Aine M. Gormley-Gallagher, Sebastian Sterl, Annette L. Hirsch, Sonia I. Seneviratne, Edouard L. Davin, and Wim Thiery
Earth Syst. Dynam., 13, 419–438, https://doi.org/10.5194/esd-13-419-2022, https://doi.org/10.5194/esd-13-419-2022, 2022
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Our results show that agricultural management can impact the local climate and highlight the need to evaluate land management in climate models. We use regression analysis on climate simulations and observations to assess irrigation and conservation agriculture impacts on warming trends. This allowed us to distinguish between the effects of land management and large-scale climate forcings such as rising CO2 concentrations and thus gain insight into the impacts under different climate regimes.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
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Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Martina Klose, Oriol Jorba, María Gonçalves Ageitos, Jeronimo Escribano, Matthew L. Dawson, Vincenzo Obiso, Enza Di Tomaso, Sara Basart, Gilbert Montané Pinto, Francesca Macchia, Paul Ginoux, Juan Guerschman, Catherine Prigent, Yue Huang, Jasper F. Kok, Ron L. Miller, and Carlos Pérez García-Pando
Geosci. Model Dev., 14, 6403–6444, https://doi.org/10.5194/gmd-14-6403-2021, https://doi.org/10.5194/gmd-14-6403-2021, 2021
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Mineral soil dust is a major atmospheric airborne particle type. We present and evaluate MONARCH, a model used for regional and global dust-weather prediction. An important feature of the model is that it allows different approximations to represent dust, ranging from more simplified to more complex treatments. Using these different treatments, MONARCH can help us better understand impacts of dust in the Earth system, such as its interactions with radiation.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
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We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
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This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Zhen Zhang, Etienne Fluet-Chouinard, Katherine Jensen, Kyle McDonald, Gustaf Hugelius, Thomas Gumbricht, Mark Carroll, Catherine Prigent, Annett Bartsch, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 2001–2023, https://doi.org/10.5194/essd-13-2001-2021, https://doi.org/10.5194/essd-13-2001-2021, 2021
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The spatiotemporal distribution of wetlands is one of the important and yet uncertain factors determining the time and locations of methane fluxes. The Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset describes the global data product used to quantify the areal dynamics of natural wetlands and how global wetlands are changing in response to climate.
Lise Kilic, Catherine Prigent, Carlos Jimenez, and Craig Donlon
Ocean Sci., 17, 455–461, https://doi.org/10.5194/os-17-455-2021, https://doi.org/10.5194/os-17-455-2021, 2021
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The Copernicus Imaging Microwave Radiometer (CIMR) is one of the high-priority satellite missions of the Copernicus program within the European Space Agency. It is designed to respond to the European Union Arctic policy. Its channels, incidence angle, precisions, and spatial resolutions have been selected to observe the Arctic Ocean with the recommendations expressed by the user communities.
In this note, we present the sensitivity analysis that has led to the choice of the CIMR channels.
Marcus Breil, Edouard L. Davin, and Diana Rechid
Biogeosciences, 18, 1499–1510, https://doi.org/10.5194/bg-18-1499-2021, https://doi.org/10.5194/bg-18-1499-2021, 2021
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The physical processes behind varying evapotranspiration rates in forests and grasslands in Europe are investigated in a regional model study with idealized afforestation scenarios. The results show that the evapotranspiration response to afforestation depends on the interplay of two counteracting factors: the transpiration facilitating characteristics of a forest and the reduced saturation deficits of forests caused by an increased surface roughness and associated lower surface temperatures.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
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Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Quentin Lejeune, Edouard L. Davin, Grégory Duveiller, Bas Crezee, Ronny Meier, Alessandro Cescatti, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 1209–1232, https://doi.org/10.5194/esd-11-1209-2020, https://doi.org/10.5194/esd-11-1209-2020, 2020
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Trees are darker than crops or grasses; hence, they absorb more solar radiation. Therefore, land cover changes modify the fraction of solar radiation reflected by the land surface (its albedo), with consequences for the climate. We apply a new statistical method to simulations conducted with 15 recent climate models and find that albedo variations due to land cover changes since 1860 have led to a decrease in the net amount of energy entering the atmosphere by −0.09 W m2 on average.
Maialen Iturbide, José M. Gutiérrez, Lincoln M. Alves, Joaquín Bedia, Ruth Cerezo-Mota, Ezequiel Cimadevilla, Antonio S. Cofiño, Alejandro Di Luca, Sergio Henrique Faria, Irina V. Gorodetskaya, Mathias Hauser, Sixto Herrera, Kevin Hennessy, Helene T. Hewitt, Richard G. Jones, Svitlana Krakovska, Rodrigo Manzanas, Daniel Martínez-Castro, Gemma T. Narisma, Intan S. Nurhati, Izidine Pinto, Sonia I. Seneviratne, Bart van den Hurk, and Carolina S. Vera
Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, https://doi.org/10.5194/essd-12-2959-2020, 2020
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We present an update of the IPCC WGI reference regions used in AR5 for the synthesis of climate change information. This revision was guided by the basic principles of climatic consistency and model representativeness (in particular for the new CMIP6 simulations). We also present a new dataset of monthly CMIP5 and CMIP6 spatially aggregated information using the new reference regions and describe a worked example of how to use this dataset to inform regional climate change studies.
Lena R. Boysen, Victor Brovkin, Julia Pongratz, David M. Lawrence, Peter Lawrence, Nicolas Vuichard, Philippe Peylin, Spencer Liddicoat, Tomohiro Hajima, Yanwu Zhang, Matthias Rocher, Christine Delire, Roland Séférian, Vivek K. Arora, Lars Nieradzik, Peter Anthoni, Wim Thiery, Marysa M. Laguë, Deborah Lawrence, and Min-Hui Lo
Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, https://doi.org/10.5194/bg-17-5615-2020, 2020
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We find a biogeophysically induced global cooling with strong carbon losses in a 20 million square kilometre idealized deforestation experiment performed by nine CMIP6 Earth system models. It takes many decades for the temperature signal to emerge, with non-local effects playing an important role. Despite a consistent experimental setup, models diverge substantially in their climate responses. This study offers unprecedented insights for understanding land use change effects in CMIP6 models.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, https://doi.org/10.5194/gmd-13-5425-2020, 2020
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To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Kathrin Wehrli, Mathias Hauser, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 855–873, https://doi.org/10.5194/esd-11-855-2020, https://doi.org/10.5194/esd-11-855-2020, 2020
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The 2018 summer was unusually hot for large areas in the Northern Hemisphere, and heatwaves on three continents led to major impacts on agriculture and society. This study investigates storylines for the extreme 2018 summer, given the observed atmospheric circulation but different levels of background global warming. The results reveal a strong contribution by the present-day level of global warming and show a dramatic outlook for similar events in a warmer climate.
Samuel Favrichon, Carlos Jimenez, and Catherine Prigent
Atmos. Meas. Tech., 13, 5481–5490, https://doi.org/10.5194/amt-13-5481-2020, https://doi.org/10.5194/amt-13-5481-2020, 2020
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Long-term monitoring of satellite-derived variables is necessary for a better understanding of the evolution of Earth parameters at global scale. However different instruments' observations used over the years need to be inter-calibrated with each other to provide meaningful information. This paper describes how a linear correction can improve the observations from the Scanning Multichannel Microwave Radiometer over continental surfaces to be more consistent with more recent radiometers.
Karina von Schuckmann, Lijing Cheng, Matthew D. Palmer, James Hansen, Caterina Tassone, Valentin Aich, Susheel Adusumilli, Hugo Beltrami, Tim Boyer, Francisco José Cuesta-Valero, Damien Desbruyères, Catia Domingues, Almudena García-García, Pierre Gentine, John Gilson, Maximilian Gorfer, Leopold Haimberger, Masayoshi Ishii, Gregory C. Johnson, Rachel Killick, Brian A. King, Gottfried Kirchengast, Nicolas Kolodziejczyk, John Lyman, Ben Marzeion, Michael Mayer, Maeva Monier, Didier Paolo Monselesan, Sarah Purkey, Dean Roemmich, Axel Schweiger, Sonia I. Seneviratne, Andrew Shepherd, Donald A. Slater, Andrea K. Steiner, Fiammetta Straneo, Mary-Louise Timmermans, and Susan E. Wijffels
Earth Syst. Sci. Data, 12, 2013–2041, https://doi.org/10.5194/essd-12-2013-2020, https://doi.org/10.5194/essd-12-2013-2020, 2020
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Understanding how much and where the heat is distributed in the Earth system is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to obtain the Earth heat inventory over the period 1960–2018.
Ramdane Alkama, Patrick C. Taylor, Lorea Garcia-San Martin, Herve Douville, Gregory Duveiller, Giovanni Forzieri, Didier Swingedouw, and Alessandro Cescatti
The Cryosphere, 14, 2673–2686, https://doi.org/10.5194/tc-14-2673-2020, https://doi.org/10.5194/tc-14-2673-2020, 2020
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The amount of solar energy absorbed by Earth is believed to strongly depend on clouds. Here, we investigate this relationship using satellite data and 32 climate models, showing that this relationship holds everywhere except over polar seas, where an increased reflection by clouds corresponds to an increase in absorbed solar radiation at the surface. This interplay between clouds and sea ice reduces by half the increase of net radiation at the surface that follows the sea ice retreat.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Marielle Saunois, Ann R. Stavert, Ben Poulter, Philippe Bousquet, Josep G. Canadell, Robert B. Jackson, Peter A. Raymond, Edward J. Dlugokencky, Sander Houweling, Prabir K. Patra, Philippe Ciais, Vivek K. Arora, David Bastviken, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Kimberly M. Carlson, Mark Carrol, Simona Castaldi, Naveen Chandra, Cyril Crevoisier, Patrick M. Crill, Kristofer Covey, Charles L. Curry, Giuseppe Etiope, Christian Frankenberg, Nicola Gedney, Michaela I. Hegglin, Lena Höglund-Isaksson, Gustaf Hugelius, Misa Ishizawa, Akihiko Ito, Greet Janssens-Maenhout, Katherine M. Jensen, Fortunat Joos, Thomas Kleinen, Paul B. Krummel, Ray L. Langenfelds, Goulven G. Laruelle, Licheng Liu, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Joe McNorton, Paul A. Miller, Joe R. Melton, Isamu Morino, Jurek Müller, Fabiola Murguia-Flores, Vaishali Naik, Yosuke Niwa, Sergio Noce, Simon O'Doherty, Robert J. Parker, Changhui Peng, Shushi Peng, Glen P. Peters, Catherine Prigent, Ronald Prinn, Michel Ramonet, Pierre Regnier, William J. Riley, Judith A. Rosentreter, Arjo Segers, Isobel J. Simpson, Hao Shi, Steven J. Smith, L. Paul Steele, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Francesco N. Tubiello, Aki Tsuruta, Nicolas Viovy, Apostolos Voulgarakis, Thomas S. Weber, Michiel van Weele, Guido R. van der Werf, Ray F. Weiss, Doug Worthy, Debra Wunch, Yi Yin, Yukio Yoshida, Wenxin Zhang, Zhen Zhang, Yuanhong Zhao, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
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Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Gregory Duveiller, Federico Filipponi, Sophia Walther, Philipp Köhler, Christian Frankenberg, Luis Guanter, and Alessandro Cescatti
Earth Syst. Sci. Data, 12, 1101–1116, https://doi.org/10.5194/essd-12-1101-2020, https://doi.org/10.5194/essd-12-1101-2020, 2020
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Sun-induced chlorophyll fluorescence is a valuable indicator of vegetation productivity, but our capacity to measure it from space using satellite remote techniques has been hampered by a lack of spatial detail. Based on prior knowledge of how ecosystems should respond to growing conditions in some modelling along with ancillary satellite observations, we provide here a new enhanced dataset with higher spatial resolution that better represents the spatial patterns of vegetation growth over land.
Ryan S. Padrón, Lukas Gudmundsson, Dominik Michel, and Sonia I. Seneviratne
Hydrol. Earth Syst. Sci., 24, 793–807, https://doi.org/10.5194/hess-24-793-2020, https://doi.org/10.5194/hess-24-793-2020, 2020
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We focus on the net exchange of water between land and air via evapotranspiration and dew during the night. We provide, for the first time, an overview of the magnitude and variability of this flux across the globe from observations and climate models. Nocturnal water loss from land is 7 % of total evapotranspiration on average and can be greater than 15 % locally. Our results highlight the relevance of this often overlooked flux, with implications for water resources and climate studies.
Edouard L. Davin, Diana Rechid, Marcus Breil, Rita M. Cardoso, Erika Coppola, Peter Hoffmann, Lisa L. Jach, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Kai Radtke, Mario Raffa, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Tölle, Kirsten Warrach-Sagi, and Volker Wulfmeyer
Earth Syst. Dynam., 11, 183–200, https://doi.org/10.5194/esd-11-183-2020, https://doi.org/10.5194/esd-11-183-2020, 2020
Lea Beusch, Lukas Gudmundsson, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 139–159, https://doi.org/10.5194/esd-11-139-2020, https://doi.org/10.5194/esd-11-139-2020, 2020
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Earth system models (ESMs) are invaluable to study the climate system but expensive to run. Here, we present a statistical tool which emulates ESMs at a negligible computational cost by creating stochastic realizations of yearly land temperature field time series. Thereby, 40 ESMs are considered, and for each ESM, a single simulation is required to train the tool. The resulting ESM-specific realizations closely resemble ESM simulations not employed during training at point to regional scales.
Christian G. Andresen, David M. Lawrence, Cathy J. Wilson, A. David McGuire, Charles Koven, Kevin Schaefer, Elchin Jafarov, Shushi Peng, Xiaodong Chen, Isabelle Gouttevin, Eleanor Burke, Sarah Chadburn, Duoying Ji, Guangsheng Chen, Daniel Hayes, and Wenxin Zhang
The Cryosphere, 14, 445–459, https://doi.org/10.5194/tc-14-445-2020, https://doi.org/10.5194/tc-14-445-2020, 2020
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Widely-used land models project near-surface drying of the terrestrial Arctic despite increases in the net water balance driven by climate change. Drying was generally associated with increases of active-layer depth and permafrost thaw in a warming climate. However, models lack important mechanisms such as thermokarst and soil subsidence that will change the hydrological regime and add to the large uncertainty in the future Arctic hydrological state and the associated permafrost carbon feedback.
Altug Ekici, Hanna Lee, David M. Lawrence, Sean C. Swenson, and Catherine Prigent
Geosci. Model Dev., 12, 5291–5300, https://doi.org/10.5194/gmd-12-5291-2019, https://doi.org/10.5194/gmd-12-5291-2019, 2019
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Ice-rich permafrost thaw can create expanding thermokarst lakes as well as shrinking large wetlands. Such processes can have major biogeochemical implications and feedbacks to climate systems by altering the pathways and rates of permafrost carbon release. We developed a new model parameterization that allows a direct representation of surface water dynamics with subsidence. Our results show increased surface water fractions around western Siberian plains and northeastern territories of Canada.
Lise Kilic, Rasmus Tage Tonboe, Catherine Prigent, and Georg Heygster
The Cryosphere, 13, 1283–1296, https://doi.org/10.5194/tc-13-1283-2019, https://doi.org/10.5194/tc-13-1283-2019, 2019
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In this study, we develop and present simple algorithms to derive the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice. This is achieved using satellite observations collocated with buoy measurements. The errors of the retrieved parameters are estimated and compared with independent data. These parameters are useful for sea ice concentration mapping, understanding sea ice properties and variability, and for atmospheric sounding applications.
Samuel Favrichon, Catherine Prigent, Carlos Jimenez, and Filipe Aires
Atmos. Meas. Tech., 12, 1531–1543, https://doi.org/10.5194/amt-12-1531-2019, https://doi.org/10.5194/amt-12-1531-2019, 2019
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Land surface parameters (such as temperature) can be extracted from passive microwave satellite observations, with less cloud contamination than in the infrared. A cloud contamination index is proposed to detect cloud contamination for multiple frequency ranges (from 10 to 190 GHz), to be applicable to the successive generations of MW instruments. Even with a reduced number of low-frequency channels over land, the index reaches an accuracy of ≥ 70 % in detecting contaminated observations.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Sophia Walther, Luis Guanter, Birgit Heim, Martin Jung, Gregory Duveiller, Aleksandra Wolanin, and Torsten Sachs
Biogeosciences, 15, 6221–6256, https://doi.org/10.5194/bg-15-6221-2018, https://doi.org/10.5194/bg-15-6221-2018, 2018
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We explored the timing of the peak of the short annual growing season in tundra ecosystems as indicated by an extensive suite of satellite indicators of vegetation productivity. Delayed peak greenness compared to peak photosynthesis is consistently found across years and land-cover classes. Plants also experience growth after optimal conditions for assimilation regarding light and temperature have passed. Our results have implications for the modelling of the circumpolar carbon balance.
Seyed Hamed Alemohammad, Jana Kolassa, Catherine Prigent, Filipe Aires, and Pierre Gentine
Hydrol. Earth Syst. Sci., 22, 5341–5356, https://doi.org/10.5194/hess-22-5341-2018, https://doi.org/10.5194/hess-22-5341-2018, 2018
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A new machine learning algorithm is developed to downscale satellite-based soil moisture estimates from their native spatial scale of 9 km to 2.25 km.
Ronny Meier, Edouard L. Davin, Quentin Lejeune, Mathias Hauser, Yan Li, Brecht Martens, Natalie M. Schultz, Shannon Sterling, and Wim Thiery
Biogeosciences, 15, 4731–4757, https://doi.org/10.5194/bg-15-4731-2018, https://doi.org/10.5194/bg-15-4731-2018, 2018
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Deforestation not only releases carbon dioxide to the atmosphere but also affects local climatic conditions by altering energy fluxes at the land surface and thereby the local temperature. Here, we evaluate the local impact of deforestation in a widely used land surface model. We find that the model reproduces the daytime warming effect of deforestation well. On the other hand, the warmer temperatures observed during night in forests are not present in this model.
Gregory Duveiller, Giovanni Forzieri, Eddy Robertson, Wei Li, Goran Georgievski, Peter Lawrence, Andy Wiltshire, Philippe Ciais, Julia Pongratz, Stephen Sitch, Almut Arneth, and Alessandro Cescatti
Earth Syst. Sci. Data, 10, 1265–1279, https://doi.org/10.5194/essd-10-1265-2018, https://doi.org/10.5194/essd-10-1265-2018, 2018
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Changing the vegetation cover of the Earth's surface can alter the local energy balance, which can result in a local warming or cooling depending on the specific vegetation transition, its timing and location, as well as on the background climate. While models can theoretically simulate these effects, their skill is not well documented across space and time. Here we provide a dedicated framework to evaluate such models against measurements derived from satellite observations.
Gordon B. Bonan, Edward G. Patton, Ian N. Harman, Keith W. Oleson, John J. Finnigan, Yaqiong Lu, and Elizabeth A. Burakowski
Geosci. Model Dev., 11, 1467–1496, https://doi.org/10.5194/gmd-11-1467-2018, https://doi.org/10.5194/gmd-11-1467-2018, 2018
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Land surface models neglect the roughness sublayer and parameterize within-canopy turbulence in an ad hoc manner. We implemented a roughness sublayer parameterization in a multilayer canopy model to test if this theory provides a tractable parameterization extending from the ground through the canopy and the roughness sublayer. The multilayer canopy improves simulations compared with the Community Land Model (CLM4.5) while also advancing the theoretical basis for surface flux parameterizations.
Nicholas C. Parazoo, Charles D. Koven, David M. Lawrence, Vladimir Romanovsky, and Charles E. Miller
The Cryosphere, 12, 123–144, https://doi.org/10.5194/tc-12-123-2018, https://doi.org/10.5194/tc-12-123-2018, 2018
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Carbon models suggest the permafrost carbon feedback (soil carbon emissions from permafrost thaw) acts as a slow, unobservable leak. We investigate if permafrost temperature provides an observable signal to detect feedbacks. We find a slow carbon feedback in warm sub-Arctic permafrost soils, but potentially rapid feedback in cold Arctic permafrost. This is surprising since the cold permafrost region is dominated by tundra and underlain by deep, cold permafrost thought impervious to such changes.
Victoria Sol Galligani, Die Wang, Milagros Alvarez Imaz, Paola Salio, and Catherine Prigent
Atmos. Meas. Tech., 10, 3627–3649, https://doi.org/10.5194/amt-10-3627-2017, https://doi.org/10.5194/amt-10-3627-2017, 2017
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Three meteorological events with deep convection and severe weather, characteristic of the SESA region, are considered. High-resolution models, a powerful tool to study convection, can be operated with different microphysics schemes (predict the development of hydrometeors, their interactions, growth, precipitation). We present a systematic evaluation of the microphysical schemes available in the WRF model by a direct comparison between satellite-based simulated and observed microwave radiances.
Seyed Hamed Alemohammad, Bin Fang, Alexandra G. Konings, Filipe Aires, Julia K. Green, Jana Kolassa, Diego Miralles, Catherine Prigent, and Pierre Gentine
Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, https://doi.org/10.5194/bg-14-4101-2017, 2017
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Water, Energy, and Carbon with Artificial Neural Networks (WECANN) is a statistically based estimate of global surface latent and sensible heat fluxes and gross primary productivity. The retrieval uses six remotely sensed observations as input, including the solar-induced fluorescence. WECANN provides estimates on a 1° × 1° geographic grid and on a monthly time scale and outperforms other global products in capturing the seasonality of the fluxes when compared to eddy covariance tower data.
Jakob Zscheischler, Rene Orth, and Sonia I. Seneviratne
Biogeosciences, 14, 3309–3320, https://doi.org/10.5194/bg-14-3309-2017, https://doi.org/10.5194/bg-14-3309-2017, 2017
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We use newly established methods to compute bivariate return periods of temperature and precipitation and relate those to crop yield variability in Europe. Most often, crop yields are lower when it is hot and dry and higher when it is cold and wet. The variability in crop yields along a specific climate gradient can be captured well by return periods aligned with these gradients. This study provides new possibilities for investigating the relationship between crop yield variability and climate.
Andrew G. Slater, David M. Lawrence, and Charles D. Koven
The Cryosphere, 11, 989–996, https://doi.org/10.5194/tc-11-989-2017, https://doi.org/10.5194/tc-11-989-2017, 2017
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This work defines a metric for evaluation of a specific model snow process, namely, heat transfer through snow into soil. Heat transfer through snow regulates the difference in air temperature versus soil temperature. Accurate representation of the snow heat transfer process is critically important for accurate representation of the current and future state of permafrost. Utilizing this metric, we can clearly identify models that can and cannot reasonably represent snow heat transfer.
Andreas Will, Naveed Akhtar, Jennifer Brauch, Marcus Breil, Edouard Davin, Ha T. M. Ho-Hagemann, Eric Maisonnave, Markus Thürkow, and Stefan Weiher
Geosci. Model Dev., 10, 1549–1586, https://doi.org/10.5194/gmd-10-1549-2017, https://doi.org/10.5194/gmd-10-1549-2017, 2017
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We present a coupled regional climate system model. The COSMO CLM regional climate model is two-way coupled via OASIS3-MCT to the land surface, regional ocean for the Mediterranean Sea, North and Baltic seas and an earth system model. The direct coupling costs of communication and horizontal interpolation are shown to be negligible even for a frequent exchange of 450 2-D fields. A procedure of finding an optimum processor configuration is presented and successfully applied to all couplings.
Martin Hirschi, Dominik Michel, Irene Lehner, and Sonia I. Seneviratne
Hydrol. Earth Syst. Sci., 21, 1809–1825, https://doi.org/10.5194/hess-21-1809-2017, https://doi.org/10.5194/hess-21-1809-2017, 2017
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We compare lysimeter and eddy covariance (EC) flux measurements of evapotranspiration at a research catchment in Switzerland. The measurements are compared on various timescales, and with respect to a 40-year long-term lysimeter time series. Overall, the lysimeter and EC measurements agree well, especially on the annual timescale. Furthermore, we identify that lack of reliable EC data during/after rainfall events significantly contributes to an underestimation of EC evapotranspiration.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
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An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
David M. Lawrence, George C. Hurtt, Almut Arneth, Victor Brovkin, Kate V. Calvin, Andrew D. Jones, Chris D. Jones, Peter J. Lawrence, Nathalie de Noblet-Ducoudré, Julia Pongratz, Sonia I. Seneviratne, and Elena Shevliakova
Geosci. Model Dev., 9, 2973–2998, https://doi.org/10.5194/gmd-9-2973-2016, https://doi.org/10.5194/gmd-9-2973-2016, 2016
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Human land-use activities have resulted in large changes to the Earth's surface, with resulting implications for climate. In the future, land-use activities are likely to expand and intensify further to meet growing demands for food, fiber, and energy. The goal of LUMIP is to take the next steps in land-use change science, and enable, coordinate, and ultimately address the most important land-use science questions in more depth and sophistication than possible in a multi-model context to date.
Bart van den Hurk, Hyungjun Kim, Gerhard Krinner, Sonia I. Seneviratne, Chris Derksen, Taikan Oki, Hervé Douville, Jeanne Colin, Agnès Ducharne, Frederique Cheruy, Nicholas Viovy, Michael J. Puma, Yoshihide Wada, Weiping Li, Binghao Jia, Andrea Alessandri, Dave M. Lawrence, Graham P. Weedon, Richard Ellis, Stefan Hagemann, Jiafu Mao, Mark G. Flanner, Matteo Zampieri, Stefano Materia, Rachel M. Law, and Justin Sheffield
Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, https://doi.org/10.5194/gmd-9-2809-2016, 2016
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This manuscript describes the setup of the CMIP6 project Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP).
Wenli Wang, Annette Rinke, John C. Moore, Duoying Ji, Xuefeng Cui, Shushi Peng, David M. Lawrence, A. David McGuire, Eleanor J. Burke, Xiaodong Chen, Bertrand Decharme, Charles Koven, Andrew MacDougall, Kazuyuki Saito, Wenxin Zhang, Ramdane Alkama, Theodore J. Bohn, Philippe Ciais, Christine Delire, Isabelle Gouttevin, Tomohiro Hajima, Gerhard Krinner, Dennis P. Lettenmaier, Paul A. Miller, Benjamin Smith, Tetsuo Sueyoshi, and Artem B. Sherstiukov
The Cryosphere, 10, 1721–1737, https://doi.org/10.5194/tc-10-1721-2016, https://doi.org/10.5194/tc-10-1721-2016, 2016
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The winter snow insulation is a key process for air–soil temperature coupling and is relevant for permafrost simulations. Differences in simulated air–soil temperature relationships and their modulation by climate conditions are found to be related to the snow model physics. Generally, models with better performance apply multilayer snow schemes.
Veronika Eyring, Mattia Righi, Axel Lauer, Martin Evaldsson, Sabrina Wenzel, Colin Jones, Alessandro Anav, Oliver Andrews, Irene Cionni, Edouard L. Davin, Clara Deser, Carsten Ehbrecht, Pierre Friedlingstein, Peter Gleckler, Klaus-Dirk Gottschaldt, Stefan Hagemann, Martin Juckes, Stephan Kindermann, John Krasting, Dominik Kunert, Richard Levine, Alexander Loew, Jarmo Mäkelä, Gill Martin, Erik Mason, Adam S. Phillips, Simon Read, Catherine Rio, Romain Roehrig, Daniel Senftleben, Andreas Sterl, Lambertus H. van Ulft, Jeremy Walton, Shiyu Wang, and Keith D. Williams
Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, https://doi.org/10.5194/gmd-9-1747-2016, 2016
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A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) in CMIP has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations.
Yan Li, Nathalie De Noblet-Ducoudré, Edouard L. Davin, Safa Motesharrei, Ning Zeng, Shuangcheng Li, and Eugenia Kalnay
Earth Syst. Dynam., 7, 167–181, https://doi.org/10.5194/esd-7-167-2016, https://doi.org/10.5194/esd-7-167-2016, 2016
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The impact of deforestation is to warm the tropics and cool the extratropics, and the magnitude of the impact depends on the spatial extent and the degree of forest loss. That also means location matters for the impact of deforestation on temperature because such an impact is largely determined by the climate condition of that region. For example, under dry and wet conditions, deforestation can have quite different climate impacts.
D. Michel, C. Jiménez, D. G. Miralles, M. Jung, M. Hirschi, A. Ershadi, B. Martens, M. F. McCabe, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 803–822, https://doi.org/10.5194/hess-20-803-2016, https://doi.org/10.5194/hess-20-803-2016, 2016
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In this study a common reference input data set from satellite and in situ data is used to run four established evapotranspiration (ET) algorithms using sub-daily and daily input on a tower scale as a testbed for a global ET product. The PT-JPL model and GLEAM provide the best performance for satellite and in situ forcing as well as for the different temporal resolutions. PM-MOD and SEBS perform less well: the PM-MOD model generally underestimates, while SEBS generally overestimates ET.
W. Wang, A. Rinke, J. C. Moore, X. Cui, D. Ji, Q. Li, N. Zhang, C. Wang, S. Zhang, D. M. Lawrence, A. D. McGuire, W. Zhang, C. Delire, C. Koven, K. Saito, A. MacDougall, E. Burke, and B. Decharme
The Cryosphere, 10, 287–306, https://doi.org/10.5194/tc-10-287-2016, https://doi.org/10.5194/tc-10-287-2016, 2016
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We use a model-ensemble approach for simulating permafrost on the Tibetan Plateau. We identify the uncertainties across models (state-of-the-art land surface models) and across methods (most commonly used methods to define permafrost).
We differentiate between uncertainties stemming from climatic driving data or from physical process parameterization, and show how these uncertainties vary seasonally and inter-annually, and how estimates are subject to the definition of permafrost used.
We differentiate between uncertainties stemming from climatic driving data or from physical process parameterization, and show how these uncertainties vary seasonally and inter-annually, and how estimates are subject to the definition of permafrost used.
S. Peng, P. Ciais, G. Krinner, T. Wang, I. Gouttevin, A. D. McGuire, D. Lawrence, E. Burke, X. Chen, B. Decharme, C. Koven, A. MacDougall, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, C. Delire, T. Hajima, D. Ji, D. P. Lettenmaier, P. A. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
The Cryosphere, 10, 179–192, https://doi.org/10.5194/tc-10-179-2016, https://doi.org/10.5194/tc-10-179-2016, 2016
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Soil temperature change is a key indicator of the dynamics of permafrost. Using nine process-based ecosystem models with permafrost processes, a large spread of soil temperature trends across the models. Air temperature and longwave downward radiation are the main drivers of soil temperature trends. Based on an emerging observation constraint method, the total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000.
R. A. Fisher, S. Muszala, M. Verteinstein, P. Lawrence, C. Xu, N. G. McDowell, R. G. Knox, C. Koven, J. Holm, B. M. Rogers, A. Spessa, D. Lawrence, and G. Bonan
Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, https://doi.org/10.5194/gmd-8-3593-2015, 2015
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Predicting the distribution of vegetation under novel climates is important, both to understand how climate change will impact ecosystem services, but also to understand how vegetation changes might affect the carbon, energy and water cycles. Historically, predictions have been heavily dependent upon observations of existing vegetation boundaries. In this paper, we attempt to predict ecosystem boundaries from the ``bottom up'', and illustrate the complexities and promise of this approach.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
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We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
W. R. Wieder, A. S. Grandy, C. M. Kallenbach, P. G. Taylor, and G. B. Bonan
Geosci. Model Dev., 8, 1789–1808, https://doi.org/10.5194/gmd-8-1789-2015, https://doi.org/10.5194/gmd-8-1789-2015, 2015
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Projecting biogeochemical responses to environmental change requires multi-scaled perspectives. However, microbes, the drivers of soil organic matter decomposition and stabilization, remain notably absent from models used to project carbon cycle–climate feedbacks. Here, we apply and evaluate representations of microbial functional diversity across scales and find that such representations may be critical to accurately project soil carbon dynamics in a changing world.
V. S. Galligani, C. Prigent, E. Defer, C. Jimenez, P. Eriksson, J.-P. Pinty, and J.-P. Chaboureau
Atmos. Meas. Tech., 8, 1605–1616, https://doi.org/10.5194/amt-8-1605-2015, https://doi.org/10.5194/amt-8-1605-2015, 2015
H. Norouzi, M. Temimi, C. Prigent, J. Turk, R. Khanbilvardi, Y. Tian, F. A. Furuzawa, and H. Masunaga
Atmos. Meas. Tech., 8, 1197–1205, https://doi.org/10.5194/amt-8-1197-2015, https://doi.org/10.5194/amt-8-1197-2015, 2015
S. Sitch, P. Friedlingstein, N. Gruber, S. D. Jones, G. Murray-Tortarolo, A. Ahlström, S. C. Doney, H. Graven, C. Heinze, C. Huntingford, S. Levis, P. E. Levy, M. Lomas, B. Poulter, N. Viovy, S. Zaehle, N. Zeng, A. Arneth, G. Bonan, L. Bopp, J. G. Canadell, F. Chevallier, P. Ciais, R. Ellis, M. Gloor, P. Peylin, S. L. Piao, C. Le Quéré, B. Smith, Z. Zhu, and R. Myneni
Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://doi.org/10.5194/bg-12-653-2015, 2015
J. Schwaab, M. Bavay, E. Davin, F. Hagedorn, F. Hüsler, M. Lehning, M. Schneebeli, E. Thürig, and P. Bebi
Biogeosciences, 12, 467–487, https://doi.org/10.5194/bg-12-467-2015, https://doi.org/10.5194/bg-12-467-2015, 2015
G. D. Hayman, F. M. O'Connor, M. Dalvi, D. B. Clark, N. Gedney, C. Huntingford, C. Prigent, M. Buchwitz, O. Schneising, J. P. Burrows, C. Wilson, N. Richards, and M. Chipperfield
Atmos. Chem. Phys., 14, 13257–13280, https://doi.org/10.5194/acp-14-13257-2014, https://doi.org/10.5194/acp-14-13257-2014, 2014
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Globally, wetlands are a major source of methane, which is the second most important greenhouse gas. We find the JULES wetland methane scheme to perform well in general, although there is a tendency for it to overpredict emissions in the tropics and underpredict them in northern latitudes. Our study highlights novel uses of satellite data as a major tool to constrain land-atmosphere methane flux models in a warming world.
G. B. Bonan, M. Williams, R. A. Fisher, and K. W. Oleson
Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, https://doi.org/10.5194/gmd-7-2193-2014, 2014
B. P. Guillod, B. Orlowsky, D. Miralles, A. J. Teuling, P. D. Blanken, N. Buchmann, P. Ciais, M. Ek, K. L. Findell, P. Gentine, B. R. Lintner, R. L. Scott, B. Van den Hurk, and S. I. Seneviratne
Atmos. Chem. Phys., 14, 8343–8367, https://doi.org/10.5194/acp-14-8343-2014, https://doi.org/10.5194/acp-14-8343-2014, 2014
S. J. Sutanto, B. van den Hurk, P. A. Dirmeyer, S. I. Seneviratne, T. Röckmann, K. E. Trenberth, E. M. Blyth, J. Wenninger, and G. Hoffmann
Hydrol. Earth Syst. Sci., 18, 2815–2827, https://doi.org/10.5194/hess-18-2815-2014, https://doi.org/10.5194/hess-18-2815-2014, 2014
W. R. Wieder, A. S. Grandy, C. M. Kallenbach, and G. B. Bonan
Biogeosciences, 11, 3899–3917, https://doi.org/10.5194/bg-11-3899-2014, https://doi.org/10.5194/bg-11-3899-2014, 2014
R. Briant, L. Menut, G. Siour, and C. Prigent
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-7-3441-2014, https://doi.org/10.5194/gmdd-7-3441-2014, 2014
Revised manuscript not accepted
F. Aemisegger, S. Pfahl, H. Sodemann, I. Lehner, S. I. Seneviratne, and H. Wernli
Atmos. Chem. Phys., 14, 4029–4054, https://doi.org/10.5194/acp-14-4029-2014, https://doi.org/10.5194/acp-14-4029-2014, 2014
S. Levis, M. D. Hartman, and G. B. Bonan
Geosci. Model Dev., 7, 613–620, https://doi.org/10.5194/gmd-7-613-2014, https://doi.org/10.5194/gmd-7-613-2014, 2014
I. Pison, B. Ringeval, P. Bousquet, C. Prigent, and F. Papa
Atmos. Chem. Phys., 13, 11609–11623, https://doi.org/10.5194/acp-13-11609-2013, https://doi.org/10.5194/acp-13-11609-2013, 2013
C. D. Koven, W. J. Riley, Z. M. Subin, J. Y. Tang, M. S. Torn, W. D. Collins, G. B. Bonan, D. M. Lawrence, and S. C. Swenson
Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, https://doi.org/10.5194/bg-10-7109-2013, 2013
L. Gudmundsson and S. I. Seneviratne
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-13191-2013, https://doi.org/10.5194/hessd-10-13191-2013, 2013
Manuscript not accepted for further review
D. Lombardozzi, J. P. Sparks, and G. Bonan
Biogeosciences, 10, 6815–6831, https://doi.org/10.5194/bg-10-6815-2013, https://doi.org/10.5194/bg-10-6815-2013, 2013
R. Orth and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3895–3911, https://doi.org/10.5194/hess-17-3895-2013, https://doi.org/10.5194/hess-17-3895-2013, 2013
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
V. Beck, C. Gerbig, T. Koch, M. M. Bela, K. M. Longo, S. R. Freitas, J. O. Kaplan, C. Prigent, P. Bergamaschi, and M. Heimann
Atmos. Chem. Phys., 13, 7961–7982, https://doi.org/10.5194/acp-13-7961-2013, https://doi.org/10.5194/acp-13-7961-2013, 2013
R. Q. Thomas, G. B. Bonan, and C. L. Goodale
Biogeosciences, 10, 3869–3887, https://doi.org/10.5194/bg-10-3869-2013, https://doi.org/10.5194/bg-10-3869-2013, 2013
R. Wania, J. R. Melton, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, G. Chen, A. V. Eliseev, P. O. Hopcroft, W. J. Riley, Z. M. Subin, H. Tian, P. M. van Bodegom, T. Kleinen, Z. C. Yu, J. S. Singarayer, S. Zürcher, D. P. Lettenmaier, D. J. Beerling, S. N. Denisov, C. Prigent, F. Papa, and J. O. Kaplan
Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, https://doi.org/10.5194/gmd-6-617-2013, 2013
B. Orlowsky and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 1765–1781, https://doi.org/10.5194/hess-17-1765-2013, https://doi.org/10.5194/hess-17-1765-2013, 2013
J. F. Tjiputra, C. Roelandt, M. Bentsen, D. M. Lawrence, T. Lorentzen, J. Schwinger, Ø. Seland, and C. Heinze
Geosci. Model Dev., 6, 301–325, https://doi.org/10.5194/gmd-6-301-2013, https://doi.org/10.5194/gmd-6-301-2013, 2013
B. M. Rogers, J. T. Randerson, and G. B. Bonan
Biogeosciences, 10, 699–718, https://doi.org/10.5194/bg-10-699-2013, https://doi.org/10.5194/bg-10-699-2013, 2013
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Impacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)
A modeling framework to understand historical and projected ocean climate change in large coupled ensembles
TriCCo v1.1.0 – a cubulation-based method for computing connected components on triangular grids
Estimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0.1
The Moist Quasi-Geostrophic Coupled Model: MQ-GCM 2.0
Cell tracking of convective rainfall: sensitivity of climate-change signal to tracking algorithm and cell definition (Cell-TAO v1.0)
Transport parameterization of the Polar SWIFT model (version 2)
Analog data assimilation for the selection of suitable general circulation models
Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0
Grid refinement in ICON v2.6.4
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
Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset
The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)
Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects
HORAYZON v1.2: an efficient and flexible ray-tracing algorithm to compute horizon and sky view factor
LPJ-GUESS/LSMv1.0: a next-generation land surface model with high ecological realism
Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44
Intercomparison of four algorithms for detecting tropical cyclones using ERA5
Inland lake temperature initialization via coupled cycling with atmospheric data assimilation
wavetrisk-2.1: an adaptive dynamical core for ocean modelling
Representing surface heterogeneity in land–atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime
AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model
The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2
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.
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.
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.
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.
Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Haipeng Lin, Elizabeth W. Lundgren, Steve Goldhaber, Steven R. H. Barrett, and Daniel J. Jacob
Geosci. Model Dev., 15, 8669–8704, https://doi.org/10.5194/gmd-15-8669-2022, https://doi.org/10.5194/gmd-15-8669-2022, 2022
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We bring the state-of-the-science chemistry module GEOS-Chem into the Community Earth System Model (CESM). We show that some known differences between results from GEOS-Chem and CESM's CAM-chem chemistry module may be due to the configuration of model meteorology rather than inherent differences in the model chemistry. This is a significant step towards a truly modular Earth system model and allows two strong but currently separate research communities to benefit from each other's advances.
Rainer Schneck, Veronika Gayler, Julia E. M. S. Nabel, Thomas Raddatz, Christian H. Reick, and Reiner Schnur
Geosci. Model Dev., 15, 8581–8611, https://doi.org/10.5194/gmd-15-8581-2022, https://doi.org/10.5194/gmd-15-8581-2022, 2022
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The versions of ICON-A and ICON-Land/JSBACHv4 used for this study constitute the first milestone in the development of the new ICON Earth System Model ICON-ESM. JSBACHv4 is the successor of JSBACHv3, and most of the parameterizations of JSBACHv4 are re-implementations from JSBACHv3. We assess and compare the performance of JSBACHv4 and JSBACHv3. Overall, the JSBACHv4 results are as good as JSBACHv3, but both models reveal the same main shortcomings, e.g. the depiction of the leaf area index.
Dave van Wees, Guido R. van der Werf, James T. Randerson, Brendan M. Rogers, Yang Chen, Sander Veraverbeke, Louis Giglio, and Douglas C. Morton
Geosci. Model Dev., 15, 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022, https://doi.org/10.5194/gmd-15-8411-2022, 2022
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We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial.
Adama Sylla, Emilia Sanchez Gomez, Juliette Mignot, and Jorge López-Parages
Geosci. Model Dev., 15, 8245–8267, https://doi.org/10.5194/gmd-15-8245-2022, https://doi.org/10.5194/gmd-15-8245-2022, 2022
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Increasing model resolution depends on the subdomain of the Canary upwelling considered. In the Iberian Peninsula, the high-resolution (HR) models do not seem to better simulate the upwelling indices, while in Morocco to the Senegalese coast, the HR models show a clear improvement. Thus increasing the resolution of a global climate model does not necessarily have to be the only way to better represent the climate system. There is still much work to be done in terms of physical parameterizations.
Jadwiga H. Richter, Daniele Visioni, Douglas G. MacMartin, David A. Bailey, Nan Rosenbloom, Brian Dobbins, Walker R. Lee, Mari Tye, and Jean-Francois Lamarque
Geosci. Model Dev., 15, 8221–8243, https://doi.org/10.5194/gmd-15-8221-2022, https://doi.org/10.5194/gmd-15-8221-2022, 2022
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Solar climate intervention using stratospheric aerosol injection is a proposed method of reducing global mean temperatures to reduce the worst consequences of climate change. We present a new modeling protocol aimed at simulating a plausible deployment of stratospheric aerosol injection and reproducibility of simulations using other Earth system models: Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI).
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
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The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Entao Yu, Rui Bai, Xia Chen, and Lifang Shao
Geosci. Model Dev., 15, 8111–8134, https://doi.org/10.5194/gmd-15-8111-2022, https://doi.org/10.5194/gmd-15-8111-2022, 2022
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A large number of simulations are conducted to investigate how different physical parameterization schemes impact surface wind simulations under stable weather conditions over the coastal regions of North China using the Weather Research and Forecasting model with a horizontal grid spacing of 0.5 km. Results indicate that the simulated wind speed is most sensitive to the planetary boundary layer schemes, followed by short-wave/long-wave radiation schemes and microphysics schemes.
Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, and Michael Duda
Geosci. Model Dev., 15, 8135–8151, https://doi.org/10.5194/gmd-15-8135-2022, https://doi.org/10.5194/gmd-15-8135-2022, 2022
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We focus on the recent development of a state-of-the-art storm-resolving global climate model and investigate how this next-generation model performs for precipitation prediction over the western USA. Results show realistic representations of precipitation with significantly enhanced snowpack over complex terrains. The model evaluation advances the unified modeling of large-scale forcing constraints and realistic fine-scale features to advance multi-scale climate predictions and changes.
Marina Martínez Montero, Michel Crucifix, Victor Couplet, Nuria Brede, and Nicola Botta
Geosci. Model Dev., 15, 8059–8084, https://doi.org/10.5194/gmd-15-8059-2022, https://doi.org/10.5194/gmd-15-8059-2022, 2022
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We present SURFER, a lightweight model that links CO2 emissions and geoengineering to ocean acidification and sea level rise from glaciers, ocean thermal expansion and Greenland and Antarctic ice sheets. The ice sheet module adequately describes the tipping points of both Greenland and Antarctica. SURFER is understandable, fast, accurate up to several thousands of years, capable of emulating results obtained by state of the art models and well suited for policy analyses.
Haopeng Fan, Siran Li, Zhongmiao Sun, Guorui Xiao, Xinxing Li, and Xiaogang Liu
EGUsphere, https://doi.org/10.5194/egusphere-2022-898, https://doi.org/10.5194/egusphere-2022-898, 2022
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The bias of traditional tropospheric zenith hydrostatic delay (ZHD) model is usually thought negligible, yet it still reaches 10 mm sometimes and would lead to mm-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 we verified the efficiency based on data from ECMWF (European Centre for Medium-Range Weather Forecasts), it turned out that ZHD biases were rectified by ~50 %.
Francisco José Cuesta-Valero, Hugo Beltrami, Stephan Gruber, Almudena García-García, and J. Fidel González-Rouco
Geosci. Model Dev., 15, 7913–7932, https://doi.org/10.5194/gmd-15-7913-2022, https://doi.org/10.5194/gmd-15-7913-2022, 2022
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Inversions of subsurface temperature profiles provide past long-term estimates of ground surface temperature histories and ground heat flux histories at timescales of decades to millennia. Theses estimates complement high-frequency proxy temperature reconstructions and are the basis for studying continental heat storage. We develop and release a new bootstrap method to derive meaningful confidence intervals for the average surface temperature and heat flux histories from any number of profiles.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
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We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Wentao Zhang, Xiangjun Shi, and Chunsong Lu
Geosci. Model Dev., 15, 7751–7766, https://doi.org/10.5194/gmd-15-7751-2022, https://doi.org/10.5194/gmd-15-7751-2022, 2022
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The two-moment bulk cloud microphysics scheme used in CAM6 was modified to consider the impacts of the ice-crystal size distribution shape parameter (μi). After that, how the μi impacts cloud microphysical processes and then climate simulations is clearly illustrated by offline tests and CAM6 model experiments. Our results and findings are useful for the further development of μi-related parameterizations.
Yona Silvy, Clément Rousset, Eric Guilyardi, Jean-Baptiste Sallée, Juliette Mignot, Christian Ethé, and Gurvan Madec
Geosci. Model Dev., 15, 7683–7713, https://doi.org/10.5194/gmd-15-7683-2022, https://doi.org/10.5194/gmd-15-7683-2022, 2022
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A modeling framework is introduced to understand and decompose the mechanisms causing the ocean temperature, salinity and circulation to change since the pre-industrial period and into 21st century scenarios of global warming. This framework aims to look at the response to changes in the winds and in heat and freshwater exchanges at the ocean interface in global climate models, throughout the 1850–2100 period, to unravel their individual effects on the changing physical structure of the ocean.
Aiko Voigt, Petra Schwer, Noam von Rotberg, and Nicole Knopf
Geosci. Model Dev., 15, 7489–7504, https://doi.org/10.5194/gmd-15-7489-2022, https://doi.org/10.5194/gmd-15-7489-2022, 2022
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In climate science, it is helpful to identify coherent objects, for example, those formed by clouds. However, many models now use unstructured grids, which makes it harder to identify coherent objects. We present a new method that solves this problem by moving model data from an unstructured triangular grid to a structured cubical grid. We implement the method in an open-source Python package and show that the method is ready to be applied to climate model data.
Jérémy Bernard, Erwan Bocher, Elisabeth Le Saux Wiederhold, François Leconte, and Valéry Masson
Geosci. Model Dev., 15, 7505–7532, https://doi.org/10.5194/gmd-15-7505-2022, https://doi.org/10.5194/gmd-15-7505-2022, 2022
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OpenStreetMap is a collaborative project aimed at creaing a free dataset containing topographical information. Since these data are available worldwide, they can be used as standard data for geoscience studies. However, most buildings miss the height information that constitutes key data for numerous fields (urban climate, noise propagation, air pollution). In this work, the building height is estimated using statistical modeling using indicators that characterize the building's environment.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
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Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-202, https://doi.org/10.5194/gmd-2022-202, 2022
Revised manuscript accepted for GMD
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Cell tracking algorithms allow the properties of a convective cell to be studied 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.
Ingo Wohltmann, Daniel Kreyling, and Ralph Lehmann
Geosci. Model Dev., 15, 7243–7255, https://doi.org/10.5194/gmd-15-7243-2022, https://doi.org/10.5194/gmd-15-7243-2022, 2022
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The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Juan Ruiz, Pierre Ailliot, Thi Tuyet Trang Chau, Pierre Le Bras, Valérie Monbet, Florian Sévellec, and Pierre Tandeo
Geosci. Model Dev., 15, 7203–7220, https://doi.org/10.5194/gmd-15-7203-2022, https://doi.org/10.5194/gmd-15-7203-2022, 2022
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We present a new approach to validate numerical simulations of the current climate. The method can take advantage of existing climate simulations produced by different centers combining an analog forecasting approach with data assimilation to quantify how well a particular model reproduces a sequence of observed values. The method can be applied with different observations types and is implemented locally in space and time significantly reducing the associated computational cost.
Chahan M. Kropf, Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch
Geosci. Model Dev., 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022, https://doi.org/10.5194/gmd-15-7177-2022, 2022
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Mathematical models are approximations, and modellers need to understand and ideally quantify the arising uncertainties. Here, we describe and showcase the first, simple-to-use, uncertainty and sensitivity analysis module of the open-source and open-access climate-risk modelling platform CLIMADA. This may help to enhance transparency and intercomparison of studies among climate-risk modellers, help focus future research, and lead to better-informed decisions on climate adaptation.
Günther Zängl, Daniel Reinert, and Florian Prill
Geosci. Model Dev., 15, 7153–7176, https://doi.org/10.5194/gmd-15-7153-2022, https://doi.org/10.5194/gmd-15-7153-2022, 2022
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This article describes the implementation of grid refinement in the ICOsahedral Nonhydrostatic (ICON) model, which has been jointly developed at several German institutions and constitutes a unified modeling system for global and regional numerical weather prediction and climate applications. The grid refinement allows using a higher resolution in regional domains and transferring the information back to the global domain by means of a feedback mechanism.
Suzanne Robinson, Ruza Ivanovic, Lauren Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul Valdes
EGUsphere, https://doi.org/10.5194/egusphere-2022-606, https://doi.org/10.5194/egusphere-2022-606, 2022
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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 alongside incorporation of Nd onto sinking particles represent the major global sources and sinks. 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.
Sébastien Gardoll and Olivier Boucher
Geosci. Model Dev., 15, 7051–7073, https://doi.org/10.5194/gmd-15-7051-2022, https://doi.org/10.5194/gmd-15-7051-2022, 2022
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Tropical cyclones (TCs) are one of the most devastating natural disasters, which justifies monitoring and prediction in the context of a changing climate. In this study, we have adapted and tested a convolutional neural network (CNN) for the classification of reanalysis outputs (ERA5 and MERRA-2 labeled by HURDAT2) according to the presence or absence of TCs. We tested the impact of interpolation and of "mixing and matching" the training and test sets on the performance of the CNN.
Marco A. Giorgetta, William Sawyer, Xavier Lapillonne, Panagiotis Adamidis, Dmitry Alexeev, Valentin Clément, Remo Dietlicher, Jan Frederik Engels, Monika Esch, Henning Franke, Claudia Frauen, Walter M. Hannah, Benjamin R. Hillman, Luis Kornblueh, Philippe Marti, Matthew R. Norman, Robert Pincus, Sebastian Rast, Daniel Reinert, Reiner Schnur, Uwe Schulzweida, and Bjorn Stevens
Geosci. Model Dev., 15, 6985–7016, https://doi.org/10.5194/gmd-15-6985-2022, https://doi.org/10.5194/gmd-15-6985-2022, 2022
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This work presents a first version of the ICON atmosphere model that works not only on CPUs, but also on GPUs. This GPU-enabled ICON version is benchmarked on two GPU machines and a CPU machine. While the weak scaling is very good on CPUs and GPUs, the strong scaling is poor on GPUs. But the high performance of GPU machines allowed for first simulations of a short period of the quasi-biennial oscillation at very high resolution with explicit convection and gravity wave forcing.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
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This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
Christian R. Steger, Benjamin Steger, and Christoph Schär
Geosci. Model Dev., 15, 6817–6840, https://doi.org/10.5194/gmd-15-6817-2022, https://doi.org/10.5194/gmd-15-6817-2022, 2022
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Terrain horizon and sky view factor are crucial quantities for many geoscientific applications; e.g. they are used to account for effects of terrain on surface radiation in climate and land surface models. Because typical terrain horizon algorithms are inefficient for high-resolution (< 30 m) elevation data, we developed a new algorithm based on a ray-tracing library. A comparison with two conventional methods revealed both its high performance and its accuracy for complex terrain.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, https://doi.org/10.5194/gmd-15-6709-2022, 2022
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We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
Jorge Baño-Medina, Rodrigo Manzanas, Ezequiel Cimadevilla, Jesús Fernández, Jose González-Abad, Antonio S. Cofiño, and José Manuel Gutiérrez
Geosci. Model Dev., 15, 6747–6758, https://doi.org/10.5194/gmd-15-6747-2022, https://doi.org/10.5194/gmd-15-6747-2022, 2022
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Deep neural networks are used to produce downscaled regional climate change projections over Europe for temperature and precipitation for the first time. The resulting dataset, DeepESD, is analyzed against state-of-the-art downscaling methodologies, reproducing more accurately the observed climate in the historical period and showing plausible future climate change signals with low computational requirements.
Stella Bourdin, Sébastien Fromang, William Dulac, Julien Cattiaux, and Fabrice Chauvin
Geosci. Model Dev., 15, 6759–6786, https://doi.org/10.5194/gmd-15-6759-2022, https://doi.org/10.5194/gmd-15-6759-2022, 2022
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When studying tropical cyclones in a large dataset, one needs objective and automatic procedures to detect their specific pattern. Applying four different such algorithms to a reconstruction of the climate, we show that the choice of the algorithm is crucial to the climatology obtained. Mainly, the algorithms differ in their sensitivity to weak storms so that they provide different frequencies and durations. We review the different options to consider for the choice of the tracking methodology.
Stanley G. Benjamin, Tatiana G. Smirnova, Eric P. James, Eric J. Anderson, Ayumi Fujisaki-Manome, John G. W. Kelley, Greg E. Mann, Andrew D. Gronewold, Philip Chu, and Sean G. T. Kelley
Geosci. Model Dev., 15, 6659–6676, https://doi.org/10.5194/gmd-15-6659-2022, https://doi.org/10.5194/gmd-15-6659-2022, 2022
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Application of 1-D lake models coupled within earth-system prediction models will improve accuracy but requires accurate initialization of lake temperatures. Here, we describe a lake initialization method by cycling within a weather prediction model to constrain lake temperature evolution. We compared these lake temperature values with other estimates and found much reduced errors (down to 1-2 K). The lake cycling initialization is now applied to two operational US NOAA weather models.
Nicholas K.-R. Kevlahan and Florian Lemarié
Geosci. Model Dev., 15, 6521–6539, https://doi.org/10.5194/gmd-15-6521-2022, https://doi.org/10.5194/gmd-15-6521-2022, 2022
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WAVETRISK-2.1 is an innovative climate model for the world's oceans. It uses state-of-the-art techniques to change the model's resolution locally, from O(100 km) to O(5 km), as the ocean changes. This dynamic adaptivity makes optimal use of available supercomputer resources, and allows two-dimensional global scales and three-dimensional submesoscales to be captured in the same simulation. WAVETRISK-2.1 is designed to be coupled its companion global atmosphere model, WAVETRISK-1.x.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
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The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
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We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493, https://doi.org/10.5194/gmd-15-6451-2022, https://doi.org/10.5194/gmd-15-6451-2022, 2022
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The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
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Short summary
We revise the roughness of the land surface in the CESM climate model. Guided by observational data, we increase the surface roughness of forests and decrease that of bare soil, snow, ice, and crops. These modifications alter simulated temperatures and wind speeds at and above the land surface considerably, in particular over desert regions. The revised model represents the diurnal variability of the land surface temperature better compared to satellite observations over most regions.
We revise the roughness of the land surface in the CESM climate model. Guided by observational...
