Articles | Volume 9, issue 11
https://doi.org/10.5194/gmd-9-4169-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-4169-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Forest soil carbon stock estimates in a nationwide inventory: evaluating performance of the ROMULv and Yasso07 models in Finland
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Tapio Linkosalo
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Mikko Peltoniemi
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Risto Sievänen
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Raisa Mäkipää
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Pekka Tamminen
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Maija Salemaa
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Tiina Nieminen
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Boris Ťupek
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Juha Heikkinen
Natural Resources Institute Finland, Natural resources and bioproduction, (LUKE), P.O. Box 2, 00791 Helsinki, Finland
Alexander Komarov
Institute of Physicochemical and Biological Problems in Soil Science of the Russian Academy of Sciences, 142290 Institutskaya ul., 2, Pushchino, Moscow, Russian Federation
deceased
Related authors
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
Short summary
Short summary
Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
Jyrki Jauhiainen, Jukka Alm, Brynhildur Bjarnadottir, Ingeborg Callesen, Jesper R. Christiansen, Nicholas Clarke, Lise Dalsgaard, Hongxing He, Sabine Jordan, Vaiva Kazanavičiūtė, Leif Klemedtsson, Ari Lauren, Andis Lazdins, Aleksi Lehtonen, Annalea Lohila, Ainars Lupikis, Ülo Mander, Kari Minkkinen, Åsa Kasimir, Mats Olsson, Paavo Ojanen, Hlynur Óskarsson, Bjarni D. Sigurdsson, Gunnhild Søgaard, Kaido Soosaar, Lars Vesterdal, and Raija Laiho
Biogeosciences, 16, 4687–4703, https://doi.org/10.5194/bg-16-4687-2019, https://doi.org/10.5194/bg-16-4687-2019, 2019
Short summary
Short summary
We collated peer-reviewed publications presenting GHG flux data for drained organic forest soils in boreal and temperate climate zones, focusing on data that have been used, or have the potential to be used, for estimating net annual soil GHG emission/removals. We evaluated the methods in data collection and identified major gaps in background/environmental data. Based on these, we developed suggestions for future GHG data collection to increase data applicability in syntheses and inventories.
Shoji Hashimoto, Kazuki Nanko, Boris Ťupek, and Aleksi Lehtonen
Geosci. Model Dev., 10, 1321–1337, https://doi.org/10.5194/gmd-10-1321-2017, https://doi.org/10.5194/gmd-10-1321-2017, 2017
Short summary
Short summary
Soil organic carbon (SOC) stock simulated by Earth system models (ESMs) and those of observational databases are not well correlated when the two are compared at fine grid scales. To identify the key factors that govern global SOC distribution, we applied a data-mining scheme to observational databases and outputs from ESMs. This study not only identifies key factors but it also presents a new approach that compares the observational databases with ESM outputs.
Boris Ťupek, Carina A. Ortiz, Shoji Hashimoto, Johan Stendahl, Jonas Dahlgren, Erik Karltun, and Aleksi Lehtonen
Biogeosciences, 13, 4439–4459, https://doi.org/10.5194/bg-13-4439-2016, https://doi.org/10.5194/bg-13-4439-2016, 2016
Short summary
Short summary
We evaluated the soil carbon stock estimates of Yasso07, Q, and CENTURY soil carbon models, used in national greenhouse gas inventories in Europe, Japan, and USA, with soil carbon stock measurements from Swedish Forest Soil National Inventories. Measurements grouped according to the gradient of soil nutrient status revealed that the models underestimated for the Swedish boreal forest soils with higher site fertility. We discussed mechanisms of underestimation and further model developments.
P. E. Kauppi, R. A. Birdsey, Y. Pan, A. Ihalainen, P. Nöjd, and A. Lehtonen
Biogeosciences, 12, 855–862, https://doi.org/10.5194/bg-12-855-2015, https://doi.org/10.5194/bg-12-855-2015, 2015
Vilna Tyystjärvi, Tiina Markkanen, Leif Backman, Maarit Raivonen, Antti Leppänen, Xuefei Li, Paavo Ojanen, Kari Minkkinen, Roosa Hautala, Mikko Peltoniemi, Jani Anttila, Raija Laiho, Annalea Lohila, Raisa Mäkipää, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2023-3037, https://doi.org/10.5194/egusphere-2023-3037, 2024
Short summary
Short summary
Drainage of boreal peatlands strongly influences soil methane fluxes with important implications to their climatic impacts. Here we simulate methane fluxes in forestry-drained and restored peatlands during the 21st century. We found that restoration turned peatlands to a source of methane but the magnitude varied regionally. In forests, changes in water table level influenced methane fluxes and in general, the sink was weaker under rotational forestry compared to continuous cover forestry.
Jyrki Jauhiainen, Juha Heikkinen, Nicholas Clarke, Hongxing He, Lise Dalsgaard, Kari Minkkinen, Paavo Ojanen, Lars Vesterdal, Jukka Alm, Aldis Butlers, Ingeborg Callesen, Sabine Jordan, Annalea Lohila, Ülo Mander, Hlynur Óskarsson, Bjarni D. Sigurdsson, Gunnhild Søgaard, Kaido Soosaar, Åsa Kasimir, Brynhildur Bjarnadottir, Andis Lazdins, and Raija Laiho
Biogeosciences, 20, 4819–4839, https://doi.org/10.5194/bg-20-4819-2023, https://doi.org/10.5194/bg-20-4819-2023, 2023
Short summary
Short summary
The study looked at published data on drained organic forest soils in boreal and temperate zones to revisit current Tier 1 default emission factors (EFs) provided by the IPCC Wetlands Supplement. We examined the possibilities of forming more site-type specific EFs and inspected the potential relevance of environmental variables for predicting annual soil greenhouse gas balances by statistical models. The results have important implications for EF revisions and national emission reporting.
Jukka Alm, Antti Wall, Jukka-Pekka Myllykangas, Paavo Ojanen, Juha Heikkinen, Helena M. Henttonen, Raija Laiho, Kari Minkkinen, Tarja Tuomainen, and Juha Mikola
Biogeosciences, 20, 3827–3855, https://doi.org/10.5194/bg-20-3827-2023, https://doi.org/10.5194/bg-20-3827-2023, 2023
Short summary
Short summary
In Finland peatlands cover one-third of land area. For half of those, with 4.3 Mha being drained for forestry, Finland reports sinks and sources of greenhouse gases in forest lands on organic soils following its UNFCCC commitment. We describe a new method for compiling soil CO2 balance that follows changes in tree volume, tree harvests and temperature. An increasing trend of emissions from 1.4 to 7.9 Mt CO2 was calculated for drained peatland forest soils in Finland for 1990–2021.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
Short summary
Short summary
Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
Maiju Linkosalmi, Juha-Pekka Tuovinen, Olli Nevalainen, Mikko Peltoniemi, Cemal M. Taniş, Ali N. Arslan, Juuso Rainne, Annalea Lohila, Tuomas Laurila, and Mika Aurela
Biogeosciences, 19, 4747–4765, https://doi.org/10.5194/bg-19-4747-2022, https://doi.org/10.5194/bg-19-4747-2022, 2022
Short summary
Short summary
Vegetation greenness was monitored with digital cameras in three northern peatlands during five growing seasons. The greenness index derived from the images was highest at the most nutrient-rich site. Greenness indicated the main phases of phenology and correlated with CO2 uptake, though this was mainly related to the common seasonal cycle. The cameras and Sentinel-2 satellite showed consistent results, but more frequent satellite data are needed for reliable detection of phenological phases.
Jarmo Mäkelä, Francesco Minunno, Tuula Aalto, Annikki Mäkelä, Tiina Markkanen, and Mikko Peltoniemi
Biogeosciences, 17, 2681–2700, https://doi.org/10.5194/bg-17-2681-2020, https://doi.org/10.5194/bg-17-2681-2020, 2020
Short summary
Short summary
We assess the relative magnitude of uncertainty sources on ecosystem indicators of the 21st century climate change on two boreal forest sites. In addition to RCP and climate model uncertainties, we included the overlooked model parameter uncertainty and management actions in our analysis. Management was the dominant uncertainty factor for the more verdant southern site, followed by RCP, climate and parameter uncertainties. The uncertainties were estimated with canonical correlation analysis.
Laura Matkala, Maija Salemaa, and Jaana Bäck
Biogeosciences, 17, 1535–1556, https://doi.org/10.5194/bg-17-1535-2020, https://doi.org/10.5194/bg-17-1535-2020, 2020
Short summary
Short summary
We studied how species number and abundance of the understorey vegetation correlates with nutrient contents of soil and tree leaves at a northern boreal forest site. The phosphorus (P) content of the humus layer showed higher correlation with vegetation than the nitrogen (N) content. Usually N is considered more important in boreal forests. The plots with high P content in humus had birch as the dominant tree species, implying that birch leaf litter is an important source of P to the plants.
Jyrki Jauhiainen, Jukka Alm, Brynhildur Bjarnadottir, Ingeborg Callesen, Jesper R. Christiansen, Nicholas Clarke, Lise Dalsgaard, Hongxing He, Sabine Jordan, Vaiva Kazanavičiūtė, Leif Klemedtsson, Ari Lauren, Andis Lazdins, Aleksi Lehtonen, Annalea Lohila, Ainars Lupikis, Ülo Mander, Kari Minkkinen, Åsa Kasimir, Mats Olsson, Paavo Ojanen, Hlynur Óskarsson, Bjarni D. Sigurdsson, Gunnhild Søgaard, Kaido Soosaar, Lars Vesterdal, and Raija Laiho
Biogeosciences, 16, 4687–4703, https://doi.org/10.5194/bg-16-4687-2019, https://doi.org/10.5194/bg-16-4687-2019, 2019
Short summary
Short summary
We collated peer-reviewed publications presenting GHG flux data for drained organic forest soils in boreal and temperate climate zones, focusing on data that have been used, or have the potential to be used, for estimating net annual soil GHG emission/removals. We evaluated the methods in data collection and identified major gaps in background/environmental data. Based on these, we developed suggestions for future GHG data collection to increase data applicability in syntheses and inventories.
Terhikki Manninen, Tuula Aalto, Tiina Markkanen, Mikko Peltoniemi, Kristin Böttcher, Sari Metsämäki, Kati Anttila, Pentti Pirinen, Antti Leppänen, and Ali Nadir Arslan
Biogeosciences, 16, 223–240, https://doi.org/10.5194/bg-16-223-2019, https://doi.org/10.5194/bg-16-223-2019, 2019
Short summary
Short summary
The surface albedo time series CLARA-A2 SAL was used to study trends in the timing of the melting season of snow and preceding albedo value in Finland during 1982–2016 to assess climate change. The results were in line with operational snow depth data, JSBACH land ecosystem model, SYKE fractional snow cover and greening-up data. In the north a clear trend to earlier snowmelt onset, increasing melting season length, and decrease in pre-melt albedo (related to increased stem volume) was observed.
Mikko Peltoniemi, Mika Aurela, Kristin Böttcher, Pasi Kolari, John Loehr, Jouni Karhu, Maiju Linkosalmi, Cemal Melih Tanis, Juha-Pekka Tuovinen, and Ali Nadir Arslan
Earth Syst. Sci. Data, 10, 173–184, https://doi.org/10.5194/essd-10-173-2018, https://doi.org/10.5194/essd-10-173-2018, 2018
Short summary
Short summary
Monitoring ecosystems using low-cost time lapse cameras has gained wide interest among researchers worldwide. Quantitative information stored in image pixels can be analysed automatically to track time-dependent phenomena, e.g. seasonal course of leaves in the canopies or snow on ground. As such, cameras can provide valuable ground references to earth observation. Here we document the ecosystem camera network we established to Finland and publish time series of images recorded between 2014–2016.
Eero Nikinmaa, Tuomo Kalliokoski, Kari Minkkinen, Jaana Bäck, Michael Boy, Yao Gao, Nina Janasik-Honkela, Janne I. Hukkinen, Maarit Kallio, Markku Kulmala, Nea Kuusinen, Annikki Mäkelä, Brent D. Matthies, Mikko Peltoniemi, Risto Sievänen, Ditte Taipale, Lauri Valsta, Anni Vanhatalo, Martin Welp, Luxi Zhou, Putian Zhou, and Frank Berninger
Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-141, https://doi.org/10.5194/bg-2017-141, 2017
Manuscript not accepted for further review
Short summary
Short summary
We estimated the impact of boreal forest management on climate, considering the effects of carbon, albedo, aerosols, and effects of industrial wood use. We made analyses both in current and warmer climate of 2050. The aerosol effect was comparable to that of carbon sequestration. Deciduous trees may have a large potential for mitigation due to their high albedo and aerosol effects. If the forests will be used more intensively and mainly for pulp and energy, the warming influence is clear.
Shoji Hashimoto, Kazuki Nanko, Boris Ťupek, and Aleksi Lehtonen
Geosci. Model Dev., 10, 1321–1337, https://doi.org/10.5194/gmd-10-1321-2017, https://doi.org/10.5194/gmd-10-1321-2017, 2017
Short summary
Short summary
Soil organic carbon (SOC) stock simulated by Earth system models (ESMs) and those of observational databases are not well correlated when the two are compared at fine grid scales. To identify the key factors that govern global SOC distribution, we applied a data-mining scheme to observational databases and outputs from ESMs. This study not only identifies key factors but it also presents a new approach that compares the observational databases with ESM outputs.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
Short summary
Short summary
After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
Marta Camino-Serrano, Elisabeth Graf Pannatier, Sara Vicca, Sebastiaan Luyssaert, Mathieu Jonard, Philippe Ciais, Bertrand Guenet, Bert Gielen, Josep Peñuelas, Jordi Sardans, Peter Waldner, Sophia Etzold, Guia Cecchini, Nicholas Clarke, Zoran Galić, Laure Gandois, Karin Hansen, Jim Johnson, Uwe Klinck, Zora Lachmanová, Antti-Jussi Lindroos, Henning Meesenburg, Tiina M. Nieminen, Tanja G. M. Sanders, Kasia Sawicka, Walter Seidling, Anne Thimonier, Elena Vanguelova, Arne Verstraeten, Lars Vesterdal, and Ivan A. Janssens
Biogeosciences, 13, 5567–5585, https://doi.org/10.5194/bg-13-5567-2016, https://doi.org/10.5194/bg-13-5567-2016, 2016
Short summary
Short summary
We investigated the long-term trends of dissolved organic carbon (DOC) in soil solution and the drivers of changes in over 100 forest monitoring plots across Europe. An overall increasing trend was detected in the organic layers, but no overall trend was found in the mineral horizons. There are strong interactions between controls acting at local and regional scales. Our findings are relevant for researchers focusing on the link between terrestrial and aquatic ecosystems and for C-cycle models.
Maiju Linkosalmi, Mika Aurela, Juha-Pekka Tuovinen, Mikko Peltoniemi, Cemal M. Tanis, Ali N. Arslan, Pasi Kolari, Kristin Böttcher, Tuula Aalto, Juuso Rainne, Juha Hatakka, and Tuomas Laurila
Geosci. Instrum. Method. Data Syst., 5, 417–426, https://doi.org/10.5194/gi-5-417-2016, https://doi.org/10.5194/gi-5-417-2016, 2016
Short summary
Short summary
Digital photography has become a widely used tool for monitoring the vegetation phenology. The seasonal cycle of the greenness index obtained from photographs correlated well with the CO2 exchange of the plants at our wetland and Scots pine forest sites. While the seasonal changes in the greenness were more obvious for the ecosystem dominated by annual plants, clear seasonal patterns were also observed for the evergreen forest.
Boris Ťupek, Carina A. Ortiz, Shoji Hashimoto, Johan Stendahl, Jonas Dahlgren, Erik Karltun, and Aleksi Lehtonen
Biogeosciences, 13, 4439–4459, https://doi.org/10.5194/bg-13-4439-2016, https://doi.org/10.5194/bg-13-4439-2016, 2016
Short summary
Short summary
We evaluated the soil carbon stock estimates of Yasso07, Q, and CENTURY soil carbon models, used in national greenhouse gas inventories in Europe, Japan, and USA, with soil carbon stock measurements from Swedish Forest Soil National Inventories. Measurements grouped according to the gradient of soil nutrient status revealed that the models underestimated for the Swedish boreal forest soils with higher site fertility. We discussed mechanisms of underestimation and further model developments.
F. Minunno, M. Peltoniemi, S. Launiainen, M. Aurela, A. Lindroth, A. Lohila, I. Mammarella, K. Minkkinen, and A. Mäkelä
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-5089-2015, https://doi.org/10.5194/gmdd-8-5089-2015, 2015
Revised manuscript not accepted
P. E. Kauppi, R. A. Birdsey, Y. Pan, A. Ihalainen, P. Nöjd, and A. Lehtonen
Biogeosciences, 12, 855–862, https://doi.org/10.5194/bg-12-855-2015, https://doi.org/10.5194/bg-12-855-2015, 2015
W. Yuan, S. Liu, W. Cai, W. Dong, J. Chen, A. Arain, P. D. Blanken, A. Cescatti, G. Wohlfahrt, T. Georgiadis, L. Genesio, D. Gianelle, A. Grelle, G. Kiely, A. Knohl, D. Liu, M. Marek, L. Merbold, L. Montagnani, O. Panferov, M. Peltoniemi, S. Rambal, A. Raschi, A. Varlagin, and J. Xia
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-6-5475-2013, https://doi.org/10.5194/gmdd-6-5475-2013, 2013
Revised manuscript not accepted
D. J. Charman, D. W. Beilman, M. Blaauw, R. K. Booth, S. Brewer, F. M. Chambers, J. A. Christen, A. Gallego-Sala, S. P. Harrison, P. D. M. Hughes, S. T. Jackson, A. Korhola, D. Mauquoy, F. J. G. Mitchell, I. C. Prentice, M. van der Linden, F. De Vleeschouwer, Z. C. Yu, J. Alm, I. E. Bauer, Y. M. C. Corish, M. Garneau, V. Hohl, Y. Huang, E. Karofeld, G. Le Roux, J. Loisel, R. Moschen, J. E. Nichols, T. M. Nieminen, G. M. MacDonald, N. R. Phadtare, N. Rausch, Ü. Sillasoo, G. T. Swindles, E.-S. Tuittila, L. Ukonmaanaho, M. Väliranta, S. van Bellen, B. van Geel, D. H. Vitt, and Y. Zhao
Biogeosciences, 10, 929–944, https://doi.org/10.5194/bg-10-929-2013, https://doi.org/10.5194/bg-10-929-2013, 2013
Related subject area
Climate and Earth system modeling
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields
An emulation-based approach for interrogating reactive transport models
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0
Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
URock 2023a: an open-source GIS-based wind model for complex urban settings
DASH: a MATLAB toolbox for paleoclimate data assimilation
Accurate Assessment of Land-Atmosphere Coupling in Climate Models Requires High Frequency Data Output
Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)
All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
Exchange-grid coupling approach for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
Short summary
Short summary
We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
Short summary
Short summary
This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
Short summary
Short summary
We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
Short summary
Short summary
Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
Short summary
Short summary
We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
Short summary
Short summary
Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
Short summary
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
Short summary
Short summary
This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
Short summary
Short summary
The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
Short summary
Short summary
Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
Short summary
Short summary
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
Short summary
Short summary
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
Short summary
Short summary
Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
Short summary
Short summary
We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
Short summary
Short summary
This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
Short summary
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Xinzhu Yu, Li Liu, Chao Sun, Qingu Jiang, Biao Zhao, Zhiyuan Zhang, Hao Yu, and Bin Wang
Geosci. Model Dev., 16, 6285–6308, https://doi.org/10.5194/gmd-16-6285-2023, https://doi.org/10.5194/gmd-16-6285-2023, 2023
Short summary
Short summary
In this paper we propose a new common, flexible, and efficient parallel I/O framework for earth system modeling based on C-Coupler2.0. CIOFC1.0 can handle data I/O in parallel and provides a configuration file format that enables users to conveniently change the I/O configurations. It can automatically make grid and time interpolation, output data with an aperiodic time series, and accelerate data I/O when the field size is large.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
Short summary
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Jean-Claude André, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Joussaume Sylvie, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-188, https://doi.org/10.5194/gmd-2023-188, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worlwide initiative to study the climate change. We analyze the metrics, which resulted from collaboration efforts among many partners and models, and describe our findigs to demonstrate the utility of our study for the scientific community. The research contributes to understand climate modelling performance on the current High-performance Computing (HPC) architectures.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
Short summary
Short summary
The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
Short summary
Short summary
The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
Short summary
Short summary
Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
EGUsphere, https://doi.org/10.5194/egusphere-2023-2048, https://doi.org/10.5194/egusphere-2023-2048, 2023
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant time scales, enable demonstration of inter-model spread in land-atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
Short summary
Short summary
The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
Short summary
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
Short summary
Short summary
Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
Short summary
Short summary
A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-166, https://doi.org/10.5194/gmd-2023-166, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean-atmosphere interactions.
Cited articles
Bivand, R. S., Pebesma, E. J., Gomez-Rubio, V., and Pebesma, E. J.: Applied spatial data analysis with R, Springer, New York, USA, 2008.
Chertov, O. G., Komarov, A. S., Nadporozhskaya, M., Bykhovets, S. S., and Zudin, S. L.: ROMUL – a model of forest soil organic matter dynamics as a substantial tool for forest ecosystem modeling, Ecol. Model., 138, 289–308, https://doi.org/10.1016/S0304-3800(00)00409-9, 2001.
Dalsgaard, L., Astrup, R., Anton-Fernandez, C., Borgen, S. K., Breidenbach, J., Lange, H., Lehtonen, A., and Liski, J.: Modeling soil carbon dynamics in northern forests: effects of spatial and temporal aggregation of climatic input data, PloS one, 11, e0149902, https://doi.org/10.1371/journal.pone.0149902, 2016.
Derome, J., Lindroos, A., and Kilponen, T.: Scientific Seminar on Forest Condition Monitoring and Ecosystem Functioning in Northern Europe under the Forest Focus and ICP Forests Programmes, Finnish Forest Research Institute, Vantaa, Finland, 2007.
Eronen, M.: The history of the Litorina Sea and associated Holocene events, Soc. Scient. Fenn. Comm. Phys.-Math., 44, 79–195, 1974.
Gamfeldt, L., Snäll, T., Bagchi, R., Jonsson, M., Gustafsson, L., Kjellander, P., Ruiz-Jaen, M. C., Fröberg, M., Stendahl, J., and Philipson, C. D.: Higher levels of multiple ecosystem services are found in forests with more tree species, Nat. Commun., 4, 1340, https://doi.org/10.1038/ncomms2328, 2013.
Grüneberg, E., Ziche, D., and Wellbrock, N.: Organic carbon stocks and sequestration rates of forest soils in Germany, Glob. Change Biol., 20, 2644–2662, https://doi.org/10.1111/gcb.12558, 2014.
Guenet, B., Moyano, F. E., Vuichard, N., Kirk, G. J. D., Bellamy, P. H., Zaehle, S., and Ciais, P.: Can we model observed soil carbon changes from a dense inventory? A case study over England and Wales using three versions of the ORCHIDEE ecosystem model (AR5, AR5-PRIM and O-CN), Geosci. Model Dev., 6, 2153–2163, https://doi.org/10.5194/gmd-6-2153-2013, 2013.
Härkönen, S., Pulkkinen, M., Duursma, R., and Mäkelä, A.: Estimating annual GPP, NPP and stem growth in Finland using summary models, Forest. Ecol. Manag., 259, 524–533, 2010.
Helmisaari, H., Leppälammi-Kujansuu, J., Sah, S., Bryant, C., and Kleja, D.: Old carbon in young fine roots in boreal forests, Biogeochemistry, 125, 37–46, https://doi.org/10.1007/s10533-015-0110-7, 2015.
Ilvesniemi, H., Levula, J., Ojansuu, R., Kolari, P., Kulmala, L., Pumpanen, J., Launiainen, S., Vesala, T., and Nikinmaa, E.: Long-term measurements of the carbon balance of a boreal Scots pine dominated forest ecosystem, Boreal Environ. Res., 14, 731–753, 2009.
Kleja, D. B., Svensson, M., Majdi, H., Jansson, P., Langvall, O., Bergkvist, B., Johansson, M., Weslien, P., Truusb, L., and Lindroth, A.: Pools and fluxes of carbon in three Norway spruce ecosystems along a climatic gradient in Sweden, Biogeochemistry, 89, 7–25, https://doi.org/10.1007/s10533-007-9136-9, 2008.
Komarov, A. S., Chertov, O. G., Nadporozhskaya, M. A., Mikhailov, A. V., Bykhovets, S. S., Zudin, S. L., Priputina, I. V., Larionova, A. A., Grabarnik, P. Y., Zubkova, E. V., Zudina, E. V., and Shanin, V. N.: Modelirovanie dinamiki organicheskogo veshchestva v lesnykh ekosistemakh (Modelling Organic Matter Dynamics in Forest Ecosystems), Nauka, Moscow, Russia, 2007 (in Russian).
Kumpula, J., Colpaert, A., and Nieminen, M.: Condition, potential recovery rate, and productivity of lichen (Cladonia spp.) ranges in the Finnish reindeer management area, Arctic, 53, 152–160, 2000.
Lehtonen, A., Sievänen, R., Mäkelä, A., Mäkipää, R., Korhonen, K. T., and Hokkanen, T.: Potential litterfall of Scots pine branches in southern Finland, Ecol. Model., 180, 305–315, https://doi.org/10.1016/j.ecolmodel.2004.04.024, 2004.
Lehtonen, A., Palviainen, M., Ojanen, P., Kalliokoski, T., Nöjd, P., Kukkola, M., Penttilä, T., Mäkipää, R., Leppälammi-Kujansuu, J., and Helmisaari, H.: Modelling fine root biomass of boreal tree stands using site and stand variables, Forest. Ecol. Manag., 359, 361–369, https://doi.org/10.1016/j.foreco.2015.06.023, 2016.
Leppälammi-Kujansuu, J., Aro, L., Salemaa, M., Hansson, K., Kleja, D. B., and Helmisaari, H.: Fine root longevity and carbon input into soil from below- and aboveground litter in climatically contrasting forests, Forest. Ecol. Manag., 326, 79–90, https://doi.org/10.1016/j.foreco.2014.03.039, 2014.
Lilja, H. and Nevalainen, R.: Developing a Digital Soil Map for Finland, chap. 5, in: Developments in Soil Science, vol. 31, edited by: Lagacherie, P., McBratney, A. B., and Voltz, M., Elsevier, Amsterdam, the Netherlands, Oxford, UK, 67–603, 2006.
Linkosalo, T., Kolari, P., and Pumpanen, J.: New decomposition rate functions based on volumetric soil water content for the ROMUL soil organic matter dynamics model, Ecol. Model., 263, 109–118, https://doi.org/10.1016/j.ecolmodel.2013.04.026, 2013.
Liski, J., Ilvesniemi, H., Mäkelä, A., and Starr, M.: Effect of soil age, forest fires and harvesting on the storage of organic carbon on podzolizing boreal forest soil, Eur. J. Soil Sci., 49, 407–416, https://doi.org/10.1046/j.1365-2389.1998.4930407.x, 1998.
Liski, J., Palosuo, T., Peltoniemi, M., and Sievänen, R.: Carbon and decomposition model Yasso for forest soils, Ecol. Model., 189, 168–182, https://doi.org/10.1016/j.ecolmodel.2005.03.005, 2005.
Liski, J., Tuomi, M., and Rasinmäki, J.: Yasso07 user-interface manual, Environment Finland, 1–14, available at: http://www.syke.fi/en-us/Research__Development/Research_and_development_projects/Projects/Soil_carbon_model_Yasso/Download (last access: 18 November 2016), 2009.
Mäkipää, R. and Heikkinen, J.: Large-scale changes in abundance of terricolous bryophytes and macrolichens in Finland, J. Veg. Sci., 14, 497–508, https://doi.org/10.1111/j.1654-1103.2003.tb02176.x, 2003.
Mälkönen, E.: Annual primary production and nutrient cycle in a birch stand, Comm. Inst. For. Fenn., 91.5, 1–35, 1977.
Merilä, P., Mustajärvi, K., Helmisaari, H., Hilli, S., Lindroos, A., Nieminen, T. M., Nöjd, P., Rautio, P., Salemaa, M., and Ukonmaanaho, L.: Above-and below-ground N stocks in coniferous boreal forests in Finland: Implications for sustainability of more intensive biomass utilization, Forest. Ecol. Manag., 311, 17–28, https://doi.org/10.1016/j.foreco.2013.06.029, 2014.
Miettinen, A.: Holocene sea-level changes and glacio-isostasy in the Gulf of Finland, Baltic Sea, Quatern. Int., 120, 91–104, https://doi.org/10.1016/j.quaint.2004.01.009, 2004.
Muukkonen, P. and Lehtonen, A.: Needle and branch biomass turnover rates of Norway spruce (Picea abies), Can. J. Forest. Res., 34, 2517–2527, https://doi.org/10.1139/X04-133, 2004.
Ortiz, C., Lundblad, M., Liski, J., Stendahl, J., Karltun, E., Lehtonen, A., and Gärdenäs, A.: Measurements and models – a comparison of quantification methods for SOC changes in forest soils, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden, 2009.
Ortiz, C. A., Liski, J., Gärdenäs, A. I., Lehtonen, A., Lundblad, M., Stendahl, J., Ågren, G. I., and Karltun, E.: Soil organic carbon stock changes in Swedish forest soils – A comparison of uncertainties and their sources through a national inventory and two simulation models, Ecol. Model., 251, 221–231, https://doi.org/10.1016/j.ecolmodel.2012.12.017, 2013.
Parfitt, R., Theng, B., Whitton, J., and Shepherd, T.: Effects of clay minerals and land use on organic matter pools, Geoderma, 75, 1–12, https://doi.org/10.1016/S0016-7061(96)00079-1, 1997.
Parton, W. J., Schimel, D. S., Cole, C. V., and Ojima, D. S.: Division S-3 – Soil microbiology and biochemistry. Analysis of factors controlling soil organic matter levels in great plains grasslands, Soil Sci. Soc. Am. J., 51, 1173–1179, https://doi.org/10.2136/sssaj1987.03615995005100050015x, 1987.
Pebesma, E. J.: Multivariable geostatistics in S: the gstat package, Comput. Geosci., 30, 683–691, https://doi.org/10.1016/j.cageo.2004.03.012, 2004.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., and R Core Team: nlme: Linear and Nonlinear Mixed Effects Models, R package version 3.1-103, available at: http://CRAN.R-project.org/package=nlme (last access: 21 November 2016), 2012.
Pumpanen, J., Ilvesniemi, H., and Hari, P.: A Process-Based Model for Predicting Soil Carbon Dioxide Efflux and Concentration, Soil Sci. Soc. Am. J., 67, 402–413, https://doi.org/10.2136/sssaj2003.4020, 2003.
Rantakari, M., Lehtonen, A., Linkosalo, T., Tuomi, M., Tamminen, P., Heikkinen, J., Liski, J., Mäkipää, R., Ilvesniemi, H., and Sievänen, R.: The Yasso07 soil carbon model – Testing against repeated soil carbon inventory, Forest. Ecol. Manag., 286, 137–147, https://doi.org/10.1016/j.foreco.2012.08.041, 2012.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2014.
Repola, J.: Biomass equations for birch in Finland, Silva Fenn., 42, 605–624, https://doi.org/10.14214/sf.236, 2008.
Repola, J.: Biomass equations for Scots pine and Norway spruce in Finland, Silva Fenn., 43, 625–647, https://doi.org/10.14214/sf.184, 2009.
Salemaa, M., Derome, J., and Nöjd, P.: Response of boreal forest vegetation to the fertility status of the organic layer along a climatic gradient, Boreal Environ. Res., 13, 48–66, 2008.
Schimel, D. S., Braswell, B. H., Holland, E. A., McKeown, R., Ojima, D. S., Painter, T. H., Parton, W. J., and Townsend, A. R.: Climatic, edaphic, and biotic controls over storage and turnover of carbon in soils, Global Biogeochem. Cy., 8, 279–293, https://doi.org/10.1029/94GB00993, 1994.
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S., and Janssens, I.: Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture, J. Adv. Model. Earth Sy., 7, 335–356, https://doi.org/10.1002/2014MS000358, 2015.
Six, J., Conant, R., Paul, E. A., and Paustian, K.: Stabilization mechanisms of soil organic matter: implications for C-saturation of soils, Plant Soil, 241, 155–176, https://doi.org/10.1023/A:1016125726789, 2002.
Skopp, J., Jawson, M. D., and Doran, J. W.: Steady-State Aerobic Microbial Activity as a Function of Soil Water Content, Soil Sci. Soc. Am. J., 54, 1619–1625, https://doi.org/10.2136/sssaj1990.03615995005400060018x, 1990.
Sohlenius, G., Sternbeck, J., Andrén, E., and Westman, P.: Holocene history of the Baltic Sea as recorded in a sediment core from the Gotland Deep, Mar. Geol., 134, 183–201, https://doi.org/10.1016/0025-3227(96)00047-3, 1996.
Statistics Finland: Greenhouse Gas Emissions in Finland 1990–2013, National Inventory Report under the UNFCCC and the Kyoto protocol, Helsinki, Finland, 2014.
Thum, T., Räisänen, P., Sevanto, S., Tuomi, M., Reick, C., Vesala, T., Raddatz, T., Aalto, T., Järvinen, H., and Altimir, N.: Soil carbon model alternatives for ECHAM5/JSBACH climate model: evaluation and impacts on global carbon cycle estimates, J. Geophys. Res., 116, G02028, https://doi.org/10.1029/2010JG001612, 2011.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
Tomppo, E., Haakana, M., Katila, M., and Peräsaari, J.: Multi-source national forest inventory – Methods and applications, Springer, Berlin, Germany, 2008.
Tomppo, E., Heikkinen, J., Henttonen, H. M., Ihalainen, A., Katila, M., Mäkelä, H., Tuomainen, T., and Vainikainen, N.: Designing and conducting a forest inventory – case: 9th National Forest Inventory of Finland, Springer, Dordrecht, Netherlands, 2011.
Tuomi, M., Rasinmäki, J., Repo, A., Vanhala, P., and Liski, J.: Soil carbon model Yasso07 graphical user interface, Environ. Modell. Softw., 26, 1358–1362, https://doi.org/10.1016/j.envsoft.2011.05.009, 2011.
Tupek, B., Mäkipää, R., Heikkinen, J., Peltoniemi, M., Ukonmaanaho, L., Hokkanen, T., Nöjd, P., Nevalainen, S., Lindgren, M., and Lehtonen, A.: Foliar turnover rates in Finland – comparing estimates from needle-cohort and litterfall-biomass methods, Boreal Env. Res., 20, 283–304, 2015.
Ťupek, B., Ortiz, C. A., Hashimoto, S., Stendahl, J., Dahlgren, J., Karltun, E., and Lehtonen, A.: Underestimation of boreal soil carbon stocks by mathematical soil carbon models linked to soil nutrient status, Biogeosciences, 13, 4439–4459, https://doi.org/10.5194/bg-13-4439-2016, 2016.
Venäläinen, A., Tuomenvirta, H., Pirinen, P., and Drebs, A.: A basic Finnish climate data set 1961–2000 – Description and illustrations, Finnish Meteorological Institute, Helsinki, Finland, 27 pp., 2005.
Viro, P. J.: Kivisyyden määrittämisestä, Summary: On the determination of stoniness, Comm. Inst. For. Fenn., 40, 1–23, 1952.
Viro, P. J.: Investigations on forest litter, Comm. Inst. For. Fenn., 45.6, 1–142, 1955.
Ylitalo, E.: Metsätilastollinen vuosikirja 2013, Skogsstatistisk årsbok – Finnish statistical yearbook of forestry, Vantaa: Official Statistics of Finland, Agriculture, Forestry and Fishery, Finnish Forest Research Institute, Vantaa, Finland, 2013.
Yuan, W., Liu, S., Dong, W., Liang, S., Zhao, S., Chen, J., Xu, W., Li, X., Barr, A., Andrew Black, T., Yan, W., Goulden, M. L., Kulmala, L., Lindroth, A., Margolis, H. A., Matsuura, Y., Moors, E., van der Molen, M., Ohta, T., Pilegaard, K., Varlagin, A., and Vesala, T.: Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome, Nat. Commun., 5, 4270, https://doi.org/10.1038/ncomms5270, 2014.
Short summary
It is known that Earth system models have challenges to predict correct levels of soil carbon stocks. Quantification of those stocks is a prerequisite for reliable prediction of future carbon exchange between biosphere and atmosphere. Here, we tested Yasso07 and ROMULv soil carbon models against empirical data from Finland. We found that both the role of understorey vegetation and the impact of drought to decomposition should be incorporated into soil models to have realistic soil carbon stocks.
It is known that Earth system models have challenges to predict correct levels of soil carbon...