Articles | Volume 11, issue 12
https://doi.org/10.5194/gmd-11-5051-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-5051-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
11 Dec 2018
Development and technical paper |
| 11 Dec 2018
| 11 Dec 2018
Evaluation of iterative Kalman smoother schemes for multi-decadal past climate analysis with comprehensive Earth system models
Javier García-Pintado
CORRESPONDING AUTHOR
MARUM – Center for Marine environmental Sciences and Department of
Geosciences, University of Bremen, Bremen, Germany
André Paul
MARUM – Center for Marine environmental Sciences and Department of
Geosciences, University of Bremen, Bremen, Germany
Related authors
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
Short summary
Short summary
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Charlotte Breitkreuz, André Paul, Stefan Mulitza, Javier García-Pintado, and Michael Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-32, https://doi.org/10.5194/gmd-2019-32, 2019
Publication in GMD not foreseen
Short summary
Short summary
We present a technique for ocean state estimation based on the combination of a simple data assimilation method with a state reduction approach. The technique proves to be very efficient and successful in reducing the model-data misfit and reconstructing a target ocean circulation from synthetic observations. In an application to Last Glacial Maximum proxy data the model-data misfit is greatly reduced but some misfit remains. Two different ocean states are found with similar model-data misfit.
Nils Weitzel, Heather Andres, Jean-Philippe Baudouin, Marie-Luise Kapsch, Uwe Mikolajewicz, Lukas Jonkers, Oliver Bothe, Elisa Ziegler, Thomas Kleinen, André Paul, and Kira Rehfeld
Clim. Past, 20, 865–890, https://doi.org/10.5194/cp-20-865-2024, https://doi.org/10.5194/cp-20-865-2024, 2024
Short summary
Short summary
The ability of climate models to faithfully reproduce past warming episodes is a valuable test considering potentially large future warming. We develop a new method to compare simulations of the last deglaciation with temperature reconstructions. We find that reconstructions differ more between regions than simulations, potentially due to deficiencies in the simulation design, models, or reconstructions. Our work is a promising step towards benchmarking simulations of past climate transitions.
Alexandre Cauquoin, Ayako Abe-Ouchi, Takashi Obase, Wing-Le Chan, André Paul, and Martin Werner
Clim. Past, 19, 1275–1294, https://doi.org/10.5194/cp-19-1275-2023, https://doi.org/10.5194/cp-19-1275-2023, 2023
Short summary
Short summary
Stable water isotopes are tracers of climate processes occurring in the hydrological cycle. They are widely used to reconstruct the past variations of polar temperature before the instrumental era thanks to their measurements in ice cores. However, the relationship between measured isotopes and temperature has large uncertainties. In our study, we investigate how the sea surface conditions (temperature, sea ice, ocean circulation) impact this relationship for a cold to warm climate change.
Takasumi Kurahashi-Nakamura, André Paul, Ute Merkel, and Michael Schulz
Clim. Past, 18, 1997–2019, https://doi.org/10.5194/cp-18-1997-2022, https://doi.org/10.5194/cp-18-1997-2022, 2022
Short summary
Short summary
With a comprehensive Earth-system model including the global carbon cycle, we simulated the climate state during the last glacial maximum. We demonstrated that the CO2 concentration in the atmosphere both in the modern (pre-industrial) age (~280 ppm) and in the glacial age (~190 ppm) can be reproduced by the model with a common configuration by giving reasonable model forcing and total ocean inventories of carbon and other biogeochemical matter for the respective ages.
Kaveh Purkiani, Matthias Haeckel, Sabine Haalboom, Katja Schmidt, Peter Urban, Iason-Zois Gazis, Henko de Stigter, André Paul, Maren Walter, and Annemiek Vink
Ocean Sci., 18, 1163–1181, https://doi.org/10.5194/os-18-1163-2022, https://doi.org/10.5194/os-18-1163-2022, 2022
Short summary
Short summary
Based on altimetry data and in situ hydrographic observations, the impacts of an anticyclone mesoscale eddy (large rotating body of water) on the seawater characteristics were investigated during a research campaign. The particular eddy presents significant anomalies on the seawater properties at 1500 m. The potential role of eddies in the seafloor and its consequential effect on the altered dispersion of mining-related sediment plumes are important to assess future mining operations.
Stefan Mulitza, Torsten Bickert, Helen C. Bostock, Cristiano M. Chiessi, Barbara Donner, Aline Govin, Naomi Harada, Enqing Huang, Heather Johnstone, Henning Kuhnert, Michael Langner, Frank Lamy, Lester Lembke-Jene, Lorraine Lisiecki, Jean Lynch-Stieglitz, Lars Max, Mahyar Mohtadi, Gesine Mollenhauer, Juan Muglia, Dirk Nürnberg, André Paul, Carsten Rühlemann, Janne Repschläger, Rajeev Saraswat, Andreas Schmittner, Elisabeth L. Sikes, Robert F. Spielhagen, and Ralf Tiedemann
Earth Syst. Sci. Data, 14, 2553–2611, https://doi.org/10.5194/essd-14-2553-2022, https://doi.org/10.5194/essd-14-2553-2022, 2022
Short summary
Short summary
Stable isotope ratios of foraminiferal shells from deep-sea sediments preserve key information on the variability of ocean circulation and ice volume. We present the first global atlas of harmonized raw downcore oxygen and carbon isotope ratios of various planktonic and benthic foraminiferal species. The atlas is a foundation for the analyses of the history of Earth system components, for finding future coring sites, and for teaching marine stratigraphy and paleoceanography.
Masa Kageyama, Sandy P. Harrison, Marie-L. Kapsch, Marcus Lofverstrom, Juan M. Lora, Uwe Mikolajewicz, Sam Sherriff-Tadano, Tristan Vadsaria, Ayako Abe-Ouchi, Nathaelle Bouttes, Deepak Chandan, Lauren J. Gregoire, Ruza F. Ivanovic, Kenji Izumi, Allegra N. LeGrande, Fanny Lhardy, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, André Paul, W. Richard Peltier, Christopher J. Poulsen, Aurélien Quiquet, Didier M. Roche, Xiaoxu Shi, Jessica E. Tierney, Paul J. Valdes, Evgeny Volodin, and Jiang Zhu
Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, https://doi.org/10.5194/cp-17-1065-2021, 2021
Short summary
Short summary
The Last Glacial Maximum (LGM; ~21 000 years ago) is a major focus for evaluating how well climate models simulate climate changes as large as those expected in the future. Here, we compare the latest climate model (CMIP6-PMIP4) to the previous one (CMIP5-PMIP3) and to reconstructions. Large-scale climate features (e.g. land–sea contrast, polar amplification) are well captured by all models, while regional changes (e.g. winter extratropical cooling, precipitations) are still poorly represented.
André Paul, Stefan Mulitza, Rüdiger Stein, and Martin Werner
Clim. Past, 17, 805–824, https://doi.org/10.5194/cp-17-805-2021, https://doi.org/10.5194/cp-17-805-2021, 2021
Short summary
Short summary
Maps and fields of near-sea-surface temperature differences between the past and present can be used to visualize and quantify climate changes and perform simulations with climate models. We used a statistical method to map sparse and scattered data for the Last Glacial Maximum time period (23 000 to 19 000 years before present) to a regular grid. The estimated global and tropical cooling would imply an equilibrium climate sensitivity in the lower to middle part of the currently accepted range.
Kaveh Purkiani, André Paul, Annemiek Vink, Maren Walter, Michael Schulz, and Matthias Haeckel
Biogeosciences, 17, 6527–6544, https://doi.org/10.5194/bg-17-6527-2020, https://doi.org/10.5194/bg-17-6527-2020, 2020
Short summary
Short summary
There has been a steady increase in interest in mining of deep-sea minerals in the eastern Pacific Ocean recently. The ocean state in this region is known to be highly influenced by rotating bodies of water (eddies), some of which can travel long distances in the ocean and impact the deeper layers of the ocean. Better insight into the variability of eddy activity in this region is of great help to mitigate the impact of the benthic ecosystem from future potential deep-sea mining activity.
Takasumi Kurahashi-Nakamura, André Paul, Guy Munhoven, Ute Merkel, and Michael Schulz
Geosci. Model Dev., 13, 825–840, https://doi.org/10.5194/gmd-13-825-2020, https://doi.org/10.5194/gmd-13-825-2020, 2020
Short summary
Short summary
Chemical processes in ocean-floor sediments have a large influence on the marine carbon cycle, hence the global climate, at long timescales. We developed a new coupling scheme for a chemical sediment model and a comprehensive climate model. The new coupled model outperformed the original uncoupled climate model in reproducing the global distribution of sediment properties. The sediment model will also act as a
bridgebetween the ocean model and paleoceanographic data.
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
Short summary
Short summary
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Charlotte Breitkreuz, André Paul, and Michael Schulz
Clim. Past Discuss., https://doi.org/10.5194/cp-2019-52, https://doi.org/10.5194/cp-2019-52, 2019
Publication in CP not foreseen
Short summary
Short summary
We combined a model simulation of the Last Glacial Maximum ocean with sea surface temperature and calcite oxygen isotope data through data assimilation. The reconstructed ocean state is very similar to the modern and it follows that the employed proxy data do not require an ocean state very different from today's. Sensitivity experiments reveal that data from the deep North Atlantic but also from the global deep Southern Ocean are most important to constrain the Atlantic overturning circulation.
Charlotte Breitkreuz, André Paul, Stefan Mulitza, Javier García-Pintado, and Michael Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-32, https://doi.org/10.5194/gmd-2019-32, 2019
Publication in GMD not foreseen
Short summary
Short summary
We present a technique for ocean state estimation based on the combination of a simple data assimilation method with a state reduction approach. The technique proves to be very efficient and successful in reducing the model-data misfit and reconstructing a target ocean circulation from synthetic observations. In an application to Last Glacial Maximum proxy data the model-data misfit is greatly reduced but some misfit remains. Two different ocean states are found with similar model-data misfit.
Rike Völpel, André Paul, Annegret Krandick, Stefan Mulitza, and Michael Schulz
Geosci. Model Dev., 10, 3125–3144, https://doi.org/10.5194/gmd-10-3125-2017, https://doi.org/10.5194/gmd-10-3125-2017, 2017
Short summary
Short summary
This study presents the implementation of stable water isotopes in the MITgcm and describes the results of an equilibrium simulation under pre-industrial conditions. The model compares well to observational data and measurements of plankton tow records and thus opens wide prospects for long-term simulations in a paleoclimatic context.
T. Kurahashi-Nakamura, M. Losch, and A. Paul
Geosci. Model Dev., 7, 419–432, https://doi.org/10.5194/gmd-7-419-2014, https://doi.org/10.5194/gmd-7-419-2014, 2014
D. Handiani, A. Paul, M. Prange, U. Merkel, L. Dupont, and X. Zhang
Clim. Past, 9, 1683–1696, https://doi.org/10.5194/cp-9-1683-2013, https://doi.org/10.5194/cp-9-1683-2013, 2013
J. C. Hargreaves, J. D. Annan, R. Ohgaito, A. Paul, and A. Abe-Ouchi
Clim. Past, 9, 811–823, https://doi.org/10.5194/cp-9-811-2013, https://doi.org/10.5194/cp-9-811-2013, 2013
Related subject area
Climate and Earth system modeling
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1
A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
Dynamic MJO forecasts using an ensemble subseasonal-to-seasonal forecast system of IAP-CAS model
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
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)
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
Short summary
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
Short summary
Short summary
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
Short summary
Short summary
All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
Short summary
Short summary
We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
Short summary
Short summary
In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
Short summary
Short summary
Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
Short summary
Short summary
Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
Short summary
Short summary
In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
Short summary
Short summary
We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Short summary
We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1, https://doi.org/10.5194/egusphere-2024-1, 2024
Short summary
Short summary
This article gives an overview introduction of the IAP-CAS S2S (sub-seasonal to seasonal) ensemble forecasting system and MJO forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its advantages but also exhibits some biases, including underdispersive ensemble, overestimated amplitude and faster propagation speed when forecasting MJO. We also provide the explanation towards these biases and prospects for further improvement of this system in the future.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
Short summary
Short summary
Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
Short summary
Short summary
Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
EGUsphere, https://doi.org/10.5194/egusphere-2024-335, https://doi.org/10.5194/egusphere-2024-335, 2024
Short summary
Short summary
Large volcanic eruptions deposit material into the upper-atmosphere, which is capable of altering temperature and wind patterns of the Earth's atmosphere for years following. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a testbed for climate attribution studies.
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.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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 timescales, 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.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
Short summary
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
Short summary
Short summary
A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
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.
Cited articles
Acevedo, W., Fallah, B., Reich, S., and Cubasch, U.: Assimilation of
pseudo-tree-ring-width observations into an atmospheric general circulation
model, Clim. Past, 13, 545–557, https://doi.org/10.5194/cp-13-545-2017,
2017. a, b
Annan, J. D., Hargreaves, J. C., Edwards, N. R., and R, M.: Parameter
estimation in an intermediate complexity Earth System Model using an
ensemble Kalman filter, Ocean Modell., 8, 135–154,
https://doi.org/10.1016/j.ocemod.2003.12.004, 2005a. a, b
Arakawa, A.: The cumulus parameterization problem: Past, present, and future,
J. Climate, 17, 2493–2525,
https://doi.org/10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2, 2004. a
Arakawa, A., Jung, J.-H., and Wu, C.-M.: Toward unification of the multiscale
modeling of the atmosphere, Atmos. Chem. Phys., 11, 3731–3742,
https://doi.org/10.5194/acp-11-3731-2011, 2011. a
Bannister, R. N.: A review of operational methods of variational and
ensemble-variational data assimilation, Q. J. Roy.
Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. a, b
Béal, D., Brasseur, P., Brankart, J.-M., Ourmières, Y., and Verron,
J.: Characterization of mixing errors in a coupled physical biogeochemical
model of the North Atlantic:
implications for nonlinear estimation using Gaussian anamorphosis, Ocean Sci., 6,
247–262, https://doi.org/10.5194/os-6-247-2010, 2010. a, b
Bell, B. M.: The Iterated Kalman Smoother as a Gauss–Newton Method, SIAM
J. Optimiz., 4, 626–636, https://doi.org/10.1137/0804035, 1994. a, b, c
Bell, B. M. and Cathey, F. W.: The iterated Kalman filter update as a
Gauss-Newton method, IEEE T. Automat. Contr., 38, 294–297,
https://doi.org/10.1109/9.250476, 1993. a
Bertino, L., Evensen, G., and Wackernagel, H.: Sequential Data Assimilation
Techniques in Oceanography, Int. Stat. Rev., 71, 223–241,
https://doi.org/10.1111/j.1751-5823.2003.tb00194.x, 2003. a, b
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the
Ensemble Transform Kalman Filter. Part I: Theoretical aspects, Mon.
Weather Rev., 129, 420–436,
https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an
iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818,
https://doi.org/10.5194/npg-20-803-2013, 2013. a, b
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q.
J. Roy. Meteor. Soc., 140, 1521–1535,
https://doi.org/10.1002/qj.2236, 2014. a, b, c
Chen, Y. and Oliver, D. S.: Ensemble Randomized Maximum Likelihood Method as an
Iterative Ensemble Smoother, Math. Geosci., 44, 1–26,
https://doi.org/10.1007/s11004-011-9376-z, 2012. a
Chìles, J.-P. and Delfiner, P.: Geostatistics: Modeling spatial uncertainty,
2nd edition, John Wiley & Sons, Ltd., 2012. a
Christiansen, B. and Ljungqvist, F. C.: Challenges and perspectives for
large-scale temperature reconstructions of the past two millennia, Rev.
Geophys., 50, 40–96, https://doi.org/10.1002/2016RG000521, 2017. a
Chuang, C. C., Kelly, J. T., Boyle, J. S., and Xie, S.: Sensitivity of aerosol
indirect effects to cloud nucleation and autoconversion parameterizations in
short-range weather forecasts during the May 2003 aerosol IOP, J.
Adv. Model. Earth Sy., 4, m09001, https://doi.org/10.1029/2012MS000161,
2012. a
Cohn, S. E.: An Introduction to Estimation Theory (Special Issue, Data
Assimilation in Meteology and Oceanography: Theory and Practice), J.
Meteorol. Soc. Jpn., 75, 257–288,
https://doi.org/10.2151/jmsj1965.75.1B_257, 1997. a
Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for
operational implementation of 4D-Var, using an incremental approach,
Q. J. Roy. Meteor. Soc., 120, 1367–1387,
https://doi.org/10.1002/qj.49712051912, 1994. a
Covey, C., Lucas, D. D., Tannahill, J., Garaizar, X., and Klein, R.: Efficient
screening of climate model sensitivity to a large number of perturbed input
parameters, J. Adv. Model. Earth Sy., 5, 598–610,
https://doi.org/10.1002/jame.20040, 2013. a
Dail, H. and Wunsch, C.: Dynamical Reconstruction of Upper-Ocean Conditions in
the Last Glacial Maximum Atlantic, J. Climate, 27, 807–823,
https://doi.org/10.1175/JCLI-D-13-00211.1, 2014. a
Dee, S. G., Steiger, N. J., Emile-Geay, J., and Hakim, G. J.: On the utility of
proxy system models for estimating climate states over the common era,
J. Adv. Model. Earth Sy., 8, 1164–1179,
https://doi.org/10.1002/2016MS000677, 2016. a
Delworth, T. L., Manabe, S., and Stouffer, R. J.: Multidecadal climate
variability in the Greenland Sea and surrounding regions: A coupled model
simulation, Geophys. Res. Lett., 24, 257–260,
https://doi.org/10.1029/96GL03927, 1997. a, b
Dennis, Jr., J. E. and Schnabel, R. B.: Numerical Methods for Unconstrained
Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16),
Soc for Industrial & Applied Math, 1996. a
Deutsch, C. V. and Journel, A. G.: GSLIB: Geostatistical Software Library and
User's Guide, Oxford UP, NY, 1998. a
Dirren, S. and Hakim, G. J.: Toward the assimilation of time-averaged
observations, Geophys. Res. Lett., 32, L04804, https://doi.org/10.1029/2004GL021444,
2005. a
Dommenget, D. and Rezny, M.: A Caveat Note on Tuning in the Development of
Coupled Climate Models, J. Adv. Model. Earth Sy., 10, 78–97,
https://doi.org/10.1002/2017MS000947, 2017. a
Doron, M., Brasseur, P., and Brankart, J.-M.: Stochastic estimation of
biogeochemical parameters of a 3D ocean coupled physical-biogeochemical
model: Twin experiments, J. Marine Syst., 87, 194–207,
https://doi.org/10.1016/j.jmarsys.2011.04.001, 2011. a
Dubinkina, S., Goosse, H., Sallaz-Damaz, Y., Crespin, E., and Crucifix, M.:
Testing a particle filter to reconstruct climate changes over the past
centuries, Int. J. Bifurcat. Chaos, 21, 3611–3618,
https://doi.org/10.1142/S0218127411030763, 2011. a
Emerick, A. A. and Reynolds, A. C.: Ensemble smoother with multiple data
assimilation, Comput. Geosci., 55, 3–15,
https://doi.org/10.1016/j.cageo.2012.03.011, 2013. a
Evans, M., Tolwinski-Ward, S., Thompson, D., and Anchukaitis, K.: Applications
of proxy system modeling in high resolution paleoclimatology, Quaternary
Sci. Rev., 76, 16–28, https://doi.org/10.1016/j.quascirev.2013.05.024, 2013. a
Evensen, G.: Inverse methods and data assimilation in nonlinear ocean models,
Physica D, 77, 108–129,
https://doi.org/10.1016/0167-2789(94)90130-9, 1994. a, b
Friedland, B.: Treatment of bias in recursive filtering,
IEEE T. Automat. Contr., 14, 359–367, https://doi.org/10.1109/TAC.1969.1099223, 1969. a
Gao, G. and Reynolds, A. C.: An Improved Implementation of the LBFGS Algorithm
for Automatic History Matching, SPE J., 11, 5–17,
https://doi.org/10.2118/90058-PA, 2006. a, b
García-Pintado, J.: rDAF v1.0.0: R data assimilation framework,
https://doi.org/10.5281/zenodo.1489131,
2018a. a, b, c
García-Pintado, J.: rdafEbm1D v1.00: rDAF interface for Ebm1D,
https://doi.org/10.5281/zenodo.1489133, 2018b. a, b
García-Pintado, J.: rdafCESM v1.0.0: rDAF interface for CESM,
https://doi.org/10.5281/zenodo.1489135, 2018c. a, b
García-Pintado, J., Neal, J. C., Mason, D. C., Dance, S. L., and Bates,
P. D.: Scheduling satellite-based SAR acquisition for sequential assimilation
of water level observations into flood modelling, J. Hydrol., 495,
252–266, https://doi.org/10.1016/j.jhydrol.2013.03.050, 2013. a
Gent, P. R. and McWilliams, J. C.: Isopycnal Mixing in Ocean Circulation
Models, J. Phys. Oceanogr., 20, 150–155,
https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a, b
Giering, R. and Kaminski, T.: Recipes for Adjoint Code Construction, ACM T.
Math. Software, 24, 437–474, https://doi.org/10.1145/293686.293695, 1998. a
Giering, R., Kaminski, T., and Slawig, T.: Generating Efficient Derivative Code
with TAF, Future Gener. Comp. Sy., 21, 1345–1355,
https://doi.org/10.1016/j.future.2004.11.003, 2005. a
Gilbert, J. C. and Lemaréchal, C.: Some numerical experiments with
variable-storage quasi-Newton algorithms, Math. Program., 45,
407–435, https://doi.org/10.1007/BF01589113, 1989. a, b
Goosse, H.: An additional step toward comprehensive paleoclimate reanalyses,
J. Adv. Model. Earth Sy., 8, 1501–1503,
https://doi.org/10.1002/2016MS000739, 2016. a
Gregory, J. M. and Tailleux, R.: Kinetic energy analysis of the response of the
Atlantic meridional overturning circulation to CO2-forced climate change,
Clim. Dynam., 37, 893–914, https://doi.org/10.1007/s00382-010-0847-6, 2011. a
Gu, Y. and Oliver, D. S.: An Iterative Ensemble Kalman Filter for Multiphase
Fluid Flow Data Assimilation, Society of Petroleum Engineers, 12, 438–446,
https://doi.org/10.2118/108438-PA, 2007. a
Hack, J. J.: Parameterization of moist convection in the National Center for
Atmospheric Research community climate model (CCM2), J. Geophys.
Res.-Atmos., 99, 5551–5568, https://doi.org/10.1029/93JD03478, 1994. a
Hakim, G. J., Emile-Geay, J., Steig, E. J., Noone, D., Anderson, D. M., Tardif,
R., Steiger, N., and Perkins, W. A.: The last millennium climate reanalysis
project: Framework and first results, J. Geophys. Res.-Atmos., 121, 6745–6764,
https://doi.org/10.1002/2016JD024751, 2016JD024751, 2016. a
Hargreaves, J. and Annan, J.: Assimilation of paleo-data in a simple Earth
system model, Clim. Dynam., 19, 371–381,
https://doi.org/10.1007/s00382-002-0241-0, 2002. a
Hargreaves, J. C., Paul, A., Ohgaito, R., Abe-Ouchi, A., and Annan, J. D.:
Are paleoclimate model ensembles consistent with the MARGO data synthesis?,
Clim. Past, 7, 917–933, https://doi.org/10.5194/cp-7-917-2011, 2011. a
Hartmann, D. L.: Global physical climatology, Academic Press, San Diego, 1994. a
Hartmann, D. L. and Short, D. A.: On the Role of Zonal Asymmetries in Climate
Change, J. Atmos. Sci., 36, 519–528,
https://doi.org/10.1175/1520-0469(1979)036<0519:OTROZA>2.0.CO;2, 1979. a
Holland, M. M., Blanchard-Wrigglesworth, E., Kay, J., and Vavrus, S.:
Initial-value predictability of Antarctic sea ice in the Community Climate
System Model 3, Geophys. Res. Lett., 40, 2121–2124,
https://doi.org/10.1002/grl.50410, 2013. a
Ide, K., Courtier, P., Ghill, M, and Lorenc, A. C.: Unified notation for Data
Assimilation: operational, sequential and variational, J. Meteorol. Soc.
Jpn., 75, 181–189, 1997. a
Janjić, T., Bormann, N., Bocquet, M., Carton, J. A., Cohn, S. E., Dance,
S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A., and Weston,
P.: On the representation error in data assimilation, Q. J.
Roy. Meteor. Soc., 144, 1257–1278, https://doi.org/10.1002/qj.3130,
2017. a
Jungclaus, J. H., Bard, E., Baroni, M., Braconnot, P., Cao, J., Chini, L. P.,
Egorova, T., Evans, M., González-Rouco, J. F., Goosse, H., Hurtt, G. C.,
Joos, F., Kaplan, J. O., Khodri, M., Klein Goldewijk, K., Krivova, N.,
LeGrande, A. N., Lorenz, S. J., Luterbacher, J., Man, W., Maycock, A. C.,
Meinshausen, M., Moberg, A., Muscheler, R., Nehrbass-Ahles, C.,
Otto-Bliesner, B. I., Phipps, S. J., Pongratz, J., Rozanov, E., Schmidt, G.
A., Schmidt, H., Schmutz, W., Schurer, A., Shapiro, A. I., Sigl, M., Smerdon,
J. E., Solanki, S. K., Timmreck, C., Toohey, M., Usoskin, I. G., Wagner, S.,
Wu, C.-J., Yeo, K. L., Zanchettin, D., Zhang, Q., and Zorita, E.: The PMIP4
contribution to CMIP6 – Part 3: The last millennium, scientific objective,
and experimental design for the PMIP4 past1000 simulations, Geosci. Model
Dev., 10, 4005–4033, https://doi.org/10.5194/gmd-10-4005-2017, 2017. a
Kageyama, M., Laîné, A., Abe-Ouchi, A., Braconnot, P., Cortijo, E.,
Crucifix,
M., de Vernal, A., Guiot, J., Hewitt, C., Kitoh, A., Kucera, M., Marti, O.,
Ohgaito, R., Otto-Bliesner, B., Peltier, W., Rosell-Melé, A., Vettoretti,
G., Weber, S., and Yu, Y.: Last Glacial Maximum temperatures over the North
Atlantic, Europe and western Siberia: a comparison between PMIP models, MARGO
sea-surface temperatures and pollen-based reconstructions, Quaternary
Sci. Rev., 25, 2082–2102,
https://doi.org/10.1016/j.quascirev.2006.02.010, 2006. a
Kageyama, M., Braconnot, P., Harrison, S. P., Haywood, A. M., Jungclaus, J.
H., Otto-Bliesner, B. L., Peterschmitt, J.-Y., Abe-Ouchi, A., Albani, S.,
Bartlein, P. J., Brierley, C., Crucifix, M., Dolan, A., Fernandez-Donado, L.,
Fischer, H., Hopcroft, P. O., Ivanovic, R. F., Lambert, F., Lunt, D. J.,
Mahowald, N. M., Peltier, W. R., Phipps, S. J., Roche, D. M., Schmidt, G. A.,
Tarasov, L., Valdes, P. J., Zhang, Q., and Zhou, T.: The PMIP4 contribution
to CMIP6 – Part 1: Overview and over-arching analysis plan, Geosci. Model
Dev., 11, 1033–1057, https://doi.org/10.5194/gmd-11-1033-2018, 2018. a, b
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A.,
Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo,
K. C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteor. Soc.,
77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a
Klein, F. and Goosse, H.: Reconstructing East African rainfall and Indian Ocean
sea surface temperatures over the last centuries using data assimilation,
Clim. Dynam., 50, 3909–3929, https://doi.org/10.1007/s00382-017-3853-0, 2017. a
Köhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F., and Fischer,
H.: A 156 kyr smoothed history of the atmospheric greenhouse gases
CO2, CH4, and N2O and their radiative forcing,
Earth Syst. Sci. Data, 9, 363–387, https://doi.org/10.5194/essd-9-363-2017,
2017. a, b
Kurahashi-Nakamura, T., Paul, A., and Losch, M.: Dynamical reconstruction of
the global ocean state during the Last Glacial Maximum, Paleoceanography, 32, 326–350,
https://doi.org/10.1002/2016PA003001, 2017. a, b
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A
review and a model with a nonlocal boundary layer parameterization, Rev.
Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994. a
Lawless, A. S.: Variational data assimilation for very large environmental
problems, in: Large Scale Inverse Problems, in: Radon series on computational and applied mathematics,
edited by: Cullen, M., Freitag,
M. A., Kindermann, S., and Scheichl, R., De Gruyter, Berlin, 13, 55–90, 2013. a
Lawless, A. S., Gratton, S., and Nichols, N. K.: An investigation of
incremental 4D-Var using non-tangent linear models, Q. J.
Roy. Meteor. Soc., 131, 459–476, https://doi.org/10.1256/qj.04.20, 2005. a, b
Liu, C., Xiao, Q., and Wang, B.: An Ensemble-Based Four-Dimensional Variational
Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test,
Mon. Weather Rev., 136, 3363–3373, https://doi.org/10.1175/2008MWR2312.1, 2008. a
Livings, D. M., Dance, S. L., and Nichols, N. K.: Unbiased Ensemble Square Root
Filters, Physica D, 237, 1021–1028,
https://doi.org/10.1016/j.physd.2008.01.005, 2008. a
Lorenc, A. C.: Analysis methods for numerical weather prediction, Q.
J. Roy. Meteor. Soc., 112, 1177–1194,
https://doi.org/10.1002/qj.49711247414, 1986. a, b
Lorenc, A. C.: Recommended nomenclature for EnVar data assimilation methods,
in: WGNE Blue Book Research Activities in Atmospheric and Oceanic Modelling,
section 01: 7–8, WMO: Geneva, Switzerland, 2013. a
Marchal, O., Waelbroeck, C., and de Verdière, A. C.: On the Movements of the
North Atlantic Subpolar Front in the Preinstrumental Past, J.
Climate, 29, 1545–1571, https://doi.org/10.1175/JCLI-D-15-0509.1, 2016. a
Marchi, S., Fichefet, T., Goosse, H., Zunz, V., Tietsche, S., Day, J. J., and
Hawkins, E.: Reemergence of Antarctic sea ice predictability and its link to
deep ocean mixing in global climate models, Clim. Dynam.,
https://doi.org/10.1007/s00382-018-4292-2, online first, 2018. a
MARGO Project Members: Constraints on the magnitude and patterns of ocean
cooling at the Last Glacial Maximum, Nat. Geosci., 2, 127–132,
https://doi.org/10.1038/ngeo411, 2009. a, b, c, d
Matheron, G.: Le krigeage disjunctive, Intern. Note N-360, Centre de
Géostatistique, Ecole des Mines de Paris, Paris, France, 40 pp., 1973. a
Meehl, G. A., Arblaster, J. M., Bitz, C. M., Chung, C. T. Y., and Teng, H.:
Antarctic sea-ice expansion between 2000 and 2014 driven by tropical Pacific
decadal climate variability, Nat. Geosci., 9, 590–596,
https://doi.org/10.1038/ngeo2751, 2016. a
Neale, R. B., Richter, R., Conley, A., Park, S., Lauritzen, P., Gettelman,
A., Williamson, D., Rash, P., Vavrus, S., Taylor, M., Collins, W., Zhang, M.,
and Lin, S.-J.: Description of the NCAR Community Atmosphere Model
(CAM4), Tech. Rep. NCAR/TN-485+STR, NCAR, 2011. a
North, G. R., Mengel, J. G., and Short, D. A.: Simple energy balance model
resolving the seasons and the continents: Application to the astronomical
theory of the ice ages, J. Geophys. Res.-Oceans, 88,
6576–6586, https://doi.org/10.1029/JC088iC11p06576, 1983. a, b
Oliver, D. S. and Chen, Y.: Improved initial sampling for the ensemble Kalman
filter, Comput. Geosci., 13, 13–27, https://doi.org/10.1007/s10596-008-9101-2,
2008. a
Ortega, P., Lehner, F., Swingedouw, D., Masson-Delmotte, V., Raible, C. C.,
Casado, M., and Yiou, P.: A model-tested North Atlantic Oscillation
reconstruction for the past millennium, Nature, 523, 71–74,
https://doi.org/10.1038/nature14518, 2015. a
Ott, E., Hunt, B., Szunyogh, I., Zimin, A., Kostelich, E., Corazza, M., Kalnay,
E., Patil, D., and Yorke, J.: A local ensemble Kalman filter for
atmospheric data assimilation, Tellus A, 56, 415–428,
https://doi.org/10.1111/j.1600-0870.2004.00076.x, 2004. a
Otto-Bliesner, B. L., Brady, E. C., Fasullo, J., Jahn, A., Landrum, L.,
Stevenson, S., Rosenbloom, N., Mai, A., and Strand, G.: Climate Variability
and Change since 850 CE: An Ensemble Approach with the Community Earth
System Model, B. Am. Meteorol. Soc., 97,
735–754, https://doi.org/10.1175/BAMS-D-14-00233.1, 2016. a, b
PAGES 2k-PMIP3 group: Continental-scale temperature variability in PMIP3
simulations and PAGES 2k regional temperature reconstructions over the past
millennium, Clim. Past, 11, 1673–1699,
https://doi.org/10.5194/cp-11-1673-2015, 2015. a
PAGES2k Consortium: A global multiproxy database for temperature
reconstructions of the Common Era, Scientific data, 4, 170088,
https://doi.org/10.1038/sdata.2017.88, 2017. a
Palmer, T. N. and Weisheimer, A.: Diagnosing the causes of bias in climate
models – why is it so hard?, Geophys. Astro. Fluid,
105, 351–365, https://doi.org/10.1080/03091929.2010.547194, 2011. a
Paul, A.: Ebm1d-ad v1.0.0: 1D energy balance model of climate with automatic
differentiation, https://doi.org/10.5281/zenodo.1489952, 2018. a, b
Paul, A. and Losch, M.: Perspectives of Parameter and State Estimation in
Paleoclimatology, in: Climate Change: Inferences from Paleoclimate and
Regional Aspects, edited by: Berger, A., Mesinger, F., and Sijacki, D.,
Springer Vienna, Vienna, 93–105, https://doi.org/10.1007/978-3-7091-0973-1_7, 2012. a, b
Paul, A. and Schäfer-Neth, C.: How to combine sparse proxy data and coupled
climate models, Quaternary Sci. Revi., 24, 1095–1107,
https://doi.org/10.1016/j.quascirev.2004.05.010, 2005. a
Rasch, P. J. and Kristjánsson, J. E.: A Comparison of the CCM3 Model
Climate Using Diagnosed and Predicted Condensate Parameterizations, J. Climate, 11, 1587–1614,
https://doi.org/10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2, 1998. a
Sakov, P. and Bocquet, M.: Asynchronous data assimilation with the EnKF in
presence of additive model error, Tellus A, 70, 1414545, https://doi.org/10.1080/16000870.2017.1414545, 2018. a
Sakov, P. and Oke, P. R.: Implications of the Form of the Ensemble
Transformation in the Ensemble Square Root Filters, Mon. Weather Rev., 136,
1042–1053, https://doi.org/10.1175/2007MWR2021.1, 2008. a
Sakov, P., Evensen, G., and Bertino, L.: Asynchronous data assimilation with
the EnKF, Tellus A, 62, 24–29, 2010. a
Sakov, P., Oliver, D. S., and Bertino, L.: An Iterative EnKF for Strongly
Nonlinear Systems, Mon. Weather Rev., 140, 1988–2004,
https://doi.org/10.1175/MWR-D-11-00176.1, 2012. a
Sakov, P., Jean-Matthieu, H., and Bocquet, M.: An iterative ensemble Kalman
filter in the presence of additive model error, Q. J.
Roy. Meteor. Soc., 144, 1297-1309, , https://doi.org/10.1002/qj.3213, 2018. a
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality
(complete samples), Biometrika, 52, 591–611,
https://doi.org/10.1093/biomet/52.3-4.591, 1965. a
Simon, E. and Bertino, L.: Application of the Gaussian anamorphosis to
assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic
with the EnKF: a twin experiment, Ocean Sci., 5, 495–510,
https://doi.org/10.5194/os-5-495-2009, 2009. a, b, c
Simon, E. and Bertino, L.: Gaussian anamorphosis extension of the DEnKF for
combined state parameter estimation: Application to a 1D ocean ecosystem
model, J. Marine Syst., 89, 1–18,
https://doi.org/10.1016/j.jmarsys.2011.07.007, 2012. a, b, c
Smith, P. J., Dance, S. L., and Nichols, N. K.: A hybrid data assimilation
scheme for model parameter estimation: Application to morphodynamic
modelling, Comput. Fluids, 46, 436–441,
https://doi.org/10.1016/j.compfluid.2011.01.010, 2011. a
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J.,
Dukowicz, J., Eden, C., Fox-Kemper, B., Gent, P., Hecht, M., Jayne, S.,
Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, N., Peacock, S.,
Vertenstein, M., and Yeager, S.: The Parallel Ocean Program (POP) Reference
Manual, Ocean
Component of the Community Climate System Model (CCSM) and Community Earth
System Model (CESM), Tech. Rep. LAUR-10-01854, Los Alamos National
Laboratory, Boulder, Colorado, 2010. a
Steiger, N. J., Hakim, G. J., Steig, E. J., Battisti, D. S., and Roe, G. H.:
Assimilation of Time-Averaged Pseudoproxies for Climate Reconstruction,
J. Climate, 27, 426–441, https://doi.org/10.1175/JCLI-D-12-00693.1, 2014. a
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker,
J. S.: Ensemble Square Root Filters, Mon. Weather Rev., 131, 1485–1490,
https://doi.org/10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2, 2003. a
Waelbroeck, C., Kiefer, T., Dokken, T., Chen, M.-T., Spero, H., Jung, S.,
Weinelt, M., Kucera, M., and Paul, A.: Constraints on surface seawater oxygen
isotope change between the Last Glacial Maximum and the Late Holocene,
Quaternary Sci. Rev., 105, 102–111,
https://doi.org/10.1016/j.quascirev.2014.09.020, 2014. a
Wang, X., Bishop, C. H., and Julier, S. J.: Which Is Better, an Ensemble of
Positive–Negative Pairs or a Centered Spherical Simplex Ensemble?, Mon.
Weather
Rev., 132, 1590–1605, https://doi.org/10.1175/1520-0493(2004)132<1590:WIBAEO>2.0.CO;2, 2004. a
Weitzel, N., Wagner, S., Sjolte, J., Klockmann, M., Bothe, O., Andres, H.,
Tarasov, L., Rehfeld, K., Zorita, E., Widmann, M., Sommer, P., Schädler, G.,
Ludwig, P., Kapp, F., Jonkers, L., García-Pintado, J., Fuhrmann, F., Dolman,
A., Dallmeyer, A., and Brücher, T.: Diving into the past – A paleo
data-model comparison workshop on the Late Glacial and Holocene, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-18-0169.1,
online first, 2018. a
Whitaker, J. S. and Hamill, T. M.: Ensemble Data Assimilation without Perturbed
Observations, Mon. Weather Rev., 130, 1913–1924,
https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2, 2002. a
Wu, Z., Reynolds, A., and Oliver, D.: Conditioning Geostatistical Models to
Two-Phase Production Data, SPE J., 3, 142–155, https://doi.org/10.2118/56855-PA,
1999. a, b
Yang, B., Qian, Y., Lin, G., Leung, R., and Zhang, Y.: Some issues in
uncertainty quantification and parameter tuning: a case study of convective
parameterization scheme in the WRF regional climate model, Atmos. Chem.
Phys., 12, 2409–2427, https://doi.org/10.5194/acp-12-2409-2012, 2012. a
Yang, B., Qian, Y., Lin, G., Leung, L. R., Rasch, P. J., Zhang, G. J.,
McFarlane, S. A., Zhao, C., Zhang, Y., Wang, H., Wang, M., and Liu, X.:
Uncertainty quantification and parameter tuning in the CAM5
Zhang-McFarlane convection scheme and impact of improved convection on the
global circulation and climate, J. Geophys. Res.-Atmos.,
118, 395–415, https://doi.org/10.1029/2012JD018213, 2013. a, b
Zanchettin, D., Bothe, O., Lehner, F., Ortega, P., Raible, C. C., and
Swingedouw, D.: Reconciling reconstructed and simulated features of the
winter Pacific/North American pattern in the early 19th century, Clim. Past,
11, 939–958, https://doi.org/10.5194/cp-11-939-2015, 2015. a
Zhang, G. and McFarlane, N. A.: Sensitivity of climate simulations to the
parameterization of cumulus convection in the Canadian climate centre
general circulation model, Atmos.-Ocean, 33, 407–446,
https://doi.org/10.1080/07055900.1995.9649539, 1995.
a
Zhou, H., Gómez-Hernández, J. J., Franssen, H.-J. H., and Li, L.: An
approach to handling non-Gaussianity of parameters and state variables in
ensemble Kalman filtering, Adv. Water Resour., 34, 844–864,
https://doi.org/10.1016/j.advwatres.2011.04.014, 2011. a
Zunz, V., Goosse, H., and Dubinkina, S.: Impact of the initialisation on the
predictability of the Southern Ocean sea ice at interannual to multi-decadal
timescales, Clim. Dynam., 44, 2267–2286,
https://doi.org/10.1007/s00382-014-2344-9, 2015. a
Short summary
Earth system models (ESMs) integrate interactions of atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate under a variety of conditions. Past climate field reconstructions with deterministic ESMs through the assimilation of climate proxies need to consider the required high computations and model non-linearity. Our tests indicate that iterative schemes based on the Kalman filter and careful sensitivity analysis are adequate for approaching the problem.
Earth system models (ESMs) integrate interactions of atmosphere, ocean, land, ice, and biosphere...
