Articles | Volume 17, issue 8
https://doi.org/10.5194/gmd-17-3409-2024
© Author(s) 2024. 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-17-3409-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
30 Apr 2024
Development and technical paper |
| 30 Apr 2024
| 30 Apr 2024
cfr (v2024.1.26): a Python package for climate field reconstruction
Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
Julien Emile-Geay
Department of Earth Sciences, University of Southern California, Los Angeles, CA, USA
Gregory J. Hakim
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Dominique Guillot
Department of Mathematical Sciences, University of Delaware, Newark, DE, USA
Deborah Khider
University of Southern California, Information Sciences Institute, Marina Del Rey, CA, USA
Robert Tardif
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Walter A. Perkins
Allen Institute for Artificial Intelligence, Seattle, WA, USA
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Eric John Mei, Gregory J. Hakim, Max Taniguchi-King, Dominik Stiller, and Alexander J. Turner
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Chemistry-climate models are used to investigate how physical climate influences the composition of the atmosphere but are slow and expensive to run. We train a linear inverse model that can replicate the behavior of chemistry-climate models at low computational cost. It captures how large-scale climate features like El Niño affect atmospheric composition and can make accurate forecasts up to a year ahead. This model enables fast hypothesis testing and estimates of past atmospheric composition.
Michael N. Evans, Lucie J. Lücke, Kevin J. Fan, and Feng Zhu
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We present a database of databases (DoD2k) for Common Era (1–2000 A.D.) paleoclimate research. The DoD2k leverages existing code and 5 curated databases, eliminates duplicates, and contains 4613 records. We analyze for common features across moisture and temperature sensitive records, and we test cave carbonate data simulations against observations. DoD2k is expected to be useful for detecting climate change on decadal timescales and for improving data models and paleoclimate reconstructions.
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Earth Syst. Sci. Data, 15, 2081–2116, https://doi.org/10.5194/essd-15-2081-2023, https://doi.org/10.5194/essd-15-2081-2023, 2023
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This paper describes geoChronR, an R package that streamlines the process of quantifying age uncertainties, propagating uncertainties through several common analyses, and visualizing the results. In addition to describing the structure and underlying theory of the package, we present five real-world use cases that illustrate common workflows in geoChronR. geoChronR is built on the Linked PaleoData framework, is open and extensible, and we welcome feedback and contributions from the community.
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Jessica A. Badgeley, Eric J. Steig, Gregory J. Hakim, and Tyler J. Fudge
Clim. Past, 16, 1325–1346, https://doi.org/10.5194/cp-16-1325-2020, https://doi.org/10.5194/cp-16-1325-2020, 2020
Robert Tardif, Gregory J. Hakim, Walter A. Perkins, Kaleb A. Horlick, Michael P. Erb, Julien Emile-Geay, David M. Anderson, Eric J. Steig, and David Noone
Clim. Past, 15, 1251–1273, https://doi.org/10.5194/cp-15-1251-2019, https://doi.org/10.5194/cp-15-1251-2019, 2019
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An updated Last Millennium Reanalysis is presented, using an expanded multi-proxy database, and proxy models representing the seasonal characteristics of proxy records, in addition to the dual sensitivity to temperature and moisture of tree-ring-width chronologies. We show enhanced skill in spatial reconstructions of key climate variables in the updated reanalysis, compared to an earlier version, resulting from the combined influences of the enhanced proxy network and improved proxy modeling.
Hansi K. A. Singh, Gregory J. Hakim, Robert Tardif, Julien Emile-Geay, and David C. Noone
Clim. Past, 14, 157–174, https://doi.org/10.5194/cp-14-157-2018, https://doi.org/10.5194/cp-14-157-2018, 2018
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Walter A. Perkins and Gregory J. Hakim
Clim. Past, 13, 421–436, https://doi.org/10.5194/cp-13-421-2017, https://doi.org/10.5194/cp-13-421-2017, 2017
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Nathan Steiger and Gregory Hakim
Clim. Past, 12, 1375–1388, https://doi.org/10.5194/cp-12-1375-2016, https://doi.org/10.5194/cp-12-1375-2016, 2016
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J. Emile-Geay and M. Tingley
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Ignoring nonlinearity in palaeoclimate records (e.g. continental run-off proxies) runs the risk of severely overstating changes in climate variability. Even with the correct model and parameters, some information is irretrievably lost by such proxies. However, we find that a simple empirical transform can do much to improve the situation, and makes them amenable to classical analyses. Doing so on two palaeo-ENSO records markedly changes some of the quantitative inferences made from such records.
M. Comboul, J. Emile-Geay, M. N. Evans, N. Mirnateghi, K. M. Cobb, and D. M. Thompson
Clim. Past, 10, 825–841, https://doi.org/10.5194/cp-10-825-2014, https://doi.org/10.5194/cp-10-825-2014, 2014
J. Wang, J. Emile-Geay, D. Guillot, J. E. Smerdon, and B. Rajaratnam
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Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
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Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
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Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
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Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
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We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
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In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
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Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Noribeth Mariscal, Louisa K. Emmons, Duseong S. Jo, Ying Xiong, Laura M. Judd, Scott J. Janz, Jiajue Chai, and Yaoxian Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-228, https://doi.org/10.5194/egusphere-2025-228, 2025
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The distribution of ozone (O3) and its precursors (NOx, VOCs) is explored using the chemistry-climate model, MUSICAv0, and evaluated using measurements from the Michigan-Ontario Ozone Source Experiment. A custom grid of ~7 km was created over Michigan. A sector-based diurnal cycle for anthropogenic nitric oxide was included in the model. This work shows that grid resolution played a more important role for O3 precursors, and the diurnal cycle significantly impacted nighttime O3 formation.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
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We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
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To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
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
Annan, J. D. and Hargreaves, J. C.: A new global reconstruction of temperature changes at the Last Glacial Maximum, Clim. Past, 9, 367–376, https://doi.org/10.5194/cp-9-367-2013, 2013. a
Annan, J. D., Hargreaves, J. C., and Mauritsen, T.: A new global surface temperature reconstruction for the Last Glacial Maximum, Clim. Past, 18, 1883–1896, https://doi.org/10.5194/cp-18-1883-2022, 2022. a
Bova, S., Rosenthal, Y., Liu, Z., Godad, S. P., and Yan, M.: Seasonal origin of the thermal maxima at the Holocene and the last interglacial, Nature, 589, 548–553, https://doi.org/10.1038/s41586-020-03155-x, 2021. a
Brady, E., Stevenson, S., Bailey, D., Liu, Z., Noone, D., Nusbaumer, J., Otto-Bliesner, B. L., Tabor, C., Tomas, R., Wong, T., Zhang, J., and Zhu, J.: The Connected Isotopic Water Cycle in the Community Earth System Model Version 1, J. Adv. Model. Earth Sy., 11, 2547–2566, https://doi.org/10.1029/2019MS001663, 2019. a, b, c, d
Büntgen, U., Allen, K., Anchukaitis, K. J., Arseneault, D., Boucher, É., Bräuning, A., Chatterjee, S., Cherubini, P., Churakova (Sidorova), O. V., Corona, C., Gennaretti, F., Grießinger, J., Guillet, S., Guiot, J., Gunnarson, B., Helama, S., Hochreuther, P., Hughes, M. K., Huybers, P., Kirdyanov, A. V., Krusic, P. J., Ludescher, J., Meier, W. J. H., Myglan, V. S., Nicolussi, K., Oppenheimer, C., Reinig, F., Salzer, M. W., Seftigen, K., Stine, A. R., Stoffel, M., St. George, S., Tejedor, E., Trevino, A., Trouet, V., Wang, J., Wilson, R., Yang, B., Xu, G., and Esper, J.: The influence of decision-making in tree ring-based climate reconstructions, Nat. Commun., 12, 3411, https://doi.org/10.1038/s41467-021-23627-6, 2021. a
Bürger, G., Fast, I., and Cubasch, U.: Climate reconstruction by regression – 32 variations on a theme, Tellus A, 58, 227–235, https://doi.org/10.1111/j.1600-0870.2006.00164.x, 2006. a
Christiansen, B. and Ljungqvist, F. C.: Challenges and Perspectives for Large-Scale Temperature Reconstructions of the Past Two Millennia, Rev. Geophys., 55, 40–96, https://doi.org/10.1002/2016RG000521, 2017. a
Cook, E. R., Woodhouse, C. A., Eakin, C. M., Meko, D. M., and Stahle, D. W.: Long-Term Aridity Changes in the Western United States, Science, 306, 1015–1018, https://doi.org/10.1126/science.1102586, 2004. a
Cook, E. R., Anchukaitis, K. J., Buckley, B. M., D'Arrigo, R. D., Jacoby, G. C., and Wright, W. E.: Asian Monsoon Failure and Megadrought During the Last Millennium, Science, 328, 486–489, https://doi.org/10.1126/science.1185188, 2010. a
Dempster, A. P., Laird, N. M., and Rubin, D. B.: Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion), J. Roy. Stat. Soc. B, 39, 1–38, 1977. 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
Dubinkina, S. and Goosse, H.: An assessment of particle filtering methods and nudging for climate state reconstructions, Clim. Past, 9, 1141–1152, https://doi.org/10.5194/cp-9-1141-2013, 2013. 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
Emile-Geay, J., Cobb, K., Mann, M., and Wittenberg, A. T.: Estimating Central Equatorial Pacific SST variability over the Past Millennium. Part 1: Methodology and Validation, J. Climate, 26, 2302–2328, https://doi.org/10.1175/JCLI-D-11-00510.1, 2013a. a
Emile-Geay, J., Cobb, K., Mann, M., and Wittenberg, A. T.: Estimating Central Equatorial Pacific SST variability over the Past Millennium. Part 2: Reconstructions and Implications, J. Climate, 26, 2329–2352, https://doi.org/10.1175/JCLI-D-11-00511.1, 2013b. a
Evans, M. N., Kaplan, A., and Cane, M. A.: Pacific sea surface temperature field reconstruction from coral δ18O data using reduced space objective analysis, Paleoceanography, 17, 7-1–7-13, https://doi.org/10.1029/2000PA000590, 2002. a
Evans, M. N., Tolwinski-Ward, S. E., Thompson, D. M., and Anchukaitis, K. J.: 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, b, c
Evensen, G.: Data Assimilation: The Ensemble Kalman Filter, 2nd Edn., Springer, ISBN 978-3-642-03710-8, 2009. a
Fang, M., Li, X., Chen, H. W., and Chen, D.: Arctic Amplification Modulated by Atlantic Multidecadal Oscillation and Greenhouse Forcing on Multidecadal to Century Scales, Nat. Commun., 13, 1865, https://doi.org/10.1038/s41467-022-29523-x, 2022. a
Franke, J., Brönnimann, S., Bhend, J., and Brugnara, Y.: A Monthly Global Paleo-Reanalysis of the Atmosphere from 1600 to 2005 for Studying Past Climatic Variations, Scientific Data, 4, 170076, https://doi.org/10.1038/sdata.2017.76, 2017. a
Friedman, J., Hastie, T., and Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso, Biostat, 9, 432–441, https://doi.org/10.1093/biostatistics/kxm045, 2008. a
Gebhardt, C., Kühl, N., Hense, A., and Litt, T.: Reconstruction of Quaternary Temperature Fields by Dynamically Consistent Smoothing, Clim. Dynam., 30, 421–437, https://doi.org/10.1007/s00382-007-0299-9, 2008. a
GISTEMP Team: The GISS Surface Temperature Analysis (GISTEMP), version 4, GISTEMP Team [data set], https://data.giss.nasa.gov/pub/gistemp/gistemp1200_GHCNv4_ERSSTv5.nc.gz, last access: 23 April 2024. a
Goosse, H., Renssen, H., Timmermann, A., Bradley, R. S., and Mann, M. E.: Using paleoclimate proxy-data to select optimal realisations in an ensemble of simulations of the climate of the past millennium, Clim. Dynam., 27, 165–184, https://doi.org/10.1007/s00382-006-0128-6, 2006. a, b
Goosse, H., Crespin, E., de Montety, A., Mann, M. E., Renssen, H., and Timmermann, A.: Reconstructing Surface Temperature Changes over the Past 600 Years Using Climate Model Simulations with Data Assimilation, J. Geophys. Res.-Atmos., 115, D09108, https://doi.org/10.1029/2009JD012737, 2010. a, b
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, 2016. a, b, c, d, e, f, g, h, i, j, k
Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in Python, Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons, J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1, 2017. a, b
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Jones, J. and Widmann, M.: Reconstructing Large-scale Variability from Palaeoclimatic Evidence by Means of Data Assimilation Through Upscaling and Nudging (DATUN), in: The KIHZ project: Towards a Synthesis of Holocene Proxy Data and Climate Models, edited by: Fischer, H., Kumke, T., Lohmann, G., Flösser, G., Miller, H., von Storch, H., and Negendank, J., Springer, Heidelberg, Berlin, New York, 171–193, ISBN 978-3-642-05826-4, 2004. a
Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems, J. Fluid. Eng.-T. ASME, 82, 35–45, https://doi.org/10.1115/1.3662552, 1960. a
Khider, D., Emile-Geay, J., Zhu, F., James, A., Landers, J., Ratnakar, V., and Gil, Y.: Pyleoclim: Paleoclimate Timeseries Analysis and Visualization With Python, Paleoceanography and Paleoclimatology, 37, e2022PA004509, https://doi.org/10.1029/2022PA004509, 2022. a
King, J., Tierney, J., Osman, M., Judd, E. J., and Anchukaitis, K. J.: DASH: a MATLAB toolbox for paleoclimate data assimilation, Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, 2023. a
King, J. M., Anchukaitis, K. J., Tierney, J. E., Hakim, G. J., Emile-Geay, J., Zhu, F., and Wilson, R.: A data assimilation approach to last millennium temperature field reconstruction using a limited high-sensitivity proxy network, J. Climate, 1, 1–64, https://doi.org/10.1175/JCLI-D-20-0661.1, 2021. a, b
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., and Willing, C.: Jupyter Notebooks – a publishing format for reproducible computational workflows, in: Positioning and Power in Academic Publishing: Players, Agents and Agendas, edited by: Loizides, F. and Schmidt, B., IOS Press, 87–90, https://doi.org/10.3233/978-1-61499-649-1-87, 2016. a
Lauritzen, S. L.: Graphical Models, Clarendon Press, Oxford, ISBN 9780198522195, 1996. a
Lenssen, N. J. L., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A., Ruedy, R., and Zyss, D.: Improvements in the GISTEMP Uncertainty Model, J. Geophys. Res.-Atmos., 124, 6307–6326, https://doi.org/10.1029/2018JD029522, 2019. a, b, c, d
Liu, H., Liu, Z., and Lu, F.: A Systematic Comparison of Particle Filter and EnKF in Assimilating Time-Averaged Observations, J. Geophys. Res.-Atmos., 122, 13155–13173, https://doi.org/10.1002/2017JD026798, 2017. a
Lough, J. M.: Climate records from corals, WIRes Clim. Change, 1, 318–331, https://doi.org/10.1002/wcc.39, 2010. a
Lyu, Z., Goosse, H., Dalaiden, Q., Klein, F., Shi, F., Wagner, S., and Braconnot, P.: Spatial Patterns of Multi–Centennial Surface Air Temperature Trends in Antarctica over 1–1000 CE: Insights from Ice Core Records and Modeling, Quaternary Sci. Rev., 271, 107205, https://doi.org/10.1016/j.quascirev.2021.107205, 2021. a, b
Malevich, S. B., Vetter, L., and Tierney, J. E.: Global Core Top Calibration of δ18O in Planktic Foraminifera to Sea Surface Temperature, Paleoceanography and Paleoclimatology, 34, 1292–1315, https://doi.org/10.1029/2019PA003576, 2019. a
Mann, M. E., Bradley, R. S., and Hughes, M. K.: Global-scale temperature patterns and climate forcing over the past six centuries, Nature, 392, 779–787, https://doi.org/10.1038/33859, 1998. a
Mann, M. E., Bradley, R. S., and Hughes, M. K.: Northern hemisphere temperatures during the past millennium: Inferences, uncertainties, and limitations, Geophys. Res. Lett., 26, 759–762, https://doi.org/10.1029/1999GL900070, 1999. a
Mann, M. E., Rutherford, S., Wahl, E., and Ammann, C.: Robustness of proxy-based climate field reconstruction methods, J. Geophys. Res.-Atmos., 112, D12109, https://doi.org/10.1029/2006JD008272, 2007. a
Mann, M. E., Zhang, Z., Hughes, M. K., Bradley, R. S., Miller, S. K., Rutherford, S., and Ni, F.: Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, P. Natl. Acad. Sci. USA, 105, 13252–13257, https://doi.org/10.1073/pnas.0805721105, 2008. a
Mann, M. E., Zhang, Z., Rutherford, S., Bradley, R. S., Hughes, M. K., Shindell, D., Ammann, C., Faluvegi, G., and Ni, F.: Global Signatures and Dynamical Origins of the Little Ice Age and Medieval Climate Anomaly, Science, 326, 1256–1260, https://doi.org/10.1126/science.1177303, 2009. a
Marcott, S. A., Shakun, J. D., Clark, P. U., and Mix, A. C.: A Reconstruction of Regional and Global Temperature for the Past 11 300 Years, Science, 339, 1198–1201, https://doi.org/10.1126/science.1228026, 2013. a
Masson-Delmotte, V., Schulz, M., Abe-Ouchi, A., Beer, J., Ganopolski, A., Rouco, J. G., Jansen, E., Lambeck, K., Luterbacher, J., Naish, T., Osborn, T., Otto-Bliesner, B., Quinn, T., Ramesh, R., Rojas, M., Shao, X., and Timmermann, A.: Information from Paleoclimate Archives, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 383–464, https://doi.org/10.1017/CBO9781107415324.013, 2013. a
McKinney, W.: Data Structures for Statistical Computing in Python, in: Proceedings of the 9th Python in Science Conference, edited by: van der Walt, S. and Millman, J., 51–56, https://doi.org/10.25080/Majora-92bf1922-00a, 2010. a
Menne, M. J., Williams, C. N., Gleason, B. E., Rennie, J. J., and Lawrimore, J. H.: The Global Historical Climatology Network Monthly Temperature Dataset, Version 4, J. Climate, 31, 9835–9854, https://doi.org/10.1175/JCLI-D-18-0094.1, 2018. a, b
Met Office: Cartopy: a cartographic python library with a Matplotlib interface, Exeter, Devon, Met Office [code], https://scitools.org.uk/cartopy (last access: 23 April 2024), 2010. a
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying Uncertainties in Global and Regional Temperature Change Using an Ensemble of Observational Estimates: The HadCRUT4 Data Set, J. Geophys. Res.-Atmos., 117, D08101, https://doi.org/10.1029/2011JD017187, 2012. a, b, c
Neukom, R., Barboza, L. A., Erb, M. P., Shi, F., Emile-Geay, J., Evans, M. N., Franke, J., Kaufman, D. S., Lücke, L., Rehfeld, K., Schurer, A., Zhu, F., Brönnimann, S., Hakim, G. J., Henley, B. J., Ljungqvist, F. C., McKay, N., Valler, V., and von Gunten, L.: Consistent multidecadal variability in global temperature reconstructions and simulations over the Common Era, Nat. Geosci., 12, 643–649, https://doi.org/10.1038/s41561-019-0400-0, 2019a. a
Neukom, R., Steiger, N., Gómez-Navarro, J. J., Wang, J., and Werner, J. P.: No evidence for globally coherent warm and cold periods over the preindustrial Common Era, Nature, 571, 550–554, https://doi.org/10.1038/s41586-019-1401-2, 2019b. a
Osman, M. B., Tierney, J. E., Zhu, J., Tardif, R., Hakim, G. J., King, J., and Poulsen, C. J.: Globally resolved surface temperatures since the Last Glacial Maximum, Nature, 599, 239–244, https://doi.org/10.1038/s41586-021-03984-4, 2021. a, b, c, d
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
PAGES2k Consortium: PAGES2k Global 2,000 Year Multiproxy Database, PAGES2k Consortium [data set], https://doi.org/10.25921/ycr3-7588, last access: 23 April 2024. a
Perkins, W. A. and Hakim, G. J.: Reconstructing paleoclimate fields using online data assimilation with a linear inverse model, Clim. Past, 13, 421–436, https://doi.org/10.5194/cp-13-421-2017, 2017. a
Perkins, W. A. and Hakim, G. J.: Coupled Atmosphere–Ocean Reconstruction of the Last Millennium Using Online Data Assimilation, Paleoceanography and Paleoclimatology, 36, e2020PA003959, https://doi.org/10.1029/2020PA003959, 2021. a
Plotly Technologies Inc.: Collaborative data science, https://plot.ly (last access: 23 April 2024), 2015. a
Rew, R. and Davis, G.: NetCDF: An Interface for Scientific Data Access, IEEE Comput. Graph., 10, 76–82, https://doi.org/10.1109/38.56302, 1990. a
Ridgwell, A., Hargreaves, J. C., Edwards, N. R., Annan, J. D., Lenton, T. M., Marsh, R., Yool, A., and Watson, A.: Marine geochemical data assimilation in an efficient Earth System Model of global biogeochemical cycling, Biogeosciences, 4, 87–104, https://doi.org/10.5194/bg-4-87-2007, 2007. a
Rutherford, S., Mann, M. E., Osborn, T. J., Bradley, R. S., Briffa, K. R., Hughes, M. K., and Jones, P. D.: Proxy-Based Northern Hemisphere Surface Temperature Reconstructions: Sensitivity to Method, Predictor Network, Target Season, and Target Domain, J. Climate, 18, 2308–2329, 2005. a
Schneider, T.: Analysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values, J. Climate, 14, 853–871, https://doi.org/10.1175/1520-0442(2001)014<0853:AOICDE>2.0.CO;2, 2001. a
Seabold, S. and Perktold, J.: statsmodels: Econometric and statistical modeling with python, in: 9th Python in Science Conference, https://doi.org/10.25080/Majora-92bf1922-011, 2010. a
Shakun, J. D., Clark, P. U., He, F., Marcott, S. A., Mix, A. C., Liu, Z., Otto-Bliesner, B., Schmittner, A., and Bard, E.: Global Warming Preceded by Increasing Carbon Dioxide Concentrations during the Last Deglaciation, Nature, 484, 49–54, https://doi.org/10.1038/nature10915, 2012. a
Shi, F., Zhao, S., Guo, Z., Goosse, H., and Yin, Q.: Multi-proxy reconstructions of May–September precipitation field in China over the past 500 years, Clim. Past, 13, 1919–1938, https://doi.org/10.5194/cp-13-1919-2017, 2017. a
Shi, F., Goosse, H., Klein, F., Zhao, S., Liu, T., and Guo, Z.: Monopole Mode of Precipitation in East Asia Modulated by the South China Sea Over the Last Four Centuries, Geophys. Res. Lett., 46, 14713–14722, https://doi.org/10.1029/2019GL085320, 2019. a, b
Smerdon, J. E. and Pollack, H. N.: Reconstructing Earth's surface temperature over the past 2000 years: the science behind the headlines, WIREs Clim. Change, 7, 746–771, https://doi.org/10.1002/wcc.418, 2016. a
Steiger, N. and Hakim, G.: Multi-timescale data assimilation for atmosphere–ocean state estimates, Clim. Past, 12, 1375–1388, https://doi.org/10.5194/cp-12-1375-2016, 2016. 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, b
Steiger, N. J., Smerdon, J. E., Cook, E. R., and Cook, B. I.: A Reconstruction of Global Hydroclimate and Dynamical Variables over the Common Era, Scientific Data, 5, 1–15, https://doi.org/10.1038/sdata.2018.86, 2018. a
Tardif, R.: The post-processed “iCESM1” last millennium simulation, https://atmos.washington.edu/~rtardif/LMR/prior, last access: 23 April 2024. a
Tardif, R., Hakim, G. J., Perkins, W. A., Horlick, K. A., Erb, M. P., Emile-Geay, J., Anderson, D. M., Steig, E. J., and Noone, D.: Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling, Clim. Past, 15, 1251–1273, https://doi.org/10.5194/cp-15-1251-2019, 2019. a, b, c, d, e, f, g, h, i, j, k
Tierney, J. E. and Tingley, M. P.: A Bayesian, Spatially-Varying Calibration Model for the TEX86 Proxy, Geochim. Cosmochim. Ac., 127, 83–106, https://doi.org/10.1016/j.gca.2013.11.026, 2014. a
Tierney, J. E. and Tingley, M. P.: BAYSPLINE: A New Calibration for the Alkenone Paleothermometer, Paleoceanography and Paleoclimatology, 33, 281–301, https://doi.org/10.1002/2017PA003201, 2018. a
Tierney, J. E., Abram, N. J., Anchukaitis, K. J., Evans, M. N., Giry, C., Kilbourne, K. H., Saenger, C. P., Wu, H. C., and Zinke, J.: Tropical sea surface temperatures for the past four centuries reconstructed from coral archives, Paleoceanography, 30, 226–252, https://doi.org/10.1002/2014PA002717, 2015. a
Tierney, J. E., Malevich, S. B., Gray, W., Vetter, L., and Thirumalai, K.: Bayesian Calibration of the
Tierney, J. E., Zhu, J., King, J., Malevich, S. B., Hakim, G. J., and Poulsen, C. J.: Glacial Cooling and Climate Sensitivity Revisited, Nature, 584, 569–573, https://doi.org/10.1038/s41586-020-2617-x, 2020. a, b, c
Tingley, M. P. and Huybers, P.: Recent temperature extremes at high northern latitudes unprecedented in the past 600 years, Nature, 496, 201–205, https://doi.org/10.1038/nature11969, 2013. a, b
Tingley, M. P. and Huybers, P. J.: A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part 1: Development and Applications to Paleoclimate Reconstruction Problems, J. Climate, 23, 2759–2781, https://doi.org/10.1175/2009JCLI3015.1, 2010a. a
Tingley, M. P. and Huybers, P. J.: A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part 2: Comparison with the Regularized Expectation-Maximization Algorithm, J. Climate, 23, 2782–2800, https://doi.org/10.1175/2009JCLI3016.1, 2010b. a
Tingley, M. P. and Li, B.: Comments on “Reconstructing the NH Mean Temperature: Can Underestimation of Trends and Variability Be Avoided?”, J. Climate, 25, 3441–3446, https://doi.org/10.1175/JCLI-D-11-00005.1, 2012. a
Tingley, M. P., Craigmile, P. F., Haran, M., Li, B., Mannshardt, E., and Rajaratnam, B.: Piecing together the past: statistical insights into paleoclimatic reconstructions, Quaternary Sci. Rev., 35, 1–22, https://doi.org/10.1016/j.quascirev.2012.01.012, 2012. a, b
Tolwinski-Ward, S. E., Evans, M. N., Hughes, M. K., and Anchukaitis, K. J.: An efficient forward model of the climate controls on interannual variation in tree-ring width, Clim. Dynam., 36, 2419–2439, https://doi.org/10.1007/s00382-010-0945-5, 2011. a, b
Tolwinski-Ward, S. E., Anchukaitis, K. J., and Evans, M. N.: Bayesian parameter estimation and interpretation for an intermediate model of tree-ring width, Clim. Past, 9, 1481–1493, https://doi.org/10.5194/cp-9-1481-2013, 2013. a, b
Vaccaro, A., Emile-Geay, J., Guillot, D., Verna, R., Morice, C., Kennedy, J., and Rajaratnam, B.: Climate field completion via Markov random fields – Application to the HadCRUT4.6 temperature dataset, J. Climate, 1, 1–66, https://doi.org/10.1175/JCLI-D-19-0814.1, 2021a. a, b, c, d, e, f, g, h, i, j, k
Vaccaro, A. D., Emile-Geay, J., Guillot, D., Verna, R., Morice, C., Kennedy, J., and Rajaratnam, B.: GraphEM-infilled HadCRUT4.6.0.0 January 1850 – December 2017, Zenodo [data set], https://doi.org/10.5281/zenodo.4601616, 2021b. a
Valler, V., Franke, J., Brugnara, Y., and Brönnimann, S.: An Updated Global Atmospheric Paleo-Reanalysis Covering the Last 400 Years, Geosci. Data J., 9, 89–107, https://doi.org/10.1002/gdj3.121, 2022. a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wang, J., Emile-Geay, J., Guillot, D., McKay, N. P., and Rajaratnam, B.: Fragility of reconstructed temperature patterns over the Common Era: Implications for model evaluation., Geophys. Res. Lett., 42, 7162–7170, https://doi.org/10.1002/2015GL065265, 2015. a, b
Waskom, M. L.: seaborn: statistical data visualization, Journal of Open Source Software, 6, 3021, https://doi.org/10.21105/joss.03021, 2021. a
Widmann, M., Goosse, H., van der Schrier, G., Schnur, R., and Barkmeijer, J.: Using data assimilation to study extratropical Northern Hemisphere climate over the last millennium, Clim. Past, 6, 627–644, https://doi.org/10.5194/cp-6-627-2010, 2010. a
Wikle, C. K. and Berliner, L. M.: A Bayesian tutorial for data assimilation, Physica D, 230, 1–16, https://doi.org/10.1016/j.physd.2006.09.017, 2007. a
Zhu, F. and Emile-Geay, J.: The GitHub repository for cfr, GitHub [code], https://github.com/fzhu2e/cfr, last access: 23 April 2024. a
Zhu, F. and Tolwinski-Ward, S.: PyVSL v0.4.0, Zenodo [code], https://doi.org/10.5281/zenodo.7967103, 2023. a
Zhu, F., Emile-Geay, J., Hakim, G. J., King, J., and Anchukaitis, K. J.: Resolving the Differences in the Simulated and Reconstructed Temperature Response to Volcanism, Geophys. Res. Lett., 47, e2019GL086908, https://doi.org/10.1029/2019GL086908, 2020. a
Zhu, F., Emile-Geay, J., Anchukaitis, K. J., Hakim, G. J., Wittenberg, A. T., Morales, M. S., Toohey, M., and King, J.: A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation, Nat. Commun., 13, 747, https://doi.org/10.1038/s41467-022-28210-1, 2022. a, b
Zhu, F., Emile-Geay, J., Anchukaitis, K. J., McKay, N. P., Stevenson, S., and Meng, Z.: The pseudoPAGES2k Dataset v1.4, Zenodo [data set], https://doi.org/10.5281/zenodo.8173256, 2023b. a, b, c
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.
Climate field reconstruction encompasses methods that estimate the evolution of climate in space...
