Articles | Volume 16, issue 19
https://doi.org/10.5194/gmd-16-5653-2023
© Author(s) 2023. 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-16-5653-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
12 Oct 2023
Methods for assessment of models |
| 12 Oct 2023
| 12 Oct 2023
DASH: a MATLAB toolbox for paleoclimate data assimilation
Jonathan King
CORRESPONDING AUTHOR
Department of Geosciences, University of Arizona, Tucson, Arizona, USA
Laboratory of Tree-Ring Research, University of Arizona, Tucson, Arizona, USA
Jessica Tierney
Department of Geosciences, University of Arizona, Tucson, Arizona, USA
Matthew Osman
Department of Geosciences, University of Arizona, Tucson, Arizona, USA
Department of Geography, University of Cambridge, Cambridge, UK
Emily J. Judd
Department of Paleobiology, Smithsonian National Museum of
Natural History, Washington, DC, USA
Kevin J. Anchukaitis
Department of Geosciences, University of Arizona, Tucson, Arizona, USA
Laboratory of Tree-Ring Research, University of Arizona, Tucson, Arizona, USA
School of Geography, Development, and Environment, University of Arizona, Tucson, Arizona, USA
Related authors
No articles found.
Julia Campbell, Christopher J. Poulsen, Jiang Zhu, Jessica E. Tierney, and Jeremy Keeler
Clim. Past, 20, 495–522, https://doi.org/10.5194/cp-20-495-2024, https://doi.org/10.5194/cp-20-495-2024, 2024
Short summary
Short summary
In this study, we use climate modeling to investigate the relative impact of CO2 and orbit on Early Eocene (~ 55 million years ago) climate and compare our modeled results to fossil records to determine the context for the Paleocene–Eocene Thermal Maximum, the most extreme hyperthermal in the Cenozoic. Our conclusions consider limitations and illustrate the importance of climate models when interpreting paleoclimate records in times of extreme warmth.
Helen Mackay, Gill Plunkett, Britta J. L. Jensen, Thomas J. Aubry, Christophe Corona, Woon Mi Kim, Matthew Toohey, Michael Sigl, Markus Stoffel, Kevin J. Anchukaitis, Christoph Raible, Matthew S. M. Bolton, Joseph G. Manning, Timothy P. Newfield, Nicola Di Cosmo, Francis Ludlow, Conor Kostick, Zhen Yang, Lisa Coyle McClung, Matthew Amesbury, Alistair Monteath, Paul D. M. Hughes, Pete G. Langdon, Dan Charman, Robert Booth, Kimberley L. Davies, Antony Blundell, and Graeme T. Swindles
Clim. Past, 18, 1475–1508, https://doi.org/10.5194/cp-18-1475-2022, https://doi.org/10.5194/cp-18-1475-2022, 2022
Short summary
Short summary
We assess the climatic and societal impact of the 852/3 CE Alaska Mount Churchill eruption using environmental reconstructions, historical records and climate simulations. The eruption is associated with significant Northern Hemisphere summer cooling, despite having only a moderate sulfate-based climate forcing potential; however, evidence of a widespread societal response is lacking. We discuss the difficulties of confirming volcanic impacts of a single eruption even when it is precisely dated.
PAGES Hydro2k Consortium
Clim. Past, 13, 1851–1900, https://doi.org/10.5194/cp-13-1851-2017, https://doi.org/10.5194/cp-13-1851-2017, 2017
Short summary
Short summary
Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited due to a paucity of modern instrumental observations. We review how proxy records of past climate and climate model simulations can be used in tandem to understand hydroclimate variability over the last 2000 years and how these tools can also inform risk assessments of future hydroclimatic extremes.
Rob Wilson, Rosanne D'Arrigo, Laia Andreu-Hayles, Rose Oelkers, Greg Wiles, Kevin Anchukaitis, and Nicole Davi
Clim. Past, 13, 1007–1022, https://doi.org/10.5194/cp-13-1007-2017, https://doi.org/10.5194/cp-13-1007-2017, 2017
Short summary
Short summary
Blue intensity shows great potential for reconstructing past summer temperatures from conifer trees growing at high latitude or the treeline. However, conifer species that express a strong colour difference between the heartwood and sapwood can impart a long-term trend bias in the resultant reconstructions. Herein, we highlight this issue using eight mountain hemlock sites across the Gulf of Alaska and explore how a non-biased reconstruction of past temperature could be derived using such data.
S. E. Tolwinski-Ward, K. J. Anchukaitis, and M. N. Evans
Clim. Past, 9, 1481–1493, https://doi.org/10.5194/cp-9-1481-2013, https://doi.org/10.5194/cp-9-1481-2013, 2013
Related subject area
Climate and Earth system modeling
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)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
The Regional Climate-Chemistry-Ecology Coupling Model RegCM-Chem (v4.6)-YIBs (v1.0): Development and Application
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields
A one-dimensional urban flow model with an Eddy-diffusivity Mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
An emulation-based approach for interrogating reactive transport models
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Subgrid-scale variability of cloud ice in the ICON-AES-1.3.00
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
INFERNO-peat v1.0.0: A representation of northern high latitude peat fires in the JULES-INFERNO global fire model
CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0
Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method
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.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
Short summary
Short summary
We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
Short summary
Short summary
Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
Short summary
Short summary
We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
Short summary
Short summary
Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
Short summary
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
Short summary
Short summary
This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
Short summary
Short summary
The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
Short summary
Short summary
Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
Short summary
Short summary
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
Short summary
Short summary
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
Short summary
Short summary
Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
Short summary
Short summary
We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
Short summary
Short summary
This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Jiachen Lu, Negin Nazarian, Melissa Hart, Scott Krayenhoff, and Alberto Martilli
EGUsphere, https://doi.org/10.5194/egusphere-2023-2811, https://doi.org/10.5194/egusphere-2023-2811, 2023
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 flux" term. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
Short summary
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
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 by either uniform erosion processes 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.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-34, https://doi.org/10.5194/gmd-2022-34, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Especially over the mid-latitudes precipiation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-GCM, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-GCM. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to a cloud ice loss and to an improvement of the process rate bias.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2023-2399, https://doi.org/10.5194/egusphere-2023-2399, 2023
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 parameterisation 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.
Xinzhu Yu, Li Liu, Chao Sun, Qingu Jiang, Biao Zhao, Zhiyuan Zhang, Hao Yu, and Bin Wang
Geosci. Model Dev., 16, 6285–6308, https://doi.org/10.5194/gmd-16-6285-2023, https://doi.org/10.5194/gmd-16-6285-2023, 2023
Short summary
Short summary
In this paper we propose a new common, flexible, and efficient parallel I/O framework for earth system modeling based on C-Coupler2.0. CIOFC1.0 can handle data I/O in parallel and provides a configuration file format that enables users to conveniently change the I/O configurations. It can automatically make grid and time interpolation, output data with an aperiodic time series, and accelerate data I/O when the field size is large.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
Short summary
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
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
Alley, R. B.: Palaeoclimatic insights into future climate challenges,
Philos. T. Roy. Soc. Lond. A-Math. 361, 1831–1849, 2003. a
Amrhein, D. E., Hakim, G. J., and Parsons, L. A.: Quantifying structural
uncertainty in paleoclimate data assimilation with an application to the last
millennium, Geophys. Res. Lett., 47, e2020GL090485, https://doi.org/10.1029/2020GL090485, 2020. a
Anchukaitis, K., Wilson, R., Briffa, K., Büntgen, U., Cook, E., D'Arrigo, R., Davi, N., Esper, J., Frank, D., Gunnarson, B., Hegerl, G., Helama, S., Klesse, S., Krusic, P., Linderholm, H., Myglan, V., Osborn, T., Zhang, P., Rydval, M., Schneider, L., Schurer, A., Wiles, G., and Zorita, E.: Last
millennium Northern Hemisphere summer temperatures from tree rings: Part
II, spatially resolved reconstructions, Quaternary Sci. Rev., 163,
1–22, 2017. a, b
Anchukaitis, K. J., Cook, E. R., Cook, B. I., Pearl, J., D'Arrigo, R., and
Wilson, R.: Coupled Modes of North Atlantic Ocean-Atmosphere Variability and
the Onset of the Little Ice Age, Geophys. Res. Lett., 46,
12417–12426, 2019. a
Ault, T., Deser, C., Newman, M., and Emile-Geay, J.: Characterizing decadal to
centennial variability in the equatorial Pacific during the last millennium,
Geophys. Res. Lett., 40, 3450–3456, 2013. a
Ault, T. R., Cole, J. E., Overpeck, J. T., Pederson, G. T., and Meko, D. M.:
Assessing the risk of persistent drought using climate model simulations and
paleoclimate data, J. Climate, 27, 7529–7549, 2014. a
Bradley, R. S.: Are there optimum sites for global paleo-temperature
reconstructions?, in: Climatic Variations and Forcing Mechanisms of the Last
2000 Years, edited by: P. D. Jones, R. S. B. and Jouzel, J.,
NATO ASI, Springer-Verlag, New York, vol. 41, 603–624, 1996. a
Brady, E., Stevenson, S., Bailey, D., Liu, Z., Noone, D., Nusbaumer, J.,
Otto-Bliesner, B., 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, 2019. a
Burke, K. D., Williams, J. W., Chandler, M. A., Haywood, A. M., Lunt, D. J.,
and Otto-Bliesner, B. L.: Pliocene and Eocene provide best analogs for
near-future climates, P. Natl. Acad. Sci. USA, 115,
13288–13293, 2018. a
Cane, M. A., Braconnot, P., Clement, A., Gildor, H., Joussaume, S., Kageyama,
M., Khodri, M., Paillard, D., Tett, S., and Zorita, E.: Progress in
paleoclimate modeling, J. Climate, 19, 5031–5057, 2006. a
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of GCM
precipitation by quantile mapping: how well do methods preserve changes in
quantiles and extremes?, J. Climate, 28, 6938–6959, 2015. a
Chen, Z.: Bayesian filtering: From Kalman filters to particle filters,
and beyond, Statistics, 182, 1–69, 2003. a
Coats, S., Smerdon, J., Stevenson, S., Fasullo, J., Otto-Bliesner, B., and
Ault, T.: Paleoclimate constraints on the spatiotemporal character of past
and future droughts, J. Climate, 33, 9883–9903, 2020. a
Cook, B. I., Cook, E. R., Anchukaitis, K. J., Seager, R., and Miller, R. L.:
Forced and unforced variability of twentieth century North American droughts
and pluvials, Clim. Dynam., 37, 1097–1110, 2011. 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, 2010. a
Crowley, T. J.: Utilization of paleoclimate results to validate projections of
a future greenhouse warming, in: Developments in atmospheric science,
Elsevier, vol. 19, 35–45, https://doi.org/10.1016/B978-0-444-88351-3.50010-6, 1991. a
Dail, H. and Wunsch, C.: Dynamical reconstruction of upper-ocean conditions in
the Last Glacial Maximum Atlantic, J. Climate, 27, 807–823, 2014. a
Dee, S.: sylvia-dee/PRYSM, Github [code], https://github.com/sylvia-dee/PRYSM, last access: 2 October 2023. a
Dee, S., Noone, D., Buenning, N., Emile-Geay, J., and Zhou, Y.: SPEEDY-IER: A
fast atmospheric GCM with water isotope physics, J. Geophys.
Res.-Atmos., 120, 73–91, 2015b. a
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate
change projections: the role of internal variability, Clim. Dynam., 38,
527–546, 2012. 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, b
Esper, J., Frank, D. C., Wilson, R. J., and Briffa, K. R.: Effect of scaling
and regression on reconstructed temperature amplitude for the past
millennium, Geophys. Res. Lett., 32, L07711, https://doi.org/10.1029/2004GL021236, 2005. a
Evans, M. N., Kaplan, A., and Cane, M. A.: Optimal sites for coral-based
reconstruction of global sea surface temperature, Paleoceanography, 13,
502–516, 1998. a
Evans, M. N., Kaplan, A., Cane, M. A., and Villalba, R.: Globality and
Optimality in Climate Field Reconstructions from Proxy Data, in:
Interhemispheric Climate Linkages, edited by: Markgraf, V.,
Cambridge University Press, Cambridge, UK, 53–72, https://doi.org/10.1016/B978-012472670-3/50007-0, 2001. 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, 2013. a
Fang, S.-W., Khodri, M., Timmreck, C., Zanchettin, D., and Jungclaus, J.:
Disentangling internal and external contributions to Atlantic multidecadal
variability over the past millennium, Geophys. Res. Lett., 48,
e2021GL095990, https://doi.org/10.1029/2021GL095990, 2021. a
Fernández-Donado, L., González-Rouco, J. F., Raible, C. C., Ammann, C. M., Barriopedro, D., García-Bustamante, E., Jungclaus, J. H., Lorenz, S. J., Luterbacher, J., Phipps, S. J., Servonnat, J., Swingedouw, D., Tett, S. F. B., Wagner, S., Yiou, P., and Zorita, E.: Large-scale temperature response to external forcing in simulations and reconstructions of the last millennium, Clim. Past, 9, 393–421, https://doi.org/10.5194/cp-9-393-2013, 2013. a
Frank, D., Esper, J., and Cook, E. R.: Adjustment for proxy number and
coherence in a large-scale temperature reconstruction, Geophys. Res.
Lett., 34, L16709, https://doi.org/10.1029/2007GL030571, 2007. a
Franke, J., Valler, V., Brönnimann, S., Neukom, R., and Jaume-Santero, F.: The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections, Clim. Past, 16, 1061–1074, https://doi.org/10.5194/cp-16-1061-2020, 2020. a, b, c, d
Galmarini, S., Cannon, A. J., Ceglar, A., Christensen, O. B.,
de Noblet-Ducoudré, N., Dentener, F., Doblas-Reyes, F. J., Dosio, A.,
Gutierrez, J. M., Iturbide, M., Jury, M., Lange, S., Loukos, H., Maiorano, A., Maraun, D., McGinnis, S., Nikulin, G., Riccio, A., Sanchez, E., Solazzo, E., Toreti, A., Vrac, M., and Zampieri, M.: Adjusting climate model bias for
agricultural impact assessment: How to cut the mustard, Climate Services, 13,
65–69, 2019. a, b
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and
three dimensions, Q. J. Roy. Meteorol. Soc., 125,
723–757, 1999. a
Gebbie, G.: How much did glacial North Atlantic water shoal?, Paleoceanography,
29, 190–209, 2014. a
Gong, D. and Wang, S.: Definition of Antarctic oscillation index, Geophys.
Res. Lett., 26, 459–462, 1999. a
Goosse, H., Crespin, E., de Montety, A., Mann, M., 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
Goosse, H., Crespin, E., Dubinkina, S., Loutre, M.-F., Mann, M. E., Renssen,
H., Sallaz-Damaz, Y., and Shindell, D.: The role of forcing and internal
dynamics in explaining the “Medieval Climate Anomaly”, Clim. Dyn.,
39, 2847–2866, 2012a. a
Goosse, H., Guiot, J., Mann, M. E., Dubinkina, S., and Sallaz-Damaz, Y.: The
Medieval Climate Anomaly in Europe: Comparison of the summer and
annual mean signals in two reconstructions and in simulations with data
assimilation, Global Planet. Change, 84, 35–47, 2012b. a
Gulev, S. K., Thorne, P. W., Ahn, J., Dentener, F. J., Domingues, C. M.,
Gerland, S., Gong, D., Kaufman, D. S., Nnamchi, H. C., Quaas, J., Rivera,
J. A., Sathyendranath, S., Smith, S. L., Trewin, B., von Shuckmann, K., and
Vose, R. S.: Changing State of the Climate System, in: Climate Change 2021:
The Physical Science Basis, Contribution of Working Group I to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change, edited by:
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C.,
Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M.,
Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T.,
Yelekçi, O., Yu, R., and Zhou, B., chap. 2, Cambridge University Press, https://doi.org/10.1017/9781009157896.004,
2021. a
Guttman, N. B.: A sensitivity analysis of the Palmer Hydrologic Drought Index,
J. Am. Water Resour. As., 27, 797–807,
1991. a
Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-dependent filtering of
background error covariance estimates in an ensemble Kalman filter, Mon.
Weather Rev., 129, 2776–2790, 2001. a
Hansen, J., Sato, M., Russell, G., and Kharecha, P.: Climate sensitivity, sea
level and atmospheric carbon dioxide, Philos. T. Roy.
Soc. A, 371, 20120294, https://doi.org/10.1098/rsta.2012.0294,
2013. a
Hargreaves, J. C. and Annan, J. D.: On the importance of paleoclimate modelling for improving predictions of future climate change, Clim. Past, 5, 803–814, https://doi.org/10.5194/cp-5-803-2009, 2009. a
Hargreaves, J. C., Abe-Ouchi, A., and Annan, J. D.: Linking glacial and future climates through an ensemble of GCM simulations, Clim. Past, 3, 77–87, https://doi.org/10.5194/cp-3-77-2007, 2007. a
Hegerl, G. C., Crowley, T. J., Hyde, W. T., and Frame, D. J.: Climate
sensitivity constrained by temperature reconstructions over the past seven
centuries, Nature, 440, 1029–1032, 2006. a
Kalman, R. E.: A new approach to linear filtering and prediction problems, J. Basic Eng., 82, 35–45, https://doi.org/10.1115/1.3662552,
1960. a
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) large ensemble project: A community
resource for studying climate change in the presence of internal climate
variability, B. Am. Meteorol. Soc., 96, 1333–1349,
2015. a
King, J.: JonKing93/DASH: DASH v4.2.2 (v4.2.2), Zenodo [code], https://doi.org/10.5281/zenodo.8277408, 2023. a
King, J. and Meko, D.: JonKing93/pdsi, Github [code], https://github.com/JonKing93/pdsi, last access: 2 October 2023. a
King, J., Tierney, J., Osman, M., Judd, E., and Anchukaitis, K.: DASH: A
MATLAB toolbox for paleoclimate data assimilation, Zenodo [code and data set],
https://doi.org/10.5281/zenodo.7545722, 2023b. 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, 34, 7091–7111, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Kurahashi-Nakamura, T., Losch, M., and Paul, A.: Can sparse proxy data constrain the strength of the Atlantic meridional overturning circulation?, Geosci. Model Dev., 7, 419–432, https://doi.org/10.5194/gmd-7-419-2014, 2014. a
Kutzbach, J. E., He, F., Vavrus, S. J., and Ruddiman, W. F.: The dependence of
equilibrium climate sensitivity on climate state: Applications to studies of
climates colder than present, Geophys. Res. Lett., 40, 3721–3726,
2013. a
LeGrand, P. and Wunsch, C.: Constraints from paleotracer data on the North
Atlantic circulation during the last glacial maximum, Paleoceanography, 10,
1011–1045, 1995. a
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, 13–155, 2017. a
Mairesse, A., Goosse, H., Mathiot, P., Wanner, H., and Dubinkina, S.: Investigating the consistency between proxy-based reconstructions and climate models using data assimilation: a mid-Holocene case study, Clim. Past, 9, 2741–2757, https://doi.org/10.5194/cp-9-2741-2013, 2013. a
Matsikaris, A., Widmann, M., and Jungclaus, J.: On-line and off-line data assimilation in palaeoclimatology: a case study, Clim. Past, 11, 81–93, https://doi.org/10.5194/cp-11-81-2015, 2015. a, b
Morales, M. S., Cook, E. R., Barichivich, J., Christie, D. A., Villalba, R.,
LeQuesne, C., Srur, A. M., Ferrero, M. E., González-Reyes, Á.,
Couvreux, F., Matskovsky, V., Aravena, J., Lara, A., Mundo, I., Rojas, F., Prieto, M., Smerdon, J., Bianchi, L., Masiokas, M., Urrutia-Jalabert, R., Rodriguez-Catón, M., Muñoz, A., Rojas-Badilla, M., Alvarez, C., Lopez, L., Luckman, B., Lister, D., Harris, I., Jones, P., Williams, A., Velazquez, G., Aliste, D., Aguilera-Betta, I., Marcotti, E., Flores, F., Muñoz, T., Cuq, E., and Boninsegna, J.: Six hundred years of South American tree rings reveal
an increase in severe hydroclimatic events since mid-20th century,
P. Natl. Acad. Sci. USA, 117, 16816–16823, 2020. a
Oke, P. R., Allen, J. S., Miller, R. N., Egbert, G. D., and Kosro, P. M.:
Assimilation of surface velocity data into a primitive equation coastal ocean
model, J. Geophys. Res.-Oceans, 107, 3122, https://doi.org/10.1029/2000JC000511, 2002. a, b
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, 2016. a
Overpeck, J. T., Otto-Bliesner, B. L., Miller, G. H., Muhs, D. R., Alley,
R. B., and Kiehl, J. T.: Paleoclimatic evidence for future ice-sheet
instability and rapid sea-level rise, Science, 311, 1747–1750, 2006. a
PaleoSENSE Project Members: Making
sense of palaeoclimate sensitivity, Nature, 491, 683–691, 2012. a
Parsons, L. A., Amrhein, D. E., Sanchez, S. C., Tardif, R., Brennan, M. K., and
Hakim, G. J.: Do Multi-Model Ensembles Improve Reconstruction Skill in
Paleoclimate Data Assimilation?, Earth Space Sci., 8, e2020EA001467, https://doi.org/10.1029/2020EA001467,
2021. a, b
Perkins, W. A. and Hakim, G.: Linear inverse modeling for coupled
atmosphere-ocean ensemble climate prediction, J. Adv. Model.
Earth Sy., 12, e2019MS001778, https://doi.org/10.1029/2019MS001778, 2020. 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., Tardif, R., and Hakim, G.: modons/LMR, Github [code], https://github.com/modons/LMR, last access: 2 October 2023. a
Rice, J. L., Woodhouse, C. A., and Lukas, J. J.: Science and Decision Making:
Water Management and Tree-Ring Data in the Western United States,
J. Am. Water Resour. As., 45, 1248–1259, 2009. a
Rohling, E. J., Marino, G., Foster, G. L., Goodwin, P. A., Von der Heydt,
A. S., and Köhler, P.: Comparing climate sensitivity, past and present,
Annu. Rev. Mar. Sci., 10, 261–288, 2018. a
Schmidt, G. A.: Enhancing the relevance of palaeoclimate model/data comparisons
for assessments of future climate change, J. Quaternary Sci., 25,
79–87, 2010. a
Schmidt, G. A., Annan, J. D., Bartlein, P. J., Cook, B. I., Guilyardi, E., Hargreaves, J. C., Harrison, S. P., Kageyama, M., LeGrande, A. N., Konecky, B., Lovejoy, S., Mann, M. E., Masson-Delmotte, V., Risi, C., Thompson, D., Timmermann, A., Tremblay, L.-B., and Yiou, P.: Using palaeo-climate comparisons to constrain future projections in CMIP5, Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, 2014. a
Sherwood, S., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M.,
Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J.,
Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C., Foster, G., Hausfather, Z., von der Heydt, A., Knutti, R., Mauritsen, T., Norris, J., Proistosescu, C., Rugenstein, M., Schmidt, G., Tokarska, K., and Zelinka, M.: An assessment of Earth's climate sensitivity using multiple lines of
evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a
Smerdon, J. E.: Climate models as a test bed for climate reconstruction
methods: pseudoproxy experiments, Wires Clim.
Change, 3, 63–77, 2012. a
Snyder, C. W.: The value of paleoclimate research in our changing climate,
Climatic Change, 100, 407–418, 2010. a
Steiger, N.: njsteiger/PHYDA-v1, Github [code], https://github.com/njsteiger/PHYDA-v1, last access: 2 October 2023. a
Steiger, N. J., Steig, E. J., Dee, S. G., Roe, G. H., and Hakim, G. J.: Climate
reconstruction using data assimilation of water isotope ratios from ice
cores, J. Geophys. Res.-Atmos., 122, 1545–1568, 2017. a
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,
Sci. Data, 5, 180086, https://doi.org/10.1038/sdata.2018.86, 2018. a, b, c
Stevenson, S., Otto-Bliesner, B., Brady, E., Nusbaumer, J., Tabor, C., Tomas,
R., Noone, D., and Liu, Z.: Volcanic eruption signatures in the
isotope-enabled last millennium ensemble, Paleoceanogr.
Paleoclim., 34, 1534–1552, 2019. 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
Tierney, J.: jesstierney/BAYMAG, Github [code], https://github.com/jesstierney/BAYMAG, last access: 2 October 2023b. a
Tierney, J.: jesstierney/BAYSPAR, Github [code], https://github.com/jesstierney/BAYSPAR, last access: 2 October 2023b. a
Tierney, J. E., Poulsen, C. J., Montañez, I. P., Bhattacharya, T., Feng,
R., Ford, H. L., Hönisch, B., Inglis, G. N., Petersen, S. V., Sagoo, N.,
Tabor, C. R., Thirumalai, K., Zhu, J., Burls, N., Foster, G., Goddéris, Y., Huber, B., Ivany, L., Turner, S., Lunt, D., McElwain, J., Mills, B., Otto-Bliesner, B., Ridgwell, A., and Zhang, Y.: Past climates inform our future, Science, 370, eaay3701, https://doi.org/10.1126/science.aay370,
2020a. a, b
Tierney, J. E., Zhu, J., Li, M., Ridgwell, A., Hakim, G. J., Poulsen, C. J.,
Whiteford, R. D., Rae, J. W., and Kump, L. R.: Spatial patterns of climate
change across the Paleocene–Eocene Thermal Maximum, P.
Natl. Acad. Sci. USA, 119, e2205326119, https://doi.org/10.1073/pnas.2205326119, 2022. a, b
Tolwinski-Ward, S.: suztolwinskiward/vslite, Github [code], https://github.com/suztolwinskiward/vslite, last access: 2 Ocotober 2023. a
Valler, V., Franke, J., and Brönnimann, S.: Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation, Clim. Past, 15, 1427–1441, https://doi.org/10.5194/cp-15-1427-2019, 2019. a
Van der Schrier, G., Jones, P., and Briffa, K.: The sensitivity of the PDSI to
the Thornthwaite and Penman-Monteith parameterizations for potential
evapotranspiration, J. Geophys. Res.-Atmos., 116, D03106, https://doi.org/10.1029/2010JD015001, 2011. a
Vrac, M.: Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction, Hydrol. Earth Syst. Sci., 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, 2018. a
Wang, Q. and Robertson, D.: Multisite probabilistic forecasting of seasonal
flows for streams with zero value occurrences, Water Resour. Res., 47, W02546, https://doi.org/10.1029/2010WR009333,
2011. a
Wang, Q., Shrestha, D. L., Robertson, D., and Pokhrel, P.: A log-sinh
transformation for data normalization and variance stabilization, Water
Resour. Res., 48, W05514, https://doi.org/10.1029/2011WR010973, 2012. a, b
Wikle, C. K. and Berliner, L. M.: A Bayesian tutorial for data assimilation,
Physica D, 230, 1–16, 2007. a
Wilson, R., Anchukaitis, K., Briffa, K. R., Büntgen, U., Cook, E.,
D'arrigo, R., Davi, N., Esper, J., Frank, D., Gunnarson, B., Hegerl G., Helama, S., Klesse, S., Krusic, P., Linderholm, H., Myglan, V., Osborn, T., Rydval, M., Schneider, L., Schurer, A., Wiles, G., Zhang, P., and Zorita, E.: Last
millennium northern hemisphere summer temperatures from tree rings: Part I:
The long term context, Quaternary Sci. Rev., 134, 1–18, 2016. a
Winguth, A., Archer, D., Maier-Reimer, E., and Mikolajewicz, U.: Paleonutrient
data analysis of the glacial Atlantic using an adjoint ocean general
circulation model, in: Inverse Methods in Global Biogeochemical Cycles,
Wiley Online Library, 171–183, https://doi.org/10.1029/GM114, 2000. a
Zhao, T., Bennett, J. C., Wang, Q., Schepen, A., Wood, A. W., Robertson, D. E.,
and Ramos, M.-H.: How suitable is quantile mapping for postprocessing GCM
precipitation forecasts?, J. Climate, 30, 3185–3196, 2017. 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,
1–9, 2022. a
Zhu, J., Liu, Z., Brady, E., Otto-Bliesner, B., Zhang, J., Noone, D., Tomas,
R., Nusbaumer, J., Wong, T., Jahn, A., and Tabor, C.: Reduced ENSO variability at
the LGM revealed by an isotope-enabled Earth system model, Geophys.
Res. Lett., 44, 6984–6992, 2017. a
Zhu, J., Poulsen, C. J., and Otto-Bliesner, B. L.: High climate sensitivity in
CMIP6 model not supported by paleoclimate, Nat. Clim. Change, 10,
378–379, 2020. a
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Gettelman, A., Bacmeister, J. T.,
Neale, R. B., Poulsen, C. J., Shaw, J. K., McGraw, Z. S., and Kay, J. E.: LGM
paleoclimate constraints inform cloud parameterizations and equilibrium
climate sensitivity in CESM2, J. Adv. Model. Earth Sy.,
e2021MS002776, https://doi.org/10.1029/2021MS002776, 2021a. a, b
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E.,
Lofverstrom, M., and DiNezio, P.: Assessment of equilibrium climate
sensitivity of the Community Earth System Model version 2 through simulation
of the Last Glacial Maximum, Geophys. Res. Lett., 48,
e2020GL091220, https://doi.org/10.1029/2020GL091220, 2021b. a
Short summary
Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Paleoclimate data assimilation is a useful method that allows researchers to combine climate...
