Articles | Volume 11, issue 12
https://doi.org/10.5194/gmd-11-5051-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-5051-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of iterative Kalman smoother schemes for multi-decadal past climate analysis with comprehensive Earth system models
Javier García-Pintado
CORRESPONDING AUTHOR
MARUM – Center for Marine environmental Sciences and Department of
Geosciences, University of Bremen, Bremen, Germany
André Paul
MARUM – Center for Marine environmental Sciences and Department of
Geosciences, University of Bremen, Bremen, Germany
Related authors
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
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Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Charlotte Breitkreuz, André Paul, Stefan Mulitza, Javier García-Pintado, and Michael Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-32, https://doi.org/10.5194/gmd-2019-32, 2019
Publication in GMD not foreseen
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We present a technique for ocean state estimation based on the combination of a simple data assimilation method with a state reduction approach. The technique proves to be very efficient and successful in reducing the model-data misfit and reconstructing a target ocean circulation from synthetic observations. In an application to Last Glacial Maximum proxy data the model-data misfit is greatly reduced but some misfit remains. Two different ocean states are found with similar model-data misfit.
Alexandre Cauquoin, Ayako Abe-Ouchi, Takashi Obase, Wing-Le Chan, André Paul, and Martin Werner
Clim. Past, 19, 1275–1294, https://doi.org/10.5194/cp-19-1275-2023, https://doi.org/10.5194/cp-19-1275-2023, 2023
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Stable water isotopes are tracers of climate processes occurring in the hydrological cycle. They are widely used to reconstruct the past variations of polar temperature before the instrumental era thanks to their measurements in ice cores. However, the relationship between measured isotopes and temperature has large uncertainties. In our study, we investigate how the sea surface conditions (temperature, sea ice, ocean circulation) impact this relationship for a cold to warm climate change.
Nils Weitzel, Heather Andres, Jean-Philippe Baudouin, Marie Kapsch, Uwe Mikolajewicz, Lukas Jonkers, Oliver Bothe, Elisa Ziegler, Thomas Kleinen, André Paul, and Kira Rehfeld
EGUsphere, https://doi.org/10.5194/egusphere-2023-986, https://doi.org/10.5194/egusphere-2023-986, 2023
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The ability of climate models to faithfully reproduce past warming episodes is a valuable test considering potentially large future warming. We develop a new method to compare simulations of the last deglaciation with temperature reconstructions. We find that reconstructions differ more between regions than simulations, potentially due to deficiencies in the simulation design, models, or reconstructions. Our work is a promising step towards benchmarking simulations of past climate transitions.
Takasumi Kurahashi-Nakamura, André Paul, Ute Merkel, and Michael Schulz
Clim. Past, 18, 1997–2019, https://doi.org/10.5194/cp-18-1997-2022, https://doi.org/10.5194/cp-18-1997-2022, 2022
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With a comprehensive Earth-system model including the global carbon cycle, we simulated the climate state during the last glacial maximum. We demonstrated that the CO2 concentration in the atmosphere both in the modern (pre-industrial) age (~280 ppm) and in the glacial age (~190 ppm) can be reproduced by the model with a common configuration by giving reasonable model forcing and total ocean inventories of carbon and other biogeochemical matter for the respective ages.
Kaveh Purkiani, Matthias Haeckel, Sabine Haalboom, Katja Schmidt, Peter Urban, Iason-Zois Gazis, Henko de Stigter, André Paul, Maren Walter, and Annemiek Vink
Ocean Sci., 18, 1163–1181, https://doi.org/10.5194/os-18-1163-2022, https://doi.org/10.5194/os-18-1163-2022, 2022
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Based on altimetry data and in situ hydrographic observations, the impacts of an anticyclone mesoscale eddy (large rotating body of water) on the seawater characteristics were investigated during a research campaign. The particular eddy presents significant anomalies on the seawater properties at 1500 m. The potential role of eddies in the seafloor and its consequential effect on the altered dispersion of mining-related sediment plumes are important to assess future mining operations.
Stefan Mulitza, Torsten Bickert, Helen C. Bostock, Cristiano M. Chiessi, Barbara Donner, Aline Govin, Naomi Harada, Enqing Huang, Heather Johnstone, Henning Kuhnert, Michael Langner, Frank Lamy, Lester Lembke-Jene, Lorraine Lisiecki, Jean Lynch-Stieglitz, Lars Max, Mahyar Mohtadi, Gesine Mollenhauer, Juan Muglia, Dirk Nürnberg, André Paul, Carsten Rühlemann, Janne Repschläger, Rajeev Saraswat, Andreas Schmittner, Elisabeth L. Sikes, Robert F. Spielhagen, and Ralf Tiedemann
Earth Syst. Sci. Data, 14, 2553–2611, https://doi.org/10.5194/essd-14-2553-2022, https://doi.org/10.5194/essd-14-2553-2022, 2022
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Stable isotope ratios of foraminiferal shells from deep-sea sediments preserve key information on the variability of ocean circulation and ice volume. We present the first global atlas of harmonized raw downcore oxygen and carbon isotope ratios of various planktonic and benthic foraminiferal species. The atlas is a foundation for the analyses of the history of Earth system components, for finding future coring sites, and for teaching marine stratigraphy and paleoceanography.
Masa Kageyama, Sandy P. Harrison, Marie-L. Kapsch, Marcus Lofverstrom, Juan M. Lora, Uwe Mikolajewicz, Sam Sherriff-Tadano, Tristan Vadsaria, Ayako Abe-Ouchi, Nathaelle Bouttes, Deepak Chandan, Lauren J. Gregoire, Ruza F. Ivanovic, Kenji Izumi, Allegra N. LeGrande, Fanny Lhardy, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, André Paul, W. Richard Peltier, Christopher J. Poulsen, Aurélien Quiquet, Didier M. Roche, Xiaoxu Shi, Jessica E. Tierney, Paul J. Valdes, Evgeny Volodin, and Jiang Zhu
Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, https://doi.org/10.5194/cp-17-1065-2021, 2021
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The Last Glacial Maximum (LGM; ~21 000 years ago) is a major focus for evaluating how well climate models simulate climate changes as large as those expected in the future. Here, we compare the latest climate model (CMIP6-PMIP4) to the previous one (CMIP5-PMIP3) and to reconstructions. Large-scale climate features (e.g. land–sea contrast, polar amplification) are well captured by all models, while regional changes (e.g. winter extratropical cooling, precipitations) are still poorly represented.
André Paul, Stefan Mulitza, Rüdiger Stein, and Martin Werner
Clim. Past, 17, 805–824, https://doi.org/10.5194/cp-17-805-2021, https://doi.org/10.5194/cp-17-805-2021, 2021
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Maps and fields of near-sea-surface temperature differences between the past and present can be used to visualize and quantify climate changes and perform simulations with climate models. We used a statistical method to map sparse and scattered data for the Last Glacial Maximum time period (23 000 to 19 000 years before present) to a regular grid. The estimated global and tropical cooling would imply an equilibrium climate sensitivity in the lower to middle part of the currently accepted range.
Kaveh Purkiani, André Paul, Annemiek Vink, Maren Walter, Michael Schulz, and Matthias Haeckel
Biogeosciences, 17, 6527–6544, https://doi.org/10.5194/bg-17-6527-2020, https://doi.org/10.5194/bg-17-6527-2020, 2020
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There has been a steady increase in interest in mining of deep-sea minerals in the eastern Pacific Ocean recently. The ocean state in this region is known to be highly influenced by rotating bodies of water (eddies), some of which can travel long distances in the ocean and impact the deeper layers of the ocean. Better insight into the variability of eddy activity in this region is of great help to mitigate the impact of the benthic ecosystem from future potential deep-sea mining activity.
Takasumi Kurahashi-Nakamura, André Paul, Guy Munhoven, Ute Merkel, and Michael Schulz
Geosci. Model Dev., 13, 825–840, https://doi.org/10.5194/gmd-13-825-2020, https://doi.org/10.5194/gmd-13-825-2020, 2020
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Chemical processes in ocean-floor sediments have a large influence on the marine carbon cycle, hence the global climate, at long timescales. We developed a new coupling scheme for a chemical sediment model and a comprehensive climate model. The new coupled model outperformed the original uncoupled climate model in reproducing the global distribution of sediment properties. The sediment model will also act as a
bridgebetween the ocean model and paleoceanographic data.
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
Short summary
Short summary
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Charlotte Breitkreuz, André Paul, and Michael Schulz
Clim. Past Discuss., https://doi.org/10.5194/cp-2019-52, https://doi.org/10.5194/cp-2019-52, 2019
Publication in CP not foreseen
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We combined a model simulation of the Last Glacial Maximum ocean with sea surface temperature and calcite oxygen isotope data through data assimilation. The reconstructed ocean state is very similar to the modern and it follows that the employed proxy data do not require an ocean state very different from today's. Sensitivity experiments reveal that data from the deep North Atlantic but also from the global deep Southern Ocean are most important to constrain the Atlantic overturning circulation.
Charlotte Breitkreuz, André Paul, Stefan Mulitza, Javier García-Pintado, and Michael Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-32, https://doi.org/10.5194/gmd-2019-32, 2019
Publication in GMD not foreseen
Short summary
Short summary
We present a technique for ocean state estimation based on the combination of a simple data assimilation method with a state reduction approach. The technique proves to be very efficient and successful in reducing the model-data misfit and reconstructing a target ocean circulation from synthetic observations. In an application to Last Glacial Maximum proxy data the model-data misfit is greatly reduced but some misfit remains. Two different ocean states are found with similar model-data misfit.
Rike Völpel, André Paul, Annegret Krandick, Stefan Mulitza, and Michael Schulz
Geosci. Model Dev., 10, 3125–3144, https://doi.org/10.5194/gmd-10-3125-2017, https://doi.org/10.5194/gmd-10-3125-2017, 2017
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This study presents the implementation of stable water isotopes in the MITgcm and describes the results of an equilibrium simulation under pre-industrial conditions. The model compares well to observational data and measurements of plankton tow records and thus opens wide prospects for long-term simulations in a paleoclimatic context.
T. Kurahashi-Nakamura, M. Losch, and A. Paul
Geosci. Model Dev., 7, 419–432, https://doi.org/10.5194/gmd-7-419-2014, https://doi.org/10.5194/gmd-7-419-2014, 2014
D. Handiani, A. Paul, M. Prange, U. Merkel, L. Dupont, and X. Zhang
Clim. Past, 9, 1683–1696, https://doi.org/10.5194/cp-9-1683-2013, https://doi.org/10.5194/cp-9-1683-2013, 2013
J. C. Hargreaves, J. D. Annan, R. Ohgaito, A. Paul, and A. Abe-Ouchi
Clim. Past, 9, 811–823, https://doi.org/10.5194/cp-9-811-2013, https://doi.org/10.5194/cp-9-811-2013, 2013
Related subject area
Climate and Earth system modeling
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
URock 2023a: an open-source GIS-based wind model for complex urban settings
DASH: a MATLAB toolbox for paleoclimate data assimilation
Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)
All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
LandInG 1.0: a toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources
Conservation of heat and mass in P-SKRIPS version 1: the coupled atmosphere–ice–ocean model of the Ross Sea
Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53
Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0
Differentiable programming for Earth system modeling
Evaluation of CMIP6 model performances in simulating fire weather spatiotemporal variability on global and regional scales
Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2
Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning
An improved method of the Globally Resolved Energy Balance model by the Bayesian networks
Assessing predicted cirrus ice properties between two deterministic ice formation parameterizations
Various ways of using empirical orthogonal functions for climate model evaluation
C-Coupler3.0: an integrated coupler infrastructure for Earth system modelling
FEOTS v0.0.0: a new offline code for the fast equilibration of tracers in the ocean
Pace v0.2: a Python-based performance-portable atmospheric model
Earth System Model Aerosol-Cloud Diagnostics Package (ESMAC Diags) Version 2: Assessments of Aerosols, Clouds and Aerosol-Cloud Interactions Through Field Campaign and Long-Term Observations
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
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The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
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The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
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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.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
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Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
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The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
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The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
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Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
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This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
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Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
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We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
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A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
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ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
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Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
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Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
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To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
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The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
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This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
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How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
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Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
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This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
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Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
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A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
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This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
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Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
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In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
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Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
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A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
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Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
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This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
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We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
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Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334, https://doi.org/10.5194/gmd-16-3313-2023, https://doi.org/10.5194/gmd-16-3313-2023, 2023
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This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
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Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
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Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
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This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
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Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
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Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
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This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
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A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
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A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
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C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
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FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
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It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
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. Discuss., https://doi.org/10.5194/gmd-2023-51, https://doi.org/10.5194/gmd-2023-51, 2023
Revised manuscript accepted for GMD
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To assess the ability of Earth System Model (ESM) predictions, we developed a tool called ESMAC Diags to understand the details of how aerosols, clouds, and ACI are represented in ESMs, and this paper describes its version 2 functionality. We compared the model predictions with measurements taken by airplanes, ships, satellites, and ground instruments over four regions over the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Cited articles
Acevedo, W., Fallah, B., Reich, S., and Cubasch, U.: Assimilation of
pseudo-tree-ring-width observations into an atmospheric general circulation
model, Clim. Past, 13, 545–557, https://doi.org/10.5194/cp-13-545-2017,
2017. a, b
Annan, J. D., Hargreaves, J. C., Edwards, N. R., and R, M.: Parameter
estimation in an intermediate complexity Earth System Model using an
ensemble Kalman filter, Ocean Modell., 8, 135–154,
https://doi.org/10.1016/j.ocemod.2003.12.004, 2005a. a, b
Arakawa, A.: The cumulus parameterization problem: Past, present, and future,
J. Climate, 17, 2493–2525,
https://doi.org/10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2, 2004. a
Arakawa, A., Jung, J.-H., and Wu, C.-M.: Toward unification of the multiscale
modeling of the atmosphere, Atmos. Chem. Phys., 11, 3731–3742,
https://doi.org/10.5194/acp-11-3731-2011, 2011. a
Bannister, R. N.: A review of operational methods of variational and
ensemble-variational data assimilation, Q. J. Roy.
Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. a, b
Béal, D., Brasseur, P., Brankart, J.-M., Ourmières, Y., and Verron,
J.: Characterization of mixing errors in a coupled physical biogeochemical
model of the North Atlantic:
implications for nonlinear estimation using Gaussian anamorphosis, Ocean Sci., 6,
247–262, https://doi.org/10.5194/os-6-247-2010, 2010. a, b
Bell, B. M.: The Iterated Kalman Smoother as a Gauss–Newton Method, SIAM
J. Optimiz., 4, 626–636, https://doi.org/10.1137/0804035, 1994. a, b, c
Bell, B. M. and Cathey, F. W.: The iterated Kalman filter update as a
Gauss-Newton method, IEEE T. Automat. Contr., 38, 294–297,
https://doi.org/10.1109/9.250476, 1993. a
Bertino, L., Evensen, G., and Wackernagel, H.: Sequential Data Assimilation
Techniques in Oceanography, Int. Stat. Rev., 71, 223–241,
https://doi.org/10.1111/j.1751-5823.2003.tb00194.x, 2003. a, b
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the
Ensemble Transform Kalman Filter. Part I: Theoretical aspects, Mon.
Weather Rev., 129, 420–436,
https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an
iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818,
https://doi.org/10.5194/npg-20-803-2013, 2013. a, b
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q.
J. Roy. Meteor. Soc., 140, 1521–1535,
https://doi.org/10.1002/qj.2236, 2014. a, b, c
Chen, Y. and Oliver, D. S.: Ensemble Randomized Maximum Likelihood Method as an
Iterative Ensemble Smoother, Math. Geosci., 44, 1–26,
https://doi.org/10.1007/s11004-011-9376-z, 2012. a
Chìles, J.-P. and Delfiner, P.: Geostatistics: Modeling spatial uncertainty,
2nd edition, John Wiley & Sons, Ltd., 2012. a
Christiansen, B. and Ljungqvist, F. C.: Challenges and perspectives for
large-scale temperature reconstructions of the past two millennia, Rev.
Geophys., 50, 40–96, https://doi.org/10.1002/2016RG000521, 2017. a
Chuang, C. C., Kelly, J. T., Boyle, J. S., and Xie, S.: Sensitivity of aerosol
indirect effects to cloud nucleation and autoconversion parameterizations in
short-range weather forecasts during the May 2003 aerosol IOP, J.
Adv. Model. Earth Sy., 4, m09001, https://doi.org/10.1029/2012MS000161,
2012. a
Cohn, S. E.: An Introduction to Estimation Theory (Special Issue, Data
Assimilation in Meteology and Oceanography: Theory and Practice), J.
Meteorol. Soc. Jpn., 75, 257–288,
https://doi.org/10.2151/jmsj1965.75.1B_257, 1997. a
Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for
operational implementation of 4D-Var, using an incremental approach,
Q. J. Roy. Meteor. Soc., 120, 1367–1387,
https://doi.org/10.1002/qj.49712051912, 1994. a
Covey, C., Lucas, D. D., Tannahill, J., Garaizar, X., and Klein, R.: Efficient
screening of climate model sensitivity to a large number of perturbed input
parameters, J. Adv. Model. Earth Sy., 5, 598–610,
https://doi.org/10.1002/jame.20040, 2013. a
Dail, H. and Wunsch, C.: Dynamical Reconstruction of Upper-Ocean Conditions in
the Last Glacial Maximum Atlantic, J. Climate, 27, 807–823,
https://doi.org/10.1175/JCLI-D-13-00211.1, 2014. a
Dee, S. G., Steiger, N. J., Emile-Geay, J., and Hakim, G. J.: On the utility of
proxy system models for estimating climate states over the common era,
J. Adv. Model. Earth Sy., 8, 1164–1179,
https://doi.org/10.1002/2016MS000677, 2016. a
Delworth, T. L., Manabe, S., and Stouffer, R. J.: Multidecadal climate
variability in the Greenland Sea and surrounding regions: A coupled model
simulation, Geophys. Res. Lett., 24, 257–260,
https://doi.org/10.1029/96GL03927, 1997. a, b
Dennis, Jr., J. E. and Schnabel, R. B.: Numerical Methods for Unconstrained
Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16),
Soc for Industrial & Applied Math, 1996. a
Deutsch, C. V. and Journel, A. G.: GSLIB: Geostatistical Software Library and
User's Guide, Oxford UP, NY, 1998. a
Dirren, S. and Hakim, G. J.: Toward the assimilation of time-averaged
observations, Geophys. Res. Lett., 32, L04804, https://doi.org/10.1029/2004GL021444,
2005. a
Dommenget, D. and Rezny, M.: A Caveat Note on Tuning in the Development of
Coupled Climate Models, J. Adv. Model. Earth Sy., 10, 78–97,
https://doi.org/10.1002/2017MS000947, 2017. a
Doron, M., Brasseur, P., and Brankart, J.-M.: Stochastic estimation of
biogeochemical parameters of a 3D ocean coupled physical-biogeochemical
model: Twin experiments, J. Marine Syst., 87, 194–207,
https://doi.org/10.1016/j.jmarsys.2011.04.001, 2011. a
Dubinkina, S., Goosse, H., Sallaz-Damaz, Y., Crespin, E., and Crucifix, M.:
Testing a particle filter to reconstruct climate changes over the past
centuries, Int. J. Bifurcat. Chaos, 21, 3611–3618,
https://doi.org/10.1142/S0218127411030763, 2011. a
Emerick, A. A. and Reynolds, A. C.: Ensemble smoother with multiple data
assimilation, Comput. Geosci., 55, 3–15,
https://doi.org/10.1016/j.cageo.2012.03.011, 2013. a
Evans, M., Tolwinski-Ward, S., Thompson, D., and Anchukaitis, K.: Applications
of proxy system modeling in high resolution paleoclimatology, Quaternary
Sci. Rev., 76, 16–28, https://doi.org/10.1016/j.quascirev.2013.05.024, 2013. a
Evensen, G.: Inverse methods and data assimilation in nonlinear ocean models,
Physica D, 77, 108–129,
https://doi.org/10.1016/0167-2789(94)90130-9, 1994. a, b
Friedland, B.: Treatment of bias in recursive filtering,
IEEE T. Automat. Contr., 14, 359–367, https://doi.org/10.1109/TAC.1969.1099223, 1969. a
Gao, G. and Reynolds, A. C.: An Improved Implementation of the LBFGS Algorithm
for Automatic History Matching, SPE J., 11, 5–17,
https://doi.org/10.2118/90058-PA, 2006. a, b
García-Pintado, J.: rDAF v1.0.0: R data assimilation framework,
https://doi.org/10.5281/zenodo.1489131,
2018a. a, b, c
García-Pintado, J.: rdafEbm1D v1.00: rDAF interface for Ebm1D,
https://doi.org/10.5281/zenodo.1489133, 2018b. a, b
García-Pintado, J.: rdafCESM v1.0.0: rDAF interface for CESM,
https://doi.org/10.5281/zenodo.1489135, 2018c. a, b
García-Pintado, J., Neal, J. C., Mason, D. C., Dance, S. L., and Bates,
P. D.: Scheduling satellite-based SAR acquisition for sequential assimilation
of water level observations into flood modelling, J. Hydrol., 495,
252–266, https://doi.org/10.1016/j.jhydrol.2013.03.050, 2013. a
Gent, P. R. and McWilliams, J. C.: Isopycnal Mixing in Ocean Circulation
Models, J. Phys. Oceanogr., 20, 150–155,
https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a, b
Giering, R. and Kaminski, T.: Recipes for Adjoint Code Construction, ACM T.
Math. Software, 24, 437–474, https://doi.org/10.1145/293686.293695, 1998. a
Giering, R., Kaminski, T., and Slawig, T.: Generating Efficient Derivative Code
with TAF, Future Gener. Comp. Sy., 21, 1345–1355,
https://doi.org/10.1016/j.future.2004.11.003, 2005. a
Gilbert, J. C. and Lemaréchal, C.: Some numerical experiments with
variable-storage quasi-Newton algorithms, Math. Program., 45,
407–435, https://doi.org/10.1007/BF01589113, 1989. a, b
Goosse, H.: An additional step toward comprehensive paleoclimate reanalyses,
J. Adv. Model. Earth Sy., 8, 1501–1503,
https://doi.org/10.1002/2016MS000739, 2016. a
Gregory, J. M. and Tailleux, R.: Kinetic energy analysis of the response of the
Atlantic meridional overturning circulation to CO2-forced climate change,
Clim. Dynam., 37, 893–914, https://doi.org/10.1007/s00382-010-0847-6, 2011. a
Gu, Y. and Oliver, D. S.: An Iterative Ensemble Kalman Filter for Multiphase
Fluid Flow Data Assimilation, Society of Petroleum Engineers, 12, 438–446,
https://doi.org/10.2118/108438-PA, 2007. a
Hack, J. J.: Parameterization of moist convection in the National Center for
Atmospheric Research community climate model (CCM2), J. Geophys.
Res.-Atmos., 99, 5551–5568, https://doi.org/10.1029/93JD03478, 1994. a
Hakim, G. J., Emile-Geay, J., Steig, E. J., Noone, D., Anderson, D. M., Tardif,
R., Steiger, N., and Perkins, W. A.: The last millennium climate reanalysis
project: Framework and first results, J. Geophys. Res.-Atmos., 121, 6745–6764,
https://doi.org/10.1002/2016JD024751, 2016JD024751, 2016. a
Hargreaves, J. and Annan, J.: Assimilation of paleo-data in a simple Earth
system model, Clim. Dynam., 19, 371–381,
https://doi.org/10.1007/s00382-002-0241-0, 2002. a
Hargreaves, J. C., Paul, A., Ohgaito, R., Abe-Ouchi, A., and Annan, J. D.:
Are paleoclimate model ensembles consistent with the MARGO data synthesis?,
Clim. Past, 7, 917–933, https://doi.org/10.5194/cp-7-917-2011, 2011. a
Hartmann, D. L.: Global physical climatology, Academic Press, San Diego, 1994. a
Hartmann, D. L. and Short, D. A.: On the Role of Zonal Asymmetries in Climate
Change, J. Atmos. Sci., 36, 519–528,
https://doi.org/10.1175/1520-0469(1979)036<0519:OTROZA>2.0.CO;2, 1979. a
Holland, M. M., Blanchard-Wrigglesworth, E., Kay, J., and Vavrus, S.:
Initial-value predictability of Antarctic sea ice in the Community Climate
System Model 3, Geophys. Res. Lett., 40, 2121–2124,
https://doi.org/10.1002/grl.50410, 2013. a
Ide, K., Courtier, P., Ghill, M, and Lorenc, A. C.: Unified notation for Data
Assimilation: operational, sequential and variational, J. Meteorol. Soc.
Jpn., 75, 181–189, 1997. a
Janjić, T., Bormann, N., Bocquet, M., Carton, J. A., Cohn, S. E., Dance,
S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A., and Weston,
P.: On the representation error in data assimilation, Q. J.
Roy. Meteor. Soc., 144, 1257–1278, https://doi.org/10.1002/qj.3130,
2017. a
Jungclaus, J. H., Bard, E., Baroni, M., Braconnot, P., Cao, J., Chini, L. P.,
Egorova, T., Evans, M., González-Rouco, J. F., Goosse, H., Hurtt, G. C.,
Joos, F., Kaplan, J. O., Khodri, M., Klein Goldewijk, K., Krivova, N.,
LeGrande, A. N., Lorenz, S. J., Luterbacher, J., Man, W., Maycock, A. C.,
Meinshausen, M., Moberg, A., Muscheler, R., Nehrbass-Ahles, C.,
Otto-Bliesner, B. I., Phipps, S. J., Pongratz, J., Rozanov, E., Schmidt, G.
A., Schmidt, H., Schmutz, W., Schurer, A., Shapiro, A. I., Sigl, M., Smerdon,
J. E., Solanki, S. K., Timmreck, C., Toohey, M., Usoskin, I. G., Wagner, S.,
Wu, C.-J., Yeo, K. L., Zanchettin, D., Zhang, Q., and Zorita, E.: The PMIP4
contribution to CMIP6 – Part 3: The last millennium, scientific objective,
and experimental design for the PMIP4 past1000 simulations, Geosci. Model
Dev., 10, 4005–4033, https://doi.org/10.5194/gmd-10-4005-2017, 2017. a
Kageyama, M., Laîné, A., Abe-Ouchi, A., Braconnot, P., Cortijo, E.,
Crucifix,
M., de Vernal, A., Guiot, J., Hewitt, C., Kitoh, A., Kucera, M., Marti, O.,
Ohgaito, R., Otto-Bliesner, B., Peltier, W., Rosell-Melé, A., Vettoretti,
G., Weber, S., and Yu, Y.: Last Glacial Maximum temperatures over the North
Atlantic, Europe and western Siberia: a comparison between PMIP models, MARGO
sea-surface temperatures and pollen-based reconstructions, Quaternary
Sci. Rev., 25, 2082–2102,
https://doi.org/10.1016/j.quascirev.2006.02.010, 2006. a
Kageyama, M., Braconnot, P., Harrison, S. P., Haywood, A. M., Jungclaus, J.
H., Otto-Bliesner, B. L., Peterschmitt, J.-Y., Abe-Ouchi, A., Albani, S.,
Bartlein, P. J., Brierley, C., Crucifix, M., Dolan, A., Fernandez-Donado, L.,
Fischer, H., Hopcroft, P. O., Ivanovic, R. F., Lambert, F., Lunt, D. J.,
Mahowald, N. M., Peltier, W. R., Phipps, S. J., Roche, D. M., Schmidt, G. A.,
Tarasov, L., Valdes, P. J., Zhang, Q., and Zhou, T.: The PMIP4 contribution
to CMIP6 – Part 1: Overview and over-arching analysis plan, Geosci. Model
Dev., 11, 1033–1057, https://doi.org/10.5194/gmd-11-1033-2018, 2018. a, b
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A.,
Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo,
K. C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteor. Soc.,
77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a
Klein, F. and Goosse, H.: Reconstructing East African rainfall and Indian Ocean
sea surface temperatures over the last centuries using data assimilation,
Clim. Dynam., 50, 3909–3929, https://doi.org/10.1007/s00382-017-3853-0, 2017. a
Köhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F., and Fischer,
H.: A 156 kyr smoothed history of the atmospheric greenhouse gases
CO2, CH4, and N2O and their radiative forcing,
Earth Syst. Sci. Data, 9, 363–387, https://doi.org/10.5194/essd-9-363-2017,
2017. a, b
Kurahashi-Nakamura, T., Paul, A., and Losch, M.: Dynamical reconstruction of
the global ocean state during the Last Glacial Maximum, Paleoceanography, 32, 326–350,
https://doi.org/10.1002/2016PA003001, 2017. a, b
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A
review and a model with a nonlocal boundary layer parameterization, Rev.
Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994. a
Lawless, A. S.: Variational data assimilation for very large environmental
problems, in: Large Scale Inverse Problems, in: Radon series on computational and applied mathematics,
edited by: Cullen, M., Freitag,
M. A., Kindermann, S., and Scheichl, R., De Gruyter, Berlin, 13, 55–90, 2013. a
Lawless, A. S., Gratton, S., and Nichols, N. K.: An investigation of
incremental 4D-Var using non-tangent linear models, Q. J.
Roy. Meteor. Soc., 131, 459–476, https://doi.org/10.1256/qj.04.20, 2005. a, b
Liu, C., Xiao, Q., and Wang, B.: An Ensemble-Based Four-Dimensional Variational
Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test,
Mon. Weather Rev., 136, 3363–3373, https://doi.org/10.1175/2008MWR2312.1, 2008. a
Livings, D. M., Dance, S. L., and Nichols, N. K.: Unbiased Ensemble Square Root
Filters, Physica D, 237, 1021–1028,
https://doi.org/10.1016/j.physd.2008.01.005, 2008. a
Lorenc, A. C.: Analysis methods for numerical weather prediction, Q.
J. Roy. Meteor. Soc., 112, 1177–1194,
https://doi.org/10.1002/qj.49711247414, 1986. a, b
Lorenc, A. C.: Recommended nomenclature for EnVar data assimilation methods,
in: WGNE Blue Book Research Activities in Atmospheric and Oceanic Modelling,
section 01: 7–8, WMO: Geneva, Switzerland, 2013. a
Marchal, O., Waelbroeck, C., and de Verdière, A. C.: On the Movements of the
North Atlantic Subpolar Front in the Preinstrumental Past, J.
Climate, 29, 1545–1571, https://doi.org/10.1175/JCLI-D-15-0509.1, 2016. a
Marchi, S., Fichefet, T., Goosse, H., Zunz, V., Tietsche, S., Day, J. J., and
Hawkins, E.: Reemergence of Antarctic sea ice predictability and its link to
deep ocean mixing in global climate models, Clim. Dynam.,
https://doi.org/10.1007/s00382-018-4292-2, online first, 2018. a
MARGO Project Members: Constraints on the magnitude and patterns of ocean
cooling at the Last Glacial Maximum, Nat. Geosci., 2, 127–132,
https://doi.org/10.1038/ngeo411, 2009. a, b, c, d
Matheron, G.: Le krigeage disjunctive, Intern. Note N-360, Centre de
Géostatistique, Ecole des Mines de Paris, Paris, France, 40 pp., 1973. a
Meehl, G. A., Arblaster, J. M., Bitz, C. M., Chung, C. T. Y., and Teng, H.:
Antarctic sea-ice expansion between 2000 and 2014 driven by tropical Pacific
decadal climate variability, Nat. Geosci., 9, 590–596,
https://doi.org/10.1038/ngeo2751, 2016. a
Neale, R. B., Richter, R., Conley, A., Park, S., Lauritzen, P., Gettelman,
A., Williamson, D., Rash, P., Vavrus, S., Taylor, M., Collins, W., Zhang, M.,
and Lin, S.-J.: Description of the NCAR Community Atmosphere Model
(CAM4), Tech. Rep. NCAR/TN-485+STR, NCAR, 2011. a
North, G. R., Mengel, J. G., and Short, D. A.: Simple energy balance model
resolving the seasons and the continents: Application to the astronomical
theory of the ice ages, J. Geophys. Res.-Oceans, 88,
6576–6586, https://doi.org/10.1029/JC088iC11p06576, 1983. a, b
Oliver, D. S. and Chen, Y.: Improved initial sampling for the ensemble Kalman
filter, Comput. Geosci., 13, 13–27, https://doi.org/10.1007/s10596-008-9101-2,
2008. a
Ortega, P., Lehner, F., Swingedouw, D., Masson-Delmotte, V., Raible, C. C.,
Casado, M., and Yiou, P.: A model-tested North Atlantic Oscillation
reconstruction for the past millennium, Nature, 523, 71–74,
https://doi.org/10.1038/nature14518, 2015. a
Ott, E., Hunt, B., Szunyogh, I., Zimin, A., Kostelich, E., Corazza, M., Kalnay,
E., Patil, D., and Yorke, J.: A local ensemble Kalman filter for
atmospheric data assimilation, Tellus A, 56, 415–428,
https://doi.org/10.1111/j.1600-0870.2004.00076.x, 2004. a
Otto-Bliesner, B. L., Brady, E. C., Fasullo, J., Jahn, A., Landrum, L.,
Stevenson, S., Rosenbloom, N., Mai, A., and Strand, G.: Climate Variability
and Change since 850 CE: An Ensemble Approach with the Community Earth
System Model, B. Am. Meteorol. Soc., 97,
735–754, https://doi.org/10.1175/BAMS-D-14-00233.1, 2016. a, b
PAGES 2k-PMIP3 group: Continental-scale temperature variability in PMIP3
simulations and PAGES 2k regional temperature reconstructions over the past
millennium, Clim. Past, 11, 1673–1699,
https://doi.org/10.5194/cp-11-1673-2015, 2015. a
PAGES2k Consortium: A global multiproxy database for temperature
reconstructions of the Common Era, Scientific data, 4, 170088,
https://doi.org/10.1038/sdata.2017.88, 2017. a
Palmer, T. N. and Weisheimer, A.: Diagnosing the causes of bias in climate
models – why is it so hard?, Geophys. Astro. Fluid,
105, 351–365, https://doi.org/10.1080/03091929.2010.547194, 2011. a
Paul, A.: Ebm1d-ad v1.0.0: 1D energy balance model of climate with automatic
differentiation, https://doi.org/10.5281/zenodo.1489952, 2018. a, b
Paul, A. and Losch, M.: Perspectives of Parameter and State Estimation in
Paleoclimatology, in: Climate Change: Inferences from Paleoclimate and
Regional Aspects, edited by: Berger, A., Mesinger, F., and Sijacki, D.,
Springer Vienna, Vienna, 93–105, https://doi.org/10.1007/978-3-7091-0973-1_7, 2012. a, b
Paul, A. and Schäfer-Neth, C.: How to combine sparse proxy data and coupled
climate models, Quaternary Sci. Revi., 24, 1095–1107,
https://doi.org/10.1016/j.quascirev.2004.05.010, 2005. a
Rasch, P. J. and Kristjánsson, J. E.: A Comparison of the CCM3 Model
Climate Using Diagnosed and Predicted Condensate Parameterizations, J. Climate, 11, 1587–1614,
https://doi.org/10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2, 1998. a
Sakov, P. and Bocquet, M.: Asynchronous data assimilation with the EnKF in
presence of additive model error, Tellus A, 70, 1414545, https://doi.org/10.1080/16000870.2017.1414545, 2018. a
Sakov, P. and Oke, P. R.: Implications of the Form of the Ensemble
Transformation in the Ensemble Square Root Filters, Mon. Weather Rev., 136,
1042–1053, https://doi.org/10.1175/2007MWR2021.1, 2008. a
Sakov, P., Evensen, G., and Bertino, L.: Asynchronous data assimilation with
the EnKF, Tellus A, 62, 24–29, 2010. a
Sakov, P., Oliver, D. S., and Bertino, L.: An Iterative EnKF for Strongly
Nonlinear Systems, Mon. Weather Rev., 140, 1988–2004,
https://doi.org/10.1175/MWR-D-11-00176.1, 2012. a
Sakov, P., Jean-Matthieu, H., and Bocquet, M.: An iterative ensemble Kalman
filter in the presence of additive model error, Q. J.
Roy. Meteor. Soc., 144, 1297-1309, , https://doi.org/10.1002/qj.3213, 2018. a
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality
(complete samples), Biometrika, 52, 591–611,
https://doi.org/10.1093/biomet/52.3-4.591, 1965. a
Simon, E. and Bertino, L.: Application of the Gaussian anamorphosis to
assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic
with the EnKF: a twin experiment, Ocean Sci., 5, 495–510,
https://doi.org/10.5194/os-5-495-2009, 2009. a, b, c
Simon, E. and Bertino, L.: Gaussian anamorphosis extension of the DEnKF for
combined state parameter estimation: Application to a 1D ocean ecosystem
model, J. Marine Syst., 89, 1–18,
https://doi.org/10.1016/j.jmarsys.2011.07.007, 2012. a, b, c
Smith, P. J., Dance, S. L., and Nichols, N. K.: A hybrid data assimilation
scheme for model parameter estimation: Application to morphodynamic
modelling, Comput. Fluids, 46, 436–441,
https://doi.org/10.1016/j.compfluid.2011.01.010, 2011. a
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J.,
Dukowicz, J., Eden, C., Fox-Kemper, B., Gent, P., Hecht, M., Jayne, S.,
Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, N., Peacock, S.,
Vertenstein, M., and Yeager, S.: The Parallel Ocean Program (POP) Reference
Manual, Ocean
Component of the Community Climate System Model (CCSM) and Community Earth
System Model (CESM), Tech. Rep. LAUR-10-01854, Los Alamos National
Laboratory, Boulder, Colorado, 2010. a
Steiger, N. J., Hakim, G. J., Steig, E. J., Battisti, D. S., and Roe, G. H.:
Assimilation of Time-Averaged Pseudoproxies for Climate Reconstruction,
J. Climate, 27, 426–441, https://doi.org/10.1175/JCLI-D-12-00693.1, 2014. a
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker,
J. S.: Ensemble Square Root Filters, Mon. Weather Rev., 131, 1485–1490,
https://doi.org/10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2, 2003. a
Waelbroeck, C., Kiefer, T., Dokken, T., Chen, M.-T., Spero, H., Jung, S.,
Weinelt, M., Kucera, M., and Paul, A.: Constraints on surface seawater oxygen
isotope change between the Last Glacial Maximum and the Late Holocene,
Quaternary Sci. Rev., 105, 102–111,
https://doi.org/10.1016/j.quascirev.2014.09.020, 2014. a
Wang, X., Bishop, C. H., and Julier, S. J.: Which Is Better, an Ensemble of
Positive–Negative Pairs or a Centered Spherical Simplex Ensemble?, Mon.
Weather
Rev., 132, 1590–1605, https://doi.org/10.1175/1520-0493(2004)132<1590:WIBAEO>2.0.CO;2, 2004. a
Weitzel, N., Wagner, S., Sjolte, J., Klockmann, M., Bothe, O., Andres, H.,
Tarasov, L., Rehfeld, K., Zorita, E., Widmann, M., Sommer, P., Schädler, G.,
Ludwig, P., Kapp, F., Jonkers, L., García-Pintado, J., Fuhrmann, F., Dolman,
A., Dallmeyer, A., and Brücher, T.: Diving into the past – A paleo
data-model comparison workshop on the Late Glacial and Holocene, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-18-0169.1,
online first, 2018. a
Whitaker, J. S. and Hamill, T. M.: Ensemble Data Assimilation without Perturbed
Observations, Mon. Weather Rev., 130, 1913–1924,
https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2, 2002. a
Wu, Z., Reynolds, A., and Oliver, D.: Conditioning Geostatistical Models to
Two-Phase Production Data, SPE J., 3, 142–155, https://doi.org/10.2118/56855-PA,
1999. a, b
Yang, B., Qian, Y., Lin, G., Leung, R., and Zhang, Y.: Some issues in
uncertainty quantification and parameter tuning: a case study of convective
parameterization scheme in the WRF regional climate model, Atmos. Chem.
Phys., 12, 2409–2427, https://doi.org/10.5194/acp-12-2409-2012, 2012. a
Yang, B., Qian, Y., Lin, G., Leung, L. R., Rasch, P. J., Zhang, G. J.,
McFarlane, S. A., Zhao, C., Zhang, Y., Wang, H., Wang, M., and Liu, X.:
Uncertainty quantification and parameter tuning in the CAM5
Zhang-McFarlane convection scheme and impact of improved convection on the
global circulation and climate, J. Geophys. Res.-Atmos.,
118, 395–415, https://doi.org/10.1029/2012JD018213, 2013. a, b
Zanchettin, D., Bothe, O., Lehner, F., Ortega, P., Raible, C. C., and
Swingedouw, D.: Reconciling reconstructed and simulated features of the
winter Pacific/North American pattern in the early 19th century, Clim. Past,
11, 939–958, https://doi.org/10.5194/cp-11-939-2015, 2015. a
Zhang, G. and McFarlane, N. A.: Sensitivity of climate simulations to the
parameterization of cumulus convection in the Canadian climate centre
general circulation model, Atmos.-Ocean, 33, 407–446,
https://doi.org/10.1080/07055900.1995.9649539, 1995.
a
Zhou, H., Gómez-Hernández, J. J., Franssen, H.-J. H., and Li, L.: An
approach to handling non-Gaussianity of parameters and state variables in
ensemble Kalman filtering, Adv. Water Resour., 34, 844–864,
https://doi.org/10.1016/j.advwatres.2011.04.014, 2011. a
Zunz, V., Goosse, H., and Dubinkina, S.: Impact of the initialisation on the
predictability of the Southern Ocean sea ice at interannual to multi-decadal
timescales, Clim. Dynam., 44, 2267–2286,
https://doi.org/10.1007/s00382-014-2344-9, 2015. a
Short summary
Earth system models (ESMs) integrate interactions of atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate under a variety of conditions. Past climate field reconstructions with deterministic ESMs through the assimilation of climate proxies need to consider the required high computations and model non-linearity. Our tests indicate that iterative schemes based on the Kalman filter and careful sensitivity analysis are adequate for approaching the problem.
Earth system models (ESMs) integrate interactions of atmosphere, ocean, land, ice, and biosphere...