Articles | Volume 14, issue 2
Development and technical paper 11 Feb 2021
Development and technical paper | 11 Feb 2021
Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0)
Zhaoyuan Yu et al.
Z. Yu, W. Luo, L. Yi, Y. Hu, and L. Yuan
Nonlin. Processes Geophys., 20, 563–570,
Z. Yu, W. Luo, L. Yi, Y. Hu, and L. Yuan
Nonlin. Processes Geophys., 20, 563–570,
Related subject area
Climate and Earth system modelingMethane chemistry in a nutshell – the new submodels CH4 (v1.0) and TRSYNC (v1.0) in MESSy (v2.54.0)Coordinating an operational data distribution network for CMIP6 dataImplementation of sequential cropping into JULESvn5.2 land-surface modelDevelopment of four-dimensional variational assimilation system based on the GRAPES–CUACE adjoint model (GRAPES–CUACE-4D-Var V1.0) and its application in emission inversionHIRM v1.0: a hybrid impulse response model for climate modeling and uncertainty analysesCLIMADA v1.4.1: towards a globally consistent adaptation options appraisal toolFORTE 2.0: a fast, parallel and flexible coupled climate modelOptimization of the sulfate aerosol hygroscopicity parameter in WRF-ChemUpdated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2)Spin-up characteristics with three types of initial fields and the restart effects on forecast accuracy in the GRAPES global forecast systemGTS v1.0: a macrophysics scheme for climate models based on a probability density functionCalibration of temperature-dependent ocean microbial processes in the cGENIE.muffin (v0.9.13) Earth system modelDescription and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulationsDiRong1.0: a distributed implementation for improving routing network generation in model couplingOverview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulationsGeospatial input data for the PALM model system 6.0: model requirements, data sources and processingExploring the parameter space of the COSMO-CLM v5.0 regional climate model for the Central Asia CORDEX domainThe benefits of increasing resolution in global and regional climate simulations for European climate extremesEuropean daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP)Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6A computationally efficient method for probabilistic local warming projections constrained by history matching and pattern scaling, demonstrated by WASP–LGRTC-1.0R2D2 v2.0: accounting for temporal dependences in multivariate bias correction via analogue rank resamplingExtending the Modular Earth Submodel System (MESSy v2.54) model hierarchy: the ECHAM/MESSy IdeaLized (EMIL) model setupBoreal summer intraseasonal oscillation in a superparameterized general circulation model: effects of air–sea coupling and ocean mean stateReduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature responseModeling land surface processes over a mountainous rainforest in Costa Rica using CLM4.5 and CLM5A new bias-correction method for precipitation over complex terrain suitable for different climate states: a case study using WRF (version 3.8.1)A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1ISSM-SLPS: geodetically compliant Sea-Level Projection System for the Ice-sheet and Sea-level System Model v4.17Newly developed aircraft routing options for air traffic simulation in the chemistry–climate model EMAC 2.53: AirTraf 2.0Quantifying CanESM5 and EAMv1 sensitivities to Mt. Pinatubo volcanic forcing for the CMIP6 historical experimentOptimizing high-resolution Community Earth System Model on a heterogeneous many-core supercomputing platformUnderstanding the development of systematic errors in the Asian Summer MonsoonMIROC-INTEG-LAND version 1: a global biogeochemical land surface model with human water management, crop growth, and land-use changeOptimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 2: Sensitivity analysis and model calibrationOptimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 1: Implementation and model behaviourThe E3SM version 1 single-column modelRadNet 1.0: exploring deep learning architectures for longwave radiative transferEfficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54Evaluation of the University of Victoria Earth System Climate Model version 2.10 (UVic ESCM 2.10)Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIPEvaluating the land-surface energy partitioning in ERA5Robust Ecosystem Demography (RED version 1.0): a parsimonious approach to modelling vegetation dynamics in Earth system modelsThe GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)Role of vegetation in representing land surface temperature in the CHTESSEL (CY45R1) and SURFEX-ISBA (v8.1) land surface models: a case study over IberiaSimulating the mid-Holocene, Last Interglacial and mid-Pliocene climate with EC-Earth3-LRTaiwan Earth System Model Version 1: description and evaluation of mean stateUsing the anomaly forcing Community Land Model (CLM 4.5) for crop yield projectionsEvaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate Model Assessment Tool (CMATv1)
Franziska Winterstein and Patrick Jöckel
Geosci. Model Dev., 14, 661–674,Short summary
Atmospheric methane is currently a hot topic in climate research. This is partly due to its chemically active nature. We introduce a simplified approach to simulate methane in climate models to enable large sensitivity studies by reducing computational cost but including the crucial feedback of methane on stratospheric water vapour. We further provide options to simulate the isotopic content of methane and to generate output for an inverse optimization technique for emission estimation.
Ruth Petrie, Sébastien Denvil, Sasha Ames, Guillaume Levavasseur, Sandro Fiore, Chris Allen, Fabrizio Antonio, Katharina Berger, Pierre-Antoine Bretonnière, Luca Cinquini, Eli Dart, Prashanth Dwarakanath, Kelsey Druken, Ben Evans, Laurent Franchistéguy, Sébastien Gardoll, Eric Gerbier, Mark Greenslade, David Hassell, Alan Iwi, Martin Juckes, Stephan Kindermann, Lukasz Lacinski, Maria Mirto, Atef Ben Nasser, Paola Nassisi, Eric Nienhouse, Sergey Nikonov, Alessandra Nuzzo, Clare Richards, Syazwan Ridzwan, Michel Rixen, Kim Serradell, Kate Snow, Ag Stephens, Martina Stockhause, Hans Vahlenkamp, and Rick Wagner
Geosci. Model Dev., 14, 629–644,Short summary
This paper describes the infrastructure that is used to distribute Coupled Model Intercomparison Project Phase 6 (CMIP6) data around the world for analysis by the climate research community. It is expected that there will be ~20 PB (petabytes) of data available for analysis. The operations team performed a series of preparation "data challenges" to ensure all components of the infrastructure were operational for when the data became available for timely data distribution and subsequent analysis.
Camilla Mathison, Andrew J. Challinor, Chetan Deva, Pete Falloon, Sébastien Garrigues, Sophie Moulin, Karina Williams, and Andy Wiltshire
Geosci. Model Dev., 14, 437–471,Short summary
Sequential cropping (also known as multiple or double cropping) is a common cropping system, particularly in tropical regions. Typically, land surface models only simulate a single crop per year. To understand how sequential crops influence surface fluxes, we implement sequential cropping in JULES to simulate all the crops grown within a year at a given location in a seamless way. We demonstrate the method using a site in Avignon, four locations in India and a regional run for two Indian states.
Chao Wang, Xingqin An, Qing Hou, Zhaobin Sun, Yanjun Li, and Jiangtao Li
Geosci. Model Dev., 14, 337–350,
Kalyn Dorheim, Steven J. Smith, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 365–375,Short summary
Simple climate models are frequently used in research and decision-making communities because of their tractability and low computational cost. Simple climate models are diverse, including highly idealized and process-based models. Here we present a hybrid approach that combines the strength of two types of simple climate models in a flexible framework. This hybrid approach has provided insights into the climate system and opens an avenue for investigating radiative forcing uncertainties.
David N. Bresch and Gabriela Aznar-Siguan
Geosci. Model Dev., 14, 351–363,Short summary
Climate change is a fact and adaptation a necessity. The Economics of Climate Adaptation methodology provides a framework to integrate risk and reward perspectives of different stakeholders, underpinned by the CLIMADA impact modelling platform. This extended version of CLIMADA enables risk assessment and options appraisal in a modular form and occasionally bespoke fashion yet with high reusability of functionalities to foster usage in interdisciplinary studies and international collaboration.
Adam T. Blaker, Manoj Joshi, Bablu Sinha, David P. Stevens, Robin S. Smith, and Joël J.-M. Hirschi
Geosci. Model Dev., 14, 275–293,Short summary
FORTE 2.0 is a flexible coupled atmosphere–ocean general circulation model that can be run on modest hardware. We present two 2000-year simulations which show that FORTE 2.0 is capable of producing a stable climate. Earlier versions of FORTE were used for a wide range of studies, ranging from aquaplanet configurations to investigating the cold European winters of 2009–2010. This paper introduces the updated model for which the code and configuration are now publicly available.
Ah-Hyun Kim, Seong Soo Yum, Dong Yeong Chang, and Minsu Park
Geosci. Model Dev., 14, 259–273,Short summary
A new method to estimate the sulfate aerosol hygroscopicity parameter (κSO4) is suggested that can consider κSO4 for two different sulfate species instead of prescribing a single κSO4 value, as in most previous studies. The new method simulates more realistic cloud droplet concentrations and, thus, a more realistic cloud albedo effect than the original method. The new method is simple and readily applicable to modeling studies investigating sulfate aerosols’ effect in aerosol–cloud interactions.
Brigitta Szabó, Melanie Weynants, and Tobias K. D. Weber
Geosci. Model Dev., 14, 151–175,Short summary
This paper presents updated European prediction algorithms (euptf2) to compute soil hydraulic parameters from easily available soil properties. The new algorithms lead to significantly better predictions and provide a built-in prediction uncertainty computation. The influence of predictor variables on predicted soil hydraulic properties is explored and practical guidance on how to use the derived PTFs is provided. A website and an R package facilitate easy application of the updated predictions.
Zhanshan Ma, Chuanfeng Zhao, Jiandong Gong, Jin Zhang, Zhe Li, Jian Sun, Yongzhu Liu, Jiong Chen, and Qingu Jiang
Geosci. Model Dev., 14, 205–221,Short summary
The spin-up in GRAPES_GFS, under different initial fields, goes through a dramatic adjustment in the first half-hour of integration and slow dynamic and thermal adjustments afterwards. It lasts for at least 6 h, with model adjustment gradually completed from lower to upper layers in the model. Thus, the forecast results, at least in the first 6 h, should be avoided when used. In addition, the spin-up process should repeat when the model simulation is interrupted.
Chein-Jung Shiu, Yi-Chi Wang, Huang-Hsiung Hsu, Wei-Ting Chen, Hua-Lu Pan, Ruiyu Sun, Yi-Hsuan Chen, and Cheng-An Chen
Geosci. Model Dev., 14, 177–204,Short summary
A cloud macrophysics scheme utilizing grid-mean hydrometeor information is developed and evaluated for climate models. The GFS–TaiESM–Sundqvist (GTS) scheme can simulate variations of cloud fraction associated with relative humidity (RH) in a more consistent way than the default scheme of CAM5.3. Through better cloud–RH distributions, the GTS scheme helps to better represent cloud fraction, cloud radiative forcing, and thermodynamic-related climatic fields in climate simulations.
Katherine A. Crichton, Jamie D. Wilson, Andy Ridgwell, and Paul N. Pearson
Geosci. Model Dev., 14, 125–149,Short summary
Temperature is a controller of metabolic processes and therefore also a controller of the ocean's biological carbon pump (BCP). We calibrate a temperature-dependent version of the BCP in the cGENIE Earth system model. Since the pre-industrial period, warming has intensified near-surface nutrient recycling, supporting production and largely offsetting stratification-induced surface nutrient limitation. But at the same time less carbon that sinks out of the surface then reaches the deep ocean.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423,Short summary
Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Hao Yu, Li Liu, Chao Sun, Ruizhe Li, Xinzhu Yu, Cheng Zhang, Zhiyuan Zhang, and Bin Wang
Geosci. Model Dev., 13, 6253–6263,Short summary
Routing network generation is a major step for initializing the data transfer functionality for model coupling. The distributed implementation for routing network generation (DiRong1.0) proposed in this paper can significantly improve the global implementation of routing network generation used in some existing coupling software, because it does not introduce any gather–broadcast communications and achieves much lower complexity in terms of time, memory, and communication.
Øyvind Seland, Mats Bentsen, Dirk Olivié, Thomas Toniazzo, Ada Gjermundsen, Lise Seland Graff, Jens Boldingh Debernard, Alok Kumar Gupta, Yan-Chun He, Alf Kirkevåg, Jörg Schwinger, Jerry Tjiputra, Kjetil Schanke Aas, Ingo Bethke, Yuanchao Fan, Jan Griesfeller, Alf Grini, Chuncheng Guo, Mehmet Ilicak, Inger Helene Hafsahl Karset, Oskar Landgren, Johan Liakka, Kine Onsum Moseid, Aleksi Nummelin, Clemens Spensberger, Hui Tang, Zhongshi Zhang, Christoph Heinze, Trond Iversen, and Michael Schulz
Geosci. Model Dev., 13, 6165–6200,Short summary
The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. The temperature and precipitation patterns has improved compared to NorESM1. The model reaches present-day warming levels to within 0.2 °C of observed temperature but with a delayed warming during the late 20th century. Under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the warming in the period of 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.1, and 3.9 K.
Wieke Heldens, Cornelia Burmeister, Farah Kanani-Sühring, Björn Maronga, Dirk Pavlik, Matthias Sühring, Julian Zeidler, and Thomas Esch
Geosci. Model Dev., 13, 5833–5873,Short summary
For realistic microclimate simulations in urban areas with PALM 6.0, detailed description of surface types, buildings and vegetation is required. This paper shows how such input data sets can be derived with the example of three German cities. Various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. The collection and preparation of input data sets is tedious. Future research aims therefore at semi-automated tools to support users.
Emmanuele Russo, Silje Lund Sørland, Ingo Kirchner, Martijn Schaap, Christoph C. Raible, and Ulrich Cubasch
Geosci. Model Dev., 13, 5779–5797,Short summary
The parameter space of the COSMO-CLM RCM is investigated for the Central Asia CORDEX domain using a perturbed physics ensemble (PPE) with different parameter values. Results show that only a subset of model parameters presents relevant changes in model performance and these changes depend on the considered region and variable: objective calibration methods are highly necessary in this case. Additionally, the results suggest the need for calibrating an RCM when targeting different domains.
Carley E. Iles, Robert Vautard, Jane Strachan, Sylvie Joussaume, Bernd R. Eggen, and Chris D. Hewitt
Geosci. Model Dev., 13, 5583–5607,Short summary
We investigate how increased resolution affects the simulation of European climate extremes for global and regional climate models to inform modelling strategies. Precipitation extremes become heavier with higher resolution, especially over mountains, wind extremes become somewhat stronger, and for temperature extremes warm biases are reduced over mountains. Differences with resolution for the global model appear to come from downscaling effects rather than improved large-scale circulation.
Marie-Estelle Demory, Ségolène Berthou, Jesús Fernández, Silje L. Sørland, Roman Brogli, Malcolm J. Roberts, Urs Beyerle, Jon Seddon, Rein Haarsma, Christoph Schär, Erasmo Buonomo, Ole B. Christensen, James M. Ciarlo ̀, Rowan Fealy, Grigory Nikulin, Daniele Peano, Dian Putrasahan, Christopher D. Roberts, Retish Senan, Christian Steger, Claas Teichmann, and Robert Vautard
Geosci. Model Dev., 13, 5485–5506,Short summary
Now that global climate models (GCMs) can run at similar resolutions to regional climate models (RCMs), one may wonder whether GCMs and RCMs provide similar regional climate information. We perform an evaluation for daily precipitation distribution in PRIMAVERA GCMs (25–50 km resolution) and CORDEX RCMs (12–50 km resolution) over Europe. We show that PRIMAVERA and CORDEX simulate similar distributions. Considering both datasets at such a resolution results in large benefits for impact studies.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464,Short summary
To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Philip Goodwin, Martin Leduc, Antti-Ilari Partanen, H. Damon Matthews, and Alex Rogers
Geosci. Model Dev., 13, 5389–5399,Short summary
Numerical climate models are used to make projections of future surface warming for different pathways of future greenhouse gas emissions, where future surface warming will vary from place to place. However, it is so expensive to run complex models using supercomputers that future projections can only be produced for a small number of possible future emissions pathways. This study presents an efficient climate model to make projections of local surface warming using a desktop computer.
Mathieu Vrac and Soulivanh Thao
Geosci. Model Dev., 13, 5367–5387,Short summary
We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations). It consists on a method reordering the ranks of the time series according to their multivariate distance to a reference time series. Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
Hella Garny, Roland Walz, Matthias Nützel, and Thomas Birner
Geosci. Model Dev., 13, 5229–5257,Short summary
Numerical models of Earth's climate system have been gaining more and more complexity over the last decades. Therefore, it is important to establish simplified models to improve process understanding. In our study, we present and document the development of a new simplified model setup within the framework of a complex climate model system that uses the same routines to calculate atmospheric dynamics as the complex model but is simplified in the representation of clouds and radiation.
Yingxia Gao, Nicholas P. Klingaman, Charlotte A. DeMott, and Pang-Chi Hsu
Geosci. Model Dev., 13, 5191–5209,Short summary
Both the air–sea coupling and ocean mean state affect the fidelity of simulated boreal summer intraseasonal oscillation (BSISO). To elucidate their relative effects on the simulated BSISO, a set of experiments was conducted using a superparameterized AGCM and its coupled version. Both air–sea coupling and cold ocean mean state improve the BSISO amplitude due to the suppression of the overestimated variance, while the former (latter) could further upgrade (degrade) the BSISO propagation.
Zebedee R. J. Nicholls, Malte Meinshausen, Jared Lewis, Robert Gieseke, Dietmar Dommenget, Kalyn Dorheim, Chen-Shuo Fan, Jan S. Fuglestvedt, Thomas Gasser, Ulrich Golüke, Philip Goodwin, Corinne Hartin, Austin P. Hope, Elmar Kriegler, Nicholas J. Leach, Davide Marchegiani, Laura A. McBride, Yann Quilcaille, Joeri Rogelj, Ross J. Salawitch, Bjørn H. Samset, Marit Sandstad, Alexey N. Shiklomanov, Ragnhild B. Skeie, Christopher J. Smith, Steve Smith, Katsumasa Tanaka, Junichi Tsutsui, and Zhiang Xie
Geosci. Model Dev., 13, 5175–5190,Short summary
Computational limits mean that we cannot run our most comprehensive climate models for all applications of interest. In such cases, reduced complexity models (RCMs) are used. Here, researchers working on 15 different models present the first systematic community effort to evaluate and compare RCMs: the Reduced Complexity Model Intercomparison Project (RCMIP). Our research ensures that users of RCMs can more easily evaluate the strengths, weaknesses and limitations of their tools.
Jaeyoung Song, Gretchen R. Miller, Anthony T. Cahill, Luiza Maria T. Aparecido, and Georgianne W. Moore
Geosci. Model Dev., 13, 5147–5173,Short summary
The performance of a land surface model (CLM4.5 and 5.0) was examined against a suite of measurements from a tropical montane rainforest in Costa Rica. Both versions failed to capture the effects of frequent rainfall events and mountainous terrain on temperature, leaf wetness, photosynthesis, and transpiration. While the new model version eliminated some errors, it still cannot precisely simulate a number of processes. This suggests that two key components of the model need modification.
Patricio Velasquez, Martina Messmer, and Christoph C. Raible
Geosci. Model Dev., 13, 5007–5027,Short summary
This work presents a new bias-correction method for precipitation that considers orographic characteristics, which can be used in studies where the latter strongly changes. The three-step correction method consists of a separation into orographic features, correction of low-intensity precipitation, and application of empirical quantile mapping. Seasonal bias induced by the global climate model is fully corrected. Rigorous cross-validations illustrate the method's applicability and robustness.
Johannes Horak, Marlis Hofer, Ethan Gutmann, Alexander Gohm, and Mathias W. Rotach
Geosci. Model Dev. Discuss.,
Preprint under review for GMDShort summary
The presented process based evaluation of the atmospheric model ICAR is conducted to derive recommendations to increase the likelihood of its results being correct for the right reasons. We conclude that a different diagnosis of the atmospheric background state is necessary, as well as a model top at an elevation of at least 10 km. Alternative boundary conditions at the top were not found effective in reducing this model top elevation. The results have wide implications for future ICAR studies.
Eric Larour, Lambert Caron, Mathieu Morlighem, Surendra Adhikari, Thomas Frederikse, Nicole-Jeanne Schlegel, Erik Ivins, Benjamin Hamlington, Robert Kopp, and Sophie Nowicki
Geosci. Model Dev., 13, 4925–4941,Short summary
ISSM-SLPS is a new projection system for future sea level that increases the resolution and accuracy of current projection systems and improves the way uncertainty is treated in such projections. This will pave the way for better inclusion of state-of-the-art results from existing intercomparison efforts carried out by the scientific community, such as GlacierMIP2 or ISMIP6, into sea-level projections.
Hiroshi Yamashita, Feijia Yin, Volker Grewe, Patrick Jöckel, Sigrun Matthes, Bastian Kern, Katrin Dahlmann, and Christine Frömming
Geosci. Model Dev., 13, 4869–4890,Short summary
This paper describes the updated submodel AirTraf 2.0 which simulates global air traffic in the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. Nine aircraft routing options have been integrated, including contrail avoidance, minimum economic costs, and minimum climate impact. Example simulations reveal characteristics of different routing options on air traffic performances. The consistency of the AirTraf simulations is verified with literature data.
Landon A. Rieger, Jason N. S. Cole, John C. Fyfe, Stephen Po-Chedley, Philip J. Cameron-Smith, Paul J. Durack, Nathan P. Gillett, and Qi Tang
Geosci. Model Dev., 13, 4831–4843,Short summary
Recently, the stratospheric aerosol forcing dataset used as an input to the Coupled Model Intercomparison Project phase 6 was updated. This work explores the impact of those changes on the modelled historical climates in the CanESM5 and EAMv1 models. Temperature differences in the stratosphere shortly after the Pinatubo eruption are found to be significant, but surface temperatures and precipitation do not show a significant change.
Shaoqing Zhang, Haohuan Fu, Lixin Wu, Yuxuan Li, Hong Wang, Yunhui Zeng, Xiaohui Duan, Wubing Wan, Li Wang, Yuan Zhuang, Hongsong Meng, Kai Xu, Ping Xu, Lin Gan, Zhao Liu, Sihai Wu, Yuhu Chen, Haining Yu, Shupeng Shi, Lanning Wang, Shiming Xu, Wei Xue, Weiguo Liu, Qiang Guo, Jie Zhang, Guanghui Zhu, Yang Tu, Jim Edwards, Allison Baker, Jianlin Yong, Man Yuan, Yangyang Yu, Qiuying Zhang, Zedong Liu, Mingkui Li, Dongning Jia, Guangwen Yang, Zhiqiang Wei, Jingshan Pan, Ping Chang, Gokhan Danabasoglu, Stephen Yeager, Nan Rosenbloom, and Ying Guo
Geosci. Model Dev., 13, 4809–4829,Short summary
Science advancement and societal needs require Earth system modelling with higher resolutions that demand tremendous computing power. We successfully scale the 10 km ocean and 25 km atmosphere high-resolution Earth system model to a new leading-edge heterogeneous supercomputer using state-of-the-art optimizing methods, promising the solution of high spatial resolution and time-varying frequency. Corresponding technical breakthroughs are of significance in modelling and HPC design communities.
Gill M. Martin, Richard C. Levine, José M. Rodriguez, and Michael Vellinga
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
Our study highlights a number of different techniques that can be employed to investigate the sources of model error. We demonstrate how this methodology can be used to identify the regions and model components responsible for the development of long-standing errors in the Asian Summer Monsoon. Once these are known, further work can be done to explore the local processes contributing to this behaviour and their sensitivity to changes in physical parameterizations and/or model resolution.
Tokuta Yokohata, Tsuguki Kinoshita, Gen Sakurai, Yadu Pokhrel, Akihiko Ito, Masashi Okada, Yusuke Satoh, Etsushi Kato, Tomoko Nitta, Shinichiro Fujimori, Farshid Felfelani, Yoshimitsu Masaki, Toshichika Iizumi, Motoki Nishimori, Naota Hanasaki, Kiyoshi Takahashi, Yoshiki Yamagata, and Seita Emori
Geosci. Model Dev., 13, 4713–4747,Short summary
The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balances, human water management, and crop growth incorporates a land-use decision-making model based on economic activities. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand.
Chia-Te Chien, Markus Pahlow, Markus Schartau, and Andreas Oschlies
Geosci. Model Dev., 13, 4691–4712,Short summary
We demonstrate sensitivities of tracers to parameters of a new optimality-based plankton–ecosystem model (OPEM) in the UVic-ESCM. We find that changes in phytoplankton subsistence nitrogen quota strongly impact the nitrogen inventory, nitrogen fixation, and elemental stoichiometry of ordinary phytoplankton and diazotrophs. We introduce a new likelihood-based metric for model calibration, and it shows the capability of constraining globally averaged oxygen, nitrate, and DIC concentrations.
Markus Pahlow, Chia-Te Chien, Lionel A. Arteaga, and Andreas Oschlies
Geosci. Model Dev., 13, 4663–4690,Short summary
The stoichiometry of marine biotic processes is important for the regulation of atmospheric CO2 and hence the global climate. We replace a simplistic, fixed-stoichiometry plankton module in an Earth system model with an optimal-regulation model with variable stoichiometry. Our model compares better to the observed carbon transfer from the surface to depth and surface nutrient distributions. This work could aid our ability to describe and project the role of marine ecosystems in the Earth system.
Peter A. Bogenschutz, Shuaiqi Tang, Peter M. Caldwell, Shaocheng Xie, Wuyin Lin, and Yao-Sheng Chen
Geosci. Model Dev., 13, 4443–4458,Short summary
This paper documents a tool that has been developed that can be used to accelerate the development and understanding of climate models. This version of the model, known as a the single-column model, is much faster to run than the full climate model, and we demonstrate that this tool can be used to quickly exploit model biases that arise due to physical processes. We show examples of how this single-column model can directly benefit the field.
Ying Liu, Rodrigo Caballero, and Joy Merwin Monteiro
Geosci. Model Dev., 13, 4399–4412,Short summary
The calculation of atmospheric radiative transfer is the most computationally expensive part of climate models. Reducing this computational burden could potentially improve our ability to simulate the earth's climate at finer scales. We propose using a statistical model – a deep neural network – to compute approximate radiative transfer in the earth's atmosphere. We demonstrate a significant reduction in computational burden as compared to a traditional scheme, especially when using GPUs.
Lars Nerger, Qi Tang, and Longjiang Mu
Geosci. Model Dev., 13, 4305–4321,Short summary
Data assimilation combines observations with numerical models to get an improved estimate of the model state. This work discusses the technical aspects of how a coupled model that simulates the ocean and the atmosphere can be augmented by data assimilation functionality provided in generic form by the open-source software PDAF (Parallel Data Assimilation Framework). A very efficient program is obtained that can be executed on high-performance computers.
Christof G. Beer, Johannes Hendricks, Mattia Righi, Bernd Heinold, Ina Tegen, Silke Groß, Daniel Sauer, Adrian Walser, and Bernadett Weinzierl
Geosci. Model Dev., 13, 4287–4303,Short summary
Mineral dust aerosol plays an important role in the climate system. Previously, dust emissions have often been represented in global models by prescribed monthly-mean emission fields representative of a specific year. We now apply an online calculation of wind-driven dust emissions. This results in an improved agreement with observations, due to a better representation of the highly variable dust emissions. Increasing the model resolution led to an additional performance gain.
Nadine Mengis, David P. Keller, Andrew H. MacDougall, Michael Eby, Nesha Wright, Katrin J. Meissner, Andreas Oschlies, Andreas Schmittner, Alexander J. MacIsaac, H. Damon Matthews, and Kirsten Zickfeld
Geosci. Model Dev., 13, 4183–4204,Short summary
In this paper, we evaluate the newest version of the University of Victoria Earth System Climate Model (UVic ESCM 2.10). Combining recent model developments as a joint effort, this version is to be used in the next phase of model intercomparison and climate change studies. The UVic ESCM 2.10 is capable of reproducing changes in historical temperature and carbon fluxes well. Additionally, the model is able to reproduce the three-dimensional distribution of many ocean tracers.
Axel Lauer, Veronika Eyring, Omar Bellprat, Lisa Bock, Bettina K. Gier, Alasdair Hunter, Ruth Lorenz, Núria Pérez-Zanón, Mattia Righi, Manuel Schlund, Daniel Senftleben, Katja Weigel, and Sabrina Zechlau
Geosci. Model Dev., 13, 4205–4228,Short summary
The Earth System Model Evaluation Tool is a community software tool designed for evaluation and analysis of climate models. New features of version 2.0 include analysis scripts for important large-scale features in climate models, diagnostics for extreme events, regional model and impact evaluation. In this paper, newly implemented climate metrics, emergent constraints for climate-relevant feedbacks and diagnostics for future model projections are described and illustrated with examples.
Brecht Martens, Dominik L. Schumacher, Hendrik Wouters, Joaquín Muñoz-Sabater, Niko E. C. Verhoest, and Diego G. Miralles
Geosci. Model Dev., 13, 4159–4181,Short summary
Climate reanalyses are widely used in different fields and an in-depth evaluation of the different variables provided by reanalyses is a necessary means to provide feedback on the quality to their users and the operational centres producing these data sets. In this study, we show the improvements of ECMWF's latest climate reanalysis (ERA5) upon its predecessor (ERA-Interim) in partitioning the available energy at the land surface.
Arthur P. K. Argles, Jonathan R. Moore, Chris Huntingford, Andrew J. Wiltshire, Anna B. Harper, Chris D. Jones, and Peter M. Cox
Geosci. Model Dev., 13, 4067–4089,Short summary
The Robust Ecosystem Demography (RED) model simulates cohorts of vegetation through mass classes. RED establishes a framework for representing demographic changes through competition, growth, and mortality across the size distribution of a forest. The steady state of the model can be solved analytically, enabling initialization. When driven by mean growth rates from a land-surface model, RED is able to fit the observed global vegetation map, giving a map of implicit mortality rates.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 3995–4018,Short summary
Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Miguel Nogueira, Clément Albergel, Souhail Boussetta, Frederico Johannsen, Isabel F. Trigo, Sofia L. Ermida, João P. A. Martins, and Emanuel Dutra
Geosci. Model Dev., 13, 3975–3993,Short summary
We used earth observations to evaluate and improve the representation of land surface temperature (LST) and vegetation coverage over Iberia in CHTESSEL and SURFEX land surface models. We demonstrate the added value of updating the vegetation types and fractions together with the representation of vegetation coverage seasonality. Results show a large reduction in daily maximum LST systematic error during warm months, with neutral impacts in other seasons.
Qiong Zhang, Qiang Li, Qiang Zhang, Ellen Berntell, Josefine Axelsson, Jie Chen, Zixuan Han, Wesley de Nooijer, Zhengyao Lu, Klaus Wyser, and Shuting Yang
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
Paleoclimate modelling has long been regarded as a strong out-of-sample test-bed of the climate models that are used for the projection of future climate changes. Here, we document the model experiments setup for the three past warm periods with EC-Earth3-LR and present the results on the large-scale features from the completed production simulations. The simulations demonstrate the good performance of the model to capture the climate response under different climate forcings.
Wei-Liang Lee, Yi-Chi Wang, Chein-Jung Shiu, I-chun Tsai, Chia-Ying Tu, Yung-Yao Lan, Jen-Ping Chen, Hua-Lu Pan, and Huang-Hsiung Hsu
Geosci. Model Dev., 13, 3887–3904,Short summary
The Taiwan Earth System Model (TaiESM) is a new climate model developed in Taiwan. It includes several new features, and therefore it can better simulate the occurrence of convective rainfall, solar energy received by mountainous surfaces, and more detail chemical processes in aerosols. TaiESM can capture the trend of global warming after 1950 well, and its overall performance in most meteorological quantities is better than the average of global models used in IPCC AR5.
Yaqiong Lu and Xianyu Yang
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
Crop growth in land surface models normally requires high temporal resolution climate data, but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. Our work provides an alternative way to use the monthly climate for crop yield projections. Such approach could be easily adopted by other crop models.
John T. Fasullo
Geosci. Model Dev., 13, 3627–3642,Short summary
The fidelity of climate model simulations included in the WCRP Coupled Model Intercomparison Project Versions 3 through 6 is evaluated using best estimates of fields considered by the community to be critical for climate change projections. The analysis benchmarks patterns of the mean state and variability (seasonal/interannual) both within and across model generations, highlighting progress and quantifying persisting biases across models.
Baker, A. H., Xu, H., Dennis, J. M., Levy, M. N., Nychka, D., Mickelson, S. A., Edwards, J., Vertenstein, M., and Wegener, A.: A methodology for evaluating the impact of data compression on climate simulation data, in: Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing, Vancouver, Canada, 23–27 June 2014.
Baker, A. H., Hammerling, D. M., Mickelson, S. A., Xu, H., Stolpe, M. B., Naveau, P., Sanderson, B., Ebert-Uphoff, I., Samarasinghe, S., De Simone, F., Carbone, F., Gencarelli, C. N., Dennis, J. M., Kay, J. E., and Lindstrom, P.: Evaluating lossy data compression on climate simulation data within a large ensemble, Geosci. Model Dev., 9, 4381–4403, https://doi.org/10.5194/gmd-9-4381-2016, 2016.
Bengua, J. A., Phien, H. N., Tuan, H. D., and Do, M. N.: Matrix product state for higher-order tensor compression and classification, IEEE Trans. Signal Process., 65, 4019–4030, https://doi.org/10.1109/TSP.2017.2703882, 2016.
Cai, J. Y., Chen, X., and Lu, P.: Non-negative weighted #csps: an effective complexity dichotomy, Comput. Sci., 6, 45–54, https://doi.org/10.1109/CCC.2011.32, 2012.
Di, S., Tao, D., Liang, X., and Franck, C.: Efficient Lossy Compression for Scientific Data Based on Pointwise Relative Error Bound, IEEE Trans. Parallel Distrib. Syst., 30, 331–345, https://doi.org/10.1109/TPDS.2018.2859932, 2019.
Diffenderfer, J., Fox, A., Hittinger, J., Sanders, G., and Lindstrom, P: Error Analysis of ZFP Compression for Floating-Point Data, SIAM J. Sci. Comput., 41, A1867–A1898, https://doi.org/10.1137/18M1168832, 2019.
Du, B., Zhang, M., Zhang, L., Hu, R., and Tao, D.: Pltd: patch-based low-rank tensor decomposition for hyperspectral images, IEEE Trans. Multimedia, 19, 67–79, https://doi.org/10.1109/TMM.2016.2608780, 2017.
Grasedyck, L.: Hierarchical Singular Value Decomposition of Tensors, SIAM J. Matrix Anal. A., 31, 2029–2054, https://doi.org/10.1137/090764189, 2010.
Jing, W., Xiang, X., and Jingming, K.: A novel multichannel audio signal compression method based on tensor representation and decomposition, China Commun., 11, 80–90, https://doi.org/10.1109/CC.2014.6825261, 2014.
Kressner, D. and Tobler, C.: Algorithm 941: htucker – a matlab toolbox for tensors in hierarchical tucker format, ACM Trans. Math. Softw., 40, 1–22, https://doi.org/10.1145/2538688, 2014.
Kuang, L., Yang, L. T., Chen, J., Hao, F., and Luo, C.: A Holistic Approach for Distributed Dimensionality Reduction of Big Data, IEEE Trans. Cloud Comput., 6, 506–518, https://doi.org/10.1109/TCC.2015.2449855, 2018.
Kuhn, M., Kunkel, J., and Ludwig, T.: Data compression for climate data, Supercomput. Front. Innov., 3, 75–94, https://doi.org/10.14529/jsfi160105, 2016.
Lakshminarasimhan, S., Shah, N., Ethier, S., Seung-Hoe Ku, Chang, C. S., Klasky, S., Latham, R., Ross, R., and Samatova, N. F.: Isabela for effective in situ compression of scientific data, Concurr. Comput., 25, 524–540, https://doi.org/10.1002/cpe.2887, 2013.
Li, D., Yang, L., Yu, Z., Hu, Y., and Yuan, L.: A Tensor-based Interpolation Method for Sparse Spatio-temporal Field Data, J. SPAT Sci., 65, 307–325, https://doi.org/10.1080/14498596.2018.1509740, 2020.
Mashhoodi, B., Stead, D., and van Timmeren, A.: Spatial homogeneity and heterogeneity of energy poverty: A neglected dimension, Ann. GIS, 25, 19–31, https://doi.org/10.1080/19475683.2018.1557253, 2019.
Moon, A., Kim, J., Zhang, J., and Son, S. W.: Lossy compression on IoT big data by exploiting spatiotemporal correlation, in: 2017 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 12–14 September 2017, 2017.
Nathanael, Hübbe, Wegener, A., Kunkel, J. M., Ling, Y., and Ludwig, T.: Evaluating lossy compression on climate data, Lect. Notes Comput. Sci., 7905, 343–356, https://doi.org/10.1007/978-3-642-38750-0_26, 2013.
Nielsen, J. E., Pawson, S., Molod, A., Auer, B., da Silva, A. M., Douglass, A. R., Duncan, B., Liang, Q., Manyin, M., Oman, L. D., Putman, W., and Wargan, K.: Chemical Mechanisms and Their Applications in the Goddard Earth Observing System (GEOS) Earth System Model, J. Adv. Model. Earth Syst., 9, 3019–3044, https://doi.org/10.1002/2017MS001011, 2017.
Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., and Muoz-Marí, J.: Inferring causation from time series in earth system sciences, Nat. Commun., 10, 2553, https://doi.org/10.1038/s41467-019-10105-3, 2019.
Shi, Q., Dai, W., Santerre, R., Li, Z., and Liu, N.: Spatially heterogeneous land surface deformation data fusion method based on an enhanced spatio-temporal random effect model, Remote Sens., 11, 1084, https://doi.org/10.3390/rs11091084, 2019.
Simmons, Fellous, J. L., Ramaswamy, V., Trenberth, K., and Shepherd, T.: Observation and integrated earth-system science: a roadmap for 2016–2025, Adv. Space Res., 57, 2037–2103, https://doi.org/10.1016/j.asr.2016.03.008, 2016.
Song, L., Park, H., Ishteva, M., Parikh, A., and Xing, E.: Hierarchical tensor decomposition of latent tree graphical models, in: 30th International Conference on Machine Learning (ICML), Atlanta, American, 16–21 June 2013.
Sudmanns, M., Tiede, D., and Baraldi, A.: Semantic and syntactic interoperability in online processing of big Earth observation data, Int. J. Digit. Earth, 11, 95–112, https://doi.org/10.1080/17538947.2017.1332112, 2018.
Suiker, A. S. J. and Chang, C. S.: Application of higher-order tensor theory for formulating enhanced continuum models, Acta Mech. Solida Sin., 142, 223–234, https://doi.org/ 10.1007/BF01190020, 2000.
Tao, D., Di, S., Guo, H., Chen, Z., and Cappello, F.: Z-checker: A Framework for Assessing Lossy Compression of Scientific Data, Int. J. High Perform. Comput. Appl., 33, 1–19, https://doi.org/10.1177/1094342017737147, 2017.
Tao, D., Di, S., Liang, X., Chen, Z., and Cappello, F.: Optimizing lossy compression rate-distortion from automatic online selection between sz and zfp, IEEE Trans. Parallel Distrib. Syst., 30, 1857–1871, https://doi.org/10.1109/TPDS.2019.2894404, 2018.
Wang, H. C., Wu, Q., Shi, L., Yu, Y. Z., and Ahuja, N.: Out-of-core tensor approximation of multi-dimensional matrices of visual data, ACM Trans. Graph., 24, 527–535, https://doi.org/10.1145/1073204.1073224, 2005.
Wu, Q., Xia, T., Chen, C., Lin, H. Y. S., Wang, H., and Yu, Y.: Hierarchical tensor approximation of multi-dimensional visual data, IEEE Trans. Vis. Comput. Graph., 14, 186–199, https://doi.org/10.1109/TVCG.2007.70406, 2008.
Yan, F., Wang, J., Liu, S., Jin, M., and Shen, Y.: Svd-based low-complexity methods for computing the intersection of k≥2 subspaces, Chinese J. Electron., 28, 430–436, https://doi.org/10.1049/cje.2019.01.013, 2019.
Yuan, L., Yu, Z., Luo, W., Hu, Y., Feng, L., and Zhu, A. X.: A hierarchical tensor-based approach to compressing, updating and querying geospatial data, IEEE T. Data En., 27, 312–325, https://doi.org/10.1109/TKDE.2014.2330829, 2015.
Zhang, Z.: Compressed code and data, Zenodo, https://doi.org/10.5281/zenodo.4384627, 2020a.
Zhang, Z.: Climate model data [Data set], Zenodo, https://doi.org/10.5281/zenodo.3997216, 2020b.
Zheng, Y., William, H., Seung Woo, S., Christoph, F., Ankit, A., Liao, W. K., and Alok, C.: Parallel Implementation of Lossy Data Compression for Temporal Data Sets, in: 2016 IEEE 23rd International Conference on High Performance Computing (HiPC), Hyderabad, India, 19–22 December 2016.
Ziv, J. and Lempel, A.: A universal algorithm for sequential data compression, IEEE Trans. Inf. Theory, 23, 337–343, https://doi.org/10.1109/TIT.1977.1055714, 2003.
Few lossy compression methods consider both the global and local multidimensional coupling correlations, which could lead to information loss in data compression. Here we develop an adaptive lossy compression method, Adaptive-HGFDR, to capture both the global and local variation of multidimensional coupling correlations and improve approximation accuracy. The method can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity.
Few lossy compression methods consider both the global and local multidimensional coupling...