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A peer-reviewed, open access journal in numerical models of the earth system, description of the earth system, climate and earth system modelling, earth and space science informatics, atmospheric sciences & biogeosciences.
Computer modelling underpins vast areas of the geosciences, and the development of those models is itself a major scientific undertaking. GMD provides the forum for the publication of new developments in software across the geosciences. We communicate the advances made in geoscientific modelling capability, and thereby provide recognition to the many scientists who undertake this form of research. Equally importantly, we provide a key link in the provenance chain of geoscientific modelling. Every time a model result informs the conclusions of a scientific paper, the reader should be able to understand the basis of that calculation, its assumptions and limitations. In other words, the details of the computer model used to produce a scientific result should themselves be published. GMD provides the venue for that publication. We take our role in establishing the provenance of scientific results very seriously. GMD publications are required, whenever legally possible, to be accompanied by complete, public, and persistent archives of the software and data used to create the results presented: our authors are required to show their working. By doing this, we provide readers with a detailed access to what models actually do that goes far beyond the summary descriptions that fit in a research paper. GMD’s scope spans the geosciences. Papers describe fluid and solid models of the atmosphere, ocean, cryosphere, surface, lithosphere, and core. Specialist publications deal with individual parametrisations, while survey papers cover entire Earth System Models. Data assimilation papers discuss the incorporation of observed data, using ensemble, variational and machine learning techniques. Technical papers cover new algorithms and ports to new hardware, as well as new standards for data and interfaces. In essence, any paper about any stage of the development of software for the geosciences is in scope.
Abstract. In 2008, the first volume of the European Geosciences Union (EGU) journal Geoscientific Model Development (GMD) was published. GMD was founded because we perceived there to be a need for a space to publish comprehensive descriptions of numerical models in the geosciences. The journal is now well established, with the submission rate increasing over time. However, there are several aspects of model publication that we believe could be further improved. In this editorial we assess the lessons learned over the first few years of the journal's life, and describe some changes to GMD's editorial policy, which will ensure that the models and model developments are published in such a way that they are of maximum value to the community. These changes to editorial policy mostly focus on improving the rigour of the review process through a stricter requirement for access to the materials necessary to test the behaviour of the models. Throughout this editorial, "must" means that the stated actions are required, and the paper cannot be published without them; "strongly encouraged" means that we encourage the action, but papers can still be published if the criteria are not met; "may" means that the action may be carried out by the authors or referees, if they so wish. We have reviewed and rationalised the manuscript types into five new categories. For all papers which are primarily based on a specific numerical model, the changes are as follows: – The paper must be accompanied by the code, or means of accessing the code, for the purpose of peer-review. If the code is normally distributed in a way which could compromise the anonymity of the referees, then the code must be made available to the editor. The referee/editor is not required to review the code in any way, but they may do so if they so wish. – All papers must include a section at the end of the paper entitled "Code availability". In this section, instructions for obtaining the code (e.g. from a supplement, or from a website) should be included; alternatively, contact information should be given where the code can be obtained on request, or the reasons why the code is not available should be clearly stated. – We strongly encourage authors to upload any user manuals associated with the code. – For models where this is practicable, we strongly encourage referees to compile the code, and run test cases supplied by the authors where appropriate. – For models which have been previously described in the "grey" literature (e.g. as internal institutional documents), we strongly encourage authors to include this grey literature as a supplement, when this is allowed by the original authors. – All papers must include a model name and version number (or other unique identifier) in the title. It is our perception that, since Geoscientific Model Development (GMD) was founded, it has become increasingly common to see model descriptions published in other more traditional journals, so we hope that our insights may be of general value to the wider geoscientific community.
The paper describes the hydrodynamic part of the coupled ice-ocean model that also includes the ecosystem predictive model. The Baltic Sea model is based on the Community Earth System Model (CESM from NCAR – National Centre for Atmospheric Research). CESM was adopted for the Baltic Sea as a coupled sea-ice model. It consists of the Community Ice CodE (CICE, model version 4.0) and the Parallel Ocean Program (POP, version 2.1). The models are linked through a coupler (CPL7), which is based on the Model Coupling Toolkit (MCT) library. The current horizontal resolution is about 2 km (1/48 degrees). The ocean model has 21 vertical levels and is forced by atmospheric fields from the European Centre for Medium Weather Forecast (ECMWF). A preliminary validation of the hydrodynamic module with in situ measurements and reanalysis from My Ocean (http://www.myocean.eu) has also been done. In the operational mode, 48-hour atmospheric forecasts provided by the UM model from the Interdisciplinary Centre for Mathematical and Computational Modelling of Warsaw University (ICM) are used. The variables presented on the website in real time for a 48-hour forecast are temperature, salinity, currents, sea surface height, ice thickness and ice coverage (http://deep.iopan.gda.pl/CEMBaltic/newlay/index.php). The embedded model of the marine ecosystem, like ice, is not taken into account in this paper.
Abstract. Geoscientific models are based on geoscientific data; hence, building better models, in the sense of attaining better predictions, often means acquiring additional data. In decision theory, questions of what additional data are expected to best improve predictions and decisions is within the realm of value of information and Bayesian optimal survey design. However, these approaches often evaluate the optimality of one additional data acquisition campaign at a time. In many real settings, certainly in those related to the exploration of Earth resources, a large sequence of data acquisition campaigns possibly needs to be planned. Geoscientific data acquisition can be expensive and time-consuming, requiring effective measurement campaign planning to optimally allocate resources. Each measurement in a data acquisition sequence has the potential to inform where best to take the following measurements; however, directly optimizing a closed-loop measurement sequence requires solving an intractable combinatoric search problem. In this work, we formulate the sequential geoscientific data acquisition problem as a partially observable Markov decision process (POMDP). We then present methodologies to solve the sequential problem using Monte Carlo planning methods. We demonstrate the effectiveness of the proposed approach on a simple 2D synthetic exploration problem. Tests show that the proposed sequential approach is significantly more effective at reducing uncertainty than conventional methods. Although our approach is discussed in the context of mineral resource exploration, it likely has bearing on other types of geoscientific model questions.
Abstract. Geoscientific models are based on geoscientific data, hence building better models, in the sense of attaining better predictions, often means acquiring additional data. In decision theory questions of what additional data is expected to best improve predictions/decisions is within the realm of value of information and Bayesian optimal survey design. However, these approaches often evaluate the optimality of one additional data acquisition campaign at a time. In many real settings, certainly in those related to the exploration of Earth resources, possibly a large sequence of data acquisition campaigns need to be planned. Geoscientific data acquisition can be expensive and time consuming, requiring effective measurement campaign planning to optimally allocate resources. Each measurement in a data acquisition sequence has the potential to inform where best to take the following measurements, however, directly optimizing a closed-loop measurement sequence requires solving an intractable combinatoric search problem. In this work, we formulate the sequential geoscientific data acquisition problem as a Partially Observable Markov Decision Process (POMDP). We then present methodologies to solve the sequential problem using Monte Carlo planning methods. We demonstrate the effectiveness of the proposed approach on a simple 2D synthetic exploration problem. Tests show that the proposed sequential approach is significantly more effective at reducing uncertainty than conventional methods. Although our approach is discussed in the context of mineral resource exploration, it likely has bearing on other types of geoscientific model questions.
<strong>Data and model outputs for the replication of the analysis made in:</strong><br> (see the published version of this article in Geoscientific Model Development, 2021 - please cite this version if you use these data)<br> C. Amory, C. Kittel, L. Le Toumelin, C. Agosta, A. Delhasse, V. Favier, and X. Fettweis: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adelie Land, East Antarctica, Geoscientific Model Development, accepted, 2021. See README.txt for a full description of the dataset content Please contact me at amory.charles@live.fr if you need other half-hourly outputs or for more details on the dataset
Input data for the PISM-MOM coupling framework as used in the Geoscientifc Model Development publication "Coupling framework (1.0) for the PISM (1.1.4) ice sheet model and the MOM5 (5.1.0) ocean model via the PICO ice shelf cavity model in an Antarctic domain" by Moritz Kreuzer, Ronja Reese, Willem Huiskamp, Stefan Petri, Torsten Albrecht, Georg Feulner and Ricarda Winkelmann.
Abstract. Recognizing the leap in high-performance computing with accelerated co-processors, we propose a lightweight approach to adapt legacy codes to next generation hardware and achieve efficiently a high degree of performance portability. We focus on abstracting the computing kernels at the loop levels based on the lightweight, preprocessor-based embedded Domain Specific Language (eDSL) concept in conjunction with Unified Memory management. We outline a set of code pre-adaptations that facilitate the proposed abstraction. In two geophysical code applications programmed in C and Fortran, we demonstrate the efficiency of the eDSL approach in adaptation to NVIDIA GPUs with: native CUDA and Kokkos eDSL backends achieving up to 10–30 fold speedup. Our experience suggests that the proposed lightweight eDSL code adaptation is less expensive in terms of Full Time Equivalent of effort than adaptation based on complex DSL approaches, even if no earlier GPU competence exists.
F.Veillon, M.Dumont, C.Amory, M.Fructus : A versatile method for computing optimized snow albedo from spectrally fixed radiative variables : VALHALLA v1.0, Geoscientific Model Development, in review, 2020. See README for a full description of the dataset content Please contact me at marie.dumont@meteo.fr if you need more details on the dataset
Abstract The year 2019 marks the thirtieth anniversary of the development of the first regional climate model (RCM), and here an overview is provided of the progress in regional modeling research and of the main challenges lying ahead. RCMs were primarily developed to provide fine‐scale climate information for impact studies, but they have evolved into general and multipurpose modeling tools. Among the main achievements in RCM research the focus is on: the development of community RCMs applicable to a wide variety of studies and regional contexts; the increase of model simulation length up to centennial scales and spatial resolutions up to convection‐permitting scales (few kilometers), leading to a better understanding of regional to local climate change signals; the development of fully coupled Regional Earth System Models; the inception of intercomparison projects culminating in the international Coordinated Regional Climate Downscaling Experiment; the extensive use of RCM simulations for impact assessments; and the involvement of the scientific community from developing countries in climate modeling research. Among the outstanding issues in need of more attention are the Added Value of using this downscaling technique; various technical aspects concerning RCM simulations; and uncertainties in RCM‐based climate projections. Future directions in RCM research are discussed, with highlight on: transition to convection‐permitting modeling systems; further development of Regional Earth System Models including the human component; next phase of the Coordinated Regional Climate Downscaling Experiment project; and use of RCMs in the distillation of actionable information for contribution to climate service activities. A brief historical overview of regional climate modeling is also presented.
Abstract The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.
Abstract Euro‐Mediterranean Centre on Climate Change coupled climate model (CMCC‐CM2) represents the new family of the global coupled climate models developed and used at CMCC. It is based on the atmospheric, land and sea ice components from the Community Earth System Model coupled with the global ocean model Nucleus for European Modeling of the Ocean. This study documents the model components, the coupling strategy, particularly for the oceanic, atmospheric, and sea ice components, and the overall model ability in reproducing the observed mean climate and main patterns of interannual variability. As a first step toward a more comprehensive, process‐oriented, validation of the model, this work analyzes a 200‐year simulation performed under constant forcing corresponding to present‐day climate conditions. In terms of mean climate, the model is able to realistically reproduce the main patterns of temperature, precipitation, and winds. Specifically, we report improvements in the representation of the sea surface temperature with respect to the previous version of the model. In terms of mean atmospheric circulation features, we notice a realistic simulation of upper tropospheric winds and midtroposphere geopotential eddies. The oceanic heat transport and the Atlantic meridional overturning circulation satisfactorily compare with present‐day observations and estimates from global ocean reanalyses. The sea ice patterns and associated seasonal variations are realistically reproduced in both hemispheres, with a better skill in winter. Main weaknesses of the simulated climate are related with the precipitation patterns, specifically in the tropical regions with large dry biases over the Amazon basin. Similarly, the seasonal precipitation associated with the monsoons, mostly over Asia, is weaker than observed. The main patterns of interannual variability in terms of dominant empirical orthogonal functions are faithfully reproduced, mostly in the Northern Hemisphere winter. In the tropics the main teleconnection patterns associated with El Niño–Southern Oscillation and with the Indian Ocean Dipole are also in good agreement with observations.
Abstract This work documents the first version of the U.S. Department of Energy (DOE) new Energy Exascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully coupled physical model designed to address DOE mission‐relevant water cycle questions. Its components include atmosphere and land (110‐km grid spacing), ocean and sea ice (60 km in the midlatitudes and 30 km at the equator and poles), and river transport (55 km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima simulations consisting of a long preindustrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO 2 forcing simulations. The model performs well overall with biases typical of other CMIP‐class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP‐class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between preindustrial (1850) and present day, the trajectory of the warming diverges from observations in the second half of the twentieth century with a period of delayed warming followed by an excessive warming trend. Using a two‐layer energy balance model, we attribute this divergence to the model's strong aerosol‐related effective radiative forcing (ERF ari+aci = −1.65 W/m 2 ) and high equilibrium climate sensitivity (ECS = 5.3 K).
Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.
forcing, which nonetheless can be represented by a simple two-layer model.
Abstract. The Palaeoclimate Modelling Intercomparison Project has expanded to include a model intercomparison for the mid-Pliocene warm period (3.29 to 2.97 million yr ago). This project is referred to as PlioMIP (the Pliocene Model Intercomparison Project). Two experiments have been agreed upon and together compose the initial phase of PlioMIP. The first (Experiment 1) is being performed with atmosphere-only climate models. The second (Experiment 2) utilises fully coupled ocean-atmosphere climate models. Following on from the publication of the experimental design and boundary conditions for Experiment 1 in Geoscientific Model Development, this paper provides the necessary description of differences and/or additions to the experimental design for Experiment 2.
Abstract. The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.
Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2 °C above present (approximately 2.7 °C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m(3) per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2 °C, whereas indicators of very severe impacts increase unabated beyond 2 °C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.
Abstract Equilibrium climate sensitivity, the global surface temperature response to CO doubling, has been persistently uncertain. Recent consensus places it likely within 1.5–4.5 K. Global climate models (GCMs), which attempt to represent all relevant physical processes, provide the most direct means of estimating climate sensitivity via CO quadrupling experiments. Here we show that the closely related effective climate sensitivity has increased substantially in Coupled Model Intercomparison Project phase 6 (CMIP6), with values spanning 1.8–5.6 K across 27 GCMs and exceeding 4.5 K in 10 of them. This (statistically insignificant) increase is primarily due to stronger positive cloud feedbacks from decreasing extratropical low cloud coverage and albedo. Both of these are tied to the physical representation of clouds which in CMIP6 models lead to weaker responses of extratropical low cloud cover and water content to unforced variations in surface temperature. Establishing the plausibility of these higher sensitivity models is imperative given their implied societal ramifications.