Author
Javier Vegas-Regidor
Bio: Javier Vegas-Regidor is an academic researcher from Barcelona Supercomputing Center. The author has contributed to research in topics: Earth system science & Software quality. The author has an hindex of 6, co-authored 7 publications receiving 118 citations.
Papers
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German Aerospace Center1, University of Bremen2, University of Turin3, Barcelona Supercomputing Center4, Max Planck Society5, Universidade Nova de Lisboa6, ENEA7, ETH Zurich8, Plymouth Marine Laboratory9, National Center for Atmospheric Research10, Swedish Meteorological and Hydrological Institute11, Met Office12, University of Arizona13, Free University of Berlin14, Alfred Wegener Institute for Polar and Marine Research15, University of Hamburg16, Central Maine Community College17, University of Reading18, Université catholique de Louvain19, Ludwig Maximilian University of Munich20, Polytechnic University of Turin21
TL;DR: Large-scale diagnostics of the second major release of the ESMValTool tool, a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models participating in the Coupled Model Intercomparison Project (CMIP), are described.
Abstract: This research has been supported by Horizon 2020 (grant nos. 641816, 727862, 641727, and 824084), the Copernicus Climate Change Service (C3S) (Metrics and Access to Global Indices for Climate Projections, MAGIC), the Helmholtz Association (Advanced Earth System Model Evaluation for CMIP, EVal4CMIP), the Deutsche Forschungsgemeinschaft (grant no. 274762653), the Federal Ministry of Education and Research (BMBF) (grant no. CMIP6-DICAD), and the European Space Agency (ESA Climate Change Initiative Climate Model User Group, ESA CCI CMUG).
70 citations
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TL;DR: The new version of ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs.
Abstract: . This paper describes the second major release of the Earth System Model
Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Compared to version 1.0, released in 2016, ESMValTool version 2.0 (v2.0) features a brand new design, with an improved interface and a revised preprocessor. It also features a significantly enhanced diagnostic part that is described in three companion papers. The new version of ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs. The new version takes advantage of state-of-the-art computational libraries and methods to deploy an efficient and user-friendly data processing. Common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. Performance tests conducted on a set of standard diagnostics show that the new version is faster than its predecessor by about a factor of 3. The performance can be further improved, up to a factor of more than 30, when the newly introduced task-based parallelization options are used, which enable the efficient exploitation of much larger computing infrastructures. ESMValTool v2.0 also includes a revised and simplified installation procedure, the setting of user-configurable options
based on modern language formats, and high code quality standards following the best practices for software development.
53 citations
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TL;DR: In this article, the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project Component A (DCPP-A) is evaluated.
Abstract: . In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and hamper the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies.
32 citations
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18 Jul 2016TL;DR: In this paper, Autosubmit, a Python-based tool that allows creating, launching and monitoring climate experiments, is introduced and could be expanded to perform any weather, air quality and climate experiment on any computing platform to ensure the efficient handling of highly-dependent jobs.
Abstract: One of the main challenges for the weather, air quality and climate science is how to efficiently perform large numbers of simulations of the Earth system on a variety of supercomputers. In particular, the climate community has developed complex computational systems to obtain climate projections and predictions. A huge amount of computational resources are needed to produce these simulations, as well as to deal with the data coming in and out from the models. Regardless of the platform, climate simulations typically consist of hundreds of programs or scripts whose workflow can be complex. In this paper, Autosubmit, a Python-based tool that allows creating, launching and monitoring climate experiments, is introduced. The experiment is defined as a sequence of jobs that Autosubmit remotely submits and manages in a transparent way to the user. The same experiment can run in more than one supercomputing platform and for different workflow configurations. Autosubmit could be expanded to perform any weather, air quality and climate experiment on any computing platform to ensure the efficient handling of highly-dependent jobs, an optimum use of available computing resources, and a user-friendly management of the experiments, including creation, documentation, start, stop, restart, live monitoring and reproduction.
31 citations
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TL;DR: The Earth System Model Evaluation Tool (ESMValTool) v2.0 as discussed by the authors was developed by a large community of scientists to facilitate the evaluation and comparison of Earth system models which are participating in the Coupled Model Intercomparison Project (CMIP).
Abstract: . This paper complements a series of now four publications that
document the release of the Earth System Model Evaluation Tool (ESMValTool)
v2.0. It describes new diagnostics on the hydrological cycle, extreme
events, impact assessment, regional evaluations, and ensemble member
selection. The diagnostics are developed by a large community of scientists
aiming to facilitate the evaluation and comparison of Earth system models
(ESMs) which are participating in the Coupled Model Intercomparison Project
(CMIP). The second release of this tool aims to support the evaluation of
ESMs participating in CMIP Phase 6 (CMIP6). Furthermore, datasets from
other models and observations can be analysed. The diagnostics for the
hydrological cycle include several precipitation and drought indices, as
well as hydroclimatic intensity and indices from the Expert Team on Climate
Change Detection and Indices (ETCCDI). The latter are also used for
identification of extreme events, for impact assessment, and to project
and characterize the risks and impacts of climate change for natural and
socio-economic systems. Further impact assessment diagnostics are included
to compute daily temperature ranges and capacity factors for wind and solar
energy generation. Regional scales can be analysed with new diagnostics
implemented for selected regions and stochastic downscaling. ESMValTool v2.0
also includes diagnostics to analyse large multi-model ensembles including
grouping and selecting ensemble members by user-specified criteria. Here, we
present examples for their capabilities based on the well-established CMIP
Phase 5 (CMIP5) dataset.
14 citations
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01 Dec 2012
Abstract: We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.
948 citations
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National Center for Atmospheric Research1, University of Colorado Boulder2, Utrecht University3, Brown University4, Cooperative Institute for Research in Environmental Sciences5, University of Toronto6, University of Wisconsin–Milwaukee7, University of California, Irvine8, Columbia University9, Pacific Northwest National Laboratory10
TL;DR: The Community Earth System Model Version 2 (CESM2) as discussed by the authors is the most recent version of the Coupled Model Intercomparison Project (CMEI) coupled model.
Abstract: An overview of the Community Earth System Model Version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1° horizontal resolution configuration of the coupled model with both the “low-top” (40 km, with limited chemistry) and “high-top” (130 km, with comprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial science and infrastructure improvements and new capabilities since its previous major release, CESM1, resulting in improved historical simulations in comparison to CESM1 and available observations. These include major reductions in low-latitude precipitation and shortwave cloud forcing biases; better representation of the Madden-Julian Oscillation; better El Nino-Southern Oscillation-related teleconnections; and a global land carbon accumulation trend that agrees well with observationally based estimates. Most tropospheric and surface features of the low- and high-top simulations are very similar to each other, so these improvements are present in both configurations. CESM2 has an equilibrium climate sensitivity of 5.1–5.3 °C, larger than in CESM1, primarily due to a combination of relatively small changes to cloud microphysics and boundary layer parameters. In contrast, CESM2's transient climate response of 1.9–2.0 °C is comparable to that of CESM1. The model outputs from these and many other simulations are available to the research community, and they represent CESM2's contributions to the Coupled Model Intercomparison Project Phase 6.
884 citations
01 Dec 2012
TL;DR: In this paper, the magnitude and evolution of parameters that characterize feedbacks in the coupled carbon-climate system are compared across nine Earth system models (ESMs), based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1.
Abstract: The magnitude and evolution of parameters that characterize feedbacks in the coupled carbon–climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1. These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO2 fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO2 concentration and to changes in temperature and other climate variables. The carbon–concentration and carbon–climate feedback parameters characterize the response of the CO2 flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon–concentration feedback to...
454 citations
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TL;DR: Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6.
Abstract: For the current generation of earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the range of equilibrium climate sensitivity (ECS, a hypothetical value of global warming at equilibrium for a doubling of CO2) is 18°C to 56°C, the largest of any generation of models dating to the 1990s Meanwhile, the range of transient climate response (TCR, the surface temperature warming around the time of CO2 doubling in a 1% per year CO2 increase simulation) for the CMIP6 models of 17°C (13°C to 30°C) is only slightly larger than for the CMIP3 and CMIP5 models Here we review and synthesize the latest developments in ECS and TCR values in CMIP, compile possible reasons for the current values as supplied by the modeling groups, and highlight future directions Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6
297 citations
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Joint Global Change Research Institute1, German Aerospace Center2, University of Bremen3, ETH Zurich4, École Polytechnique5, University of Exeter6, University of Leeds7, Met Office8, University of Denver9, Centre national de la recherche scientifique10, Netherlands Environmental Assessment Agency11, International Institute for Applied Systems Analysis12, University of Melbourne13, University of Maryland, College Park14, Potsdam Institute for Climate Impact Research15, National Center for Atmospheric Research16, Goddard Institute for Space Studies17, University of Paris18, Max Planck Society19, University of Hamburg20, Korea Meteorological Administration21, Commonwealth Scientific and Industrial Research Organisation22, Central Maine Community College23, Geophysical Fluid Dynamics Laboratory24, Korean Ocean Research and Development Institute25, Pukyong National University26, Nanjing University of Information Science and Technology27, Norwegian Meteorological Institute28, Indian Institute of Tropical Meteorology29, Ontario Ministry of Natural Resources30, University of Toulouse31, Alfred Wegener Institute for Polar and Marine Research32, Oak Ridge National Laboratory33, Deutscher Wetterdienst34, University of Arizona35, Japan Agency for Marine-Earth Science and Technology36, Lawrence Livermore National Laboratory37, Swedish Meteorological and Hydrological Institute38, China Meteorological Administration39, Danish Meteorological Institute40, Chinese Academy of Sciences41
TL;DR: In this paper, the authors present a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations, focusing mainly on the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation.
Abstract: . The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5–95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20–28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s.
190 citations