Author
Valeriu Predoi
Bio: Valeriu Predoi is an academic researcher from University of Reading. 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 266 citations.
Papers
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Met Office1, University of Leeds2, National Oceanography Centre, Southampton3, University of Reading4, Plymouth Marine Laboratory5, University of Cambridge6, University of Exeter7, Wellington Management Company8, University of Leicester9, British Antarctic Survey10, Commonwealth Scientific and Industrial Research Organisation11
TL;DR: The United Kingdom Earth System Model UKESM1 as discussed by the authors was developed and tuned to achieve acceptable performance in key physical and Earth system quantities, and discuss the challenges involved in mitigating biases in a model with complex connections between its components.
Abstract: We document the development of the first version of the United Kingdom Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2‐ES, with enhancements to all component models and new feedback mechanisms. These include: a new core physical model with a well‐resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric‐stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane and nitrous oxide; two‐moment, five‐species, modal aerosol; and ocean biogeochemistry with two‐way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities, and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall the model performs well, with a stable pre‐industrial state, and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger‐than‐observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealised simulations show a high climate sensitivity relative to previous generations of models: equilibrium climate sensitivity (ECS) is 5.4 K, transient climate response (TCR) ranges from 2.68 K to 2.85 K, and transient climate response to cumulative emissions (TCRE) is 2.49 K/TtC to 2.66 K/TtC.
416 citations
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University of Bremen1, German Aerospace Center2, 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: More than 40 model groups worldwide are participating in the Coupled model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present, and future climate change.
Abstract: More than 40 model groups worldwide are participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present, and future climate change. Here, we use the Earth System Model Evaluation Tool (ESMValTool) to assess the performance of the CMIP6 ensemble compared to the previous generations CMIP3 and CMIP5. While CMIP5 models did not capture the observed pause in the increase in global mean surface temperature between 1998 and 2013, the historical CMIP6 simulations agree well with the observed recent temperature increase, but some models have difficulties in reproducing the observed global mean surface temperature record of the second half of the 20th century. While systematic biases in annual mean surface temperature and precipitation remain in the CMIP6 multi-model mean, individual models and high-resolution versions of the models show significant reductions in many long-standing biases. Some improvements are also found in the vertical temperature, water vapor and zonal wind speed distributions, and root mean square errors for selected fields are generally smaller with reduced inter-model spread and higher average skill in the correlation patterns relative to observations. An emerging property of the CMIP6 ensemble is a higher effective climate sensitivity with an increased range between 2.3 and 5.6 K. A possible reason for this increase in some models is improvements in cloud representation resulting in stronger shortwave cloud feedbacks than in their predecessor versions.
46 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
Cited by
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École Normale Supérieure1, University of Exeter2, Norwich Research Park3, University of Groningen4, Wageningen University and Research Centre5, Max Planck Society6, Ludwig Maximilian University of Munich7, Commonwealth Scientific and Industrial Research Organisation8, Université Paris-Saclay9, Stanford University10, National Oceanic and Atmospheric Administration11, National Institute for Space Research12, University of Southampton13, Bermuda Institute of Ocean Sciences14, PSL Research University15, Japan Agency for Marine-Earth Science and Technology16, National Institute for Environmental Studies17, University of Maryland, College Park18, University of Leeds19, International Institute of Minnesota20, Flanders Marine Institute21, ETH Zurich22, University of East Anglia23, German Aerospace Center24, Woods Hole Research Center25, University of Illinois at Urbana–Champaign26, University of Toulouse27, Japan Meteorological Agency28, Plymouth Marine Laboratory29, University of Paris30, Hobart Corporation31, Oeschger Centre for Climate Change Research32, Tsinghua University33, National Center for Atmospheric Research34, Appalachian State University35, University of Colorado Boulder36, University of Washington37, Atlantic Oceanographic and Meteorological Laboratory38, Princeton University39, Met Office40, Leibniz Institute of Marine Sciences41, Auburn University42, University of Tasmania43, VU University Amsterdam44, Oak Ridge National Laboratory45, Sun Yat-sen University46, Nanjing University47
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ± 0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).
1,764 citations
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University of Exeter1, École Normale Supérieure2, Norwich Research Park3, Alfred Wegener Institute for Polar and Marine Research4, University of Groningen5, Wageningen University and Research Centre6, Max Planck Society7, Ludwig Maximilian University of Munich8, Commonwealth Scientific and Industrial Research Organisation9, Centre national de la recherche scientifique10, Stanford University11, Karlsruhe Institute of Technology12, Atlantic Oceanographic and Meteorological Laboratory13, Cooperative Institute for Marine and Atmospheric Studies14, Bjerknes Centre for Climate Research15, Geophysical Institute, University of Bergen16, Japan Agency for Marine-Earth Science and Technology17, University of Maryland, College Park18, National Institute of Water and Atmospheric Research19, National Oceanic and Atmospheric Administration20, Appalachian State University21, Flanders Marine Institute22, Augsburg College23, ETH Zurich24, Leibniz Institute of Marine Sciences25, University of East Anglia26, Woods Hole Research Center27, University of Illinois at Urbana–Champaign28, University of Hong Kong29, Utrecht University30, Netherlands Environmental Assessment Agency31, University of Paris32, Hobart Corporation33, University of Tasmania34, University of Bern35, National Center for Atmospheric Research36, University of Reading37, Cooperative Institute for Research in Environmental Sciences38, National Institute for Environmental Studies39, Russian Academy of Sciences40, Goddard Space Flight Center41, Leibniz Institute for Baltic Sea Research42, Princeton University43, Met Office44, Lund University45, Auburn University46, Food and Agriculture Organization47, VU University Amsterdam48
TL;DR: In this article, the authors describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land use change, and show that the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere is a measure of imperfect data and understanding of the contemporary carbon cycle.
Abstract: . Accurate assessment of anthropogenic carbon dioxide ( CO2 ) emissions and
their redistribution among the atmosphere, ocean, and terrestrial biosphere
– the “global carbon budget” – is important to better understand the
global carbon cycle, support the development of climate policies, and
project future climate change. Here we describe data sets and methodology to
quantify the five major components of the global carbon budget and their
uncertainties. Fossil CO2 emissions ( EFF ) are based on energy
statistics and cement production data, while emissions from land use change
( ELUC ), mainly deforestation, are based on land use and land use change
data and bookkeeping models. Atmospheric CO2 concentration is measured
directly and its growth rate ( GATM ) is computed from the annual changes
in concentration. The ocean CO2 sink ( SOCEAN ) and terrestrial
CO2 sink ( SLAND ) are estimated with global process models
constrained by observations. The resulting carbon budget imbalance
( BIM ), the difference between the estimated total emissions and the
estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a
measure of imperfect data and understanding of the contemporary carbon
cycle. All uncertainties are reported as ±1σ . For the last
decade available (2009–2018), EFF was 9.5±0.5 GtC yr −1 ,
ELUC 1.5±0.7 GtC yr −1 , GATM 4.9±0.02 GtC yr −1 ( 2.3±0.01 ppm yr −1 ), SOCEAN 2.5±0.6 GtC yr −1 , and SLAND 3.2±0.6 GtC yr −1 , with a budget
imbalance BIM of 0.4 GtC yr −1 indicating overestimated emissions
and/or underestimated sinks. For the year 2018 alone, the growth in EFF was
about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr −1 , reaching 10 GtC yr −1 for the first time in history,
ELUC was 1.5±0.7 GtC yr −1 , for total anthropogenic
CO2 emissions of 11.5±0.9 GtC yr −1 ( 42.5±3.3 GtCO2 ). Also for 2018, GATM was 5.1±0.2 GtC yr −1 ( 2.4±0.1 ppm yr −1 ), SOCEAN was 2.6±0.6 GtC yr −1 , and SLAND was 3.5±0.7 GtC yr −1 , with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of
−0.2 % to 1.5 %) based on national emissions projections for China, the
USA, the EU, and India and projections of gross domestic product corrected
for recent changes in the carbon intensity of the economy for the rest of
the world. Overall, the mean and trend in the five components of the global
carbon budget are consistently estimated over the period 1959–2018, but
discrepancies of up to 1 GtC yr −1 persist for the representation of
semi-decadal variability in CO2 fluxes. A detailed comparison among
individual estimates and the introduction of a broad range of observations
shows (1) no consensus in the mean and trend in land use change emissions
over the last decade, (2) a persistent low agreement between the different
methods on the magnitude of the land CO2 flux in the northern
extra-tropics, and (3) an apparent underestimation of the CO2
variability by ocean models outside the tropics. This living data update
documents changes in the methods and data sets used in this new global
carbon budget and the progress in understanding of the global carbon cycle
compared with previous publications of this data set (Le Quere et
al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by
this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein
et al., 2019).
981 citations
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