Author
Bettina K. Gier
Other affiliations: German Aerospace Center
Bio: Bettina K. Gier is an academic researcher from University of Bremen. The author has contributed to research in topics: Earth system science & Climate model. The author has an hindex of 5, co-authored 8 publications receiving 274 citations. Previous affiliations of Bettina K. Gier include German Aerospace Center.
Papers
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German Aerospace Center1, University of Bremen2, University of Exeter3, Lawrence Livermore National Laboratory4, University of New South Wales5, University of California, Berkeley6, Lawrence Berkeley National Laboratory7, University of California, Los Angeles8, University of Tennessee9, Oak Ridge National Laboratory10, University of Maryland, College Park11, Institute of Arctic and Alpine Research12, Met Office13, Geophysical Fluid Dynamics Laboratory14, ETH Zurich15, Cooperative Institute for Research in Environmental Sciences16, National Center for Atmospheric Research17, Goddard Institute for Space Studies18, University of Arizona19
TL;DR: The authors discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting.
Abstract: Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.
397 citations
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University of Bremen1, German Aerospace Center2, University of Turin3, Barcelona Supercomputing Center4, Universidade Nova de Lisboa5, Max Planck Society6, 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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University of Bremen1, German Aerospace Center2, University of Leicester3, Netherlands Institute for Space Research4, Heidelberg University5, Ludwig Maximilian University of Munich6, Japan Aerospace Exploration Agency7, National Institute for Environmental Studies8, Jet Propulsion Laboratory9, Colorado State University10
TL;DR: In this paper, a comparison of satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Nino Southern Oscillation (ENSO) indices is made.
Abstract: . The growth rate of atmospheric carbon dioxide ( CO2 ) reflects the net
effect of emissions and uptake resulting from anthropogenic and natural
carbon sources and sinks. Annual mean CO2 growth rates have been
determined from satellite retrievals of column-averaged dry-air mole fractions
of CO2 , i.e. XCO2 , for the years 2003 to 2016. The XCO2
growth rates agree with National Oceanic and Atmospheric Administration
(NOAA) growth rates from CO2 surface observations within the uncertainty
of the satellite-derived growth rates (mean difference ± standard
deviation: 0.0±0.3 ppm year −1 ; R : 0.82). This new and independent data
set confirms record-large growth rates of around 3 ppm year −1
in 2015 and 2016, which are attributed to the 2015–2016 El Nino. Based on a comparison of
the satellite-derived growth rates with human CO2 emissions from fossil
fuel combustion and with El Nino Southern Oscillation (ENSO) indices, we
estimate by how much the impact of ENSO dominates the impact of fossil-fuel-burning-related emissions in explaining the variance of the atmospheric
CO2 growth rate. Our analysis shows that the ENSO impact on CO2
growth rate variations dominates that of human emissions throughout the
period 2003–2016 but in particular during the period 2010–2016 due to strong
La Nina and El Nino events. Using the derived growth rates and their
uncertainties, we estimate the probability that the impact of ENSO on the
variability is larger than the impact of human emissions to be 63 % for the
time period 2003–2016. If the time period is restricted to 2010–2016, this
probability increases to 94 %.
27 citations
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TL;DR: The Earth System Model Evaluation Tool (ESMValTool) as discussed by the authors is a community diagnostics and performance metrics tool for evaluation and analysis of Earth system models (ESMs) designed to facilitate a more comprehensive and rapid comparison of single or multiple models participating in the coupled model intercomparison project (CMIP).
Abstract: . The Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for evaluation and analysis of Earth system models (ESMs) is designed to facilitate a more comprehensive and rapid comparison of single or multiple models participating in the coupled model intercomparison project (CMIP). The ESM results can be compared against observations or reanalysis data as well as against other models including predecessor versions of the same model. The updated and extended version 2.0 of the ESMValTool includes several new analysis scripts such as large-scale diagnostics for evaluation of ESMs as well as diagnostics for extreme events, regional model and impact evaluation. In this paper, the newly implemented climate metrics such as effective climate sensitivity (ECS) and transient climate response (TCR) as well as emergent constraints for various climate-relevant feedbacks and diagnostics for future projections from ESMs are described and illustrated with examples using results from the well-established model ensemble CMIP5. The emergent constraints implemented include ECS, snow-albedo effect, climate-carbon cycle feedback, hydrologic cycle intensification, future Indian summer monsoon precipitation, and year of disappearance of summer Arctic sea ice. The diagnostics included in ESMValTool v2.0 to analyze future climate projections from ESMs include analysis scripts to reproduce selected figures of chapter 12 of the Intergovernmental Panel on Climate Change’s (IPCC) Fifth Assessment report (AR5) and various multi-model statistics.
21 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
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German Aerospace Center1, Imperial College London2, Wageningen University and Research Centre3, Clarkson University4, University of Valencia5, VU University Amsterdam6, Potsdam Institute for Climate Impact Research7, University of California, San Diego8, Carnegie Mellon University9, Max Planck Society10, University of Copenhagen11, University of Amsterdam12, Oeschger Centre for Climate Change Research13, University of Bern14, ETH Zurich15
TL;DR: An overview of causal inference frameworks is given, promising applications and methodological challenges are identified, and a causality benchmark platform is initiated to close the gap between method users and developers.
Abstract: The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
460 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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National Center for Atmospheric Research1, Pusan National University2, Cornell University3, Geophysical Fluid Dynamics Laboratory4, University of Texas at Austin5, Lamont–Doherty Earth Observatory6, University of Paris7, University of Victoria8, Northwestern University9, University of Colorado Boulder10, Cooperative Institute for Research in Environmental Sciences11, ETH Zurich12, Institute of Arctic and Alpine Research13, Max Planck Society14, University of California, Los Angeles15, Hokkaido University16, University of California, Irvine17, University of Exeter18
TL;DR: In this article, a collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences.
Abstract: Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed. Climate change detection is confounded by internal variability, but recent initial-condition large ensembles (LEs) have begun addressing this issue. This Perspective discusses the value of multi-model LEs, the challenges of providing them and their role in future climate change research.
426 citations