Improvements in the terrestrial carbon cycle in CMIP models evaluated with satellite observations

With impacts of climate change already noticeable in every region of the world, understanding and accurately simulating the drivers of climate change is crucial. In particular, the global carbon cycle and its responses to changing carbon dioxide (CO2) emissions plays an important role. This thesis aims to identify important improvements and key processes relevant to accurately simulate the carbon cycle under climate change and to provide recommendations for further model developments. This is achieved by a comprehensive evaluation of historical simulations from earth system models (ESMs) participating in the last two phases of the Coupled Model Intercomparison Project (CMIP) with satellite observations. In a first study of this thesis, column-average CO2 mole fraction (XCO2) from CMIP5 and CMIP6 emission-driven ESM simulations are compared to satellite observations. A previously found discrepancy between a negative trend of the seasonal cycle amplitude (SCA) with increasing XCO2 in the northern midlatitudes shown by the observations with models showing an insignificant trend could be attributed to spatial sampling. Furthermore, while ESMs overestimate mean and growth rate of XCO2 while underestimating the SCA, the CMIP6 ensemble performs better than CMIP5 ensemble. In a second study, the present-day land carbon cycle is evaluated. While some long-standing biases could be resolved in CMIP6, such as the photosynthesis overestimation which was resolved through the inclusion of the interactive nitrogen cycle, other biases remain. Despite the increased process complexity in emission-driven simulations that fully account for the influence of climate-carbon feedbacks on atmospheric CO2, they perform just as well as CO2 concentration-driven simulations. Therefore, both the use of emission-driven over concentration-driven simulations, as well as the inclusion of interactive nitrogen cycles are recommended as a default setting for future CMIP phases

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIP

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 (v2.0) 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 constraints on 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 further 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.This research has been supported by the Horizon 2020 Framework Programme (CRESCENDO (grant no. 641816), 4C (grant no. 821003), and IS-ENES3 (grant no. 824084)), the Copernicus Climate Change Service (C3S) (Metrics and Access to Global Indices for Climate Change Projections (MAGIC)), the Federal Ministry of Education and Research (BMBF) (CMIP6-DICAD), the European Space Agency (ESA Climate Change Initiative Climate Model User Group (ESA CCI CMUG)) and the Helmholtz Association (Advanced Earth System Model Evaluation for CMIP (EVal4CMIP)).Peer Reviewed"Article signat per 13 autors/es: Axel Lauer, Veronika Eyring, Omar Bellprat, Lisa Bock, Bettina K. Gier, Alasdair Hunter, Ruth Lorenz, Núria Pérez-Zanón, Mattia Righi, Manuel Schlund, Daniel Senftleben, Katja Weigel, and Sabrina Zechlau"Postprint (published version

Earth System Model Evaluation Tool (ESMValTool) v2.0 - diagnostics for emergent constraints and future projections from Earth system models in CMIP

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 constraints on 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 further 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

Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003-2016

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%

Spatially resolved evaluation of Earth system models with satellite column averaged CO2

Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. By comparing the simulations with satellite observations, in this study we find slight improvements in the ESMs participating in the new Phase 6 (CMIP6) compared to CMIP5. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). The satellite data are a combined data product covering the period 2003­-2014 based on the SCIAMACHY/ENVISAT (2003-2012) and TANSO-FTS/GOSAT (2009-2014) instruments. In this study the Observations for Model Intercomparisons Project (Obs4MIPs) format data product version 3 (O4Mv3) with a spatial resolution of 5° × 5° and monthly time resolution has been used. Comparisons of XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6, with differences in the absolute concentrations of up to approximately 20 ppmv relative to the satellite observations. The multi-model mean has a bias of approximately +10 and +2 ppmv in CMIP5 and CMIP6, respectively. The derived atmospheric XCO2 growth rate (GR) is typically slightly overestimated in the models, with a slightly better average and lower spread for CMIP6. The interannual variability of the growth rate is well reproduced in the multi-model mean. All models capture the expected increase of the seasonal cycle amplitude (SCA) with increasing latitude, but most models underestimate the SCA. Most models from both ensembles show a positive trend of the SCA over the period 2003-2014, i.e. an increase of the SCA with XCO2, similar to in situ ground-based measurements. In contrast, the combined satellite product shows a negative trend over this period. Any SCA derived from sampled data can only be considered an "effective" SCA, as sampling can remove the peaks or troughs. This negative trend can be reproduced by the models when sampling them as the observations. The average effective SCA in the models is higher when using the SCIAMACHY/ENVISAT instead of the TANSO-FTS/GOSAT mean data coverage mask, overall leading to a negative trend over the full period similar to the combined satellite product. Models with a larger difference in the average effective SCA between the two coverages also show a stronger trend reversal. Therefore, this trend reversal in the satellite data is due to sampling characteristics, more specifically the different data coverage of the two satellites that can be reproduced by the models if sampled the same way. Overall, the CMIP6 ensemble shows better agreement with the satellite data in all considered quantities (XCO2, GR, SCA and trend in SCA), with the biggest improvement in the mean XCO2 content of the atmosphere. This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth System Models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations

Spatially resolved evaluation of Earth system models with satellite column-averaged CO<SUB>2</SUB>

International audienceEarth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission-driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6. Compared to the satellite observations, the models have a bias of +25 to -20 ppmv in CMIP5 and +20 to -15 ppmv in CMIP6, with the multi-model mean biases at +10 and +2 ppmv, respectively. The derived mean atmospheric XCO2 growth rate (GR) of 2.0 ppmv yr-1 is overestimated by 0.4 ppmv yr-1 in CMIP5 and 0.3 ppmv yr-1 in CMIP6 for the multi-model mean, with a good reproduction of the interannual variability. All models capture the expected increase of the seasonal cycle amplitude (SCA) with increasing latitude, but most models underestimate the SCA. Any SCA derived from data with missing values can only be considered an "effective" SCA, as the missing values could occur at the peaks or troughs. The satellite data are a combined data product covering the period 2003-2014 based on the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat (2003-2012) and Thermal And Near infrared Sensor for carbon Observation Fourier transform spectrometer/Greenhouse Gases Observing Satellite (TANSO-FTS/GOSAT) (2009-2014) instruments. While the combined satellite product shows a strong negative trend of decreasing effective SCA with increasing XCO2 in the northern midlatitudes, both CMIP ensembles instead show a non-significant positive trend in the multi-model mean. The negative trend is reproduced by the models when sampling them as the observations, attributing it to sampling characteristics. Applying a mask of the mean data coverage of each satellite to the models, the effective SCA is higher for the SCIAMACHY/Envisat mask than when using the TANSO-FTS/GOSAT mask. This induces an artificial negative trend when using observational sampling over the full period, as SCIAMACHY/Envisat covers the early period until 2012, with TANSO-FTS/GOSAT measurements starting in 2009. Overall, the CMIP6 ensemble shows better agreement with the satellite data than the CMIP5 ensemble in all considered quantities (XCO2, GR, SCA and trend in SCA). This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth system models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP

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.This research has been supported by the Copernicus Climate Change Service (C3S) (Metrics and Access to Global Indices for Climate Projections (C3S-MAGIC), C3S_34a Lot 2), the Helmholtz-Gemeinschaft (Advanced Earth System Model Evaluation for CMIP (grant no. EVal4CMIP)), the Horizon 2020 Framework Programme, H2020 Societal Challenges (CRESCENDO (grant no. 641816)), the Federal Ministry of Education and Research (BMBF) (grant no. CMIP6-DICAD), and the Horizon 2020 Framework Programme, H2020 Excellent Science (IS-ENES3 (grant no. 824084)). The article processing charges for this open-access publication were covered by the University of Bremen.Peer Reviewed"Article signat per 26 autors/es: Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyrin "Postprint (published version

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIP

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 (v2.0) 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 constraints on 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 further 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.ISSN:1991-9603ISSN:1991-959

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation and analysis of Earth system models in CMIP

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 25 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)

Earth System Model Evaluation Tool (ESMValTool) v2.0-diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP

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 userspecified criteria. Here, we present examples for their capabilities based on the well-established CMIP Phase 5 (CMIP5) dataset