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.

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 Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño 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 Niña and El Niño 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 %.

Abstract. The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of (1) an easy-to-install, well-documented Python package providing the core functionalities (ESMValCore) that performs common preprocessing operations and (2) a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability, the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top–down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klimarechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5.