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.
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.
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