| 04 May 2026
Spin-up in humidity and temperature and its consequences for convective diagnostics: a Model Uncertainty Model Intercomparison Project experiment
Abstract. We analyse the evolution of convective diagnostics such as mixed-layer convective available potential energy (CAPE), level of neutral buoyancy and precipitation rate as a function of lead time in the model uncertainty model-intercomparison project. Four model physics packages are exposed to common dynamics to form a large single-column model dataset. We analyse tendencies in an equatorial band over the Indian Ocean out to 6 hr lead time over one month. We prescribe dynamics and initial conditions from an ICON-DYAMOND simulation after coarse-graining to 0.2 degrees. The physics suites represent state-of-the-art global numerical weather and climate prediction models.
Correlation analysis shows that the spatial mean change of CAPE is not associated with precipitation rate, but it correlates very well with mean mixed-layer drying across our suites. This systematic drying occurs below 700 hPa in some suites, especially in the first hour. The sub-grid physics adjusts the initialised ICON state towards the native climate of each physics suite, in particular at low levels.
We apply a column-by-column empirical orthogonal function (EOF) analysis to a two-layer representation of physics and dynamics tendencies, CAPE tendency and precipitation rate. The first EOF is associated with free-tropospheric tendencies and nearly all precipitation variability, with neat compensation between physics and dynamics tendencies. The second and third EOFs of each suite indicate that a imbalance between these terms in the mixed-layer correlates with the CAPE change at least one of them, which are explained by temperature and humidity adjustments, but with little imprint on precipitation.
Convective physics are a vital part of climate models but lead to lots of uncertainty and biases. As convective processes tend to occur on spatial scales smaller than can be resolved by most models, they must be parameterized. These parameterizations are vital to understand as they control lots of model results, but are enormously complex and have effects that are hard to isolate.
In this manuscript, the authors place data from a full GCM into single column models to see how the physics model interacts with the column when it is isolated from the dynamics. They find that all the physics models tend to decrease CAPE through boundary layer drying, and that there are free troposphere changes associated with precipitation.
The manuscript is interesting and the framework for analyzing model biases is valid. However, the analysis could be much more thorough and the results are not very general. Additionally, the manuscript has serious grammatical errors and inappropriately cites “In preparation” references.
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