How different are deterministic physics suites when coupled to fixed model dynamics and why?
Journal of the European Meteorological Society Elsevier 5 (2026) 100041
Abstract:
It is often difficult to attribute uncertainty and errors in atmospheric models to designated model components. This is because sub-grid parameterised processes interact strongly with the large-scale transport represented by the explicit model dynamics. We carry out experiments with prescribed large-scale dynamics and different sub-grid physics suites. This dataset has been constructed for the Model Uncertainty Model Intercomparison Project (MUMIP), in which each suite forecasts sub-grid tendencies at a 22km grid. The common dynamics is derived from a convection-permitting benchmark: an ICON DYAMOND experiment (2.5km grid). We compare four different physics suites for atmospheric models in an Indian Ocean experiment. We analyse their joint PDFs of precipitation and associated physics tendencies for a full month, where precipitation is used to diagnose uncertainty of convective activity. We find that all physics suites produce very similar precipitation amounts, with very high correlations between models, i.e., > 0.95 at the native grid. However, the convection-permitting benchmark is more dissimilar from each of the physics suites, with correlations of ≈ 0.80. Similarly, we show that the vertically averaged physics tendencies in the free-troposphere are highly similar between the four physics suites, yet different if reconstructed for the benchmark. The water vapour sink is very closely linked with precipitation in the four physics suites. This suggests that the coarse-grid models are overconfident. We hypothese is that variation in unresolved convective structures can lead to variation in the dynamics, following a given amount of latent heating at fine grids, but not in our physics suites. The difference appears to be caused by the explicit interactions between gravity waves, occurring at fine grids only Groot et al. (2024) We assess whether their non-linear feedback from convective precipitation systems explains our joint PDFs of precipitation. The slightly exponential curve supports the interaction mechanism. These findings are further evidence for a non-linear feedback between convective organisation/aggregation and dynamics. This feedback has been studied earlier in a real-case study with ICON by looking from the fixed-physics rather than the fixed-dynamics perspective. Our current results may indicate that sub-grid physics with stochastic physics perturbations emulate convective organisation effects.Investigating How the Fractions Skill Score and Brier Divergence Skill Score Reflect Forecast Error
Monthly Weather Review American Meteorological Society (2026)
Abstract:
<jats:title>Abstract</jats:title> <jats:p>Meaningful scores for forecast verification are essential for developing reliable forecasts, and there has been much effort to develop scores that align well with human perceptions of forecast quality. Whilst many of these scores have intuitive interpretations, relatively little is known about how these scores rank different forecasts, and how scores reflect forecast error. We theoretically explore the behaviour of two scores that fall within the ‘neighbourhood’ paradigm of spatial verification; the Fractions Skill Score (FSS) and Brier Divergence Skill Score (BDnSS). We investigate how each score ranks forecasts with two types of error; errors in the mean frequency (corresponding to intensity or shape errors) and errors in the standard deviation (corresponding to errors in spatial structure, such as blurring or excess noise). We find that under many situations the FSS assigns higher scores to forecasts that over-predict mean frequency, thus theoretically confirming the need to use the FSS with percentile thresholds. Both scores generally assign higher scores to forecasts with lower neighbourhood standard deviation, a reflection of the ‘double penalty’ problem; however, we observe that size of this effect is larger for the BDnSS than the FSS, showing that the FSS under some situations is less susceptible to the double penalty problem than the BDnSS.</jats:p>From climate projections to trajectory-conditioned local predictions
(2026)
From climate projections to trajectory-conditioned local predictions
Under review (2026)
Abstract:
Regional climate projections are usually sharpened by constraining models’ forced responses toward observations, a step that filters out internal variability and works well mainly where the signal-to-noise ratio is high. Here we side-step this limitation with deep learning, treating internal variability not as noise to remove but as part of the future to predict and of the uncertainty to report. A U-Net trained on 290 simulations from 40 CMIP6 Earth System Models maps the 1980 to 2025 evolution of surface air temperature onto decadal-mean monthly climatologies from the 2030s to the 2090s under SSP2-4.5, keeping the forced response and internal variability together. Tested on entirely withheld model families, the forecasts are skilful and empirically calibrated out-of-sample. Within a model's own ensemble, a forecast predicts the future of the specific member it was given more accurately than the futures of that model's other members, a skill that persists to the 2090s, notably over the subpolar North Atlantic, Europe, North America and Southern Ocean. The forecasts also generalise to a next-generation eddy-rich model whose resolved physics lies outside the training distribution, supporting application to real-world data. When applied to observations, the framework reduces uncertainty in global-mean warming similarly to observational constraints, while also providing spatially coherent local predictions. This shows that our deep learning approach can estimate the distribution of future local climate, conditioned on the recent observed trajectory, encompassing uncertainty arising from both forced response and internal variability realisations. This extends observationally constrained projection toward calibrated predictions at the local scale and seasonal resolution where adaptation decisions are made.
Supplementary material to "From climate projections to trajectory-conditioned local predictions"
(2026)