arXiv subjects

Edward Groot

Publications and source records attributed to Edward Groot.

3 recordsLinked to original sources

arXiv2026

How Do AI Climate Models Respond to Warming Across Climate Zones?

Regional climate zones are expected to shift under global warming. Whether AI climate models have learned to generalize climate-zone distributions under warming in a physically meaningful way affects their suitability for climate projection. We address this question by applying a K\"oppen-Geiger climate-zone decomposition to AIMIP Phase 1 models under prescribed +4K SST forcing and comparing their responses to physics-based AMIP models. Using this diagnostic, we compare baseline classification skill, per-zone responses in temperature, precipitation, and near-surface specific humidity, and the spatial structure of departures from physics-based models. All AI models considered reproduce the 1979-2014 ERA5 climatology within the physics-based models' range, but only the hybrid physics-AI model NeuralGCM-HRD reorganizes zones in agreement with established thermodynamic and hydrological scaling relations. The remaining emulators have distinct failure modes traceable to their architectural treatment of land cells. A physically consistent climate-zone response is therefore necessary for AI models intended for climate projection.

physics.ao-ph↗

arXiv2026

How different are deterministic physics suites when coupled to fixed model dynamics and why?

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. Precipitation is selected because it is a dominant uncertainty in the models that redistributes large amounts of heat. We find that all physics suites produce very similar precipitation amounts, with very high correlations between models, which exceed 0.95 at the native grid. However, the convection-permitting benchmark is more dissimilar from each of the physics suites, with correlations of $\approx$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 abstract length limit of ArXiv requires you to proceed in the PDF.

physics.ao-ph↗

arXiv2025

When tiny convective spread affects a midlatitude jet: spread sequence

We investigate the evolution of spread over three days in a numerical ensemble experiment starting from tiny initial condition uncertainty. We simulate a real event during which three mesoscale convective systems occur in close proximity to the midlatitude jet. The spread evolution is compared with an existing conceptual three-stage model. Each system follows the first stage, characterised by development of convective variability. Nevertheless, we find significant variation among the systems in their propensity to interact with the jet stream, which characterises conceptual stage 2. One exemplary convective system follows the conceptual evolution of Baumgart et al., i.e., convective uncertainty initially projects onto the jet by upper-tropospheric outflow, which further amplifies spread through nonlinear growth as it propagates downstream. Rossby-like dispersion in the downstream spread is strongly associated with the convective variability. In contrast, for another convective system, convective variability projects onto the local anticyclonic flow aloft. Subsequently, this anticyclonic perturbation hardly (if at all) projects convective uncertainty onto the particularly straight jet stream, which truncates the conceptual evolution. For the third system, negligible fingerprints of second and third stages are identified. Alongside convective heating, longwave radiation jointly dominates the spread evolution near the convective systems (as opposed to earlier studies). Longwave-radiative tendencies of convective anvils outlive the accompanied heating tendencies and extend spatially. Furthermore, we link convective variability of the exemplary system directly to longwave-radiative tendencies. Therefore, longwave radiation appears to contributes substantially to stages 1 and 2 here. Finally, we identify flow dependence of the impact of convection on the jet. (Truncated abstract)

physics.ao-ph↗