Abstract Although it has been demonstrated that data-driven models can outperform physical models in simulating mean states, their capabilities to simulate extremes and their generalizability across climate scenarios remain questionable. This study compares Pangu-Weather to the physics-based ICON-CLM model in simulating peak of the July 2019 European summer heatwave and its unfolding in +2, +3, and +4 K future warmer scenarios, following a storyline approach. We initialize both Pangu-Weather and ICON-CLM using global storyline simulations from the coupled climate model AWI-CM1, which were spectrally nudged towards ERA5 reanalysis to maintain the large-scale flow and to accurately assess the impacts on heatwave severity due to thermodynamic changes only. To quantify uncertainties in Pangu-Weather’s predictions, the random field perturbation (RFP) technique is used to generate probabilistic forecasts. The study reveals that Pangu-Weather shows a systematic cold bias of about 2 – 3° C over the heatwave core region in both present and future warmer scenarios. Pangu-Weather fails to capture maximum 2-m temperature (T 2 m ) exceeding 45° C in +3 and over a limited area in +4 K global warming scenarios compared to ICON-CLM, indicating its limitations to accurately predict out-of-sample extremes even in an ensemble framework. Further, the maximum T 2 m response is linearly dependent on global warming levels consistent with ICON-CLM simulations but with reduced warming amplification. Additionally, it shows that Pangu-Weather needs enough time to leave the trajectory specified by forcing data. Our study emphasizes that the storyline approach and comparison with physics-based models show a promising way to test data-driven models for their applicability to warmer climate conditions.
Abstract The boreal summer Asian–Australian monsoon (BSAAM) comprises several regional subsystems traditionally studied in isolation. This study provides a holistic perspective on the BSAAM and its modulation by pan-tropical sea surface temperature anomalies (SSTAs). The first mode (BSAAM1) captures Western Pacific Subtropical High anomalies and enhanced rainfall over India and the Yangtze–Huaihe River Basin. The second mode (BSAAM2) reflects Australian High variations, linked to increased rainfall over South China and the northern Bay of Bengal coast. BSAAM1 is associated with a tripole SSTA pattern spanning the Indian Ocean-Western Pacific, central–eastern Pacific, and tropical Atlantic (IOWP–CEP–TA tripole), while BSAAM2 corresponds to another tripole across the western Indian Ocean, Maritime Continent, and tropical CEP (WIO–MC–TCEP tripole). Both tripoles modulate their respective BSAAM modes through remote SST effects and local air-sea coupling. The seasonal evolution of these tripoles also differs markedly. BSAAM1 is governed by persistent SSTAs in the IOWP and TA, with rapid CEP cooling driven by thermodynamic wind-evaporation effects associated with Victoria Mode and dynamic zonal advection. BSAAM2 is driven by SSTA persistence in the MC, coupled with rapid warming in the WIO and TCEP, induced by thermodynamic wind-evaporation effects and dynamic zonal advection, respectively. Leveraging these physical insights, physically based empirical models are developed that outperform dynamical models in predicting the BSAAM, with forecast lead times extended to the preceding winter. This study presents a comprehensive understanding of how pan-tropical SSTAs modulate the BSAAM interannual variability and improve BSAAM seasonal predictability.
Abstract The increasing interannual variability of Antarctic sea ice near West Antarctica underscores the need to understand how El Niño–Southern Oscillation (ENSO) teleconnections may shape future sea ice changes. Using simulations from the Coupled Model Intercomparison Project Phase 6, this study investigates the projected impacts of near-future ENSO variability on Antarctic sea ice. ENSO teleconnections are found to primarily influence Antarctic sea ice from austral winter through spring, and this influence is projected to strengthen in the near future. We show that the strengthened teleconnection is driven mainly by enhanced tropical forcing with the structural changes in tropical sea surface temperatures and precipitation, with a smaller contribution from changes in the midlatitude jet waveguide. These mechanisms are further supported by idealized nudging experiment using a dry dynamic core model. The strengthened teleconnection deepens the ENSO-induced mid-tropospheric circulation anomaly near West Antarctica, enhancing warm-air advection and the resulting sea ice anomalies. Overall, these changes are projected to increase Antarctic sea ice variability in the near future, highlighting the importance of understanding how evolving ENSO teleconnections may modulate Antarctic climate variability.
Abstract Despite forecasting a La Niña of marginal strength, the dynamical forecast models of the North American Multi-Model Ensemble (NMME) initialized in the boreal fall of 2024 predicted La Niña-like temperature and precipitation patterns of considerable strength over North America for the following winter. In fact, the forecast temperature and precipitation patterns were even stronger than those from the fall of 2023, when we were entering one of the strongest El Niños on record. Here, we explore the role of tropical ocean warmth outside of the Niño-3.4 region in explaining the apparent mismatch between the predicted El Niño-Southern Oscillation (ENSO) amplitude and North American teleconnection forecast amplitudes between the two winters. Specifically, we explore the degree to which the use of the relative Niño-3.4 index, which accounts for this basin-wide warming, can account for the predicted temperature and precipitation forecast amplitudes. We find that the NMME predicted North American precipitation forecast signal is more closely related to the relative rather than the traditional Niño-3.4 index over the 1991-2024 period. The temperature forecast relationships, however, are less straightforward to interpret because the trend in the traditional Niño-3.4 index correlates with the long-term warming trend over North America, which is an important component of the seasonal temperature signal. We also find that the precipitation and temperature forecast amplitudes in the winters and 2023/24 and 2024/25 were more consistent with moderate ENSO events, as determined by the relative but not the traditional index. Overall, these findings strengthen the argument for monitoring ENSO with the relative Niño-3.4 index in a warming climate.
Abstract The climate system displays a seasonal cycle not only in its mean state, but also in predictability and variance. In particular, month-dependent autocorrelation functions (ACFs) reveal seasonal variations in persistence and are used routinely to characterize such phenomena as the spring predictability barrier in El Niño forecasts and the wintertime reemergence of sea surface temperature anomalies. However, detection of such behavior in climate data has often relied on heuristic procedures tailored to specific applications that are difficult to generalize to other applications. This paper proposes a general approach to detecting month-dependent ACFs based on Periodic Autoregressive (PAR) models, which extend standard autoregressive models by allowing parameters to vary with month. Specifically, a likelihood ratio test is used to determine if a PAR provides a significantly better fit than a stationary model with month-invariant parameters. Externally forced variability is incorporated through additional forcing terms. The resulting model provides a self-consistent representation of both internal and forced variability. Applying the test to observational records of sea surface temperature reveals distinct regions with statistically significant month-dependence in their ACFs. These include the tropical Pacific, where the method identifies the canonical spring predictability barrier, and several midlatitude regions displaying the statistical signature of reemergence. The test also identifies a previously undocumented seasonal rebound in the ACF over the Guinea Dome region of the northeastern tropical Atlantic. It is further shown that the widely used Seasonal Autoregressive (SAR) model cannot capture in a parsimonious manner the spring predictability barrier or reemergence.
