This study aims to investigate the projections of air temperature and precipitation climate extremes in the Brazilian Legal Amazon (BLA) through the end of the century. The Brazilian Daily Weather Gridded Data dataset is used to characterise the present climate, and 12 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), under the Shared Socioeconomic Pathways scenario SSP5-8.5, are employed to assess future changes relative to the reference period (1995–2014). Student’s t-test is employed to evaluate the robustness of the projected changes. Results show that warming is projected to persist throughout the BLA, but at a lower rate in the north-east (NE) than in other areas. In the south (S) and north-western (NW) sectors, the highest maximum temperatures will increase by an average of 7°C by the end of the century. Regarding precipitation extremes, in the NW, which is currently the wettest portion of the BLA, models project a reduction in consecutive wet days (CWDs) and an increase in the amount of daily rainfall that exceeds the 95th percentile of wet days (R95p). In the NE, by the end of the century, a 30-day increase in the total number of consecutive dry days per year (CDDs) is projected, combined with a reduction in annual rainfall of ~270 mm. In the south of the BLA, models project an increase across the entire sector, with an additional 15 days per year, along with a reduction in annual rainfall. The projected changes in precipitation exhibit strong seasonality, concentrating primarily in the transitional seasons and the beginning of the rainy season, rather than occurring uniformly throughout the year. Also, the projected warming intensifies during the dry season and austral spring, especially in the NW region. In this sense, it is observed that, in the future, the climate in the BLA will become warmer, keeping the trends observed in the present climate, as shown in other previous studies. However, rainfall patterns differ between regions: while in the NE and S the climate will become drier, in the NW, the intensity of extreme rainfall is expected to increase significantly by the end of the century.
Baselines matter in our perception of which is the climate ‘normal’ and what benchmark we use to track climate changes. Use of recent baselines grounds expectations about historical and future climates in recent lived experience, but differences between even similar recent periods shape our perceptions and interpretations of climate information. Here, we examine various examples of the effect of changing standard World Meteorological Organisation baselines (1961–90 and 1991–2020), as well as the pre-industrial baseline of 1850–1900, in the context of climate change messaging in Australia. We find the choice of baseline notably affects the messaging, with different connotations in two different contexts. The first context is where there are ongoing and attributable trends, such as in temperature, sea level and some localised precipitation changes. Here, the baseline grounds our perception of ‘normal’ to a particular point in an ongoing trend, and this normal is increasingly different from current lived experience. Also, in this context, reported ranges of change are smaller for more recent baselines. The second context is where there are weak climate change trends, and differences between baselines are primarily due to climate variability, such as most regional precipitation changes. Here, the baseline also grounds the perception of the climate ‘normal’, not as a particular point on an ongoing trend but rather in a particular state of climate variability. Here, a challenge can be to convey plausible future changes due to climate change where there is currently only a weak signal overwhelmed by variability. This study demonstrates the importance of baselines in climate change messaging and gives a simple framework for considering some key questions such as signal and variability.
Indonesia, as a tropical maritime continent, exhibits a seasonal rainfall pattern influenced by the El Niño–Southern Oscillation (ENSO). Variations in the Wet Season Onset (WSO) and Dry Season Onset (DSO) have broad implications for agriculture, water resources and disaster mitigation. This study aims to analyse the climatology and interannual variability of WSO and DSO over the Java–Bali region (104–116°E), using satellite-based rainfall data (1998–2024). The results show a clear monsoonal pattern, with maximum (minimum) rainfall from December to February (June to August). Climatologically, WSO (DSO) propagates eastward (westward) at ~21 km day–¹ (19 km day–¹) with an onset difference of ~57 (64) days between western Java and Bali. WSO is coherent across all four regions, whereas DSO is coherent across three Java regions (weaker coherence with Bali), consistent with a stronger and more spatially uniform ENSO relationship for WSO. Despite the ENSO predictability barrier, DSO remains significantly related to the Relative Oceanic Niño Index (RONI), with earlier DSO associated more strongly with the developing positive ENSO than with the decaying event from the preceding wet season. El Niño accelerates westward propagation of DSO, whereas La Niña slows DSO and WSO propagation. For WSO, El Niño delays the onset timing with propagation speed comparable with climatology, whereas La Niña advances onset with slower progression. Hence, ENSO affects seasonal onset timing and modulates the longitudinal propagation pace of monsoonal systems. These findings highlight monsoon spatio-temporal propagation as a key atmospheric response to ENSO over Java–Bali, with implications for climate prediction, agricultural planning and hydrometeorological risk management.
Bahía Blanca (38.7°S, 62.2°W) experienced an unprecedented rainfall event on 7 March 2025, officially documented by the Servicio Meteorológico Nacional (SMN) as a historical record with an estimated return period exceeding 100 years. This study provides an observationally based diagnosis of the event, testing the hypothesis that record precipitation resulted from the coupling between upper-level jet-streak-associated divergence, deep ascent and exceptional moisture transport. We combine integrated multi-satellite retrievals for global precipitation measurement (IMERG) satellite precipitation with ERA5 (fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis) reanalysis fields and adopt event-consistent daily metrics (24-h windows) to characterise the dynamical and thermodynamical environment. IMERG reveals a spatially organised precipitation footprint co-located with a persistent corridor of enhanced integrated vapour transport (IVT) toward south-eastern Buenos Aires Province. Over the upstream region of Bahía Blanca, upper-level diagnostics show an anomalous jet-streak configuration at 200 hPa (along-flow acceleration percentile 99.3%) concurrent with extremely rare divergence at 200 hPa (percentile 99.8%) and exceptionally strong deep ascent (minimum ω daily metric, percentile 99.9%). Vertical diagnostics indicate that moisture transport was not confined to the boundary layer: layer-integrated fluxes show substantial contributions from mid-to-upper tropospheric levels (700–300 hPa) consistent with a deep warm conveyor belt like moist airstream during the event window. Together, the results support a proximal mechanism in which an anomalous upper-level jet-streak induced extreme divergence and deep ascent that, when coupled with record-level moisture transport, produced the observed catastrophic rainfall.
Post-processing in Numerical Weather Prediction (NWP) is essential for transforming raw model outputs into forecasts that are both accurate and actionable. This study investigates a dynamic weighting approach for multi-model blending, in which the contribution of each model is adjusted in real time based on recent performance, using root mean square error (RMSE) as a skill metric. We benchmark this approach against three baseline approaches: equal weighting, optimised static weighting and spatially static weighting derived from a training period. Experiments are conducted using deterministic and ensemble model outputs from two global NWP systems (Australian Community Climate and Earth System Simulator, ACCESS, and European Centre for Medium-Range Weather Forecasts, ECMWF) over two seasonal periods and are verified against both gridded analyses and surface station observations. Results show that dynamic weighting consistently improves forecast skill relative to both equal and static weighting approaches, particularly at longer lead times, and closely matches the performance of the operational Australian Bureau of Meteorology’s calibrated and bias-corrected IMPROVER products, even though the experiments presented here omit the bias-correction and calibration stages applied operationally. This suggests that adaptive performance-based weighting alone can recover a substantial fraction of the skill typically attributed to more complex post-processing.
