International Journal of Climatology最新文献

Previous studies are more oriented toward the impacts of snow cover on seasonal mean or annual extreme climate over local and remote regions, rather than on intraseasonal variability in extreme climate. In this study, the influence of Eurasian spring snowmelt (SSD, Spring Snow water equivalent Differences) on intraseasonal extreme minimum temperature (T_min) variability in May and June (MJ) over northeastern China and the relevant physical mechanisms during 1979–2018 are investigated. Results show that the dominant mode of Eurasian SSD features an east–west dipole pattern, characterised by reversed SSD anomalies over Siberia and Europe. Decreased Siberian SSD contributes to enlarged T_min variability in MJ over northeastern China. The days with area-averaged T_min anomalies over northeastern China greater than the 90th (90P) and less than the 10th (10P) percentiles are selected to explore the relevant mechanisms. Deficient Siberian SSD corresponds to excessive evapotranspiration and snow cover in MJ with a more pronounced increase in 10P, resulting in anomalous thermal conditions and thereby modulating downstream atmospheric circulation variability. The anomalous atmospheric circulations are conducive to increased TN90P (warm nights) and TN10P (cold nights), as well as lower TNn (minimum T_min) and higher TNx (maximum T_min). Thus, increased intraseasonal T_min variability appears over northeastern China due to a larger T_min range and more TN90P and TN10P occurrences. Moreover, SSD is a key driver for T_min variability in MJ over northeastern China, and the North Atlantic sea surface temperature provides atmospheric circulations that favour the formation of SSD distributions. The present findings highlight that Eurasian SSD is an important indicator for T_min variability predictability over northeastern China.

Croatian territory is diverse and specific, composed of coastal, mountainous and plain parts. Thus, the differences between Croatian regions in climate and weather conditions during specific atmospheric circulation situations are significant. Since 1965, circulation types (CTs) in Croatia have been determined according to the subjective Poje's classification. Given various restrictions of this classification regarding its application in a synoptic-climatological context, the application and evaluation of automated circulation classification (CC) procedures for the Croatian domain is sought. Hence, a representative selection of classification methods available from the catalogue established within the COST733 Action (‘Harmonisation and Applications of Weather Types Classifications for European Regions’) is applied and analysed regarding their synoptic skill performance with a focus on precipitation using four different datasets of surface observations for evaluation. To obtain a comprehensive assessment of the most appropriate CT approach for precipitation in Croatia, as a supplement to basic classification methods, specific settings (including location and size of the spatial domain, temporal domain, number of types and variables considered for classification) are taken into account. Thereby, various quantitative metrics to measure the synoptic skill are utilised and, in addition, CCs are used in an exemplary application for investigating the synoptic climatology of drought events in Croatia. Although no single superior CC becomes apparent from these systematic evaluation steps, valuable indications of advantageous features of CCs do emerge. Annual-based CCs from the threshold-based and optimisation methods with 18 CTs and for a Croatia-centered domain tend towards the highest synoptic skill overall. A further improvement seems to be achieved by explicitly considering a vorticity index in the classification. This provides information on suitable existing CCs and important starting points for the intended development of an optimised CC for assessing spatiotemporal precipitation variability in Croatia.

Increasing the resolution of atmospheric or oceanic components in the global climate models (GCM) tends to improve the representation of regional climate. However, the magnitude and consistency of this improvement are critical in determining modelling strategies. This study examines the HighResMIP model developed within the PRIMAVERA project to identify the best performing model along with their resolution for the mean and standard deviation of sea surface temperature (SST) as well as two temperature-based marine extremes (marine heatwaves; MHWs and marine cold spells; MCSs) over the Tropical Indian Ocean (TIO). Notably, HighResMIP models are configured to run at least two different resolutions, with resolution increases applied either to the atmospheric or oceanic components. This study also investigates whether increasing the resolution of these components leads to overall improvements or potential degradations in model performance. Results suggest refining the resolution of either the atmospheric or oceanic component improves the simulation of SST, MHWs, and MCSs, bringing them closer to observed values. However, the extent of the improvement depends on the region, specific metric being evaluated (MHWs/MCSs count, intensity, duration) and model used. Additionally, over a few regions, the model performance is found to be degraded when the resolution is increased, suggesting that inevitably increasing the horizontal resolution alone may not be enough to reduce the persistent biases. Furthermore, in many cases, the improvements achieved by increasing resolution are offset by simultaneous degradations. Hence, overall, benefits appear to be limited but still considerable.

As one of the key components of the Asian summer monsoon system in boreal summer, the upper-tropospheric tropical easterly jet (TEJ) is significantly modulated by the El Niño-Southern Oscillation (ENSO) on the interannual time scale. In this study, we report how the El Niño decay rate prominently controls the TEJ's behaviours from the post-El Niño spring to summer. For the slow-decaying (SD) El Niño cases, a warmer-than-usual sea surface temperature anomaly (SSTA) pattern in the tropical central-eastern Pacific sustains from the mature winter to decaying summer. Consequently, the associated equatorial Kelvin wave response will decelerate the TEJ in its outflow and core regions in early summer. Moreover, the SD El Niño SSTA pattern induces a pair of anomalous upper-tropospheric anticyclones over the western Pacific, where the TEJ usually inflows in the shape of double easterly jets. These anticyclone responses will strengthen the south branch of double easterly jets and weaken the north one branch. The fast-decaying (FD) El Niño cases experience a phase transition into a La Niña-like cooling pattern in the decaying phase. The FD El Niño-induced atmospheric circulation is almost a mirror to the counterpart induced by the SD El Niño cases in early summer. That is, the FD El Niño cases aid in a weaker-than-normal south branch of double easterly jets and a stronger-than-normal north branch in the western Pacific, as well as an acceleration of the entire TEJ. These results foreground the significant influences of the El Niño decaying pace on the TEJ in early summer and indicate that the temporal diversity in the ENSO events should be considered for further understanding of the ENSO impacts on the Asian summer monsoon system.

The Upper Blue Nile Basin, a vital water tower for northeastern Africa, faces growing climate risks that threaten its water resources and agricultural systems. While CMIP6 General Circulation Models (GCMs) represent the state-of-the-art in climate projection, their performance in tropical highlands remains uncertain due to complex topography and localised climate processes. This study evaluates five CMIP6 models (ACCESS-ESM1-5, GFDL-CM4, GFDL-ESM4, INM-CM4-8, MRI-ESM2-0) against observed data (1990–2014) using a multi-metric framework combining statistical indices (MR, KGE) and extreme climate indicators (RX1day, SU25, CSDI). Results reveal distinct model strengths: GFDL-ESM4 and INM-CM4-8 showed superior rainfall simulation (KGE > 0.6 for daily scales), while ACCESS-ESM1-5 exhibited systematic wet biases (> 30% in main rainy season). Temperature simulations demonstrated strong elevation dependence, with GFDL-ESM4 achieving the lowest errors (RMSE < 2°C) but persistent cold biases at high elevations. The ensemble mean outperformed individual models, reducing precipitation biases by 25%–40% and improving extreme event detection (20% higher skill for R95p heavy rainfall). Three key findings emerge: (1) Model performance varies substantially by temporal scale and elevation zone, necessitating context-specific selection; (2) Ensemble approaches effectively mitigate individual model biases, particularly for temperature extremes; (3) Current models systematically underestimate rainfall intensity (RX1day errors up to 40 mm) and drought duration (CDD detection < 50% accuracy). These results advance global understanding of GCM limitations in tropical highlands while providing actionable insights for the Nile Basin. The demonstrated elevation-dependent biases have implications for other mountainous regions, suggesting CMIP7 should prioritise orographic process representation. For practitioners, we establish a transferable framework for model evaluation and recommend ensemble-weighted projections for climate adaptation planning in data-scarce highland regions.

As a pioneering component of the Coupled Model Intercomparison Project Phase 6 (CMIP6), the High-Resolution Model Intercomparison Project (HighResMIP) addresses critical resolution limitations in global climate models. This study evaluates three HighResMIP models (25–100 km resolution) in simulating precipitation over China (1961–2014) and projects changes in extremes during 2015–2050 relative to 1985–2014. We quantify impacts of horizontal resolution and ocean–atmosphere coupling, revealing that: high-resolution configurations improve extreme precipitation correlations by 0.1–0.25 (notably for consecutive dry days), yet yield < 5% improvement in mean precipitation accuracy. Projections indicate heavy precipitation days (R95p) will increase 15%–30% faster than total annual precipitation, while maximum dry spells (CDD) show high uncertainty (±22%–35% regional variability). Resolution effects amplify precipitation changes by 3%–8% in complex terrain. Ocean coupling moderates extremes by 10%–15% in continental interiors but intensifies southeastern droughts by 6–12 days/year. These findings demonstrate that while enhanced resolution improves regional precipitation forecasts, its effectiveness is spatially heterogeneous and requires complementary advances in model physics.

Accurate and reliable daily runoff forecasting plays a vital role in water resource management, flood warning and operational scheduling. However, runoff prediction is challenging due to its nonlinear and non-stationary nature, influenced by climate change, topography and human activities. To improve forecasting accuracy, this study proposes a hybrid TCN-BiLSTM model optimised by the Fruit Fly Optimization Algorithm (FOA) for daily runoff prediction in the Xijiang River basin. The model first utilises the Temporal Convolutional Network (TCN) to extract temporal features, then employs the Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal dependencies, and finally optimises key hyperparameters of the model using the FOA to enhance overall performance. Taking the four hydrological stations in the Xijiang River basin, including WX, WZ, DHJK and GG, as examples, the model exhibits outstanding performance in both single-step and multi-step prediction tasks. Taking the WX station as a representative example, the model achieved an MSE, MAE, and R² of 0.888 × 10⁶ m³/s, 0.530 × 10³ m³/s and 0.960 on the test set, respectively. Compared with the BiLSTM model, the MSE and MAE decreased by 63.27% and 40.69%, while the R² increased by 7.49%. Compared with the TCN model, the MSE and MAE decreased by 59.60% and 39.71%, with an R² improvement of 6.31%. Relative to the TCN-BiLSTM model, the MSE and MAE were reduced by 43.15% and 26.38%, and the R² increased by 3.11%. Moreover, the R² values for the test sets at all four stations reached 0.955 or higher, further confirming the model's stability and generalisation capability across multiple regions. The results indicate that the FOA-TCN-BiLSTM model demonstrates significant advantages in enhancing runoff prediction accuracy and generalisation, making it particularly suitable for practical engineering applications such as flood forecasting, water resource management, and regional hydrological risk assessment, thus holding promising application prospects.

This study investigates interannual changes and long-term trends in the covariability of summer (June–August) and autumn (September–November) tropical cyclone (TC) frequency over the western North Pacific (WNP) during 1961–2024. By using an empirical orthogonal function transformation, summer and autumn WNP TC frequency changes are converted to in-phase and out-of-phase changes. In-phase covariability is primarily controlled by the spring Pacific meridional mode, with more (fewer) summer and autumn TCs during its positive (negative) phase. Out-of-phase covariability is mainly linked to El Niño-Southern Oscillation (ENSO) during the subsequent winter, with more (fewer) summer TCs but fewer (more) autumn TCs for a developing El Niño (La Niña). When considering different PMM-ENSO relationships, in-phase covariability occurs in PMM-only and opposite-signed PMM-ENSO years, while out-of-phase covariability is found in ENSO-only and same-signed PMM-ENSO years. These results can be explained by consistent (opposite) simultaneous correlations between TC frequency and the PMM (ENSO) index during summer and autumn. Additionally, given the increasing amplitude and influence of the PMM on ENSO in recent decades, same-signed PMM-ENSO years are occurring more frequently, leading to summer and autumn TC frequency exhibiting strengthened out-of-phase covariability but weakened in-phase covariability, respectively.