International Journal of Climatology最新文献

Accurate prediction of precipitation is of paramount importance for effective planning of future water resources. In this study, we focused on the improvement and evaluation of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation ensemble-based seasonal precipitation prediction product, designated (SEASonal prediction of precipitation (SEAS5)). Three selected linear regression methods, namely ordinary least squares (OLS), flexible least squares (FLS) and the quantile-quantile (Q-Q) methods, were used to develop a correction procedure. The watershed of Lake Urmia was selected as a case study. The application of these augmentation methods has yielded encouraging results, demonstrating an improvement in the statistical metrics of SEAS5 precipitation forecasts for the first and second-coming months. However, all linear projection methods improve the performance of the SEAS5 products. The Q-Q method has shown the highest efficiency among the methods, playing a significant role in improving the accuracy of the hindcast precipitation. A variety of statistics (deterministic, forecast skill and uncertainty scores) were used to evaluate the effectiveness of both the raw and enhanced SEAS5 products. These analyses provide a comprehensive understanding of the performance of the SEAS5 product in its original form and after augmentation. The results highlight the potential of the linear projection method (specifically Q-Q method) to improve the accuracy of hindcast precipitation and provide valuable insights for water resource planning in the study area.

On February 22, 2023, North America experienced an unusual weather phenomenon characterised by extreme cold in the west and extreme warmth in the east, reflecting a surface air temperature (SAT) seesaw pattern that significantly impacted human activities in the region. Utilising reanalysis data, we analysed this case as well as similar events in long-term climate records. The results indicate that this extreme temperature mode is caused by a hybrid evolution of atypical North Pacific Oscillation (NPO) and Pacific-North American (PNA) phase relationships, transitioning from a dominant positive NPO phase to a strong negative PNA phase, while maintaining and developing Pacific blocking, which we refer to as a hybrid transition of atmospheric regimes. Further analysis revealed that the anomalously weak meridional potential vorticity gradient (PVy) in southern Alaska prior to the event provided favourable conditions for the northward movement and intensification of the anticyclone in the central North Pacific, serving as a key driving factor for the transition of circulation mode from positive NPO to negative PNA. Additionally, significant anomalous outgoing longwave radiation (OLR) in the tropical Indian Ocean prior to the event indicated enhanced convective activity in that region, which may have triggered meridional Rossby waves, thereby influencing the transition between NPO and PNA in mid to high latitudes. Thus, the anomalous PVy in southern Alaska and the OLR from the tropical Indian Ocean may serve as potential precursor indicators for such extreme weather events.

Development and dissemination of seasonal forecasts are integral components of the climate services provided by numerous meteorological services worldwide, offering estimates of meteorological variables on a seasonal time scale to aid local warning systems and decision-making processes. The World Meteorological Organization (WMO) recommends that operational seasonal forecasts be objective and that the process be traceable and reproducible, including the selection and calibration of models. Following these guidelines, the Chilean Meteorological Service (Dirección Meteorológica de Chile, DMC) has implemented the next generation of seasonal forecasts, NextGen-Chile. This new forecast system is based on a multi-model ensemble using state-of-the-art general circulation models (GCMs) from the calibrated North American Multi-Model Ensemble (NMME) project. The forecasts from the GCMs are calibrated using a canonical correlation analysis-based regression with a homogenised dataset of ground stations. The system is completed with two statistical models built using canonical correlation analysis on sea surface temperature (SST) in the ENSO and the Southwestern Pacific regions. Individually calibrated GCMs and statistical models are combined by weighing their hindcast skill to construct the final calibrated multi-model ensemble (CMME) prediction. A verification analysis of probabilistic re-forecasts during 2019–2021 has been performed, adding an average-based ensemble forecast (CMME-Mean). The CMME models outperformed the individual models in discrimination and showed less seasonal variability in performance than the individual models, adding consistency to the forecast. All metrics analysed during the verification process were maximised in the central region of Chile, which could be attributed to the high concentration of ground stations in the central region and the definition of a central region-centred domain for the CCA calculation. Looking into the near future of NextGen-Chile, a Flexible Seasonal Forecast is introduced as a more comprehensive approach for seasonal forecasts, allowing users and stakeholders to access information beyond the tercile seasonal forecast approach.

The frequency and intensity of heatwaves over southwestern China during spring and early summer have been increased significantly during 1980–2022. Until now, the physical mechanisms for these changes in heatwaves remain unclear. Here, we show that these increases in heatwaves can be attributed to the changes in both local soil moisture–temperature coupling processes and teleconnection patterns across Eurasia. On the one hand, the third dominant mode of teleconnection patterns across Eurasia exhibits more pronounced and meridionally-elongated features after the 2000s, leading to more southeastward-shifted positive geopotential height anomalies towards southwestern China, favouring increases in the regional heatwaves. On the other hand, the intensified coupling strength of soil moisture–temperature further amplifies the heatwaves over southwestern China, in particular for compound drought and heatwave (CDHW) events. This intensified soil moisture–temperature coupling is attributed to the identical phase transitions of heat anomalies and temperature anomalies after the 2000s. Distinct characteristics and drivers of CDHW and non_CDHW over southwestern China are also discussed.

A dynamical model is used as a simple GCM to perform historical forecasts for rainfall in northeastern Brazil for the years 1982–2020. The model is forced by empirically derived source terms and includes basic parameterisations to simulate vertical diffusion, convection and condensation. Ensemble forecasts with 38 members are initiated on 1st January using persisted tropical sea surface temperature anomalies (SSTAs). Rainfall forecast performance is evaluated for the February–April (FMA) rainy season. The model reproduces the climatological precipitation in the specified Nordeste region with a mainly dry bias as the model rainfall maximum is displaced in the the northwest. Hindcasts for interannual rainfall anomalies correlate with observed values (r = 0.46) and model variance is weaker than observed. A further set of forecast experiments with SSTAs restricted to the three major ocean basins reveals that most of the forecast skill can be attributed to the Pacific, despite the model's greater sensitivity to Atlantic SSTAs. The sum of results from the three ocean basins is close to the full hindcast result. Finally, a set of 128 forecast runs with idealised SSTAs placed regularly within the tropics is carried out to calibrate the response of modelled rainfall to remote influences. An influence function is diagnosed in the form of a tropical distribution of northeastern Brazil rainfall in mm/day per unit SSTA. It is strongly concentrated in the tropical Atlantic, with dry/wet conditions resulting from positive SSTAs in the northern/southern tropical Atlantic, in keeping with the observed covariance. The influence function is the used to construct a linear approximation to the forecast performance of the simple GCM. It has similar skill but stronger variance, and the skill is partitioned differently between Atlantic and Pacific influences.

Extreme precipitation events (EPEs) are becoming increasingly frequent and intense in southeastern South America (SESA). The limited rain gauge network in SESA could be overcome using satellite-based synthetic precipitation data. This study analyses the capability of satellite products IMERG Final Run V06, PERSIANN, PERSIANN CCS-CDR and PDIR-NOW in capturing extreme precipitation characteristics over SESA in the 2001–2020 period. EPEs were characterised by annual maximum values, maximum monthly values, and the 95th and 99th percentiles of precipitation time series. Statistical metrics were applied to evaluate the efficiency of satellite products in representing EPEs compared to observational data. Extreme events characterised by the number of very wet days (R95p), extremely wet days (R99p), and the simple daily intensity index (SDII) were also evaluated. Our results suggest that IMERG and PERSIANN CCS-CDR accurately represent the annual maximum precipitation averages and provide the best estimates of the maximum precipitation and the average number of events across various precipitation thresholds. IMERG exhibits the lowest BIAS and RMSE for the 95th percentile and performs well in representing R95p and R99p indices. IMERG also accurately represents the average number of events across various precipitation thresholds, although it overestimates precipitation at the 0.1–5 mm threshold. In contrast, uncalibrated products like PERSIANN and PDIR-NOW exhibit less consistent performance, often underestimating lower-intensity events (< 50 mm) and overestimating higher-intensity events (> 50 mm). PERSIANN tends to overestimate SDII values and displays higher error rates for the 95th percentile, while PDIR-NOW overestimates R95p and R99p indices and estimates SDII with poor performance. Although there are challenges in high-altitude areas and coastal regions, IMERG and PERSIANN CCS-CDR show promise in detecting extreme events, particularly for precipitation thresholds above 100 mm. Our findings provide a basis for developing Intensity-Duration-Frequency (IDF) curves, essential for hydrological planning, in future work using combined satellite datasets.

Understanding seasonal rainfall patterns and variability is crucial for managing water resources, pastoral and agricultural activities in Ethiopia, an especially climate-vulnerable country. This study examines trends in March–April–May (MAM) rainfall from 1981 to 2022, emphasising the relationships between climate indices, atmospheric circulation patterns, and variations in precipitation. By utilising daily CHIRPS rainfall data and monthly sea surface temperature (SST), sea level pressure (SLP) and other reanalysis datasets, we applied extreme indices analysis, correlation analysis and Student's t-tests to compare climate factors and rainfall during two distinct periods 1(981–2001 and 2002–2022) and assess changes relative to the 1991–2020 long-term mean. Our findings reveal a notable shift towards warmer sea surface temperature (SST) phases in key ocean basins, with significant positive correlations (r > 0.45, p < 0.05) between Ethiopian MAM rainfall and SSTs in the Mediterranean, Northwest Pacific, Northern Atlantic and Western Indian Ocean. Precipitation patterns shifted from above-average to below-average rainfall, aligning with opposite trends in the tropical central Indian Ocean. A long-term trend analysis revealed a marked decrease in rainy days across northeast, east, central, south and southeastern Ethiopia during 2002–2022, with an increase in consecutive dry days and a decrease in consecutive wet days, with statistical significance at the 95% confidence level. The period from 2002 to 2022 was characterised by La Niña-like conditions and a negative Pacific Decadal Oscillation, which had a considerable impact on rainfall patterns. Changes in large-scale atmospheric circulation reduced moisture transport to Ethiopia, leading to drier conditions. These findings enhance our understanding of Ethiopian rainfall variability and its drivers, crucial for improving early warning systems and developing climate adaptation strategies in the region.

The zonal movement of both the western Pacific subtropical high (WPSH) and the North African subtropical high (NASH) correlates well with the summer precipitation in the arid region of Northwest China (ARNWC), but there is not enough understanding of the interdecadal change in the interannual relationship between the zonal position linkage of the two subtropical highs and summer precipitation in the ARNWC under different interdecadal backgrounds. Based on the observed precipitation data at 101 stations in the ARNWC, the NCEP–NCAR reanalysis data, and Hadley Centre surface sea temperature (SST) data for 1961–2022, the interdecadal change in the interannual relationship between the zonal position linkage of the two subtropical highs and summer precipitation in the ARNWC is investigated. Results show that the summer precipitation has experienced two distinct periods (dry period: 1961–1986; wet period: 1987–2022), and the zonal position linkage of the two subtropical highs correlates well with the summer precipitation in the eastern part of the ARNWC in the dry period and the Tarim Basin in the wet period, respectively. When the NASH and the WPSH move in opposite directions, the water vapour is directly (indirectly) transported from the eastern Asian monsoon region (tropical Indo-Pacific Ocean) in the dry (wet) period. The Asian-Pacific Oscillation (APO) and the sea surface temperature (SST) in the Indo-Pacific warm pool both contribute to the zonal movement of the two subtropical highs in the dry period, but in the wet period, only the SST in the Indo-Pacific warm pool plays an important role in modulating the zonal position linkage of the NASH and the WPSH.

Drought is a major concern in Turkey, significantly affecting agriculture, water resources and the economy, especially in the Central Anatolia region with a semiarid steppe and dry-sub-humid climate. This study aims to develop an optimal forecasting model for Standardised Precipitation Evapotranspiration Index (SPEI) values over various periods (1–24 months) using data from 50 stations in the Central Anatolia region. It compares statistical forecasting and machine learning methods, finding that machine learning algorithms, particularly the Bayesian Recurrent Neural Network, outperform statistical approaches. The results show a consistent increase in drought severity and highlight the robust performance of top models across different SPEI periods. The study provides a benchmark for future research on forecasting models and underscores the need for effective drought mitigation and adaptation strategies. The incorporation of advanced machine learning algorithms, such as the Bayesian Recurrent Neural Network, and their comparison with traditional statistical methods highlight the potential for more accurate and adaptive drought forecasting models.

The Coupled Model Intercomparison Project phase Six (CMIP6) lacks wave climate projections, emphasising the critical need to select the most accurate CMIP6 model winds for projecting wave climate. This study focuses on evaluating and selecting the optimal CMIP6 model wind fields for the Indian Ocean wave climate projections. A 35-year (1980–2014) wind-wave climate simulation of the Indian Ocean (IO) using the third-generation wave model WAVEWATCH-III (WW3), forced with seven CMIP6 Global Climate Models (BCC-CSM2-HR, EC-Earth3, CMCC-CM2-SR, GFDL-ESM4, CNRM-CM6-1-HR, HadGEM3-GC31-MM and MPI-ESM1-2-HR), is generated and validated against in situ buoy observations and ERA5 reanalysis data. Statistical analyses revealed that MPI, BCC and EC are the most accurate in representing wave characteristics in the IO, exhibiting strong correlations with observations and effectively capturing inter-annual variability. Extreme wave analysis shows that MPI, BCC and EC model wind-forced wave simulations match well with ERA5 data. The top three models (MPI, BCC and EC) are then selected for the composite analysis to assess their capability to reproduce the climate mode impacts on IO wave climate. EC performs best in capturing wave fields under El-Nino Southern Oscillation, Southern Annular Mode, and Indian Ocean Dipole influences, followed by BCC and MPI. Thus, the study identifies BCC, MPI and EC as the optimal CMIP6 models for the Indian Ocean wave projections.