International Journal of Climatology最新文献

Based on Coupled Model Intercomparison Project phase 6 (CMIP6) simulations, we found that the frequency and intensity of daytime–nighttime compound hot extremes (HEs) in the mid-high latitudes of Asia (MHA) are expected to increase. The most significant increase is anticipated under the shared socioeconomic pathway (SSP) 5-8.5, while the smallest increase is expected under SSP1-2.6. Notably, unlike the decreasing trends of independent HEs since 2050 under the high emission scenarios, the compound HEs, which comprise the largest proportion, are expected to continuously increase and intensify. To better understand the impact of these changes on human society, we also focused on changes in population exposed to HEs. The findings reveal that population exposure to compound and nighttime HEs is projected to increase most rapidly under SSP3-7.0, with estimates indicating increases of 10.06 and 3.80 times, respectively, by the end of the century. The most significant increases are expected in the mid-latitudes, where changes in HEs are most pronounced. Climate change is the primary driver behind the rising population exposure to compound and nighttime HEs, with its impact expected to grow over time. Conversely, exposure to daytime HEs is primarily influenced by population changes, particularly in urban areas. Therefore, effective climate change mitigation and adaptive strategies are crucial to reducing future population exposure to HEs in MHA.

Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric-hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large-eddy simulations (LES) mode within WRF, coupled with single-layer urban canopy models (SLUCM), to enable even finer-scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid-scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2-m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture-related parameters from the parent for child domain initialization yields a notable reduction in mean and root-mean-squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Sea surface temperature (SST) is a significant climatic variable that affects the climate of the Earth. Monitoring a location's SST pattern is useful for several research areas, including weather forecasting and climate change. In this study, the emerging hot spot and cold spot patterns of SST in the Mediterranean and Black Sea Marine System (MBMS) were examined, the spatial distribution characteristics and temporal changes of SST in the sub-basins were analysed, and future predictions were made. A distinctive aspect of the research lies in the introduction of novel techniques, specifically the application of space time cube and evolving hot spot analysis, for visualising and evaluating SST in the MBMS. This approach sets the study apart by pioneering the utilisation of these methods in this particular context. In the examined region, SST demonstrates a decreasing trend from east to west and from south to north. The forecast suggests that this spatial distribution pattern will persist in 2033, further accentuated by the intensification of the warming effect. Nine different time series clusters are defined within this distribution pattern. Although it changes seasonally, the prevailing statistically significant hot spots in the study area are primarily characterised by new hot spots, intensifying hot spots, sporadic hot spots and oscillating hot spots. The trends of hot and cold spot clusters, along with SST values, were assessed for all sub-basins in the MBMS. Conversely, the observed clustering category among statistically significant cold spots is identified as persistent cold spots, diminishing cold spots, sporadic cold spots, oscillating cold spots and historical cold spots. The spatiotemporal analysis in this research has provided notable insights, offering a spatial context to the previously explored temporal trends of SST in the MBMS.

Current East Asian winter monsoon (EAWM) indices effectively depict the associated high- and low-latitude atmospheric circulations. However, the spatial dynamics of the winter coldness within the monsoon domain are not well adequately represented by EAWM indices. We introduce a novel approach to classify winter temperatures based on both their co-variability and their mean values. We classified the EAWM domain into three distinct modes: northern (ranging from −27°C to −15°C), central (−14°C to 5°C), and southern (6°C to 27°C). The northern mode, characterised by intense coldness, correlates with a strengthened westerlies that traps Arctic cold air masses during the positive phase of the Arctic Oscillation (AO). In contrast, the southern mode is primarily influenced by low-latitude oceanic and atmospheric patterns, particularly for near-coast areas. The central mode, representing an interplay of both high and low-latitude processes, encapsulates the comprehensive characteristics of the EAWM. Our analysis reveals a notable shift in the relationships among the northern, central, and southern modes around 1990. Prior to this year, the EAWM was predominantly influenced by northern atmospheric patterns, while there is a discernible increase in the influence of low-latitude drivers afterwards. This shift may be linked to the significant warming in the western Pacific and Indian Oceans, underscoring the heightened role of low-latitude drivers on the EAWM.

Regional cold winters have occurred frequently in Eurasia since the beginning of the 21st century, increasing the interannual variability in winter temperatures and increasing the difficulty of prediction. In this study, we evaluate the performance of Climate Forecast version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP) in predicting winter temperature anomalies over the Northern Hemisphere and find that CFSv2 has significantly lower temperature prediction ability for cold winters in the mid–high latitudes of Eurasia since the 21st century. This is mainly due to the stronger response to global warming and the weaker response to sea ice anomalies in the preceding autumn in CFSv2 than the in reanalysis. Accordingly, two targeted correction methods have been developed to improve the prediction ability, with the first method removing the linear temperature trend of CFSv2 predictions and the second method considering the effects of autumn Arctic Sea ice anomalies via a dynamical–statistical correction approach (DSCA). Both methods can effectively improve the prediction ability of winter temperature anomalies in the mid–high latitudes of Eurasia, especially in cold winters. The anomaly correlation coefficient (ACC) increased from −0.03 to 0.13 before and after the modification by the DSCA, and from −0.12 to 0.25 for cold winters. The DSCA significantly reduced the root mean square error (RMSE) of the CFSv2 predictions by approximately 10%.

Climate extreme events are intensifying globally, posing increasing risks across various sectors. Understanding climate extremes' spatiotemporal patterns and responses to climate change is crucial for effective management, especially on a regional scale. This study examines temperature and precipitation extremes, as well as compound dry-hot events (CDHEs), in the Ishikari River basin (IRB) of Northeastern Japan, an area of significant socioeconomic importance. We focus on spatiotemporal analysis under multiple scenarios of temperature/precipitation extremes and CDHEs based on statistical downscaled datasets from the Coupled Model Intercomparison Project Phase 6. Results indicate that IRB underwent increased trends of extreme hot periods, extreme droughts, and heavy rainfalls during 1985–2014, which are significantly affected by the North Pacific Oscillation and Southern Oscillation Index. Future projections show that warming temperatures and less rainfall shift asymmetrical impacts on temperature and precipitation extremes, expecting increased warm spells and CDHEs but increased wet durations and less heavy rainfalls. Emission scenarios analysis suggests low-emission scenarios (SSP1-2.6) could mitigate their exacerbations, especially for CDHEs (decreased by 139%). Moreover, spatial-pattern analysis reveals regional heterogeneity in temperature and precipitation extremes, with northern mountainous regions more susceptible to thermal extremes and southern plain regions (e.g., Sapporo city) experiencing prolonged drought and CDHEs. This study provides valuable insights into climate risk management and adaptation strategies.

This paper presents a detailed spatio-temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high-resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR-DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year⁻¹ for PRCPTOT, 0.7 day year⁻¹ for R10mm, 0.2 day year⁻¹ for R20mm, 0.01 mm year⁻¹ for Rx1day, 0.03 mm year⁻¹ for Rx5day, 0.4 day year⁻¹ for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year⁻¹. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non-significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long-term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.

In the context of global warming, the rise in extreme precipitation events in high-altitude headwater areas has introduced greater hydrological uncertainty. However, the limited understanding of the physical mechanisms driving extreme precipitation in these areas hinders efforts to mitigate the potential rise in future precipitation risks. This study analysed the extreme precipitation events in the headwater area of the Yellow River (HAYR) from May to September each year from 2015 to 2020 using satellite-based data from Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory and Integrated Multi-satellite Retrievals for GPM (IMERG). The results show that stratiform precipitation (SP) determines the spatial extent of extreme precipitation events, while convective precipitation (CP) largely affects the rainfall intensity. Statistical analysis from different extreme precipitation events indicates that the rain rate of CP is 2 to 3 times higher than that of SP, thus zones of intense precipitation in the study area are normally dominated by CP. Vertically, the topographic lifting in complex mountainous regions exerts opposite effects on the precipitation rates of SP and CP, weakening the precipitation intensity of SP while enhancing that of CP. The peak precipitation rate in the midstream and downstream regions is observed at approximately 5 km, whereas the upstream region displays a distinctive double-peaked distribution, with one peak at 8.5 km and another near the surface. This study provides a better understanding of the interior structure evolution process of plateau precipitation, as well as the associated microphysical properties, and highlights some insights to improve microphysical parameterization in the future model developments.

As global surface temperatures have increased with human-induced climate change, notable compound climate extremes in the New Zealand (NZ) region associated with atmospheric heatwaves (AHWs) and marine heatwaves (MHWs) have occurred in the past 6 years. Natural modes of variability that also played a key role regionally include the Interdecadal Pacific Oscillation (IPO), El Niño/Southern Oscillation (ENSO) and changes in the location and strength of the westerlies as seen in the Southern Annular Mode (SAM). Along with mean warming of 0.8°C since 1900, a negative phase of the IPO, La Niña phase of ENSO and a strongly positive SAM contributed to five compound warm extremes in the extended austral summer seasons (NDJFM) of 1934/35, 2017/18, 2018/19, 2021/22 and 2022/23. These are the most intense coupled ocean/atmosphere (MHWs/AHWs) heatwaves on record with average temperature anomalies over land and sea +0.8°C to 1.1°C above 1991–2020 averages. The number of days above 25°C and above the 90th percentile of maximum temperature has increased, while the number of nights below 0°C and below the 10th percentile has decreased. Coastal waters around NZ recently experienced their longest MHW in the satellite era (1982-present) of 289 days through 2023. The estimated recurrence interval reduces from 1 in 300-years for the AHW event during the 1930s climate to a 1 in 25-year event for the most recent decade. Consequences include major loss of ice of almost one-third volume from Southern Alps glaciers from 2017 to 2021 with rapid melt of seasonal snow in all four cases. Above-average temperatures in the December/January grape flowering period resulted in advances in veraison (the onset of ripening); and higher-than-average grape yields in 2022 and 2023 vintages. Marine impacts include widespread sea-sponge bleaching around northern and southern NZ.