International Journal of Climatology最新文献

This study investigates the projected changes in the diurnal temperature range (DTR) over India and explains its considerable spatial heterogeneity from a 20-km resolution coupled regional climate model (RSM-ROMS) integration. The RSM-ROMS is driven at the lateral boundaries by the Community Climate System Model version 4 (CCSM4) model. Observations reveal spatial heterogeneity in DTR trends with significant declining trends at many grid points interspersed with areas of either increasing or insignificant trends of DTR during each of the four seasons. The present-day simulations from RSM-ROMS show reasonable skill in simulating the daily maximum temperature (Tmax) and minimum temperature (Tmin) over India. Our results show a significant decrease in DTR over the Gangetic Plains in boreal winter and fall seasons and over southeastern India during boreal summer in the projected mid-21st century climate under the RCP 8.5 emission scenario. The future reduction in DTR over Region-1 (over Bihar and the eastern regions of Uttar Pradesh) during December–February (−0.86°C) and over Region-3 (over the rain shadow regions of Peninsular India) during June–September (−0.49°C) is attributed to large changes in surface radiative fluxes, with some of the decrease in downward short wave flux attributed to an increase in high cloud cover at the time of Tmax while there is a considerable increase in downward longwave flux in the mid-21st century climate. The enthalpy fluxes at the time of Tmax also act to reduce the rate of its warming. As a result, the warming rate of Tmax is less compared with the corresponding warming rate of Tmin, which leads to a reduction of the DTR in some regions that display a significant reduction in future climate. In contrast, Region-2 (over Rajasthan) and Region-4 (over northeast India) exhibit insignificant DTR changes in the mid-21st century climate for lack of asymmetrical changes in Tmin and Tmax.

The Indo-Pacific warm pool (IPWP), enclosed by a 28°C isotherm, is vital in controlling atmospheric circulations affecting monsoonal flow. The warming trend of sea surface temperatures (SSTs) over the IPWP has expanded the IPWP region. This study examines the impact of the IPWP warming on the Indian summer monsoon rainfall (ISMR) patterns using ERA5 reanalysis and India Meteorological Department rainfall records based on station data from 1959 to 2021. Analyses based on correlation, regression and composite anomalies show the complex relationship between recent decades of IPWP expansion/warming and monsoon circulation. However, the effects of regional IPWP SST warming changes on the ISMR pattern remain unexplored. Here, we explore the changes in the monsoonal circulation owing to the warming and expansion of IPWP, by comparing two equal periods (1959–1989 and 1990–2021). The responses of monsoons to IPWP warming in these two periods revealed some interesting facts, but the complexity remained. Further, we examined the composite impacts of IPWP SST warming in three categories, that is, very cool, usual and extremely warm, on the dynamics of monsoon circulations. The very cool IPWP is associated with the dry monsoon, while the extremely warm IPWP produces copious rainfall over southern India and dryness over eastern north India. The study confirms the non-linear relationship between IPWP warming and ISMR, which has been investigated in detail.

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.