Theoretical and Applied Climatology最新文献

The seasonal prediction of the Indian summer monsoon (ISM) and Monsoon Intraseasonal Oscillations (MISO), as well as the Madden Julian Oscillations (MJO) that strongly modulate MISO, is important to the country for water and crop management. We have analyzed the precipitation, convection, and cloud in the selected models from the sixth generation Coupled Model Intercomparison Projects (CMIP6). This study highlights the significant differences in simulating MISO and MJO features between the selected CMIP6 models and their possible reasons. The mean and intraseasonal features of MISO and MJO varied significantly in CMIP6 models, which may impact better depiction of convection and total cloud fraction. The probability distributions of rainfall and OLR in CMIP6 models also indicate significant variations in simulating the MISO and mean ISM. The results demonstrate the importance of cloud and convection in CMIP6 models to depict realistic MISO and MJO and provide a road map for improving ISM climate prediction and projections.

The analysis of extreme rainfall parameters, particularly rainfall intensities, plays a serious role in the protection, productivity, and resilience of hydrological systems against storms and floods. This is especially important in arid and semi-arid regions like Pakistan, where inclusive long-term rainfall data with short aggregation periods is limited. Addressing this need, the current study develops intensity-duration-frequency (IDF) curves using rainfall data from four cities across different elevations and geographical regions within Pakistan. By statistically fitting the Gumbel distribution to observed data at different durations (1 h, 6 h, 12 h, and 24 h), the study originates rainfall intensities for distinct return periods. The analysis discloses an average annual rainfall of 25.42 mm, 9.62 mm, 9.25 mm, and 28.02 mm, with standard deviations of 6.45 mm, 9.67 mm, 7.50 mm, and 11.96 mm for Lahore, Karachi, Quetta, and Peshawar, respectively, based on data from 2001 to 2022. Notably, the assessed rainfall intensities for various return periods (2, 5, 10, and 25 years) are higher in mountainous regions compared to interior and coastal regions. Additionally, the study develops empirical parameters for the IDF formula for each city through a linear regression technique, allowing the prediction of rainfall intensities based on desired return periods. Finally, contour maps for all the parameters were created, which can be used to determine IDF relationships for un-gauged locations. These outcomes underscore the vulnerability of mountainous regions to extreme rainfall events, focus the necessity for updated infrastructure and robust flood management strategies. The derived IDF curves and empirical parameters offer valuable tools for policymakers and urban planners to plan effective interventions aimed at mitigating the adverse impacts of extreme rainfall in Pakistan.

Lake Urmia is one of the world’s largest hyper-saline lakes, which has faced a severe drop in water level in the last three decades. To study the effects of irrigated agriculture development on the hydrology of the Lake Urmia basin, a regional climate model, RegCM, was used to simulate the hydroclimate of the basin under different land use and land cover (LULC) scenarios. The findings demonstrated that the growth of irrigated agriculture increases actual evapotranspiration, and affects other components of water and energy balances. Under the past scenario, the lake’s water right is fully provided. But, under the current scenario, only about 42.2% of the lake’s water right is supplied, and under the future scenario, even the agricultural sector will face a water deficit. Regarding the implementation of the Urmia Lake Restoration Program (ULRP) strategy of reducing water consumption by 40% in the agricultural sector, 59.8% and 15.3% of lake’s water right is provided under current and future scenarios, respectively and if other solutions (water transfer from Kani Sib dam and Silweh dam) are used, 85.3% and 40.8% of the lake’s water right is supplied under current and future scenarios, respectively. Considering the effect of climate change on the hydroclimatic conditions of the basin, it is necessary to study the combined effects of LULC change and climate change on the water balance of Lake Urmia.

Afghanistan faces severe air quality issues in major cities due to various sources like transportation, domestic energy use, and industrial activity. This study investigates PM2.5 spatiotemporal variability and its future relationship with six meteorological variables: precipitation, temperature, dewpoint temperature, wind speed, boundary layer height and surface pressure. This study aims to assess the spatiotemporal variability of PM2.5 concentrations in Afghanistan and derive models for predicting PM2.5 from the six variables. Satellite-measured PM2.5 and six reanalyses (ERA5) meteorological datasets for 1998–2020 were used as predictors. Three machine learning models, AdaBoost, Random Forest (RF), and Support Vector Machine (SVM), were used to develop the annual and seasonal PM2.5 concentration prediction model. Results suggest PM2.5 levels ranging from 60–80 µg/m³ in northern, southern, and western regions, while other areas experience lower levels (12–50 µg/m³). The lowest PM2.5 concentrations are in the Hindu Kush mountain range. Summer exhibited the highest PM2.5 concentrations, reaching a maximum of 137.4 µg/m³ and an average of 48.5 µg/m³. Among the prediction models, RF performed best in predicting PM2.5 across Afghanistan, as evidenced by the evaluation metrics: NRMSE (59.2), RSR (0.59), rSD (0.75), and higher values of NSE (0.65), R² (0.65), and KGE (0.68). The geographical and seasonal distribution of observed PM2.5 distribution was very similar to the PM2.5 estimated using RF compared to the other two models. The analysis showed that air temperature, precipitation, wind speeds, and boundary layer heights play significant roles in PM2.5 distribution. However, the relationship between precipitation and PM2.5 was more pronounced than other meteorological variables.

Changes in rainfall and drought significantly impact agriculture and water management, making it vital for effective planning and management. This study aimed to analyze rainfall trends and drought conditions in the Awash River Basin, Ethiopia. Twenty meteorological (1985–2021) and nine streamflow (1985–2014) station data were used to analyze rainfall trends, variability, and drought conditions based on the Mann–Kendall test, innovative trend analysis, standardized precipitation index, agricultural standardized precipitation index, reconnaissance drought index, effective reconnaissance drought index, and streamflow drought index. Based on Mann–Kendall’s test results rainfall during the Bega season showed a decreasing trend at all stations while Tsedey and Kiremt seasons showed an increasing trend at the majority of the stations. However, the Belg season and annual rainfall showed no clear trend at the majority of the stations. A significant (p < 0.05) increase at Debre Berhan and a decrease at Awash7kilo and Ginchi were observed in annual rainfall by 44.1, 102.4, and 116.4 mm per decade, respectively. The innovative trend analysis revealed the Tsedey and Bega seasons showed increasing and decreasing trends in the majority of the stations for all rainfall categories, respectively. However, the annual, Belg, and Kiremt rainfall showed no clear trend in the majority of the stations for different rainfall categories. Annual rainfall showed increasing (Debre Berhan, Mojo, and Sheno) and decreasing (Awash7kilo, Dire Dawa, and Ginchi) trends for all rainfall categories. Generally, there is high variability in rainfall during Tsedey, Bega, and Belg, moderate and low variability during Kiremt, and annual with moderate and irregular rainfall distribution for the majority of the stations. The drought analysis revealed that 15.7, 17.3, 30.7, and 16.3% of drought periods were detected with annual standardized precipitation, agricultural standardized precipitation, reconnaissance drought, and effective reconnaissance drought indices, respectively. Hydrological drought conditions also showed a high probability of occurrence amounting to 47.6 and 48.2% for annual and three-month with severe indices of about -2.58 and -4.26 found at Awash Melka Sedi and Metehara gauge stations, respectively. Moderate to extreme hydrometeorological droughts have occurred approximately every six to eight years, with significant drought events recorded in 1987/88, 1991/92, 1996/97, 2001/02, 2003/04, 2014/15, and 2016/17. The results could have paramount importance for water resource policies and planning for rainfall variability and drought management and adaptation strategies in the Awash River basin.

The increased meteorological drought conditions are very prominent in the Ganga-Brahmaputra (GB) basin due to the impacts of climate change. In the context of meteorological drought in India, particularly within the GB basin, this study explores the effectiveness of the Microwave Integrated Drought Index (MIDI). The study analyses the use of microwave dataset combined with optical remote sensing data for meteorological drought assessment for 18 years (2003–2020). The MIDI was calculated for the month of October, using multiple datasets (Precipitation (Chips, Cmorph, Persiann CDR, Persiann CCS CDR), Temperature (MODIS Land Surface Temperature (LST)), and Soil Moisture (Climate Change Initiative Soil MoistureCCISMv.02.2)) and their ensemble. MODIS-based Enhanced Vegetation Index (EVI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI) were calculated from 1991 to 2020, to understand the previous conditions of drought as well as for correlation analysis. After the analysis of drought conditions based on MIDI, the major drought years observed in the Ganga-Brahmaputra basin were 2011–2012, 2014–2015, 2017–2018, and 2020. The MIDIs were then correlated with the SPI, SPEI, and EVI where the highest significant correlation was found between MIDI and SPEI (0.876), emphasizing the importance of incorporating diverse environmental factors for a comprehensive understanding of drought dynamics. The highest correlation was observed with Chirps precipitation-based MIDI (0.87 to 0.83) and the lowest with MIDI CDR and CCS CDR (0.29 and 0.37 respectively) specifically in the Brahmaputra basin. The various precipitation products reflected different characteristics in their behaviour for different topography that can be analyzed for better monitoring.

Spatiotemporal change in precipitation induced by climate change can be a concern for riverine disasters. The relationship between precipitation and flow discharge in the 8 rivers from northeast to southwest of Japan (either mainstream or tributary) in which neither manmade dam nor reservoir is present was investigated based on observed data at the stream gauging stations managed by the Ministry of Land, Infrastructure, Transport and Tourism of the Japanese government. Observed data of air temperature, precipitation, etc. by the Japan Meteorological Agency vicinity of the gauging station in the 8 rivers basins were also used in the analyses. The precipitation concentration index (PCI) which is the Gini coefficient of precipitation during a year, and the Gini coefficient of flow discharge were computed for about half a century. The results reveal that annual maximum flow discharge can be related closely to the extreme rainfall events such as annual maximum daily or hourly precipitation. Obtained trends of the PCI were all positive and statistically significant at the 1% level, indicating that an in – equality in rainfall distribution during a year has been accelerated as the air temperature has risen in the basins. Whereas obtained trends of the Gini coefficient of the flow discharge were either positive or negative, and very weakly correlated with the trends in the PCI. Temporal precipitation distributions in a year have changed in the 8 rivers basin, i.e. light rain days (0 – 1 mm/day) have increased whereas rain days with 1 – 10 mm/day have decreased, while no such trend is seen in flow discharge in the 8 rivers. The interaction between surface and subsurface flows, and soil moisture may play important roles in moderating the effects of spatiotemporal change in precipitation. The flow discharge, however, can increase immediately in response to the precipitation when rainfall intensity is sufficiently strong.

Global warming is incurring diverse climate changes across different regimes, where high-resolution models provide valuable insights of the regional climate changes for guiding social adaptation and mitigation. Thus, this study is aimed to investigate the capability of high-resolution models in simulating the historical climate (1980–2014) over the Yangtze River Delta (YRD) region, and examine the possible regional climate change in the near future (2031–2050). Data from the highresSST-present and highresSST-future experiments of 5 CMIP6 HighResMIP models (FGOALS-f3-H, HiRAM-SIT-HR, NICAM16-8S, MRI-AGCM3-2-S, and MRI-AGCM3-2-H) were analyzed together with the daily station observations by China Meteorological Administration. Results show that the models generally well simulate the regional means and extreme events of the daily-mean temperature and precipitation over the YRD region for the historical period. The temperature is underestimated in the southern YRD (especially in summer and autumn), causing underestimated meridional gradient. In contrast, the precipitation spatial distribution closely matches observations in all seasons, showing a marked improvement over results from low-resolution models. For the near-future period, the daily-mean temperature is projected to increase by 1.4 ℃, which nearly persists throughout the year and is only slightly milder in winter. The daily-mean precipitation may increase by 0.2 mm day⁻¹ (~ 6%), with the largest increase in summer (0.4 mm day⁻¹) and a slight decrease in winter. Meanwhile, the occurrences of extreme hot events and heavy-precipitation events are increased across the YRD region. Given the substantial implications of these possible imminent changes, more effort is warranted to reduce model uncertainties for enhanced validation.

Spatial pattern and variability of sea surface temperature SST and their teleconnections with rainfall dynamics in the Gulf of Guinea (GOG) were examined in this study. SST and rainfall data of 50 years (1970–2022) were obtained from ERA5 and NOAA CPC at 0.25° x × 0.25° and 0.5° × 0.5° spatial resolution from longitudes 10°W and 8°W, 6^oW, 4^oW, and 2^oW and latitudes 15°N, 5^oN, 3^oN, 15^oS, 5^oS, and 3^oS, distributed along the Gulf of Guinea (GOG) respectively. Analysis was further carried out on twelve rainfall gridded stations distributed along the Gulf of Guinea (GoG) for the characterization of the Rainfall-SST teleconnection across the region while the relationship between the rainfall-SST anomalies, seasonal, inter-annual, and decadal scales was carried out using correlation analyses and composites. Interpolation of the meteorological variables was carried out using Inverse Distance Weight (IDW) from the ArcGIS Spatial Analyst Tool, Ferret, and CDO which were further employed to generate the seasonal and decadal rainfall and SST maps and statistical analysis of the study area. The result of the decadal and seasonal analysis of SST variability from 1970–1980,1980–1990, 1990–2000,2000–2010,2010–2020 and 2022 indicate that SST was highest from 2010 to 2020 at 28.91 °C and fluctuated between (28.49 °C) in the 1970–1980 and (28.08 °C) for the 1980–1990 decade While the seasonal pattern of SST showed marked variability with March–April and May(MAM) recording 29.34 °C with the lowest being in June-July–August(JJA) at 28.7 °C. In terms of decadal analysis of rainfall, the period 2010–2020 recorded the highest amount of rainfall along the coast (3,145.5 mm-3,928.3 mm while 1970–1980 recorded the lowest amount of rainfall (2,650–3.310 mm. To investigate the teleconnection between of SST and rainfall dynamics, statistical analysis was used where the SST values were plotted against seasonal rainfall in 11 stations namely Abidjan, Banjul, Accra, Guinea, Conakry, Cotonou, Dakar Doula, Freetown, Lagos, Lome, and Monrovia. The outcome of the statistical analysis and Standardized Anomaly Index used indicate that Banjul, Cotonou, Dakar, and Doula exhibited statistically insignificant correlation at 0.05 confidence level while Abidjan, Accra, Lagos, Lome, Freetown, and Monrovia showed positive and statistically significant correlation. The spatial pattern of seasonal rainfall climatology categorized into DJF, MAM, JJA, and SON reveals that JJA and SON produced 80% of rainfall in the Coastal GOG followed by MAM. The study affirmed that warm and cold tongues exist in the GOG alongside positive teleconnection and that the spatial variability of SST observed in this study corresponds positively with the decadal and seasonal variability of rainfall.

The shifting thermal environment brought by global warming presents new concerns for urban residents. However, the lack of urban presentation in the global and regional climate models limits the ability of these models to provide useful information at the urban scale. This study examines the impact of 1.5 °C and 2 °C global warming levels (GWL1.5 and GWL2) on the future summer of Beijing, China. A new statistical-dynamical downscaling (SDD) method was applied using available Coordinated Regional Climate Downscaling Experiment (CORDEX) ensemble data to downscale climate projections across Beijing at different GWLs. The results showed that the maximum air temperature increase reached 3.5 °C and 4 °C at GWL1.5 and GWL2, respectively, in the central urban area of Beijing. The historical urban heat island (UHI) intensity first increased to 2.48 ± 0.97/1.02 ± 0.58 °C in GWL1.5 and then decreased to 2.24 ± 0.98/0.90 ± 0.69 °C in GWL2 at 22:00/09:00. Under GWL1.5, the UHI effect is greater in the eastern metropolitan areas (> 2 °C) than in the western regions (0.5–1.5 °C). The highest daytime and nighttime UHIs occurred mostly in LCZ154 (open high-rise area). The intensity, duration, and frequency of future heat waves (HWs) are increasing, especially in urban areas under GWL2. Climate information on UHIs and HWs under the Paris Agreement would be very helpful for stakeholders and city planners to develop near-term future local adaptation policies.