Meteorology and Atmospheric Physics最新文献

This study examines the skill of the North American Multi-Model Ensemble (NMME) seasonal precipitation forecast and the influence of tropical sea surface temperature (SST) anomalies and their teleconnections on precipitation prediction skill over Central Africa (CA). The skill is assessed for December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON) seasons, at 0-, 3-, and 6- month lead time. Results show that for all seasons and at all lead times, models used in this study have tendency to overestimate the observed SSTs over the tropical areas. The multi-model ensemble mean (MME) generally succeeds in capturing the spatial differences in the seasonal mean climatology of precipitation and clearly determines the bi-modal and uni-modal natures of observed precipitation over CA. The El Ninõ-Southern Oscillation 3.4 index (Ninõ3.4), Indian Ocean Dipole (IOD) western pole index (IODWP), and IOD eastern pole index (IODEP) teleconnections with tropical SST are well represented by the MME at all seasons and lead times with a pattern correlation coefficient (PCC) >0.6. The quality of these teleconnections decreases when the lead time increases. The Ninõ3.4-induced precipitation’s teleconnection is better represented in MAM at all lead times, and it is found that precipitation is reinforced over northern CA during the El Ninõ years and weakened during the La Niña years. IODWP and IODEP teleconnections with CA precipitation are well represented in MAM and SON, with PCC > 0.8. The IODWP and IODEP could be a very good indicators to predict the increase or decrease of precipitation in CA during MAM and SON seasons.

Understanding the spatial and temporal patterns of drought and their connection with major climate indices is crucial for creating early warning and drought mitigation strategies. This study analyzed hydrological drought variability and its association with global climate indices in the Sahel Region of Nigeria. Before conducting drought analysis, temperature and precipitation data were verified for consistency using three homogeneity tests. The study utilized six synoptic stations across the area to identify drought periods through the Standardized Precipitation Evapotranspiration Index (SPEI). Drought characteristics such as duration, severity, and amplitude were examined using SPEI data. Trend and variability in drought patterns were assessed with Mann–Kendall trend analysis and wavelet analysis, respectively. The relationship between large climate indices and drought was explored using Pearson correlation analysis. Trend analysis indicated an increase in drought occurrences, with significant findings in four stations. Wavelet analysis identified the 2–4 and 4–8 year bands as crucial for understanding SPEI drought patterns. Correlation analysis showed the influence of various climate trends on concurrent climate events, ranking the impact of climate indices on drought as MEI/SOI > NAO > AMO > DMI. Coherence analysis found significant correlations between ENSO and SPEI, and NAO and SPEI, in the 2–7 and > 8-year bands, respectively. Phase differences suggested that severe wet and dry periods align with La Nina and El Nino events, with strong El Nino events and AMO negative phases mainly causing severe droughts in the area.

The main functions of climate normals are twofold. They offer a reference point for evaluating recent or ongoing observations and form the basis for various climate datasets that rely on anomalies. Additionally, they are frequently employed to predict the probable conditions that one might encounter in a specific area. The World Meteorological Organization (WMO) advises regularly reviewing climate normals every decade to keep up with the evolving climate. Atmospheric Science and Meteorological Research Center (ASMERC) is proud to release “Iran Climate Normals” for the periods of 1981–2010 and 1991–2020 including a suite of monthly and annual statistics that are based on temperature, precipitation, sea-level pressure, vapor pressure, station-level pressure, snow-depth, wind speed, visibility, soil temperature, relative humidity, dew point, and cloud amount measurements. This study documents the procedures used for quality control, homogenization of daily observations, and calculation of normal values. For each station and each parameter, the results of the outliers due to the error and the homogeneity assessment are reported. Out of all the parameters, the soil temperature has the highest error percentage. However, this does not necessarily imply that it has the most measurement errors; it could be due to the ease of detecting errors for this specific parameter. Of the 143 stations, 56 had a breakpoint recorded in two parameters or more at a specific point in time. According to the analysis of the temperature and precipitation parameters, (a) the new normal of mean, maximum, and minimum temperatures are 0.47, 0.5, and 0.6 °C above the 1981–2010 period; (b) the normal annual precipitation has increased by an average of 5.4 mm in 1991–2020 compared to 1981–2010; (c) comparing the two periods, the changes in precipitation normals vary in different parts of Iran and different months, while the temperature normals increase in all stations across Iran except for four stations (Gorgan, Kerman, Shiraz, Bandar-e Lengeh); (d) changes in the fourth quintile of monthly precipitation are more than average, and minimum temperature changes are higher than maximum and mean temperatures; and (e) generally, the latter period is characterized by a warmer climate almost across Iran, wetter conditions over the Zagros mountain range and the western part of the Caspian Sea coasts, and drier conditions over the east, center, and west of Iran.

Abstract

Satellite-based precipitation estimates and global reanalysis products bear the promise of supporting the development of accurate and timely climate information for end users in sub-Sharan Africa. The accuracy of these global models, however, may be reduced in data-scarce regions and should be carefully evaluated. This study evaluates the performance of ERA5 reanalysis data and CHIRPS precipitation data against ground-based measurements from 167 rain gauges in Ethiopia, a region with complex topography and diverse climates. Focusing over a 38-year period (1981–2018), our study utilizes a point-to-pixel analysis to compare daily, monthly, seasonal, and annual precipitation data, conducting an evaluation based on continuous and categorical metrics. Our findings indicate that over Ethiopia CHIRPS generally outperforms ERA5, particularly in high-altitude areas, demonstrating a better capability in detecting high-intensity rainfall events. Both datasets, however, exhibit lower performance in Ethiopia's lowland regions, possibly the influence of sparse rain gauge networks informing gridded datasets. Notably, both CHIRPS and ERA5 were found to underestimate rainfall variability, with CHIRPS displaying a slight advantage in representing the erratic nature of Ethiopian rainfall. The study’s results highlight considerable performance differences between CHIRPS and ERA5 across varying Ethiopian landscapes and climatic conditions. CHIRPS’ effectiveness in high-altitude regions, especially for daily rainfall estimation, emphasizes its suitability in similar geographic contexts. Conversely, the lesser performance of ERA5 in these areas suggests a need for refined calibration and validation processes, particularly for complex terrains. These insights are essential for the application of satellite-based and reanalysis of rainfall data in meteorological, agricultural, and hydrological contexts, particularly in topographically and climatically diverse regions.

Heat waves are prolonged periods of excessively hot weather, which can have significant impacts on human health, agriculture, and the environment. Climate change has been linked to an increase in the frequency, intensity, and duration of heat waves. As the global average temperature rises, heat waves are becoming more common and more severe. The Arabian Peninsula is warming at a faster rate as compared to the globe in the recent decades. In this paper, the mild, moderate, severe, and extreme heat waves defined by 85th, 90th, 95th and 99th percentile, respectively, are analyzed over Saudi Arabia using historical daily maximum and minimum temperature observations for the period 1985–2021. The large number of mild heat waves are observed all over Saudi Arabia while extreme heat waves are dominant in the northwestern region. Moderate and severe heat waves are observed less in both the Red Sea and the Arabian Gulf coastal regions. The heat waves are intense in the northern and central areas as compared to other regions of the country. Heat wave frequency, intensity and length in Saudi Arabia are in increasing trends, along with the increase in the heat wave season length. Heat wave frequency and intensity are largely observed during the ENSO La Nina and neutral phases along with NAO negative phase as well as IOD negative and neutral phases. However, further investigation is required to see the occurrence of heat waves in different climate zones over Saudi Arabia at various seasons and their teleconnection to large-scale circulations.

Raindrop size distribution (DSD) plays a significant role in understanding the microphysical process of rainfall and the quantitative precipitation estimation (QPE) in hydrology, especially in urban environments which has spatial and temporal variability. In this study, the seasonal variation in DSD and its response to cloud regimes over two contrasting coastal sites (i.e. Kolkata (22.58° N, 88.45° E) and Trivandrum (8.43° N, 76.98° E) of India obtained using laser precipitation monitor (LPM) disdrometer for more than 2 years are investigated. The results show a significant difference in DSD spectra between Kolkata and Trivandrum. It is observed that the smaller-size (< 0.5 mm) particles are more dominant over Trivandrum than at Kolkata. During the monsoon, larger raindrops (D > 2 mm) dominate over Kolkata when compared with Trivandrum and clear separations in DSD were observed in the pre-monsoon season. The percentage contribution of the rain types to the total rainfall duration over Kolkata (Trivandrum) is found to be about 74.13% (80.50%), 18.97% (15.35%) and 6.98% (4.13%) for stratiform, transition and convective, respectively. In the convective rain, the smaller (mid-size, 1 < D < 3 mm and large, D > 3 mm) drops concentrations are higher (lower) over Trivandrum, while mid-size and larger (smaller, D < 0.5 mm) drops are higher (lower) over Kolkata. The convective rains are dominated by continental/maritime and maritime over Kolkata and Trivandrum, respectively. As the rain rate increases, the DSD spectra have larger widths with peaks around diameter D ~ 0.5 mm over both the locations. Further, the empirical relations between reflectivity (Z) and rain rate (R) were established, which are found to be different for different rain types. In each rain type, the Z-R relationship over Kolkata (Trivandrum) is Z = 56.4*R^1.94 (Z = 21.3*R^2.18), Z = 118.8*R^1.89 (Z = 106.4*R^1.83), and Z = 388.0*R^1.54 (Z = 303.1*R^1.38) for convective, transition and stratiform rains, respectively. These results clearly indicate that the two locations are dominated by different cloud systems and microphysical processes. Therefore, the present results are expected to provide a better understanding of regional DSD variability and Z-R relationship with seasons, rain types and cloud microphysical processes, which is the significance of the present study.

This study investigates the trend in the projected rainfall and temperature over undivided Sudan and its major cities of political, trade, and agricultural significance under two different Representative Concentration Pathways (RCPs; RCP2.6 and RCP8.5). Available high-resolution datasets from the Coordinated Regional Climate Downscaling Experiment- Coordinated Output for Regional Evaluations (CORDEX-CORE) at a resolution of 25 km along with their ensemble are considered. The study analyzes projected climate conditions, with a specific emphasis on the near future (2036–2060) and far future (2071–2095). The rainfall distribution is projected to decline across South Sudan (undivided Sudan) under RCP2.6 (RCP8.5). The projected temperature is significantly increasing while rainfall is decreasing across all cities, with these trends being more pronounced under the RCP8.5 scenario. These changes could potentially result in various climate extremes such as severe heatwaves, droughts, and wildfires, which could have significant impacts on the ecosystems, agriculture, public health and ultimately, the livelihood and socio-economic condition of the people. The findings of the study will assist the governments, local administration and town planners in formulating short-term and long-term strategies for adaptation and mitigation, aimed at reducing the impacts of climate change. The study suggests specific measures to address the extreme heat and water deficit at the local scale, hence making it a valuable policy document for addressing the changing climate in undivided Sudan.

This study examines the combined influence of El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on Upper Troposphere Lower Stratosphere (UTLS) ozone variability. The investigation employs data from the Microwave Limb Sounder (MLS) aboard the Aura Satellite and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, spanning the period 2005–2020 across tropical latitudes (20º N–20º S). Three specific events were chosen for analysis: a strong La Niña event in 2010, the co-occurrence of El Niño and moderate IOD in 2015, and a robust IOD event in 2019. During years marked by the simultaneous occurrence of ENSO and IOD events, the UTLS (100 hPa altitude is considered for the present study. 82 hPa is the altitude just above the tropopause, therefore also shown in the results) ozone mixing ratio demonstrates a decline in absolute values. The Quasi-biennial Oscillation (QBO) was also investigated, revealing a synchronized variation with the ozone anomaly in the UTLS region. Furthermore, the calculated eddy heat flux, utilized as a proxy for the Brewer–Dobson Circulation (BDC), aligns with the UTLS ozone anomalies, indicating a positive (negative) anomaly during periods of intense tropical downwelling (upwelling). To quantitatively elucidate the contributions of ENSO, IOD, and QBO to the observed ozone anomaly, a multivariate linear regression analysis was executed utilizing the least square method. The findings underscore that a notable fraction—about one-fourth of the observed UTLS ozone anomaly within the study timeframe (2005–2020) can be attributed collectively to ENSO, IOD, and QBO. This preliminary exploration underscores the substantial role played by large-scale climate drivers emanating from the Pacific and Indian oceans in shaping UTLS ozone distribution. These insights emphasize the significance of considering these climatic influences when examining the intricate dynamics and variability of UTLS ozone patterns.