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- Impact of climate change on the behaviour of solar radiation using
AFR-CORDEX model over West Africa-
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Abstract: Abstract The study evaluated the impact of climate change on incoming solar radiation (RSDS) in West Africa by comparing observed data from the CMSAF solar products (SARAH and CLARA-A1) for the period 1983–2019 with simulated data from the AFR-CORDEX models (RegCM-4.7 and CCCma-canRCM4) for the historical period (1983–2004) and various RCP emission scenarios (2.6, 4.5, 8.5) for 2005–2099. The values of the RCP in parentheses signify the level of increasing radiative forcings due to varying emission controls. Assessment metrics like correlation coefficient (R), Taylor Skill Score (TSS), and root mean square errors (RMSE) were employed for comparative analysis on annual and seasonal timescales. The analyses revealed annual mean RSDS intensities of 256.22 for SARAH, 238.53 for CLARA-A1, 270.81 for Historical, 270.26 for RCP 2.6, 255.90 for RCP 4.5, and 271.93 for the RCP 8.5 scenarios in watts per square metres. The TSS analyses showed average agreement values between observed CMSAF and simulated AFR-CORDEX solar radiation with values of 0.8450 and 0.8575 with historical, 0.8750 and 0.8600 with RCP 2.6, 0.9025 and 0.8550 with RCP 4.5, and 0.8675 and 0.8525 with RCP 8.5 scenarios for SARAH and CLARA-A1 respectively. All the metrics showed better agreement with SARAH than CLARA-A1, likely due to the associated cloud influence on CLARA-A1. Notably, the CORDEX-CCCma-canRCM4 model under RCP 4.5 demonstrated the highest accuracy, with an average correlation of 0.82 and a mean TSS of 0.90 against the SARAH reference dataset. The results suggest the AFR-CORDEX model, particularly the CCCma-canRCM4 for RCP 4.5 scenario, could reliably predict solar radiation and inform climate change impacts on solar energy potential in West Africa under moderate emission conditions.
PubDate: 2024-08-24
- Urban heat island characteristics of Yangtze river delta in a heatwave
month of 2017-
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Abstract: Abstract The analysis of urban thermal environment based on Local Climate Zone (LCZ) is helpful to understand the fine structure of urban heat island (UHI), so as to provide a scientific basis for urban ecological environment management. This research focused on the three biggest cities, Shanghai, Nanjing and Hangzhou, in Yangtze River Delta (YRD) and the UHI characteristics in a heatwave month (July 2017) were investigated. Based on the observations of automatic weather stations, the spatiotemporal characteristics of air temperature and canopy urban heat island intensity (UHII) of each LCZ in three cities under different weather conditions were compared and analyzed by using the LCZ clustering method, and the effects of water bodies, urban greening and sea breeze on urban heat island were discussed. Results show that the air temperature and urban heat island intensity of different LCZs would vary due to the differences in urban geometry, building materials, the proportion of impervious surface and anthropogenic heat. The LCZ based UHII in the three YRD typical cities showed similar characteristics: compact high-rise (LCZ 1), compact mid-rise (LCZ 2) and open mid-rise (LCZ 5) had higher UHII while sparsely built (LCZ 9) had lower UHII. The diurnal variation of UHII in the three cities are different: the UHII diurnal curves of Nanjing and Hangzhou were “U” type, while that of Shanghai was shallow “W” type, which was because Shanghai was vulnerable to sea breeze during the summer day. In addition to land and sea location, large water bodies and urban greening would also impact the spatiotemporal patterns of urban thermal environment.
PubDate: 2024-08-03
- Variations in air-sea heat fluxes during lifetime of intense tropical
cyclones over the Bay of Bengal-
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Abstract: Abstract In this study, we have tried to find out the influence of air-sea heat fluxes (particularly the Surface Latent Heat Flux (SLHF) and the Surface Sensible Heat Flux (SSHF)) on the intensity of Intense Tropical Cyclones’ (ITCs’). We have analysed 32 ITCs which originated in the Bay of Bengal (BoB) during 1990–2019. We have used IMD best track data for track and vital parameters of ITCs. We have also used high resolution (0.25°×0.25°) ERA5 SLHF, SSHF and SST data for their variations during the lifetime of ITCs. It is observed that the SLHFmax during the whole lifetime and the study period is highly correlated with ITCs’ intensity (i.e. with the central pressure (CP) and the maximum sustained wind speed (MSW)) whereas the SSHFmax shows weak correlations with ITCs’ intensity. This suggests the strong association between the SLHFmax and ITCs intensity and strong latent heat flux exchange between the ocean and atmosphere during the whole lifetime and the study period. Similar results are observed in the pre-monsoon and the post-monsoon season. In the pre-monsoon season the association of SLHFmax with the ITCs intensity is stronger than the post-monsoon season due to strong background conditions, pointing towards the strong air-sea interaction. The SLHFmax in the growing and the decaying stage are also highly correlated with ITCs’ intensity but correlation coefficients of ITCs’ intensity with the SLHFmax in the decaying stage are slightly higher than those of in the growing stage. It is also found that the SSHFmax has an appreciable correlation with ITCs’ intensity during the growing stage whereas it has negligible correlation with ITCs’ intensity during the decaying stage pointing towards the influence of sensible heat flux in the growing stage of ITCs. It is also noticed that during rapid decay (RD) the SLHFmax is highly correlated with ITCs’ intensity possibly due to cold wakes which rapidly diminishes the SLHF but during rapid intensification the SLHF does not increase so rapidly due to the sluggish WISHE feedback, hence the SLHFmax during rapid intensification (RI) is not appreciably correlated with ITCs’ intensity.
PubDate: 2024-07-04
- Validation of ERA5 rainfall data over the South Pacific Region: case study
of Fiji Islands-
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Abstract: Abstract Rainfall variability has a significant impact on hydrological cycle. Understanding rainfall variability over Fiji Islands is important for decision-making in the backdrop of global warming. Reanalysis rainfall products are commonly used to overcome observed data quality challenges especially over ungauged highland areas. However, an evaluation of reanalysed datasets is important to ensure accurate and reliable climate information generated using such datasets, especially for small Island with high variable topography like Fiji. This work aims to validate the spatiotemporal performance of European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis rainfall (ERA5) data against ground-based station data from 19 stations for the period 1971–2020 over Fiji Islands. Correlation coefficient and difference statistics: bias, and root mean square error, are used to assess the performance of the data. Further, common Empirical Orthogonal Function (common EOFs) analysis was used to evaluate spatiotemporal performance of ERA5 datasets. The results of the station-by-station comparison shows that interpolated ERA5 annual rainfall matches the corresponding results from rain gauges remarkably well for many stations. The correlation coefficient values range from 0.5 to 0.85, while the bias spans from a negative 282 to a positive 575, and the root mean square error (RMSE) varies between 285 and 662 mm for the annual rainfall across the study area. However, there is overestimation and underestimation of the observed rainfall by ERA5 datasets. The leading common EOF principal component for annual rainfall suggests that the inter-annual variability in ERA5 dataset is generally consistent with observed station datasets, cross validation results indicated high scores (correlations of 0.82), with limited spatial variation. This work presents a reliable data assessment of the ERA5 data over Fiji Islands, indicating there is good match of the annual observed rain gauged station data and ERA5. The findings give accuracy references for further use of the ERA5 data in understanding rainfall variability and change over the region.
PubDate: 2024-06-28
- Large-scale dynamics of extreme precipitation in the tropical Andes:
combining weather radar observations and reanalysis data-
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Abstract: Abstract Extreme precipitation events are a global threat to human wellbeing, they are the main cause of natural disasters such as flooding and landslides. Floods are particularly harmful because they can trigger infectious disease outbreaks, cause damage to essential infrastructure, impact food and water security, and even affect the mental health of the population. Climate change has caused an intensification of extreme precipitation events globally in the last decades, including the tropics, which receive more than half of the global precipitation. In this context, the understanding of the climate dynamics related to extreme precipitation events is vital for climate change adaptation. Such understanding has been particularly limited in the tropical Andes, primarily because of the paucity of data in the region. In the present study, we used data from a weather radar installed at 4450 m a.s.l. in the southern Ecuadorian Andes and ERA5 reanalysis to examine the large-scale dynamics associated with extreme precipitation events in the tropical Andes during the 2015–2022 period. We found that extreme rainfall in the Interandean valley is connected to local positive near surface temperature and CAPE anomalies that extend to the eastern slopes of the Andes, which cause strong thermal convection and intense afternoon events. On the other hand, extreme rainfall over the western slopes and the coastal plains is associated with El Niño-like conditions that produce Mesoscale Convective Systems that survive well after sunset and cause extreme nighttime precipitation. The present study allowed us to reveal the connections between large-scale dynamics and extreme precipitation for the first time in the southern Ecuadorian Andes. The outcome of the present study could be useful in future research to improve forecasting of extreme precipitation events and early warning systems in the region.
PubDate: 2024-06-10
DOI: 10.1007/s00703-024-01022-2
- Atmospheric processes dominate the changes in autumn rain hours over the
Sichuan Basin: 1960–2018-
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Abstract: Abstract Autumn rain around Sichuan Basin (ARSB) in the downstream of Tibetan Plateau, is one of China’s six important rain seasons, with the longest precipitation frequency. In measuring the ARSB, precipitation amount and precipitation day were usually used, which do not represent the most remarkable feature which is the continuous light rain. This paper investigates the daily rain hours (DRH) during ARSB over 1960–2018 using observations from 371 stations. The annual cycle of DRH shows a two-peak distribution, with the second peak indicating the ARSB and a clearer peak than the total rainfall. The southern stations and northern stations (south/north of 30°N) show different climatology, such as the occurrence day of the DRH peak, the duration of ARSB season, etc. The DRH decreased significantly in the northern/southern stations from 1960 to 1997, then rebounded to around the 1970s’ level by the 2010s, which was dominated by changes in the rain event count but not the rain event duration. Our analyses reveal two local weather systems, namely the Huabei High and the Southwest Low, contributing to the high DRH during ARSB season. Changes in northeasterly and southerly winds induced changes in low-level moisture convergence and hence help explain the non-linear decadal trends of DRH. For example, the easterly wind in the south of Huabei high weakened before 1998 and strengthened after 1998. These results highlighted the importance of using high temporal-resolution data in measuring the changes in precipitation and considering local atmospheric environments in the attribution of climate change.
PubDate: 2024-06-07
DOI: 10.1007/s00703-024-01024-0
- Improved temperature prediction using deep residual networks in Hunan
Province, China-
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Abstract: Abstract Utilizing the real-time grid dataset CLDAS spanning from 2017 to 2020 and an optimal set of variables from the ECMWF IFS model, we developed a temperature prediction model based on Spatiotemporal Stacked ResNet (Res-STS). To assess the efficacy of the Res-STS model, we devised two additional modelling schemes: the first, denoted as the LSTM scheme, replaced the spatiotemporal stacking technology with long short-term memory (LSTM) networks on the basis of the Res-STS model, while the second, denoted as the T2m scheme, replaced the optimal factor set with a single meteorological element, specifically 2 m temperature. Consequently, we generated three sets of hourly temperature and daily maximum/minimum temperature forecasts. A comparative analysis was then conducted, pitting the predictions from the three schemes against those from the IFS model to evaluate the performance of the Res-STS model. The findings showed a weak unimodal pattern in the Mean Absolute Error (MAE) of hourly forecasts across all models, with errors peaking in the afternoon when the daily maximum temperature was reached. All schemes revealed superior and more consistent performance in the minimum temperature prediction compared to their maximum temperature counterparts. Notable errors were observed in high-altitude areas. The Res-STS scheme, using the Res-STS model with spatiotemporal stacking technology, outperformed the IFS model, especially in forecasting the maximum temperature. This improvement extended throughout the entire province, particularly in high-altitude mountainous regions. Comparing with the alternative schemes, the better performance of the Res-STS scheme underscored the greater contribution of the spatiotemporal stacking technology compared to the optimal factor set. Particularly in high-altitude regions, the Res-STS scheme demonstrated superior performance over the T2m scheme, showing its capability to reduce forecast errors caused by complex terrain, where the optimal factor set played a crucial role.
PubDate: 2024-06-07
DOI: 10.1007/s00703-024-01023-1
- Deterministic ensemble Kalman filter based on two localization techniques
for mitigating sampling errors with a quasi-geostrophic model-
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Abstract: Abstract In the ensemble Kalman filter (EnKF) framework for data assimilation, a limited ensemble size results in a spurious sampling error and underestimation of the background error covariance. Localization techniques have been explored to mitigate the sampling errors induced by ensemble-based assimilation approaches. In this study, we incorporate a local analysis (LA) scheme and a covariance filter (CL) scheme into the deterministic EnKF (DEnKF), thus formulating a DEnKF method with local analysis (DEnKF(LA)) and a DEnKF method with covariance localization (DEnKF(CL)), respectively. To verify the effectiveness of the proposed algorithm, we perform a series of experiments with a large-scale (quasi-geostrophic) model. The results show that the DEnKF(LA) method yields better assimilation results than the DEnKF(CL), the traditional EnKF with a covariance filter (EnKF(CL)) and the ensemble square-root filter with local analysis (EnSRF(LA)) in terms of various assimilation parameters (i.e., ensemble size, localization, inflation, and observation error variance). In addition, the DEnKF(CL) and DEnKF(LA) methods both yield well-constrained analyses, as determined by examining the spatial state distributions of the true field, the analysis field and the average root-mean-square errors (RMSEs) field for the various methods. Overall, the DEnKF(LA) method clearly outperforms the other data assimilation methods. Hence, the proposed DEnKF(LA) method has good prospects for application in atmospheric and oceanic models.
PubDate: 2024-05-20
DOI: 10.1007/s00703-024-01015-1
- Comparative testing of selected solar radiation shields under hot
Mediterranean summer conditions-
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Abstract: Abstract In this study, a set of four commercially available naturally ventilated radiation shields and a reference mechanically ventilated weather station were tested during three months of a hot Mediterranean summer in southern Europe. The results indicate that among the selected radiation shields, the naturally ventilated shield identified as shelter 2 with its inner temperature sensor has the best overall performance and could be used as a reference radiation shield, as its statistical errors were of the same order of magnitude compared with the reference mechanically ventilated weather station.
PubDate: 2024-05-16
DOI: 10.1007/s00703-024-01021-3
- Correction: Aerosol optical depth and water vapor variability assessed
through autocorrelation analysis-
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PubDate: 2024-05-09
DOI: 10.1007/s00703-024-01019-x
- Analysis of a hailstorm in the south of Minas Gerais state on October 13,
2020-
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Abstract: Abstract On October 13th, 2020, at approximately 1940 UTC, a hailstorm struck the city of Itajubá, located in the south of the Minas Gerais state, Brazil. This hailstorm produced hail with a diameter of 5 cm causing damages in roofs of houses and shelters. In this sense, the objective of this study is to describe the synoptic-scale environment that lead to the “ingredients” necessary for the mesoscale development of the storm, and to provide a description of cloud microphysical and lightning properties. Several data sources were used in this study as: surface observations, reanalysis data, and atmospheric remote sensing information. The synoptic-scale environment conducive to storm formation was associated with an inverted trough at surface and a shortwave trough at upper-level levels, which were important to organize upward movements in the atmosphere. High reflectivity (> 60 dBZ) was registered in the convective cell from 1940 to 2010 UTC, according to the São Roque radar data, indicating the presence of large raindrops and/or hail on the ground. The total lightning rates increased from the beginning of the storm, reaching ~ 80 lightning/5 min around 20 min before the hail precipitation, which occurred at 1920 UTC. This study highlights the importance of associating synoptic and physical information for understanding the environment and the main features of hailstorms. It also emphasizes the significance of producing information that can aid in nowcasting tools.
PubDate: 2024-05-08
DOI: 10.1007/s00703-024-01020-4
- Tele-connections of atmospheric oscillations on streamflow data in Turkey
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Abstract: Abstract The climate indices demonstrate temporal and spatial variability of large-scale ocean–atmosphere patterns. This study has been carried out to analyze the streamflow data in Turkey to understand the effects of climate indices such as the Southern Oscillation (SO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO). The periodical relationship of the streamflow data of Turkey over different atmospheric oscillations was investigated. For this purpose, the average annual and seasonal flows at the current 72 stations in other regions of Turkey have been studied. In this context, the correlation analysis determined the relationship between NAO, AO, SO indices, and stream-flows. Besides, the original observed data were separated into parts by discrete wavelet transform to obtain the periodic components. The correlations between the found periodical components and atmospheric indices were also examined. The correlations between the streamflow and the AO/NAO showed a strong negative relationship during the annual/winter and spring periods, especially in western Turkey. Besides, the periodic components showed us the multi-annual connections between the global indices and the streamflow data of Turkey.
PubDate: 2024-04-15
DOI: 10.1007/s00703-024-01014-2
- Climate forecast skill and teleconnections on seasonal time scales over
Central Africa based on the North American Multi-Model Ensemble (NMME)-
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Abstract: Abstract 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.
PubDate: 2024-04-05
DOI: 10.1007/s00703-024-01018-y
- Impact of climate teleconnections on hydrological drought in the Sahel
Region of Nigeria (SRN)-
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Abstract: Abstract 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.
PubDate: 2024-04-05
DOI: 10.1007/s00703-024-01016-0
- Evaluation of ERA5 and CHIRPS rainfall estimates against observations
across Ethiopia-
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Abstract: 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.
PubDate: 2024-04-03
DOI: 10.1007/s00703-024-01008-0
- Climatological standard normals of IRAN, for the period 1981–2010 and
1991–2020: precipitation and temperature-
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Abstract: Abstract 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.
PubDate: 2024-04-03
DOI: 10.1007/s00703-024-01013-3
- A case study on the impact of real-time land cover changes in the
intertidal zone on coastal meteorological predictions using a coupled
atmosphere–ocean model-
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Abstract: Abstract The effect of time-varying land cover within the intertidal zone on meteorological predictions was evaluated on the west coast of South Korea during the summer of 2020 (July 3–8). Time-varying spatial distributions of the intertidal zone were generated using a coupled atmosphere–ocean model and Landsat-8 satellite data. Meteorological simulations were conducted under two land cover conditions: time-varying (referred to as TIDE) and fixed land cover (no tidal effect) within the intertidal zone (referred to as CTRL). In general, the results of TIDE simulation exhibited good agreement with the observed meteorological data when compared to the CTRL simulations. The most significant air temperature (Temp) and relative humidity (RH) variations in the entire intertidal zone occurred during low tide in the daytime, showing an average Temp increase of + 2.0 ℃ and a RH decrease of − 9.2% compared to the CTRL simulation. Conversely, during nighttime, the Temp decreased by an average of − 0.5 ℃ and the RH increased by + 0.1%. These variations were primarily attributed to changes in the physical properties of the soil surface. Furthermore, the nighttime wind speed (WS) during low tide in the entire intertidal zone exhibited the most significant decrease, averaging − 0.4 m s−1, mainly due to an increase in surface roughness length. Conversely, daytime WS showed the slight increase, averaging 0.2 m s−1, due to an intensified Temp gradient between the intertidal zone and the open sea. This study underscores the non-negligible influence of land cover changes within the intertidal zone on meteorological conditions in inland areas.
PubDate: 2024-03-31
DOI: 10.1007/s00703-024-01009-z
- Aerosol optical depth and water vapor variability assessed through
autocorrelation analysis-
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Abstract: Abstract Numerous studies globally have centered on atmospheric air pollution due to its profound health and climate effects. NASA’s AERONET (National Aeronautics and Space Administration - AErosol RObotic NETwork) network has been one of the world’s leading tools for accessing the physical properties of atmospheric aerosols from various sources, mainly anthropogenic ones. This study proposes a new approach to evaluate the Aerosol Optical Depth (AOD) and precipitable water vapor (PWV) seasonality and the influence of short-term perturbations, such as the presence of local and regional aerosol sources or meteorological events, based on the temporal autocorrelation function (ACF). We introduce the adimensional seasonal assessment autocorrelation function, \(\Delta _{{{\text{ACF,k}}}}\) , as a parameter to quantify the influence of the short-term perturbation, and we use its average, \(\langle \Delta _{{{\text{ACF,k}}}} \rangle\) , as a proxy for seasonality loss. The smaller \(\langle \Delta _{{{\text{ACF,k}}}} \rangle\) , the lower the influence of high-frequency perturbations on seasonality. Nine AERONET network sites in South America with different environmental characteristics were evaluated. The selected sites were São Paulo, Rio Branco, Manaus, ATTO (Amazon Tall Tower Observatory), Alta Floresta, Ji-Paraná, Cuiabá, Arica, and La Paz. The results showed that sites with less local anthropogenic aerosol sources acting as short-term perturbations had pronounced AOD seasonality and a linear relationship between the ACF functions of AOD, PWV, and the simulated direct solar radiation. As local anthropogenic sources become more prominent, the AOD ACF is attenuated and has less amplitude in seasonal oscillations. In addition, the relationship between AOD and PWV ACF becomes more attenuated. Buenos Aires has shown to be the most affected site, with \(\langle \Delta _{{{\text{ACF,AOD}}}} \rangle\) of 0.47, followed by São Paulo and La Paz. The areas in the Amazonian deforestation arc had relatively close average \(\Delta _{{{\text{ACF,AOD}}}}\) , with Alta Floresta representing the most influenced by short-term perturbations. Central Amazonian sites had the lowest \(\Delta _{{{\text{ACF,AOD}}}}\) averages, of about 0.25, which means that constant local anthropogenic sources do not dominate the AOD seasonality and that the wet deposition still plays an essential role in regulating the aerosol sources in the atmosphere. In contrast, the behavior of \(\langle \Delta _{{{\text{ACF,PWV}}}} \rangle\) in the Amazon region varies mainly due to meteorological influences, with the highest values observed in the central region, likely related to the high amount of water vapor in the atmosphere, and more pronounced seasonality near deforestation arcs and major cities. The proposed method eliminates the need for a reference site when comparing seasonalities of different time series, enabling valid comparisons across different areas without a comparative reference point. The method can be further applied to other atmospheric time series, including greenhouse gases.
PubDate: 2024-03-31
DOI: 10.1007/s00703-024-01011-5
- Observed heatwaves characteristics and variability over Saudi Arabia
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Abstract: Abstract 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.
PubDate: 2024-03-28
DOI: 10.1007/s00703-024-01010-6
- Seasonal dependence of characteristics of rain drop size distribution over
two different climatic zones of India-
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Abstract: Abstract 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*R1.94 (Z = 21.3*R2.18), Z = 118.8*R1.89 (Z = 106.4*R1.83), and Z = 388.0*R1.54 (Z = 303.1*R1.38) 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.
PubDate: 2024-03-27
DOI: 10.1007/s00703-024-01012-4
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