Modern-Era Retrospective Analysis For Research And Applications Version 2 Research Articles

Exploring the factors responsible for aerosol asymmetric trends over Indo-Gangetic Plain using remote sensing observations

The present study investigates the influencing factors responsible for the asymmetry in aerosol optical depth (AOD) trends using long-term datasets (2003–2019) over western and eastern Indo-Gangetic Plain (IGP) regions during the pre-monsoon season. Analysis from Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) for different aerosols illustrates that dust aerosols dominate over the western IGP (W-IGP), while sulphate and carbonaceous aerosols (black carbon (BC) and organic carbon (OC)) majorly contributed to the total AOD over the eastern IGP (E-IGP). Our study reveals a significant decline in AOD over the W-IGP, while a rising trend over E-IGP from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. A dipole pattern in AOD trends over IGP indicates the aerosol loading from combined effects of various natural and anthropogenic emissions under favourable meteorological conditions over the W-IGP and E-IGP, respectively. Furthermore, the declining AOD trend over W-IGP is mainly attributed to increased pre-monsoonal rainfall, which supports the wet deposition, increases soil moisture, thus reducing soil erodibility, and correlates strongly with meteorological factors. The rising AOD trend over the E-IGP appears to be influenced by increased anthropogenic emissions (i.e., BC, OC, and sulphate) from the industrialization of the region, decreased rainfall, and enhanced westerly-induced advection of aerosols from W-IGP. Our study indicates that the regional meteorological variables and anthropogenic sources influence changes in the AOD trends over the IGP region.

Historical footprints and future projections of global dust burden from bias-corrected CMIP6 models

Dust aerosols significantly affect the Earth’s climate, not only as a source of radiation, but also as ice nuclei, cloud condensation nuclei and thus affect CO2 exchange between the atmosphere and the ocean. However, there are large deviations in dust model simulations due to limited observations on a global scale. Based on ten initial Climate Models Intercomparison Project Phase Six (CMIP6) models, the multi-model ensemble (MME) approximately underestimates future changes in global dust mass loading (DML) by 7–21%, under four scenarios of shared socioeconomic pathways (SSPs). Therefore, this study primarily constrains the CMIP6 simulations under various emission scenarios by applying an equidistant cumulative distribution function (EDCDF) method combined with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) datasets based on observation assimilation. We find that the results (19.0–26.1 Tg) for 2000–2014 are closer to MERRA2 (20.0–24.8 Tg) than the initial simulations (4.4–37.5 Tg), with model deviation reduced by up to 75.6%. We emphasize that the DML during 2081–2100 is expected to increase significantly by 0.023 g m–2 in North Africa and the Atlantic region, while decreasing by 0.006 g m–2 in the Middle East and East Asia. In comparison with internal variability and scenario uncertainty, model uncertainty accounts for more than 70% of total uncertainty. When bias correction is applied, model uncertainty significantly decreases by 65% to 90%, resulting in a similar variance contribution to internal variability.

Evaluation of MERRA-2 and MODIS C6.1 aerosol products over Indonesia

Atmospheric aerosols play an essential role in atmospheric chemistry and climate. Amidst the issue of climate change, analysis of at-mospheric aerosols based on satellite and reanalysis is needed to estimate radiative forcing by aerosols, completing the spatial gap obtained from ground-based observation data. This research aims to evaluate the satellite and reanalysis aerosol products including Modern-Era Retrospective analy-sis for Research and Applications Version 2 (MERRA-2) and Moderate Res-olution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) over In-donesia in the recent two decades, 2001 - 2020. The long-term performance of MERRA-2 and MODIS C6.1 is evaluated using the Aerosol Robotic Net-work (AERONET)-derived AOD. In general, the results show that the cor-relation of the two data models is good. The average correlation between AOD data obtained from AERONET to MERRA-2 and MODIS C6.1 was 0.8 and 0.5 at all locations, respectively. Meanwhile, the average RMSE val-ues for MERRA-2 and MODIS C6.1 were 0.24 and 0.26 at all locations, respectively. This study successfully assessed the performance of MERRA-2 and MODIS C6.1 in Indonesia as an initial study. This initial study verifies that MERRA-2 and MODIS C6.1 can be used to complement surface data in Indonesia.

Influence of pollution control measures on the reduction of black carbon in an urban site of megacity, Tianjin, China based on ground-monitored and MERRA-2 reanalysis data

The concentration of particulate matter (PM) has been reduced significantly with the implementation of air pollution control plans in Tianjin. However, as an important component of PM that can lead to global warming and adverse health effects, the influence of pollution control measures (PCM) on black carbon (BC) has been less studied. In this study, ten years of BC concentration satellite-based reanalysis data were collected from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and their reliability was verified using ground-monitored BC data. Using the proposed Kolmogorov-Zurbenko and artificial neural network (KZ-ANN) model, the influences of meteorology and emission measures were separated. The results indicated that the overall meteorological conditions were not conducive to BC diffusion, especially in autumn and winter with low temperature, surface solar radiation, boundary layer height, and high atmospheric pressure, all of which increased the BC concentration. This study also found that although a significant reduction in BC emissions was observed in Tianjin (the total emissions of BC in 2020 dropped by 52 % compared with the level in 2013), the change in emission-influenced BC was relatively low (the concentration of emission-influenced BC in 2022 dropped by only 2.39 % compared to that in 2013). The reduction of emission-influenced BC concentration during the air pollution prevention control and action plan (APPC) was higher than the level during of the three-year action plan for winning the blue sky defense war (abbreviated as the Blue Sky Defense War). In addition, the lockdown measures during the Corona Virus Disease 2019 (COVID-19) did not have beneficial effect on the reduction of emission-influenced BC concentration. This phenomenon can be explained by the long-range transport of BC from surrounding areas, which was also proven by the results of the backward trajectory analysis. Therefore, efforts on emissions reduction in Tianjin were diminished. It is necessary to cooperate with the governments in surrounding areas to implement joint BC control measures, especially in autumn and winter.

Long-Term MERRA-2 Reanalysis Data Indicate Atmospheric Environmental Changes for Three Major Concentrating-Solar-Power-Plant Project Areas in Xinjiang, China

The characteristics, distributions, and trends of the aerosol optical depth (AOD) and dust aerosol optical depth (DAOD) of three major concentrating solar power (CSP)-plant project areas (Hami, Turpan, and Ruoqiang) in Xinjiang, China were investigated and analyzed during 1980–2022 using the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis products. The monthly variation, seasonal variation, inter-annual variation, distributions of AOD and DAOD, and proportions of dust in the aerosols in these three CSP-plant project areas were computed and analyzed. Overall, the annual mean AOD at 550 nm in the Turpan project area was the highest (0.20–0.36), while Ruoqiang had the lowest annual mean AOD at 550 nm (0.13–0.30), and the annual mean AOD at 550 nm in Hami was distributed between 0.17 and 0.33. After 2010, the change in the rate of the annual mean AOD showed an overall downward trend in Hami and Ruoqiang, indicating that the atmospheric environmental changes in both areas were more favorable for the operation of CSP plants. In the project areas of Hami, Turpan, and Ruoqiang, more than 90% of the AOD values were mainly in ranges 0.10–0.30, 0.10–0.35, and 0.05–0.30, respectively. As expected, the AOD values in spring and summer were significantly higher than those in autumn and winter in the three study areas. In spring, the dust contents (i.e., ratios of DAOD to AOD) were the highest, accounting for 64% (Hami), 67% (Turpan), and 69% (Ruoqiang) of the total aerosol contents. In all three areas, the proportions of dust in aerosols in spring have shown an increasing trend since 2000, suggesting that the negative impact of the dust on the power generation efficiency in these areas has gradually been increasing. Therefore, it is recommended that the CSP plants in Hami, Turpan, and Ruoqiang develop a strategy for cleaning heliostats, especially in spring, to reduce the impact of dust adhesion on the efficiency of the CSP plants.

JRA55 is the best reanalysis representing observed near-surface wind speeds over India

Reanalyses are often regarded as the most reliable datasets that give a comprehensive picture of meteorological parameters all over the globe. They are the first choice of dataset for researchers interested in analysing meteorological variables. Recognizing their importance, several agencies worldwide produce reanalysis datasets using different methods. This study aims to reduce the uncertainty associated with choosing among these reanalyses by identifying the best reanalysis representing the near-surface wind speeds over India. For this purpose, 10 m wind speed data from six reanalyses are used: National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (NCEP1), National Centers for Environmental Prediction-Department of Energy reanalysis (NCEP2), Japanese 55-year Reanalysis (JRA55), Indian Monsoon Data Assimilation and Analysis (IMDAA), European Centre for Medium-Range Weather Forecasts reanalysis version 5 (ERA5), and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2). The evaluation is done by comparing the spatially-averaged reanalyses against the station observations obtained from National Centers for Environmental Information (NCEI) over seven homogenous climate zones of India for the 1980–2020 period. Results demonstrate wide divergence between the reanalyses and observations in terms of monthly mean statistics, inter-annual variability (IAV), and annual mean trend. Despite the differences, JRA55 best represents the trends, IAV, and shows the closest resemblance to observations. On an annual scale, the 10 m wind speed observations over India have a weak correlation with the Nino 3.4 index; however, they have a significant negative correlation with the Dipole Mode index (DMI), implying that SST anomalies over the Indian Ocean have a considerable impact on the weather and climate patterns of the Indian subcontinent. JRA55 closely reflects these relationships with large-scale ocean-atmospheric features as well. Overall, we conclude that JRA55 can be considered a proxy for 10 m wind speed observations over India.

The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data

Abstract. The adjoint of the GEOS-Chem (Goddard Earth Observing System with Chemistry) model has been widely used to constrain the sources of atmospheric compositions. Here, we designed a new framework to facilitate emission inventory updates in the adjoint of the GEOS-Chem model. The major advantage of this new framework is good readability and extensibility, which allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) meteorological data, which allows us to perform long-term analyses with consistent meteorological data for the period 1979–present. The performances of the developed capabilities were evaluated with the following steps: (1) diagnostic outputs of carbon monoxide (CO) sources and sinks to ensure the correct reading and use of emission inventories, (2) forward simulations to compare the modeled surface and column CO concentrations among various model versions, (3) backward simulations to compare adjoint gradients of global CO concentrations to CO emissions with finite-difference gradients, and (4) observing system simulation experiments (OSSEs) to evaluate the model performance in 4D variational (4D-Var) assimilations. Finally, an example application of 4D-Var assimilation was presented to constrain anthropogenic CO emissions in 2015 by assimilating Measurement of Pollution in the Troposphere (MOPITT) CO observations. The capabilities developed in this work are important for better applications of the adjoint of the GEOS-Chem model in the future. These capabilities will be submitted to the standard GEOS-Chem adjoint code base for better development of the community of the adjoint of the GEOS-Chem model.

Impact of regional climate patterns on the biomass burning emissions and transport over Peninsular Southeast Asia, 2000-2019

The regional climate variability in peninsular Southeast Asia (PSEA) can influence springtime biomass burning (BB) aerosol emissions and associated transport patterns. To comprehend the interannual variation of regional climate and its impact on PSEA BB, a diagnostic analysis based on the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) observations from 2000 to 2019 has presented. Employing principal component, composite, and correlation analyses, this study identified four climatic factors governing the emission and transport of PSEA BB aerosols: (i) a low-level anticyclone (suppressed monsoon trough) in the Bay of Bengal, (ii) the relative strength of the anticyclone over the South China Sea, (iii) the Pacific subtropical high, and (iv) low-level westerlies from PSEA to Taiwan. Additionally, BB emissions and transport significantly correlate with the El Niño-Southern Oscillation (ENSO). In the El Niño year, increased anticyclones in the Bay of Bengal and South China Sea accompanied the stronger westerlies, which enhanced BB aerosol emission and transport. The diagnostic results of this study can contribute to a better understanding and improved model simulations of aerosol-climate interactions in South and Southeast Asian monsoon regions.

Validation of MERRA-2 AOT Modeling Data over China Using SIAVNET Measurement

The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) Aerosol Optical Thickness (AOT) dataset is a consistent and comprehensive dataset combining observations from various satellite instruments and other sources with a numerical model, supporting climate studies, atmospheric modeling, air quality monitoring, and environmental research. Due to the uneven and sparse distribution of the Aerosol Robotic Network (AERONET) in China, the validation for the MERRA-2 AOT dataset over China is inadequate. The construction of the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is helpful to compensate for MERRA-2 AOT dataset validation over China. The validation results show that the accuracy of the MERRA-2 AOT goes down along with the aerosol loading in the atmosphere increase. In general, when the AOT is less than 1.0, the slope can reach 0.712 with R2 = 0.584. The percentage of data pairs that fall within the GCOS minimum requirement is less than 60%. Research also shows that MERRA-2 has a lower simulation quality of AOT at high altitudes than at low altitudes in China. Additionally, MERRA-2’s AOT simulation quality varies by season. Simulated quality is worst in spring, improving in subsequent seasons. During the winter season, simulations are of the highest quality.

Multiparameter study for a new ground-based telescope in Egypt

ABSTRACT A multiparameter analysis was conducted to evaluate the impact of meteorological parameters, night sky brightness and seismic hazards on proposed sites for the new optical/infrared Egyptian astronomical telescope. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data set is used to obtain the following meteorological parameters: total cloud coverage fraction, precipitable water vapour, relative humidity, wind speed and direction, and air temperature. To estimate the aerosol optical depth, we used the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). Light pollution over the candidate sites was measured using the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band. In order to assess the seismic hazards for the candidate sites, the seismic input in terms of maximum acceleration and response spectra was computed using a physics-based ground motion approach to assess the seismic hazards and, consequently, the designation of a seismic-resistant structure for the proposed sites. Of the seven nominated sites, two sites are found to have the best measurements and might be considered as future sites for the new Egyptian astronomical telescope. The first site is located in the south of the Sinai peninsula, while the second site is located in the Red Sea mountains region.

Vortex preconditioning of the 2021 sudden stratospheric warming: barotropic–baroclinic instability associated with the double westerly jets

Abstract. This study explores the abrupt split of the polar vortex in the upper stratosphere prior to a recent sudden stratospheric warming event on 5 January 2021 (SSW21) and the mechanisms of vortex preconditioning by using the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) global reanalysis data. SSW21 is preceded by the highly distorted polar vortex that was initially displaced from the pole but eventually split at the onset date. Vortex splitting is most significant in the upper stratosphere (1 hPa altitude) accompanied by the anomalous growth of westward-propagating planetary waves (PWs) of zonal wavenumber (ZWN) 2 (WPW2). While previous studies have suggested the East Asian trough as a potential source for the abnormal WPW2 growth, the prominent westward-propagating nature cannot be explained satisfactorily by the upward propagation of the quasi-stationary ZWN2 fluxes in the troposphere. More importantly, WPW2 exhibits an obvious in situ excitation signature within the barotropically and baroclinically destabilized stratosphere, dominated by the easterlies descending from the stratopause containing the WPW2 critical levels. This suggests that the vortex split is attributed to the WPW2 generated in situ within the stratosphere via instability. Vortex destabilization is achieved as the double-jet structure consisting of a subtropical mesospheric core and a polar stratospheric core develops into SSW21 by encouraging the anomalous dissipation of the upward-propagating tropospheric ZWN1 PWs. This double-jet configuration is likely a favorable precursor for SSW onset, not only for the SSW21 but generally for most SSWs, through promoting the anomalous growth of unstable PWs as well as the enhancement of the tropospheric PW dissipation.

Comparisons of reanalysis and measured lower‐troposphere winds over a portion of equatorial Africa

AbstractDespite lower‐troposphere wind flow being a major control of rainfall in equatorial Africa, no studies have systematically compared winds from multiple reanalyses nor have attempted to validate the wind directions. Therefore, the objectives of this study are to assess differences in wind directions among multiple reanalysis products and compare reanalysis winds with wind measurements made at weather stations. The study region is western Uganda, part of the transition region between western equatorial Africa (WEA) and eastern equatorial Africa (EEA). Four‐times daily (i.e., 0Z, 6Z, 12Z and 18Z) 10‐m and 850‐hPa winds from 1980 to 2021 are obtained for ECMWF Reanalysis v5 (ERA5), Japan Meteorological Agency 55‐year Reanalysis (JRA55), Modern‐Era Retrospective analysis for Research and Applications Version 2 (MERRA2), NCEP‐NCAR Reanalysis 1 (R1) and NCEP/DOE Reanalysis II (R2). Wind measurements at 10 m and 850 hPa are obtained for six weather stations and two weather stations, respectively. Agreements between pairs of products and between measurements and reanalysis estimates are determined. In addition, differences between reanalyses and measurements with respect to wind vectors are calculated. Results show that the majority of reanalyses have western Uganda within the prevailing easterly flow over EEA and east of the prevailing westerly flow over WEA. Ten‐meter wind measurements also show easterly flow being prevalent throughout western Uganda. R1 is unique among the products due to a relatively large number of westerly days. However, much of the westerly flow is likely artificial, based on station data. MERRA2 has large easterly biases. JRA55 is much more accurate than the other products at reproducing the intra‐annual frequencies of wind directions. JRA55 and ERA5 are the least biased products based on the magnitudes of difference vectors. Therefore, it is recommended that JRA55 and ERA5 continue to be used in examinations of winds in western Uganda.

Comparison of the Performance of the GRASP and MERRA2 Models in Reproducing Tropospheric Aerosol Layers

Two approaches, based on Generalized Retrieval of Aerosol and Surface Properties (GRASP) and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) models, are investigated for reproducing aerosol layers in the troposphere. The GRASP algorithm is supplied with synergistic LIDAR and sunphotometer measurements to obtain aerosol extinction profiles. MERRA-2 is an atmospheric reanalysis coupling model that includes an external mixture of sea salt, dust, organic carbon, black carbon, and sulfate aerosols. A data set from Racibórz observatory, obtained with LIDAR and a sunphotometer in the 2017–2020 period, is analysed with GRASP along with the closest grid point data given by MERRA-2. The models demonstrate satisfactory agreement, yet some discrepancies were observed, indicating the presence of biases. For vertically integrated profiles, the correlation coefficient (R) between aerosol optical thickness was calculated to be 0.84, indicating a strong linear relationship. The Pearson correlation coefficient calculated between profiles for the selected altitude sectors varies between 0.428 and 0.824, indicating moderate to good agreement at all altitudes. GRASP shows denser aerosol layers in the mid-troposphere, while MERRA-2 gives higher aerosol extinctions throughout the high troposphere to low stratosphere region. Moreover, GRASP does not provide vertical variability in the extinction profile near the ground, due to a lack of data in the LIDAR’s incomplete overlap range. Lastly, the aerosol layer identification and type recognition are validated with statistical analysis of air mass backward trajectories with endpoints spatially and temporally collocated with individual identified layers. These reveal potential source regions that are located within areas known to be significant sources for the different identified aerosol types.

Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China

A refined classification of aerosol types is essential to identify and control air pollution sources. This study focused on improving the resolution and accuracy of aerosol optical depth (AOD) and further refining the classification of aerosol types in China. We validated the accuracy of the AOD acquired using the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) and Copernicus Atmosphere Monitoring Service (CAMS) by comparing it with that acquired using from the Aeronet Robotic Network (AERONET). We simulated the AOD with high spatial resolution and accuracy based on the extremely randomized trees (ERT), adaptive boosting (AdaBoost), and gradient boosting decision trees (GBDT) models and identified aerosol types based on the Angstrom Exponent (AE) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the calibrated AOD. The results showed that CAMS overestimates AOD (21.4 %) and MERRA2 underestimates AOD (−17.3 %). Among the three machine learning models, the ERT model performed best, with a determination coefficient (R2) of 0.825 and the root-mean-square error (RMSE) of 0.174. Biomass burning/urban-industrial aerosols dominated China, with the largest contributions to southern, eastern, and central China in spring and summer. Clean continental aerosols contributed the most to southwestern China in fall and winter, whereas desert dust aerosols contributed the most to northwestern and eastern China in spring.

Modeling atmosphere–land interactions at a rainforest site – a case study using Amazon Tall Tower Observatory (ATTO) measurements and reanalysis data

Abstract. Modeling the interactions between atmosphere and soil at a forest site remains a challenging task. Using tower measurements from the Amazon Tall Tower Observatory (ATTO) in the rainforest, we evaluated the performance of the land surface model JSBACH, focusing especially on processes influenced by the forest canopy. As a first step, we analyzed whether high-resolution global reanalysis data sets are suitable to be used as land surface model forcing. Namely, we used data from the fifth-generation ECMWF atmospheric reanalysis of the global climate (ERA5) and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). Comparing 5 years of ATTO measurements to near-surface reanalysis data, we found a substantial underestimation of wind speeds by about 1 m s−1. ERA5 captures monthly mean temperatures quite well but overestimates annual mean precipitation by 30 %. Contrarily, MERRA-2 overestimates monthly mean temperatures in the dry season (August–October) by more than 1 K, while mean precipitation biases are small. To test how much the choice of reanalysis data set and the reanalysis biases affect the results of the land surface model, we performed spin-up and model runs using either ERA5 or MERRA-2 and with and without a bias correction for precipitation and wind speed and compared the results. The choice of reanalysis data set results in large differences of up to 1.3 K for soil temperatures and 20 % for soil water content, which are non-negligible, especially in the first weeks after spin-up. Correcting wind speed and precipitation biases also notably changes the land surface model results – especially in the dry season. Based on these results, we constructed an optimized forcing data set using bias-corrected ERA5 data for the spin-up period and ATTO measurements for a model run of 2 years and compared the results to observations to identify model shortcomings. Generally, the shape of the soil water profile is not reproduced correctly, which might be related to a lack of vertical variability of soil properties or of the root density. The model also shows a positive soil temperature bias and overestimates the penetration depth of the diurnal cycle. To tackle this issue, potential improvements can be made by improving the processes related to the storage and vertical transport of energy. For instance, incorporating a distinct canopy layer into the model could be a viable solution.

Formation of an unprecedented yellow snow episode in Xinjiang on December 1, 2018

On 1 December 2018, a heavy yellow snow fell in Urumqi (87°37′E, 43°47′N) — the largest city of northwest China's Xinjiang province, which was the first case that the yellow snow has been observed in winter. The air parcel trajectories obtained from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the dust surface mass concentration from Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) were adopted to identify the potential sources and transport paths of pollutants responsible for this yellow snow episode. The meteorological situation and the European Center for Medium-Range Weather Forecasts (ECMWF) forecast products have been utilized to analyze the supportive meteorological conditions. The results showed that the heavy snow in Urumqi was contaminated by the yellow dust originated in Karamay of Xinjiang province. The strong surface winds in Karamay lifted large amounts of dust into the atmosphere. Then the airborne dusts were transported to Urumqi rapidly by strong low-level winds, where precipitation in connection with the upper trough and the cold front lead to the yellow snow episode. This study can provide important scientific significance for predicting this kind of event (yellow snow).

Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002–2019) over Pakistan using ARIMAX model and cross-wavelet analysis

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002–2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2–8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

Understanding day–night differences in dust aerosols over the dust belt of North Africa, the Middle East, and Asia

Abstract. Utilizing the well-calibrated, high-spectral-resolution equal-quality performance of daytime and nighttime (09:30 and 21:30 local solar Equator-crossing time (local solar ECT)) products of the Infrared Atmospheric Sounder Interferometer (IASI) from the Laboratoire de Météorologie Dynamique (LMD), this study investigates the day–night differences in dust aerosols over the global dust belt of North Africa, the Middle East, and Asia. Both daytime dust optical depth (DOD) and nighttime DOD at 10 µm show high consistency with solar and lunar observations of coarse-mode aerosol optical depth (CAOD) from AErosol RObotic NETwork (AERONET) sites across the dust belt, with correlation coefficients of 0.8–0.9 for most sites. Both IASI DOD and dust layer height show a significant (95 % confidence level) day–night difference over the major dust sources within the dust belt. Daytime DOD over the central to northern Sahara, the central to eastern Arabian Peninsula, and the Taklamakan Desert is significantly higher than that of nighttime but lower than nighttime over the southern Sahel to the Guinea Coast and the western to central Indian subcontinent in the annual mean. The magnitude of the day–night differences in DOD is larger and more evident in boreal winter and spring than in other seasons. The positive day–night differences in DOD (i.e., higher daytime values than nighttime) over the central Sahara, the Middle East, and Asia are likely associated with greater dust emissions driven by higher dust uplift potential (DUP) and stronger wind speeds during daytime. Dust layer heights demonstrate negative day–night differences over dust source regions in the central Sahara, central Arabian Peninsula, and Taklamakan Desert and positive height differences in the southern Sahel to the Guinea Coast, southern parts of the Arabian Peninsula, and large parts of the Indian subcontinent. The higher dust layer height over the Guinea Coast and the Indian subcontinent during the daytime is associated with a deeper planetary boundary layer height and greater convective instability during daytime than nighttime, which promotes vertical transport and mixing of dust aerosols. The corresponding lower daytime DOD over the Sahel and the Indian subcontinent indicates a possible dilution of dust aerosols when they are transported to higher altitudes by convection where they are more susceptible to horizontal transport. Ground-based observations of dust show surface PM10 concentration and CAOD exhibit a spatially varying diurnal cycle across the dust belt. CAOD and PM10 concentrations generally peak in late morning and from late afternoon to midnight in the Sahel and in early afternoon and around early morning in the Middle East, the timings of which are largely consistent with the day–night differences in IASI DOD. It is also found that DOD from reanalysis products (e.g., Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and ECMWF Atmospheric Composition Reanalysis 4 (EAC4)) failed to capture the day–night differences in IASI DOD in large parts of the dust belt except in small dust source hotspots over North Africa.

Electronic Atlas of Climatic Changes in the Western Russian Arctic in 1950–2021 as Geoinformatic Support of Railway Development

The Arctic zone of the Russian Federation is one of the most intensively developing regions of the country. Amongst the major domains of economic and industrial growth and improvement is transport infrastructure and particularly the railway network. This area is being exposed to negative factors of rapid climate change that can significantly affect and compromise this activity. Thus, it is vital to take them into account during design, construction, and operation of the railway infrastructure facilities. This work details the production of a digital atlas comprising the 1950–2021 dynamics of the main hydrometeorological parameters: air and soil temperature, precipitation, wind speed, air and soil humidity, and snow cover thickness. The maps are based on climatic data derived from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis. In total there are 459, which are arranged into 7 chapters. The atlas geographically covers the western part of the Russian Arctic encompassing the regions of quite intensive transport development, which includes the construction of the Northern Latitudinal Railway. Original algorithms of geospatial data processing and their further representation as well as the maps compiled in GIS environment are discussed. Comprehensive analysis of climatic changes in the region of the Russian Arctic including detailed quantitative evaluation over 40 years is given. In the Discussion, we focus on those changes of the regional climate which, from our point of view, are the most significant for consideration by railway operators. The obtained results contribute to framing the theoretical basis of design, development, and sustainable operation of the railway infrastructure in the Arctic and facilitate the decision-making process. This is the first experience of building a specialized climatic cartographic product for the needs of the Russian railways, and to our knowledge the first atlas such as that in the world. In the future, the amassed experience may be transferred to other regions of the Russian Federation as well as similar regions in Canada, Sweden and Highland China that are also subject to significant climate change.

Model-based insights into aerosol perturbation on pristine continental convective precipitation

Abstract. The Tibetan Plateau (TP) is of great importance for weather and climate due to its role as a heat and water resource. Relatively clean aerosol conditions over the Plateau make the study on the aerosol–cloud–precipitation interactions in this pristine continental region distinctive. In order to investigate the impacts of aerosols on small-scale convection processes over the TP, a convective event with precipitation observed on 24 July 2014 in Naqu was selected to explore the influence of aerosols on the onset and intensity of precipitation. We use the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis to derive the cloud condensation nuclei (CCN) number concentration, which can be regarded as the real-time background. These values are adopted to initialize the regional Weather Research Forecast (WRF) 4.0 meteorological model and to simulate the onset of convective events and the formation of precipitation. Four sets of experiments, named clean (1/10 CCN), control (default setting), Tibetan Plateau (CCN calculated from MERRA-2 reanalysis), and polluted (10 times CCN), were adopted for our simulations. A detailed analysis of the microphysical processes shows that the conversion of cloud water into rain is enhanced by small increases in aerosol concentration, while it is suppressed by larger increases in concentration. However, the transformation of cloud water to graupel and the development of convective clouds are favored under a polluted situation. As a result, the onset of the precipitation is delayed and cold-rain intensity increases.