Key Meteorological Variables Research Articles

Deep Learning‐Based Precipitation Simulation for Tropical Cyclones, Mesoscale Convective Systems, and Atmospheric Rivers in East Asia

AbstractDifferent types of weather events, including tropical cyclones (TCs), mesoscale convective systems (MCSs), and atmospheric rivers (ARs), significantly impact precipitation patterns in East Asia. This study pioneers the application of deep learning (DL) methods, including convolutional neural network, U‐Net, and Attention U‐Net models, to simulate precipitation associated with these weather events. The spatial permutation method is also used to identify key meteorological variables for accurately generating precipitation in DL models. The DL models trained on all timeslots consistently surpass the performance of state‐of‐the‐art numerical simulations, although their efficacy slightly diminishes during extreme weather events. This outperformance is attributed to the appropriate emphasis on key variables that capture precipitation processes, such as low‐level moisture and mid‐level pressure fields. However, new DL models trained separately for TCs, MCSs, and ARs using clipped precipitation as the output does not exceed the performance of the previous DL models. Among all input features, moisture variables contribute the most to precipitation at low intensity, while the importance of other variables increases for more intense precipitation, although some discrepancies vary across models and event types. The spatial results further reveal the detailed locations of variables that are essential for accurately simulating precipitation related to weather events, such as areas of high specific humidity and strong winds. DL models could also acquire useful information from region remote to the events to improve the simulation. Overall, DL models serve as promising tools for simulating and enhancing our understanding of precipitation patterns associated with various weather events in East Asia.

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

BackgroundThe Weather Research and Forecasting (WRF) Model is an exceptional software for mesoscale climate modeling. It is extensively used to simulate key meteorological variables, including temperature, rainfall, and wind.MethodsThis study thoroughly examined the effectiveness of the WRF model in generating precise wind data for assessing the potential of wind power in Burundi. A meticulous evaluation of various combinations of model physics parameterization schemes was conducted to ensure accuracy.By comparing the simulated data with measurements from four meteorological stations and utilizing statistical metrics such as root-mean-square error (RMSE) and bias, the accuracy of the WRF model was determined.Results The findings of the study uncovered that utilizing WRF Single-Moment 3-Class (WSM3) for microphysics, Grell-Devenyi ensemble for cumulus physics, and Yonsei University for planetary boundary layer yields highly accurate wind data results for Burundi.Furthermore, the WRF model was utilized to create detailed seasonal and annual mean wind maps with a high resolution. ConclusionThese maps demonstrated that the western part of Burundi experiences higher wind speeds (ranging from 4 to 9.7 m/s) during the dry seasons revealing the potential for wind energy harvesting in the different areas of Burundi.

ROTATE: A Coordinate System for Analyzing Atmospheric Rivers

AbstractThis study introduces the ROTated Atmospheric river coordinaTE (ROTATE) system — a novel storm‐centric coordinate system designed specifically for analyzing atmospheric rivers (ARs). It effectively preserves key AR signals in the time mean that may be lost or obscured in simple averaging due to diverse AR orientations and shapes. By applying the ROTATE system, we compared climatological characteristics for northern hemisphere ARs. Composites of four key meteorological variables, integrated vapor transport, integrated water vapor, precipitation, and windspeed, indicate distinct and clearer patterns of ARs compared to the conventional non‐rotated AR centroid‐based compositing approach. Moreover, the ROTATE system improves precipitation rates, particularly around the AR center and its head and tail regions, providing more distinct delineations of the precipitation signals between landfalling and oceanic ARs. Overall, the ROTATE system has the potential to serve as a valuable tool for better comparing and understanding the characteristics, processes, and impacts of ARs across different regions.

Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data

This study investigates monthly streamflow modeling at Kale and Durucasu stations in the Black Sea Region of Turkey using remote sensing data. The analysis incorporates key meteorological variables, including air temperature, relative humidity, soil wetness, wind speed, and precipitation. The study also investigates the accuracy of multivariate adaptive regression (MARS) with Kmeans clustering (MARS-Kmeans) by comparing it with single MARS, M5 model tree (M5Tree), random forest regression (RF), multilayer perceptron neural network (MLP). In the first modeling stage, principal component regression is applied to diverse input combinations, both with and without lagged streamflow (Q), resulting in twenty-three and twenty input combinations, respectively. Results demonstrate the critical role of including lagged Q for improved model accuracy, as models without lagged Q exhibit significant performance degradation. The second stage involves a comparative analysis of the MARS-Kmeans model with other machine-learning models, utilizing the best-input combination. MARS-Kmeans, incorporating three clusters, consistently outperforms other models, showcasing superior accuracy in predicting monthly streamflow.

Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.

A Large Ensemble Global Dataset for Climate Impact Assessments

We present a self-consistent, large ensemble, high-resolution global dataset of long‐term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) − bias-correction (BC) method to climate projections from the MIT Integrated Global System Model (IGSM). Four emission scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2 °C and Paris 1.5 °C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. We demonstrate the dataset’s ability to represent climate-change responses across various regions of the globe. This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

ENSO‐Related Fire Weather Changes in Southeast and Equatorial Asia: A Quantitative Evaluation Using Fire Weather Index

AbstractInterannual variability of tropical fire activity has been linked to the El Niño‐Southern Oscillation (ENSO). Here we analyze changes in fire and simultaneous fire weather related to ENSO over Southeast (SEAS) and Equatorial Asia (EQAS) and quantitively evaluate the contribution of key meteorological variables to the fire weather variations for the past decades. During the fire seasons, large increases in fires emerge in El Niño years, which greatly coincides with the more fire‐susceptible weather conditions compared to La Niña years. Moreover, the fires exhibit a more significant correlation with the ENSO index in El Niño years, mainly due to the influence of asymmetry response of the fire weather to ENSO. Composite analysis shows that it is the positive anomalous low‐level geopotential height and less water vapor transportation over SEAS (March–May) and EQAS (August–October) during El Niño that forms the fire‐favoring weather. By conducting tests in calculating fire weather index (FWI) under different ideal conditions, we identified the daily precipitation as the dominant driver of FWI variation, with the contribution reaching 65.5%–77.5% and 60.9%–90.5% for SEAS and EQAS respectively, while decreased relative humidity induces nearly a quarter of increases in FWI in El Niño years. Changes in temperature and wind speed, however, rarely perturb the fire weather for both SEAS and EQAS regions. The quantitative evaluations of individual meteorological variables in shaping fire weather as well as their asymmetrical response in ENSO years could support fire management during fire seasons in Tropical Asia.

A seamless approach for evaluating climate models across spatial scales

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.

An independent framework-based evapotranspiration model (IFEM) for dual-source: From field to regional scale

The triangle/trapezoid framework model (TFM) is a widely used approach for mapping regional evapotranspiration (ET) based on thermal infrared remote sensing imagery. However, the homogeneity assumption for meteorological forcing and surface properties involved in TFM contradicts the characteristics of heterogeneous regions, leading to high uncertainty at regional scale, especially under energy-limited or strong advection conditions. Therefore, this study examined the linear relationship between soil moisture availability (ΛSM), evapotranspiration efficiency (ΛET) and radiation surface temperature (Trad), and developed an Independent Framework-based dual-source ET Model (IFEM) for heterogeneous regions. By fixing two key meteorological forcing variables, air temperature (Ta) and vapor pressure deficit (VPD), and three surface characteristic parameters, aerodynamic resistance (ra), albedo (α) and the ratio of soil heat flux to net radiation (Γ), the IFEM constructs a uniform fractional vegetation cover (fc)- Trad space. In this uniform fc-Trad space, isolines of ΛSM and ΛET are aligned with those of Trad, and an independent trapezoid framework is constructed for each pixel of RS images. It avoids the systematic bias caused by the distorted trapezoid space involved in traditional TFMs. The proposed model was applied in a hydrologically semi-enclosed irrigation area, the Hetao Irrigation District, with Landsat and MODIS datasets from 2016 to 2020 to evaluate its performance at the field and region scales. Firstly, the IFEM was capable of retrieving latent heat with a mean absolute error (MAE) of 34.43 W m−2 and a mean absolute percentage error (MAPE) of 8.68%, compared with the measurements from flux towers. Secondly, the IFEM reasonably separated the soil (MAPE = 21.33%) and canopy (MAPE = 14.33%) components from ET, compared with the soil evaporation observed by micro-lysimeters and the plant transpiration calculated by sap velocity upscaling technology. Thirdly, the IFEM showed high accuracy in estimating annual total ET with a percentage error range within ±3.0%, compared against the regional water balance calculations. Overall, the IFEM demonstrated high robustness and accuracy for field-scale and regional-scale applications, validating its potential as a reliable method for mapping regional ET in heterogeneous regions.

Local and global sensitivity analysis and its contributing factors in reference crop evapotranspiration

Abstract Sensitivity analysis (SA) intends to identify the key meteorological variables that affect the performance of reference crop evapotranspiration (ET0) models. It is of importance in assessing the variability of meteorological variables and ET0, especially in the face of increasing climate uncertainties. However, the surging of inconsistencies resulting from global changes in meteorological conditions due to climate change have impacted the ET0 model estimation in different regions, with detrimental effects on water resources and crop production. Therefore, efficient SA is necessary to evaluate the impact of changes in meteorological variables that influence ET0 model estimation. This mini review analyses the various SA methods applied in the field of ET0, based on a comprehensive and comparative analysis of existing SA methods from all around the world. The study discusses the advantages and disadvantages of each SA method, as well as the factors affecting the SA of ET0. The study also provides future prospects that may contribute to more solid and powerful analysis for ET0 model estimations and conclusions.

An empirical analysis of Delhi's air quality throughout different COVID-19 pandemic waves

Delhi was one of India's COVID-19 hotspots, with significant death rates during the year 2021. This study looked at the link between COVID-19 cases in Delhi, and key meteorological variables. The study found that COVID-19 cases during the second wave (P2-March- May 2021) were much higher than during the first wave (P1-Jan-Feb 2021) in Delhi. During P1 (Jan-Feb 2021) the mean PM2.5, PM10, NO2 and CO concentrations were greater than that of P2 (March-May 2021) while the reverse happened for SO2 and O3. Spearman correlation test indicated that COVID-19 cases maintained a significant positive correlation with the high temperature of P2 (March-May 2021) and high humidity of P1 (Jan-Feb 2021) in line with the accepted notion that COVID-19 transmitted favourably in hot and humid climates. The Multilayer perceptron (MLP) model indicated that COVID-19 spread was supported by air pollutants and climate variables like PM2.5, NO2, RH, and WS in P1(Jan-Feb 2021) and PM2.5 and O3 in P2 (March-May 2021). Owing to chemical coupling, across all six monitoring stations, O3 maintained an inverse relationship with NO2 throughout the COVID-19 phases in Delhi. The city dwellers had health risks also due to PM pollution at varying degrees, indicated by high hazard quotients (HQs), requiring lowering of air pollution concentrations on an urgent basis.

Elucidating the prediction capability of neural network model for estimation of crop water stress index of rice

ABSTRACT The crop water stress index is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN) are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, and canopy temperature). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. For the lower CWSI baseline of rice, a linear relationship between air and canopy temperature difference (Tc-Ta) and, air vapour pressure deficit (AVPD) was developed, whereas air temperature plus 3°C was taken for the upper CWSI baselines. The performance of SOM and FF-BP-ANN were compared by computing Nash–Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R2). It is found that FF-BP-ANN (R2 = 0.97, NSE = 0.92, d = 0.95, RMSE = 0.006) performs better than SOM (R2 = 0.88, NSE = 0.87, d = 0.9, RMSE = 0.075).

Region-dependent meteorological conditions for the winter cold hazards with and without precipitation in China

Cold hazard is one of the major meteorological disasters in winter. However, the meteorological conditions for the cold hazard events vary significantly with both the feature of the event and the region of occurrence. This study divides winter cold hazard events in China into three categories based on the daily gridded dataset of cold hazards from November 1980 to March 2020: events without wintry precipitation (hazardous low temperature, abrupt temperature drop, and/or freezing), with wintry precipitation only (hazardous sleet and/or snowstorm), and with both. The region-dependent multivariate meteorological conditions for each category of cold hazards are investigated using ERA5 reanalysis data. Results show that the surface air temperature (T2m) and its anomaly (T2m_anom) are lower than climatology during cold hazards. But the difference in T2m among provinces exceeds 30 °C, and even for the same province, the difference among different categories of cold hazards exceeds 10 °C. The region- and category-dependent differences of T2m_anom and daily temperature drop (∆T24) are also large, about 5 °C and 2 °C d−1, respectively. The Multivariate Empirical Orthogonal Function analysis has further been applied to not only the abovementioned temperature-related variables but also the precipitation-related variables (i.e., daily accumulated total precipitation, daily accumulated snowfall, and daily mean snow depth) in the middle and lower Yangtze River region, which reveals the event-mean state and spatial–temporal coupling evolution during the progression of the event for the selected key meteorological variables. The meteorological conditions for cold hazards put forward by this study could provide region-dependent and category-dependent reference for the prediction and warning of cold hazards.

Performances of climatic indicators from seasonal forecasts for ecosystem management: The case of Central Europe and the Mediterranean

Climate change is affecting the interannual variability and the seasonal cycle for key meteorological variables, as well as the frequency, intensity, duration and timing of out-of-the-norm and extreme events. In such a context, seasonal forecasts (i.e., climate predictions from a few weeks to several months ahead) are compelling instruments able to anticipate upcoming climate risks and guide tactical decisions. In this study, we assess the performance of seasonal forecasts for the summer period across Central Europe and the Mediterranean region in predicting some key indicators serving agriculture and forestry sectors, i.e., Potential EvapoTranspiration (PET), Potential Soil Moisture Deficit (PSMD), and Fire Weather Index (FWI). We exploit authoritative climate data from Copernicus Climate Data Store, i.e., ERA5 reanalyses and hindcasts from CMCC SPSv3 and ECMWF SEAS5 seasonal prediction systems (SPSs), to evaluate, using both deterministic and probabilistic evaluation scores, differences in performance across the two SPSs, two start dates (March and May) and four correction techniques applied to overcome modelling bias, namely bias correction (BC), calibration (CAL), quantile mapping (QM) and detrending (DET). Results show that seasonal predictions of PET perform better in Western and Eastern Europe and some areas of North Africa. PSMD predictions follow a similar spatial pattern as PET, except that for some areas in Central (Eastern) Europe in which the performance increases (decreases). FWI predictions reveal better results in some areas of the Iberian Peninsula, North-Western Africa, Balkan Peninsula, and Ukraine. Results also suggest that QM might be the most suitable technique for bias correction. Furthermore, the start date of the forecast might imply varying correlation significance, with the start date closest to the forecasted period not always being the best. Overall, our study suggests the potential usefulness of seasonal forecasts for decision making under different geographical and environmental contexts, considering sensitivity inherent to different processing chains.

Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

AbstractA multivariate bias correction based on N‐dimensional probability density function transform (MBCn) technique is applied to four different high‐resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near‐surface air temperature, near‐surface maximum air temperature, near‐surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias‐correction on the historical (1980–2005) period, the inter‐variable relationships, and the measures of spatio‐temporal consistency are investigated. The focus is on the discrepancies between the original and the bias‐corrected results over five agro‐ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara.

FORECASTING AND ON THE INFLUENCE OF CLIMATIC FACTORS ON RISING DENGUE INCIDENCE IN BAGUIO CITY, PHILIPPINES

Dengue fever or dengue has been a concern for individuals living in Baguio City, Philippines. Every year, incidence counts rise during rainy seasons experienced from June to October. Several researches suggest that meteorological factors have great influence on the life, growth, and reproduction of dengue-carrying mosquitoes, resulting in higher dengue incidence in the area. With the continuing rise of dengue incidence in Baguio City, we aim to forecast dengue incidence in the area for the year 2019, starting from January until the end of the outbreak period in the area. Here, we use the projections package of R as it involves the serial interval distribution and 𝑅𝑡 value of dengue incidence. We also aim to use multiple linear regression analysis to determine if meteorological factors have significant effects in the rise of dengue incidence in the city. With the inclusion of time-varying reproduction number and serial interval distribution of dengue, we projected that dengue incidence may reach up to 101 cases by June 16, 2021, and without further actions, cases may rise up to 529 cases by August 29, 2021. Based on the average two-year period, such increase is attributed to relative humidity and average temperature as these are the most significant factors associated with dengue incidence based on the MLR analysis. The highest and mean maximum temperatures remain as key meteorological variables that influence dengue incidence in the city. As early as possible, local officials are recommended to uphold proper safety and health procedures in preventing the spread of dengue in Baguio City.

Investigation on atmospheric dispersion of limestone particulates at the sea port

In this study, low-level dispersion of particulates discharged during handling of limestone at the harbour is modelled using AERMOD software. The influences of key meteorological and operational variables on distribution of particulates are also studied. The key variables such as wind velocity, limestone water level, relative humidity, ambient temperature, and limestone drop elevation are considered for this study. In low-level emissions (0 m to 8 m from sea level) atmospheric temperature and relative humidity have a weak negative correlation with particulate concentration. Wind velocity, limestone moisture content and limestone drop elevation have a considerable effect on the distribution of particulates. At wind velocities (<3m/s) the limestone particles continue hovering in the atmosphere for a longer period and reach longer distance from the source of the particulate pollution. The concentration of particulates decreases with rise in limestone moister level. For low-level emissions, limestone moisture level and limestone drop elevation are important operational variables in dispersion of emissions. The model results are validated with measured values of the particulate concentrations and it is observed that predicted levels of PM10 and PM2.5 are in concurrence to that of measured levels of PM10 and PM2.5 with mean error of 21 % and 19.37 % respectively.

Variations in airborne pollen and spores in urban Guangzhou and their relationships with meteorological variables

Airborne pollen causes various types of allergies in humans, and the extent of allergic infection is related to the presence of different types of sporo–pollen and existing meteorological conditions in a certain area. Therefore, an aeropalynological study of 72 airborne samples with a hydrofluoric acid (HF) treatment was conducted in the Haizhu district of Guangzhou, China, in 2016, to identify the temporal variations in airborne sporo–pollen and the relationship between airborne sporo–pollen concentrations and different meteorological variables in Guangzhou, China. Forty-five types of airborne pollen, seven types of airborne spores, and some undetermined sporo–pollen taxa were identified with two separate plant habitats occurring during this period (from January to December 2016): arboreal pollen (tree-based) and non-arboreal pollen (herb, shrub, aquatic, liane, etc.). Furthermore, the daily records of four key meteorological variables (temperature, precipitation, relative humidity, and wind speed) were acquired to distinguish the pollen seasons and correlated with Spearman's rho test to establish a pollen-weather data book with the seasonal variations. The two leading seasons were identified based on pollen abundance: spring and autumn. Among them, the primary dominant sporo–pollen families during the spring season were Poaceae, Pinaceae, Euphorbiaceae, Moraceae, Microlepia sp., and Polypodiaceae. Conversely, Artemisia sp., Asteraceae, Cyperaceae, Poaceae, Alnus sp., Corylus sp., Myrtaceae, and Rosaceae were the dominant pollen species during autumn. However, few pollen grains were identified in January, May–July, and December. The statistical analysis revealed that temperature had both positive and negative correlations with sporo–pollen concentrations. However, precipitation and relative humidity had a strong impact on the sporo–pollen dispersion and exhibited a negative correlation with the sporo–pollen concentrations. The wind speed had a positive but strong correlation with the sporo–pollen concentration during the study period. Some inconsistent results were found due to environmental variations, vegetation type, and climate change around the study area. This study will facilitate the identification of pollen seasons to prevent the occurrence of pollen-related allergies in the Guangzhou city area.

Continuous monitoring of surface water vapour isotopic compositions at Neumayer Station III, East Antarctica

Abstract. In this study, the first fully continuous monitoring of water vapour isotopic composition at Neumayer Station III, Antarctica, during the 2-year period from February 2017 to January 2019 is presented. Seasonal and synoptic-scale variations in both stable water isotopes H218O and HDO are reported, and their links to variations in key meteorological variables are analysed. In addition, the diurnal cycle of isotope variations during the summer months (December and January 2017/18 and 2018/19) has been examined. Changes in local temperature and specific humidity are the main drivers for the variability in δ18O and δD in vapour at Neumayer Station III, on both seasonal and shorter timescales. In contrast to the measured δ18O and δD variations, no seasonal cycle in the Deuterium excess signal (d) in vapour is detected. However, a rather high uncertainty in measured d values especially in austral winter limits the confidence of this finding. Overall, the d signal shows a stronger inverse correlation with specific humidity than with temperature, and this inverse correlation between d and specific humidity is stronger for the cloudy-sky conditions than for clear-sky conditions during summertime. Back-trajectory simulations performed with the FLEXPART model show that seasonal and synoptic variations in δ18O and δD in vapour coincide with changes in the main sources of water vapour transported to Neumayer Station III. In general, moisture transport pathways from the east lead to higher temperatures and more enriched δ18O values in vapour, while weather situations with southerly winds lead to lower temperatures and more depleted δ18O values. However, on several occasions, δ18O variations linked to wind direction changes were observed, which were not accompanied by a corresponding temperature change. Comparing isotopic compositions of water vapour at Neumayer Station III and snow samples taken in the vicinity of the station reveals almost identical slopes, both for the δ18O–δD relation and for the temperature–δ18O relation.

Impact of climate change on "evaporation paradox" in province of Jiangsu in southeastern China.

Contrary to the common expectation that the reference evapotranspiration (ETo), which is an indicator of the atmospheric evaporation capability, increases in warming climate, the decline of the ETo has been reported worldwide, and this contradiction between the expected increasing ETo and the observed decreasing one is now termed the “evaporation paradox”. Based on the updated meteorological data (1960–2019), we separately detected the spatiotemporal characteristics and the causes of the “evaporation paradox” in three subregions, namely Huaibei, Jianghuai, and Sunan, and throughout the entire province of Jiangsu in southeastern China. Different from the reported continuous unidirectional variations in the ETo, in the province of Jiangsu, it generally showed a decreasing trend before 1990 but followed an increasing trend from 1990 to 2019, which led to the different characteristics of the “evaporation paradox” in the periods from 1960 to 1989, from 1990 to 2019, and from 1960 to 2019. In the first 30 years, the reduction of the wind speed (WS) was the main reason for the decreased ETo, which consequently gave rise to the “evaporation paradox” in spring and winter in the Huaibei region and only in winter in the two other subregions and throughout the entire province. We noticed that the “evaporation paradox” in spring in the Sunan region was expressed by the decreased daily mean air temperature (Tmean) and the increased ETo which was chiefly induced by the decreased relative humidity (RH) and the increased vapor pressure deficit (VPD). After 1990, the decreased WS also dominated the decreased ETo and resulted in the “evaporation paradox” in winter in the Jianghuai region. Furthermore, the decreased sunshine hour (SH) was the main factor influencing the decreased ETo, thereby inducing the “evaporation paradox” in summer and autumn in the Jianghuai region and only in autumn in the Huaibei region and throughout the whole province from 1990 to 2019. In the whole study period from 1960 to 2019, the decreased SH was also found to be responsible for the decreased ETo and for the “evaporation paradox” in summer in all the subregions and throughout the whole province. However, regarding the “evaporation paradox” in autumn, in winter, and in the entire year in the Huaibei region and throughout the whole province, the observed decreased ETo was largely due to the reduced WS from 1960 to 2019. In summary, in addition to the air temperature, the ETo has shifted due to the other meteorological variables (especially the WS, the SH, and the VPD) and shaped the unique spatiotemporal characteristics of the “evaporation paradox” in the province of Jiangsu in southeastern China. Moreover, future studies and simulations addressing the regional climate change and hydrological cycles should take account of the changeable key meteorological variables in different subregions and seasons of the province of Jiangsu.