Theoretical and Applied Climatology最新文献

This work investigates the spatio-temporal variability of planetary boundary layer height (PBLH) characteristics by leveraging multi-decadal (1980–2019) data from India’s first high-resolution regional atmospheric reanalysis–IMDAA, in conjunction with ERA5 and MERRA-2. The spatial variability in the seasonal and annual climatological mean PBLH obtained from IMDAA agrees well with ERA5 and MERRA-2, albeit with some differences. The IMDAA and ERA5 PBLH exhibit a high correlation (> 0.6) over the entire India and also show a significant positive (negative) correlation with MERRA-2 over northwest and central (southern and eastern) Indian regions. However, IMDAA tends to overestimate ERA5 PBLH ( ~ < 500 m) and underestimate MERRA-2 PBLH ( ~ > 500 m) during all seasons. Despite these discrepancies, IMDAA successfully captures the diurnal changes in PBLH similar to ERA5 and MERRA-2. Furthermore, the evaluation of IMDAA PBLH in conjunction with other meteorological factors suggests that PBLH exhibits a negative correlation with relative humidity (RH), indicating a decrease in PBLH as RH increases. On the other hand, PBLH shows positive correlations with surface temperature and surface zonal winds. Surface sensible and latent heat flux exhibit positive and negative correlations with PBLH, respectively, over Indian sub-regions throughout all seasons. Moreover, IMDAA realistically represents the declining trend of PBLH (-1.1 to -76.2 m decade^− 1) compared to ERA5 in India during all seasons. The results from IMDAA, in concurrence with other reanalyses, demonstrate that the decreasing trend in PBLH over India is associated with rising surface temperatures and weakening surface zonal winds. This trend is also attributed to increasing latent heat flux and decreasing sensible heat flux. The changes in surface fluxes over India are attributed to the intensification of Indian monsoon rainfall in the last three decades. Moreover, El Niño appears to be an important control on PBLH variability over India during different seasons, which is realistically represented by IMDAA as in ERA5 and MERRA-2.

Landslides triggered by extreme rainfall events often cause losses of life, property damage, and environmental alterations. While past studies have assessed landslide hazards using various indices, how to select rainfall indices in rainfall-induced landslide hazard assessment is still a challenge due to the spatiotemporal asymmetry of rainfall indices. In this study, we employed three machine-learning models, namely the Random forest (RF), Support vector machine (SVM), and logistics regression models, and developed an extreme rainfall index-based model to evaluate rainfall-induced landslide hazards. To eliminate the effect of spatiotemporal asymmetry in indices, we selected six extreme rainfall indices that are highly correlated with rainfall-induced landslides and tested 63 combinations. Over the past four decades, extreme rainfall events have become more frequent and intense. Both the number and type of rainfall indices affected the assessment results of landslides in the study area. The RF model showed a better accuracy in landslide hazard assessments than did the other two models. To better predict rainfall-induced landslide hazards, an optimal model based on three extreme rainfall indices, i.e., PSSPTOT, R25mm, and Rx5day, was proposed for the study area. With climate change, the study area may encounter more intense rainfall events and experience high levels of rainfall-induced landslide hazards. Compared to the baseline, landslide hazards in the study area are projected to increase by 9.9% and 11.9% in the 2030s (2021–2050). Areas with high- and very high- levels of landslide hazards will account for more than 50% of the study area and will be mainly distributed in the central and eastern parts of the study area. This study suggested an optimal combination of extreme precipitation indicies and provided scientific information about rainfall-induced landslide hazard management under climate change.

Air temperature holds significant importance in microclimate and environmental health studies, playing a crucial role in weather regulation. There is a need to develop a reliable model capable of accurately capturing air temperature variations. In this study, we focused on the Amazon-Cerrado transitional forest, constructing a robust predictive model for hourly temperature fluctuations. This forest, situated approximately 50 km northwest of Sinop, Mato Grosso, Brazil, is a transitional area, making it important to investigate its climatic behavior and ecosystems. We estimated air temperature using machine learning techniques such as Random Forest, Gradient Boosting, Multilayer Perceptron, and Support Vector Regressor, aiming to evaluate the most effective models based on relevant metrics. Performance assessments were conducted during both dry and rainy seasons to verify their adaptability. The top-performing Random Forest model demonstrated Willmott and Spearman indexes above 0.97. The air relative humidity, solar radiation, and volumetric soil water content were identified as the most important features, evaluated with Willmott and Spearman indexes above 0.95 in a model with such dimensionality reduction. These results underscore the efficacy of machine learning techniques in accurately estimating air temperature.

Precipitation plays a vital role in various fields, including hydroclimatic modeling, climate change studies, agricultural optimization, and water resources management. Precipitation data can be obtained through observational measurements using the rain gauge approach or as Gridded precipitation products (GPP) derived from satellites or atmospheric models. GPPs provide optimized global estimates of climate data without spatial or temporal gaps, making them a valuable solution for areas with sparse or nonexistent rain gauges. However, it is essential to assess their reliability and limitations across different time scales and regions before usage. This study aims to evaluate the accuracy of two specific GPP datasets, ERA5 and MERRA-2, in comparison with two observational datasets, focusing on the Tocantins-Araguaia watershed and Pará river estuary in Brazil. The results show that both GPPs, ERA5 and MERRA-2, captured the overall precipitation regime for the analyzed period. However, discrepancies emerged, particularly at the daily and annual scales, with better agreement observed at monthly and climatology scales when compared to observational datasets. ERA5 demonstrated a higher number of acceptable stations compared to MERRA-2. Although both reanalysis products showed good agreement in climatological analysis, a more detailed evaluation revealed shortcomings in simulating precipitation during the dry season. While GPPs offer consistent time series with higher temporal and spatial resolutions, the observational precipitation data is deemed the most suitable input for hydrological-hydrodynamic modeling in the Tocantins-Araguaia watershed. Its widespread coverage, numerous rain gauges, and accurate representation of reality make it an ideal choice for hydrological modeling in the region.

In this study we defined the association of SACZ episodes with ENOS phases during the 2000–2021 summer seasons, considering November to March months, and the circulation associated patterns. Each SACZ episode was classified when OLR values were below 220 W m^− 2 and precipitable water values above 45 kg m^− 2 for more than three consecutive days. The association between ONI and the annual number of days with SACZ and the number of SACZ episodes shows linear correlation of -0.44 and − 0.34, respectively, showing the prevalence of SACZ episodes during the La Niña phase. Analysis of the entire series shows a linear annual mean increase of ~ 16 days with SACZ episodes from 2000 up to 2021. Sea level pressure anomalies between El Niño and La Niña periods present meridional dipole patterns between northern and southern South Atlantic, including the southamerican continent. OLR anomalies fields present negative (positive) values during LN (EN) periods over the northern South America extending to SACZ areas, helping to explain the higher number of SACZ episodes in LN (63 episodes) than in EN (29 episodes) periods. Both SLP and OLR anomalous patterns are associated with higher moisture convergence in the SACZ area in LN than in EN periods. Analysis of very dry (2014–2015) and very rainy (2020–2021) summer seasons over southeastern South America, illustrating El Niño and La Niña periods, respectively, shows strengthened upward movement over southwestern South America and weakened upward movement over northeastern in the former summer season and the opposite signals in the second one. These patterns were associated with typical circulation at low and high tropospheric levels: the upper level cyclonic vortex and the subtropical South Atlantic high pressure displacement to continental areas during the very dry period, 2014–2015, and the displacement of both systems, in upper and low levels, to the ocean during the very rainy period, 2020–2021.

The diurnal cycle is an important mode of climatic variability associated with different aspects of micro, meso and large scale meteorological phenomena. Thus, we performed a study of the space-time variability of the diurnal cycle of precipitation with hourly sampling and covering all regions of Brazil. The dataset was collected during the period of 13-year, from 1st January 2008 to 31th December 2020. We used data from 411 rain gauges installed in automatic weather stations. To evaluate regional aspects, we conducted a cluster analysis with different configurations (4, 5 and 6 groups). We identified a considerable heterogeneity in the hour of maximum precipitation in Brazil and three main types of diurnal cycle were observed: (i) maximum precipitation at mid- to late afternoon associated with strong local convection activity; (ii) diurnal cycle with intense precipitation during nighttime at the Amazon basin, the coast of Northeast Brazil and the Southern region; (iii) semidiurnal cycles with low precipitation rate at the Northeast Brazil.

Coffea arabica, a vital cash crop in Yunnan Province’s plateaus(YN), comprises 98% of China’s total coffee output in both cultivation area and yield. In this study, the average annual temperature (Tyear), the average temperature of the coldest month(Tcoldest), annual precipitation (Ryear) and precipitation from February to March (R2–3) were used to assess the climatic suitability of Coffea arabica cultivation in YN, to understand the possible expansion of the crop in future scenarios The simulated outputs of the regional climate model RegCM4 driven by three global climate models (HadGEM2-ES, MPI-ESM-MR and NorESM1-M) were used, and the ensemble average method was applied to obtain the ensemble model results. The suitability of Coffea arabica cultivation in YN for the base period (1981–2010) and three future periods (2021–2030, 2031–2040, 2041–2050) under three emission scenarios (RCP2.6, RCP4.5, RCP8.5) was analyzed. The results showed that the suitable planting area of small-grain coffee in YN increased significantly under the three models and the aggregate model, it expanded to the north and east, and the unsuitable planting area decreased sharply. The optimum areas of the northern part of southwestern YN and of the western, eastern, and central parts of southeastern YN were enlarged, while the suitability grade of the southern part was improved. In most parts of southeastern YN in particular, the areas that were not suitable or were less suitable for small-grain coffee cultivation became suitable or even the most suitable, and the suitability grade improvement and area expansion were considerable. Among the three models, the largest increase was obtained with the MPI-ESM-MR model, the smallest increase with the HadGEM2-ES model, and the largest decrease with the MPI-ESM-MR model from 2041 to 2050 (55.2%) under the RCP8.5. The largest increases in the most suitable area were 65.5% and 64.5%, which were obtained under the RCP8.5 with the NorESM1-M and MPI-ESM-MR models, respectively, from 2041 to 2050. Under RCP2.6 and RCP4.5, the change is similar to that of RCP8.5, but the increase is lower than that of RCP8.5.

As a global issue, water shortage has attracted much attention from the society. Artificial rain enhancement (ARE) is an effective way to exploit cloud water resources and solve water shortage, but the timing of operation is always a key problem that ARE is facing. The fluctuating properties of water vapor content (WVC) are intricately tied to the choice of operational timing, so accurately predicting the evolution of WVC holds paramount importance when determining the optimal operational timing. However, most of the proposed forecasting methods are limited to simple time series forecasting, and do not pay attention to the complex characteristics of the original data and the shortcomings of a single model prediction. Therefore, the prediction accuracy is difficult to meet the requirements of increasingly refined meteorological services. To tackle this challenge, a new hybrid prediction model, including data reconstruction strategy, benchmark model and improved multi-objective optimization algorithm, is proposed in our research by combining advanced theoretical research of artificial intelligence and data preprocessing ideas. The microwave radiometer WVC observation data at high altitude of Qilian Mountains in China is taken as a case study. By comparing 12 mainstream models, it can be concluded that: The model developed in this study achieves the highest prediction accuracy, and the mean MAPE of the three data sets at 2, 4, 6 and 8 prediction steps is 1.23%, 1.33%, 1.37% and 1.52%, respectively. This result verifies the superiority and practical value of the proposed model in predicting WVC under complex terrain conditions, and provides an excellent solution for accurate prediction of WVC.

Atmospheric moisture transport is crucial for understanding New Zealand’s climate dynamics, particularly with respect to extreme precipitation events. While the majority of previous studies have focussed on Atmospheric Rivers (ARs), this study examines the entire spectrum of water vapour transport and its link to extreme precipitation using 40 years (1981–2020) of Integrated Water Vapour Transport (IVT) data over the region. Although ARs are important drivers of extreme precipitation, they are infrequent as they account for less than 10% of total moisture transport at most coastal locations. Extreme water vapour transport (defined by the 90th percentile IVT threshold) corresponds more closely with precipitation extremes than ARs alone, even using an expanded AR detection range. Here, IVT is classified into strength categories from weak to strong. Over the study period, all but the weakest category of IVT has increased in frequency of occurrence over most of the South Island, while decreasing in northern North Island. Similarly, monthly IVT anomaly trends show a positive trend in the South Island and negative trend in the northern North Island during warmer months. Separate analysis of moisture weighted wind speeds (UV) and total column water vapour (TCWV) revealed that even though the dynamic component of IVT has decreased in many locations, the increase in TCWV across New Zealand is the driving factor underpinning the IVT trends. Correspondingly, these findings indicate the importance of analysis both dynamic and thermodynamic factors in seeking to understand hydrometeorological variation and when investigating the responses to climate change.

Urban expansion has made thermal conditions a significant concern in the city of Oran. The daily dynamics of transportation and industrial activities can result in high temperatures, which can cause stress for residents, particularly during the summer. In this study, Landsat 8 data were used to extract Land Surface Temperature (LST) for July 18, 2015, and July 15, 2020. Anthropogenic, microclimatic, and atmospheric pollutant variables and a Random Forest (RF) model were employed to predict temperatures for 2025. The results revealed that 26% of the study area is characterized by low temperatures that do not exceed 33 °C; this area consists mainly of forests and water surfaces. 25% exhibit extreme temperatures exceeding 42 °C, with the industrial zone and port of Oran being the main heat sources. Additionally, with 48% of the study area, built-up areas and bare land are characterized by mean temperatures ranging between 33.87 °C and 42.28 °C. With a mean temperature of 37.27 °C, the simulation for 2025 shows that temperatures are expected to decrease by 0.53 °C, with forests and water surfaces being the main classes. Our findings provide valuable information on the future thermal balance of cities and can assist planners in designing more effective medium and long-term policies from both environmental and tourism perspectives.