Theoretical and Applied Climatology最新文献

Analysis of the daily rainfall records from 43 synoptic stations of the Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) from 1979 to 2019 reveals that the wettest station in the Philippines is in Hinatuan City, Surigao del Sur, in eastern Mindanao Island in terms of the mean annual total rainfall. Despite being located at a low elevation (∼ 3 m above sea level), the mean annual total rainfall in this station is about 4554 mm, which is approximately 700 mm more than the mean annual total rainfall in Baguio City station, the station with the highest elevation (∼ 1500 m above sea level) in the country. Further analysis of the statistical characteristics of rainfall and comparison with other stations in terms of intensity, frequency, duration (i.e., short (1 − 2 days), medium (3 − 7 days), long (8 − 14 days), and very long (> 14 days) events), and 95th percentile extremes show that this station ranks first in terms of the frequency of wet months (200–500 mm month^− 1) and heavy rainfall months (> 500 mm month^− 1), mean monthly rainfall amounts from January to April, and the mean rainfall amount in the short duration category. The contributions of multiscale factors such as Tropical Cyclones (TCs), Low Pressure Systems (LPSs), and the Madden-Julian Oscillation (MJO) to the rainfall extremes over Hinatuan City station are also quantified. The results show that TCs, LPSs, and MJO contribute about 0–5%, 0–38%, 3–38% to the monthly extremes over Hinatuan City station, respectively. Cases when TCs or LPSs are located within 1100 km radius centered at Hinatuan City station while MJO is active were also found and their contributions to the monthly extremes are 0–4% and 0–12%, respectively. The largest portion of the extremes are associated with other unaccounted factors, which contribute about 49–71%. The results of this study may serve as a basis for future characterization of the spatial variation of rainfall including the variations in extremes and their potential causes over the Philippines

Freezing rain poses significant challenges to power systems, particularly in terms of safety and operational efficiency. This study introduces the latest NASA Earth Exchange Global Daily Downscaled Projections dataset, NEX-GDDP-CMIP6, which enhances the spatial resolution compared to conventional CMIP6 models, thereby offering new potentials for high-resolution climate modeling. Using this advanced dataset, we conducted a comparative analysis to assess its performance in simulating key meteorological factors relevant to freezing rain in Guizhou, China—a region known for its complex terrain and susceptibility to winter icing events. Our analysis indicates that NEX-GDDP-CMIP6 more accurately simulates surface air temperature (tas) and relative humidity (hurs) over complex terrains compared to generic CMIP6 models, especially the best multi-model ensemble (BMME). The BMME projections show a notable decrease in freezing rain days in January in Guizhou, from an average of 12 to 4 by the century’s end (2071–2100), alongside a substantial decrease in the affected area. Additionally, the study highlights that the position of the Yunnan-Guizhou quasi-stationary front (YGQSF) remains unchanged under different emission scenarios. Only minor changes in intensity are observed in small areas, with the equivalent potential temperature gradient decreasing from 0.2 K·km⁻¹ to 0.1 K·km⁻¹. Concurrently, tas and tasmin exhibit a uniform warming trend. This study projects a shrinkage of the winter ice-prone zone in Guizhou, associated with escalated emission levels, with the remaining impacted region retreating to the province’s western portion by the end of this century. Overall, our research underscores the importance of high-resolution datasets like NEX-GDDP-CMIP6 for accurate climate projections and informs regional adaptation strategies, as its projection aligns with recent trends of decreased icing events.

Wildfires are catastrophes of natural origin or initiated by human activities with high disruptive potential. "Portugal, located in western Iberia, has recently experienced several large fire events, including megafires, due to a combination of factors such as orography, vegetation, climate, and socio-demographic conditions that contribute to fuel accumulation.". One approach to studying fire danger is to use fire weather indices that are commonly used to quantify meteorological conditions that can lead to fire ignition and spread. This study aims to provide high-resolution (~ 6 km) future projections of the Fire Weather Index (FWI) for Portugal using the Weather Research and Forecasting (WRF) model, forced by the Max Planck Institute (MPI) model from the CMIP6 suite, under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP58.5) for the present period (1995–2014) and two future periods (2046–2065 and 2081–2100). The results show good agreement between FWI and its subcomponents from the WRF and reanalysis. The modelled FWI reproduced the climatological distribution of fire danger Projections indicate an increase in days with very high to extreme fire danger (FWI > 38) across all scenarios and time frames, with the southern and northeastern regions experiencing the most significant changes. The southern and northeastern parts of the territory experienced the largest changes, indicating significant changes between the scenarios and regions. This study suggests that FWI and its subcomponents should be investigated further. Our results highlight the importance of creating new adaptation measures, especially in the areas most at risk, prepared in advance by different players and authorities, so that the increasing risk of wildfires can be mitigated in the future.

Some aspects of the variability of Convective Available Potential Energy (CAPE) in the Mediterranean region are examined for the 83-year period 1940–2022 with the use of the daily ERA5 database. The mean monthly patterns, the linear trends of monthly time series and the inter-annual variations of the frequency of extreme CAPE cases are studied, while a classification of spatially extended extreme CAPE events is performed with the combined use of Factor Analysis and k-means Cluster Analysis. According to the results, the spatial distribution of CAPE presents a remarkable seasonal variability due to the strong seasonality of other relevant climatic variables. Also, there is a statistically significant increase of the mean monthly CAPE in most parts of the Mediterranean region in all seasons except spring. This increase appears also in the frequency of extreme cases and in the frequency of the summer spatially extended extreme events. The above positive trends are in line with other signals of the ongoing climate change in the Mediterranean region.

Atmospheric aerosols (aerosol optical depth, AOD) and green cover (normalized difference vegetation index, NDVI) significantly affect the radiation balance of a region and thereby modify the land surface temperature (LST). We have examined the long-term (2000–2017) temporal association between these variables using Wavelet Local Multiple Correlation (WLMC) analysis across six geographically separated areas representing different climatic zones of India. Spearman’s correlation between the variables indicates a mix of positive and negative correlations for varying seasons across the climatic zones. The non-stationary co-movement of multivariate correlation structure among the variables has been resolved by applying Maximal Overlap Discrete Wavelet Transform and WLMC analyses. Results show that the multivariate correlation integrates well beyond quarterly and biannual scales (16–32 weeks) for all zones. Daytime and nighttime LST explain the correlation structure in the data in zones from almost all climatic regions, except from central India where AOD and NDVI are the dominant variables. To some extent, NDVI plays an important role in eastern Indian region. The WLMC analysis confirms that the most reliable information in the multivariate spatial-temporal data at the regional scale can be suitably investigated. Regional climate models in this regard can further investigate the dynamics of the dominant variable in affecting the regional energy budget based on the WLMC analysis. The study has potential applications in forecasting extreme climate disasters and planning preemptive mitigation strategies.

Empirical climate classification is a process that makes environmental conditions understandable to humans by using climatic elements. Köppen-Geiger (KG) is a popular climate classification method that uses long-term precipitation and temperature data to classify climate into five primary groups. However, long-term continuous meteorological data is heavily exposed to data scarcity, particularly in a national scale. This research study addresses this challenge by leveraging satellite imageries, multilinear regression models and spatial interpolation within the context of entire country of Iran between 2016 and 2019. Accordingly, this study examined statistical relationship between 14 explanatory variables under four main categories of MODIS-LST, MODIS-NDVI, MODIS-TVDI, GPM-precipitation and SRTM-DEM against ground-based precipitation and temperature data (dependent variables). The spatial interpolation model (i.e. Krigging and Co-krigging) was directly developed from weather observation station datasets. A total of 332 synoptic stations were selected, 67% of which were used in modeling and the remaining 33% in testing. Accuracy assessment was performed with Kappa statistics. Overall, this research study developed three KG classification maps. These include a map per precipitation and temperature from regression model and spatial interpolation and a point-based maps from unused climate data in modelling. This study identified three KG main climate groups of arid, warm temperate and snow and eight KG sub-groups of hot desert, cold steppe, cold desert, hot steppe, warm temperate climate with dry hot summer, snow climate with dry hot summer, warm temperate climate with dry warm summer and snow climate with dry warm summer. A comparison between those maps (kappa = 0.75) showed the higher accuracy of regression-based KG maps against spatial interpolation maps. This study contributes to a more detailed monitor of climate change across countries and regions with sparse distribution of weather observation data.

Extreme weather events have become more frequent and severe with ongoing climate change, with a huge implication for the agricultural sector and detrimental effects on crop yield. In this study, we compare several combinations of climate indices and utilized the Least Absolute Shrinkage and Selection Operator (LASSO) to explain the probabilities of substantial drops in silage maize yield (here defined as “yield shock” by using a 15th percentile as threshold) in Germany between 1999 and 2020. We compare the variable importance and the predictability skill of six combinations of climate indices using the Matthews Correlation Coefficient (MCC). Finally, we delve into year-to-year predictions by comparing them against the historical series and examining the variables contributing to high and low predicted yield shock probabilities. We find that cold conditions during April and hot and/or dry conditions during July increase the chance of silage maize yield shock. Moreover, a combination of simple variables (e.g. total precipitation) and complex variables (e.g. cumulative cold under cold nights) enhances predictive accuracy. Lastly, we find that the years with higher predicted yield shock probabilities are characterized mainly by relatively hotter and drier conditions during July compared to years with lower yield shock probabilities. Our findings enhance our understanding of how weather impacts maize crop yield shocks and underscore the importance of considering complex variables and using effective selection methods, particularly when addressing climate-related events.

Climate change is increasingly recognized as a critical factor driving shifts in the distribution of dominant tree species within various forest ecosystems, including evergreen, deciduous, and thorn forests. These shifts pose significant threats to biodiversity and the essential ecosystem services that forests provide. In Tamil Nadu, India, where forest ecosystems are integral to both ecological balance and local livelihoods, there is an urgent need to predict potential changes in species distributions under future climate scenarios to inform effective conservation strategies. This study addresses this need by utilizing the MaxEnt species distribution model to assess the habitat suitability of dominant tree species in these forest types. The analysis spans current conditions (baseline period 1985–2014) and future projections (2021–2050) under the SSP2-4.5 emissions scenario, leveraging bioclimatic variables at a 1 km resolution. Key climatic factors such as annual mean temperature, precipitation of the driest month, and precipitation seasonality were identified as major drivers of habitat suitability, particularly in the Eastern and Western Ghats of Tamil Nadu. Model projections suggest a potential decrease in suitable habitat area by 32% for evergreen species and 18% for deciduous species, whereas thorn forest species might experience a 71% increase in suitable area. These findings underscore the critical need for targeted conservation actions to mitigate anticipated habitat losses and bolster the resilience of these vital forest ecosystems in the face of ongoing climate change.

The study delves into the complexities of prolonged El Niño (PE) and La Niña (PL) events, examining their behaviour, dynamics, and representation in climate models participating in CMIP6. These events deviate from the typical cycles of the El Niño-Southern Oscillation (ENSO) system and significantly impact global weather patterns and socioeconomic systems. The study aims to enhance our understanding of these multi-year ENSO events through a comparative analysis of observational data and model simulations. Observational data reveal the distinct characteristics of PE and PL events, with prolonged warming or cooling anomalies persisting in the equatorial Pacific beyond the usual timeframe associated with canonical El Niño (CE) and La Niña (CL) events. However, while climate models generally capture the general trend of sustained warming or cooling, discrepancies exist in the magnitude and timing of SST anomalies, particularly during peak phases. The analysis highlights limitations in the ability of current climate models to simulate consecutive El Niño events following PE events and strong El Niño events preceding PL events accurately. Furthermore, discrepancies in the representation of subsurface oceanic dynamics and zonal wind stress patterns underscore challenges in capturing the intricate interactions driving ENSO variability. The study emphasizes the importance of refining climate models to capture better the intricacies of prolonged ENSO events, which have significant implications for future climate projections and adaptation strategies.

We present the analysis of the spatio-temporal changes in absolute extreme temperatures of the coldest night (TNn), coldest day (TXn), and the hottest night (TNx) and, hottest day (TXx) as defined by the Expert Team on Climate Change Detection and Indices in the Mediterranean Region (MedR) for the period 1950–2023 using ERA5-Land reanalysis gridded dataset. Results show that the annual and seasonal frequencies of TNn and TXn have significantly decreased, while the frequencies of TNx and TXx have increased over the last 74 years in the MedR particularly during hot periods of the year. Since 1950, the magnitude of change in the annual TNn is higher than all absolute extreme temperature indices in the MedR, with more pronounced trends in winter in the western MedR. The hottest year in the MedR since 1950 was 2023, when 35% of the highest absolute maximum temperatures were recorded. According to the results of Pettitt’s test, the most significant change point for MedR was in the late 1980s for the absolute extreme cold temperature indices and in the late 1990s for the absolute extreme warm temperature indices. Spatial differences in warming rates are observed for all absolute extreme temperature indices in the MedR. The increase in temperatures, particularly TXx and TNn, is much more pronounced in Western Mediterranean (WMed) during the annual and summer season than in the eastern Mediterranean (EMed).