Tag: Climate Change Downscaling Techniques

Abstract

Skin exposome encapsulates all internal and environmental exposures that affect skin health. Of these, photo-pollution refers to the combined effect on human skin of the simultaneous exposure to solar radiation (especially UV) and air pollution. Providing personalised photo-pollution exposure warnings and dose monitoring to an individual through a smartphone app could help in reducing skin ageing and degradation as well as in managing skin conditions (for example Atopic Dermatitis). However, accurate monitoring is challenging without a potentially expensive or cumbersome sensor device. In this work we present an innovative satellite-based air pollutant monitoring software service, ExpoPol, developed by siHealth Ltd. ExpoPol synthesises several inputs including live satellite imagery in real-time into an artificial intelligence (AI) model to provide assessment of the exposure of a smartphone user to relevant air pollutants, such as nitrogen oxides (NO_x), poly-aromatic hydrocarbons (PAH) and ozone (O₃). When combined with siHealth’s patented technology HappySun® for solar radiation monitoring, ExpoPol can effectively provide a sensor-less personal skin photo-pollution dosimetry. By downscaling satellite data using local geographic data, ExpoPol is capable of monitoring pollutants with street-level resolution and global coverage in near real-time. We evaluate the accuracy of ExpoPol against ground-station monitoring data for three pollutants across three continental regions (Europe, Asia, North America) and find R² values of 0.62, 0.65, 0.74 for PM₁₀, PM_2.5, NO₂ respectively. ExpoPol is shown to be significantly more accurate than a state-of-the-art global atmospheric forecasting system (CAMS) over the same ground-station dataset and provide data on much finer spatial resolutions. The presented system can support the real-time automatic assessment of the user’s skin exposome, anywhere and anytime. This paves the way for the development of mobile applications empowering users and clinicians to make informed decisions about skin health, or assisting dermocosmetic manufacturers in the creation of personalised products for personal care (e.g., skin ageing prevention or hair care).

Abstract

High temperature warning indicators play a pivotal role in meteorological departments, serving as crucial criteria for issuing warnings that guide both social production and daily life. Despite their importance, limited studies have explored the relationship between different global warming levels and changes in high temperature warning indicators. In this study, we analyze data from 2,419 meteorological stations over China and utilize the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine historical changes in high temperature warning indicators used by the China Meteorological Administration. We evaluate model performance and estimate future changes in these indicators using an annual cycle bias correction method. The results indicate that since 1961, the number of high temperature days (TX35d and TX40d) and length of season (TX40d and TX40l) with daily maximum temperature reaching or exceeding 35°C and 40°C have increased over China. The intensity of high temperatures (TXx) has strengthened and the geographical extent affected by high temperatures has expanded. In 2022, the occurrence of 40°C high temperatures surges, with Eastern China experiencing a two-day increase in TX40d and an extended seasonal length in TX40l by over five days. While CMIP6 models have underestimated the high temperature indictors associated with 35°C during historical periods, notable difference is not observed between the models and observations for TX40d and TX40l, given their rare occurrence. However, future projections, after bias correction, indicate that the increasing trends for 35°C and 40°C high temperature days and length of season become more pronounced than the raw projection, suggesting a more severe increase than that anticipated originally. As global warming intensifies, the high temperature days and length of season are projected to increase non-linearly, while the intensity of high temperatures is expected to increase linearly. For every 1°C increase in global temperature, the intensity is projected to rise by approximately 1.4°C. The impact of high temperatures is expanding, with the major hotspot for China located in the eastern and northwestern regions. Under 5°C global warming, certain regions in China may experience prolonged extreme high temperatures. For instance, 40°C high temperature days in areas like North China and the Yangtze River Basin could increase by about 32 d, and the length of season could extend by approximately 100 d.

Abstract

High temperature warning indicators play a pivotal role in meteorological departments, serving as crucial criteria for issuing warnings that guide both social production and daily life. Despite their importance, limited studies have explored the relationship between different global warming levels and changes in high temperature warning indicators. In this study, we analyze data from 2,419 meteorological stations over China and utilize the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine historical changes in high temperature warning indicators used by the China Meteorological Administration. We evaluate model performance and estimate future changes in these indicators using an annual cycle bias correction method. The results indicate that since 1961, the number of high temperature days (TX35d and TX40d) and length of season (TX40d and TX40l) with daily maximum temperature reaching or exceeding 35°C and 40°C have increased over China. The intensity of high temperatures (TXx) has strengthened and the geographical extent affected by high temperatures has expanded. In 2022, the occurrence of 40°C high temperatures surges, with Eastern China experiencing a two-day increase in TX40d and an extended seasonal length in TX40l by over five days. While CMIP6 models have underestimated the high temperature indictors associated with 35°C during historical periods, notable difference is not observed between the models and observations for TX40d and TX40l, given their rare occurrence. However, future projections, after bias correction, indicate that the increasing trends for 35°C and 40°C high temperature days and length of season become more pronounced than the raw projection, suggesting a more severe increase than that anticipated originally. As global warming intensifies, the high temperature days and length of season are projected to increase non-linearly, while the intensity of high temperatures is expected to increase linearly. For every 1°C increase in global temperature, the intensity is projected to rise by approximately 1.4°C. The impact of high temperatures is expanding, with the major hotspot for China located in the eastern and northwestern regions. Under 5°C global warming, certain regions in China may experience prolonged extreme high temperatures. For instance, 40°C high temperature days in areas like North China and the Yangtze River Basin could increase by about 32 d, and the length of season could extend by approximately 100 d.

Abstract

In recent years climate prediction has obtained more attention to mitigate the impact of natural disasters caused by climatic variability. Efficient and effective climate prediction helps palliate negative consequences and allows favourable conditions for managing the resources optimally through proper planning. Due to the environmental, geopolitical and economic consequences, forecasting of atmospheric and oceanic parameters still results in a challenging task. An efficient prediction technique named Sea Lion Autoregressive Deer Hunting Optimization-based Deep Recurrent Neural Network (SLArDHO-based Deep RNN) is developed in this research to predict the oceanic and atmospheric parameters. The extraction of technical indicators makes the devised method create optimal and accurate prediction outcomes by employing the deep learning framework. The classifier uses more training samples and this can be generated by augmenting the data samples using the oversampling method. The atmospheric and the oceanic parameters are considered for the prediction strategy using the Deep RNN classifier. Here, the weights of the Deep RNN classifier are optimally tuned by the SLArDHO algorithm to find the best value based on the fitness function. The devised method obtains minimum mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) of 0.020, 0.142, and 0.029 for the All India Rainfall Index (AIRI) dataset.

Abstract

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of − 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Abstract

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of − 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Abstract

In the rapidly evolving mountain cryosphere, snow avalanches threaten livelihoods, settlements and infrastructure. In this Review, we analyse past and projected impacts of climate change on avalanche activity and the associated risks. The limited availability of comprehensive datasets, the potential confounding factors and the limitations of statistical approaches can make it difficult to identify trends in avalanche activity. However, available data indicate a general decrease in avalanche number, size, seasonality and active paths at low elevations, and an increase in the proportion of wet avalanches relative to dry avalanches. Increased snowfall at high elevations can lead to peaks in avalanche activity and an increase in the number of wet and slush-like avalanches. Activity patterns gradually shift from low to high elevations under continued warming. These changes affect avalanche risk; however, risk is also influenced by factors such as land use and the growth or decline of human settlements. The impact of these factors varies across diverse mountain environments, making it challenging to predict how risk will evolve under a changing climate. Therefore, future research should aim to couple an improved systemic understanding of the impacts of these factors with slope-scale projections of avalanche hazards and risks to support sustainable mountain development and adaptation strategies.

Abstract

In the rapidly evolving mountain cryosphere, snow avalanches threaten livelihoods, settlements and infrastructure. In this Review, we analyse past and projected impacts of climate change on avalanche activity and the associated risks. The limited availability of comprehensive datasets, the potential confounding factors and the limitations of statistical approaches can make it difficult to identify trends in avalanche activity. However, available data indicate a general decrease in avalanche number, size, seasonality and active paths at low elevations, and an increase in the proportion of wet avalanches relative to dry avalanches. Increased snowfall at high elevations can lead to peaks in avalanche activity and an increase in the number of wet and slush-like avalanches. Activity patterns gradually shift from low to high elevations under continued warming. These changes affect avalanche risk; however, risk is also influenced by factors such as land use and the growth or decline of human settlements. The impact of these factors varies across diverse mountain environments, making it challenging to predict how risk will evolve under a changing climate. Therefore, future research should aim to couple an improved systemic understanding of the impacts of these factors with slope-scale projections of avalanche hazards and risks to support sustainable mountain development and adaptation strategies.

Abstract

The aim of this investigation is to identify a representative set of climate model projections for the Awash Basin using accessible general circulation model (GCM) predictors from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive. Various approaches were employed to systematically shortlist and select suitable climate models under two representative concentration pathways (RCPs). For RCP4.5, 105 GCMs were used and for RCP8.5, 78 GCMs were used to select the best performance models for a climate change impact study in this basin. These approaches are combined in the current study to create a three-step sequential procedure for choosing climate models: (1) initial selection of climate models based on the range of projected changes in climatic means, (2) refined selection based on the range of projected changes in climatic extremes, and (3) final selection based on the ability of the climate models to simulate historical climate changes between 1971–2000 and 2071–2100 were analyzed. Five corners of possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold, and the 50th percentile of the temperatures) were used. A total of 25 GCMs were selected for RCP4.5 and RCP8.5 based on the range of expected mean temperature and rainfall change. Based on the range of extreme changes, 10 GCMs were chosen. Five GCMs were ultimately selected for each RCP4.5 and RCP8.5 by merging all three stages. The findings of this study will contribute valuable insights to better understand and adapt to the impacts of climate change in the Awash River Basin and similar regions.

Abstract

The aim of this investigation is to identify a representative set of climate model projections for the Awash Basin using accessible general circulation model (GCM) predictors from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive. Various approaches were employed to systematically shortlist and select suitable climate models under two representative concentration pathways (RCPs). For RCP4.5, 105 GCMs were used and for RCP8.5, 78 GCMs were used to select the best performance models for a climate change impact study in this basin. These approaches are combined in the current study to create a three-step sequential procedure for choosing climate models: (1) initial selection of climate models based on the range of projected changes in climatic means, (2) refined selection based on the range of projected changes in climatic extremes, and (3) final selection based on the ability of the climate models to simulate historical climate changes between 1971–2000 and 2071–2100 were analyzed. Five corners of possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold, and the 50th percentile of the temperatures) were used. A total of 25 GCMs were selected for RCP4.5 and RCP8.5 based on the range of expected mean temperature and rainfall change. Based on the range of extreme changes, 10 GCMs were chosen. Five GCMs were ultimately selected for each RCP4.5 and RCP8.5 by merging all three stages. The findings of this study will contribute valuable insights to better understand and adapt to the impacts of climate change in the Awash River Basin and similar regions.