Climate Change Studies Research Articles

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document}, NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model’s capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model’s superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

Evaluation of Sea Surface Temperature from the Satellite Observations and Model Output using In Situ Measurement dataset: Study area Panjang Island, Banten

Abstract Sea surface temperature (SST) is a crucial parameter in climate change studies, and it can be obtained from various sources, including in-situ measurements, satellite observations, reanalysis, and models. However, due to the limited availability of in-situ data, researchers often rely on satellite observations and models, all of which are susceptible to errors. This study aims to improve the accuracy of SST data from satellite observations and models by evaluating and calibration the data through comparison with in-situ SST data using time series analysis. The study areas are Panjang Island, Banten, with a period from April 15, 2022, to February 7, 2023. The data was collected from satellite (OISST V2.1), model (Hycom GOFS V3.0-3.1), and in-situ (loggers HOBO U-24 and TidBit V2.1). The results show that satellite-derived SST values perform better than the model based on higher satellite correlation coefficient values (r = 0.853) compared to the model (r = 0.685). In addition, satellite data also have lower error rate ( x ¯ b i a s = 0.445°C; σ bias = 0.304°C; RMSE = 0.538°C) than model ( x ¯ b i a s = 0.472°C; σ bias = 0.32°C; RMSE = 0.57°C). The variance tests show no significant difference for satellite (p-value = 0.785) and model (p-value = 0.346) data against in-situ data, while the mean tests reveal disparities (p-value < 0.001). The calibration process reduces error values for both satellite ( x ¯ b i a s = 0.276°C; σ bias = 0.225°C; RMSE = 0.356°C) and model data ( x ¯ b i a s = 0.398°C; σ bias = 0.298°C; RMSE = 0.397°C), but the correlation coefficients remain stable at 0.853 (satellite) and 0.685 (model). In conclusion, satellite observations provide a more precise picture of the SST conditions on Panjang Island, Banten and the calibration process can improve the quality of satellite model data.

Drivers of the Extreme Early Spring Glacier Melt of 2022 on the Central Tibetan Plateau

AbstractExtreme glacier melt has important implications for climate change studies and glacier‐related hazards. Few attempts have been made to study the characteristics and mechanisms of extreme melt on the Tibetan Plateau (TP) and surroundings, one of the largest concentrations of glaciers beyond the polar regions. To address this gap, we investigated the magnitude and possible drivers of anomalous early spring glacier melt in 2022 on the central TP using multi‐source observations (October 2020–July 2022) of Kuoqionggangri and Sangqu glaciers and an energy and mass balance model. Our results show that anomalous melt of both glaciers occurred in early spring (18 March–10 April 2022), when glaciers are traditionally expected to be frozen. There were 89 cumulative hours with air temperatures above 0°C, with a positive cumulative air temperature of 121.80°C hour at 5,700 m on Kuoqionggangri Glacier. The glacier‐wide cumulative melts on Kuoqionggangri and Sangqu glaciers from 18 March to 10 April 2022 were 22 ± 14.2 and 48 ± 37.5 mm w.e., respectively. Such anomalous glacier melt was driven by high incoming shortwave radiation and sensible heat flux, which were influenced by low cloud fraction and precipitation, and high air temperature during early spring. The weakening and northward‐shift of the westerlies, coupled with descending air motions over the central TP, reduced cloud fraction and hindered the transport of cold, moist air to the central TP. These changes occurred both in the central TP and the Himalaya, indicating the influence of macroscale atmospheric circulation patterns on this extreme glacier melt.

Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020–2050

Assessment of current and future growth in the global rooftop area is important for understanding and planning for a robust and sustainable decentralised energy system. These estimates are also important for urban planning studies and designing sustainable cities thereby forwarding the ethos of the Sustainable Development Goals 7 (clean energy), 11 (sustainable cities), 13 (climate action) and 15 (life on land). Here, we develop a machine learning framework that trains on big data containing ~700 million open-source building footprints, global land cover, road, and population datasets to generate globally harmonised estimates of growth in rooftop area for five different future growth narratives covered by Shared Socioeconomic Pathways. The dataset provides estimates for ~3.5 million fishnet tiles of 1/8 degree spatial resolution with data on gross rooftop area for five growth narratives covering years 2020–2050 in decadal time steps. This single harmonised global dataset can be used for climate change, energy transition, biodiversity, urban planning, and disaster risk management studies covering continental to conurbation geospatial levels.

MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)

Abstract. Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at https://doi.org/10.5281/zenodo.10005826 (Jiang et al., 2023a).

From test case to trusted tool: Lithuania’s evolving SWAT system for water and agricultural management

Lithuania’s development of the river modelling system (RMS) exemplifies an institutional development and application of integrated modelling for water and agricultural management. What started as a test case, continued to develop focusing on environmental compliance with the EU regulations. Currently, the RMS is a part of decision-making. By incorporating the soil and water assessment tool (SWAT) model and comprehensive data sources, the system facilitates in-depth analysis and policy formulation. Applications in water management plans, pollution assessments, and climate change studies demonstrate the reliability of RMS. Despite data quality and skill retention challenges, institutional commitment and collaboration ensure the RMS’s persistence. This experience emphasizes the value of sustained investment in integrated modelling systems for achieving sustainable environmental governance and signifies Lithuania’s shift towards data driven green transition practices.

A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate

This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.

Amplification of the discrepancy between simplified and physics-based wet-bulb globe temperatures in a warmer climate

The Simplified Wet Bulb Globe Temperature (sWBGT) is widely used in heat stress assessments for climate-change studies, but its limitations have not been thoroughly explored. Building on recent critiques of sWBGT's use for current climate on global scale, this study examines sWBGT's biases using dynamically-downscaled sub-daily climate projections under multiple future emission scenarios. The analysis is aimed at understanding caveats in the application of sWBGT and the uncertainties in existing climate change analysis dependent on sWBGT. Results indicate sWBGT's biases are heavily influenced by local near-surface air temperature, with overestimation of heat stress in East Asia regions, particularly hot and humid areas, due to static assumptions of radiation and wind speed. This overestimation is amplified in warmer climates, leading to exaggerated projected heat stress increases in future. In contrast, underestimations are found for heat stress levels attributed to low wind speeds and strong radiations, such as over the Tibetan Plateau and certain extreme events. Additionally, sWBGT underestimates variability in extreme heatwave events compared to WBGT in both current and future climates, irrespective of overestimation in absolute heatwave intensities. This study emphasizes the limitations of sWBGT, especially in future warmer climates. Importance of sub-daily data for capturing daily maximum heat stress level and reflecting diurnal variations in different components is also discussed. In conclusion, we recommend using Liljegren's model (i.e., physics-based calculation) with high-resolution sub-daily climate data for more accurate outdoor heat stress assessments in climate change studies.

Experimental Study on Ecological and Biogeochemical Role of Calcimycocavites in Understanding Carbon Fluxes in the Age of Global Warming and Climate Change

The present work reports the results of calcimycocavitological studies on the seashells from beaches of north Goa, India yielding distinct microfungal forms known as calcimycocavites which are important in global climate change studies. SEM studies of the calcareous shell fragments revealed the micro tunneling behavior of the fungi. Digital analysis of SEM images using Mountain Premium 7.2 software revealed the fine topography of calcimycocavites hyphae along with unidentified presumptive biomineral encrustations. The biodegradation of mycocavitogenic insoluble Calcium Oxalate by oxalotrophic members of bacterial communities may occur using bacterial Oxalate decarboxylase and formate dehydrogenase finally releasing Carbon Di Oxide and water and Calcium oxide to the local nutrient pool. Calcimycocavites may be thus involved in the biogeochemical cycling of Carbon and Calcium in the Ocean-Beach environment. The ecological, biological, and biogeochemical implications of the findings are presented concerning the possible role of calcimycocavites in Calcium and Carbon cycling by breakdown of the calcareous shells and release of inorganic and organic components in the ecosystem. The role of calcimycocavites is stressed in global climate studies.

Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations

Abstract. Error metrics are useful for evaluating model performance and have been used extensively in climate change studies. Despite the abundance of error metrics in the literature, most studies use only one or two metrics. Since each metric evaluates a specific aspect of the relationship between the reference data and model data, restricting the comparison to just one or two metrics limits the range of insights derived from the analysis. This study proposes a new framework and composite error metrics called Bergen metrics to summarize the overall performance of climate models and to ease interpretation of results from multiple error metrics. The framework of Bergen metrics are based on the p norm, and the first norm is selected to evaluate the climate models. The framework includes the application of a non-parametric clustering technique to multiple error metrics to reduce the number of error metrics with minimum information loss. An example of Bergen metrics is provided through its application to the large ensemble of regional climate simulations available from the EURO-CORDEX initiative. This study calculates 38 different error metrics to assess the performance of 89 regional climate simulations of precipitation and temperature over Europe. The non-parametric clustering technique is applied to these 38 metrics to reduce the number of metrics to be used in Bergen metrics for eight different sub-regions in Europe. These provide useful information about the performance of the error metrics in different regions. Results show it is possible to observe contradictory behaviour among error metrics when examining a single model. Therefore, the study also underscores the significance of employing multiple error metrics depending on the specific use case to achieve a thorough understanding of the model behaviour.

Selection of multiple ensemble representative CMIP5 climate models for climate change study in developing river basin: the case of Awash River Basin, Ethiopia

Selection of multiple ensemble representative CMIP5 climate models for climate change study in developing river basin: the case of Awash River Basin, Ethiopia

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-Sensor Consistency Using Radiative Transfer Modeling

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

A Data-Driven Probabilistic Network Approach to Assess Model Similarity in CMIP Ensembles

Abstract The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple global climate models (GCMs) from different modeling centers with some shared building blocks and interdependencies. Applications typically follow the “model democracy” approach which might have significant implications in the resulting products (e.g., large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori approach seeks similarity in the GCMs’ output variables and is thus data-driven. In this study, we apply probabilistic network models (PNMs), a well-established machine learning technique, as a new a posteriori method to measure intermodel similarities. The proposed methodology is applied to surface temperature fields of the historical experiments from the CMIP5 multimodel ensemble and different reanalysis gridded datasets. PNMs are able to learn the complex spatial dependency structures present in climate data, including teleconnections operating on multiple spatial scales, characteristic of the underlying GCM. A distance metric building on the resulting PNMs is applied to characterize GCM model dependencies. The results of this approach are in line with those obtained with more traditional methods but have further explanatory potential building on probabilistic model querying. Significance Statement The present study proposes the use of probabilistic network models (PNMs) to quantify model similarity within ensembles of global climate models (GCMs). This is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. When applied to climate data (gridded global surface temperature in this study), PNMs encode the relevant spatial dependencies (local and remote connections). Similarities among the PNMs resulting from different GCMs can be quantified and are shown to capture similar GCM formulations reported in previous studies. Differently to other machine learning methods previously applied to this problem, PNMs are fully explainable (allowing probabilistic querying) and are applicable to high-dimensional gridded raw data.

Optical flow of temperature reveals climate change patterns for agriculture and forestry

The study of climate change's impact on terrestrial ecosystems is crucial for achieving societal and economic sustainability and human well-being. Domains like agriculture and forestry respond to climate change similarly to other terrestrial natural biomes, experiencing reductions in productivity, migration, and loss of biodiversity. However, there is a research gap in understanding climate velocity for different land cover and land use types, which could reveal vulnerability patterns in agriculture and forestry. To address this gap, we present the first study of climate velocity for major land cover and land use classes with a focus on major crop growing regions and functional forest types at the continental level. To enable this analysis, we introduce a new approach for estimating climate velocity based on optical flow. This approach allows us to conduct a global study, improve ecosystem interconnection representation, and reduce the model's sensitivity to noisy data and parametrization. Our study highlights the significant vulnerability to climate change of agriculture as a land cover type with a climate velocity 15% higher than of other vegetation land cover classes. Furthermore, the analysis of climate velocity for functional tree types reveals the highest expected impact on deciduous and evergreen needleleaf trees primarily concentrated in regions with the highest climate velocity, especially in sub-polar and temperate climates. Conversely, tropical forests dominated by evergreen broadleaf tree types exhibits marked variations in climate velocity with the highest rates in South America and Africa and three times lower in Asia. Additionally, our analysis of climate velocity for agriculture shows that North American croplands experience the highest climate velocity exceeding the global average by 32%. This trend is consistent for major crop types such as soybean that have higher climate velocity than globally for 49%, rapeseed and sunflower for 39%, cotton for 35%, maize for 32%, and wheat for 30%. Notably, areas dedicated to cereals, sugar beet, and sunflowers are located in zones with climate velocities higher than the global average by 15%, 12%, and 9%, respectively. These patterns indicate potential shifts in the agriculture and forestry structures in the near future that coherent with recent climate change studies and will have significant impact on the socio-economic dimension of humanity sustainability.

Regional climate models and bias correction methods for rainfall-runoff modeling in Katar watershed, Ethiopia

Systematic errors in regional climate models (RCMs) hinder their implementation and lead to uncertainties in regional hydrological climate change studies. As a result, checking the accuracy of climate model simulations and applying bias correction are preliminary methods for achieving consistent findings. Therefore, identifying suitable RCM models for bias correction is important for providing reliable inputs for evaluating climate change impacts. The impacts of bias correction methods on streamflow were assessed on the Katar catchment within the Lake Ziway subbasin using coordinated regional climate downscaling experiments with a spatial resolution of 50 km (CORDEX-44) RCMs through the Integrated Hydrological Modelling System (IHMS) version 6.3. This study evaluated fourteen RCM models under five precipitation and three temperature bias correction methods for the Katar catchment. Statistical approaches, such as bias (PBIAS), the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of variation (CV), the coefficient of determination (R2), and the relative volume error (RVE), are used for performance analysis. GERICS-MPI, RAC4-NOAA-2G, and CCLM4-NCCR-AFR-22 have better performances for both rainfall and temprature. The empirical cumulative distribution function (ECDF) method performed best in removing bias from the frequency-based statistics of rainfall and streamflow, followed by the power transformation (PT), distribution mapping (DM), local intensity scaling (LOCI), and linear scaling (LS) methods. Specifically, for temperature, the VARI and DM methods perform better in frequency-based statistics than the LS method. The performance of hydrological modeling is strongly affected by the selection of rainfall bias correction methods. In addition, the effect of the temperature bias correction method was not significant. The adequacy of the BCM depends on the RCM models and regional context. Therefore, the BCM implementation procedure can be adapted from region to region. This study revealed that the performance of the RCM models differed and that the errors in the RCM model outputs were reduced by the use of bias correction methods.

Research on Wind Speed Prediction Method Based on Feature Fusion

Wind speed occupies a central position in the study of atmospheric circulation and climate change, playing a crucial role in the accuracy and reliability of meteorological forecasting models. The current meteorological observation network, especially in remote and mountainous areas, faces challenges due to insufficient data resulting from low station density. This limitation hampers a comprehensive understanding of wind field dynamics, subsequently affecting the performance of forecasting models. To enhance the performance of meteorological prediction models, this study adopts an innovative approach that combines boxplot difference indexing and feature-level fusion techniques to filter key predictive features and reduce information redundancy. Furthermore, by integrating spatial location information, the contribution of geographic data to wind field prediction is enhanced, improving the quality of the fused data and thereby increasing the accuracy of model predictions. Experimental comparisons demonstrate that the integrated wind field dataset, post feature fusion, achieves better results across various deep learning models.

Women's experiences and sustainable adaptation: a socio-ecological study of climate change in the Himalayas

Women's experiences and sustainable adaptation: a socio-ecological study of climate change in the Himalayas

Assessment of climate change in different regions of Karnataka state

At the regional level, climate change has significant influences on crop productivity and food security. A climate change study was carried out using different parametric indices like rainfall attributes, temperature, and humidity from 58 years of climatic data (1964-2017) in Karnataka. The climatic period was divided into the Pre-climate change period- P1 (1964-1990) and the climate change period- P2 (1991-2017) with 27 years. The result shows annual rainfall and rainy days were increased in South Interior Karnataka (SIK) and Malnad regions and reduced in North Interior Karnataka (NIK) and Coastal regions. Dakshina Kannada, Yadgir, Kalabarugi, Udupi and Kodagu districts showed a significant reduction in receiving rainfall and an increase in Shivamogga, Hassan, Kolar and Chitradurga districts from the P1 to P2 period. NIK and SIK regions are highly prone to drought vulnerability compared to Malnad and Coastal regions. The occurrence of droughts wasincreasing,the temperature trend is increased and the relative humidity trend is decreasing in the P2 period. Assessment of climate variability in P1 and P2 helps to adopt preciseuse of water, nutrient and different crop-specific management in different zones of Karnataka.

Investigation of rainfall disaggregation with flexible timescales based on point process models

Analyzing high-temporal-resolution rainfall data with disaggregation tools is vital for understanding precipitation variability, especially in climate change studies with limited access to such data. To facilitate such study for tropical region, we proposed a Point-Process Modelling and Rainfall Disaggregation (PPMRD) framework for rainfall disaggregation with flexible time scales. The framework was tested on two tropical sites in Singapore, using six point-process models and four disaggregation schemes (24 h to 5 min, 3 h to 5 min, 24 h to 1 h, and 6 h to 1 h). Most models excelled fundamental statistics and extreme value analysis. The Bartlett-Lewis Rectangular Pulse Seven parameter (BLRP7) model was found to be the best performer for simulating and disaggregating tropical rainfall. The study findings emphasized the utility of disaggregation models in generating fine-resolution time series for analyzing rainfall extremes. However, it is crucial to underscore the importance of employing ensembles to comprehensively address uncertainties arising from the stochastic nature of rainfall. The study also emphasized the importance of model selection in the proposed framework, as the disaggregation method depends on the generator’s performance. The PPMRD surpasses traditional methods by handling subdaily upper-level series disaggregation to minute-level rainfall. This advancement offers greater capabilities and flexibility, promising benefits for generating high-temporal-resolution rainfall data in hydrological modeling and climate change impact studies.

Evaluation of the climate models used by the IPCC based on the position of the ITCZ in the Equatorial South Atlantic

AbstractA number of the uncertainties in climate projections for the end of the century are due to the difficulty of general circulation models (GCMs) to represent large‐scale precipitating systems such as the Intertropical Convergence Zone (ITCZ) in the historical period. In this study, we evaluate the Coupled Model Intercomparison Project Phase 5 and 6 models based on the position of the ITCZ in the South Atlantic during the period in which it is climatologically further south (February–May). This period characterizes the rainy season of the Brazilian semiarid. The models were ranked according to two objective methods and validated through a qualitative analysis. The first method considered a success rate to evaluate the correct position of the areas with maximum precipitation and minimum pressure and meridional wind as representative of the mean position of the ITCZ. Among the 15 coupled GCMs analysed for each CMIP, those that best represented the position of the ITCZ were HADGEM.2‐AO, MIROC5 and CANESM2 from CMIP5 and CESM2 and MIROC6 from CMIP6. These models represented well the following characteristics of the ITCZ in February–May: maximum precipitation near the equator at the height of the coast of South America and with the wind convergence zone sloping north towards the African continent. The most common problem noticed in the other GCMs was to displace the ITCZ to the south of its observed mean position within the period February–May. Also, some GCMs presented a double ITCZ (GISS‐E2‐H, GDFL‐CM3 and IPSL‐CM5A‐LR from CMIP5 and INM‐CM5‐0 from CMIP6) following two high TSM cores. One GCM (ACCESS1‐3 from CMIP5) presented a relatively good performance, but the zone of greater precipitation was shifted to the east, being located in the centre of the Atlantic basin. Knowing how the GCMs represent the ITCZ is a criterion that must be considered when choosing the GCMs for climate change studies.