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- Another scanning test of trend change in regression coefficients applied
to monthly temperature on global land and sea surfaces-
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Abstract: Abstract Two algorithms have been proposed in this paper. One is another scanning t test of trend change points in regression slope coefficients in two phases, along with a coherency analysis of trend changes between two time series. It is different from the previously published scanning Fmax test of trend changes. The second is a fuzzy weighted moving average (FWMA). Then, the algorithms were applied to two series of monthly temperatures over global land and ocean surfaces for 1850–2021. The applied results show that significant changes in segment trends appeared in two gradations on the interdecadal and intradecadal scales. All subsample regression models were found to fit well with those data. Global warming started in April 1976 with a good coherency of warming trends between land and sea. The global warming “hiatus” mainly occurred in SST cooling from November 2001 to April 2008, but was not evinced over land on interdecadal scales. The “land/sea warming contrast” was detected only in their anomaly series, but disappeared in their standardized differences. We might refer to the anomalies in distribution N(0,s) as “perceptual” indicators and refer to the standardized differences in N(0,1) as “net” indexes.
PubDate: 2023-09-28
- A CMIP6 multi-model ensemble-based analysis of potential climate change
impacts on irrigation water demand and supply using SWAT and CROPWAT
models: a case study of Akmese Dam, Turkey-
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Abstract: Abstract This study details an integrated framework for assessing the water supply reliability of a multi-purpose reservoir under different climate change scenarios, with the case of the Akmese Project in northwest Turkey. In this assessment, the precipitation and temperature simulations of 24 Global Circulation Models (GCMs) from the Couple Model Intercomparison Project phase 6 (CMIP6) are analyzed using two statistical bias correction methods, namely, linear scaling and distribution mapping, to produce the best-performing multi-model ensemble predictions under two different Shared Socio-economic Pathway (SSP) scenarios (SSP245 and SSP585). The future inflow rates of the Akmese reservoir are simulated using the Soil and Water Assessment Tool (SWAT) model. The CROPWAT model is utilized to estimate crop water and crop irrigation requirements under the projected climate conditions. The effects of changing climate on the lake evaporation rates are also taken into consideration in analyzing the future reservoir water availability for domestic usages, irrigation demands, and downstream environmental flow requirements. The 25-year monthly reservoir operations are conducted with the changing inputs of the projected inflows, lake evaporation rates, and irrigation requirements for the historical period of 1990–2014 and near-, mid-, and long-future periods of 2025–2049, 2050–2074, and 2075–2099, respectively. The results indicate that the projected changes in the hydro-climatic conditions of the Akmese Basin will adversely impact the reservoir water availability. Under the high-forcing scenario SSP585, 9.26 and 22.11% of the total water demand, and 20.17 and 38.89% of the total irrigation requirement cannot be supplied, in turn, in the mid- and long-future periods.
PubDate: 2023-09-27
- Terrestrial water storage and climate variability study of the Volta River
Basin, West Africa-
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Abstract: Abstract The Volta Basin in West Africa plays a crucial role in supporting the livelihoods of millions of people, and effective management of its water resources is essential for climate change adaptation. This study utilized remote sensing technology, specifically the Gravity Recovery and Climate Experiment (GRACE), to assess terrestrial water storage (TWS) and its response to climate variability within the Volta Basin. The methodology involved integrating GRACE data with ground-based measurements, climate models, and other satellite observations to enhance the accuracy of TWS assessment. Despite numerous studies conducted within the basin, this research employed additional statistical techniques such as Independent Component Analysis (ICA) and El Niño Southern Oscillation (ENSO). It also utilized Climate Hazard Group Infrared Precipitation with Station (CHIRPS) to determine variations in TWS and climate variability observed within the Volta Basin. The results provide valuable insights into TWS dynamics, highlighting the complex interplay between precipitation patterns, groundwater storage, and surface water availability. Also, it was revealed that rainfall signals were strongest in the northernmost part of the basin, reaching a maximum value of 10 mm, while the lowest value of 5.5 mm was recorded in the southern part of the basin. Similarly, TWS signals were highest in the northern and lowest in the southern part of the basin, exhibiting values related to that of rainfall. Additionally, the highest TWS value of 250 mm was identified between 2010 and 2012. The increase in TWS during this period correlates with the occurrence of La Niña that happened between 2010 and 2012. This study offers essential information for water resource management, drought monitoring, flood forecasting, and climate change adaptation strategies not only within the Volta Basin but also in other basins across the globe.
PubDate: 2023-09-26
- Multiscale analysis of drought, heatwaves, and compound events in the
Brazilian Pantanal in 2019–2021-
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Abstract: Abstract In this study, a comprehensive multiscale analysis of compounding drought-heat events in the Pantanal region is presented. The goal is to assess the multiscale nature of drought and determine whether the combined effects of drought and heatwaves, as driving factors, are more relevant than the effects of each event separately. The study describes a persistent interannual extreme event characterized by drought and heatwaves in the Pantanal, lasting from 2019 to 2021. The extreme event involved a prolonged dry season, a shortened and delayed rainy season, and persistent heatwaves, resulting in the emergence of drought-heat compound events. Despite experiencing consecutive months of increasing drought hazards and a delayed rainy season in late 2020 to 2021, the northern Pantanal region was unable to recover from the water deficit accumulated due to water stress in the previous year. This emphasizes the long-lasting impacts of compound events on water availability and ecosystem health. Furthermore, the study suggests that interannual water stress played a crucial role in explaining the context that led to record-breaking daily maximum temperatures during the austral spring of 2020. The regions most at risk for such compound extreme events are the northern and central Pantanal. Looking at longer timescales, the analysis of compound drought-heat events can provide insights essential for understanding and preventing their impacts, particularly those that could trigger fire outbreaks.
PubDate: 2023-09-26
- Performance evaluation of climate models in the simulation of
precipitation and average temperature in the Brazilian Cerrado-
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Abstract: Abstract Expected changes in climate variables, such as precipitation and temperature, can change the hydrological regime, impacting water availability in already stressed watersheds. The predictions indicate annual surface temperature increase trends in the Cerrado region. Climate models are essential tools in predicting future climate. To increase the degree of confidence in the projections of these climate models, it is necessary to understand the performance of the models and identify and correct the biases observed in the climate variables simulated by them. This study aimed to evaluate global climate models nested with regional climate models in the simulation of precipitation and average temperature in localities in the Brazilian Cerrado. A comparison of historical data from climate models (Eta-HadGEM2-ES, Eta-MIROC5, Eta-BESM, and Eta-CANESM2) was carried out with data observed at the climatological stations present in the area through statistical metrics. In general, the model with the best statistical fits for precipitation and average temperature in the Cerrado region was Eta-HadGEM2-ES. The Run, Mann-Kendall, Pettitt, and Sen’s slope tests demonstrated that a reduction in precipitation and an increase in temperature are expected in the studied locations in the Cerrado region by the end of the 21st century.
PubDate: 2023-09-26
- Highlighting climate change by applying statistical tests and climate
indices to the temperature of Kébir Rhumel watershed, Algeria-
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Abstract: Abstract A number of statistical methods (Mann-Kendall, Pettitt test, etc.) and climatic indices (Emberger, Euverte) were used to highlight climate change in the Kébir Rhumel watershed, at seven geographically selected stations, by studying temperature on a monthly scale. The time series used was from 1901 to 2021 (121 years). The results show a breakpoint in the series, identified at 1980, for all the stations. Analysis of the monthly temperature values, before and after the breakpoint, shows an increase of between 1.08 and 1.18 °C. The graphical representation of the climatic indices indicates that most of the stations moved from the sub-humid stage (before the break) to the semi-arid stage (after the break). Euverte’s method displays that the temperatures for 80% of the months are shifting towards a warmer, less humid climate.
PubDate: 2023-09-22
- Analysis of the spatial variability of temperature with the aim of
improving the location of wind machines-
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Abstract: Abstract Spring frosts after budburst are responsible for crop losses and threaten local economies. As global warming brings forward the phenological stages of plants, they are increasingly confronted with long periods of frost. Nowadays, many solutions exist to fight frost, including the wind machines that dot the Quincy vineyard in France. However, their placements often do not consider the spatial variability of spring frost, leading to some zones being insufficiently protected, while others may have overprotection. The temperature within a specific area can vary spatially due to factors such as the meteorological conditions, the topography of the site, and the soil characteristics. Radiative frost can cause the temperature in an area to differ by a few degrees Kelvin, creating colder areas that must be known to protect plants from frost effects. This study, transferable to any other orchard equipped with wind machines, first analyzes the spatial variability of the minimum temperature according to the type of frost. Meteorological variables from a national synoptic weather station, topographic parameters, and local daily minimum temperatures from a network of connected sensors scattered throughout the vineyard are retrieved for the last three spring seasons of 2020, 2021, and 2022. Then, thanks to hierarchical ascendant clusterings, the spatial variability of temperatures is linked to the synoptic situation and the topography of the domain. In a second step, the current implantation of the wind machines is compared with the frost areas previously identified to propose an optimal positioning for the wind machines in the Quincy vineyard.
PubDate: 2023-09-21
- COVID-19 and the impact of climatic parameters: a case study of Bangladesh
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Abstract: Abstract This study examines the relationship between climatic factors and the prevalence of COVID-19 in Bangladesh. The Pearson correlation coefficient, the Spearman correlation coefficient, and Kendall's correlation coefficient have all been used to assess the intensity and direction of the relationship between climatic factors and COVID-19. The lagged effects of climatic parameters on COVID-19 daily confirmed cases from Bangladesh are being investigated using the Auto Regressive Distributed Lag (ARDL) model. As a result, one non-climatic variable, such as a daily lab test, is considered a control variable. As climatic variables, average temperature (°C), average humidity (percent), average rainfall (mm), and average wind speed (km/h) were well chosen and the same time one environmental variable (a proxy of air quality) like average particulate matter ( \({PM}_{2.5})\) is considered into account. The time series data used in this analysis was from May 1, 2020, to April 14, 2021. The findings of correlation analysis indicate that there is an important /strong, significant, and positive relationship between COVID-19 widespread and temperature (°C), humidity (percent), rainfall (mm), and wind speed (km/h), whereas there is a negative, weak, and significant relationship between \({PM}_{2.5}\) and COVID-19 widespread. In addition, the ARDL findings suggest that temperature (°C), \({PM}_{2.5}\) , and wind speed (km/h) have major lag effects on COVID-19 in Bangladesh, while humidity (percent) and rainfall (mm) have negligible lag effects. This study will be helpful to environmental activists and policymakers in creating future sustainable improvement plans for climate and weather conditions in Bangladesh.
PubDate: 2023-09-20
- The hottest center: characteristics of high temperatures in midsummer of
2022 in Chongqing and its comparison with 2006-
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Abstract: Abstract In the midsummer of 2022, a rare regional high-temperature event occurred in the Yangtze River Basin. Chongqing, as the center of this event, also experienced its strongest high temperature since 1961. In this paper, the characteristics of this event in Chongqing are reviewed in detail and compared with those of 2006. We have confirmed the following conclusions with detailed data: (1) In the midsummer of 2022, the number of days that the daily maximum temperature is exceeding 35 ℃ in Chongqing was the most since 1961, with the number of days that the daily maximum temperature above 40 ℃ increasing significantly compared with 2006. (2) The impact range of this event in Chongqing was quite extensive, especially the range with the daily maximum temperature exceeding 40 ℃ was the widest since 1961. (3) The high-temperature event was more extreme in 2022 than that in 2006. The maximum of daily maximum temperature was 45 ℃, and this is the first time that China has recorded a high temperature of 45 ℃ outside Turpan. There were 5 stations with daily maximum temperature exceeding 44 ℃, accounting for 1/3 of the number of stations with such temperature (15 stations) in China. (4) In the midsummer of 2022, Chongqing was dominated by the compound high-temperature day. Compared with 2006, the daytime high-temperature day decreased significantly, while the compound one increased obviously.
PubDate: 2023-09-20
- Daily precipitation concentration and Shannon’s entropy characteristics:
spatial and temporal variability in Iran, 1966–2018-
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Abstract: Abstract Iran’s diverse climate and topography lead to variable precipitation distribution over different time scales. This study examines daily rainfall distribution across 37 Iranian synoptic stations for 53 years (1966–2018) using the daily precipitation concentration index (CI) and Shannon’s entropy (H). The analysis reveals significant insights into precipitation patterns and their implications. The concentration index (CI) highlights irregular daily precipitation, particularly along the Persian Gulf and Caspian Sea coastlines, and in southeastern Iran, where CI values range from 0.58 to 0.72. In contrast, Shannon’s entropy indicates higher entropy in northern and northwestern Iran, reflecting more uniform northwest rainfall (entropy values 6.48 to 11.63), while entropy decreases from northwest to south and southeast, indicating varying levels of precipitation evenness. The Lorenz asymmetric coefficient (LAC) indicates uneven rainfall distribution, characterized by light rain in the west/northwest and heavy rain elsewhere. The interplay between diminished precipitation, heightened entropy, and increased rainfall variability contributes to an elevated likelihood of droughts and flooding in the southern regions. For instance, regions like the Persian Gulf coast have CI values of 0.71, indicating moderate-high rainfall concentration. In the north, where CI values range from 0.57 to 0.71, amplified rainfall, entropy, and variability enhance the risk of flooding. Notably, the Shannon entropy index responds more significantly to changes in rainfall uniformity compared to increased daily precipitation classes, thereby significantly impacting the concentration index (CI). These findings provide valuable insights into the spatial and temporal dynamics of precipitation, with implications for risk assessment, and water resource management.
PubDate: 2023-09-19
- High-performance prediction model combining minimum redundancy maximum
relevance, circulant spectrum analysis, and machine learning methods for
daily and peak streamflow-
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Abstract: Abstract Streamflow predictions play a crucial role in the planning and management of water structures. However, accurately predicting streamflow data, which exhibits nonlinear and nonstationary characteristics, is a challenging problem. In this study, a novel approach was proposed for the prediction of both overall and peak streamflows, aiming to achieve high performance. The data used included precipitation and streamflow time series, as well as lagged data from the empirical mode decomposition (EMD), variational mode decomposition (VMD), and circulant spectrum analysis (ciSSA) subbands. The minimum redundancy maximum relevance (MRMR) method was employed for feature selection from these datasets. The selected features were used to develop daily streamflow prediction models using Gaussian process regression (GPR), ensemble (boosting and bagging), support vector regression (SVR), and artificial neural network (ANN) methods. The performance of the developed MRMR-, EMD-MRMR-, VMD-MRMR-, and ciSSA-MRMR-machine learning models was evaluated using mean squared error (MSE), mean absolute error (MAE), correlation coefficient (R), and determination coefficient (R2) metrics. Additionally, the Bland-Altman plots and the Kruskal–Wallis test were used to determine the statistical significance of the results. According to the results, the ciSSA-MRMR-machine learning models achieved higher performance compared to the other models (R2 value of 0.956, an MSE of 0.0001, and an MAE of 0.0049 for overall streamflow prediction). For peak streamflow prediction, the ciSSA-MRMR-ANN model yielded an R2 value of 0.956, an MSE of 0.0002, and an MAE of 0.0217. It was observed that the proposed method significantly improved the prediction of not only overall streamflow but also peak streamflow values.
PubDate: 2023-09-19
- Trend and variability analysis in rainfall and temperature records over
Van Province, Türkiye-
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Abstract: Abstract The problem of detecting hydrometeorological trends is still a concern. In this study, monthly, seasonal, and annual temperature and precipitation variations in the Van Province of Türkiye are assessed using both traditional such as Mann–Kendall (MK) and Spearman’s Rho (SRHO) and graphical such as Innovative trend significance test (ITA-ST) and Innovative polygon trend analysis (IPTA) methods. In order to determine trend slope and change-point detection, Sen’s Slope and Sequential Mann–Kendall tests are also used, respectively. The findings show that the MK, SRHO, and ITA-ST approaches detect a decreasing trend for annual total precipitation at the Erciş and Başkale stations. Additionally, the MAM and SON seasons at Erciş and the SON season at Gevaş stations also show a noticeably decreasing trend. All stations have an increasing trend at a 95% significant level for annual mean temperatures. Except Gevaş station, all four seasons have an increasing trend for temperature series. Results from the IPTA show that for precipitation data, the transition from May to June exhibits the highest trend length. In addition, for temperature data, the minimum trend length and slope are captured in the transition from July to August. In general, ITA-ST and IPTA are superior to MK and SRHO at identifying trends and that the outcomes of these methods are significantly more suitable to visual inspection and linguistic interpretation. Different hydro-climatological variables can be analyzed more flexibly and thoroughly utilizing innovative methodologies.
PubDate: 2023-09-18
- Trends and variability in precipitation across Turkey: a multimethod
statistical analysis-
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Abstract: Abstract Analyzing trends in precipitation data is crucial for understanding the effects of climate change and making informed decisions about water management and crop patterns. The objective of the presented study was to investigate precipitation trends, analyze temporal and spatial variations and identify potential change points in Turkey throughout the period from 1980 to 2019. Precipitation data were analyzed for both regional and 81 meteorological stations in Turkey on a monthly, seasonal, and annual basis. Spearman rank correlation and Mann–Kendall tests were utilized to detect possible trends and Sen’s slope test to estimate the magnitude of change throughout the entire time series. The average precipitation amount of Turkey was determined 639.2 mm between the years 1980 and 2019. While Central Anatolian and Eastern Anatolian regions had below 639.2 mm, other regions were above. The range of seasonal precipitation values were found for winter 128.7–320.8 mm, 108.9–260.0 mm for spring, 43.9–109.3 mm for summer, and 79.7–238.4 mm for autumn. The analysis of the data revealed no significant increase or decrease in annual values on a regional basis, with the greatest change on a seasonal basis being observed in the winter. The 40-year trends of annual precipitation data belonging to 81 stations were decreasing in 23 provinces and increasing in 58 provinces, and 11 of them (14% of the total) were found to be statistically significant. Moreover, November was found to be a month of particular significance in terms of precipitation changes across the country, with a decrease observed in 80 out of 81 provinces. Spatial distribution analysis showed that the magnitude of variation in precipitation decreased as one moved from the southern to the northern regions of the country.
PubDate: 2023-09-18
- Long-term forecast of heatwave incidents in China based on numerical
weather prediction-
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Abstract: Abstract Climate change increases the frequency, duration, and intensity of extreme heatday (HD) and heatwave (HW) events. Accurate forecasting of HD and HW is crucial for disaster risk reduction. This study aimed to forecast HD and HW in 51 major cities across China by using the Global Ensemble Forecasting System (GEFS) v2 data and employing statistical downscaling. The results showed that extreme gradient boosting (XGBoost) and equidistant cumulative distribution function matching (EDCDFm) significantly enhanced the accuracy of GEFS-v2 data. However, the forecast performance declined with lead time, with a slowdown observed after 8 days. EDCDFm demonstrated the best performance in forecasting HD (1–16 days) with average values of probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) at 49.2%, 44.3%, and 35.4%, respectively, reaching an average POD as high as 70.12% in some regions. For HW (number (HWN), the longest duration (HWD), frequency (HWF), and EDCDFm achieved the most accurate forecasts (1–16 days), with average POD, FAR, and CSI values of 53.9%, 20.9%, and 45.4% for HWN; 50.6%, 22.8%, and 41.4% for HWD; and 55.6%, 18.6%, and 48.5% for HWF, respectively. Some regions achieved a minimum POD of 81.2% and a minimum CSI of 67.9%. Finally, EDCDFm exhibited even more pronounced improvements in forecasting HD and HW in the later period (9–16 days). Therefore, the use of EDCDFm enabled the effective prediction of HD and HW.
PubDate: 2023-09-16
- Utilizing advanced and modified conventional trend methods to evaluate
multi-temporal variations in rainfall characteristics over India-
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Abstract: Abstract Adequate and consistent rainfall is essential for sustaining water resources, agricultural production, and overall economy of a nation. To explore the variability and changes in rainfall system, the most common and widely employed conventional trend methods are linear regression (LR) and Mann-Kendall (MK) trend tests. These methods are often subject to inconsistent results with respect to the extent of changes reported in rainfall patterns. This study utilizes the advanced LR i.e., quantile regression (QR) and the modified MK (m-MK) trend methods to investigate the retrospective rainfall characteristics and associated trends for multi-temporal periods i.e., long-term (1951–2020), bifurcated (pre-1985 and post-1985), and most-recent (2000–2020), over different climate zones of India. Furthermore, temporal evolution and trend consistency (stability) were examined by comparing multi-temporal slope coefficients at various quantiles (τ) of rainfall distribution. The long-term trends in general rainfall characteristics (GRCs) exhibited drying patterns, while opposite increasing trends were observed in extreme rainfall characteristics (ERCs) for most of the study region. The results of QR at median tail (τ = 0.5) were more or less consistent with the results of m-MK test. Interestingly, an increase in the trend significance and magnitude was observed at higher quantiles (τ > 0.8). The bifurcated and long-term periods showed contrasting results in rainfall characteristics, suggesting trend instability whereas during pre-1985, post-1985, and most-recent periods, the temporal evolution of GRCs revealed a systematic increment in positive trend significance. Altogether, the advanced and modified trend assessment in the present research compliments conventional trend methods with improved trend detection and trend consistency identification.
PubDate: 2023-09-16
- Evaluation of long-term changes in water balances in the Nepal Himalayas
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Abstract: Abstract The Himalayan region is one of the world’s youngest and greatest mountain ranges, serving as the source of several major rivers in Asia. Nepal, situated in the middle of the Himalayas, has a diverse climate that varies from tropical to polar, with highly heterogeneous precipitation patterns and hydrological regimes. This study analyzes long-term changes in water balance parameters from 1961 to 2020 across Nepal using TerraClimate (a global gridded dataset), splitting the period into two 30-year phases, reflecting pre-development (1961–1990) and development (1991–2020). The water balance parameters include precipitation, actual and potential evapotranspiration, runoff, climate water deficit, soil moisture, minimum temperature, and maximum temperature. These parameters illustrate the various in- and out-fluxes of a hydrologic system and the net change in water storage. This study also examines the temporal variation and monotonic trends in water balance in different physiographical and spatial regions. We divide the country into nine regions, and additionally, look at seasonal and annual scales. Results show that maximum and minimum temperatures have an increasing trend and, therefore, indicate a positive deviation during the later period (1991–2020) compared to the earlier period (1961–1990) in all regions. Furthermore, we find that precipitation and soil moisture are slightly decreasing in the later period, while climate water deficit is increasing, indicating increased water stress, particularly in the western mountains. In addition, several springs in the mountains of Nepal are disappearing, which indicates an increase in water scarcity and vulnerability. Seasonally, post-monsoon and winter temperatures are increasing faster than pre-monsoon and monsoon temperatures. Looking at the precipitation pattern, monsoon and post-monsoon precipitation are decreasing overall, and pre-monsoon precipitation is mostly increasing. These findings can help to better comprehend water availability in a region and guide water management strategies.
PubDate: 2023-09-16
- Effective climate sensitivity distributions from a 1D model of global
ocean and land temperature trends, 1970–2021-
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Abstract: Abstract Current theoretically based Earth system models (ESMs) produce Effective Climate Sensitivities (EffCS) that range over a factor of three, with 80% of those models producing stronger global warming trends for 1970–2021 than do observations. To make a more observationally based estimate of EffCS, a 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a full range of three model free parameters are evaluated to produce fits to a range of observed surface temperature trends (± 2σ) from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived EffCS are larger than over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.86 °C global EffCS, ± 34% range 1.48–2.15 °C) than the older Representative Concentration Pathway forcing (RCP6, 2.49 °C global average EffCS, ± 34% range 2.04–2.87 °C). The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone. The results are consistent with previous observation-based studies that concluded EffCS during the observational period is on the low end of the range produced by current ESMs.
PubDate: 2023-09-16
- Estimation of solar radiation in data-scarce subtropical region using
ensemble learning models based on a novel CART-based feature selection-
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Abstract: Abstract Solar radiation estimation is essential with increasing energy demands for industrial and agricultural purposes to create a cleaner environment, negotiate climate change impacts, and attain sustainable development. However, the maintenance and operation of solar radiation measurements are costly due to the lack of pyranometers or their failure; hence, obtaining reliable solar radiation data is challenging in many subtropical regions. Despite its importance, a few studies use machine learning algorithms for solar radiation estimation in Bangladesh. To this end, this study contributes to filling the gap twofold. First, we presented the potentials of ensemble models, such as Bagging-REPT (reduced error pruning tree), random forest (RF), and Bagging-RF, which were compared to three standalone models, namely, Gaussian process regression (GPR), artificial neural network (ANN), and support vector machine (SVM), for estimating daily global solar radiation in three Bangladeshi regions. Second, we explore the optimal input parameters influencing solar radiation change at the regional scale using a classification and regression tree (CART)-based feature selection tool. Satellite-derived ERA5 reanalysis and NASA POWER project datasets were used as input parameters. The performance of the models was compared using performance evaluation metrics like correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), index of agreement (IA), and Taylor diagram. Results suggested that the RF model performed 5.47–37.22% better than the standalone models in estimating daily solar radiation at Chuadanga in terms of RMSE. Besides, the other ensemble model Bagging-RF showed 14.9–25.03% and 11.46–30.97% greater performances in Dinajpur and Satkhira than the conventional models in RMSE metric. Besides, this study may provide knowledge to the policymakers to make critical judgments on future energy yield, efficiency, productivity, and operation, which are essential elements for investments and solar energy conversion applications in the subtropical areas of the world.
PubDate: 2023-09-15
- A new Monte Carlo Feature Selection (MCFS) algorithm-based weighting
scheme for multi-model ensemble of precipitation-
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Abstract: Abstract Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles.
PubDate: 2023-09-15
- Impact of climate change on potential distribution and altitudinal shift
of critically endangered Amentotaxus assamica D.K. Ferguson in Arunachal
Pradesh Himalaya, India-
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Abstract: Abstract Climate change has significantly affected the potential distribution and altitudinal shift of several plant species. Amentotaxus assamicus being one of the critically endangered gymnosperms under the family Taxaceac is restricted only to a few pockets of Arunachal Pradesh with low population size. The current study aims to analyze the current distribution of A. assamica in the state using key environmental parameters and to predict the potential suitable habitat in accordance with two IPCC representative concentration pathway (RCP) scenarios. The future potential distribution was projected for two possible climate scenarios (RCP 4.5 and RCP 8.5) given by three various global climate models (GCMs), viz., BC_CSM 1.1 (BC), CCSM4 (CC), and CNRM-CM5 (CN). A total of 36 independent localities of A. assamica were used to model the current species distribution along with 23 environmental variables, including bioclimatic parameters, elevation, global land cover, and soil data. To run the future simulations, IPCC AR5 scenarios were used for 19 bioclimatic variables. Maxent modeling was used for the current distribution of A. assamica in Arunachal Pradesh, India, through 10 duplicate runs which showed the test AUC average of 0.905 as well as a standard deviation of 0.057. Soil available nitrogen at 15 cm depth was found to contribute the maximum in the model accounting for 38.2% followed by soil nitrogen at 5 cm depth (21.8%). Bio 4, Bio 6, Bio 7, and Bio 19 were the key variables that contributed to varying extent in all the three GCMs consisting of two scenarios each. Under the high suitability zone, the optimistic scenario (RCP 4.5; 3618.25 km2) represented the maximum area followed by RCP 8.5 (3269.89 km2) whereas the lowest in the current distribution model revealed as 2909.64 km2. Furthermore, the high suitability distribution range in terms of altitudinal regime shifted from 270 msl of lowest elevation in the current distribution to the 966 msl in the RCP 4.5 scenario and 894 msl in the RCP 8.5 scenario. The altitudinal shift of the distribution found in the futuristic model is significant, and the species’ lower range of altitudinal distribution has clearly shifted upward. The findings of this study would be useful in determining quantified future climate space for the species and allow the conservation managers to formulate appropriate conservation strategies.
PubDate: 2023-09-14
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