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- Beyond the ordinary metrics on the evaluation of historical Euro-CORDEX
simulations over Türkiye: the mutual information approach-
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Abstract: Previous studies applied a large variety of performance metrics to evaluate the global and regional climate model simulations; however, there is still a huge debate to justify the rationale for the chosen metrics. The performance of sixty Euro-CORDEX temperature and precipitation simulations was investigated in temporal and spatial approaches through various common metrics over Türkiye. In addition, several mutual information (MI) methods based on the information theory were evaluated as state-of-the-art alternative metrics and compared with the applied common metrics. Based on the average of outputs over the ensemble of the driving models for the annual temperature, the MBE, MASE, MRAE, and NSE are presenting a similar pattern on the rank of the simulations. The MPI with 0.3 on MBE, NCC with 2.7, 2.9, and − 10.4 on MASE, MRAE, and NSE, respectively, and IPSL-LR and NOAA with 0.1 on the modified KGE represented the least errors. The ICHEC with respective 15, 0.1, 1.01, and − 4 for the MBE, MASE, MRAE, and NSE presented the lowest errors for the similar above-mentioned analysis except for the precipitation. The MPI and CNRM with 0.37, 0.37, and 0.08 obtained the highest outcomes on the KNN, mixed and noisy KNN, respectively. We conclude that the ability of mutual information to capture nonlinear relationships is very beneficial. Finally, it is also suggested to undertake these analyses for other hydroclimatic variables for future studies to gain a comprehensive insight into the performance of MI methods.
PubDate: 2023-06-04
- Quantifying uncertainties related to observational datasets used as
reference for regional climate model evaluation over complex topography
— a case study for the wettest year 2010 in the Carpathian region-
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Abstract: Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, the uncertainty of observations affects the evaluation. This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. Besides precipitation and temperature, our method uses geographic variables (e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. correlation analysis and permutation test, was carried out. Furthermore, we used a special metric, the reduction of error (RE) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). We focused on the Carpathian region, because of its unique orographic and climatic conditions. The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901. The results show that CarpatClim is wetter than E-OBS, while temperature is similar over the lowland; however, E-OBS is significantly warmer than CarpatClim over the mountains. By the RE metric, RegCM has improvement against the LBCs over mountains for temperature and areas with dense station network for precipitation. Nevertheless, there are significant differences in the results depending on which observational dataset was used concerning precipitation. The evaluation method can be applied to other datasets, different time periods and areas. It is also suitable to find dataset errors, which is also exemplified in this paper.
PubDate: 2023-06-03
- The Kolkata Cyclone 1864: loss of life and its effects in Bengal, India
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Abstract: The great Kolkata Cyclone of 5 October 1864 is still felt in the hearts of people in Bengal after 158 years. Almost every year, the inhabitants of Bengal are frequently threatened by various types of cyclones. In the last three centuries, from 1737 to 2021, the people of Bengal have witnessed more than one hundred fifty cyclones, tornadoes, and typhoons. Cyclones that occurred in the Bay of Bengal in October of 1737, 1864, 1874, 1876, and 1942 were severe and caused great loss of life and property. But the Kolkata Cyclone on the 5 October 1864 was one of the deadliest cyclones of Bengal. It was also one of the most destructive cyclones in the world. The devastation of the cyclone was terrible. It appeared that the total loss of life by the cyclone in Bengal actually reported by competent authorities was not more than sixty thousand. The loss of life in the Bengal had been partially ascertained. This paper is a case study of a natural calamity that swept over the Bengal on 5 October 1864 with reference to broader questions relating to climate and human settlement. The most striking part of the description is the human casualties. Finally, it shows how the effects of this natural calamity devastated thousands of lives in then-Bengal.
PubDate: 2023-06-03
- Geostatistical modelling of rainfall in Fars Province of Iran using
non-Gaussian spatial process-
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Abstract: Prediction of response values is a primary goal in many applications. The standard approach to this problem is kriging which is essentially a linear prediction using optimal least squares interpolation of the random field. However, the optimal predictor is not necessarily a linear one unless the geostatistical data support the Gaussian model. As data often exhibit non-normality, some of the most effective spatial processes are reviewed in the current study. The usefulness of the presented models is demonstrated based on the prediction of rainfall levels in Fars Province, Iran. The measurements were taken from 100 stations. To assess the predictive performance of the evaluated models, 15 stations were randomly withheld. Subsequently, the predicted values at these locations were evaluated against the measured ones. The results of the study indicated that, comparing to some well-known models, the skew Gaussian model introduced in this article demonstrated a better performance in the prediction.
PubDate: 2023-06-03
- Association between circulation weather types and hospital admissions due
to COPD in Changchun, a city in Northeast China-
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Abstract: Studies suggested that atmospheric circulation types were significantly associated with health. In the current study, we evaluated the association between circulation weather types (CWTs) and chronic obstructive pulmonary disease (COPD) in a city of northeast China using an automatic classification scheme. Thirteen main circulation types were selected. Results indicated that specific weather types increased hospital admissions due to COPD. For the whole group, high admission indices (AI) were always associated with the types of anticyclonic-northerly (AN) and anticyclonic-northwesterly (ANW). Stratified by gender, males were found to be most affected by ANW, followed by southerly (S), westerly (W), and southwesterly (SW), while females were more affected by northerly (N), followed by ANW and W. Regarding the age, NW and W types caused more COPD hospital admissions for those younger than 65 years, while AN and ANW types pronounced more for those older than 65 years. In general, weather types characterized by cold and dry characteristics have stronger impact on COPD.
PubDate: 2023-06-02
- Assessment of the effect of climate fluctuations and human activities on
vegetation dynamics and its vulnerability-
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Abstract: It is necessary to evaluate the impacts of climate change and human activities on vegetation dynamics. The purpose of this study is to assess the linkage of vegetation cover and climate fluctuations and distinguish the contribution of climate fluctuations and human activities on vegetation and its vulnerability in Namak Lake basin, Iran. For this purpose, changes in the enhanced vegetation index (EVI) in association with standardized precipitation evapotranspiration index (SPEI) were analyzed. The roles of climate fluctuations and human activities on vegetation vulnerability were then assessed in various land use land cover (LULC) classes using the residual analysis and probability of vegetation vulnerability index (PVVI). The EVI and land surface temperature (LST) maps for the period 2001–2019 were obtained from MOD13A2 and MOD11A2 products of MODIS, respectively. The results indicated that vegetation cover was mainly dependent on short-term climatic changes and their correlation decreased with increasing time scale of SPEI. It reflected that short-term water availability was vital for vegetation growth. Also, the sparse vegetation cover was mainly more vulnerable to climate fluctuations. Residual analysis showed that the vegetation dynamics was intensively attributed to the climate fluctuations, so that climate fluctuations affected vegetation cover in 78.94% of the basin, while 15.58% was affected by human activities and 5.48% was affected by both factors. The value of PVVI in the regions affected by climatic change was the highest (55.99); in the regions affected by human activities, it was lower (50.40); and in the regions affected by both factors, it was between the other two numbers (50.94). Therefore, climate fluctuations and human activities were both driving factors affecting vegetation covers over time in Namak Lake basin.
PubDate: 2023-06-02
- Analysis of rainfall and temperature using deep learning model
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Abstract: The uncertainty of climatic or weather variations makes human adaptation a challenging task. Though lots of techno developments have taken place addressing the need of predictions and forecasting of climatic behavior, the uncertainty of atmospheric and geoprocesses poses a severe challenge to the effectivity of efforts in dealing with disasters. It has become very crucial to understand the future atmospheric uncertainty process and further predict and forecast for analysis of different activities based on geographical locations. Climate parameters are mandatorily required to improve system activities and output analysis of acquired data. For a sustainable adaptation, this is very important on how the uncertainty of variations in climatic parameters is dealt and the decisions based upon appropriate forecasting model are taken. The work has primarily focused on the development of an appropriate model, training the model with massive monthly rainfall and temperature data which can be used to analyze any climatic predictions and provide support to deal with adverse future situations. This model determines the appropriate amount of rainfall and temperature varied during the dry season and helps set a relationship with other input variables like humidity and maximum and minimum temperature. The model has been trained and tested using the obtained data and performed well with better accuracy. The model developed using long short-term memory algorithm of deep learning for predicting rainfall and temperature climatic variables is supported with the study of the main factors affecting monthly climatic change analysis. Monthly precipitation, rainfall intensity, and temperature have been used in the multipurpose prediction model to analyze the monthly and yearly climatic change prediction. The model has also been optimized for precision, accuracy, and computational speed, which are affected by number of iterations, neurons of a hidden layers, epochs, and batch size.
PubDate: 2023-05-31
- Dependency of LSA and LST to topographic factors in Iran, based on remote
sensing data-
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Abstract: Land surface albedo (LSA) and land surface temperature (LST) are used in many environmental studies. Linking topographical factors (altitude, aspect, and slope) with LST and LSA plays an essential role in climate modeling, environmental changes, hydrology, energy balance, architecture, etc. Knowing the relationship between them is of particular importance. In this research, the data of two remote sensing products that are Modis Terra and Aqua (for the period of 2000–2019) were used to investigate the link between topographical factors, LSA, and LST. Investigating the relationship between the altitude, aspect, and slope with LST showed that this parameter is strongly influenced by topographical factors; an increase in altitude, aspect, and slope leads to a decrease in LST. The correlation coefficients of altitude, aspect, and slope with the LST are estimated to be − 0.968, − 0.927, and − 0.684, respectively. The results of linking topographical factors with albedo showed that this parameter has a strong link with altitude, so as the altitude increases, there would be an increase in albedo. But there is no significant relationship between aspect and slope considering the low correlation coefficients. The correlation coefficients of latitude, aspect, and slope with LSA are 0.95, 0.087, and 0.18 respectively. Therefore, the altitude, aspect, and slope are important factors affecting the LST in Iran. Altitude also plays a vital role in the Iran’s LSA.
PubDate: 2023-05-31
- Evaluating the relation between meteorological drought and hydrological
drought, and the precipitation distribution for drought classes and return
periods over the upper Tigris River catchment-
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Abstract: This study focuses on the assessment of successive drought relations by using the standardized precipitation index, standardized precipitation evapotranspiration index, and the streamflow drought index. The main goal is to propose lag times between drought events using factors like longitude and elevation and to construct maps that cover precipitation threshold values, and critical precipitation values corresponding to return periods using meteorological indices. For this purpose, monthly streamflow datasets of 42 stations and monthly meteorological datasets of 25 stations from 1972 to 2011 were used. Results indicate that mean elevations of the sub-catchments showed a decisive role in the amount of drought delay. The sub-catchments located in the low altitudes showed no delay in translation, whereas the sub-catchments located in the highly elevated regions showed 2-month delay in the monthly time scale. Moreover, the success of drought relations is more pronounced with temperature datasets, especially in the highly elevated regions for greater drought periods. In the second part, the spatial variation of the precipitation in defining the threshold values depicts that although there is some variety in the precipitation values for time scales less than 12 months, there is no visual difference between the two indices for yearly time scales. And, the mild and extreme droughts are obtained for yearly precipitation values of less than 628 and 427 mm, respectively. With calculations in return-period precipitation amounts, it is inferred that temperature is a strong dataset in defining precipitation values for return periods greater than 10 years and duration time less than 5 months. Since the findings in this study present physical and practical value, they can be key for stakeholders, policymakers, and end users in water allocation studies. Furthermore, it can be useful in ungauged points with missing data and therefore, if necessary, modification in crop patterns and changes in land use for specific areas can be done. And this study can be more beneficial by adding datasets covering climate change scenarios as future work.
PubDate: 2023-05-31
- Prediction of evapotranspiration and soil moisture in different rice
growth stages through improved salp swarm based feature optimization and
ensembled machine learning algorithm-
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Abstract: Rice cultivation demands adequate soil water balance in each growth stage and estimation of evapotranspiration and soil moisture are the most contributing factors for determining this. Evapotranspiration prediction is essential as it indicates rice water demand in advance depending on several environmental conditions and soil moisture prediction helps to judiciously schedule irrigation based on soil water balance. Several environmental parameters are having impact on evapotranspiration and soil moisture prediction. In order to select correlated environmental parameters with evapotranspiration and soil moisture, salp swarm algorithm which is improved using opposition based learning, local search algorithm, and a control parameter called inertia weight (ISSA) is used. Feature Weighted K-Nearest Neighbor is consolidated with ISSA to assess the quality of selected environmental parameters. Evapotranspiration is predicted using individual machine learning models and ensemble learning but individual machine learning models suffers from high bias and variance in prediction and cannot provide the desired prediction accuracy. Boosting outperform all the models with Mean Absolute Error (MAE) [0.10, 0.03, 0.05], Mean Squared Error (MSE) [0.15, 0.09, 0.109], Root Mean Square Error (RMSE) [0.387, 0.300, 0.330], Nash Sutcliffe Efficiency (NSE) [0.959, 0.948, 0.692] and Coefficient of determination ( \(R^{2}\) ) [0.962, 0.941, 0.697] for evapotranspiration prediction in vegetative, reproductive and ripening growth stages respectively using selected features. Soil moisture is also predicted where Boosting also outperforms other methods with MAE [0.168, 0.131, 0.08], MSE [0.425, 0.142, 0.128], RMSE [0.651, 0.376, 0.357], NSE [0.912, 0.928, 0.698] and \(R^{2}\) [0.918, 0.932, 0.717] in every rice growth stage using selected features.
PubDate: 2023-05-29
- Biometeorological conditions of urban and suburban areas in Bosnia and
Herzegovina-
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Abstract: In this study, an assessment of the thermal conditions and a comparative analysis of the urban and suburban surroundings of Sarajevo (SA) and Banja Luka (BL), Bosnia and Herzegovina, were performed. The study covers the period 2001–2020 and uses hourly observations at 0 h, 6 h, 12 h, and 18 h Universal Time Coordinated. Values of modified physiological equivalent temperature (mPET), one of the commonly used indices, were calculated from basic climate data using the RayMan model. The study results indicate higher and more frequent heat stress in the urban compared to the suburban surrounding during the summer and higher and more frequent cold stress in suburban areas. Due to the climatic characteristics of the area, SA has a higher frequency of cold stress categories than BL, while BL has a higher frequency of heat stress categories. Mean daily and monthly mPET values indicate the mPET urban-suburban difference that follows the definition of the urban heat island. The largest differences between urban and suburban areas appear in midday and evening. However, in the warm part of the year, morning mPETs were lower in urban than suburban surroundings, possibly due to the lack of sun at the urban meteorological stations caused by the layout of buildings. The analysis also revealed unexpected differences between urban and suburban values of meteorological elements in certain parts of the day in SA, showing the used urban station in SA is not the most suitable for urban climatological research.
PubDate: 2023-05-29
- Uncovering the hydro-meteorological drivers responsible for forest fires
utilizing geospatial techniques-
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Abstract: Forest fires have become a growing concern worldwide, with climate change exacerbating their frequency and intensity. In the Simlipal region of India, forest fires are relatively rare; however, in 2021, significant damage occurred in the buffer area’s forests. Understanding the driving factors behind these events is essential for developing effective management strategies. This study investigates the impact of hydro-meteorological conditions on forest fire causes in the Simlipal region by analyzing Terra climatic data and geo-statistics for the period of 1984 to 2021. Long-term trends were determined using non-parametric tests on the Google Earth Engine (GEE) cloud computing platform. Our findings reveal that the maximum burned area location has a decreasing trend in Land Surface Temperature (LST), with a small portion (<10%) showing an increasing trend (0.02 °C/year) near burned locations. Wind speed is decreasing at a rate of −0.006 m/s/year. The sudden forest fires are caused by the combined effect of increasing LST and decreasing wind speed in some areas (<10% of the region). However, the major factor contributing to forest fires in the entire area is the rising trend of annual potential water deficit and actual evapotranspiration, as well as an increasing trend of minimum temperature. The soil moisture deficit during the summer season, especially between 2012 and 2021, contributed to forest fires in the burned area. The soil moisture deficit during the summer season, particularly from 2012 to 2021, played a significant role in the occurrence of forest fires in the affected area. The study emphasized the need for increased attention to this region in order to preserve biodiversity, which was assessed through an analysis of burned severity mapping in GEE (Google Earth Engine). These findings have important implications for future forest management strategies in the Simlipal region. Climate variability is likely to exacerbate the frequency and intensity of forest fires in the region, necessitating effective management strategies to mitigate their impact. Such strategies could involve improving fire prevention and control measures, such as creating fire breaks and increasing the availability of fire-fighting equipment, as well as enhancing forest monitoring systems to detect potential fires early. Additionally, efforts to address climate change, proper management of land use practices, and reduce greenhouse gas emissions could help to mitigate the future impacts of forest fires in the Simlipal region and elsewhere.
PubDate: 2023-05-29
- Analysis of the memory mechanism in the pan evaporation phenomenon by the
band similarity method-
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Abstract: In this study, band similarity (BS) method as a new approach, which allows investigation of the memory features of the evaporation phenomenon, was applied on 7 different meteorological data in addition to the monthly pan evaporation data of Beysehir district of Konya city, located in the middle regions of Turkey. The models required for BS were generated with the artificial bee colony (ABC) optimization algorithm. As a result of the study, it has been observed that ABC optimization algorithm produced sufficient evaporation models. Subsequently, it was concluded that the BS method significantly improved the ABC results by using the temporal similarity mechanism. In this direction, it has been observed that the evaporation phenomenon studied remembers its own past. As a result of the BS method, it can be mentioned that there is a seasonal effect in the memory properties. While the memory weakens in the months when evaporation is high and low, it gets stronger especially in the spring and autumn months. Therefore, it has been concluded that the changes of the parameters affecting evaporation have a more intense effect on memory compared to their intensities. It is thought that this study differs from other studies in the literature because the pan evaporation phenomenon was evaluated from a different perspective and the BS method, which is a new method, and was applied for the first time on a hydrological parameter.
PubDate: 2023-05-29
- Future extreme high-temperature risk in the Beijing-Tianjin-Hebei urban
agglomeration of China based on a regional climate model coupled with
urban parameterization scheme-
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Abstract: Projecting the future extreme high-temperature risk under the background of global warming and urbanization is essential to the collaborative development of Beijing, Tianjin, and Hebei (BTH). In this study, based on the global climate simulation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the fine land-cover data, we use the Weather Research and Forecast (WRF) model coupled with the building effect parameterization (BEP) and building energy model (BEM) at 3-km grid spacing to project the changes in the intensity, frequency, and risk of extreme high temperature over the BTH urban agglomeration. The results show that under the future shared socioeconomic pathway scenario (SSP245), the average extreme high-temperature intensity (EHI) in the BTH will increase by 0.71 °C and 2.12 °C in the middle and late twenty-first century, respectively, compared with that in the reference period (2005–2014), which are 0.23 ℃ and 0.58 ℃ more than that only considering global warming, respectively. The average extreme high-temperature frequency (EHF) will increase by 99 h and 200 h, 53 h and 72 h more than that considering only climate change, respectively. The average high-temperature risk in the BTH for 20-year and 50-year return periods will increase by 1.9 times and 2.4 times in the middle twenty-first century, respectively, and expand to 8.0 times and 12.9 times in the late twenty-first century, respectively. Therefore, it is necessary to take adaptation approaches to reduce the future risk of extreme high-temperature events in the BTH.
PubDate: 2023-05-27
- Detection and quantification of seasonal human heat and cold stress
frequencies in representative existing and future urban canyons: the case
of Ankara-
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Abstract: Based upon a ‘human-centred approach’, combinations of existing and new methodologies were applied to determine how Ankara’s morphological characteristics influenced the magnitude/frequency of Cold Stress (CS) and Heat Stress (HS) to detect/quantify seasonal and yearly human thermal stress frequency. To quantify these conditions upon the human biometeorological system, the Physiologically Equivalent Temperature (PET) was utilised by processing climatic variables from Ankara’s Meteorological Station (AMS). In situ assessments of human thermophysiological thresholds were undertaken within characteristic existing/future Urban Canyon Cases (UCCs), with a further stipulation of three interior Reference Points (RPs). Indoor PET values were moreover calculated within a stereotypical vulnerable residential dwelling. Seasonal frequencies revealed that winter PET values frequently ranged between 0.0 and − 19.9 °C, with corresponding summer values frequently ranging between 35.1 and 46.0 °C. Accounting for Ankara’s urban morphology, yearly frequency of No Thermal Stress remained at ~ 48%, CS remained at ~ 26%, and HS ~ 28%. HS varied the most between the eight evaluated Aspect Ratios (ARs). It reduced by up to 7.1% (114 min) within the Centre (RPC) area of UCCs with an orientation of 90°. Out of twelve orientations, the highest HS frequency took place between 105 and 135°. Including in UCC3.50, the frequency of HS almost always remained above 72% (2592 min). Graphical 
PubDate: 2023-05-27
- Spatial analysis and optimization of raingauge stations network in urban
catchment using Weather Research and Forecasting model-
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Abstract: Hydrological modelling, especially in urban catchments, relies heavily on accurate rainfall data collection. Therefore, rainfall estimation and establishing a network of raingauge stations is essential, especially for regions with a limited number of stations. To simulate rainfall, the Weather Research and Forecasting (WRF) model was used in this study based on the six schemes including Lin, WSM3, WSM5, WSM6, WDM5, and WDM6. Furthermore, optimal spatial design of raingauge networks has been achieved using geostatistical and deterministic interpolation methods of Radial Basis Function (RBF), Local Polynomial Interpolation (LPI), Co-Kriging (COK), Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), and Empirical Bayesian Kriging (EBK). Hence, the error reduction in estimating rainfall on non-station points was considered as an indicator to determine the optimal location of stations. This study was conducted in the Sabzevar urban catchment in northeastern Iran, which faces significant flood damages in a few raingauge stations. Initially, the 24-h rainfall data on the five events (from winter to spring 2019 ~ 2020), covering all types of rainfall associated with the seasons, were selected for analysis. The results revealed that among the WRF model schemes, the Lin was chosen as the most desirable scheme to simulate the rainfall in the catchment. A positive verification criterion result between 0.65 and 1 also shows that rainfall values can be estimated efficiently by this scheme over a distance of 18.85 km from the observational raingauge station. Furthermore, based on the interpolation results, the RBF method with the highest R2 (98%) was the most accurate method for the optimal location of the stations in non-station points, i.e., the WRF model outputs with observational stations. Overally, it can be concluded that using the WRF meteorological model combined with the RBF interpolation method could be appropriate for simulating the rainfall, designing the raingauge stations, and forecasting highly accurate 24-h rainfall, especially for the urban catchment without stations. The proposed approach used in this study is also recommended to develop the optimal design of raingauge networks of non-station points in large catchments.
PubDate: 2023-05-27
- Temporal resolution of climate pressures on façades in Oxford
1815–2021-
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Abstract: Changes in climate will exert increasing pressure on heritage, so standard climate metrics need to be tuned to heritage threats. Historical meteorological records are commonly available as monthly summaries, with few offering daily observations as daily readings may not have been taken or yet digitised. As data averaged over longer intervals misses short weather events, we investigate the extent to which temporal resolution is important for assessing climate pressures on façades. The Radcliffe Meteorological Station, Oxford, UK, provides the longest continual record of daily temperature and precipitation measurements in the UK. We use this record to assess the role of temporal scale in heritage climate parameters relating to (i) sunshine and warmth, (ii) rainy days and (iii) freezing events. Where there is a linear relationship between daily and monthly scale data, monthly observations can be interpolated as heritage climate parameters. However, for the majority of parameters, daily data was required to capture the variability in the datasets. We argue for the increased availability of daily observations to help assess the threat of climate to heritage.
PubDate: 2023-05-26
- Solar global irradiance from actinometric degree data for Montsouris
(Paris) 1873–1877-
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Abstract: In the past, long-term recordings of solar radiation energy were not commonly conducted. However, several observations using the Arago-Davy actinometer were made in different parts of the world during the nineteenth century. In this paper, we propose a method to convert actinometric degree data into information on global solar irradiance on a horizontal surface. We utilized hourly actinometric degree data from the Montsouris Observatory in Paris recorded between 1873 and 1877. Three models were tested for estimating solar global irradiance at ground level from actinometric degrees. Despite the quality of the solar irradiance data provided by the Twentieth Century Reanalysis Project version 3 (20CRv3) was subjected to criticisms, these data are used here as a reference, taking into account the lack of similar data for the nineteenth century. One of the main challenges in this study is the fact that the solar irradiance incident on the Arago-Davy actinometer is not global solar irradiance on horizontal surface, \({G}_{H}\) , a quantity which is usually measured and recorded. To address this issue, a proxy value of \({G}_{H}\) , named \({G}_{H}^{+}\) , is defined by using the first sub-model of the Ferrel-Pouillet model (named FP1). The analysis showed that \({G}_{H}^{+}\) is suitable for estimating both hourly and daily averaged values of the global solar irradiance \({G}_{H}\) . However, the model FP1 requires two input parameters, namely the actinometric degree \(D\) and the bright-bulb thermometer temperature \({t}_{{\mathrm{bright}}-{\mathrm{bulb}}}\) , while most long-term observations on the globe have data only for \(D\) . This makes the FP1 model less useful in these cases. To address this, a method was proposed that relies only on \(D\) as an input parameter. This method provides hourly and daily averaged irradiance data that are similar to those obtained using the FP1 model with two input parameters. The accuracy of this method is expected to remain the same in colder climates but decrease in warmer climates. The proposed method also provides credible lower and upper bounds for the interval of variation of monthly averaged irradiance. Additionally, at the level of monthly averaged solar irradiance data, the proposed method and the 20CRv3 project seem to support each other.
PubDate: 2023-05-24
- Correction to: Competing effects of vegetation on summer temperature in
North Korea-
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PubDate: 2023-05-24
- Future changes in heatwaves characteristics in Romania
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Abstract: The changes in the characteristics of heatwaves over Romania have been analyzed using the excess heat factor calculated for two climate change scenarios (RCP4.5 and RCP8.5) from the EURO-CORDEX project. The changes were evaluated for the near future (2021–2050) using the historical period (1971–2000) as reference. The frequency of occurrence and the duration of heatwaves is projected to increase for both climate scenarios in particular over southern Romania. In this region, the percentage of change in the near future for the number of heatwaves is between 50 and 60% for the RCP4.5 scenario and 60–80% for the RCP8.5 scenario. Also for the same region, the duration of heatwaves will increase by 30–50% for the RCP4.5 scenario and 60–80% for the RCP8.5 scenario. These results indicate that the human exposure to heatwaves will increase in Romania in the near future. To increase awareness on heatwaves and their impact, we propose a series of immediate actions that include (1) improving the communication of the impact of heatwaves, (2) identification of the regions where the population is more vulnerable to heatwaves, and (3) better understanding of the mortality and morbidity associate with heatwaves in Romania.
PubDate: 2023-05-23
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