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Melting layer usually exists in precipitation where ice particles gradually melt into liquid particles. Utilizing the detection from the Dual-frequency Precipitation Radar onboard Global Precipitation Measurement Mission Core Observatory during 2018-2022, this paper investigates the quasi-global climatological features of Melting layer over 65°S ~65°N and analyzes the relationships with elevation in the golden case involving the section along 32.5°N across China. The distribution of Melting layer is latitude-dependent: Melting layer is higher in 30°S~30°N and decrease toward the mid-and high-latitude, which are generally lower in 30°S~65°S throughout the year. The height and thickness of Melting layer change more dramatically in mid-and high latitudes than in low latitudes for diurnal variations. The higher elevation terrain has the lower Melting layer top height than other terrains, yet the geometric thickness of Melting layer is also thinner. A golden case is selected to represent the analysis of Melting layer play role in rainfall. When the Melting layer grow thinner with steeply rising elevation due to the thermal change and orographic uplift, there is weak rain with a greater number of smaller raindrops in the golden case. However, Melting layer can get widen and raindrops can get sufficient growth as the elevation declines. [ABSTRACT FROM AUTHOR]

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

The applicability of the four-parameter (4P) kappa distribution to fit the annual maximum (AM) and the peak over threshold (POT) extreme precipitation series in the Amman Zarqa Basin (AZB), Jordan, is assessed. This paper presents a practical, easy-to-implement, yet accurate method in light of the method of L-moments for parameter estimation of 4P kappa. Based on the results from the data analysis of daily precipitation records during a period that exceeded 50 years at 23 weather stations, the 4P kappa distribution can fit the precipitation extremes for both the AM and POT series for each weather station well. The 4P kappa distribution can be employed in place of its three-parameter distributions typically selected for simulating precipitation extremes (i.e., generalized extreme value (GEV), generalized logistic (GLO) distribution for AM series, and generalized Pareto (GP) distribution for POT series). The bootstrap resampling for performance evaluation beyond the dataset length shows that the 4P kappa distribution outperforms its three special cases (GLO, GEV, and GP distributions) in the context of modeling the AM and POT series for all sampling sizes. Additionally, in representing the distribution's upper right tail quantile estimations, the 4P kappa distribution performed better and, in some circumstances, was comparable to its three special cases (GLO, GEV, and GP distributions). The outcomes of this study and the method employed should lay the groundwork for wider implementation of the 4P kappa distribution in hydrological and climate science applications, as well as in other disciplines. [ABSTRACT FROM AUTHOR]

The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

The Ångström–Prescott equation defines generically the relationship between solar energy available at ground level and sunshine duration. From the very beginning in the history of the solar energy converters, the equation was extensively used to estimate the amount of collectable solar energy. In the present paper, we propose a different way of looking to the Ångström–Prescott equation, namely, to use it for the estimation of cloud and clear sky atmospheric transmittances. The key tools are the mathematical expressions which give the clear sky and cloud transmittances in terms of the Ångström–Prescott empirical coefficients. The theory is verified by comparing the mean all-sky atmospheric transmittance (innovatively inferred from the cloud and clear sky atmospheric transmittances) with the measured clearness index. Reasonably good concordance between the two quantities has been found. It was shown that the best performance is obtained with the linear Ångström–Prescott equation. Higher-order polynomial equations tend to overfit the measurements. Examples are also shown about how to use the linear Ångström–Prescott equation to obtain information about the time series stationarity and the seasonal sensitivity of the cloud and clear-sky transmittances. The present point of view gives the perspective to estimate past cloud and clear-sky climatology in places where the Ångström–Prescott equation has been fitted to the sunshine data. [ABSTRACT FROM AUTHOR]

To study the climatology of thunderstorm activity (TA) in the south of the Russian Far East according to data of 34 weather stations and data of the World Wide Lightning Location Network (WWLLN) for 2009–2018, the optimal radii of the circular area of the WWLLN data sample centered at each station were obtained. The optimal radii, at which the correspondence between data of thunderstorm observations and lightning detections at weather stations is maximum, were estimated by two statistical methods based on the calculation of the Threat Score and the difference of days with TA. Taking into account the previously published results, the values of the optimal radii vary from 5 to 39 km and, on average, in the south of the Russian Far East are 23 km. Differences in the estimates of radii for the mainland, coastal, and island (Sakhalin) stations were noted, the average values for which were 25 km, 23 km, and 19.5 km, respectively, as well as for nighttime and daytime. In the south of the Far East as a whole and at the mainland and coastal stations separately, the "nighttime" optimal radii are on average larger than the "daytime" ones, on Sakhalin—on the contrary. The values of optimal radii obtained by two methods were used to estimate the average annual number of lightning WWLLN days for each weather station, which were compared with the average annual number of thunderstorm days based on human observations. It was found that the difference between them at all stations was within ±6 days/year and up to 1.3 days/year, the root mean square deviations were 2.4 days/year and 0.4 days/year for the first and second methods, respectively. It is shown that the WWLLN data reflect the main features of the intra-annual TA variation according to weather station data, and also make it possible to identify individual thunderstorms during TA variation at a diurnal scale, including those not noted in the reports of weather stations, and mesoscale structures formed by cumulus clouds in the spatial distribution of lightning. [ABSTRACT FROM AUTHOR]

An objective cyclone detection and tracking analysis is performed for the Mediterranean region with the use of 6-hourly (00, 06, 12, and 18 UTC) 1° × 1° mean sea-level pressure data obtained from the ERA5 database for the period 1950–2018. At first, the main cyclogenesis and high-density areas of cyclones are identified. Next, principal component analysis and cluster analysis are performed, classifying the detected cyclone trajectories into 12 clusters. In the following step, the application of the above methodology, this time on the intra-annual variations of the 12 cyclone clusters' frequencies leads to the objective definition of four seasons, which generally correspond to the conventional ones, but they present differences in their limits and duration. The results of this method are also compared with the ones of two other methods for defining the seasons, which are based (i) on the long-term mean intra-annual variations of various meteorological parameters and (ii) on the intra-annual variations of the frequencies of defined weather types, revealing larger winter and shorter spring seasons. Finally, a composition of all three aforementioned approaches is attempted, leading to the following seasons: "winter" (November 16–March 25) which lasts more than 4 months, "spring" (March 26–June 11) with a duration of approximately 2.5 months, "summer" (June 12–September 12) which lasts about 3 months, and "autumn" (September 13–November 15) with a duration of about 2 months. The long-term changes of the above seasons' characteristics are also examined. [ABSTRACT FROM AUTHOR]

This work proposes a probabilistic forecast of seasonal precipitation in the basins of the Limay, Neuquén, and Negro rivers in the north of Argentine Patagonia. The Comahue region is particularly important because part of the country's hydroelectric energy is generated there. The amount of winter precipitation modulates the flow of rivers, and therefore, prior knowledge of possible precipitation thresholds is very useful for decision-makers. Ensembles made up of statistical models that explain more than 50% of the precipitation were used and were generated with multiple techniques such as linear regression, generalized additive models, support vector regression, and artificial neural networks. The result showed that the forecasts are better in Limay and Neuquén river basins in winter than in Negro River basin. Brier Skill Score values indicate that the probabilistic forecast is better than the climatology in winter, in Neuquén and Limay basins for below and above normal categories. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau Vortex (TPV) is an important low-pressure system that affects the weather and climate in the Qinghai-Tibet Plateau. It is of great importance to study the water cycle of the TPV to further understand the TPV and its resulting disastrous weather. This study is different from the previous studies of TPV cases or of the atmospheric water cycle of the Qinghai Tibet Plateau. Focusing on the moving TPV weather system, from the perspective of climatology, the characteristics of the atmospheric water cycle of the TPV and the atmospheric circulation that affects the activity of the TPV water cycle are studied using statistical methods based on the reanalysis data. The results show that the TPV water cycle is mainly affected by external circulation. The overall water vapour utilization efficiency of the TPV water cycle first decreases and then increases, and its activity degree first decreases and then increases. The high-value area of TPV precipitation is located in the middle of the plateau and annual TPV precipitation has a good correlation with TPV water cycle activity. The large-scale system is closely related to the vortex water cycle of the plateau. When the intensity of the South Asian high (SAH) is relatively strong, the West Pacific subtropical high (WPSH) advances westward, and there is a stronger warm rising area over the main body of the plateau. When the southerly airflow on the south side of the low-level plateau is strong, the water cycle of the TPV is more active, easily moves eastward and produces more precipitation, and vice versa. [ABSTRACT FROM AUTHOR]

Specifying seasons is one of the most significant and arduous parts of climatology studies. In the present study, seasons are defined to apparent temperature index. For determining changes in the length, start, and end dates of the season in Iran in line with global climate change, in this research we used daily data of apparent temperature for 32 synoptic stations with locations and topography over a 60-year period (1959–2018). Temperature indices used for determining the start of seasons are thresholds of apparent temperatures of 0 and 20 degrees, with a 10-day continuation and without reverting to the initial conditions during this period for winter and summer, respectively. In the same vein, transitional seasons were specified and changes were analyzed using linear regression. The findings revealed that, in various regions, the start of spring and summer occur earlier while the start of fall and winter are delayed. The biggest statistically significant changes are observed at the start of the spring and fall seasons, as the increase is 2.25 days for summer and 1.5 days for winter per decade. Therefore, the duration of cold seasons have decreased and the duration of warm seasons have increased in climatic regions. These changes are statistically significant in foothill and high-altitude regions; the hot and dry climatic range is expanding with the increase and decrease of the summer and winter length, respectively. However, considering the inhomogeneity of altitude and geographical phenomena in the Iranian plateau and its flat northern and southern regions, these changes are not uniform or equivalent. We can say that, overall, climate change, as a direct factor influencing season change and climatic belts, plays a more influential role in comparison with local factors. [ABSTRACT FROM AUTHOR]

Temperature has a control on growth, development and yield of crops. Extreme high and low temperatures can be a limiting factor on crops. Extreme temperatures have accompanied global average temperature increase. Investigating the changes in indices derived using daily minimum and maximum temperatures may provide information about how extreme temperatures related to crop production have changed under global warming. In this sense, it is aimed in this study to investigate the climatology and monotonic temporal trends of a number of temperature-based agroclimatic indices across western Anatolia, Türkiye. The indices included number of frost days (NFD), first fall/winter frost (FFF), last winter/spring frost (LSF), frost period (FP), accumulated frost (AF), plant heat stress day (PHSD), plant heat stress accumulation (PHSA), growing season start (GSS), growing season end (GSE), growing season length (GSL) and growing degree-day (GDD). Significance of trends was assessed using Mann–Kendall test, and trend rates were quantified by Sen's slope estimator. The results firstly showed that southern low-elevation stations have the lowest frequency, intensity and duration of frost as a result of rarely occurring frost events. These stations are also characterized by the highest GDD, the earliest GSS, the latest GSE and hence the longest GSL, even with never-ending growing seasons in many years. They are also exposed to the highest heat stress in terms of frequency and intensity. On the other hand, northern high-elevation stations have completely opposite character for all indices in comparison to southern low-elevation stations. Trend analysis revealed that frequency of frost events had decreasing trends (i.e., decreased NFD), accompanied by later onset of FFF, earlier onset of LSF and hence shortened FP and decreased AF. As for growing season indices, in general, GSS occurred earlier, GSE occurred later and GSL extended. Growing degree-day and heat stress indices had substantial increasing trends, implying that, over the course of study period, the study area had more thermal accumulation and exposed to more heat stress that may hinder crop development. [ABSTRACT FROM AUTHOR]

Temperature inversions are a common feature of the Arctic climate, affecting the surface energy budget and planetary boundary layer transports. This study investigates the evolution of large-scale temperature inversions (between 925 hPa and 2 m) in the context of a changing climate. To this end, two five-member regional climate model (RCM) ensembles, driven by the Canadian Earth System Model, spanning the 1950–2099 period, corresponding to two greenhouse gas emission scenarios (RCP 4.5 and 8.5), are considered. An ERA-Interim driven simulation for the 1979–2005 period is also considered to assess model performance. A comparison of observed atmospheric soundings with the boundary layer variations in the reanalysis-driven simulation indicates that the model captures the temperature inversion characteristics reasonably well, with some positive biases in the temperature inversion strength and frequency. The transient regional climate change simulations suggest substantial decreases in both temperature inversion strength and frequency in winter in future climate for both emission scenarios. These changes are consistent with the reduced sea ice cover and the associated increase in cloud cover that reduce the surface radiative cooling necessary for the formation of strong temperature inversions. Some increases in the frequency and strength of temperature inversions are projected for summer over the Arctic Ocean, possibly linked with increased poleward moisture transport. [ABSTRACT FROM AUTHOR]

The present study evaluates five observed gridded precipitation datasets [Global Precipitation Climatology Centre (GPCC), Climate Prediction Centre (CPC), Climatic Research Unit (CRU), Cressman Interpolated High-resolution Gauge-based Gridded Observations (CIHGGO), and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI)] and five reanalysis products [ERA-Interim and ERA5 of European Centre for Medium-Range Weather Forecasts, Twentieth Century Reanalysis (20CR), Japanese 55-year Reanalysis (JRA55), and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2)] against surface precipitation gauge data as reference for the period 1981–2015 over Pakistan. The performance of the above gridded datasets is assessed using six statistical metrics: correlation coefficient, relative bias, root mean square error, mean absolute error, wet/dry years, and precipitation centroid on monthly, seasonal [summer (June–September), and winter (December–March)], and annual timescales. Our results show that GPCC has overall much better performance across an entire country (avg. correlation > 0.95) in terms of all timescales and statistical metrics used. EWEMBI1 displays comparable results to GPCC with higher correlation and lower error values and thus can be ranked as the second-best performing observed gridded precipitation dataset. On the other hand, reanalysis products are found relatively weak in approximating the spatiotemporal distribution of precipitation, especially over complex northern areas of Pakistan. However, ERA5 exhibits a comparatively good positive linear relationship with surface precipitation gauge data at monthly (0.92), seasonal [0.89 (summer) to 0.98 (winter)], and annual (0.87) timescales, which may be attributed to an advanced data assimilation technique and model dynamics employed in the generation of the data. [ABSTRACT FROM AUTHOR]

The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used for spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2–4.5, and 5–8.5) and two future time horizons, early (2020–2059) and late (2060–2099). The compromise programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975–2014). Three models, namely, ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2–0, showed the highest skill in simulating all three variables (precipitation, maximum temperature, and minimum temperature), and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5 − 2.5 °C, 2.7 − 4.3 °C, and 4.5 − 5.3 °C and minimum temperature by 1.3 − 1.8 °C, 2.2 − 3.5 °C, and 4.6 − 5.2 °C for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, in the later period. Meanwhile, the changes in precipitation in the range of − 15 − 18%, − 36 − 47%, and − 40 − 68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter. [ABSTRACT FROM AUTHOR]

Lightning is an electrical discharge — a 'spark' or 'flash' as charged regions in the atmosphere instantly balance themselves through this discharge. It is a beautiful and deadly naturally occurring phenomenon. In June 2020, more than a hundred people died in the state Bihar of India only in 3 days' span due to lightning events. In this study, Lightning Imaging Sensor (LIS) from the Tropical Rainfall Measuring Mission (TRMM) satellite with a very high spatial resolution of 0.1° has been utilized to create the lightning climatology of India for 16 years during the period 1998 to 2013. Diurnal, monthly, and seasonal variations in the occurrence of lightning flash rate density have also been analyzed. TRMM satellite Low-Resolution Monthly Time Series (LRMTS) with 2.5° resolution datasets have been used for lightning trend analysis. The diurnal lightning event mainly occurs in the afternoon/evening time duration (1400–1900 h) around 0.001 flashes/km2/h. The highest lightning occurred in the month of May (0.04 flashes/km2/day) and the least in December (0.005 flashes/km2/day). The distribution of lightning flash counts by season over India landmass is mainly in pre-monsoon months (MAM) which ranges from 0.248 to 0.491 flashes/km2/day, and monsoon (JJA) ranges from 0.284 to 0.451 flashes/km2/day and decreases afterward. Spatially, the distribution of lightning flash density is seen over the North-Eastern region along with Bangladesh, Bihar, Jharkhand, Orissa, and Jammu and Kashmir region. The pre-monsoon (MAM) season shows a positive trend of lightning around 0.04 flashes in the North-Eastern region and the post-monsoon (SON) shows a negative trend of − 0.021 in central India. The CAPE and K-Index have positively correlated with the flash rate density seasonally but CAPE is more significantly correlated around 0.83 during pre-monsoon and monsoon season. This study also focused on finding out the district wise lightning hotspots regions of India. Rajouri and Reasi district in Jammu and Kashmir has the highest lightning with 121 and 115 flashes/km2/yr, respectively. [ABSTRACT FROM AUTHOR]

Colombia's lightning climatology was studied using 16 years of high-resolution data from the Tropical Rainfall Measuring Mission Lightning Imaging Sensor (TRMM LIS). The findings present the climatology of the country organized in two ways: where lightning occurs and when lightning occurs. The authors sorted data using Colombia's six natural regions and conditional mean values for day, month, quarter, and year for frequency occurrence. The findings show a high mean flash rate density (FRD) around 70 fl km - 2 year - 1 in 15 % of the territory. Most of this activity concentrates in the northern part of the country between latitude 7 ∘ N and 11 ∘ N. Regarding time patterns, lightning reaches its maximum in most of the territory two times a year with a transition period marked by a steep fall. But, the areas with higher activity seem to have a single lightning season, with a maximum around August. These findings confirm the need to consider lightning activity based on regional contexts. During the day, activity is more probable from afternoon to evening in the southern part of the country; meanwhile, it tends to go from afternoon to early morning in northern regions. [ABSTRACT FROM AUTHOR]

The variability in duration of withdrawal phase of southwest monsoon over India is often witnessed to influence the northeast monsoon rainfall of India as withdrawal phase is confined between the cessation of southwest monsoon and onset of northeast monsoon. The influence of withdrawal process of southwest monsoon on the variability in southwest monsoon rainfall has been well discussed; however, its impact on the variability in northeast monsoon rainfall is yet to explore. The present investigation is carried out from 1986 to 2016 to identify the important changes associated with withdrawal phase of southwest monsoon and the related variability in northeast monsoon rainfall. Different withdrawal events from 1986 to 2016 is thus divided into three phases: long withdrawal phase (LWP) (> 40 days), medium withdrawal phase (MWP) (30–40 days) and short withdrawal phase (SWP) (20–30 days). It is observed from the study that factors responsible for the variable withdrawal phase of southwest monsoon shifts more towards south/southeast directions during SWP compared to LWP. LWP is accompanied with low rainfall activity along with short length of rainy season over southern peninsular India during the northeast monsoon. Conversely, a reverse result is observed in the case of SWP. The result further shows that LWP and SWP are associated with opposite discernible features during the north–south movement of the monsoon system; however, MWP acts as a mediator and may be considered as standard or normal withdrawal phase of southwest monsoon based on 30 years (1986–2016) climatology. Early/delayed withdrawal of southwest monsoon has no association with the duration of withdrawal phase. [ABSTRACT FROM AUTHOR]

The ability of any climate model to reproduce present-day climate fields is very important for the confidence in the projections of the future state of the same fields. It is against this background that the performance of the Global Coupled configuration 2 (GC2) of the Unified Model (UM), a Global Climate Model (GCM), was evaluated over the West African region (a climate-sensitive region) for two periods on 3 climatological time scales for precipitation (using the Global Precipitation Climatology Center data) and 2-m temperature where the Climate Research Unit Time Series dataset was used. The GC2's performance is also compared with 4 Coupled Model Intercomparison Project phase 6 (CMIP6) GCMs. Results indicate that spatial biases are of similar patterns across the domain for the two study periods for both variables, demonstrating the consistency of the model in reproducing the two climate variables over West Africa (WA). The simulation of temperature by the UM is better than precipitation. Biases are larger in the UM and the CMIP6 GCMs than the levels of observational uncertainties for precipitation, but for temperature, biases in all the models are within the levels of uncertainties found in the observations. Also, the holistic performance of the UM in the two fields is relatively better than the 4 CMIP6 GCMs. The two variables are also simulated better in the southern parts of the region than the northern parts of the domain by the UM. The analysis of the UM suggests a strong linkage between the two variables and therefore, a detailed investigation in how the model reproduces precipitation and temperature-based processes over WA is recommended in order to enhance the model's simulations of these climate variables. Also, the model is recommended for future investigations of precipitation and temperature climatologies over WA. [ABSTRACT FROM AUTHOR]

This study examines the seasonal forecast of the North American Multi-Model Ensemble (NMME) over Central Africa (CA), which encompasses a region of the world where the economies of the countries are highly dependent on agriculture and livestock breeding. Following many regional climate perspectives, we evaluated the seasonal forecast over the 4 seasons: December to February (DJF), March to May (MAM), June to August (JJA), September to November (SON) between 0 and 5 months lead time before the beginning of each season. Deterministic and categorical approaches which focus on the rainfall variable were used to assess NMME ensemble mean (MME). The observed and predicted rainfalls have been divided into three categories: below normal, normal, and above normal. The results show that for 0 to 2 months lead time, the MME reproduces well the peak rainfall of the Atlantic coast and in the East of Democratic Republic of Congo in MAM and SON between 9 and 10 mm/day. Again in the same lead time interval, values of correlation coefficients (R) between the MME and the Global Precipitation Climatology Center (GPCC) reference observation of all seasons are greater than 0.72. For 3 to 5 months lead time, lower values of R are observed. It follows that probabilities of detection (POD) are greater than 50% for all different normal seasons and less than 45% for below and above normal seasons. On the other hand, high false alarm (FAR) values and low Critical Success Index (CSI) values are observed for both below and above normal seasons. From our results, one can argue that the NMME seems to be an interesting tool during the first three forecasting lead times in CA capable of providing important seasonal characteristics before the start of each season, which will allow proper consideration of meteorological phenomena. [ABSTRACT FROM AUTHOR]

Precipitation irregularity constitutes a constraint for natural systems and socio-economic activities, particularly in water-scarce environments. Standard variability statistics such as the standard deviation, variance, and coefficient of variation do not consider the chronological order of these values. In Climatology, however, the temporal order of meteorological events is a relevant factor that can affect natural and socio-economic systems. In order to evaluate the disparity between consecutive values in precipitation series, we applied the Consecutive Disparity Index (D) to the monthly grid with the highest spatial resolution (10×10 km) existing in Peninsular Spain for the period December 1915–November 2015. Monthly, seasonal, and annual D values show an increase from north to southwest, especially in July and August. The D values for the month-to-month correlative series and for monthly mean precipitation reveal a relatively similar pattern. In the latter case, however, the low values are recorded towards southern Spain, following some mountain ranges in the Centre-East of the territory. Monthly, seasonal, and annual precipitation values are also negatively correlated with the corresponding D values. [ABSTRACT FROM AUTHOR]

This study compared precipitation projections of CMIP5 and CMIP6 GCMs over Yulin City, China. The performance of CMIP5 and CMIP6 GCMs in replicating Global Precipitation Climatology Centre (GPCC) precipitation climatology of the city was evaluated using different statistical metrics. The best performing GCMs common to both CMIP5 and CMIP6 were finally selected and subsequently downscaled to GPCC resolution using linear scaling method to assess spatiotemporal changes in precipitation in the basin. The study revealed BCC.CSM1.1(m), IPSL.CM5A.LR, MRI.CGCM3, and MIROC5 of CMIP5 and their equivalents BCC-CSM2-MR, IPSL-CM6A-LR, MRI.ESM2.0, and MIRCO6 of CMIP6 as the most suitable GCMs for the projection of precipitation in Yulin. This study revealed changes in precipitation in the range of −14.0 to 0.0% and −22.0 to 0.2% during 2021−2060 for RCP4.5 and SSP245 scenarios, respectively. The precipitation was projected to decrease more during 2061–2100 for both the scenarios. The highest decrease of −29.7 to −22.0% was projected by MRI-ESM-2-0 for SSP2-45, while −28.0 to −20.0% by MIROC5 for RCP4.5. For RCP8.5 and SSP5-85 scenarios, precipitation was projected to decrease in the range of −17.0 to −2.0% and −32.0 to 0.0%, respectively, during 2021–2060 by most of the GCMs. An increase in precipitation up to 12.3% was projected only by IPSL-CM5A-LR for RCP85 for this period. A further decrease in precipitation was projected by all GCMs during 2061−2100 for both RCP8.5 and SSP5-85 scenarios. The highest decrease was projected by MIROC5 (−40.2 to −29.0%) for RCP8.5 and IPSL-CM6A-LR (−40.2 to −26.0%) for SSP5-85. Overall, the results revealed a higher decrease in precipitation in Yulin City by CMIP6 GCMs compared to that projected by their corresponding GCMs of CMIP5 for both scenarios. This study can be of significance in the planning and mitigation of climate change as it gives insight into the expected changes in precipitation and the possibility of the choices of the best performing GCMs. [ABSTRACT FROM AUTHOR]

It is widely known that the North Atlantic Oscillation (NAO) has an influence on precipitation in Europe. There are other modes of variability such as the East Atlantic (EA) pattern, the East Atlantic/West Russia pattern (EA/WR), the Scandinavia pattern (SCAND), and the Pacific/North American pattern (PNA), which characterize the climate variability in the Northern Hemisphere. In this study, the influence of the EA/WR pattern on precipitation over Europe is examined, with a focus on Serbia. In addition, we explore the combined effects of positive and negative phases of the NAO and EA pattern with the EA/WR pattern, on precipitation over Europe and Serbia. Precipitation data are obtained from the Global Precipitation Climatology Centre (GPCC) and 15 stations from the national observational network in Serbia. Precipitation anomalies have been explored in relation to positive and negative phases of the EA/WR pattern using the monthly GPCC dataset. For the negative (positive) EA/WR phase, positive (negative) precipitation anomalies prevail over the Balkan Peninsula and central Mediterranean in January, February, May, and November, while a negative (positive) one dominates in March, April, September, and during the summer months. There is a strong positive (negative) signal over Portugal in October and over Greece and the Ionian Sea in December, for the positive (negative) EA/WR phase. The precipitation anomalies associated with the negative phase of the EA/WR pattern reflect above (up to 6 mm) average precipitation over Serbia. The positive phase of the EA/WR pattern caused precipitation anomalies below (up to − 8 mm) average precipitation over Serbia. We find that the NAO and EA patterns enhance the influence of the EA/WR pattern on precipitation changes over Serbia. High positive values of precipitation anomalies existed over Serbia for the negative phases of the EA/WR pattern and NAO (up to 12 mm), and EA pattern (up to 10 mm). Negative precipitation anomalies (up to − 12 mm) over Serbia are caused by the positive phase of the EA/WR pattern in combination with the positive phases of the NAO and EA pattern. [ABSTRACT FROM AUTHOR]

During strong El Niño events, below-average rainfall is expected in large parts of southern Africa. The 1992 El Niño season was associated with one of the worst drought episodes in large parts of South Africa. Using reanalysis data set from NCEP-NCAR, this study examined circulation types (CTs) in Africa south of the equator that are statistically related to the El Niño signal in the southwest Indian Ocean and the implication of this relationship during the 1992 drought episode in South Africa. A statistically significant correlation was found between the above-average Nino 3.4 index and a CT that features widespread cyclonic activity in the tropical southwest Indian Ocean, coupled with a weaker state of the south Indian Ocean high-pressure. During the analysis period, it was found that the El Niño signal enhanced the amplitude of the aforementioned CT. The impacts of the El Niño signal on CTs in southern Africa, which could have contributed to the 1992 severe drought episode in South Africa, were reflected in (i) robust decrease in the frequency of occurrence of the austral summer climatology pattern of atmospheric circulation that favors southeasterly moisture fluxes, advected by the South Indian Ocean high-pressure; (ii) modulation of easterly moisture fluxes, advected by the South Atlantic Ocean high-pressure, ridging south of South Africa; (iii) and enhancement of the amplitude of CTs that both enhances subsidence over South Africa, and associated with the dominance of westerlies across the Agulhas current. Under the ssp585 scenario, the analyzed climate models suggested that the impact of radiative heating on the CT significantly related to El Niño might result in an anomalous increase in surface pressure at the eastern parts of South Africa. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are major meteorological hazards for coastal communities around the world. Among the most active TC development basins are the North Atlantic (NATL) and the eastern North Pacific (ENP), both of which bring impacts to the USA and other countries of North America. This research assesses the climatology of occurrence dates of nth (e.g., 1st and 2nd,) TCs and hurricanes in both basins, as well as occurrence dates of accumulated cyclone energy thresholds. We use statistical methods to develop a base climatology of occurrence dates and assess their trends over time, as well as correlate early season nth TC and hurricane occurrence dates with overall seasonal activity. We find that the climatologies of NATL and ENP occurrence dates exhibit similarities but unique differences that relate to the TC climatologies of their basins. In addition, we find that NATL nth TC occurrence dates generally have been trending earlier with time, while no trends were identified for the ENP or for hurricanes in either basin. Lastly, we find that early season TCs possess the ability to postulate overall seasonal activity in both basins, the strongest correlations in each basin coming from the 3rd and 4th TCs in the NATL and the 5th and 6th TCs in the ENP. Results of this study have utility in improving seasonal TC forecasts for the NATL and ENP made by 1 September and 1 August, respectively, as well as providing a baseline for modeling studies. [ABSTRACT FROM AUTHOR]