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Climate models provide information that resource managers, policy makers, and researchers can use when planning for the future. While this information is valuable in the broad sense, the low spatial-resolution often lacks local details that resource managers and decision makers need to plan for their communities. Therefore, statistically downscaled climate projections provide a high-resolution output and offer local information that is more beneficial than coarse-resolution global climate model output. In the Red River Basin, located in the south-central U.S., this detailed information is used to develop long-term water plans. This area is prone to drought conditions and heavy precipitation events, and studies have consistently estimated that this will continue in the future. This paper introduces a dataset of statistically downscaled climate projections of daily minimum and maximum temperature and daily precipitation that is a useful tool for studies regarding climatological and hydrological aspects in the region. The dataset was created using two quantile mapping techniques to downscale the CCSM4, MPI-ESM-LR, and MIROC5 model outputs to a 0.1-degree spatial resolution. Furthermore, we describe the added value of coproduction of knowledge between climate scientists and end users, or in this case impacts modelers and decision makers, for creating climate projections that can be used for climate risk assessments. A case study of the data's development and application is provided, detecting the mean daily changes in temperature and precipitation through the end of the century in the Red River Basin for two representative concentration pathways. After applying the users' inputs to develop the datasets, results for this example estimate an increase in mean daily precipitation in the eastern portion of the basin and as much as a 15% decline in the west by the end of the century. Furthermore, mean daily temperature is expected to rise across the entire basin in all scenarios by up to 6–7°C. [ABSTRACT FROM AUTHOR]

The Abbay River Basin, which originates in Ethiopia, is a major tributary and main source of the Nile River Basin. Land cover and vegetation in the Abbay River Basin is highly susceptible to climate change. This study was conducted to investigate the trends of climate change for a period of thirty-six years (1980–2016) within selected stations of the basin by using the innovative trend analysis method, Mann–Kendall test, and Sen's slope estimator test to investigate the mean annual precipitation and temperature variables. Changes in land cover and vegetation in the Abbay River Basin were studied for a period of thirteen years (2001–2013) by using remote sensing, GIS analysis, land cover classification, and vegetation detection methods to assess the land cover and vegetation in the basin. In addition, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Transformation Matrix were employed to analyze the spatial and temporal patterns of land cover and vegetation impacted by changes in climate. The result reflects that the trend of average annual temperature was remarkably increased (Φ = 0.12, Z = 0.75) in the 36-year period, and the temperature was increased by 0.5°C, although precipitation had slightly decreased during the same period. In the thirteen years' period, forest land and water resource decreased by 3429.62 km2 and 81.45 km2, respectively. In contrast, an increment was observed in grassland (2779.33 km2), cultivated land (535.34 km2), bare land (43.08 km2), urban land (0.65 km2), and wetland (152.66 km2) in the same period. In the study, it was also observed a decrease of an NDVI value by 0.1 was observed in 2013 in the southern part of the basin. The findings of the present study illustrate a significant change in eco-hydrological conditions in the ARB with an adverse impact on the environment. Hydroclimatic changes caused the increase in temperature and decreasing trend in precipitation which significantly impacted the land cover and vegetation in the basin. The changes in land cover were mostly caused by global and local climate influence which mainly affects the hydroclimate and eco-hydrology systems of the basin. The result is consistent with that of the previous studies conducted elsewhere. The findings of this paper could help researchers to understand the eco-hydrological condition of the study basin and become a foundation for further studies. [ABSTRACT FROM AUTHOR]

Utilising climate funds properly to reduce the impact of potential risks of climate change at the local level is essential for successful adaptation to climate change. Climate change has been disrupting the lives of millions of households along the coastal region of Bangladesh. The country has allocated support from its national funds and accessed international funds for the implementation of adaptation interventions. With the focus of the scientific community on climate finance mechanisms and governance at the global and the national level, there is a lacuna in empirical evidence of how climate finance affects risk appraisal and engagement in adaptation measures at the local level. This paper aims to examine how the support from climate finance affects risk appraisal in terms of the perceived probability and severity and the factors which influence risk appraisal. A field survey was conducted on 240 climate finance recipient households (CF HHs) and 120 nonclimate finance recipient households (non-CF HHs) in Galachipa Upazila of Patuakhali District in coastal Bangladesh. The results indicate that both CF and non-CF HHs experience a high probability of facing climatic events in the future; however, CF HHs anticipated a higher severity of impacts of climatic events on different dimensions of their households. With higher income and social capital, the overall risk appraisal decreases for CF HHs. CF HHs have higher engagement in adaptation measures and social groups and maintain alternative sources of income. Climate finance played a critical role in supporting households in understanding the risks that they were facing, assisting them in exploring as well as enhancing their engagement in adaptation options. [ABSTRACT FROM AUTHOR]

This paper tries to identify one of the reasons for the poor land skin temperature simulated by a climate model over Greenland. It first compares ICEsat surface height measurements over Greenland with those used by the model and reveals that the surface height of Greenland prescribed in the National Center for Atmospheric Research (NCAR) Community Climate System Model/Community Land Model version 3 (CCSM/CLM3) differs greatly from the satellite measurements from National Aeronautics and Space Administration (NASA) ICEsat at edges and central glacier regions. This deficiency, in part, leads to underestimated skin temperatures at coastal regions-the areas where significant ice sheet melt is observed. Furthermore, sensitivity studies reveal that surface skin temperature simulations of Greenland would be significantly improved if the more accurate surface height is used. The problem of the height used in current global climate model is mainly due to the fact that the model has to use coarse grid size, and within one grid, land surface height has high heterogeneity. How to assign a proper surface height for each model grid and meanwhile adequately present the high heterogeneity of land surface is a great challenge in current model development. [ABSTRACT FROM AUTHOR]

The detection of possible changes in extreme climate events, in terms of the frequency, intensity as well as duration assumes profound importance on the local, regional, and national scales, due to the associated critical socioeconomic consequences. Therefore, an attempt ismade in this paper to evaluate various aspects of future projections of precipitation extremes over India, as projected by a state-of-art regional climatemodeling system, known as PRECIS (Providing REgional Climates for Impacts Studies) towards the end of the 21st century (that is, 2071-2100) using standardized indices. Study reveals that PRECIS simulations under scenarios of increasing greenhouse gas concentration and sulphate aerosols indicate marked increase in precipitation towards the end of the 21st century and is expected to increase throughout the year. However the changes in daily precipitation and the precipitation extremes during summer monsoon (June through September) season are prominent than during the rest of year. PRECIS simulations under both A2 and B2 scenarios indicate increase in frequency of heavy precipitation events and also enhancement in their intensity towards the end of the 21st century. Both A2 and B2 scenarios show similar patterns of projected changes in the precipitation extremes towards the end of the 21st century. However, the magnitudes of changes in B2 scenario are on the lower side. [ABSTRACT FROM AUTHOR]

One of the most important natural aerosol systems at the global level is marine aerosol that comprises both organic and inorganic components of primary and secondary origin. The present paper reviews some new results on primary and secondary organic marine aerosol, achieved during the EU project MAP (Marine Aerosol Production), comparing them with those reported in the recent literature. Marine aerosol samples collected at the coastal site ofMaceHead, Ireland, show a chemical composition trend that is influenced by the oceanic biological activity cycle, in agreement with other observations. Laboratory experiments show that seaspray aerosol from biologically active sea water can be highly enriched in organics, and the authors highlight the need for further studies on the atmospheric fate of such primary organics. With regard to the secondary fraction of organic aerosol, the average chemical composition and molecular tracer (methanesulfonic-acid, amines) distribution could be successfully characterized by adopting a multitechnique analytical approach. [ABSTRACT FROM AUTHOR]

Myanmar's climate is heavily influenced by its geographic location and relief. Located between the Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM), Myanmar's climate is distinguished by the alternation of seasons known as the monsoon. The north-south direction of peaks and valleys creates a pattern of alternate zones of heavy and scanty precipitation during both the northeast and southwest monsoons. The majority of the rainfall has come from Myanmar's southwest monsoon (MSwM), which is Myanmar's rainy season (summer in global terms, June–September). This study explained both threshold-based and nonthreshold-based objective definitions of the onset and withdrawal of large-scale MSwM. The seasonal transitions in MSwM circulation and precipitation are convincingly represented by the new index, which is based on change point detection of the atmospheric moisture flow converging in the MSwM region (10–28 N, 92–102 E). A transition in vertically integrated moisture transport (VIMT), the reversal of surface winds, and an increase in precipitation may also be considered when defining MSwM onset objectively. We also define a change point of the MSwM (CPI) index for MSwM onset and withdrawal dates. The climatological mean onset of MSwM is day 135 (May 14), withdrawal is day 278 (October 4), and the total season length is 144 days. We are investigating spatial patterns of rainfall progression at and after the start of the monsoon, rather than transitions within a single region of the MSwM. The local southwest monsoon duration is well correlated with the CPI duration on interannual timescales, particularly in the peak rainfall regions, with a delay (advance) in large-scale onset or withdrawal associated with a delay (advance) of onset or withdrawal by local index. Hence, the next phase of this research is to study the maintenance and break of the monsoon to understand the underlying physical processes governing the monsoon circulation. The results of this study provide a possibility to reconstruct Myanmar's monsoon climate dynamics, and the findings of this study can help unravel many remaining questions regarding the greater Asian monsoon system's variability. [ABSTRACT FROM AUTHOR]

This paper analyzed the surface conditions and boundary-layer climate of regional haze events and heavy haze in southern Jiangsu Province in China. There are 5 types with the surface conditions which are equalized pressure (EQP), the advancing edge of a cold front (ACF), the base of high pressure (BOH), the backside of high pressure (BAH), the inverted trough of low pressure (INT), and saddle pressure (SAP) with the haze days. At that time, 4 types are divided with the regional haze events and each of which has a different boundary-layer structure. During heavy haze, the surface mainly experiences EQP, ACF, BOH, BAH, and INT which also have different boundary-layer structures. [ABSTRACT FROM AUTHOR]

The aviation industry has a global economic impact of $2.7 trillion (including direct, indirect, induced, and tourism catalytic effects) and contributes 3.6 percent of global GDP. Weather is one of the most essential elements impacting how an aircraft runs and how safely it can fly. The correlation coefficient is the most significant index explaining the relationship between variables and can result in teleconnection patterns of climate indices. El Nino-Southern Oscillation (ENSO) and India Ocean Dipole (IOD) were used in this study based on the ERA5 reanalysis dataset for 30 years (1991–2020). Myanmar's Yangon International Airport has recorded more than 119874 times of observation data from 2009 to 2019. The mean percentage of occurrences of weather elements is calculated for each month and each season. Analysis of flight delay and accident data was obtained statistically from the Aviation Safety Network (ASN). According to the monthly delay index, July, August, and March are the maximum delay index months, and the correlation value between aircraft movement and delays is maximum in July and August and minimum in January and February. After examining numerous characteristics of Yangon International Airport, we identified which elements had a big impact on operations through vital interviews with operators, the accident case study section, and climatology analysis. As a result, we identified two meteorological occurrences: thunderstorm rain (TSRA) and fog (FG) are of high frequency and TSRA poses a larger risk than FG for aviation operation. The maximum frequency (%) of thunderstorm occurrences was 22% in July and the minimum was 1% in January. Annual frequency analysis revealed that TSRA days are becoming more common year after year as a result of global climate change. According to a spatial gridded analysis by ERA5 reanalysis data (1991–2020), the annual convective available potential energy (CAPE) values over local airport regions, the Bay of Bengal (BOB), the western equatorial Pacific, and the South China Sea show a positive correlation with convective rainfall. In contrast, negative convective inhibition (CIN) anomalies have been observed over the same areas as above, except for the western part of BOB along the Indian Coast. The primary innovation is that we look at the effects of thunderstorms on airport operations before determining their link with ENSO and the IOD individually and then combining them during their full phases. This raises a new question and a new possibility for viewing climatology from a new perspective. [ABSTRACT FROM AUTHOR]

Water resources in Northern Thailand have been less explored with regard to the impact on hydrology that the future climate would have. For this study, three regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX) of Coupled Model Intercomparison Project 5 (CMIP5) were used to project future climate of the upper Nan River basin. Future climate data of ACCESS_CCAM, MPI_ESM_CCAM, and CNRM_CCAM under Representation Concentration Pathways RCP4.5 and RCP8.5 were bias-corrected by the linear scaling method and subsequently drove the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) to simulate future streamflow. This study compared baseline (1988–2005) climate and streamflow values with future time scales during 2020–2039 (2030s), 2040–2069 (2050s), and 2070–2099 (2080s). The upper Nan River basin will become warmer in future with highest increases in the maximum temperature of 3.8°C/year for MPI_ESM and minimum temperature of 3.6°C/year for ACCESS_CCAM under RCP8.5 during 2080s. The magnitude of changes and directions in mean monthly precipitation varies, with the highest increase of 109 mm for ACESSS_CCAM under RCP 4.5 in September and highest decrease of 77 mm in July for CNRM, during 2080s. Average of RCM combinations shows that decreases will be in ranges of −5.5 to −48.9% for annual flows, −31 to −47% for rainy season flows, and −47 to −67% for winter season flows. Increases in summer seasonal flows will be between 14 and 58%. Projection of future temperature levels indicates that higher increases will be during the latter part of the 20th century, and in general, the increases in the minimum temperature will be higher than those in the maximum temperature. The results of this study will be useful for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset negative impacts of future changes in climate. In addition, the results will also be valuable for agriculturists and hydropower planners. [ABSTRACT FROM AUTHOR]

Based on the daily climate data from 839 meteorological stations covering the 2014–2017 period in the mainland of China, the Universal Thermal Climate Indices (UTCI) were calculated and the UTCI of 247 national 5A tourist attractions in the mainland of China are obtained with ordinary kriging interpolation method. Then, a spatial analysis of all the attractions was carried out based on UTCI. The results showed that the mainland of China's annual average UTCI is generally distributed as strip-belts along a latitudinal direction and the climate comfort level gradually decreases from south to north. Significant regional differences in climate comfort results are obtained between the southeast coastal areas and the northwest inland. It was found that the number of attractions with the best climate comfort level is relatively high in spring and autumn while it is less in summer and winter. Considering the climate comfort levels, the attractions are grouped into five categories of "comfortable during spring and autumn," "comfortable during winter," "comfortable during spring, autumn, and winter," "comfortable during spring, summer, and autumn," and "uncomfortable during the four seasons" to carry out the study for determining the most convenient period of the year in terms of climate comfort. [ABSTRACT FROM AUTHOR]

Heavy rainfall events, typically associated with tropical cyclones (TCs), provoke intense flooding, consequently causing severe losses to life and property. Therefore, the amount and distribution of rain associated with TCs must be forecasted precisely within a reasonable time to guarantee the protection of lives and goods. In this study, the skill of the Numerical Tool for Hurricane Forecast (NTHF) for determining rainfall pattern, average rainfall, rainfall volume, and extreme amounts of rain observed during TCs is evaluated against Tropical Rainfall Measuring Mission (TRMM) data. A sample comprising nine systems formed in the North Atlantic basin from 2016 to 2018 is used, where the analysis begins 24 h before landfall. Several statistical indices characterising the abilities of the NTHF and climatology and persistence model for rainfalls (R-CLIPER) for forecasting rain as measured by the TRMM are calculated at 24, 48, and 72 h forecasts for each TC and averaged. The model under consideration presents better forecasting skills than the R-CLIPER for all the attributes evaluated and demonstrates similar performances compared with models reported in the literature. The proposed model predicts the average rainfall well and presents a good description of the rain pattern. However, its forecast of extreme rain is only applicable for 24 h. [ABSTRACT FROM AUTHOR]

The evaluation of climate comfort for tourism can provide information for tourists selecting destinations and tourism operators. Understanding how climate conditions for tourism evolve is increasingly important for strategic tourism planning, particularly in rapidly developing tourism markets like China in a changing climate. Multidimensional climate indices are needed to evaluate climate for tourism, and previous studies in China have used the much criticized "climate index" with low resolution climate data. This study uses the Holiday Climate Index (HCI) and daily data from 775 weather stations to examine interregional differences in the tourist climate comfortable period (TCCP) across China and summarizes the spatiotemporal evolution of TCCP from 1981 to 2010 in a changing climate. Overall, most areas in China have an "excellent" climate for tourism, such that tourists may visit anytime with many choices available. The TCCP in most regions shows an increasing trend, and China benefits more from positive effects of climate change in climatic conditions for tourism, especially in spring and autumn. These results can provide some scientific evidence for understanding human settlement environmental constructions and further contribute in improving local or regional resilience responding to global climate change. [ABSTRACT FROM AUTHOR]

Socioeconomic drought is one of the most frequent natural disasters in the world and is closely related to human life. The main cause of socioeconomic drought is the contradiction between water supply and demand; hence, as local reservoirs play a major role in improving water supply and coping with extreme climate, it is reasonable to estimate socioeconomic drought based on reservoir operations. The multivariate standardized reliability and resilience index (MSRRI) is utilized to evaluate socioeconomic drought, considering the characteristics of reservoir management and storage water resources. Therefore, with the MSRRI, this study takes the Heihe River Basin in northwestern China, which is controlled by two reservoirs dominating the upstream and downstream regions, as a case study to reveal the evolution characteristics of socioeconomic drought in the basin and the external impacts of climate variability. The results showed that (1) the drought intensity in the up-midstream region is stronger than that in the downstream region; in view of the hysteresis in the downstream region, the occurrence of drought in the up-midstream region could be regarded as an early warning to implement preventive measures in the downstream region; (2) an increasing trend in socioeconomic drought throughout the basin exists on both monthly and annual scales, which indicates that the increasing possibility of drought should be effectively addressed; (3) cross wavelet analysis indicated that the large-scale climate indices contribute to the variations in the socioeconomic droughts throughout the basin, indicating that climate variability may provide a reference for managers to deal with socioeconomic drought in the HRB. [ABSTRACT FROM AUTHOR]

A Multi-Model Ensemble (MME) based seasonal rainfall forecast customization tool called FOCUS was developed for Myanmar in order to provide improved seasonal rainfall forecast to the country. The tool was developed using hindcast data from 7 Global Climate Models (GCMs) and observed rainfall data from 49 meteorological surface observatories for the period of 1982 to 2011 from the Department of Meteorology and Hydrology. Based on the homogeneity in terms of the rainfall received annually, the country was divided into six climatological zones. Three different operational MME techniques, namely, (a) arithmetic mean (AM-MME), (b) weighted average (WA-MME), and (c) supervised principal component regression (PCR-MME), were used and built-in to the tool developed. For this study, all 7 GCMs were initialized with forecast data of May month to predict the rainfall during June to September (JJAS) period, which is the predominant rainfall season for Myanmar. The predictability of raw GCMs, bias-corrected GCMs, and the MMEs was evaluated using RMSE, correlation coefficients, and standard deviations. The probabilistic forecasts for the terciles were also evaluated using the relative operating characteristics (ROC) scores, to quantify the uncertainty in the GCMs. The results suggested that MME forecasts have shown improved performance (RMSE = 1.29), compared to the raw individual models (ECMWF, which is comparatively better among the selected models) with RMSE = 4.4 and bias-corrected RMSE = 4.3, over Myanmar. Specifically, WA-MME (CC = 0.64) and PCR-MME (CC = 0.68) methods have shown significant improvement in the high rainfall (delta) zone compared with WA-MME (CC = 0.57) and PCR-MME (CC = 0.56) techniques for the southern zone. The PCR method suggests higher predictability skill for the upper tercile (ROC = 0.78) and lower tercile categories (ROC = 0.85) for the delta region and is less skillful over lower rainfall zones like dry zones with ROC = 0.6 and 0.63 for upper and lower terciles, respectively. The model is thus suggested to perform relatively well over the higher rainfall (Wet) zones compared to the lower (Dry) zone during the JJAS period. [ABSTRACT FROM AUTHOR]

The study aims to analyze climate variability and farmers' perception in Southern Ethiopia. Gridded annual temperature and precipitation data were obtained from the National Meteorological Agency (NMA) of Ethiopia for the period between 1983 and 2014. Using a multistage sampling technique, 403 farm households were surveyed to substantiate farmers' perceptions about climate variability and change. The study applied a nonparametric Sen's slope estimator and Mann–Kendall's trend tests to detect the magnitude and statistical significance of climate variability and binary logit regression model to find factors influencing farm households' perceptions about climate variability over three agroecological zones (AEZs). The trend analysis reveals that positive trends were observed in the annual maximum temperature, 0.02°C/year (p<0.01) in the lowland and 0.04°C/year (p<0.01) in the highland AEZs. The positive trend in annual minimum temperature was consistent in all AEZs and significant (p<0.01). An upward trend in the annual total rainfall (10 mm/year) (p<0.05) was recorded in the midland AEZ. Over 60% of farmers have perceived increasing temperature and decreasing rainfall in all AEZs. However, farmers' perception about rainfall in the midland AEZ contradicts with meteorological analysis. Results from the binary logit model inform that farmers' climate change perceptions are significantly influenced by their access to climate and market information, agroecology, education, agricultural input, and village market distance. Based on these results, it is recommended to enhance farm households' capacity by providing timely weather and climate information along with institutional actions such as agricultural extension services. [ABSTRACT FROM AUTHOR]

In the present study, five-year of precipitation features (PFs) datasets, based on Global Precipitation Measurement (GPM), are used to investigate the global and regional characteristics of extreme rainfall events (EREs). The EREs are defined based on the PFs area, depth (maximum height of radar reflectivity), and the rain rate and called them largest, deepest, and intense EREs, respectively. The EREs are divided into top 10%, 1%, 0.1%, and 0.01% based on their frequency of occurrences. It is observed that occurrences of EREs belonging to less than top 0.01% EREs follow the tropical rainfall climatology over the tropics based on all the parameters. Subtropical oceanic areas consist of a higher frequency of largest EREs, whereas tropical land areas consist of the higher number of deepest EREs. The most intense EREs (top 0.01%) are uniformly distributed over tropical areas and subtropical oceans, and spatial distribution shows that a deepest ERE belongs to intense EREs in the tropical land areas. Large differences between the precipitation contribution from the largest and deepest EREs are seen; for example, the top 1% of largest EREs contribute to ∼80.7% of Earth's precipitation, whereas the corresponding percentage for deepest EREs is only 53%. On the regional and seasonal scale, South Asia (SAsia) and South America (SA) nearly show common features, as oceanic and land areas consist of largest and deepest EREs, respectively, and contribute to higher precipitation. Subtropical latitudes over South America, including Sierra de Cordoba and La Plata basin, consist of deepest and intense EREs and match with those of the Indo-Gangetic plain over South Asia, which also shows the similar characteristics. EREs based on various parameters are strongly linked over SAsia compared to SA. For example, the largest top 10% EREs have a higher probability to be part of the top 10% deepest and intense EREs over SAsia. The seasonal and regional water budget reveals different characteristics, as in the southern hemisphere, the deeper EREs contribute to the higher fraction of rainfall, but over SAsia, the shallower EREs could also contribute to significant rainfall. [ABSTRACT FROM AUTHOR]

Ecosystem water use efficiency (WUE) and carbon use efficiency (CUE), as two of the most important ecological indicators of an ecosystem, represent the carbon assimilation rate of unit water consumption and the capacity of transferring carbon from the atmosphere to potential carbon sinks. Revealing WUE and CUE changes and their impact factors is vital for regional carbon-water interactions and carbon budget assessment. Climate affects carbon and water processes differently. Compared to WUE, the variations in CUE in response to climate factors and human activity remain inadequately understood. In this study, ecosystem-level WUE and CUE variations in the Songnen Plain (SNP), Northeast China, during 2001–2015, were investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The relationships between WUE, CUE, main climate factors, and human impacts were explored. The results showed that ecosystem WUE and CUE have fluctuated over time, with regional average values of 1.319 gC·kg−1H2O and 0.516, respectively. Deciduous broad-leaved forests had the highest average WUE but the lowest CUE. The multiyear average CUE of grassland ranked in first place, while the lowest WUE indicated that a lesser capacity of net productivity was generated by the use of limited water supply. WUE and CUE showed a downward trend in most areas of the SNP, indicating that the carbon sequestration capacity of the terrestrial ecosystem became weaker in the past 15 years. Annual precipitation and relative humidity had positive influences on WUE and CUE in more than 60% of the study area. The total annual sunshine duration and annual average temperature negatively affected WUE and CUE in most areas. Human activities had a positive effect on ecosystem WUE changes in the SNP but might inhibit CUE variations. Our findings aid in understanding the biological regulation mechanisms of carbon-water cycle coupling and provide a scientific basis for formulating sustainable regional development strategies and guiding water and land resources management. [ABSTRACT FROM AUTHOR]

This study analyzed the long-term memory (LTM) in precipitation over Bénin synoptic stations from 1951 to 2010 using the detrended fluctuation analysis (DFA) method. Results reveal the existence of positive long-term memory characteristic in rainfall field. DFA exponent values are different regarding the concerned synoptic stations, reflecting the effect of geographical position and climate on the LTM. These values were related to the type of climate. The best DFA1-4 method depends on the geographical position of the studied station. However, DFA2 is generally the best in terms of spatial average from DFA1 to DFA4. In Bénin synoptic stations, except the Parakou station, the long-term temporal correlations are systematically the source of multifractality in rainfall. Except Natitingou, the strength of long-term memory characteristic decreases each twenty years in the study period. Considering the fractal approach, our results show that the subperiod 1991–2010 is not really a transition period as shown before. Thus, the drought is prolonging until 2010. So, fractal theory reveals more Bénin climatic characteristics. [ABSTRACT FROM AUTHOR]

Based on the Backward Four-Dimensional Variational Data Assimilation (Backward-4DVar) system with the Advanced Regional Eta-coordinate Model (AREM), which is capable of assimilating radio occultation data, a heavy rainfall case study is performed using GPS radio occultation (GPS RO) data and routine GTS data on July 5, 2007. The case study results indicate that the use of radio occultation data after quality control can improve the quality of the analysis to be similar to that of the observations and, thus, have a positive effect when improving 24-hour rainfall forecasts. Batch tests for 119 days from May to August during the flood season in 2009 show that only the use of GPS RO data can make positive improvements in both 24-hour and 48-hour regional rainfall forecasts and obtain a better B score for 24-hour forecasts and better TS score for 48-hour forecasts. When using radio occultation refractivity data and conventional radiosonde data, the results indicate that radio occultation refractivity data can achieve a better performance for 48-hour forecasts of light rain and heavy rain. [ABSTRACT FROM AUTHOR]

A selected number of global climate models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) were evaluated over the Volta Basin for precipitation. Biases in models were computed by taking the differences between the averages over the period (1950–2004) of the models and the observation, normalized by the average of the observed for the annual and seasonal timescales. The Community Earth System Model, version 1-Biogeochemistry (CESM1-BGC), the Community Climate System Model Version 4 (CCSM4), the Max Planck Institute Earth System Model, Medium Range (MPI-ESM-MR), the Norwegian Earth System Model (NorESM1-M), and the multimodel ensemble mean were able to simulate the observed climatological mean of the annual total precipitation well (average biases of 1.9% to 7.5%) and hence were selected for the seasonal and monthly timescales. Overall, all the models (CESM1-BGC, CCSM4, MPI-ESM-MR, and NorESM1-M) scored relatively low for correlation (<0.5) but simulated the observed temporal variability differently ranging from 1.0 to 3.0 for the seasonal total. For the annual cycle of the monthly total, the CESM1-BGC, the MPI-ESM-MR, and the NorESM1-M were able to simulate the peak of the observed rainy season well in the Soudano-Sahel, the Sahel, and the entire basin, respectively, while all the models had difficulty in simulating the bimodal pattern of the Guinea Coast. The ensemble mean shows high performance compared to the individual models in various timescales. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) climatology, the thermodynamic and dynamical variables, and teleconnections influencing MCSs development are assessed for the Paute basin (PB) in the Ecuadorian Andes from 2000 to 2009. The seasonality of MCSs occurrence shows a bimodal pattern, with higher occurrence during March-April (MA) and October-November (ON), analogous to the regional rainfall seasonality. The diurnal cycle of MCSs shows a clear nocturnal occurrence, especially during the MA and ON periods. Interestingly, despite the higher occurrence of MCSs during the rainy seasons, the monthly size relative frequency remains fairly constant throughout the year. On the east of the PB, the persistent high convective available potential and low convective inhibition values from midday to nighttime are likely related to the nocturnal development of the MCSs. A significant positive correlation between the MCSs occurrence to the west of the PB and the Trans-Niño index was found, suggesting that ENSO is an important source of interannual variability of MCSs frequency with increasing development of MCSs during warm ENSO phases. On the east of the PB, the variability of MCSs is positively correlated to the tropical Atlantic sea surface temperature anomalies south of the equator, due to the variability of the Atlantic subtropical anticyclone, showing main departures from this relation when anomalous conditions occur in the tropical Pacific due to ENSO. [ABSTRACT FROM AUTHOR]

Over the last six to seven decades, there has been a substantial increase in atmospheric research to better understand the dynamics and evolution of atmospheric blocking events. It is well known that atmospheric blocking serves as a catalyst for increasing the frequency of atmospheric flow regime stagnation and forecast unpredictability. This study built upon the results of previous work by expanding upon the findings of various climatologies and case studies. This work analyzes specific trends observed in association with atmospheric blocking predominantly across the central and eastern Pacific Ocean. Such trends include the relationship between the size, duration, and onset position of atmospheric blocking events and the frequency, duration, and intensity of heavy rainfall events across the central United States. A strong focus is placed on examining the duration and spatial extent of atmospheric blocking which has been found to influence the intensity of heavy rainfall events. The goal is to further bridge the gap between the location and duration of blocking highs and the intensity, duration, and frequency of heavy rainfall events which occur downstream of such blocking events. [ABSTRACT FROM AUTHOR]

After a tropical cyclone (TC) making landfall, the numerical model output sea level pressure (SLP) presents many small-scale perturbations which significantly influence the positioning of the TC center. To fix the problem, Barnes filter with weighting parameters C=2500 and G=0.35 is used to remove these perturbations. A case study of TC Fung-Wong which landed China in 2008 shows that Barnes filter not only cleanly removes these perturbations, but also well preserves the TC signals. Meanwhile, the centers (track) obtained from SLP processed with Barnes filter are much closer to the observations than that from SLP without Barnes filter. Based on the distance difference (DD) between the TC center determined by SLP with/without Barnes filter and observation, statistics analysis of 12 TCs which landed China during 2005–2015 shows that in most cases (about 85%) the DDs are small (between −30 km and 30 km), while in a few cases (about 15%) the DDs are large (greater than 30 km even 70 km). This further verifies that the TC centers identified from SLP with Barnes filter are more accurate compared to that directly obtained from model output SLP. Moreover, the TC track identified with Barnes filter is much smoother than that without Barnes filter. [ABSTRACT FROM AUTHOR]

Lake Victoria, Africa, supports millions of people. To produce reliable climate projections, it is desirable to successfully model the rainfall over the lake accurately. An initial step is taken here with customization of the Weather, Research, and Forecast (WRF) model. Of particular interest is an asymmetrical rainfall pattern across the lake basin, due to a diurnal land-lake breeze. The main aim is to present a customization framework for use over the lake. This framework is developed by conducting several series of model runs to investigate aspects of the customization. The runs are analyzed using Tropical Rainfall Measuring Mission rainfall data and Climatic Research Unit temperature data. The study shows that the choice of parameters and lake surface temperature initialization can significantly alter the results. Also, the optimal physics combinations for the climatology may not necessarily be suitable for all circumstances, such as extreme years. The study concludes that WRF is unable to reproduce the pattern across the lake. The temperature of the lake is too cold and this prevents the diurnal land-lake breeze reversal. Overall, this study highlights the importance of customizing a model to the region of research and presents a framework through which this may be achieved. [ABSTRACT FROM AUTHOR]

Monthly averaged tropospheric ozone residual (TOR) data from TOMS and OMI during the period 1979-2009 are used to study the spatial distribution of tropospheric column ozone within the landmass of the Indian subcontinent, the Tibetan plateau in the north and the Bay of Bengal in the south. The climatologicalmean shows seasonal maxima in spring andminima in winter in all the regions. The oceanic regions exhibit broad summermaximum and the maximumtominimum ratio is the lowest for these regions. The concentration of tropospheric column ozone is found to be highest in North Eastern India (NE) and the Indo Gangetic plains (IGP). NE ozone concentration exceeds that of IGP during spring whereas in post monsoon and winter reverse is the case. In the monsoon season, O3 levels in the two regions are equal. The spring time highest level of tropospheric column ozone over NE region is found to be associated with highest incidence of lightning and biomass burning activity. The Stratosphere-Troposphere exchange is also found to contribute to the enhanced level of ozone in spring in NE India. A net decrease in tropospheric ozone concentration over NE during the period 1979 to 2009 has been observed. [ABSTRACT FROM AUTHOR]

This is a mesoscale modeling study of land/sea breeze in the vicinity of Bushehr, Iran which is on the coast of the Persian Gulf. Two days in September, 2002 are studied using the model presented in Estoque Model (1990) (hereafter referred to as EsM90). The EsM90 produces a realistic day-night wind field somewhat in agreement with observations provided by the Port and Shipping Organization of Iran. The study demonstrates that themodel has a 3-hour delay predicting the time of the maximumof sea breeze, but accurately predicts when the end of the sea breeze occurs. Accurate estimates near mountains at the edge of the modeled region require a more complex simulation. The study shows that a reliable modeling of a complicated coastal environment like Bushehr not only depends on land/sea breezes but also on elevations and prevailing winds. This dependence is especially important when local thermal forcings are weak, for example, during late afternoon and at night. [ABSTRACT FROM AUTHOR]

Climate development with possible anthropogenic effects occurs on a background of natural climatic variations, which may be considerable, and especially in the Arctic. Natural climate variations however remain poorly understood, although they remain important for discriminating between natural and anthropogenic influences on current climate change. Using the Svalbard (78?N) surface air temperature record 1912-2010 as an example, we here suggest a road ahead to identify and describe such natural climate variations. By means of Fourier and wavelet analysis the record is decomposed into time-frequency space, to extract information on periodic signals and their amplitude and variation over time. By this we identify several cyclic variations on the time scale investigated. These oscillations are present in the annual record, as well as in seasonal subsets of the record. Using only three oscillations it is possible to hindcast the Svalbard temperature record well. We suggest that such persistent oscillations may be used for forecasting the overall features of future temperature changes for a limited period, about 10-25% of the record length. Our main focus is on identifying the character of recurrent natural temperature variations, but we also comment briefly on possible physical explanations for some of the identified cyclic variations. [ABSTRACT FROM AUTHOR]