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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]

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]

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]

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]

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]

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]

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]

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]