Study on Temporal Variations of Surface Temperature and Rainfall at Conakry Airport, Guinea: 1960–2016

Abstract

The monthly averaged data time series of temperatures and rainfall without interruption of Conakry Airport (9.34° N 13.37° W, Guinea) from 1960 to 2016 were used. Inter-annual and annual changes in temperature and rainfall were investigated. Then, different models: Mann-Kendall Test, Multi-Linear-Regression analysis, Theil-Sen’s slope estimates and wavelet analysis where used for trend analysis and the dependency with these climate forcings. Results showed an increase in temperature with semi-annual and annual cycles. A sharp and abrupt rise in the temperature in 1998 was found. The results of study have shown increasing trends for temperature (about 0.21°/year). A decrease in rainfall (about −8.14 mm/year) is found since the end of 1960s and annual cycle with a maximum value of about 1118.3 mm recorded in August in average. The coherence between the two parameters and climate indices: El Niño 3.4, Atlantic Meridional Mode, Tropical Northern Atlantic and Atlantic Niño, were investigated. Thus, there is a clear need for increased and integrated research efforts in climate parameters variations to improve knowledge in climate change.

Impact of Climate Variability on Crop Yield in Kalahandi, Bolangir, and Koraput Districts of Odisha, India

“…El Nino affects the flow of moisture-bearing winds from the cooler oceans towards India, and negatively affects the summer monsoon, i.e., the south-west monsoon, which accounts for over 70% of annual rainfall; therefore, El Nino conditions play a significant role in affecting summer monsoon, which in turn affects the crop yield. Hence, rainfall and temperature deviation [13][14][15] from season to season greatly affects soil water availability to crops and thus poses crop production risks. It is easy to comprehend such annual variation, in a changing climate [16,17] that could affect crop production [18] and growth.…”

Surface Temperature Trend Estimation over 12 Sites in Guinea Using 57 Years of Ground-Based Data

“…It can cause problems when we fit the model and interpret the results. For the present study, it was previously mentioned that there is a strong correlation between the TNA and AMM [19]. Indeed, a simple correlation analysis between the five forcings aforementioned is depicted in Figure 4.…”

“…Thus, there are several methods of trend analysis, and the choice of the desired method depends on the study context. For instance, the Mann-Kendall (MK) trend test [26,27], multilinear regression [28,29] and Trend-Run model [14] were used in order to investigate different atmospheric parameters [19,30,31].…”

“…Based on our knowledge of understanding, a very few studies on temperature variability have been conducted in some sites in Guinea [18,19], but a trend analysis using this specific method has not yet been done. In this context, we applied the Trend-Run model and Mann-Kendall trend test for a trend analysis of temperatures over Guinean regions.…”

Spatio-Temporal Variability and Trend of Rainfall and Its Association with Pacific Ocean Sea Surface Temperature in West Harerge Zone, Eastern Ethiopia

“…Time series monthly Pacific Ocean SST data in the NINO3.4 region were downloaded from the National Oceanic and Atmospheric Administration (NOAA) satellite mission website (http://www.cgd.ucar.edu/cas/catalog/climind/TNI_N34/index.html). The NINO3.4 index mean SST was calculated by taking the spatial average SST within the NINO3.4 region, which extends from 5 ⸰ N to 5 ⸰ S latitude and from 120 • W to 170 • W longitude (in the Pacific Ocean) (Loua et al 2019;Yin et al 2020). According to Babu (2009) and Zaroug (2010), NINO 3.4 SST data has characteristics of both NINO3 and NINO4.…”