1. Quality Control of Daily Air Temperature (Tx, Tn) and Relative Humidity Data
Objective
This study presents a comprehensive quality control procedure applied to daily meteorological time series, focusing on three key climate variables: maximum temperature (Tx), minimum temperature (Tn), and relative humidity. The primary goal is to ensure the reliability and consistency of the data prior to further climatological or environmental analyses.
Methodology
The quality control strategy incorporates multiple statistical techniques:
Peak and valley analysis to detect unusual daily fluctuations,
Outlier detection using both the Z-score method (based on standard deviations from the mean) and the Interquartile Range (IQR) method (based on percentiles), which are compared for robustness,
Homogeneity testing to identify non-climatic breaks or shifts in the time series,
Breakpoint detection to locate significant change points that may indicate instrumentation errors or station relocations.
Findings & Applications
The application of these methods allows for the identification and flagging of suspect data points, thereby improving the overall quality of the climate database. The work also briefly touches upon the potential intersection with health data, suggesting that such quality-controlled climate records could be used for epidemiological studies (e.g., heatwave impact on public health).
Conclusion & Perspectives
The work provides a set of recommendations for routine quality assurance of climate data. Future perspectives include the automation of the QC process, integration with additional variables, and deeper exploration of climate-health linkages.
2. Comprehensive Analysis and Validation of Temperature Data at Djibouti Airport (1966–2019)
Context & Objective
This study presents a statistical analysis of temperature records collected at the Djibouti Airport meteorological station over a 54-year period (1966–2019). The objective is to characterize long-term trends, variability, and potential shifts in local surface air temperature (Tas) in one of the hottest and most climate-sensitive regions of the world.
Data & Methodology
The analysis is based on daily and monthly temperature observations from the Djibouti Airport station. Statistical techniques are applied to detect:
Long-term trends (warming or cooling),
Interannual and seasonal variability,
Possible breakpoints or abrupt changes in the time series.
Expected Outcomes
The findings will provide a reliable baseline for understanding local climate evolution in Djibouti, support validation of satellite and reanalysis products (e.g., ERA5, CHIRPS), and contribute to climate risk assessments (heatwaves, energy demand, health impacts). This work also serves as a foundation for future downscaling and projection studies.
3. Study of Temporal Variability of Temperature in Djibouti (1966–2019)
Context & Problem Statement
This study investigates the temporal variability of daily minimum and maximum temperatures recorded at the Djibouti Aviation Station over the period 1966–2019. A major challenge addressed is the homogeneity problem in the daily temperature series, which may result from station relocations, instrumentation changes, or other non-climatic factors.
Objectives
The research has three main objectives:
To detect and correct inhomogeneities in the daily temperature time series using statistical breakpoint detection methods.
To characterize the seasonality and long-term trends in temperature variability.
To evaluate the relationships between temperature and other climatic phenomena, including humidity and Sea Surface Temperature (SST).
Data & Methodology
The analysis relies on two main datasets provided by the U.S. National Oceanic and Atmospheric Administration (NOAA):
GSOD (Global Summary of the Day) – for the Djibouti-Ambouli station (coordinates: 11.550°N, 43.133°E) and Djibouti station (11.550°N, 43.150°E).
GHCN (Global Historical Climatology Network) – for additional reference and cross-validation (including the King Abdullah Bin Abdulaziz station in Saudi Arabia as a regional reference).
Homogeneity testing is performed using Pettitt's test on deseasonalized series to identify change points (breakpoints) in the temperature records.
Expected Outcomes & Perspectives
The study will:
Provide a homogenized, reliable temperature dataset for Djibouti,
Quantify the influence of large-scale drivers (SST, humidity) on local temperature extremes,
Support climate monitoring and early warning systems in the region.
Future perspectives include extending the analysis to other variables, improving statistical downscaling, and contributing to climate adaptation planning.
4. Interannual Variability of Daily Maximum (TX) and Minimum (TN) Temperatures (1953–2023)
Context & Objective
This study investigates the interannual variability of daily maximum (TX) and minimum (TN) temperatures in the Republic of Djibouti over a 70-year period (1953–2023). The primary objective is to understand how large-scale climate drivers influence local temperature patterns across different seasons.
Methodology
The analysis focuses on the relationships between temperature extremes and key modes of climate variability, including:
ENSO (El Ni帽o–Southern Oscillation) – during both the JJA (June–July–August) and DJF (December–January–February) seasons,
IOD (Indian Ocean Dipole) – during JJA and DJF,
Sea Surface Temperature (SST) – analyzed separately for DJF and JJA,
MJO (Madden–Julian Oscillation) – during DJF,
Seasonal transitions, particularly from JJA to SON (September–October–November).
Expected Outcomes
By examining these teleconnections, the study aims to identify which large-scale oceanic–atmospheric phenomena most significantly modulate daily temperature extremes in Djibouti. The results will contribute to a better understanding of regional climate dynamics and support future climate adaptation strategies.
5. Future Precipitation Projections in the Republic of Djibouti by 2100
Context & Objective
The Republic of Djibouti faces significant climatic challenges, particularly regarding water resources and natural hazard prevention. However, the country suffers from a scarcity of long-term, high-quality daily observational data. This study aims to fill this gap by using statistical downscaling techniques applied to outputs from Global Climate Models (GCMs) and Regional Climate Models (RCMs). The main objectives are to predict future precipitation trends and variability in Djibouti by the year 2100.
Data & Models
The research relies on:
Observational/reference datasets: CHIRPSv.2 and ERA5-Land,
Model simulations from CMIP5, CMIP6, and the CORDEX regional downscaling initiative.
Key Findings
Daily precipitation from CHIRPSv.2 and ERA5-Land closely resembles the observed interannual variability, making them suitable reference datasets.
Under the RCP 4.5 and RCP 8.5 scenarios (CMIP5-CORDEX-CMIP6 ensembles), total annual precipitation is projected to increase between 4.36 and 23.03 mm/year by 2100.
The mean precipitation variation is estimated to range from −3.85% to +625.59%, corresponding to an absolute range of 142.89 to 1078.9 mm/year between 2006 and 2100.
Conclusions & Perspectives
The study confirms a general upward trend in precipitation, though with high inter-model and inter-scenario variability.
The results will be used to produce a recommendation document aimed at strengthening natural disaster prevention in a context of climate change. Specific applications include:
Identifying months with precipitation exceeding 500 mm/year (flood risk),
Determining maximum drought duration at short, medium, and long term.
馃Л Synthesis and Future Directions
Taken together, these five complementary studies form a coherent and integrated research framework for climate science in Djibouti. They address the full data value chain – from quality control and homogenization of observations, through statistical analysis of trends and variability, to future projections of temperature and precipitation under climate change.
Key Cross-Cutting Themes:
Data reliability is ensured through rigorous QC and homogeneity testing.
Understanding of current climate is achieved through trend analysis and teleconnection studies.
Future preparedness is supported through downscaled projections and risk assessments.
Future Research Perspectives:
Automation of quality control procedures for operational use,
Integration of additional climate variables (wind, radiation, evapotranspiration),
Deeper exploration of climate–health linkages,
Development of user-friendly climate services for decision-makers and local communities,
Enhancement of statistical and dynamical downscaling techniques.
馃摎 Keywords
Climate variability · Temperature trends · Precipitation projections · Quality control · Homogeneity testing · Downscaling · ENSO · IOD · SST · Djibouti · Horn of Africa · CMIP5 · CMIP6 · CORDEX · CHIRPS · ERA5-Land · Pettitt's test · Climate adaptation
馃檹 Acknowledgements
The author gratefully acknowledges the Centre d'脡tudes et de Recherche de Djibouti (CERD) et Centre de Recherches de climatologie (CRC) for their institutional support. Thanks are also extended to the National Oceanic and Atmospheric Administration (NOAA) for providing open-access climate data (GSOD, GHCN), and to the CMIP5/CMIP6 and CORDEX programs for making climate model outputs available.
Contact:
Abdi-Basid ADAN
Email: abdi-basid@outlook.com
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