In this study, we assess the spatiotemporal projections of precipitation over Algeria derived from the multi-model ensemble mean (MME) of eleven global climate model datasets within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The patterns of six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices were analyzed for two future time slices: the near-future period 2021–2040 and far-future period 2081–2100 relative to the reference period (1995–2014), under two Shared Socio-economic Pathways (SSP) scenarios: medium emission SSP2-4.5 and worst-case scenario (SSP5-8.5). These indices cover those representing the consecutive dry days CDD, simple daily precipitation intensity (SDII), very heavy precipitation days (R20mm), consecutive dry days (CDD), consecutive wet days (CWD) and very wet days (R95p). The MME Projections show a substantial reduction in total precipitation over most parts of the country by the mid and the end of the twenty-first century. The northern region close to the Mediterranean Sea will experience the highest drying, particularly during the October–December season. The reduction in precipitation would exacerbate the prevailing protracted drought over the country and drastically alter the environmental infrastructure. The projections depict an increase of the very heavy precipitation days and the very wet days by the end of the century and under both scenarios, hence, this could increase the flooding and landslides risk over the country, albeit with reduced total precipitation. The findings of this study are useful for hydrological related preparedness in the era of global warming and climate change.
Abstract Algeria is vulnerable to climate extremes due to its large surface, growing population, and diverse valuable and fragile ecosystems. We assess in this paper the spatiotemporal projections of extreme temperatures over Algeria derived from the adjusted multi-model ensemble mean (MME) data derived from eleven daily historical simulations of CMIP6-GCMs models that participated in IPCC Sixth Assessment Report (AR6), considered the most authoritative source on climate change. We assess the projected spatial patterns of twelve extreme temperature indices defined by the Expert Team on Climate Change Detection and Indices. The evolution of the projected changes is examined for two future time periods: the mid-future 2041–2070 and the far future 2071–2100, relative to the baseline period 1985–2014, under three Shared Socio-economic Pathways (SSP) scenarios: low emission SSP1-2.6; medium emission SSP2-4.5 and high emission scenario (SSP5-8.5). The selected climate indices reflect the intensity (TXx, TNx, TXn, TNn and DTR), frequency (TX90p and TN10p) and duration (WSDI, SU, CSDI, TR, and FD) of the extreme thermal events. The MME Projections show a heightened warming. Future Climate features depict a continuous increase in the occurrence of hot days and nights and an amplification of the intensity of the extreme temperature and an extension of the heat wave duration period. These positive changes are likely to be more important by the end of the 21st century in the southern region than in the northern one and under SSP5-8.5 than in the remaining scenarios, with the exception of the occurrence of summer days (SU), where the northern region is projected to experience relatively more summer days as compared to the southern region. A stabilization of the upsurge trend is remarkably observed for most indices under SSP1-2.6 starting from the 2050s. In the last decades, Algeria has been experiencing recurrently the impacts of extreme weather leading to irreversible impacts. The projected extreme climate events in Algeria underscore the urgency of climate change mitigation and adaptation measures. The future changes depicted in this study should help to assess the distribution of the impacts across different regions of Algeria in order to enhance resilience, establish the appropriate adaptation responses and improve disaster preparedness.
Abstract Precipitation nowcasting is very important to secure individuals and property against adverse events that may be triggered, as well as to optimize the management of water resources and so-called weather-sensitive economic activities. Achieving effective temporal and spatial nowcasting resolution poses additional challenges to the meteorological community due to the chaotic dynamics that characterize the atmosphere as well as the spatio-temporal variability of this phenomenon. In this work, we aim to use deep learning architectures as Convolutional Neural Network or/and Recurrent Neural Network for the prediction of precipitation to better capture the spatio-temporal variability of meteorological data which are complex. We are particularly interested in rainfall data from northeastern Algeria, where the previously prediction was based on Numerical Weather Prediction. Two types of meteorological data will be used. Firstly, we predict precipitations on synoptic data and secondly, we predict precipitation using synoptic data and previous numerical predicted data obtained by Numerical Weather Prediction. The results were evaluated according to several metrics and a comparison between different approaches was conducted showing the effectiveness of Deep Learning in this field.
We assess in this paper the spatiotemporal projections of extreme temperature indices over Algeria derived from the adjusted multi-model ensemble mean (MME) data derived from 11 daily historical simulations of CMIP6-GCMs models that participated in the IPCC Sixth Assessment Report (AR6). The projected spatial patterns of 12 extreme temperature indices defined by the Expert Team on Climate Change Detection and Indices are assessed for two future time periods: the mid-future 2041–2070 and the far future 2071–2100, relative to the baseline period 1985–2014 and under three Shared Socio-economic Pathways (SSP) scenarios: low emission SSP1-2.6; medium emission SSP2-4.5 and high emission scenario (SSP5-8.5). The selected climate indices reflect the intensity (TXx, TNx, TXn, TNn and DTR), frequency (TX90p and TN10p) and duration (WSDI, SU, CSDI, TR and FD) of the extreme thermal events. The MME Projections show a global heightened warming over Algeria. Future Climate features depict a continuous increase in the occurrence of hot days by the end of the century reaching 60% for SSP5-8.5 and an amplification of the intensity of the extreme temperature of about 6 ∘ C for SSP5-8.5 and an extension of the heat wave duration period of about 80 days in the north and 100 days in the south of the country compared to the historical period. However, the study shows a projected simultaneous decline in the cold spell duration of 7 days and in the frost days reaching 25 days. A stabilization of the upsurge trend is remarkably observed for most indices under SSP1-2.6 starting from the 2050s. The future changes depicted in this study should help to assess the distribution of the impacts across different regions of Algeria in order to enhance resilience, establish the appropriate adaptation responses and improve disaster preparedness.
In this study, we assess the future changes in minimum temperature (T-min), maximum temperature (T-max), and precipitation (PRCP) for the three periods the 2020s (2011–2040), the 2050s (2041–2070), and the 2080s (2071–2100), with respect to the reference period 1981–2010 over Algeria focusing on a validation of the Statistical DownScaling Model (SDSM). In this approach, to underpin our analysis, we evaluate statistically the SDSM performance by simulating the historical temperatures and precipitation. The NCEP reanalysis data and CanESM2 predictors of three future scenarios, RCP2.6, RCP4.5, and RCP8.5 are used for model calibration and future projection, respectively. The projected climate changes resulting from the application of SDSM show a convincing consistency with those unveiled in previous studies over Algeria based on dynamical regional climate model outputs conducted in the context of Middle East-North Africa region. By the end of the century, the results exhibit strong warming for both extreme temperatures under the worst-case scenario (RCP 8.5), it is more pronounced for the T-max and over the Algerian Sahara region. Under the optimistic scenario (RCP2.6), the strength of the warming is expected to increase for both extreme temperatures. The projected changes of precipitation revealed for all scenarios several discrepancies with significant decrease over the northwest region and central Sahara, while nonsignificant change is projected for the center and eastern coastal regions. Our findings corroborate previous studies using sophisticated tools by demonstrating that Algeria’s climate is expected to warm further in the future. These primary findings could give an overview of the application of the statistical modeling approach using SDSM over a semi-arid and arid vulnerable region like Algeria and would extend our knowledge in the climate-modelling field for the North Africa zone by providing an added value to the existing GCMs and regional climate projections. In addition, reliable information regarding the magnitude of future changes at local scale may be used in impact models to assess changes of other key economic sector variables such as water resources management, energy and agriculture.
