ARTICLE | doi:10.20944/preprints202012.0782.v1
Online: 31 December 2020 (09:29:01 CET)
This study employed 15 CMIP6 GCMs and evaluated their ability to simulate rainfall over Uganda during 1981-2019. The models and the ensemble mean were assessed based on the ability to reproduce the annual climatologyseasonal rainfall distribution, trend, and statistical metrics, including mean bias error, root mean square error, and pattern correlation coefficient. The Taylor diagram and Taylor skill score (TSS) were used in ranking the models. The models performance varies greatly from one season to the other. The models reproduced the observed bimodal rainfall pattern of March to May (MAM) and September to November (SON) rains occurring over the region. Some models slightly overestimated, while some slightly underestimated, the MAM rainfall. However, there was a high rainfall overestimation during SON by most models. The models showed a positive spatial correlation with observed dataset, whereas a low correlation was shown interannually. Some models could not capture the rainfall patterns around local-scale features, for example, around the Lake Victoria basin and mountainous areas. The best performing models identified in the study include GFDL-ESM4, BCC-CMC-MR, IPSL-CM6A-LR, CanESM5, GDFL-CM4-gr1, and GFDL-CM4-gr2. The models CNRM-CM6-1 and CNRM-ESM2 underestimated rainfall throughout the annual cycle and mean climatology. However, these two models better reproduced the spatial trends of rainfall during both MAM and SON. The model spread in CMIP6 over the study area calls for further investigation on the attributions and possible implementation of robust approaches of Machine learning to minimize the biases.
ARTICLE | doi:10.20944/preprints202101.0188.v1
Online: 11 January 2021 (12:30:55 CET)
The present study analyzed seasonal (i.e., Dec-Jan [DJF] and June – August [JJA]) temperature change for the near (2025-2054) and far future (2070-2099) under SSP245, SSP370, and SSP585 scenarios over Pakistan. The anomalies, Mann-Kendall trend tests, Sequential Mann-Kendall trend test (SQMK), and probability density frequency (PDF) analysis were used to investigate future mean temperature variations. The DJF season projected higher increase in temperature in the northern (3.8 oC, 5.1 oC and 6.5 oC), followed by central regions (3.8 oC, 4.9 oC and 6.4 oC) under SSP245, SSP370 and SSP585 scenarios, respectively. The central region is likely to record significant increase in JJA (3.0 oC, 4.4 oC and 5.4 oC) mean temperature in far future under the given SSP scenarios. Compared to historical (PDF), the far future DJF temperature changes revealed significant higher warming over northern, central and then over southern regions under most of SSP scenarios. The southern regions are projected to possible rise in far future JJA temperatures by 2.7 oC, 3.3 oC and 4.3 oC, under SSP245, SSP370 and SSP585, respectively. The PDFs for JJA further verify the highest positive abrupt shift in temperature across the central region and then southern region. The future diverse seasonal temperature changes supports further examination of the associated mechanisms and factors responsible for temperature changes to address climate change.
ARTICLE | doi:10.20944/preprints202101.0112.v1
Subject: Earth Sciences, Atmospheric Science Keywords: CMIP6; extreme precipitation; model evaluation; east Africa
Online: 6 January 2021 (11:37:37 CET)
This paper presents an analysis of precipitation extremes over the East African region. The study employs six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) to evaluate possible climate change. Observed datasets and CMIP6 simulations and projections are employed to assess the changes during the two main rainfall seasons of March to May (MAM) and October to December (OND). The study evaluated the capability of CMIP6 simulations in reproducing the observed extreme events during the period 1995 – 2014. Our results show that the multi-model ensemble (herein referred to as MME) of CMIP6 models can depict the observed spatial distribution of precipitation extremes for both seasons, albeit with some noticeable exceptions in some indices. Overall, MME's assessment yields considerable confidence in CMIP6 to be employed for the projection of extreme events over the study area. Analysis of extreme estimations shows an increase (decrease) in CDD (CWD) during 2081 – 2100 relative to the baseline period in both seasons. Moreover, SDII, R95p, R20mm, and PRCPTOT demonstrate significant OND estimates compared to the MAM season. The spatial variation for extreme incidences shows likely intensification over Uganda and most parts of Kenya, while reduction is observed over the Tanzania region. The increase in projected extremes during two main rainfall seasons poses a significant threat to the sustainability of societal infrastructure and ecosystem wellbeing. The results from these analyses present an opportunity to understand the emergence of extreme events and the capability of model outputs from CMIP6 in estimating the projected changes. More studies are encouraged to examine the underlying physical features modulating the occurrence of extremes incidences projected for relevant policies.
ARTICLE | doi:10.20944/preprints202208.0275.v2
Subject: Earth Sciences, Environmental Sciences Keywords: projections; CMIP6; climate; impacts; health; malaria; Malaria; Senegal
Online: 16 August 2022 (05:46:38 CEST)
Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on the of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite's reproductive cycle inside the mosquito. The intensity and frequency of the rainfall modulate the development of the mosquito population. In this study, the Liverpool Malaria Model (LMM) is used to simulate the spatio-temporal variation of the malaria incidence in Senegal. The simulations are based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and biased-corrected CMIP6 models, separating historical and projections for 3 Shared Socio-economic Pathways scenarios (SSP126, SSP245 and SSP585). Our results highlight a strong increase in temperatures, especially towards eastern Senegal under the SSP245 but mainly the SSP585 scenarios. The ability of the LMM model to simulate the seasonality of malaria incidence is assessed. The model reveals a period of high malaria transmission between September and November with a maximum reached in October. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and for the extreme scenario. This study is importance for the planning, prioritization, and implementation of control activities in Senegal.
ARTICLE | doi:10.20944/preprints202101.0611.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Mean surface temperature; CMIP6; evaluation; projections; East Africa
Online: 29 January 2021 (11:35:29 CET)
This study evaluates the historical mean surface temperature (hereafter T2m) and examines how T2m changes over East Africa (EA) in the 21st century using CMIP6 models. An evaluation was conducted based on mean state, trends, and statistical metrics (Bias, Correlation Coefficient, Root Mean Square Difference, and Taylor skill score). For future projections over EA, five best performing CMIP6 models (based on their performance ranking in historical mean temperature simulations) under the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 scenarios were employed. The historical simulations reveal an overestimation of the mean annual T2m cycle over the study region with fewer models depicting underestimations. Further, CMIP6 models reproduce the spatial and temporal trends within the observed range proximity. Overall, the best performing models are as follows: FGOALS-g3, HadGEM-GC31-LL, MPI-ESM2-LR, CNRM-CM6-1, and IPSL-CM6A-LR. During the three-time slice under consideration, the Multi Model Ensemble (MME) project many changes during the late period (2080 – 2100) with expected mean changes at 2.4 °C for SSP2-4.5 and 4.4 °C for the SSP5-8.5 scenario. The magnitude of change based on Sen’s slope estimator and Mann-Kendall test reveal significant increasing tendencies with projections of 0.24°C decade-1 (0.65°C decade-1) under SSP2-4.5 (SSP5-8.5) scenarios. The findings from this study illustrate higher warming in the latest model outputs of CMIP6 relative to its predecessor, despite identical instantaneous radiative forcing.
ARTICLE | doi:10.20944/preprints202107.0584.v1
Subject: Earth Sciences, Atmospheric Science Keywords: CMIP6; HighResMIP; ScenarioMIP; Lake Victoria; Climate change; East Africa
Online: 26 July 2021 (14:39:44 CEST)
In late/early 2019/2020, unprecedented high-water-levels were observed in Lake Victoria causing massive flooding in the low-lying lake-adjacent areas and disrupting human and natural systems in the Lake Victoria Basin (LVB). The high lake water-level coincided with unusually heavy and prolonged 2019 June to December precipitation in the LVB. The current study estimates future precipitation patterns over the LVB using HighResMIP and ScenarioMIP general circulation model (GCM) simulations from the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Results show that HighResMIP and ScenarioMIP simulations can adequately reproduce LVB’s precipitation patterns – albeit with location-specific biases. Generally, the GCM simulations tend to over-estimate precipitation patterns over Lake Victoria while under-estimating precipitation patterns over the lake-adjacent areas. Projections show significant future precipitation changes over the LVB relative to the 1970-1999 baseline, with more pronounced changes over the lake than in lake-adjacent areas. Overall, mean annual precipitation is projected to increase by about 18% and 31% by the end of the century, under SSP2-4.5 and SSP5-8.5 scenarios, respectively. Additionally, mean daily precipitation intensity (SDII) is projected to increase by up-to 14% while the maximum 5-day precipitation values (RX5Day) increase by up-to 71% under the SSP5-8.5 scenario. Heavy precipitation events, represented by the width of the right tail distribution of precipitation (99p-90p), are projected to increase by 50% and 94% under SSP2-4.5 and SSP5-8.5, respectively. Given that direct precipitation accounts for about 80% of Lake Victoria’s water budget, the lake’s future water-level fluctuations are likely to be more rampant and unpredictable under the changing climate. Hence, enhanced production and use of climate services is recommended to minimize the risk posed by potentially high water-level fluctuations in Lake Victoria and, ultimately, enhance the socio-economic safety of communities in the LVB.
ARTICLE | doi:10.20944/preprints202107.0575.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Lake Victoria; Climate change; Return periods; Detection and Attribution; DAMIP; CMIP6
Online: 26 July 2021 (13:42:00 CEST)
This study investigated the influence of land-use and precipitation change and variability on Lake Victoria’s water-level fluctuations. Extreme precipitation events, corresponding to extreme water-levels, over the lake and its catchment area were identified and their return periods estimated by fitting them into a generalized extreme value (GEV) distribution. Using general circulation models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6)’s Detection & Attribution Model Intercomparison Project (DAMIP), an assessment of the potential contribution of human-induced climate change on the observed precipitation patterns over the study area was done. The greatest precipitation anomalies for the period 1900-2020 were recorded in 1961’s October-December (OND) season and 2019’s June-August (JJA) and OND seasons, corresponding to the period when the highest water-levels were recorded in Lake Victoria. While land-use change in the study domain was observed, extended and unusually heavy June to December 2019 precipitation bore the greatest responsibility for the 2019/2020 high water-levels in Lake Victoria. The OND precipitation event of 2019 was a 1-in-52-year event compared to the 1961’s 1-in-693 years. Differences in return periods at various parts of the lake imply a high spatial climate variability within the lake itself. An analysis of the fraction of attributable risk (FAR) showed natural variability to have a greater influence on the JJA and OND precipitation patterns over Lake Victoria than human-induced climate change. However, variability over the land area of the study domain was mainly driven by human-induced climate change rather than natural variability, implying a unique climate system over Lake Victoria. Findings from the current study enhance the understanding of Lake Victoria’s water budget and motivate for further research to inform effective strategies on the planning and use of Lake Victoria’s water resources in a changing climate.