Evaluation and Projection of Mean Surface Temperature Using CMIP6 Models Over East Africa

Ayugi, Brian; Ngoma, Hamida; Babaousmail, Hassen; Karim, Rizwan; Iyakaremye, Vedaste; Sian, Kenny Thiam Choy Lim Kam; Ongoma, Victor

doi:10.20944/preprints202101.0611.v1

Cited by 3 publications

(6 citation statements)

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“…Since this study and ours mostly considered different GCMs and different temperature parameters; mean temperature in the former and maximum and minimum temperature in the later, in their ability to replicate observed temperature, conclusions as to the generally performing models based on both studies is undeterminable. This is because, though some common models such as IPSL-CM6A-LR and MPI-ESM2-LR were found to have good performances as seen in (Ayugi et al, 2021b) for mean temperature and in this present study for minimum temperature. However, IPSL-CM6A-LR was found to be the second least ranking model for maximum temperature in this study.…”

Section: Discussionmentioning

confidence: 45%

“…In the eastern part of East Africa, covering Kenya, Rwanda, Tanzania, Uganda, and Burundi, thirteen CMIP6 temperature GCMs were used in evaluating mean surface temperature (Ayugi et al, 2021b) by employing statistical metrics, mean state, and trends. The study found that most of the GCMs overestimated the mean annual temperature with few underestimating it.…”

Section: Discussionmentioning

confidence: 99%

“…Studies have been conducted in assessing the performances of GCMs in many parts of the globe using different statistical measures (Rivera and Arnould, 2020;Sreelatha and Anand Raj, 2019). However, there can be challenges in decision making as to which GCMs performed best due to contradictions in the outputs from different statistical measures (Ayugi et al, 2021b;Klutse et al, 2021). This emphasizes the implementation of a compromise solution which can consider tradeoffs among the measures for selecting the best models.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria

Shiru

Chung

2021

Theor Appl Climatol

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This study assessed the performances of 13 GCMs of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984Nigeria during -2014 in order to identify the best GCMs for multi model ensemble aggregation for climate projection. The study uses the monthly full reanalysis precipitation product Version 6 of Global Precipitation Climatology Centre and the maximum and minimum temperature CRU version TS v. 3.23 products of Climatic Research Unit as reference data. The study applied five statistical indices namely, normalized root mean square error, percentage of bias, Nash-Sutcliffe efficiency, and coefficient of determination; and volumetric efficiency. Compromise programming (CP) was then used in the aggregation of the scores of the different GCMs for the variables. Spatial assessment, probability distribution function, Taylor diagram, and mean monthly assessments were used in confirming the findings from the CP. The study revealed that CP was able to uniformly evaluate the GCMs even though there were some contradictory results in the statistical indicators. Spatial assessment of the GCMs in relation to the observed showed the highest ranked GCMs by the CP were able to better reproduce the observed properties. The least ranking GCMs were observed to have both spatially overestimated or underestimated precipitation and temperature over the study area. In combination with the other measures, the GCMs were ranked using the final scores from the CP. IPSL-CM6A-LR, NESM3, CMCC-CM2-SR5, and ACCESS-ESM1-5 were the highest ranking GCMs for precipitation. For maximum temperature, INM.CM4-8, BCC-CSM2-MR, MRI-ESM2-0, and ACCESS-ESM1-5 ranked the highest while AWI-CM-1-1-MR, IPSL-CM6A-LR, INM.CM5-0, and CanESM5 ranked the highest for minimum temperature.

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Section: Discussionmentioning

confidence: 45%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria

Shiru

Chung

2021

Theor Appl Climatol

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“…The CRU data is preferred over other gridded datasets because the data have been developed from a relatively large number of in‐situ data with a longer temporal scale (Harris et al ., 2014; Ahmed et al ., 2019). Several studies have tested the CRU data and its validity in various parts of Africa (Shiru et al ., 2019, 2020; Lim Kam Sian et al ., 2021; Ayugi et al ., 2021b).…”

Section: Methodsmentioning

confidence: 99%

“…Statistical approaches such as root mean square difference (RMSD), correlation coefficient (CC), and mean bias (B) were used to determine the skilfulness of the CMIP6 GCMs in reproducing Tmin, Tmax, and Tmean against the corresponding CRU datasets. Other studies (e.g., Babaousmail et al ., 2019, 2021; Ongoma et al ., 2019; Ayugi et al ., 2020, 2021b, 2021a; Ngoma et al ., 2021a, 2021b) have used the metrics in evaluating model simulation of climate variables. The mathematical formulas of the metrics employed are shown in Equations () and ();

B goodbreak= \frac{1}{N} \sum_{k = 1}^{N} ((), M_{i} goodbreak- O_{i}) RMSD goodbreak= \sqrt{\frac{1}{N} \sum_{k = 1}^{N} {(M_{normali} - O_{normali})}^{2}}

CC goodbreak= \frac{\sum_{k = 1}^{n} ((), O_{i} goodbreak- \overset{O_{normali}}{true}) ((), M_{i} goodbreak- \overset{M_{normali}}{true})}{\sqrt{\sum_{k = 1}^{n} {((), O_{i} goodbreak- \overset{O_{normali}}{true})}^{2} \sum_{k = 1}^{n} {((), M_{i} goodbreak- \overset{M_{normali}}{true})}^{2}}}

…”

Section: Methodsmentioning

confidence: 99%

Observed and Coupled Model Intercomparison Project 6 multimodel simulated changes in near‐surface temperature properties over Ghana during the 20th century

Oduro

Ayugi

et al. 2021

Intl Journal of Climatology

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This article investigates seasonal and annual trends of near-surface temperature (NST) during the last century over Ghana. The ability of 24 global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate abrupt mean temperature changes in Ghana from 1980 to 2014 was assessed. The trends' magnitude and abrupt changes were detected using Sen Slope Estimator (SSE) and sequential Mann-Kendall (SQMK) tests, respectively. Root mean square difference, correlation coefficient, and mean bias (B) were used to determine the skilfulness of GCMs in simulating NST relative to Climatic Research Unit (CRU TS4.04) datasets. Ghana's Southern belt is characterized by low temperatures, averaging 25 C to 27 C, whilst the northern belt is characterized by high temperatures (29-31 C). The inter-annual variability of temperature over Ghana exhibits an increasing trend. Findings depict a significant increase in both the annual and seasonal temperatures of Tmin and Tmean, with a steady increase in Tmax. Results revealed the rate of increase had been higher in the country's northern regions (0.5 C) than in the south (0.3-0.4 C) in recent decades. The model performances for the interannual variability of annual and FMAM temperatures are better than their JJAS seasonal performances. The study's overall model ranking shows that Ghana's best performing models for annual, FMAM and JJAS seasons are INM-CM5-0, NorCPM1, and KACE-1-0-G, respectively. Observations and comparisons provide useful reference values for a comprehensive assessment of various applications.

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Recent Observed Changes in Extreme High‐Temperature Events and Associated Meteorological Conditions over Africa

Iyakaremye

Zeng

Ullah

et al. 2022

Intl Journal of Climatology

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More frequent and intensified high-temperature extremes are acceptable indicators of global warming, which pose serious socio-economic impacts. In the present research, the Climate Prediction Center daily minimum and maximum temperature are used to characterize high temperatures into intensity of hot days (TXx), hot nights (TNx), and frequency indices based on the 90th percentile of hot days (TX90p) and hot nights (TN90p) as defined by the Expert Team on Climate Change Detection and Indices from 1981 to 2020 over Africa. The regression approach based on iterative reweighted least squares and correlation analyses are used to estimate the trends in extreme high-temperature events and their relationship with various meteorological variables. Furthermore, the Pruned exact linear time algorithm is used to assess if the maximum temperature experienced an abrupt change. Results show that many parts of Africa experienced more frequent and intensified hot days and nights in the current period (1998-2020) compared to the recent past (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997), suggesting a clear shift in climate. Thus, we used the climatological mean differences between meteorological parameters before and after the breakpoint to assess the relationship between atmospheric conditions and extreme high-temperature events. As a result, we found that the current period observed an increase in the geopotential height at 500 hPa and reduced total cloud cover, as well as an increase in upward longwave radiation resulting in an upsurge in the frequency of hot days over many African sub-regions. The present study's findings provide useful information for future planning and development of early warning systems to cope with risks associated with hot extremes.

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Evaluation and Projection of Mean Surface Temperature Using CMIP6 Models Over East Africa

Cited by 3 publications

References 9 publications

Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria

Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria

Observed and Coupled Model Intercomparison Project 6 multimodel simulated changes in near‐surface temperature properties over Ghana during the 20th century

Recent Observed Changes in Extreme High‐Temperature Events and Associated Meteorological Conditions over Africa

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