Precipitation Extremes in Dynamically Downscaled Climate Scenarios over the Greater Horn of Africa

Shiferaw, Andualem; Tadesse, Tsegaye; Rowe, Clinton M.; Oglesby, Robert J.

doi:10.3390/atmos9030112

Cited by 43 publications

(27 citation statements)

References 46 publications

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“…To compare the simulation abilities of CMIP5 and CMIP6, the pattern correlation coefficient (PCC; Cai et al ., 2018; Shiferaw et al ., 2018; Rivera and Arnould, 2020) was calculated based on spatial scale, and the calculation formula was as follows:

PCC = \frac{\sum_{m = 1}^{M} ((), y_{m} - \overset{y}{true}) ((), o_{m} - \overset{o}{true})}{{([], \sum_{m = 1}^{M} {(y_{m} - \overset{false¯}{y})}^{2} \sum_{m = 1}^{M} {(o_{m} - \overset{false¯}{o})}^{2})}^{\frac{1}{2}}},

where y and o are the GCMs and observed data at the m th grid point, respectively, and the overbars refer to the mean value over the M grid points. The PCC ranges from −1 to 1, with values closer to 1 indicating a greater ability of the GCMs to capture the spatial patterns of the observed series over the region.…”

Section: Methodsmentioning

confidence: 99%

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Lun

Liu

Cheng

et al. 2021

Intl Journal of Climatology

126

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General circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs for each climate variable, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid‐west and northern edge of the TP. Furthermore, CMIP6 offered a better performance of precipitation in summer and autumn. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1°C and had a smaller bias in spring and winter. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2°C in each elevation zone, with the minimum bias of −0.22°C distributed in the region with altitudes from 3,000 to 4,000 m, while the biases of CMIP5 MMEs in the region of 4,000–5,000 m and over 5,000 m were smaller than those of CMIP6 MMEs. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. GCMs of CMIP6 perform better than those of CMIP5 for precipitation and temperature over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.

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PCC = \frac{\sum_{m = 1}^{M} ((), y_{m} - \overset{y}{true}) ((), o_{m} - \overset{o}{true})}{{([], \sum_{m = 1}^{M} {(y_{m} - \overset{false¯}{y})}^{2} \sum_{m = 1}^{M} {(o_{m} - \overset{false¯}{o})}^{2})}^{\frac{1}{2}}},

Section: Methodsmentioning

confidence: 99%

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Lun

Liu

Cheng

et al. 2021

Intl Journal of Climatology

126

View full text Add to dashboard Cite

show abstract

“…CHIRPS is updated regularly (ftp://ftp.chg.ucsb.edu/pub/org/chg/products) and the second version provides an improved daily precipitation product (1981–present). Due to the high quality and spatial resolution (0.05°), CHIRPS is used for assessing and monitoring of extreme weather and climate events and hydrological projections in remote and data scarce regions of Africa (Funk et al ., ; Katsanos et al ., ; Petitta et al ., ; Shiferaw et al ., ).…”

Section: Study Area and Datamentioning

confidence: 97%

Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania

Gebrechorkos

Hülsmann

Bernhofer

2018

Intl Journal of Climatology

164

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East Africa is one of the most vulnerable regions of Africa to extreme weather and climate events. Regional and local information on climate extremes is critical for monitoring and managing the impacts and developing sustainable adaptation measures. However, this type of information is not readily available at the necessary spatial resolution. Therefore, here we test trends and variability of temperature (1979-2010) and precipitation (1981-2016) extremes in East Africa, particularly Ethiopia, Kenya, and Tanzania, at a spatial resolution of 0.1 and 0.05 , respectively, using the indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We use gridded data sets with high accuracy and resolution from the Terrestrial Hydrology Research Group, University of Princeton and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Trends of 19 indices are computed by fitting a linear model and using the nonparametric Mann-Kendall test and the magnitude of change is computed using the Sen's slope method. The results show an increasing trend in monthly maximum and minimum values of daily maximum and minimum temperature in large parts of the region. This is accompanied by significant increasing trends in warm nights (TN90p), warm days (TX90p), warm spell duration index (WSDI), and summer days index (SU). In addition, cold days (TX10p) and cold nights (TN10p) showed a significant decreasing trend. In general, the results show an increasing tendency in temperatures extremes, which is in line with rising global mean temperature. In addition, most of the temperature extremes observed after 2000 are warmer than the longterm mean . Precipitation indices, on the other hand, showed increasing and decreasing trends in Ethiopia, Kenya, and Tanzania, but no general pattern. The outcomes enable identifying hot spot areas and planning of adaptation and mitigation measures at much finer spatial scale than previously possible.

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“…Rapid population growth and highly variable climate conditions may increase future risks of water scarcity in the region. Hydrological impact studies, which are critical for water resources planning, have been hindered by the coarse global circulation model (GCM) resolutions not capable of capturing the small-scale rainfall patterns, large uncertainty in both GCM and regional climate models (RCM) rainfall projections and the lack of model verification using measured streamflow in the region (Otieno and Anyah 2013;Endris et al 2016;Shiferaw et al 2018).…”

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confidence: 99%

Streamflow response to climate change in the Greater Horn of Africa

et al. 2019

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The Greater Horn of Africa region increasingly experiences high risk of water scarcity. A combination of frequent droughts, rapid population growth and rising urbanisation has reduced streamflow and intensified water abstraction, causing water and food shortages. Estimates of future streamflow changes in the region have so far been highly uncertain and evaluations using ground-based measurements are still limited. Here, future streamflow changes are estimated using a distributed hydrological model forced with an ensemble of high-resolution climate simulations produced using the European community Earth-System Model v3.1. The simulated streamflow is evaluated using observed data from 29 stations from river basins across different climate zones in the region. Evaluation results show large sub-regional variations in the performance of simulated streamflow. The sign and magnitude of future streamflow changes vary between climate simulations and river basins, highlighting the uncertainties in the hydrologic projections. Overall, the streamflow projections indicate large (seasonal, long-term mean and extreme) streamflow decreases for all major rivers in Ethiopia and increases in the equatorial parts of the region at the end of the century. The ensemble mean shows a 10 to 25% decrease in the long-term mean flow in Ethiopia and a 10% increase in the equatorial part of the region in 2080s. Similarly, there is a substantial change in high flows in 2080s, with up to − 50% reduction in the northern and 50% increase in the equatorial parts of the region. These findings are critical because the rivers provide water supply to a rapidly changing socio-economy of the region. Water scarcity is one of the pressing global challenges, with wide-ranging societal and environmental impacts. It affects food security, energy production, human health and the

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Precipitation Extremes in Dynamically Downscaled Climate Scenarios over the Greater Horn of Africa

Cited by 43 publications

References 46 publications

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania

Streamflow response to climate change in the Greater Horn of Africa

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