Understanding a high resolution regional climate model's ability in simulating tropical East Africa climate variability and change

Includes bibliographical referencesThe main aim of this thesis is to investigate the potential benefits of increasing resolution in regional climate models in the simulation of climate variability and change over East Africa. This study is based on two high resolution regional climate simulations with a horizontal resolution of 50km and 10km, respectively. These represent present day climate and a projection of future climate change over East Africa. The regional climate model (RCM) used here is HIRHAM5, which is driven by the global circulation model (ECHAM5). Downscaled ECHAM5 output is used to drive the 50km HIRHAM5 simulation for the period 1950-2100, and output from this simulation is used to drive the 10km simulation for three time slices: 1980-1999, representative for present-day climate and two time slices for near future (2046-2065) and far future (2080- 2099), respectively. HIRHAM5 is evaluated with respect to the observed mean climatologies of rainfall, surface temperature and surface winds over East Africa, and representations of the observed annual cycles and inter-annual variability of rainfall and surface temperature. This study utilizes reanalysis and observational datasets: a hindcast of HIRHAM5 forced with ERA Interim, as well as two observation datasets for temperature and rainfall. Since reanalyses aim to make "best use" of all available observations by making a physically consistent representation continuous in time and space, and since there is a paucity of observations over many parts of Africa, the ERAI reanalysis is also used as a best estimate for model evaluation. Additionally, for evaluation of the bimodal nature of East Africa's rainfall, especially over Tanzania, three stations run by the Tanzania Meteorological Agency were used. The model data used in th is evaluation ranges from 1980 to 2006 iv HIRHAM5 demonstrates reasonable skill in the reproduction of observed patterns of mean climatology of rainfall, surface temperature and winds over East Africa. Moreover, the patterns of annual cycles of rainfall and surface temperature in the bimodal nature of East Africa are well represented. Furthermore, the model showed reasonable skill in the representation of the inter- annual variability and ENSO signals as suggested by the observation. Despite these strengths, HIRHAM5 shows some shortcomings. One weakness of the model is the simulation of the magnitude of a given variable over a specific region. For example, HIRHAM5 driven by ERAI underestimates rainfall and overestimates surface temperature over the entire domain of East Africa. The higher resolution HIRHAM5 (10km resolution) overestimates rainfall over high ground. The model bias could be due in part to the inadequacy of the observation networks in East Africa, represented in this thesis by the CRU and FEWS datasets. However, these two datasets draw on some different sources and neither do they have the same resolution. FEWS is a high resolution data (0.1 o ) gridded satellite-derived precipitation estimate covering the entire African continent while CRU datasets is a relatively low resolution (0.5 o ) dataset based on rain gauge monthly precipitation only; in addition , near surface temperature is also available. As no reliable wind observations exist, wind data was taken from the ERA-Interim reanalysis. The different observational datasets do not agree particularly well, which impedes evaluating the quality of the HIRHAM5 simulations, in particular the high resolution one. So while the higher resolution HIRHAM5 appears to be generally reliable, caution must be exercised in formulating conclusions from the results, especially over high ground and remote areas without adequate observation data. Under these constraints, the results suggest HIRHAM5 may be useful for assessing climate variability and change over East Africa. A weakness of the analysis presented here is that only one combination of GCM and RCM could be investigated in depth due to computer and time constraints. Therefore the results presented here, if used in application for climate change adaptation, should be considered in conjunction with a broader suite of data, such from the CORDEX programme. This has potential to increase the reliability of information about climate variability and change at a regional to local level necessary for impact assessment

Assessment of the Off-season Rainfall of January to February 2020 and Its Socio Economic Implications in Tanzania: A Case Study of the Northern Coast of Tanzania

This article examines the off season rainfall in northern coast Tanzania (NCT) including Zanzibar which occurred in January and February 2020 (JF). Like the JF rainfalls of 2001, 2004, 2010, 2016 and 2018, the JF (2020) rainfall was more unique in damages including loss of lives, properties and infrastructures. The study used the NCEP/NCAR reanalysis data to examine the cause of uniqueness of JF rainfall in 2001, 2004, 2010, 2016, 2018 and 2020 over NCT and Zanzibar. These datasets include monthly mean u, v wind at 850, 700, 500, and 200 mb; SSTs, mean sea level pressure (MSLP) anomalies, Dipole Mode Index (DMI), and monthly rainfall from NCT and Zanzibar stations. Datasets were processed and calculated into long term, seasonal, and monthly averages, indeed, Precipitation Index (PI) was calculated. Correlation analysis between the rainfall (December to January), SST, DMI and 850 mb wind vectors; and long-term percentage contribution of investigated parameters was calculated. Results revealed significant positive and negative correlations between JF rainfall, SSTs and DMI. Moreover, JFs of 2004 and 2016 had higher rainfalls of 443 mm with percentage contribution of up to 406%, while January and February, 2020 had the highest of 269.1 and 101.1mm in Zanzibar and 295 and 146.1 mm over and NCT areas, with highest January long-term rainfall contribution of 356% in Zanzibar and 526% over NCT. The DJF (2019/20) had the highest rainfall record of 649.5 mm in Zanzibar contributing up to 286%, while JF 2000 rainfall had a good spatial and temporal distribution over most NCT areas. JF, 2020 rainfall had impacts of more than 20 people died in Lindi and several infrastructures including Kiyegeya Bridge in Morogoro were damaged. Conclusively, more research works on understanding the dynamics of wet and dry JF seasons should be conducted

Environmental Associated Emotional Distress and the Dangers of Climate Change for Pastoralist Mental Health

Pastoralists in the Horn of Africa are among the most vulnerable populations to climate change yet little is known about how environmental change shapes their wellbeing and mental health. This paper presents a formative study into the relations between emotion, wellbeing and water security among pastoralist communities in Afar, Ethiopia. It uses focus group and interview data to demonstrate the close relationship between environmental conditions and emotional wellbeing, and shows how current water insecurity leads to extreme worry and fatigue among the studied population, especially in the dry season. In the context of difficulties of translating mental health clinical classifications and diagnostic tools in cross-cultural settings, the paper argues the inductive study of emotion may be a useful approach for studying environmental determined wellbeing outcomes among marginal populations in the light of understanding climate change impacts

Projected effects of 1.5 °C and 2 °C global warming levels on the intra-seasonal rainfall characteristics over the Greater Horn of Africa

This study examines the effects of 1.5 °C and 2 °C global warming levels (GWLs) on intra-seasonal rainfall characteristics over the Greater Horn of Africa. The impacts are analysed based on the outputs of a 25-member regional multi-model ensemble from the Coordinated Regional Climate Downscaling Experiment project. The regional climate models were driven by Coupled Model Intercomparison Project Phase 5 Global Climate Models for historical and future (RCP8.5) periods. We analyse the three major seasons over the region, namely March–May, June–September, and October–December. Results indicate widespread robust changes in the mean intra-seasonal rainfall characteristics at 1.5 °C and 2 °C GWLs especially for the June–September and October–December seasons. The March–May season is projected to shift for both GWL scenarios with the season starting and ending early. During the June–September season, there is a robust indication of delayed onset, reduction in consecutive wet days and shortening of the length of rainy season over parts of the northern sector under 2 °C GWL. During the October–December season, the region is projected to have late-onset, delayed cessation, reduced consecutive wet days and a longer season over most of the equatorial region under the 2 °C GWL. These results indicate that it is crucial to limit the GWL to below 1.5 °C as the differences between the 1.5 °C and 2 °C GWLs in some cases exacerbates changes in the intra-seasonal rainfall characteristics over the Greater Horn of Africa