ABSTRACT
The Turkana low-level jet stream (TJ) is important to climatic conditions over northern Kenya and East Africa. The representation of the TJ in climate models varies due to the TJ interaction with Turkana channel that is influenced by model resolution and influences the model representation of the regional climate. This study compares features of the TJ in data from Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations with European Center for Medium- range Weather Forecasting atmospheric Re-Analysis version 5 (ERA5). The study presents analysis of spatial structure and annual cycle of the TJ, and relates strength of the TJ with topographic formation of the Turkana channel rainfall in the models and reanalysis (RA). Considering study period between 1981 and 2014, models reveal climatological wind speeds that match those of the reanalysis from the ERA5 at the jet entrance (13 m/s) but lower magnitudes of wind speed and vertical shears compared to ERA5 within the Turkana channel. The models with slowest wind speeds, have a flattened Turkana channel and fail to exhibit the terrain constriction at 37°E which otherwise aids in accelerating winds to form a jet core. Furthermore, they fail to represent the narrowing of the channel as in ERA5, thereby forming blocking walls in the channel, forcing vertical ascent and mixing, and weakening shear. This boosting of ascent motion promotes rainfall formation and enhances wet anomalies at the exit of the TJ when the jet stream is weaker. By applying a new narrowing index, we demonstrate the need to improve topography details in the CMIP6 models, particularly those with resolution coarser than 1.5°, in order to properly simulate the TJ and the observed rainfall over the northwestern areas of eastern Africa.
TABLE OF CONTENTS
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABREVIATIONS AND ACRONYMS viii
LIST OF FIGURES x
LIST OF TABLES x
CHAPTER ONE
INTRODUCTION
1.1 Background of the study 1
1.1 Statement of the Problem 2
1.2 Study Objectives 3
1.3 Research questions: 3
1.4 Justification of the Study 3
1.5 Significance of the Study 4
1.6 The Study Area 4
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction 7
2.2 Known mechanism of formation and maintenance of Low-level jet streams 7
2.3 Known features of the TJ and the TJ connection with the Turkana channel 8
2.4 Influence of the Turkana Jet stream on regional climate 9
2.5 The TJ and the East African Rainfall in climate models 10
2.6 Conceptual framework used in the study 11
CHAPTER THREE
DATA AND METHODS
3.1 Introduction 13
3.2 Datasets 13
3.3 Methods 15
3.3.1 Characterization of the TJ in climatology 15
3.3.2 TJ connection with the Turkana channel 16
3.3.3 TJ influence on rainfall 17
3.4 Assumptions and limitations 17
CHAPTER FOUR
RESULTS AND DISCUSSION
4.1 Introduction 19
4.2 Characteristics of the TJ 19
4.2.1 Seasonal cycle of the TJ in CMIP6 model climatology 19
4.2.2 Spatial Structure in climatology of the TJ in CMIP6 models 23
4.2.3 Mean vertical flow in the Turkana channel 25
4.3 Topographic influence on the TJ in CMIP6 27
4.3.1 Turkana channel in CMIP6 27
4.3.2 Relationship between TJ and Topography in historical AMIP 29
4.4 Relationship between TJ and East African climate in CMIP6 33
CHAPTER FIVE
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1 Introduction 37
5.2 Summary 37
5.3 Conclusions 38
5.4 Recommendation 38
ABREVIATIONS AND ACRONYMS
ACCESS-CM2 Australian Community Climate and Earth System Simulator coupled model, version 2
ACCESS-ESM1-5 Australian Community Climate and Earth System Simulator Earth System model
AMIP Atmospheric Model Intercomparison Project
BCC-CSM2-MR Beijing Climate Center, Climate System Model
BCC-ESM1 Beijing Climate Center, Earth System Model
CAMS-CSM1-0 Chinese Academy of Meteorological Sciences Climate System Model
CanESM5 Fifth Generation Canadian Earth System Model
CESM2 Community Earth System Model version 2
CMIP5 Coupled Model Intercomparison Project Phase 5
CMIP6 Coupled Model Intercomparison Project Phase 6
CNRM-CM6-1 Centre National de Recherches Meteorologiques Coupled Model version 6
CNRM-ESM2-1 Centre National de Recherches Meteorologiques Earth System model version 2
EALLJ East Africa Low-Level Jet stream
ECMWF European Center for Medium-range Weather Forecasting
ERA5 ECMWF atmospheric Re-Analysis version 5
ERA-Interim ECMWF Re-Analysis -Interim version
FGOALS-f3-L Flexible Global Ocean-Atmosphere-Land System, finite- volume version 3
FGOALS-g3 Flexible Global Ocean-Atmosphere-Land System, grid-point version 3
GFDL-CM4 Geophysical Fluid Dynamics Laboratory Climate model version 4
HadGEM3-GC31-LL Hadley Centre Global Environment Model Global Coupled configuration 3.1, Low resolution
HadGEM3-GC31-MM Hadley Centre Global Environment Model Global Coupled configuration 3.1, medium resolution
INM-CM4-8 Institute of Numerical Mathematics Climate Model
INM-CM5-0 Institute of Numerical Mathematics Climate Model
IPSL-CM6A-LR Institut Pierre-Simon Laplace climate model low resolution
JJAS June-July-August-September
JRA-55 Japanese 55-year Re-Analysis
MERRA2 Modern-Era Retrospective analysis for Research and Applications, version 2
MIROC6 Sixth version of the Model for Interdisciplinary Research on Climate
MPI-ESM1-2-HR Max Planck Institute Earth System Model, High resolution
MRI-ESM2-0 Meteorological Research Institute Earth System Model, version 2
NCEP CFSR National Center for Environmental Prediction (NCEP) Climate Forecast System Reanalysis
NESM3 NUIST Earth System Model (NESM) version 3
NorCPM1 Norwegian Convection Permitting Model version 1
NorESM2-LM Norwegian Earth System Model, version 2
RA Re-Analysis
SAM0-UNICON Seoul National University Atmosphere Model Version 0 with a Unified Convection Scheme
TaiESM1 Taiwan Earth System Model 1.0
TJ Turkana Jet stream
WCRP World Climate Research Programme
LIST OF FIGURES
Figure 1. Map of East Africa showing the country boundaries, terrain 6
Figure 2. A schematic showing of key elements referred to in this work. 12
Figure 3. Mean mean wind vector at 850 millibars (m/s) for different datasets 22
Figure 4. Climatological wind speeds from surface to 500 mb wind speed. 24
Figure 5. Climatology of vertical cross-section of omega (Pascals per second) 26
Figure 6. Estimated cross sectional area of Turkana Channel -TC (in square kilometers) 29
Figure 7. Mean zonal change in cross-sectional area of Turkana channel. 30
Figure 8. Dependence of the TJ occurrence on mean zonal change in cross-sectional area of Turkana channel. 32
Figure 9. Climatological precipitation during the June to September season for the years 1980 to 2014. 36
LIST OF TABLES
Table 1: Name and description of CMIP6 models used in this study. 14
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
Over northern Kenya, there exists a low-level jet stream which has great socio-economic potential for the region as well as being an important mechanism for moisture transport from the western Indian Ocean to some interior parts of northwestern East Africa (Vizy and Cook, 2019; Nicholson, 2014). This fast-flowing current of air, which is now called the Turkana low-level Jet stream (TJ), gained recognition among aviators who frequently experienced turbulence when landing and taking off from the region (Indeje et al., 2001). The understanding of the TJ benefited from short field campaigns by Kinuthia and Asnani (1982) and Kinuthia (1992). The mean wind speeds in range 10 - 13 m/s culminate at 850 mb (Nicholson, 2016) in the year. Occasional ascents of pilot balloons during field campaigns in Kinuthia (1992) indicated the TJ is strongest during late night and early morning with associated wind speeds in range 30 to 50 m/s. The TJ is fragmented into two branches in the channel which merge at the mid-portion of the channel to form a jet core (Kinuthia and Asnani, 1982; Indeje et al., 2001). Previous studies have related the TJ to the observed aridity over parts of Eastern Africa (Trewartha, 1981; Nicholson, 1996; Ba and Nicholson, 1998; Sun et al., 1999; Indeje et al., 2001; Nicholson, 2016). It is for this reason that the inclusion of the TJ in dynamical models is important to the representation of rainfall over the northern Kenya region (Sun et al., 1999; Indeje et al. 2001; King et al., 2021).
Eastern Africa (EA) is a generally dry region, found in an otherwise wet equatorial belt (Camberlin, 2018). The region is hyper-arid at the tip of the Horn and near the Egyptian border with annual rainfall below 150 mm (Njenga at al., 2014), an apparent extension of desert conditions from the bordering middle-East and Sahara respectively. An extensive area featuring less than 400 mm annual rainfall stretches from eastern Ethiopia through northwestern Kenya, to Lake Turkana (Nicholson, 2016; Camberlin, 2018). Extreme drought events have been common, exacerbated by climate systems that perturbs rainfall received, whose manifestations have increased notably since 2005. Between 2015 and 2016, drought prevailed over most parts of Ethiopia related to El Nino conditions (Sjoukje et al 2015), while 2016 and 2017 exhibited drought over the Greater Horn related to La Nina (Uhe et al., 2015). Similarly, three drought events occurred in the seven years alone (2005/2006, 2008/2009, 2010/2011) compared with only seven events in 30 years (1975 to 2004). The recent cyclicity in drought impacts has heightened interests in climate information from models to inform disaster risk management and increase people’s adaptive capacity to climate extremes (Nicholson, 2014; Kilavi et al., 2018).
Climate model information has led to understanding of the TJ and its influence on the climate in East Africa. Increased studies on the TJ using current generation of climate models and improved datasets can lead to a more quantitative understanding of the TJ as well as its impacts. The aridity is linked to regional topography and wind flow. Due to the north-south orientation of the east African highlands, advection of moist air from Congo-basin into the arid area is blocked (Slingo et al., 2005). Lower-tropospheric (at 850 mb level) divergence matched by subsidence at mid troposphere characterizes the arid sub-regions (Yang et al., 2015). The large-scale divergence is linked with the TJ as fast-moving winds cause drop in pressure due to Bernoulli effect (Indeje et al., 2001) and interact with terrain within the channel, inducing frictional divergence (Ba and Nicholson, 1998). The divergence at lower atmosphere favors stable atmospheric conditions and dry climate over the plain and low plateau over coastal Tanzania, Kenya and Somali border, to the Nile plains of Sudan and South Sudan (Nicholson, 2016). The TJ also transports large amounts of moisture that promote rainfall formation at the jet exit in western parts of South Sudan and Ethiopia during the northern summer (Vizy and Cook, 2019). The strength of the TJ varies with large scale systems that drive rainfall over East Africa during October to December (Sun et al., 1999; King et al., 2021).
While the TJ is considered important to rainfall, some climate models such as the Community Atmosphere Model can exhibit weak low-level jet streams due to their low resolutions (Acosta and Huber, 2017). Climate models contributing to CMIP5 shows weaker TJ than in ERA5 during June to September months and comparable strength of the TJ in the remaining months of the year (King et al., 2021). However, reasons for why the models represent the TJ the way they do, including the influence of the TJ on the airflow over the region, and how this affects the weather over the region in models, is not well understood.
1.2 Study Objectives
The overall objective of this study was to characterize features of TJ in the advanced CMIP6 models, understand how the features of the TJ might be related to topography in the models and how the differences in the model TJ might impact on the simulated rainfall.
To achieve the overall objectives above, the study involved the following:
1. Assessing how the TJ is characterized in current generation of climate models focusing on climatological location, and strength characteristics.
2. Determining how the TJ characteristics are influenced by the Turkana channel
3. Establishing extent to which varied representation of the TJ in the models affects how rainfall is simulated over north west of East Africa.
1.3 Research questions:
a) What are the climatic characteristics of the TJ in CMIP6 models?
b) How does the resolution and the representation of topography in the models be related to features of the TJ in CMIP6 models?
c) Is the simulated rainfall related to features of the simulated TJ?
1.4 Justification of the Study
This work focuses on characteristics of the TJ in CMIP6 models which are the latest generation of models available for us to study the climate in the past and anticipate future changes. It compares the characteristics of the TJ with the EAR5 that is optimized to give the best match to what would be observed in observational data. Specifically, the study discusses model representation of the strength, structure and position of TJ along the Turkana channel whose varied representation with respect to observations may also induce biases in the simulated rainfall.
Model simulation of TJ features introduces a measure of uncertainty because of the lack of observational datasets. But since it is understood that some low-resolution models ignore low-level jet streams (Acosta and Huber, 2017), there is the opportunity to refine our understanding of the jet stream characteristics and influence on rainfall by using the recently available CMIP6 and ERA5 with high spatial resolution.
1.5 Significance of the Study
The ERA5 leverages on continuous development process that forecast models have undergone and to offer higher spatial and temporal resolution in reanalysis, historical climate products. Development in modeling is a continuous process and whereas better products are getting available, there is the need to understand model uncertainties and build confidence in them. Through evaluating the model representation of a key feature to rainfall in the region, this study informs both model development process and model use as argued in James et al., (2017).
1.6 The Study Area
East Africa encompasses varied terrain, characterized by elevation gradients ranging from the highest elevation point at 5, 395 m above mean sea level (Mount Kilimanjaro) to the lowest elevation point of 153 m below mean sea level, in Djibouti. This culminates in a north-south organization of elevated terrain (Figure 1) that separates East Africa into two regions.
The Ethiopian highlands (Ethiopian Massif) are the most expansive (McCann, 1995) with about 50% if it elevated above 1,500 m above mean sea level. The highlands are characterized by a mixture of tablelands, valleys and several summits elevated at about 4,000 m above mean sea level. The Turkana channel boarders the Ethiopian highlands to the south and characterizes the low elevation terrain over northern Kenya. Other highlands extend from Kenya-Uganda boarder to central Kenya with a terrain gap that intercepts the Kenya-Uganda boarder to central Kenya high terrain elongation, known as the Rift Valley. Kenyan highlands peak at Mount Kenya with an elevation of 5,199 m above mean sea level. In Tanzania, Mount Kilimanjaro protrudes with an elevation of 5,895 m and in Uganda, Mount Elgon is elevated at 4,321 m above mean sea level.
To the west of the Kenyan highlands, lays a fairly flat expanded elevation at about 1,000 m that contains Lake Victoria.
Regions around the equator on the globe are generally wet, although other areas are wetter than others induced by varied topography. The wet climate supports tropical rain forests which are characterized by lofty trees and dense undergrowth. However, the climatic conditions of East Africa do not support such growth that otherwise proceed throughout the year without seasons. Parts of the East African region which are to the west of the Nile valley exhibit a peak in rainfall season during the boreal summer of June-July-August (JJA) and those to the east showing bimodal regimes; the March-April-May (MAM) long rain and the September-October-November (SON) short rain seasons (Nicholson, 2018). The rainfall seasons shifts back to a unimodal regime further to the south, that peak when the season is summer in the southern hemisphere; December-January- February (DJF). The rainfall regimes are largely attributed to the North-South movement of trade winds convergence zone (Henderson et al., 1949; Miller 1971).
The East Africa is hyper-arid at the tip of the Horn of Africa and near the Egyptian border with annual rainfall below 150 mm (Njenga at al., 2014), an apparent extension of desert conditions from the bordering middle-East and Sahara respectively. An extensive area featuring less than 400 mm annual rainfall stretches from eastern Ethiopia through northwestern Kenya, to Lake Turkana (Nicholson, 2016; Camberlin, 2018).
The aridity exhibited to the east of the north-south oriented East African highlands is related to the wind that is associated with TJ whose flow is divergent. The west of the East African highland is wetter due to the influence of the zonal winds from the Congo Air Boundary (Howard and Washington 2019). The zonal advection of the moisture-laden wind from the Congo basin to the drier east is impeded by the terrain barrier (Slingo et al., 2005).
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