Accepted Manuscript Potential relation between equatorial sea surface temperatures and historic water level variability for Lake Turkana, Kenya Chris Bloszies, Steven L. Forman PII: DOI: Reference:
S0022-1694(14)00781-1 http://dx.doi.org/10.1016/j.jhydrol.2014.10.001 HYDROL 19950
To appear in:
Journal of Hydrology
Received Date: Revised Date: Accepted Date:
2 December 2013 19 June 2014 1 October 2014
Please cite this article as: Bloszies, C., Forman, S.L., Potential relation between equatorial sea surface temperatures and historic water level variability for Lake Turkana, Kenya, Journal of Hydrology (2014), doi: http://dx.doi.org/ 10.1016/j.jhydrol.2014.10.001
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Potential relation between equatorial sea surface temperatures and historic water level variability for Lake Turkana, Kenya
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Chris Bloszies*, Dept. of Earth and Environmental Sciences, University of Illinois at Chicago, 845 W. Taylor St., Chicago, IL 60607,
[email protected]
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Steven L. Forman, Dept. of Geology, Baylor University, One Bear Place #97354 Waco, TX 76798-7354
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*Corresponding Author
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Keywords: Lake Turkana, historic lake level, monsoon variability, Indian Ocean, Atlantic Ocean, East Africa
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Abstract Water level in Lake Turkana, Kenya in the past ca. 150 years is controlled primarily from the biannual passage
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of the East and West African monsoon, with rainfall volume related partially to sea surface temperatures (SSTs) in
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the Western Indian and East Atlantic oceans. Empirical orthogonal function analyses show significant correlation
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between Eastern Atlantic or Western Indian SSTs and lake level anomalies, with the first mode accounting for 66%
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and 55% of the variability. The primary geographic loadings are consistent with a Gulf of Guinea moisture source
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and positive Indian Ocean Dipole (IOD) state. The second mode explains 10% of variability, and reflects the
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westward extension of an Indian Ocean cool pool, potentially indicative of a normal to a negative IOD state. There
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is significant spatial correlation between basin rainfall anomalies associated with Eastern Atlantic SSTs and a low in
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the continental divide between the Kenyan and the Ethiopian highlands, which is a passage for moisture from the
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Congo Basin. Linear regression analysis with Bootstrap sampling and Monte Carlo simulations define numeric
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relations between Western Indian and Eastern Atlantic SSTs and lake level change for AD 1992 to 2013. The
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monthly and yearly lake level reconstructions based on this numeric analysis capture the decadal-scale variability
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and the 15 m drop in water level in the early 20th century. Meter-scale variability in lake level since ca. AD 1930 is
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associated with precipitation sourced from the Western Indian Ocean with IOD variability, whereas the 15 m drop in
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water level in the early 20th century may reflect a profound decrease in moisture from Atlantic/Congo Basin source.
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These numerical solutions are poised to reconstruct water level variations in the past ca. 300 years for Lake Turkana
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with new proxy records of SSTs from the Western Indian Ocean and the Gulf of Guinea.
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1. Introduction
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A critical system for understanding climate dynamics in the 21st century is monsoonal circulation on global and
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meso-scales (Fig. 1). Uncertainty remains on how this complex system will respond to subdecadal oscillations (e.g.
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the Indian Ocean Dipole) in sea surface temperatures (SSTs), anthropogenic atmospheric and oceanic warming, and
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land-use changes in the tropics (e.g. Ackerley et al., 2011; Christensen, 2007; Giannini, 2010; Nicholson, 2009;
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Wang et al., 2012). Global climate models predict a substantial increase in rainfall for equatorial regions with
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elevated greenhouse gases in the 21st century, often associated with an intensified and a spatially extended monsoon
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(Cherchi et al., 2011; Hsu et al., 2012). These models depict warmer SSTs in the equatorial oceans in response to a
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rise in global air temperatures, which may increase the severity of the West African and Indian monsoons (Ashok
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and Saji, 2007; Biasutti, 2013), but may have limited effect on the East African Monsoon (Shongwe et al., 2011;
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Williams et al., 2012). The predicted distribution of precipitation over equatorial Africa is spatially diverse (Cook
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and Vizy, 2012), with drought forecast to deepen in the western Sahel, an expected rainfall deficit for southern
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Africa, and a concomitant increase in rainfall across Equatorial Africa (Biasutti, 2013; Hoerling et al., 2006). A
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possible wetter 21st century monsoon is reinforced by a mesoscale climate model for East Africa which predicts a
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19% and 22% increase in precipitation for the boreal fall and boreal winter, respectively for the Lake Turkana Basin,
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Kenya (Shongwe et al., 2011). However, since the 1970s, annual rainfall has decreased substantially across East
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Africa (Funk et al., 2005), with a 14% drop for Ethiopia (Viste et al., 2013), and a ~20% fall for southern Sudan
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(Funk et al., 2011). Also, seasonal precipitation over East Africa for the boreal winter has declined ~15% since
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1999, punctuated by severe droughts in AD 2004 to 2005, 2009 and 2010 to 2011 (Lyon and DeWitt, 2012). These
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20th and 21st century droughts are associated with a precipitous drop in crop production and decreased food security,
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leading to famine and political unrest in Ethiopia, northwest Kenya and southern Sudan (Funk et al., 2005).
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A number of mechanisms have been proposed to explain 20th and 21st century drought in West and Central
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Africa, particularly the Sahel (Giannini, 2010; Giannini et al., 2003; Nicholson, 2013). One hypothesized cause of
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sustained drought for the 1960s to the 1980s is desertification from intensified human land use and animal
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overgrazing (Kucharski et al., 2013; Nicholson et al., 1998). However, with increased precipitation and “re-
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greening” in the Sahel in the late 20th century broader climate controls are inferred, with warming of the equatorial
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Atlantic and the Indian oceans and related shifts in moisture transport and upper air flow (e.g. Folland et al., 1986;
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Hastenrath et al., 1993). Subsequent climate modeling tuned to SST variability captures broadly the timing and
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extent of these droughts and are associated with changes in the intensity of the West and East African monsoons
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(Behera et al., 2005; Goddard and Graham, 1999; Hoerling et al., 2006; Riddle and Cook, 2008; Vizy and Cook,
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2001). Higher resolution reconstructions (< 2.5° x 2.5°), with input of vetted historical climate data (e.g.
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NCEP/NCAR; Kucharski et al., 2013), yield increased fidelity on the footprint and severity of these African
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droughts when forced with a combination of land-use changes, interannual SST variability and basin-wide increase
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of interdecadal SSTs; the later attributed to the global rise in air and ocean temperatures in the 20th and 21st centuries
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(Biasutti, 2013; Giannini et al., 2008; Giannini et al., 2003). Recently, increased SSTs in the southern tropical Indian
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Ocean associated with enhanced convection, precipitation and heightened subsidence over north Africa is invoked
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for decreased moisture transport from Atlantic-derived sources to the Horn of Africa in the past 30 years (Du et al.,
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2013; Lyon and DeWitt, 2012; Ummenhofer et al., 2009).
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Interannual variability in East African rainfall in the past 30 years is hypothesized to be broadly linked to warm
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pool temperatures in the Western Indian Ocean. A variety of statistical analyses indicate a robust relation between
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Indian Ocean SSTs and rainfall intensity for East Africa (Black et al., 2003; Mutai et al., 2012; Omondi et al., 2012).
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Particularly, floods during the boreal winter in 1997, 2008 and 2010 are attributed to positive Indian Ocean Dipole
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(IOD) states (Fig. 1a; Behera et al., 2005; Birkett et al., 1999; Hastenrath et al., 2010). These extreme rainfall events
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appear coeval with a meter-scale rise in water level for many East African lakes in 1997 and 2006 to 2007 (Becker
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et al., 2010; Ricko et al., 2011b). Wet conditions in 1998, 2008 and 2010 are also associated with a strong zonal
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West African Monsoon with anomalously high SSTs in the Eastern Equatorial Atlantic Ocean (Fig. 1b; Williams et
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al., 2012). Intensification of East and West African monsoons with warming SSTs may be reflected in peak water
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levels for Lake Turkana (Ricko et al., 2011a; Velpuri et al., 2012), which include a rise of >4 m in 1994, ~2 m in
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1997 and 2008 and ~1 m in 2010 (Velpuri et al., 2012). The floods following the anomalous rainfall in 1997/1998
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are implicated in the increased incidence of Malaria across East Africa, particularly in drier upland areas
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(Hashizume et al., 2012; Linthicum et al., 1999).
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Prior analyses of climate dynamics indicate that there is a plausible, but non-linear link between SSTs in the
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equatorial oceans and the strength of the East and West African monsoons, which appears to modulate interannual to
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interdecadal rainfall variability in equatorial East Africa (Black, 2005; Goddard and Graham, 1999; Omondi et al.,
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2012). This study further examines the potential links between variable SSTs for the Indian and Atlantic oceans and
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changes in East African precipitation, reflected in water level of Lake Turkana in the past ca. 150 years.
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Specifically, this empirically-based statistical analysis attempts to partition by season, the moisture from Atlantic-
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and Indian-derived sources, associated with the passage of the East and West African monsoons (Fig. 1c). This
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analysis evaluates if there is a plausible link between changes in SSTs for the Atlantic and Indian oceans from AD
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1992 to 2013 and ultimately variations in water level for Lake Turkana. In turn, the fidelity of this numeric relation
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between SSTs and lake level changes is tested against a longer water level record from ca. AD 1857 to 1992 for
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Lake Turkana. Finally, this study attempts to present a systematic approach to quantify pre-historical changes in
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hydrologic balance for the Lake Turkana Basin with input of a single proxy from two oceanic proxy sources. This
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approach is capable of reproducing water levels prior to AD 1888, a period for which our present understanding of
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East African hydroclimate is largely inferred with at best decadal to multi-decadal resolution (e.g.) (Nicholson,
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1988; Verschuren et al., 2000).
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2. Lake Turkana hydrology and climatology
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Lake Turkana is the largest lake in an arid environment (Sombroek et al., 1982) and is located within the East
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African Rift Valley. Water levels are sustained mostly by discharge from the Omo River, sourced in the Ethiopian
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Highlands, and the Kerio and Turkwel rivers which drain the Kenyan Highlands (Fig. 2). The Omo River catchment
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(~73,000 km2) occupies about 50% of the area for the Lake Turkana Basin, and yields ~90% of the annual water
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contribution (Avery, 2010), equivalent to 2.3 ± 0.6 m in lake level. The lake presently has no outlets and the only
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significant loss of water is by evaporation from the lake surface. The annual estimated evaporation rate is 2.63 m/yr,
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and is associated with an annual drop in lake level of ~0.5 m in the boreal winter (Avery, 2010; Hopson, 1982). The
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remaining ~10% of water flow to Lake Turkana is assumed to be from the discharge of the Kerio and Turkwel rivers
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and ephemeral streams and is equivalent to 0.13 m/yr of lake level (Avery, 2010).
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Approximately >80% of the rainfall into the watershed of Lake Turkana during the 20th century occurs between
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March and November with the biannual passage of the east and the west African monsoons, referred to as the
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“short” and “long” rains (Avery, 2010). The “long” rains occur from early March to early June and reflect the
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northward expansion of the Intertropical Convergence Zone (ITCZ) over East Africa. The “long” rains reach a
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northward limit in late July and early August coeval with maximum discharge for the Omo River (Cheung et al.,
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2008; Viste et al., 2013). The “short” rains are associated with the southward passage of the ITCZ, which may
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deliver less rainfall than the “long” rains, though there is significant interannual variability (Camberlin and Okoola,
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2003). These rains usually last from late September to early November, though the onset, and termination are
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variable with rainfall persisting at times into early January (Black et al., 2003; Diro et al., 2011). To account for this
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intraseasonal variability, the “long” rains are defined as the period between March and June, with the “short” rains
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between October and January.
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Anomalous SSTs in the Western Indian Ocean in the 20th and 21st centuries may modulate atmospheric
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convection, the availability of precipitable water for East Africa, and effect the strength and the duration of passage
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of the East African Monsoon (Black et al., 2003; Goddard and Graham, 1999; Saji and Yamagata, 2003). Warm
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SSTs (~28 to 29 °C) adjacent to East Africa may enhance atmospheric convergence and result in increased “short”
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rain season precipitation across Kenya, Ethiopia and Somalia. On balance, cooler SSTs (~ 24 to 25 °C) result in a
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strengthened dry Turkana Jet (Kinuthia and Asnani, 1982; Nicholson, 1996), associated with appreciably less vapor
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transport, often below the threshold for precipitable water (cf. Marchant et al., 2007; Nicholson, 1996). Typically,
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the East African Monsoon in the boreal fall is associated with a relatively cool SSTs in the Western Indian Ocean
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(~26 to 27 °C), with SSTs in the Eastern Indian Ocean comparatively ~2 to 3 °C warmer. This gradient in ocean
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surface temperature reflects strong, westerly zonal winds across the Indian Ocean with equatorial convergence
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which induces west to east surface currents associated with the Wyrtki Jet (Fig. 1b; Hastenrath et al., 1993; Wyrtki,
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1973). Variations in the strength of the westerly winds in the boreal autumn modulates the west to east SST gradient
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in the Indian Ocean with weak equatorial westerlies associated with a diminished Wyrtki Jet (Fig. 1a; Hastenrath et
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al., 1993; Wyrtki, 1973). A weakened Wyrtki Jet may result in warm water to pool against the Kenyan coast (Xie et
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al., 2002), with suppressed upwelling (Murtugudde and Busalacchi, 1999). A zone of low pressure often strengthens
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over this warmer sea surface, which may reverse Walker Circulation over the Indian Ocean, further slowing the
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Wyrtki Jet and sustaining warm SSTs along coastal Kenya (Hastenrath et al., 2010; Webster et al., 1999). Surface
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convergence of moist air farther inland and air mass ascension over the Ethiopian and the Kenyan highlands
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augments the intensity of the southward passage of the ITCZ, which can result in increased precipitation with the
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“short” rains (Camberlin and Philippon, 2002).
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Recent studies have linked anomalous SSTs for the Western Indian Ocean to changes in the equatorial zonal
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SSTs gradient, expressed as the Indian Ocean Dipole (IOD) and quantified as the Dipole Mode Index (DMI; Abram
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et al., 2008; Saji et al., 1999). The DMI is derived from a 140 year record of SSTs from the western (Seychelles) and
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eastern (Sumatra) Indian Ocean (Fig. 1c; Saji et al., 1999) to quantify changes in the SST gradient across the Indian
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Ocean. IOD events are defined by >1-σ deviations in the DMI record, with a ‘positive index’ event generally
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reflected by warm Western Indian Ocean temperatures and a cooler Indo-Pacific Warm Pool (Saji et al., 1999).
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Coral records spanning part of the past 7 ka from the Indian Ocean indicate the persistence of this ocean-atmosphere
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dipole mechanism on decadal timescales (Abram et al., 2009). Coral records with subannual resolution from the
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warm pool in the Western Indian Ocean show a significant correlation with rainfall and rainfall proxies for East
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Africa (Damassa et al., 2006; Kayanne et al., 2006). Satellite observations of water mass changes (GRACE) for
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lakes Turkana, Victoria, Tanganyika, and Malawi indicate that meter-scale variability in lake level changes for 2006
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to 2007 are significantly correlated to zonal changes in the SST gradient of the Indian Ocean, typical of IOD events
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(Becker et al., 2010).
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Another potential significant source of moisture to Lake Turkana is from the West African Monsoon, with the
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zonal advection of Atlantic-derived moisture from the Congo Basin (Williams et al., 2012). Rainfall over the Lake
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Turkana Basin may be associated with a zone of convergence between Indian and Atlantic oceans derived-air
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masses; an extension of the Congo Air Boundary (CAB; see Fig. 1c), which is often coincident with a precipitation
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maximum over central Africa (Nicholson, 2000). The West African Monsoon occurs with the ITCZ passage across
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west and central Africa with precipitation amount apparently modulated by SSTs in the Eastern Indian Ocean (EIO)
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(Giannini et al., 2003; Nicholson, 2008). Interannual SST fluctuations in the Eastern Atlantic Ocean (EAO),
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specifically for the Gulf of Guinea, appear to be spatially isolated, associated with the zonal Tropical Atlantic
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variability (Diro et al., 2011; Lamb, 1978; Xie and Carton, 2004). The West African Monsoon is bimodal with two
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annual precipitation peaks, concurrent with the “long” and “short” rains for East Africa, and with meridional
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passage of the tropical rainfall belt from ~10 °N to ~15 °S (Nicholson, 2008).
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3. Materials
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This study analyzed monthly mean SST data from the Indian and Atlantic oceans for January, 1992 to April,
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2013 and time series of similar duration of water level variability for Lake Turkana derived from satellite
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measurements. The record of SSTs for the Western Indian and Eastern Atlantic oceans is derived from the NOAA
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Optimum Interpolation global gridded dataset (OISST V2;
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http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html; Reynolds et al., 2002). These data are either
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direct measurements of SSTs or satellite-derived SST estimates (1° by 1° resolution) and are averaged to yield
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monthly mean SSTs (see Fig. 1c) for the Western Indian Ocean (WIO; Fig. 3a), the Eastern Indian Ocean (EIO; Fig.
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3b), and the Eastern Atlantic Ocean (EAO), specifically the Gulf of Guinea (Fig. 3d). These records of SSTs are
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presented as monthly mean anomalies from the 21-yr average (SSTAs). Further, a measure of zonal SST gradient is
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derived from the monthly difference of SSTAs between WIO and EIO (hereafter ∆SST), which is significantly
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correlated (r = 0.93; p<0.005) with the DMI record (Fig. 3c) (Saji et al., 1999). A range of SST variability is
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represented in the time series of SSTAs for the WIO, with positive IOD events during 1997, 2006 and 2009, which
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are also expressed in the DMI record and the ∆SST time series. Additionally, monthly mean anomalies in SSTs are
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extracted from the NOAA Extended reconstructed SST dataset (ERSST v3b;
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http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.html; Smith et al., 2008) from 1854 to present for the
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Western Indian, the Eastern Indian and the Eastern Atlantic oceans. The extended SST records for the Indian and
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Atlantic oceans show a noticeable and sustained rise since 1950 and have been linearly de-trended for that period.
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Further, SSTs data prior to 1950 have increasing error and uncertainty because of sparseness of data and
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interpolations, which mutes variability (Smith et al., 2008). Also, anomalously warm SSTs between AD 1939 and
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1941 (Kennedy et al., 2011; Reynolds et al., 2002) and cool SSTs in AD 1945 (Thompson et al., 2008) reflect
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significant data artefacts, with changes in situ measurement of water temperatures. Thus, SSTs between 1936 and
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1946 are normalized against a 2-yr moving average to remove interannual anomalies, yet preserving annual
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fluctuations.
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The record of lake level from November 1992 to May 2013 is remotely derived from measured lake surface
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elevation every ~10 days with the passage of the TOPEX/Poseidon, Jason-1 and Jason-2/OSTM satellites over Lake
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Turkana (http://www.pecad.fas.usda.gov/lakes/images/lake0093.TPJO.2.txt). The satellite measurements of lake
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surface elevation is the basis for a time series of 30-day changes in water level and is presented as a record of
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monthly lake level anomaly (Fig. 3e). An important time series for this analysis is a synthesized lake level record
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between AD 1857 and 2012 (Avery, 2010; Johnson and Malala, 2009) which has variable resolution from annual to
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multi-decadal reflecting constraining data from geomorphic observations (Nicholson, 1988), historical accounts
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(Butzer, 1971), direct gauging (Hopson, 1982) and post-1992 satellite measurements (see Fig. 10a). This historic
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lake record reflects interpolations between ~30 annual estimates of lake level, most prior to AD 1970 (Butzer,
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1971). These measurements are discontinuous and the magnitude of peak lake level at ca. AD 1880 and 1900 is
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based principally on observations of the elevation of delta surfaces for the Omo River. Many Inferred lake high and
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low stands between AD 1900 to AD 1950 are constrained by a single observation (Butzer, 1971). Lake level
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variability between AD 1950 and AD 1961 was measured from a gauge stationed in Ferguson’s Gulf, which drains
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when lake level falls below 362.3 m and thus, is insensitive to lower lake levels [Avery, 2010].
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Rainfall amounts for the Turkana Basin are extracted from the global 1° x 1° gridded time series of precipitation
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(GPCC v6; http://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html). The GPCC v6 dataset combines
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precipitation monitoring product for the years 1901 to 2010, and the “first guess” product for 2010 to April 2013.
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The data used in this study is from January 1992 to April 2013 to closely match the water level record for Lake
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Turkana, and then decomposed into monthly anomalies for the Lake Turkana Basin.
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4. Results
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4.1 The potential delay between changes in SSTs and Lake Turkana water level anomalies
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Recent studies of monsoon variability indicate a significant relation to SST fluctuations and often with a
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predictable delay associated with a moisture trajectory from an oceanic evaporative source to precipitation on the
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adjacent landmass. This delay is associated with low level (850 hPa) transport of atmospheric moisture derived from
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the WIO warm pool associated with modification to the zonal Walker Circulation and the passage of the ITCZ
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(Mutai and Ward, 2000; Yamagata et al., 2004). Climate modeling of the seasonal variability of the IOD indicates a
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significant correlation between Indian Ocean SSTs and anomalous precipitation for East Africa, with a lag of ~3-
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months (Behera et al., 2005). However, other attempts to predict East African rainfall cite a shorter delay (~15 to 10
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days) for 850 hPa wind anomalies as a proxy for heightened equatorial SSTs in the WIO (Mutai and Ward, 2000).
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Similarly, rainfall events in East Africa are linked to a ‘westerly surge’ of Atlantic-derived moisture in the 850 hPa
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wind field, which occurs ~20 days prior, resulting from SST variability in the Gulf of Guinea (Mutai and Ward,
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2000; Tazalika and Jury, 2008). A hydrologic model infers a ~55 to ~100 day delay between precipitation in the
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Turkana catchment and subsequent lake level response (Ricko et al., 2011b), although this lag is larger than assumed
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(3 to 43 days) for a hydrologic model based on quantifying net-basin supply components (Velpuri et al., 2012).
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The potential temporal offset between SSTAs and lake level change is assessed through lagged correlation
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analyses of the respective monthly anomalies. These analyses directly compare monthly anomalies for a lag between
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0 and 6 months, with the highest Pearson coefficient indicating the most probable lag (Fig. 4). These monthly time
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series which comprise the years AD 1993 to AD 2013 represent a dataset with n = 252, and when pared down by
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season these datasets have n = 80. The most significant correlation is associated with a 1-month lag between SSTAs
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in the Western Indian (r=0.33; p<0.005) and Eastern Atlantic (r=0.16; p<0.05) oceans and the corresponding lake
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level anomaly. Further, significant correlations are apparent seasonally, specifically between October to January
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SSTAs in the Western Indian (r=0.59; p<0.005) and Eastern Atlantic (r=0.34; p<0.005) oceans and lake level
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anomaly from November to February, which indicates a potential delay of one month. However, the highest
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apparent correlation is associated with a delay of less than a month (r=0.37; p<0.005) between SSTAs (March to
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June) in the WIO and lake level change. Surprisingly, the correlation coefficient is the highest (r=0.18; p≈0.11) for a
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3 month lag between Eastern Atlantic SSTAs (March to June), and lake level anomaly (June to September), though
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of low significance. Thus, in this study we assume a 30 day or less delay between changes in SSTs in the WIO and
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resultant water level response for Lake Turkana, similar to previous studies (Mutai and Ward, 2000; Tazalika and
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Jury, 2008; Velpuri et al., 2012). However, a delay of 90 days is applied between changes in SSTs (March to June)
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in the Gulf of Guinea and lake level anomalies, reflecting far traveled and recycled moisture from the Congo Basin
238
(Cook and Vizy, 2012; Levin et al., 2009; Vizy and Cook, 2001; Williams et al., 2012).
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4.2 Empirical orthogonal function analysis of SSTs and Lake Turkana Basin rainfall variability
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Empirical orthogonal function (EOF) analysis is used to deduce temporal and spatial signatures of descending
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modes of climatological variability, which facilitates multidimensional comparison between potentially associated
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SSTAs and rainfall variability (e.g. Goddard and Graham, 1999; Mutai and Ward, 2000). Prior studies have
243
employed EOF analysis to deduce the geographic impact of IOD-type variability on rainfall anomalies for East
244
Africa (Bowden and Semazzi, 2007; Omondi et al., 2012) and changes in water volume for the East African Great
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Lakes (Becker et al., 2010). Thus, an anomalous rise in water level for Lake Turkana may result from SST
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anomalies, like with IOD-type variability (Avery, 2010; Becker et al., 2010), though this hydroclimatic connection
247
has yet to quantified statistically. This analysis relates the first EOF modes between time series of SSTAs and lake
248
level anomaly and between Turkana Basin rainfall and lake level changes. Significant correlations exist between
249
primary SSTA modes for the Eastern Atlantic (r=0.21, p<0.005) and the Western Indian oceans (r=0.35, p<0.005)
250
and lake level anomaly, which explains 66% and 55% of the variability, respectively (Fig. 5a and 5c). As, expected
251
there is a high significance between lake level anomaly and rainfall in the Lake Turkana Basin (r=0.73; p<0.005)
252
with this association reflecting about 50% of total variability (Fig. 5b). The primary geographic loading (EOF1) for
253
the Eastern Atlantic and Western Indian oceans are consistent with the spatial signature of precipitation with tropical
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Atlantic SSTs variability (Fig. 5d) and positive IOD events (Fig. 5f), respectively (Saji et al., 1999; Vizy and Cook,
255
2001; Yamagata et al., 2004). The EOF2 explains 10.7% of variability and is broadly associated with the negative
256
mode of the tropical Atlantic variability, reflected by the prominent equatorial tongue of low SSTs extending
257
westward which occurs with easterly zonal wind shear and upwelling events in the Gulf of Guinea (Polo et al., 2013;
258
Vizy and Cook, 2002). The second mode (EOF2) for the Western Indian Ocean explains 10% of variability, and
259
reflects the westward extension of the equatorial cool water in the Indian Ocean, potentially indicative of a normal to
260
a negative IOD state (Saji et al., 1999; Williams and Funk, 2011).
261
Spatial correlation analysis between the principal temporal mode of SSTAs and rainfall anomaly for the Lake
262
Turkana Basin is used to deduce areas of the catchment associated with moisture derived from Western Indian and
263
Eastern Atlantic oceanic sources (Fig. 6). These principal temporal modes have been smoothed using a 3-month
264
moving average to emphasize areas of high statistical association on a yearly timescale, and smoothed for potential
265
varying atmospheric delays. The geographic distribution of correlations between rainfall anomalies and primary
266
mode of Western Indian SSTAs indicate a spatially diverse signature (Fig. 6b) potentially increasing in significance
267
with proximity to the Kenyan coast. The spatial correlation between basin rainfall anomalies and the primary mode
268
of variability for Eastern Atlantic SSTAs indicate a region of high significance in the lower Omo River catchment
269
(Fig. 6a). This area of higher loadings reflects a topographic saddle between the Kenyan and Ethiopian highlands,
270
with the lowest elevations (~450 masl) for the ~6000 km long continental divide (Indeje et al., 2001; Kinuthia and
271
Asnani, 1982). The spatial significance of this low continental divide is recognized previously as an orographic
272
pathways for Atlantic/Congo-derived moisture (e.g. Diro et al., 2011; Viste et al., 2013; Vizy and Cook, 2001).
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4.3 Is there a correlation between equatorial SST and Lake level anomalies?
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The influence of anomalous equatorial SSTs on rainfall for East Africa has been inferred on annual (Goddard
275
and Graham, 1999) and interannual timescales (Manatsa et al., 2012; Omondi et al., 2012), and for the “short” and
276
“long” rain seasons (Black et al., 2003; Camberlin and Okoola, 2003). Many studies indicate a potentially non-linear
277
response of East African rainfall to sea surface warming (Black et al., 2003; Ummenhofer et al., 2009), where
278
elevated seasonal SSTs in the WIO are associated with excess rainfall. Black et al. (2003) analyzed late 20th century
279
SSTs in the Indian Ocean, and rainfall anomalies for East Africa and demonstrated that excessive precipitation in
280
1997 is associated with elevated SSTs in the WIO with a >1.5 °C rise of the monthly mean SST. In turn, a δ 18O/16O
281
time series for coral records spanning the late 20th century from off of the Kenyan coast significantly correlates (r2 =
282
0.76; p<0.005) SST variability in the WIO with rainfall variations for East Africa (Kayanne et al., 2006). Recent
283
studies suggest that moisture transport into East Africa as a result of IOD-type events is enhanced by the zonal SST
284
gradient in the Indian Ocean, and the associated 850 hPa wind anomalies (Nicholson, 2013; Ummenhofer et al.,
285
2009). Our analysis expands on these observations by investigating the intraseasonal relation between anomalous
286
equatorial SSTs, during the short and long rains for East Africa and a possible response of water levels for Lake
287
Turkana. Specifically, a 21 year time series of SSTAs for the Eastern Atlantic and the Western Indian oceans, and
288
the SST gradient across the equatorial Indian Ocean, is partitioned by season and by SST state. Then the statistical
289
relation is evaluated between SST and lake level anomalies. Linear regression and associated correlation statistics
290
are augmented with Bootstrap sampling, and a function with errors is computed from 2000 Monte Carlo simulations
291
(Efron and Tibshirani, 1993; Zoubir, 1993).
292
This analysis focused on SSTAs for the Indian Ocean during the long and short rain seasons. The SST state for
293
the Indian Ocean is indicated by the averaged seasonal ∆SST, which reflects the zonal SST gradient for the
294
equatorial Indian Ocean. A positive SST state for the zonal Indian Ocean is recognized when the seasonal average
295
∆SST is >0.25σ, with a negative SST gradient defined by a seasonal averaged ∆SST <0.25σ. This is consistent with
296
the DMI record (Saji et al. (1999), with a positive SST gradient in the Indian Ocean often indicative of a positive
297
IOD event (Black et al., 2003; Cai and Qiu, 2013; Saji et al., 1999). Another regression analysis is conducted with a
298
positive SST state for the WIO warm pool is identified by a seasonally averaged SSTA of >0.25σ; negative SST
299
state is defined by a seasonally averaged SSTA <0.25σ. This ranking captures the anomalous increase in SSTs off
300
the Kenyan Coast associated with increased rainfall for East Africa (e.g. Goddard and Graham, 1999), while
301
isolating these SSTAs from processes which reflect basin-scale anomalies (cf. Black et al., 2003; Ummenhofer et al.,
302
2009). In the Eastern Atlantic (Gulf of Guinea), for all subsequent regression analyses, a positive SST state is
303
indicated by a seasonal averaged SSTA >0.25σ, whereas a negative SST state is defined as <0.25σ than the seasonal
304
mean SSTA.
305
Input time series consist of monthly SSTA or ∆SST values, systematically separated by the average seasonal
306
SSTA, and regressed against the corresponding month value for lake level anomaly, with the appropriate month
307
delay according to the lagged correlation analysis. For example, for the Western Indian Ocean during the “short
308
rains”, an average of October to January SSTAs above 0.25σ will select these four monthly values such that the
309
October SSTA value is paired with the lake level anomaly for November for one month lag. The value of 0.25σ is
310
chosen to systematically separate monthly SSTA or ∆SST values that result from a seasonal climate anomaly like
311
the IOD, while preserving a large sample size to ensure statistical robustness.
312
Statistical metrics for linear regression analyses indicate that there are significant, positive correlations between
313
Indian and Atlantic oceans SSTAs and changes in Lake Turkana water level (Figs. 7 and 8). The strongest apparent
314
correlation (r=0.78; p<0.005) is between SSTAs in the WIO from October to January, for a positive SST state (i.e.
315
seasonal averaged SSTA>0.25σ), and lake level anomaly from November to February (Fig. 7a). In contrast, for the
316
negative SST state (i.e. seasonal averaged SSTA<0.25σ) in the Indian Ocean, there is no significant association
317
between SSTAs and lake level changes (Fig. 7b). Further, the shift to a positive statistical correlation with an
318
increase in seasonal SSTA state of <0.25σ to >0.25σ may reflect a threshold-type response in seasonal precipitation
319
in East Africa, as inferred previously (Black et al. (2003). There is a significant apparent correlation (r=0.36;
320
p<0.05) between positive SSTAs for the EAO from October to January and lake level anomaly (Fig. 7e); with low
321
associated significance for the negative SST state (Fig. 7f). The only apparent significant correlation is for the long
322
rain season (March to June) between negative anomalies of SSTAs for the WIO and lake level (r=0.28; p<0.05; Fig.
323
8b), with low significance for the corresponding positive state of SSTAs (Fig. 8a). There appears to be low or no
324
appreciable statistical correlation between ∆SST (Figs. 8c and 8d), SSTAs in the EAO (Figs. 8e and 8f) and lake
325
level anomaly from March to June.
326
4.4 What is the validity of modeling Lake Turkana water level variability with changes in Western Indian and
327
Eastern Atlantic SSTs?
328
The EOF and linear regression analysis indicates a varied response of Lake Turkana water level to monthly
329
changes in SSTs for the Atlantic and Indian oceans, during the “long” and “short” rain seasons. We underscore that
330
the statistical relations between SSTs and lake level are apparent, reflecting the limited time span of data (21 years)
331
and the simplistic assumptions of linear regression analyses, though augmented by Bootstrap analysis and Monte
332
Carlo simulations. There are documented climatic dynamics which associate SST variability, particularly in the
333
Indian Ocean that appears to control precipitation receipts and lake level for East Africa (Becker et al., 2010; Birkett
334
et al., 1999; Black et al., 2003; Ummenhofer et al., 2009). Further, there are less significant relations during the
335
“long” rains between SSTAs in the Indian and Atlantic oceans and lake level anomaly which may reflect complex
336
and multiple sources of moisture, including the Gulf of Guinea, recycled air masses from the Congo Basin, and the
337
WIO with the northward migration of the ITCZ (Vizy and Cook, 2002; Polo et al., 2013).
338
To evaluate the fidelity between SST anomalies in the WIO and EAO and water level change for Lake Turkana
339
the results of the regression analysis are used to constrain these variables (Table 1) and tested against a longer record
340
of lake level (see Fig. 10a). The numeric solutions are derived from the regression analysis of the 21-year time series
341
of SSTAs and corresponding water level variability for Lake Turkana. These four equations describe lake level
342
variations for input of SSTAs from warm pools in the EAO and WIO (Fig. 3). Elevated WIO warm pool
343
temperatures are closely associated with increased rainfall for East Africa, rather than ∆SST across the equatorial
344
Indian Ocean (Ummenhofer et al., 2009). The four regression equations for positive SST states bound model output.
345
We quantitatively adjust the model output to match variations revealed in the satellite derived record of water level
346
variations for Lake Turkana. Also, to compensate for evaporation an annual lake fall of ~0.5 m/yr is subtracted,
347
consistent with the gauged lake level record (Avery, 2010; Ferguson and Harbott, 1982). Basin precipitation (see
348
Fig. 9) is inferred by partitioning the water volume for modelled water level changes for a lake with surface area of
349
7,334 km2 over a catchment area of ~148,600 km2 (cf. Forman et al., 2014). Finally, the model output is
350
parameterized to reflect the empirical seasonal rainfall distribution between 1992 and 2013; associated weightings
351
suggest long rain contribution is twice that of the short rains with seasonal moisture contributions from the Indian
352
Ocean comprising roughly three times that of the Atlantic Ocean (Table 1).
353
There are significant correlations (r = 0.85; p<0.005) between modeled rain fall and the empirical values for
354
annual rainfall contributions from the short rain season (Fig.9c). In contrast, the annual precipitation associated with
355
the long rain season is usually underestimated (Fig. 9b) and the associated correlation is not significant though there
356
are notable exceptions in AD 1998 and 2008-2009. The modeled total rainfall contribution is significantly correlated
357
(r=0.48; p<0.005) with the lake-level derived record of rainfall (Fig. 9a). The model calibration assumes a dominant
358
contribution from the WIO warm pool for both the “long” and “short” rains and simulations reflect a minimal, and
359
relatively consistent Atlantic-derived rainfall contribution of ~30 mm/yr (Figs. 10a and 10b). Monthly analysis
360
indicates the highest correlation coefficients and significance occurs during positive IOD events in 1997, 2006 and
361
2008-2011, when moisture is predominately sourced from the WIO (Cai et al., 2009; Saji et al., 1999; Ummenhofer
362
et al., 2009).
363
The functions defined from the 21-yr record of SST anomalies and calibrated lake level response (Table 1) are
364
tested against a longer record of SST anomalies from AD 1857 to 1992 (Fig. 10a) and evaluated against
365
corresponding change in water level for Lake Turkana (Fig. 10b). An implicit and untested assumption is that the
366
apparent SSTs to lake level relationship on an interannual to annual timescale for AD 1992 to 2013 is applicable for
367
a longer simulation of water level variability for Lake Turkana. Simulations of lake level are hind-cast from an
368
average water plain for AD 2012 at +1.2 m. The corresponding uncertainty for lake levels are calculated from the 1-
369
σ error associated with the Monte Carlo/bootstrap analysis for linear regression statistics (see Figs. 7 and 8) and is
370
summed cumulatively (Fig. 10b). The solutions to the model equations are weighted relative to the associated
371
correlation coefficients, identical to model lake level in the late 20th and early 21st centuries (Table 1). Also
372
incorporate seasonal variations in evaporative loss (equivalent to 0.5 m/yr), with scaled output defined in the 21-yr
373
calibration period (Fig. 10b). Further, the coefficients of these four functions, which quantify monthly lake
374
variations based on SSTAs, are weighted according to the associated correlations and resolve two functions which
375
describe annual lake level changes for input of annual SSTAs for the WIO and the EAO (Table 1).
376
There is an apparent significant correlation between the hind-cast lake level, based on monthly input SSTAs,
377
and the empirically-derived lake level for AD 1857 to 2013 (r = 0.90; p < 0.005; Fig. 10b). A simplified model
378
calculation with input of annually resolved SSTAs captures the multi-decadal lake level variability, with
379
considerable significance (r=0.85; p< 0.005; Fig. 10b). The monthly-based simulation captures well the 17 m fall in
380
lake level between Ad 1890 and 1940. However, on annual to decadal timescales there are a number of low
381
amplitude (< 2 m) discontinuities between the simulated and empirical-based lake level prior to AD 1950. Further,
382
pronounced ~3 m variability which includes the modeled low water level of -2.5 m between AD 1957 and 1940 is
383
not represented in measured lake level record, though there is a noticeable gap in this record between AD 1938 and
384
1948. Single observations that constrain high and low lake stands between AD 1920 and 1938 are inconsistently
385
replicated, with discrepancies in magnitude and timing of peaks. There is an inconsistency with the timing of
386
simulated lake level fluctuations between AD 1857 and AD 1910 with one broad and sustained peak, which differs
387
from the distinct double peak in lake level in the empirical record, though this couplet is not well constrained
388
chronological These second order discrepancies between simulated and historic lake levels pre AD 1955 may reflect
389
discontinuous and relative observations of water levels (Avery, 2010; Butzer, 1971; Johnson and Malala, 2009;
390
Nicholson, 1988) or degraded resolution of the extended SST record (ERSST), which exhibits increased uncertainty
391
prior to 1950 (Smith et al., 2008).
392
5. Discussion
393
The EOF analysis (Figs. 5a-5c) and associated correlations (Fig. 5d-5f) indicate a significant relation amongst
394
changes in basin rainfall and lake level, and SST variability for the WIO warm pool. The principal mode of Lake
395
Turkana Basin rainfall, as a 3-month moving average, is significantly correlated with lake level anomaly (r=0.73;
396
p<0.005; Fig. 5b). In contrast, there is lower statistical association between Atlantic SSTAs and lake level change,
397
though the EOF spatial signature is centered on the Gulf of Guinea, an important source area for Atlantic-derived
398
moisture (Cook and Vizy, 2012; Levin et al., 2009). The highest spatial correlation between Eastern Atlantic SSTAs
399
and lake level variability is for the Kenyan Highlands which may be linked to low level westerly wind anomalies
400
(Williams et al., 2012) and a low topographic divide, which may be a preferential pathway for Atlantic/Congo Basin
401
derived moisture (Fig. 6a) (Hession and Moore, 2011; Indeje et al., 2001; Kinuthia and Asnani, 1982).
402
There are significant spatial correlations between Western Indian Ocean SSTAs and rainfall for the Ethiopian
403
Highlands and the southernmost catchment of the Turkana Basin (Fig. 6b). The first EOF mode, associated with
404
58% of the rainfall variability in the Basin, has a similar footprint indicating the primacy of moisture sources from
405
the southern Ethiopian Highlands and catchments of the Turkwel and Kerio rivers (Fig. 6c). The second EOF
406
explains 16% of the rainfall variability with a distinct spatial signature for the northern part of the catchment (Fig.
407
6d), potentially reflecting advected moisture from an Indian Ocean source. There also appears to be a spatial
408
precipitation dipole in East African possibly linked to SST variability; Kenyan and eastern Ethiopian rainfall is
409
associated with Indian Ocean-derived moisture and varies in anti-phase with western Ethiopia and Uganda,
410
associated with Atlantic Ocean-derived moisture (Bowden and Semazzi, 2007; Omondi et al., 2012; Tierney et al.,
411
2013). The spatial pattern for correlations between Turkana Basin rainfall anomalies and the first temporal mode of
412
variability for SSTAs appear to reflect this dipole, with positive significance for the WIO associated with the
413
southwestern portion of the Lake Turkana Basin (Fig. 6b).
414
The state of the SSTs in the Western Indian Ocean (WIO) has an inferred effect on precipitation variability for
415
the Turkana Basin and thus, lake level. Elevated SSTAs (>0.25 σ) have an apparent significant relation to lake level
416
change, particularly during the “short” rains (Fig 7a). Lower, yet significant correlations exist between the Indian
417
Ocean SST gradient (∆SST) and lake level gains with the “short” rains (Fig. 7c). There is an apparent less
418
significant relation between Eastern Atlantic SSTAs and lake level change for the same period (Fig. 7e). In contrast,
419
lower SSTAs (<0.25 σ) in the Indian and Atlantic oceans have no apparent statistical significance (p ≥ 0.22) with
420
changes in lake volume during the “short” rains (Figs. 7b, 7d and 7f). There are weak to insignificant relations
421
between SSTAs or ∆SST for the Indian and Atlantic oceans and registered changes in lake volume during the “long”
422
rains (Fig. 8). Though the time series is short (21 years), this analysis indicates a potential threshold-like response
423
with SSTAs ~>0.25σ for the WIO warm pool for above average rainfall and associated rise in lake level (Black et
424
al., 2003).
425
Our analyses show consistently low statistical significance between SSTAs for the EAO and WIO and lake
426
level changes during the “long” rain season. Other parameters such as cloudiness, wind vectors and sea level
427
pressure differences in the EAO yield significant relations with precipitation in the Greater Horn of Africa (Williams
428
et al., 2011). There is a complex derivation of moisture during the “long” rains which involves the northward
429
movement of ITCZ with variable sources from cyclonic activity born in the Gulf Guinea, recycled moisture from the
430
Sudd and Congo Basin, and advection from the Indian Ocean (Levin et al., 2009; Williams et al., 2012). Isotopic
431
analyses of meteoric rainfall in the Ethiopian Highlands reveal variable moisture sources with mostly positive δ18O
432
values (~-0.2 ‰) relative to Kenyan rainfall (~-2.5 ‰) (Levin et al., 2009). These distinct isotopic differences
433
indicate that rain fall in Kenya is mostly derived from the Indian Ocean; whereas Omo River discharge sourced from
434
the Ethiopian Plateau reflects variable moisture contributions from the Congo Basin, and other Atlantic-derived
435
sources. Further, one tree-ring time series for Juniperus procera from the Ethiopian Highlands for the period AD
436
1915 to AD 2010 show a >2 ‰ decline in cellulose δ18O values since ca. AD 1990, associated with decreased
437
moisture contributions from the Congo Basin, which is implicated in pervasive drought conditions in the past decade
438
(Williams et al., 2012).
439
Recent statistical analyses indicate IOD events and associated SST variance may have increased in the past
440
century (Manatsa et al., 2012), and is a likely causative factor for increased precipitation variability in East Africa,
441
with potential linkages to broader-scale warming (Manatsa and Behera, 2013). Aridity in the 21st century for Central
442
and East Africa is associated with an increase in vertical energy flux over the southern tropical Indian Ocean that
443
leads to dry static energy exported to the East Africa, increasing atmospheric stability and subsidence (Williams et
444
al., 2012). Thus, the pronounced >15m drop in lake level through the 20th century may reflect decreasing Congo
445
Basin derived-moisture (Williams et al., 2012) with meter-scale variability in lake level attributed to increase in SST
446
variance for WIO warm pool (Manatsa et al., 2012). An earlier pluvial period in East Africa ca. AD 1680 to AD
447
1765 is also inferred to reflect positive in SSTs anomalies in the western Indian Ocean (Tierney et al., 2013;
448
Ummenhofer et al., 2009).
449
The inferred water level variability for Lake Turkana in the past ca. 150 years is consistent with other
450
hydrologic records from East Africa, particularly from Lake Naivasha in southeast Kenya (Fig. 10c). Instrumental
451
measurements, historic observations and proxy records for many lakes in East Africa indicate peak water levels at
452
ca. AD 1870 and 1900 (e.g. Nicholson, 2000; Verschuren et al., 2001), the latter coincident with an inferred
453
historical high stand for Lake Turkana (Butzer, 1971). Our model, based on monthly SSTA, replicates a number of
454
meter-scale rises in lake level between AD 1920 and 1990 (Fig. 10b). However, significant peaks in lake level at AD
455
1916 and 1899 are not fully resolved, though a broad high stand reconstructed between AD 1860 and 1890 is to
456
similar heights (within 2 sigma errors) of the record high stand (Fig. 10b). Lake level reconstructions based on
457
annually averaged SSTA show multi-decadal scale variability consistent with the historical record for Lake Turkana
458
(Fig. 10).
459
This study indicates that a robust approach to modeling water level variability for Lake Turkana is dependent on
460
monthly SSTA input for the Western Indian Ocean and the Gulf of Guinea. However, proxies such as coral bands,
461
are typically resolved annually (Cole et al., 2000; Damassa et al., 2006; Nakamura et al., 2011), though seasonal
462
resolution may be possible (Kayanne et al., 2006). Thus, we have developed an annual SSTA-lake level model based
463
on corresponding monthly parameterization which can hind-cast past lake level variations, with a highly significant
464
correlation (r=0.95; p<0.005). However, a clear deficiency of this model remains the inability to hind-cast lake
465
levels beyond the temporal limit of instrumental SST data (AD 1854). Further, we assume a dominance of the
466
western Indian Ocean as the prime moisture source, rather than Atlantic/Congo Basin derived sources. One tree-ring
467
time series from the Ethiopian plateau indicates that the flux of Atlantic-derived moisture has progressively
468
diminished in the past 100 years (Williams et al., 2012). This approach to lake level reconstructions is constrained
469
by the short instrumental record of high quality SSTs measurements in WIO and EAO. Coral records from the WIO
470
are important proxy records that could extend temporally this lake level reconstruction exercise. Currently, the
471
longest coral record for the WIO warm pool is a discontinuous time series of inferred changes in SSTs for the much
472
of the 17th and 20th centuries (Damassa et al., 2006) and continuous coral records for the Gulf of Guinea are limited
473
to the 20th century (Swart et al., 1998). A critical datum deduced from early observations and our modeling is that
474
water level for Lake Turkana was 5 ± 5 m at AD 1857 and is a “starting” water level for further hind-cast
475
simulations with proxy SST input. Recent field research of relict beaches around the shores of Lake Turkana have
476
deduced decadal and subdecadal water level changes in the past 1000 years (Forman et al., 2014) and in tandem with
477
SST-based modeling of lake level change should yield new insights on water supply and climate variability for East
478
Africa.
479
6. Conclusion
480
There appears to be a significant spatial and temporal relation between variability in SSTs in the WIO and EAO
481
and water level changes for Lake Turkana in the past ca. 21 years. EOF analyses indicate that significant
482
correlations exist between primary SSTA modes for the Eastern Atlantic and the Western Indian oceans and lake
483
level anomaly, which explain 66% and 55% of the variability, respectively. The primary geographic loading for the
484
Eastern Atlantic and Western Indian oceans are consistent with the spatial signature of precipitation with tropical
485
Atlantic SSTs variability and positive IOD events (Ruiz-Barradas et al., 2003; Saji et al., 1999; Vizy and Cook,
486
2001; Yamagata et al., 2004). The second mode explains 10% of variability, and reflects the westward extension of
487
the equatorial cool water in the Indian Ocean, potentially indicative of a normal to a negative IOD state (Saji et al.,
488
1999; Williams and Funk, 2011). There is significant spatial correlation between basin rainfall anomalies associated
489
with Eastern Atlantic SSTAs and a low in the continental divide between the Kenyan and Ethiopian highlands,
490
which is a passage for moisture from the Congo Basin.
491
Linear regression analysis augmented by Bootstrap sampling and Monte Carlo simulations define numeric
492
relations between WIO and EAO SSTs and lake level change for AD 1992 to 2013. The robustness of this numeric
493
solution to reconstructed lake level is evaluated against the historic record from 1857 to 1992. The monthly and
494
yearly lake level reconstructions capture the decadal-scale variability and the 15 m drop in water level in the early
495
20th century. This model assumes the primacy of the WIO as a source of moisture, particularly during the East
496
African Monsoon, which is consistent with decreasing influence of Atlantic-derived moisture on East Africa
497
hydrology through the 20th century (cf. Williams et al., 2012). Thus, meter-scale variability in lake level may be
498
related to variable precipitation sourced from the WIO with IOD variability, whereas the 15 m drop in early 20th
499
century may reflect a profound decrease in moisture from Atlantic/Congo Basin sources.
500
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700
Figure 1: Central Africa, Indian and Atlantic Ocean basins and associated zonal climate patterns. Arrows signify
701
zonal and vertical wind vectors. (a) Walker Circulation associated with Indian Ocean Dipole (IOD) events, and
702
enhanced East African Monsoon precipitation. (b) Zonal circulation patterns for the West African Monsoon. (c)
703
Distribution of climate zones included in this analysis. Red boxes delineate Eastern Atlantic (EAO; 5°W-10°E by
704
5°N-10°S), Western Indian (WIO; 50-70°E by 10°N-10°S) and Eastern Indian oceans (EIO; 90°E-110°E by 0°-
705
10°S), from (Saji et al., 1999). The Lake Turkana Basin represented by red border. Intertropical Inter-Convergence
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Zone (ITCZ) and Congo Air Boundary (CAB) represented by brown lines. Adapted from (Nicholson, 1996; Webster
707
et al., 1999; Williams and Funk, 2011).
708 709
Figure 2: Elevation and watershed limits of the Lake Turkana Basin (thick red lines) and discussed locations. Major
710
rivers are delimited (blue lines), sub-basins (thin red lines) and zones of limited drainage (hatched fill). Drainage
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boundaries and elevation calculated from Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010).
712 713
Figure 3: Monthly time series used in statistical analyses between November 1993 and April 2013 for (a) Western
714
Indian (WIO), (b) Eastern Indian (EIO), (c) Monthly difference between Western and Eastern Indian Ocean (∆SST;
715
green line), which correlates significantly (r=0.93) with the Dipole Mode Index (DMI; orange line), (d) Eastern
716
Atlantic (EAO) sea surface temperature anomalies (SSTA), (e) monthly anomaly of lake level changes.
717 718
Figure 4: Lagged correlation analysis between (a) Western Indian Ocean and (b) Eastern Indian Ocean SSTAs and
719
lake level anomaly. The “short” rain and “long” rain periods refer to the SSTAs from October to January and March
720
to June, respectively, with the lake level time series appropriately lagged.
721 722
Figure 5: Results of Empirical Orthogonal Function (EOF) analysis of equatorial SST and rainfall anomalies for the
723
Lake Turkana Basin, and comparison with the time-series of lake level variability. Correlation of PCA 1 and lake
724
level anomaly, delayed by one month (see Fig. 4) for (a) the EAO, (b) Turkana Basin rainfall, and (c) the WIO;
725
spatial distribution of EOF 1, (d) for the EAO, (e) Turkana Basin rainfall and (f) the WIO.
726
727
Figure 6: Spatial correlation between gridded time series of rainfall anomaly for the Lake Turkana Basin and PCA1
728
of SSTAs for (a) the EAO and (b) the WIO. These time series have been smoothed with a 3-month moving average
729
to reveal annual associations. Significance shown by dotted black lines. For comparison, (c) primary and (d)
730
secondary spatial EOF modes are shown. Black lines for EOFs signify zero variability.
731 732
Figure 7: Linear regressions for the “short” rain season (October to January) between SSTAs and lake level
733
anomaly. Red diamonds represent average seasonal SSTA>0.25σ and blue diamonds are average seasonal
734
SSTA<0.25σ. Regression parameters and associated statistical metrics reflect Monte Carlo bootstrap sampling
735
(n=2000), mean and 1-σ error.
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Figure 8: As in Figure 7, but for the “long” rain season, from March to June.
738 739
Figure 9: Comparison of model-derived and empirical rainfall for the Lake Turkana Basin by season and source
740
between 1993 and 2012. (a) Rainfall contribution from the “short” and “long” rain seasons as a percentage of yearly
741
total, for model-derived vs. empirical rainfall. Note reverse scale for “long” rains. (b) Absolute “long” rain
742
precipitation by year. (c) Absolute “short” rain precipitation by year. (d) Modeled rainfall associated with the WIO
743
(solid line) and the EAO (dotted line). (e) Correlation between empirical and modeled rainfall with monthly
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resolution on a yearly and seasonal basis.
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Figure 10: Comparison of instrumental records of SSTs and lake level variations for AD 1857 to 2012. (a) Input
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SSTAs for the EAO (blue line) and WIO (red line) monthly model simulation. Grey boxes reflect periods of
748
adjustment of the SSTA timeseries: (i) between AD 1936 and 1946 the 2 year moving average is removed to
749
eliminate significant data artefacts (see text), and (ii) post 1955 linear adjustment removes a pronounced warming
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trend. The thin black line reflects the 0.25-σ threshold. (b) Historic and simulated water levels for Lake Turkana.
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Periods of lake level record denoted by numerals: (I) pre-AD 1888 from Nicholson (1988), (II) between 1888 and
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1970 data and lake record from Butzer (1971), (III) between 1971 and 1992 reflects annually gauging until 1975,
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intermittent measures until monthly gauging began in 1988 (Avery, 2010). (IV) post-November 1992, monthly data
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from remote satellite measurement. Dashed lines represent periods of uncertain water level measures. Period IV
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reflects our model calibration period. Red line based on input of monthly SSTAs and associated 1-σ error. Blue line
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reflects input of annual SSTAs, offset by 10 m for clarity. Water levels are relative to the 2008 datum of 362 masl.
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(c) Lake Naivasha water level record for AD 1857 to 2012. Combined instrumental record (black line; Stoof-
758
Leichsenring et al., 2011), lake core record (black dashed line; Verschuren, 2001) and inference from vegetated
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shorelines (grey dotted line; Nicholson, 1988).
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Table 1: Linear regression statistics used in modeling Input SSTs
Input SST period
W. Indian SSTAs (WIO)
"Short" rains (October to January) "Long" rains (March to June)
E. Atlantic SSTAs (EAO)
Delay in lake level response (months) 1 0
"Short" rains (October to January)
1
"Long" rains (March to June)
3
W. Indian (Annual) E. Atlantic (Annual) a Statistically significant at the 99% level (p < 0.005) b Statistically significant at the 90% level (p < 0.05) Annual SSTAs
763 764 765 766
Criteria for SST state
equation of best fit
r
R2
Relative model weighting
Positive Negative Positive Negative Positive Negative Positive Negative Positive Positive
Y = 1.3488*X – 0.8292 Y = 0.0623*X – 0.2958 Y = 0.2745*X – 0.0889 Y = 0.3517*X – 0.0787 Y = 0.707*X – 0.2736 Y = –0.0124*X – 0.3192 Y = 0.4231*X – 0.1593 Y = 0.1521*X – 0.0103 Y = 0.0902*X – 0.051 Y = 0.0314*X – 0.012
0.78a 0.08 0.18 0.28b 0.37b 0.02 0.17 0.18 -
0.61a ~0.01 ~0.03 0.08b 0.14b ~0 ~0.03 ~0.03 -
25.0% 50.0% 7.5% 17.5% 75.0% 25.0%
767 768 769 770 771 772
• • • • •
SST anomalies appear to correlate significantly with Lake Turkana water level Regressions of SST anomalies and lake level change augmented by Bootstrap sampling. Increases in lake level linked to positive Indian Ocean SST anomalies in boreal fall. Post ca. 1930 meter-scale variability is linked to variations in Indian Ocean SST. Historical drop of 15m may reflect reduced role of Atlantic-derived sources.