Recent summer precipitation trends in the Greater Horn of Africa and the emerging role of Indian Ocean sea surface temperature
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We utilize a variety of climate datasets to examine impacts of two mechanisms on precipitation in the Greater Horn of Africa (GHA) during northern-hemisphere summer. First, surface-pressure gradients draw moist air toward the GHA from the tropical Atlantic Ocean and Congo Basin. Variability of the strength of these gradients strongly influences GHA precipitation totals and accounts for important phenomena such as the 1960s–1980s rainfall decline and devastating 1984 drought. Following the 1980s, precipitation variability became increasingly influenced by the southern tropical Indian Ocean (STIO) region. Within this region, increases in sea-surface temperature, evaporation, and precipitation are linked with increased exports of dry mid-tropospheric air from the STIO region toward the GHA. Convergence of dry air above the GHA reduces local convection and precipitation. It also produces a clockwise circulation response near the ground that reduces moisture transports from the Congo Basin. Because precipitation originating in the Congo Basin has a unique isotopic signature, records of moisture transports from the Congo Basin may be preserved in the isotopic composition of annual tree rings in the Ethiopian Highlands. A negative trend in tree-ring oxygen-18 during the past half century suggests a decline in the proportion of precipitation originating from the Congo Basin. This trend may not be part of a natural cycle that will soon rebound because climate models characterize Indian Ocean warming as a principal signature of greenhouse-gas induced climate change. We therefore expect surface warming in the STIO region to continue to negatively impact GHA precipitation during northern-hemisphere summer.
KeywordsAfrican monsoon Indian Ocean warming Drought Moisture transports Energy flux Tree rings Stable isotopes Global warming Food security
At the eastern boundary of the Sahel lies the Greater Horn of Africa (GHA), a region of northeastern Africa with highly food-insecure countries that are particularly vulnerable to interannual and decadal swings in precipitation totals (Funk et al. 2008; Funk and Brown 2009). Both the GHA and Sahel receive the bulk of their precipitation during the June–September (JJAS) boreal summer season. These regions experienced a significant decline in JJAS precipitation during the 1970s and 1980s due to anomalous warming of the South Atlantic and Indian Oceans (Folland et al. 1986; Hastenrath 1990; Giannini et al. 2003; Hoerling et al. 2006; Cook 2008) and subsequent shifts in moisture transports and upper-air flow (Nicholson 2000, 2009b; Nicholson and Grist 2003). Following the 1980s, warming in the North Atlantic appears to have caused a precipitation recovery throughout much of the Sahel (Giannini et al. 2008; Hagos and Cook 2008). In the GHA, however, the extent of post-drought recovery is unclear. Recent studies suggest drying over GHA may have continued into the 2000s (Seleshi and Zanke 2004; Funk et al. 2011b), but the cause of this potential drying and decoupling of precipitation trends in the GHA from those of the Sahel remains unexplored.
Previous studies have identified a variety of drivers of JJAS precipitation and drought in the GHA. While JJAS wind trajectories toward the GHA are quite variable from year to year, the rainforest region of the Congo Basin appears to be the main moisture source to Sudan and much of the Ethiopian Highlands, and the Indian Ocean is the main moisture source for eastern portions of Ethiopia and Kenya (Camberlin 1997; Gatebe et al. 1999; Riddle and Cook 2008; Levin et al. 2009). South of the center of the North African surface low, deep convection and enhanced moisture transport from the south and west fuel monsoonal precipitation during JJAS in the Sahel and GHA. Historically, for both the Sahel and GHA, the primary driver of interannual and longer-term variability of summer precipitation is the north-south displacement of jets, zone of maximum convection, and southern boundary of the thermal low over northern Africa (Nicholson 2009b), all of which appear to be influenced by tropical sea-surface temperature (SST) anomalies (Bhatt 1989; Whetton and Rutherfurd 1994; Camberlin 1995, 1997; Riddle and Cook 2008; Kucharski et al. 2009; Segele et al. 2009a). Some research also suggests that increased tropospheric aerosol concentration works to suppress the northward migration of the JJAS rainbelt into northern Africa, potentially reducing precipitation in the GHA (Rotstayn and Lohmann 2002). Further, relationships exist between the strength of the Indian monsoon and GHA precipitation (Camberlin 1997). Overall, drought in the GHA tends to occur when there is a reduction in the amount of moisture reaching the GHA from the tropical Atlantic Ocean and Congo Basin region.
Recently, a number of studies suggested that increased SSTs in the STIO may cause large-scale changes in atmospheric circulation and divert important moisture transports away from the GHA, altering previous relationships between regional circulation and precipitation (Verdin et al. 2005; Funk et al. 2008; Hagos and Cook 2008; Williams and Funk 2011). In an analysis of boreal spring (March–June, MAMJ), Williams and Funk (2011) found that higher SSTs in the STIO have driven large increases in convection and precipitation over the STIO. The resulting increase in the amount of diabatic energy released during precipitation has led to increased divergence of dry static energy (DSE) in the mid- and upper-troposphere. During MAMJ, this intensified outflow of mid- and upper-tropospheric DSE from the STIO has increased subsidence over northern Africa and decreased moisture transport into the GHA for at least the past 30 years. The decrease in the amount of moisture transported into the GHA region appears to have caused a reduction in what MAMJ precipitation totals would have otherwise been in the absence of increased SSTs in the STIO. Therefore, increased SSTs in the STIO and subsequent changes in moisture transports may signify a long-term alteration in how interannual variability in atmospheric circulation impacts interannual variability in precipitation totals throughout much of the GHA.
While no existing data sources detail the true sources of GHA precipitation, the composition of stable isotopes in Ethiopian rainwater suggests that most of the moisture is derived from the rainforests of the Congo Basin (Sonntag et al. 1979; Rozanski et al. 1996; Levin et al. 2009). Rain-water molecules in the Ethiopian Highlands and surrounding areas have exceptionally high levels of the heavy stable isotope of oxygen (18O) (Dansgaard 1964; Levin et al. 2009). The high level of 18O relative to 16O (hereafter referred to using the standard δ18O terminology) is evidence that the Congo Basin is a primary source of moisture to the GHA because moisture transpired by the wet forests of the Congo Basin does not undergo isotopic fractionation (Zimmermann et al. 1967; Salati et al. 1979; Gat and Matsui 1991). Unfortunately, isotopic records of precipitation throughout the GHA are inadequate to determine whether there is a trend in the proportion of precipitation coming from the humid continental interior. Conveniently, trees use rainwater to create cellulose during photosynthesis, and δ18O in precipitation is partially conserved in cellulose (Yapp and Epstein 1982; Roden and Ehleringer 1999a; Roden et al. 2000; McCarroll and Loader 2004; Gessler et al. 2009). In trees that produce annual growth rings, the isotopic composition of cellulose in tree rings may reflect an annual record of δ18O in precipitation (Roden and Ehleringer 1999a; Robertson et al. 2001; Anchukaitis and Evans 2010). We will investigate whether this is the case for trees in the Ethiopian Highlands.
In this study we use a unique network of over 1,200 publicly and privately available gauge records from northern and eastern Africa to calculate a best-estimate record of JJAS precipitation for the GHA through 2009. Where sufficient precipitation data are available, we quantify the degree to which the GHA has recovered relative to the 1970–1989 mean. We then identify the atmospheric mechanisms that governed interannual variability in GHA precipitation during the period of 1948–1989 and determine whether the relationships between GHA precipitation and these mechanisms remained stable from 1990 to 2009. We utilize reanalysis climate data and a tree-ring isotope record from the Ethiopian Highlands to test the possibility that rapidly rising SSTs in the Indian Ocean have acted to suppress JJAS precipitation in the GHA following the peak of the Sahel drought in the 1980s.
2 Data and methods
2.1 Precipitation data
2.2 Seasonal precipitation record for the Greater Horn of Africa
To calculate a time series of JJAS precipitation for the GHA, we first standardized (converted to z-scores) each grid cell’s time series by subtracting its mean and dividing by its standard deviation. We then calculated the region’s average standardized anomaly from each JJAS season during 1948–2009. The resulting time series of mean seasonal anomalies for the region accounts for 59% of the interseasonal variability throughout the region. This mean precipitation time series is most representative of precipitation in the central portion of the GHA (r = 0.9) and less representative toward the boundaries (r = 0.6–0.7). Similar to what has already been established by several studies focusing on the Sahel (e.g., Nicholson 2009a), precipitation varies fairly ubiquitously across the GHA as a whole (i.e., wet seasons are wet everywhere and dry seasons are dry everywhere). Superimposed upon this regional mode of variability is a dipole-like pattern that causes the north and south to experience anomalies of opposite sign (hence the lower correlations near the boundaries). We explore sub-regional variability later in the drought-diagnostics portion of this paper.
It is crucial to this study that the CHG-CLIM data accurately reflect the direction of precipitation trends in the GHA following the Sahel precipitation decline that ended in the 1980s. Gaps in publicly available data and the cost of privately held data from eastern Africa, especially from Ethiopia, have caused previous studies to exclude parts of the GHA (e.g., Nicholson et al. 2003; Nicholson 2005). A recent study that employed data (137 gauge stations) purchased from the Ethiopian Meteorological Agency found good agreement with global gridded precipitation estimates for boreal summer months. The authors concluded that the purchased dataset is more accurate for Ethiopia than the global products, although they did not compare multi-decade trends among the various products (Dinku et al. 2008). The Ethiopian station data used in our study were also purchased from the Ethiopian Meteorological Agency, and are similar in quality and quantity to those examined by Dinku et al. (2008).
Rate of precipitation change in the GHA and Sahel regions from the 1970–1989 period to the 1990–2009 period
Funk et al. (2011b)
Xie and Arkin (1997)
Chen et al. (2002)
Mitchell and Jones (2005)
Uppala et al. (2005)
Rudolf and Schneider (2005)
Adler et al. (2003)
Smith et al. (2010)
Kalnay et al. (1996)
Kanamitsu et al. (2002)
Willmott and Matsuura (1995)
2.3 Precipitation and drought diagnostics
We used rotated principal components analysis (Richman 1986) to split the GHA into two (northern and southern) sub-regions. The sub-regional precipitation records accounted for 75% of variability in the north and 68% in the south.
In order to evaluate Indian Monsoon-GHA precipitation relationships (e.g., Camberlin 1997) we compared JJAS Bombay SLP to the north and south sub-regional CHG-CLIM precipitation time series using monthly station SLP data from the Global Historical Climate Network (GHCN, (ftp.ncdc.noaa.gov/pub/data/ghcn/v2/). If Bombay SLP did not adequately represent a regional precipitation record, we considered monthly SLP data from 1,107 alternate stations in Africa, southern Eurasia, and the Maritime Continent (see the “Appendix” for a description of SLP data processing). We calculated the correlation between precipitation and SLP at every pair of stations (including Bombay) from 1953 to 1988. We identified the pair of regions (clusters of stations) where SLP records could either be summed or subtracted to most accurately estimate JJAS precipitation.
We used three different reanalysis data sets to analyze how sub-regional precipitation variability is related to large-scale atmospheric circulation and vapor transports. For the period 1948–1988 we used the NCEP/NCAR (Kalnay et al. 1996) and ECMWF ERA-40 (Uppala et al. 2005) reanalysis climate datasets. To test whether relationships established with SLP during 1948–1988 were stationary after the peak of the Sahel drought in 1988, we analyzed trends in both the original NCEP/NCAR reanalysis and NCEP-DOE Reanalysis 2 data (NCEP2, available for 1979–2009, Kanamitsu et al. 2002). NCEP2 uses an updated forecast model and data-assimilation system from the NCEP/NCAR reanalysis and it includes satellite data. We visualized trends in, and correlations with, wind-vector data using quiver-plot maps. In these maps, the x-axis of an arrow refers to the zonal (u) wind component and the y-axis refers to the meridional (v) wind component. In vertical profile maps depicting trends in, or correlations with, both horizontal and vertical wind velocity along a horizontal transect, the x-axis refers to the horizontal velocity component parallel to the transect and the y-axis refers to the vertical velocity component.
Informed by previous research (e.g., Gill 1980, 1982; Funk et al. 2008; Hagos and Cook 2008; Williams and Funk 2011), we investigated whether trends in Indian Ocean SSTs have impacted atmospheric circulation and moisture transports in ways that would influence GHA precipitation. We focused on the STIO region, defined here as 15°S–0°S, 55°E–90°E. We evaluated correlations between average JJAS SST in the STIO (NOAA extended SST reanalysis) and spatial fields of four variables: total cloud cover (NCEP2), net upward surface energy flux (NCEP2), dry static energy (DSE, NCEP2), and precipitation (GPCP v2.1). We calculated energy fluxes using definitions provided by Trenberth and Stepaniak (2003a). Net surface energy flux comprises the net upward radiation flux from the ocean’s surface plus the upward fluxes of sensible and latent heat from the ocean’s surface. DSE is the sum of the energy contained in the temperature and geopotential height of an air column.
We evaluated how higher SSTs in the STIO during recent decades have impacted interannual relationships between SLP and JJAS precipitation in the GHA. For each of the two GHA sub-regions, we classified JJAS seasons from 1948 to 2009 based upon whether SLP conditions were favorable for precipitation (“wet SLP” and “dry SLP”), and whether SSTs in the STIO were warm or cool. We designated all seasons with neutral SLP or SST conditions to a fifth “neutral” class. We evaluated mean JJAS precipitation (CHG-CLIM and Global Precipitation Climatology Centre (GPCC)) in the GHA during each of the five classes of seasons.
2.4 Moisture source information in tree-ring oxygen-18
At the compound of the Debrebirkan Selassie church in Gondar, Northwest Ethiopia (12°37′N, 37°29′E), 5 mm diameter cores were obtained from Juniperus procera trees in May 2007 as part of a larger sampling campaign (Wils et al. 2011). Successful cross-dating between 32 trees from five sites in the North Gondar zone was achieved by comparison of the wood anatomy directly on the surface of the samples and skeleton plotting (Stokes and Smiley 1968). Cross-dating was evaluated using the computer program COFECHA (Holmes 1983; Grissino-Mayer 2001) and the annual nature of the tree rings was confirmed by AMS radiocarbon dating (Wils et al. 2010, 2011).
Oxygen isotope ratios were measured on one tree core in a pilot study to obtain a preliminary insight into the potential environmental sensitivity of this isotopic variable in tree rings. Slivers were cut from absolutely dated annual growth rings representing years 1905–2003. Lignin was oxidized by the in situ generation of chlorine dioxide (ClO2) and hemicelluloses were hydrolyzed from the resulting holocellulose to yield α-cellulose (Loader et al. 1997; Rinne et al. 2005). After sample homogenization, 0.30–0.35 mg of dry α-cellulose was weighted into silver vessels and pyrolysed over glassy carbon at 1,090°C. Oxygen isotope ratios were measured using a PDZ Europe 20-20 mass spectrometer interfaced to a Europa ANCA GSL elemental analyzer at Swansea University (Loader et al. 2008). Oxygen isotope ratios are expressed as per mille (‰) deviations relative to the VSMOW standard (Coplen 1995). Analytical precision was typically 0.3‰ as was illustrated by a repeat analysis of samples from 1910 (mean = 34.67‰, standard deviation = 0.3‰, n = 15).
Two influential variables on tree-ring δ18O are the δ18O of precipitation and the amount of isotopic enrichment of leaf water that occurs during transpiration (Roden and Ehleringer 1999a; Robertson et al. 2001; McCarroll and Loader 2004). Leaf-water enrichment is driven by the water vapor pressure deficit (VPD) of the atmosphere under a wide range of environmental conditions (Roden and Ehleringer 1999b). To test the impact of evaporative enrichment from leaves, we statistically regressed the tree-ring δ18O record against monthly and multi-month VPD, as estimated by NCEP2 reanalysis. A positive relationship between tree-ring δ18O and VPD would suggest evaporative enrichment is an important driver of interannual tree-ring δ18O variability. A non-positive relationship would suggest tree-ring δ18O variability is dominated by variations in δ18O of precipitation.
We compared monthly NCEP2 reanalysis data to tree-ring δ18O to test relationships between tree-ring δ18O and variables suspected of influencing the source of precipitation in the GHA. Variables chosen were anti-cyclonic circulation of atmospheric moisture over the Gulf of Guinea and STIO. A good inverse proxy for anti-cyclonic moisture transports is cloud cover. For each month and range of months, we calculated the mean cloud frequency over the southern Gulf of Guinea (5°S–25°S, 15°W–20°E) and western STIO (2.5°S–10°S, 50°E–67.5°E). We then found the range of months when this cloud-cover correlated most strongly (and negatively) with tree-ring δ18O. Exploratory analysis revealed that another good anti-cyclonic moisture transport index is the sum of meridional moisture transports over southern Africa to the east of the Gulf of Guinea (0°S–20°S, 15°E–30°E) and zonal moisture transports in the western STIO (2.5°S–10°S, 50°E–67.5°E). We repeated the analysis described above for this alternate index. For visualization of the more general circulation features associated with variability of tree-ring δ18O, we also compared tree-ring δ18O to the circulation fields evaluated throughout this study.
3 Results and discussion
3.1 JJAS precipitation during 1948–2009
JJAS precipitation declined substantially throughout much of the GHA during the 1948–2009 period. Figure 2a is a time series of standardized JJAS precipitation totals in the GHA region considered in this study, outlined in red in Fig. 2b, c. Declines during the 1960s–1980s occurred in concert with the well-known reductions throughout the Sahel (the Sahel region considered in this study is outlined in yellow in Fig. 2b, c). GHA precipitation decreased by approximately 1 standard deviation during the 1950–1989 period (Fig. 2a), corresponding to decreases of over 30 mm per decade throughout much of the Ethiopian Highlands, over 60 mm per decade in western Sudan, and little change in southern Sudan, northern Uganda, and western Kenya.
Following the 1980s, JJAS precipitation continued to decline in the GHA while there was no decline in precipitation throughout much of the Sahel region west of Sudan (Fig. 2). During 1990–2009, mean GHA precipitation was reduced by more than 0.5 standard deviation units compared to 1970–1989. Exceptions were observed in some areas in northeastern Ethiopia and northern Sudan, where post-1989 precipitation increased by over 0.5 standard deviation units. Notably, Fig. 14 in “Appendix” shows a similar spatial structure in recent precipitation trends for the MAMJ season. The similarity in spatial patterns of precipitation trends during JJAS and MAMJ suggests the same mechanism may be suppressing precipitation during both seasons despite the fact that the circulation patterns that cause precipitation during these two season are quite different.
Since station coverage has decreased over the past 20 years (Fig. 2c), we considered 10 alternate gridded estimates of JJAS precipitation in addition to our CHG-CLIM data. For the GHA and Sahel regions, Table 1 lists each additional dataset’s rate of change in mean JJAS precipitation total comparing the 1970–1989 to the 1990–2009 period. Eight of the 10 alternate datasets agree with CHG-CLIM that JJAS precipitation did not increase in the GHA after the 1970–1989 period. For the Sahel region, 7 of 10 alternate datasets agree that 1990–2009 totals exceeded 1970–1989 totals. In addition, 9 out of 10 alternate datasets agree with CHG-CLIM that precipitation in the GHA either declined more or recovered less than in the Sahel. Notably, not all precipitation datasets cover the entire 1970–2009 period. If the negative post-1970 precipitation trend indicated by most datasets is real, datasets beginning in 1979 may indicate artificially low precipitation totals for 1970–1989 and datasets ending before 2009 may indicate artificially high totals for 1990–2009. Despite this bias, 5 of 6 datasets not covering the entire 1970–2009 still agree on reductions in GHA precipitation.
3.2 Drivers of GHA precipitation and drought during 1948–1988
While correlations between reanalysis atmospheric circulation (including moisture transports) and JJAS precipitation in the southern GHA were weaker than for the northern GHA (Fig. 7), precipitation records for both regions correlate with moisture transport traveling from the Gulf of Guinea and across the Congo Basin (Fig. 7a, b, d). A main difference between drivers of north versus south GHA precipitation is that southwesterly cyclonic circulation is not necessary to deliver moist air to the southern GHA. Instead, southern GHA precipitation correlates with purely westerly flow across Africa between the surface and 500 hPa. This westerly flow is associated with enhanced surface low-pressure over western India and the western Indian Ocean north of Madagascar (Fig. 7a, b), consistent with the results of the SLP analysis. Southern GHA precipitation also correlated with ascending motion over western Africa and on the eastern boundary of the STIO (Fig. 7c, d). Correlations with ascending motion were particularly strong over the STIO and weaker over the GHA in the ECMWF analysis. In both reanalyses, correlation with ascending motion was generally confined to the mid- and upper-troposphere between 600 and 200 hPa. The correlation fields indicate that enhanced convection in rainy seasons corresponds with an enhanced TEJ, as was the case for northern GHA precipitation. Previous work has shown that an enhanced TEJ contributes to enhanced instability throughout the atmospheric column during the north African summer monsoon (Nicholson 2009b; Segele et al. 2009a, b).
3.3 Post-1988 climate trends and drought diagnostics
From 1989 to 2009, the SLP gradient between Sudan and the southern Mediterranean coast strengthened and Bombay SLP declined (Fig. 5b, c). According to the strong statistical relationships between SLP and GHA precipitation, these trends should be associated with increasing JJAS precipitation throughout the GHA region (north and south). The fact that GHA precipitation did not rebound following the 1980s suggests that a secondary factor worked to suppress it and alter the previously established relationships with SLP.
Regression of various calculations of JJAS energy flux, total cloud cover, and precipitation in the STIO (15°S–0°S, 55°E–90°E) versus NOAA JJAS SST in the STIO
(W m−2 °C−1)
Increases in SST, upward energy flux, cloud cover, and precipitation over the STIO have been accompanied by diabatic heating in the mid-troposphere due to the conversion of latent energy into DSE during condensation (Trenberth and Stepaniak 2003b). Figure 9e shows that STIO surface warming corresponds with a significant increase in the amount of NCEP2 DSE exported from the region (56 Wm−2 per degree warming in the STIO). As was the case for GPCP precipitation, divergence of DSE increased most rapidly in the northwestern portion of the STIO. In this region, DSE correlated well (r > 0.77, P < 0.0001) with GPCP precipitation. Agreement between these two independently derived data sets, and their correlation with STIO SSTs, testifies to the strength of the inter-connection between STIO SSTs, precipitation in the STIO region, and the large-scale energetic response in the atmosphere.
Increasing exports of DSE from the STIO region appear to be associated with increasing imports of DSE into the GHA (Fig. 9e). While Fig. 9e does not explicitly link divergence of DSE over the STIO to convergence of DSE over the GHA, the overturning trend shown in Fig. 8c does. This atmospheric response is in agreement with previous climate modeling work showing that an off-equatorial positive heating anomaly should lead to decreased convective activity over eastern Africa via a Gill-type response (e.g., Gill 1980, 1982; Funk et al. 2008). This is further supported by Fig. 8c, d, which shows that upward vertical velocities over the Ethiopian Highlands have slowed by as much as 0.06 Pa s−1 (a figure close to the climatological mean indicated in Fig. 4c, d). Finally, reduced convection over the GHA also reduces westerly moisture transport from the Congo Basin (Fig. 8b, d). This is particularly apparent in maps comparing mean states of atmospheric circulation during the 1948–1988 and 1989–2009 periods in Fig. 18b, d in “Appendix” .
For each of the 5 classes, maps of mean CHG-CLIM (top) and GPCC (bottom) precipitation anomaly appear below the box plots in Figs. 10 and 11. It is clear from these maps that the regional tendencies described above generally hold throughout the GHA. The impact of increasing SSTs in the STIO appears to be particularly strong in the western Ethiopian Highlands, northern Uganda, and the Nairobi region of Kenya. In these regions, precipitation totals during “wet SLP/warm STIO” seasons are similar to, or even lower than, totals during “dry SLP/cool STIO” seasons (Fig. 11). This suggests that on a decadal scale, circulation dynamics associated with increasing SSTs in the STIO are becoming comparably important in dictating JJAS precipitation totals in the southern GHA as are the circulation dynamics associated with Bombay SLP variability. The relatively high gauge density throughout the Ethiopian Highlands, Uganda, and Nairobi regions (Fig. 1c) provides confidence that this finding is robust.
3.5 Tree-ring δ18O and large-scale atmospheric circulation and moisture transports
Tree-ring δ18O correlated positively but insignificantly with JJAS VPD during 1979–2003. Positive relationships were stronger when we considered VPD later in the growing season, but none were statistically significant. The weakness of the positive correlation is partly because VPD increased from 1979 to 2003 while δ18O decreased. When linear trends are removed from δ18O and VPD records, November-December VPD correlates with tree-ring δ18O positively and significantly (r = 0.51, P = 0.0099). Maximum correlation with VPD probably occurs during November and December because this is when VPD finally grows large enough following the humid monsoon season to draw down soil moisture and cause evaporative fractionation within leaves as the tree increases its water-use efficiency.
The relatively weak relationships between δ18O and VPD, and the opposing long-term trends, suggest tree-ring δ18O may be influenced by a secondary climate factor in such a strong way as to offset the relationship between increasing VPD and evaporative enrichment. We hypothesized this secondary factor is the proportion of Highlands precipitation derived from the Congo Basin. This is an impossible variable to quantify, but an adequate proxy may be the sum of moisture transports entering the GHA from the Congo Basin area. Indeed, seasonal sums of vertically integrated zonal and meridional vapor transports (NCEP2) on the southwestern border of the GHA (2.5°N–5°N, 22.5°E–30°E) correlate positively and significantly with tree-ring δ18O (r = 0.48, P = 0.0144). This relationship is no stronger than the relationship with VPD, however, because the moisture transport and δ18O time series share a common trend.
An all-encompassing representation of anti-cyclonic anomalies in these regions is total cloud cover, which is derived from satellite data in the NCEP2 reanalysis. The sum of JJAS cloud frequency within the southern Gulf of Guinea (5°S–25°S, 15°W–20°E) and western STIO (2.5°S–10°S, 50°E–67.5°E) regions correlated negatively and significantly with tree-ring δ18O (r = −0.71, P < 0.0001) during 1979–2003. JJAS was also the range of months when this cloud-cover index correlated most strongly with δ18O. Importantly, the strength of this negative relationship is not solely due to contrasting long-term trends. When linear trends were removed from both time series, correlation was still negative and significant (r = −0.60, P = 0.0017).
Further, the amount of anomalous vapor flow away from these regions represents how the anti-cyclonic anomalies relate to moisture transports from the Congo Basin toward the GHA. The maps in Fig. 13a, b indicate that δ18O is positively correlated with westerly vapor transports away from the STIO as well as southerly anomalies immediately to the east of the Gulf of Guinea, which at the equator turn into southwesterly anomalies toward the GHA. When vertically integrated JJAS zonal vapor transports over the western STIO (2.5°S–10°S, 50°E–67.5°E) are added to vertically integrated JJAS meridional vapor transports to the east of the Gulf of Guinea (0°S–20°S, 15°E–30°E), this vapor transport index correlates positively and significantly with tree-ring δ18O (r = 0.72, P < 0.0001). The correlation strengthened when only the July–September period was considered (r = 0.76, P < 0.0001). Detrended, these time series still correlate positively and significantly (for JJAS, r = 0.66, P = 0.0003).
Because the indices of cloud cover and vapor transports for the STIO and southern Gulf of Guinea correlated far more strongly with tree-ring δ18O than did VPD, we treated a combination of these two time series as the primary predictor of δ18O (red line in Fig. 12a). This time series of the standardized vapor-transport record minus the standardized cloud-cover record, termed the “anti-cyclonic index,” correlates better with δ18O than does either record independently (r = 0.80, P < 0.0001). After correlation with the anti-cyclonic index was accounted for, the residual δ18O record correlated positively and significantly with November-December VPD (r = 0.57, P = 0.0029, Fig. 12b).
This analysis is suggestive of a powerful link between increasing temperatures, evaporation, convection, precipitation, and divergence of DSE in the STIO and declining moisture transports from the Congo Basin toward the GHA. This serves as a call to action for the collection and measurement of additional tree-ring δ18O records from the Ethiopian Highlands, as tree-ring δ18O may hold valuable clues about historical variability in the source of moisture and how that variability has related to large-scale climate forcing.
As pressure on earth’s resources increases, identifying and understanding decadal climate variations will become increasingly important. While work with global climate models will certainly continue to be very important, “forensic climatology” that analyzes the cause of observed droughts is another critical mode of inquiry. We show the GHA is a prime target for such inquiry because this region has experienced a declining trend in summertime monsoonal precipitation for approximately the past 60 years (Figs. 2 and 5). JJAS precipitation was particularly low in 2008 and 2009, causing the US government to spend over $2.3 billion on food aid in Sudan, Ethiopia, Kenya, and Uganda.1 At present, parts of the GHA are in the midst of their lowest annual rainfall year in at least the past 60 years,2 contributing to steep increases in food prices and child malnutrition.3 On a longer time scale, lake-bed pollen records link precipitation variability in tropical eastern Africa with several major fluctuations in the amount of forest cover since the last glacial maximum (Bonnefille and Chalié 2000). It is therefore clear that GHA precipitation totals are capable of undergoing extreme low-frequency swings, likely in response to alterations in large-scale ocean and atmospheric circulations.
To gain insight into whether ongoing precipitation declines in the GHA can be expected to continue or rebound in coming decades, we compared records of GHA precipitation from 1948 to 2009 to records of large-scale atmospheric circulation. During the classic Sahel precipitation decline that ended in the late 1980s, precipitation in the northern GHA correlated very strongly with the sea-level pressure (SLP) gradient between Sudan and the southern coast of the Mediterranean Sea (r = 0.86, 1948–1988). Variability in this SLP gradient during 1948–1988 corresponds with the multi-decade decline in precipitation across the Sahel and GHA, and also corresponds with interannual variations specific to the northern GHA. In particular, the devastating drought year of 1984, which led to the deaths of at least several hundreds of thousands of Ethiopians and Sudanese (De Waal 1989; Kidane 1989), is clearly indicated by a weak Sudan-Mediterranean SLP gradient (Fig. 5b). For the southern GHA, 1948–1988 precipitation correlated negatively (r = −0.83) with average SLP in Bombay (Fig. 5c). These results indicate that prior to and during the Sahel drought, the strength of the summer monsoon in much of the GHA was strongly associated with the same large-scale climate processes that governed the strength of the monsoon throughout much of the Sahel to the west.
Notably, relationships between regional precipitation and these SLP indices have weakened in recent years. During 1990–2009, mean JJAS rainfall totals in the north and south GHA continued to decline even though the SLP indices discussed above trended in directions associated with more favorable precipitation conditions (Fig. 5). Evaluations of recent trends in atmospheric circulation and moisture transports (Fig. 8) suggest that warming in the STIO accounts for a substantial portion of these recent rainfall declines. Given strong and consistent surface winds in the STIO, and the already warm SSTs in this region, surface warming in the STIO during the past half century has led to large increases in STIO evaporation. Increased evaporation has dramatically increased the vertical flux of energy into the STIO atmosphere. This change has been accompanied by increases in STIO precipitation and the export of dry static energy (DSE) toward eastern Africa (Fig. 9). These DSE exports have had two negative impacts on GHA rainfall. First, they have increased DSE convergence over the GHA, increasing atmospheric stability and subsidence. Increased convergence of DSE over the GHA has had a secondary (but not necessarily less important) impact on westerly moisture transports from the Congo Basin. As DSE divergence has increased over the STIO and convergence of DSE increased over the GHA (Fig. 9e), ridging over the GHA has reduced moisture transport toward the GHA from the Congo Basin (Fig. 8, Fig. 18 in “Appendix”).
Unfortunately, long observational records do not exist to tell us whether moisture transports from the moist Congo Basin have truly been redirected away from the GHA. Our analysis suggests, however, that clues may exist in the stable isotopic composition of annual growth rings in trees from the Ethiopian Highlands. While tree-ring δ18O is often positively influenced by warm, dry conditions due to evaporative enrichment of leaf water, our analysis indicates that these conditions have only a secondary impact on tree-ring δ18O. We found that large-scale circulation features that affect transport of isotopically enriched atmospheric moisture from the Congo Basin toward the GHA are the primary influences on tree-ring δ18O (Figs. 12 and 13). While this finding comes from only one tree and replication from additional samples will be crucial, this tree-ring analysis corroborates our other findings and may serve as a novel approach toward providing an answer to the difficult and complicated question of how large-scale climate variability impacts moisture transports into the GHA.
According to the latest report by the Intergovernmental Panel on Climate Change, observations of decreased JJAS precipitation in the GHA are in contrast to model projections of unchanged summer precipitation (Christiansen et al. 2007). Importantly, there is high uncertainty in model projections of precipitation and high variability among models regarding projections of precipitation in the GHA (Christiansen et al. 2007). While these models do not converge upon a connection between JJAS precipitation in the GHA and greenhouse-gas induced global climate change, data presented here suggest recent (post-1980s) drying trends can be largely attributed to a cascade of climate changes linked to warming trends in the STIO. Climate modeling studies implicate greenhouse gas and aerosol emissions as the primary causes of recent STIO surface warming (Pierce et al. 2006; Alory et al. 2007). Although warming-induced increases in evaporation and cloud cover work to dampen surface warming (Du and Xie 2008), climate models predict tropical Indian Ocean SSTs will continue increasing (Meehl et al. 2007). Resulting increases in evaporation, rainfall, and divergence of DSE in the STIO region (Fig. 9) are linked to diminished rainfall in the GHA following the classic Sahel drought of the 1970s and 1980s (Fig. 2). Unfortunately, these declines have occurred in extremely fragile and water-insecure regions where they have likely exacerbated social conflict (Funk et al. 2011a) and drought-induced forest mortality (Allen et al. 2010). The fact that swings in precipitation appear to have significantly impacted forest cover in the past (Bonnefille and Chalié 2000) suggests that GHA forest cover is at risk of decline due to declining JJAS precipitation. Beyond the direct influence that water shortage has on humans in the GHA, drought-driven forest mortality in the Ethiopian Highlands and Ugandan rainforest would likely have indirect effects on humans by influencing regional climate, the global carbon balance, and human access to important natural resources.
This research was supported by the U.S. Agency for International Development Famine Early Warning System Network under U.S. Geological Survey Cooperative Agreement #G09AC00001 and the National Aeronautics and Space Administration under Precipitation Science Grant #NNX07AG266. Park Williams and Sara Rauscher gratefully acknowledge support from the U.S. Department of Energy through the LANL/LDRD Program. Iain Robertson is supported by the Climate Change Consortium of Wales. Marcin Koprowski was supported by an International Incoming Short Visit from the Royal Society (UK). Ethiopian precipitation data were provided by the Ethiopian Meteorological Agency. Thanks to Tufa Dinku for assisting the authors in obtaining this data purchase, providing the best possible foundation for an analysis of trends in Ethiopia. Thanks to Greg Husak for his invaluable part in obtaining CHG funding. Comments by Amy McNally, Brian Osborn, Charles Jones, Ellen Mosley-Thompson, Harikishan Jayanthi, Kevin Anchukaitis, Kyle Cavanaugh, Leila Carvalho, Lonnie Thompson, Naomi Levin, Tom Adamson, and two anonymous reviewers greatly improved the quality of the manuscript. NCEP/NCAR data were accessed online through the NOAA Earth System Research Laboratory (ESRL) Physical Sciences Division (PSD, http://www.esrl.noaa.gov/psd). ECMWF data were produced by the European Centre for Medium-Range Weather Forecasts ERA-40 project (http://www.data.ecmwf.int/data/). GHCN data were accessed from the National Climate Data Center (ftp://ncdc.noaa.gov/pub/data/ghcn/v2/).
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