1. Introduction
The societies of the coastal regions of the Greater Horn of Africa (GHA) experience two distinct rainy seasons: the “long” rains in the boreal spring, known locally as the gu in Somali or masika in Swahili, and the “short” rains in the boreal fall, known locally as the deyr in Somali or vuli in Swahili, with the long rains having, on average, higher total seasonal rainfall. These rainy seasons interrupt the long (jilaal in Somali) and short (xagaa in Somali) dry periods, forming a climate that is unique both in its aridity and for the shape of its annual rainfall climatology.
In fact, the GHA is a dramatic outlier in mean rainfall for its latitude (Fig. 1), with mean rainfall substantially below the next-driest land area of the tropics, the Brazilian sertão, and, in certain locations, similarly dry as the equatorial Pacific cold tongue regions. Authors have connected this dryness with the prevailing low-level divergence and atmospheric descent that persist in the midtroposphere even during the rainy seasons (see, e.g., Yang et al. 2015; Nicholson 2017). This descent is modulated by the global system of equatorial Walker circulations, with ascent over the Maritime Continent associated with large-scale descent of dry, stable air over the western Indian Ocean and the Horn of Africa, and climatological westerlies at the equator off the coast (King et al. 2021; Limbu and Tan 2019; Zhao and Cook 2021). While a closed circulation cell may not reach all the way to the western Indian Ocean for the whole year (Hastenrath 2000), convection over the Maritime Continent and upper-level easterlies can still modulate the stability outside of the boreal fall when the Indian Ocean Walker cell is most coherent (Liebmann et al. 2017; Zhao and Cook 2021).
Mean tropical rainfall for the period 1982–2021. (top) Meridional average across 3°S–12.5°N for three different observational data products. (bottom) Map of GPCP rainfall. Note that CHIRPS (orange dots) and GPCC (purple dots) are land-only products; for longitudes with both ocean and land grid cells, they are not expected to perfectly match the global GPCP product. Vertical red lines identify the GHA; dotted black lines in the map show the extent of meridional averaging in the top panel. The GHA is, by a substantial amount, the driest region for its latitude, with GPCP showing rainfall at some locations to be even lower than over the equatorial Pacific cold tongue region, the other driest area in the tropics.
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
Further work has examined the role of the topography in maintaining the region’s aridity, with the presence of the Ethiopian and Kenyan highlands, which reach up to 4500 and 3500 m, respectively (and with individual volcanic peaks, such as Mount Kenya/Kirinyaga, reaching above 5000 m), playing a key role. These largely prevent the advection of moist air from the Congo Basin and the Atlantic Ocean, particularly compared to the climate of the Ethiopian highlands themselves (Levin et al. 2009). Correspondingly, removing topography in modeling experiments results in a wetter Horn of Africa (Sepulchre et al. 2006), even during the seasons that are currently dry [e.g., see Boos and Kuang (2010) and Wei and Bordoni (2016) for the xagaa short dry season during the South Asian monsoon]. Furthermore, the Turkana jet, an orographically forced, low-level easterly jet passing through the Turkana Channel, a gap between the Kenyan and Ethiopian highlands over Lake Turkana, diverts part of the low-level circulation in all seasons and may be associated with decreased summer rainfall in northeastern Kenya and descent and divergence throughout the region (Indeje et al. 2001; Nicholson 2016).
Intriguingly, the aridity of eastern Africa is not an immutable fact of the current position of geologic landmasses. The Holocene alone has seen several longer-term pluvial and drought periods, generally associated with continental cooling (e.g., during the Little Ice Age) or warming, respectively (Verschuren et al. 2000; Tierney et al. 2011, 2015). In other words, the regional aridity cannot be the result of topography alone but can only be understood in concert with an analysis of the dynamical drivers of the local hydroclimate.
A second notable element of the GHA climate is the presence of two distinct rainy seasons overlapping with the equinoctial months (e.g., Trewartha 1981; Herrmann and Mohr 2011; Yang et al. 2015), though both the long and short rains generally peak after the equinox. This double-peaked climate reaches to the northern tips of the Horn of Africa at 12°N, which is far beyond the northern extent of the equatorial African region with two peaks to the west of the GHA [Figs. 2b,d; the study region with a double-peaked climatology (shaded in green), farther north than surrounding areas]. While many regions along the equator experience two seasonal peaks in rainfall due to the shift in the intertropical convergence zone (ITCZ), the GHA seasons are unrelated to this phenomenon. Though definitions of the ITCZ vary, none apply to the GHA rainy seasons; the regional climate is divergent, on average, even during the wet periods (Yang et al. 2015; Nicholson 2018). Uniquely, the GHA is the only longitude at which an ITCZ cannot be defined in any season. Furthermore, paleoclimate records on changes in the position of the ITCZ tend to not match up with shifts in rainfall proxies (Tierney et al. 2015). Instead, authors have emphasized the joint role of atmospheric and oceanic circulations in the Indian Ocean Basin to explain the seasonal march of rainfall.
Region with double-peaked rainy season in the GHA. (d) Map showing the natural log ratio of the first to the second harmonic of 1981–2021 CHIRPS rainfall. The region of study is any land pixel in the green area within the black box (representing 3°S–12.5°N and east of 32°E), where the second harmonic is larger than the first (i.e., where the rainfall has a double-peaked seasonal cycle [illustrated in (b)]). This region is largely composed of the lowlands of southeastern Ethiopia, Somalia, and eastern Kenya but excludes a thin coastal strip in southern Somalia, where the rainfall climatology consists of a dominant peak in the boreal summer. Also shown are representative rainfall climatologies for surrounding single-peaked regions: (a) Ethiopia and (c) Tanzania.
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
During the jilaal long dry season, lasting generally from December to March, surface winds are northeasterly and relatively dry, passing over cooler northern Indian Ocean waters and running along isolines of constant humidity, thereby limiting onshore advection (Yang et al. 2015; Liebmann et al. 2017). These winds flow down the pressure gradient from the Arabian high, which is at its strongest at this time of year (Vizy and Cook 2020). In the upper troposphere, winds are strong and easterly, consistent with a Gill-like response to off-equatorial heating in the Southern Hemisphere (Liebmann et al. 2017).
About 2 weeks before the onset of the gu season, these upper-level easterlies abruptly weaken, presaging a sudden spike in CAPE and an abrupt onset of rainfall progressing from southwest (∼10–20 March, on average) to northeast (∼10–20 April) (Liebmann et al. 2017; Dunning et al. 2016). The Mascarene high strengthens, together with a breakdown of the South Indian convergence zone, reversing the geopotential height gradient in the western Indian Ocean. Consequently, low-level winds transition from northeasterly to southeasterly. During this season, SSTs off the coast of the GHA are at their warmest, and these low-level winds travel over warm SSTs, advecting moist, less stable air onto the continent (Yang et al. 2015; Vizy and Cook 2020). Occasional westerly advection of moisture from the Congo Basin can occur during the long rains; these events tend to produce the strongest rainfall in March–May for parts of Kenya in particular (Finney et al. 2019).
The xagaa short dry season begins as the large-scale circulation patterns over the Indian Ocean develop into the South Asian monsoon. Like the onset, the demise of the long rains progresses from the southwest (∼30 April–15 May) to the northeast (∼20 May–10 June). The strong, low-level, southerly Findlater or Somali jet develops along the coast, feeding into the monsoon circulation; the low-level circulation becomes more divergent (Yang et al. 2015; Vizy and Cook 2020). This jet forces strong upwelling along the coast, decreasing SSTs to their lowest annual levels and further stabilizing the lower troposphere (Slingo et al. 2005; Murtugudde et al. 2007). As with the start, the end of rainfall occurs rather abruptly (Liebmann et al. 2017), with the timing of the end of the long rains being correlated with the South Asian summer monsoon onset measured in Kerala, with a lead of 12 days, on average (Camberlin et al. 2010). Throughout this season, high-level easterlies return, consistent now with a Gill-like response to Northern Hemisphere heating (Liebmann et al. 2017).
While most of the region is dry in the boreal summer, a thin, coastal strip between roughly 42° and 45°E, including the major cities of Kismayo and Mogadishu, experiences a single annual peak in rainfall in June (see the thin strip with a single-peaked rainfall climatology by the Kenyan and southern Somali coast in Fig. 2). Older literature classifies this peak as an extension of the long rains [e.g., see Camberlin and Planchon (1997) for its occurrence farther south in Kenya]. United Nations bulletins instead refer to them as occurring during the xagaa short dry season, while some local sources identify them as part of a separate, local rainy season called the hhagaayo (e.g., Galaal 1992).
At the end of the South Asian monsoon, the Somali jet weakens, SSTs recover, and the low-level winds begin their transition from southerly to northerly. The deyr rains occur during this transition, as near-surface winds turn onshore and pass over relatively warm SSTs, bringing moist, less stable air onto the continent (Yang et al. 2015). The onset of the short rains progresses roughly from north (∼5 September) to south (∼25 October). During this season, the Indian Ocean Walker cell is most developed (Hastenrath 2000), and atmospheric ascent over the GHA is shallower than during the long rains (Zhao and Cook 2021; Schwarzwald et al. 2023). Finally, surface winds finish their transition to northeasterly, and the short rains end, bringing back the long dry season, with the demise progressing roughly north (∼25 October) to south (∼5–20 December).
Variability in both rainy seasons is substantial on interannual (e.g., Nicholson 2017; Vellinga and Milton 2018; Vizy and Cook 2020) and interdecadal (Tierney et al. 2013, 2015; Walker et al. 2020) scales, with interannual variability being greater in the short than the long rains. Long rain droughts seem to have increased in frequency over recent decades, with dramatic consequences for regional food security (Funk et al. 2008, 2018), despite global climate models (GCMs) projecting, on average, increases in GHA rainfall, a discrepancy sometimes referred to as the East African rainfall paradox in the literature (Lyon and DeWitt 2012; Lyon 2014; Rowell et al. 2015; Lyon and Vigaud 2017; Wainwright et al. 2019; Walker et al. 2020). Recent research has focused on possible anthropogenic sources for the observed drought increase, particularly on remote forcing through changes in Pacific Ocean SSTs and subsequent modulations of the Indian Ocean branch of the Walker circulation (Funk et al. 2018), which GCMs are currently largely unable to replicate when forced with historical greenhouse gas and aerosol inputs (Heede et al. 2021; Seager et al. 2022).
Disentangling the relative pathways of these influences on the climatology and variability of GHA rainfall and their interactions with each other remains a complex problem in both observations and GCMs. GCMs, in general, have difficulties simulating key aspects of the climatology of the GHA, including simulating long rains that are too weak and short rains that are too strong (Yang et al. 2014; Dunning et al. 2017; Schwarzwald et al. 2023). Understanding the seasonal cycle of rains in the GHA is a prerequisite to understanding why our state-of-the-art models are unable to accurately simulate regional processes, and understanding GHA biases in models is a prerequisite for improving GCM performance in the future so they can provide more reliable predictions of variability and projections of forced change. In this paper, we reexamine the seasonal cycle of GHA rains adopting a moist static energy (MSE) framework.
A rising parcel becomes saturated at its lifting condensation level. Since MSE is approximately conserved under adiabatic ascent and through phase changes between liquid water and water vapor, to first order, the difference between surface moist static energy (hs) and saturation moist static energy (
Previous studies have looked at MSE in the GHA primarily in monthly data. Several studies, using 700 hPa as a reference level for
In this study, we examine the seasonal cycle and variability of the difference between hs and
2. Data
Throughout this study, we use daily or higher-frequency data whenever possible to accurately characterize the timing of the rainy seasons (e.g., see Camberlin and Okoola 2003). Rainy seasons are often less than 2 months long and do not neatly match up with the Gregorian calendar; monthly data, therefore, often conflate both rainy and dry periods. Furthermore, subseasonal variability, apparent even in monthly data, suggests that higher temporal resolution data are needed to fully resolve the relevant dynamics (e.g., Camberlin and Philippon 2002). See Table 1 for a summary of data products used.
Data products used. If the resolution is not the same in both horizontal dimensions, resolution is shown as lat × lon. Unless otherwise stated, main text figures show rainfall data from CHIRPS, SST data from OISST, and circulation, humidity, and temperature data from MERRA2.
a. Rainfall data
We use daily precipitation data for 1981–2021 from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset (Funk et al. 2015), which combines satellite data from the Tropical Rainfall Measuring Mission (TRMM) satellite with interpolated rain gauge products and an elevation model. Evaluation of any dataset in the Horn of Africa is complicated by lack of a dense network of reference rain gauge observations (e.g., Dinku 2018). The observational network is particularly sparse in the double-peaked region of the GHA; Dinku et al. (2018), for example, for their evaluation, did not have access to station data in eastern Kenya and access to few in the Somali region of Ethiopia. Among studied data products, however, CHIRPS tends to outperform other commonly used datasets in the GHA, such as datasets based on satellite data alone (Ayehu et al. 2018; Dinku et al. 2018). CHIRPS overestimates the frequency of rainfall in parts of Ethiopia, but rainfall in those extra events tends to be minimal (Ayehu et al. 2018).
To validate CHIRPS results, we replicate our analysis using daily data from the Global Precipitation Climatology Project (GPCP; Adler et al. 2018) and from the Integrated Multi-satellitE Retrievals for GPM (IMERG) data product (Huffman et al. 2019). The former emphasizes data homogeneity in its construction, while the latter provides very high resolution data at the expense of greater changes in data sources over its available time period (Huffman 2022). IMERG uses a rotating satellite “ensemble of opportunity”; though it includes the TRMM satellite used by CHIRPS and, therefore, is not entirely independent, it assimilates data from 10 other satellites as well. All three datasets have a similar climatology over the GHA (Fig. S1 in the online supplemental material).
To place GHA rainfall in the context of rainfall across the tropics, in Fig. 1 we additionally show monthly rainfall data from the Global Precipitation Climatology Centre (GPCC; Becker et al. 2013) and the GPCP (Adler et al. 2018), spanning 1982–2021 to accommodate GPCC’s later start. Apart from Fig. 1, all other main text figures with rainfall show CHIRPS precipitation; validation results are shown in supplemental materials.
b. Atmospheric data
We use 1981–2021 daily reanalysis data from three reanalysis data products: the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2; Bosilovich et al. 2015), fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5; Hersbach et al. 2020), and the Japanese 55-year Reanalysis (JRA-55) of the Japan Meteorological Agency (JMA) (Kobayashi et al. 2015). From all reanalysis products, daily temperature; humidity; surface radiation fluxes; top-of-atmosphere, shortwave downwelling, and shortwave fluxes; low cloud cover; and zonal winds are used. From MERRA-2, we additionally use 3-hourly temperature and humidity.
The 650-hPa pressure level is used for values in the free troposphere; this tends to be the pressure level with maximum
In addition to the paucity of surface observations mentioned above, evaluation of atmospheric variables in reanalysis products is further hindered by the lack of in situ observations at higher pressure levels. Furthermore, though a reanalysis’ precipitation can be compared to observational products such as CHIRPS as a test of a product’s performance, in general, rainfall fields in reanalyses are forecast, not assimilated, and thus are subject to additional biases compared to variables better constrained by assimilated observations. For example, ERA5’s precipitation fields are known to be substantially more biased than that of CHIRPS over the GHA (e.g., Steinkopf and Engelbrecht 2022) and are particularly wet throughout the year compared not only to CHIRPS but also the other reanalyses studied (Fig. S3). Of the three products, JRA-55 has the closest correspondence between its forecasted precipitation climatology and that of CHIRPS; however, few evaluations of the product have been published in the region.
Given the limited ability to evaluate reanalysis products in the region, the choice of any particular one remains largely arbitrary. Therefore, we repeat all analyses over the GHA using all three reanalysis products listed. In general, all produce qualitatively similar results for the analysis in this paper. Unless otherwise noted, main text figures show data from MERRA-2, with differences between reanalysis products highlighted where relevant and shown in Figs. S1 and S3.
c. SST data
We use 1981–2021 daily SST data for the Indian Ocean from the daily Optimum Interpolation Sea Surface Temperature (OISST), version 2.1, record (Huang et al. 2021). OISST is a 0.25° gridded product blending in situ ship and buoy measurements with satellite-derived estimates from the Advanced Very High Resolution Radiometer (AVHRR); though it has been found to be biased slightly low compared to in situ measurements, this discrepancy is small [e.g., ∼0.08°C vs Argo floats in Huang et al. (2021)].
d. Region of interest
Following common practice in studies of the GHA, we define an area of interest based on the extent of the region with two distinct peaks in rainfall each year. For much of this study, we consider every location in daily CHIRPS rainfall data on land between 32°E and the Indian Ocean and between 3°S and 12.5°N where the second harmonic is of greater magnitude than the first, that is, for which the 1 yr−1 frequency has less power than the 2 yr−1 frequency. This corresponds to the area shown in green in the box in Fig. 2d, and is referred to as the “double-peaked region” throughout.
e. Time frame
All analysis is conducted from 1981 to 2021 to use the longest time frame that spans all observational products analyzed, with one exception: Fig. 1 begins in 1982 to accommodate the shorter time frame of the GPCC dataset. This time span notably excludes part of the recent “triple dip” La Niña; resultant limitations are noted where relevant.
3. Methods
a. Calculating MSE
Values of
Since variability in z tends to be minimal at a given pressure level, we generally ignore the influence of geopotential height anomalies when discussing
b. Defining seasonal extent
We additionally examine seasonally specific statistics of rainfall and MSE. Since the long and short rains rarely line up well with calendar months, commonly used 3-monthly averages (such as March–May for the long rains) aggregate across both wet and dry periods, which are governed by different dynamics (Riddle and Cook 2008; Schwarzwald et al. 2023).
To identify seasonal onsets and demises, we use the method of Dunning et al. (2016) based on inflection points in cumulative precipitation rate. This method was specifically designed for regions with a double-peaked rainfall climatology. For each CHIRPS grid cell in the double-peaked region, the onset and demise of the rainy seasons in each individual year from 1981 to 2021 are calculated. In sections 4 and 5, we compare across all gridcell days that are experiencing a given rainy season (long or short rains). In section 5, we additionally show tropical maps of seasonal composites of the horizontal circulation u and SSTs during the rainy and dry seasons; these use the average onset and demise across the whole double-peaked region in each year.
We compare seasonal averages for the long and short rains, applying the Dunning et al. (2016) method to March–May (MAM) and October–December (OND) averages, respectively, where relevant. Note that in section 7 and in figures analyzing interannual rainfall changes, we only use MAM and OND averages to characterize interannual differences, highlighting changes in overall seasonal strength instead of seasonal intensity.
c. Regional averages and regridding
Whenever possible, all datasets are analyzed at their native resolution. All maps show data on their original grid.
Regional averages are calculated across the CHIRPS double-peaked region, with averages weighted by the area of each grid cell. For CHIRPS rainfall data, all grid cells within this double-peaked region are used. When calculating regional averages for reanalysis products, which do not share a grid with CHIRPS, we regrid the CHIRPS ratio of the second harmonic to the first harmonic of the rainfall seasonal cycle (see section 3d) to the reanalysis grid. All grid cells for which this ratio is >1 after regridding are defined as part of the double-peaked region and are used in regional averages. In Fig. 3, reanalysis
Statistics of CHIRPS rainfall in the double-peaked region, broken up by the
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
4. Moist static instability is generally a condition for rainfall
a. Rainfall where and when is high
During both the long and the short rains, rain tends to fall where and when
It tends to rain more (Figs. 3d,e) and more frequently (Figs. 3f,g) when
The long and short rains in Fig. 3 are calculated at the gridcell level, i.e., all data points in the histograms are between each grid cell’s local onsets and demises in each year. Even within these seasons, however, the median gridcell day is stable, on average. Only 42% of gridcell days experiencing the long rains and 33% of gridcell days experiencing the short rains have average daily
Seasonal differences exist, however. In line with the long rains producing, on average, more rain, the distribution of
b. The climatologies of and rainfall
Just as rainfall is higher on days when and locations where
The seasonal cycle of the percentage of grid cells in the double-peaked area where
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
Figure 5a shows the 1981–2021 average climatology of rainfall and
The seasonal cycle of P,
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
Despite the nonlinearity of the relationship between
Double-peaked region average rainfall vs (a),(b) double-peaked region average
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
5. The seasonal cycle of large-scale stability is composed of different seasonal cycles of its components
We have shown in the preceding section that
a. Surface temperature
Near-surface air temperature has a double-peaked climatology (red dot–dashed line in Fig. 5b, showing cpTs, and red lines in Fig. 7), though it is phase shifted from that of rainfall and
The seasonal cycle of MERRA-2 near-surface air temperature Ts (red) and (a) surface upwelling longwave radiation, (b) top-of-atmosphere (TOA) downwelling shortwave radiation, (c) surface net downwelling shortwave radiation, (d) advection of Ts into the region, (e) low cloud fraction, (f) upwelling latent heat, (g) upwelling sensible heat, and (h) OISST SSTs off the GHA coast (all in blue). The shading shows the interannual 0.25–0.75 IQR of each seasonal cycle. All atmospheric variables are averaged over the GHA double-peaked region. In (h), the solid line and shading are the average over only the closest grid cell to the GHA coast for each latitude between 3°S and 10°N; the dashed line is the average SST over a box west of 55°E and between 3°S and 10°N.
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
Note that incoming solar radiation is seasonally asymmetric (Fig. 7b). The September insolation peak is weaker than the March peak, due to the eccentricity of Earth’s orbit, and the July minimum is higher than the December minimum because the study region is largely located in the Northern Hemisphere.
Despite this, the coldest season is the boreal summer during the xagaa short dry season. During this season, SSTs in the western Indian Ocean are at their annual minimum, largely due to the advection of relatively cold air over the ocean from the Southern Hemisphere winter (Ongoma and Chen 2017; Chatterjee et al. 2019) (Figs. 7d and 9e). Upwelling along the Somali coast due to the alongshore monsoon flow further cools SSTs in the northern part of the region, especially toward the start and peak of the South Asian monsoon season (Schott and McCreary 2001; Chatterjee et al. 2019) (Fig. 7h). While most of the onshore flow crosses south of the primary upwelling zone, SSTs are lower along the coast than off the coast throughout the year, especially during the peak upwelling season in the boreal summer (Fig. 7h). In addition to the cold cross-equatorial advection, low clouds driven by low SSTs block incoming solar radiation (Figs. 7c,e), further cooling and stabilizing the region. At the end of this season, temperatures rise again in anticipation of the equinox, leading into the short rains.
The second annual Ts minimum occurs during the boreal winter, corresponding to the jilaal long dry season. During this season, temperatures begin lower, after falling during the short rains; however, temperatures, on average, do not drop to the level of the boreal summer minimum, despite this being the Northern Hemisphere winter season. The annual minimum in cloud cover (Fig. 7e) increases surface insolation, especially approaching the March equinox when incoming solar radiation is at its annual maximum. Low clouds are suppressed by relatively higher SSTs, with coastal downwelling associated with the northeasterly flow partially balanced by the influx of cold, continental air (Figs. 7h and 9a) (Murtugudde et al. 2007).
Note the differences in the seasonal cycles of Ts and tropospheric temperature. The preseason peak in Ts before the long rains, therefore, sets up the instability, countering the stabilizing force of increased
As is expected for tropical climates, interannual variability in cpTs is substantially lower than interannual variability in Lυqs, and slightly lower than the same in
Interannual variability of MERRA-2 seasonal mean double-peaked region anomalies vs the annual mean of the temperature (red) and humidity (blue) components of hs (gray) and anomalies of
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
b. Surface humidity
The component of
During the rainy seasons, interannual variability in qs, as measured by the 0.25–0.75 interquartile range across years, is highest during the short rains (Fig. 8). Across all seasons, variability of qs is highest during the boreal winter, but, on average, strongly negative values of
The seasonal cycle of surface humidity is largely a function of the seasonal cycle of the surface circulation (Fig. 9). The moisture supply for the GHA is primarily the Indian Ocean; consequently, changes in the atmospheric circulation over the Indian Ocean Basin throughout the year modulate the moisture transport and moisture convergence or divergence over the GHA. During the boreal winter, strong northeasterly winds transport limited moisture over the study region (Figs. 9a,b). This air originates in dry continental Asia and only passes briefly over relatively cool SSTs before passing by the GHA, where the circulation is divergent and its onshore component is weak (Fig. 9a); (Yang et al. 2015; Vizy and Cook 2020). Similarly, during the boreal summer, the monsoon winds and the Somali jet transport moisture over the study region (Figs. 9e,f) but tend to diverge, with part of the circulation passing through the Turkana Gap into the Congo Basin (Yang et al. 2015; Nicholson 2016). Thus, in both dry seasons, the flow over the GHA is from the winter hemisphere with cooler SSTs and drier air, reducing hs and favoring stability and low precipitation.
Seasonal average MERRA-2 (left) surface circulation u and (right) surface moisture transport uq, with seasons defined using the average onset and demise dates across the double-peaked region. Shading shows
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
In contrast, during the rainy seasons in between the boreal winter and summer monsoons, moisture transport has a strong onshore component. During the long rains, this wind arrives after passing over relatively warm SSTs over the southern Indian Ocean at the end of its summer, while during the short rains, the flow is similarly onshore but from cooler SSTs following Southern Hemisphere winter (Figs. 9c,g and 7h). The seasonal cycles of flow directions and SSTs in the western Indian Ocean thus explain the seasonal cycle of surface humidity; these seasonal cycles are fundamentally caused by the swing between the northern summer and winter Asian monsoons.
The surface moisture climatology combines with the surface temperature climatology to produce seasonal maxima of hs during the long and short rains. During the long rains in particular, this average 5.3 kJ kg−1 anomaly over the annual mean is geographically coherent with positive hs anomalies over much of the tropics in the Atlantic and Indian Ocean basins (Fig. 10b). During the short rains, the pattern is more localized, while much of the study region experiences mild positive hs anomalies on the order of roughly 2 kJ kg−1 (in line with similar signals in the Sahel and southwestern Africa), the Indian Ocean, Congo Basin, and the tropical and South Atlantic all have negative hs anomalies (Fig. 10d).
Maps of surface h and u anomalies vs the annual mean, showing the 1981–2021 average over each season, with seasons defined using the average onset and demise dates across the double-peaked region. Green contour shows double-peaked region, for reference.
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
c. Tropospheric temperature and
If a near-surface parcel’s moist static energy hs is greater than
In contrast to the surface, local thermodynamic variables analyzed above,
Interannual variability in
Though convection can increase tropospheric temperature and, therefore,
This connection is, however, seasonally dependent and can be geographically limited; the GHA seasonal cycle for
Maps of 650-hPa
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
6. A working theory of the seasonal cycle of precipitation in the GHA
These results allow us to present a general theory about the two biggest peculiarities of the GHA climate: its aridity and its two distinct rainy seasons.
The double-peaked region’s aridity can be explained by the fact that
Even the rainy seasons are drier than would be expected for this latitude. During the long rains, convection is made more difficult by a seasonal maximum in
We now pursue this analysis for each season separately, beginning with the boreal winter dry period. At the height of the jilaal long dry season in January, surface moisture is at its annual minimum, while temperature at the surface and at 650 hPa is at its annual average. Dry northeasterly winds, which have a continental origin, carry little moisture into the GHA; consequently,
The long rains thus begin when the northeasterlies weaken and surface moisture sharply increases, increasing hs such that
During the xagaa short dry period in the boreal summer, during the peak of the south Asian monsoon, surface humidity remains substantially higher than during the boreal winter. However, surface temperature is anomalously low, with southeasterly inflow from the winter hemisphere where strong winds have cooled the SST, while 650-hPa temperature remains anomalously high. Therefore,
Shortly before the onset of the short rains, 650-hPa
7. Interannual changes in explain interannual changes in regional rainfall
A theory of the seasonal cycle of precipitation in the GHA can be tested to see if it explains differences in the seasonal cycle between different periods. Figure 12 shows year-on-year changes in rainfall and both
Interannual changes in double-peaked region average rainfall vs interannual changes in (a),(b) double-peaked region average
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
The linearity of this relationship is not necessarily mirrored at the level of individual locations. Interannual changes in
Illustration of interannual variations in rainfall and
Citation: Journal of Climate 36, 20; 10.1175/JCLI-D-23-0126.1
Nevertheless, regional average changes in
8. Discussion and conclusions
In seeking to explain the aridity and double-peaked seasonal cycle of rainfall in the GHA, we have shown close correspondences between large-scale stability and rainfall. In areas and at times when
However, though powerful, the
Indeed, several further questions about the relationship between
Nevertheless, the
Many studies of the tropical climate assume a form of the weak temperature gradient (WTG), which assumes that, due to the low strength of the Coriolis force in the tropics,
Further studies could also apply the
Finally,
Acknowledgments.
The authors are grateful for insightful conversations with Adam Sobel, David Rowell, and Sarah Smith and for constructive comments from three anonymous reviewers. The authors thank the International Research Institute for Climate and Society (IRI) for institutional and computational support. This work is partially undertaken as part of the Columbia World Project, ACToday, Columbia University, New York, NY. K.S. was also supported by the Graduate School of Arts and Sciences and the Department of Earth and Environmental Sciences at Columbia University. R.S. was supported by NSF Award OCE-22-19829.
Data availability statement.
Data needed to replicate main text figures and analysis are available at https://doi.org/10.5281/zenodo.8092600. All code needed to replicate this study is available at https://doi.org/10.5281/zenodo.8092685; the latest version is stored at https://github.com/ks905383/gha_stability. All other data are available by request.
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