Earth and Planetary Science Letters 522 (2019) 20–29

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Earth and Planetary Science Letters

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Both differential and equatorial heating contributed to African variations during the mid-Holocene ∗ Ori Adam a, , Tapio Schneider b, Yehouda Enzel a, Jay Quade c a The Institute of Earth Sciences, The Hebrew University of Jerusalem, Givat Ram, Jerusalem 91904, Israel b California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA c Department of Geosciences, University of Arizona, 1040 E. 4th Street, Tucson, AZ 85721, USA a r t i c l e i n f o a b s t r a c t

Article history: The Sahara was significantly greener 11-5 kya and during multiple earlier interglacial periods. But the Received 28 January 2019 mechanisms related to the greening of the Sahara remain uncertain as most climate models severely Received in revised form 15 May 2019 underestimate past wet conditions over north Africa. The variations in the African monsoon related to Accepted 17 June 2019 the greening of the Sahara are thought to be associated with the variations in the inter-hemispheric Available online xxxx differential heating of Earth, caused by orbital variations. However, how orbital variations affect regional Editor: L. Robinson climate is not well understood. Using recent theory that relates the position of the tropical belt to Keywords: the atmospheric energy budget, we study the effect of orbital forcing during the mid-Holocene on the PMIP3 African monsoon in simulations provided by the third phase of the Paleo Model Intercomparison Project African Humid Period (PMIP3). We find that energy fluxes in the African sector are related to orbital forcing in a complex Green Sahara manner. Contrary to generally accepted theory, orbital modulation of seasonal differential heating alone atmospheric energy budget is shown to be a weak driver of African monsoon variations. Instead, net atmospheric heating near energy flux the equator, which modulates the intensity and extent of seasonal migrations of the tropical rain belt, mid-Holocene is an important but overlooked driver of African monsoon variations. A conceptual framework that relates African monsoon variations to both equatorial and inter-hemispheric differential solar heating is presented. © 2019 Elsevier B.V. All rights reserved.

1. Introduction orbital variations to different climatic states of the African mon- soon. During the wet phase of the Sahara in the early and mid- Extensive geophysical data indicate that the Sahara was sig- Holocene, known as the African Humid Period (AHP), parts of nificantly wetter and greener during the early and mid-Holocene the Sahara were vegetated, contained permanent lakes, and sus- (11-5 kya) and during multiple earlier interglacial periods (deMe- tained human populations in regions that are uninhabitable in nocal et al., 2000). Yet most modern climate models underestimate the present climate (deMenocal et al., 2000;Kuper and Kröpelin, the past greening of the Sahara, suggesting critical physical pro- 2006). In part, the AHP is coeval with wet conditions in east- cesses are misrepresented in these models (Harrison et al., 2014; ern Africa (the East African Humid Period; Gasse, 2000;Tierney Claussen et al., 2017). The changes in the Saharan are et al., 2011), which, like the AHP, are not well understood and not generally understood to be paced by variations in Earth’s orbital well captured by climate models (Liu et al., 2014, 2004; Tierney parameters, which regulate the differential heating of the hemi- et al., 2011). The drying of the Sahara began around 6 kya. It is spheres that drives the seasonal migrations of the tropical rain generally understood to have occurred alongside cooling in the belt. However, the relation of the zonally uniform changes in in- northern hemisphere (though the extent and timing of the cooling solation caused by orbital variations to regional precipitation vari- trend remain unclear, Renssen et al., 2012;Marcott et al., 2013; ations is not well understood (e.g., Liu et al., 2017). Here, using Liu et al., 2014;Marsicek et al., 2018), as evidenced by various energetic constraints on the position of the tropical rain belt, we proxies of Sahara and Sahel precipitation (Marcott et al., 2013; provide a conceptual framework for understanding the relation of Shanahan et al., 2015) and by archaeological findings (Manning and Timpson, 2014). The extent and distribution of the greening of the Sahara during the AHP remains a matter of some debate (see * Corresponding author. Quade et al., 2018 for a review). Nevertheless, most modern cli- E-mail address: [email protected] (O. Adam). mate models underestimate even the low end of plausible greening https://doi.org/10.1016/j.epsl.2019.06.019 0012-821X/© 2019 Elsevier B.V. All rights reserved. O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29 21

Fig. 1. Change in annual-mean precipitation over northern Africa between the mid-Holocene (6 kya) and preindustrial conditions in PMIP3 models. (a) Ensemble-mean change in annual-mean precipitation and the positions of the African ITCZ (solid) and EFE (dashed) in African boreal summer (July–September) during the mid-Holocene (red) and ◦ ◦ preindustrial conditions (blue). (b) Zonal average of the change in annual-mean precipitation over land (20 W–40 E) for the ensemble mean (thick black) and individual models (colors). Upper and lower estimates of the minimal change in precipitation required to sustain a Saharan steppe during the mid-Holocene are shown shaded in green (Jolly et al., 1998). estimates (Joussaume et al., 1999); they are an order of magnitude 2. Data and methods short of estimates at the upper end (Armitage et al., 2007;Drake et al., 2011). While the mechanisms behind the discrepancies be- 2.1. Data tween models and proxy data remain unclear, improved agreement between simulations and data in some models with vegetation Our analysis is based on monthly data from 12 PMIP3 models and dust feedbacks suggest surface feedbacks are important for re- (Fig. 1) and uses simulations of mid-Holocene and preindustrial producing the greening of the Sahara (Claussen and Gayler, 1997; conditions. In models for which an ensemble of runs exists, only Levis et al., 2014; Patricola and Cook, 2007; Swann et al., 2014; the first realization of each experiment is analyzed. Seasonal and Pausata et al., 2016; Gaetani et al., 2017). annual-mean climatologies are calculated from the first available ◦ ◦ Due to the tendency of the tropical rain belt to migrate toward 100 years, interpolated to a 1 × 1 horizontal grid. The boundaries ◦ ◦ and intensify in a differentially warming hemisphere (Chiang and of the African sector are defined as 20 W–40 E. The sector-mean Friedman, 2012; Schneider et al., 2014), the greening of the Sahara results were found to be qualitatively insensitive to the exact defi- is generally attributed to Earth’s orbital precession, which modu- nition of the western and eastern boundaries of the African sector, ∼ ◦ lates seasonal differential heating. However, recent theory, model- within a range of 10 of each boundary. ing studies, and paleorecords indicate that precession alone cannot The land cover and dust concentrations in PMIP3 simulations explain the greening of the Sahara (Claussen et al., 2017). First, re- are prescribed to be the same as during preindustrial conditions. gional energy fluxes are not clearly related to the zonally-uniform This limitation of PMIP3 models may account for the general dry heating associated with orbital variations (Adam et al., 2016b; bias in these models over Africa during the mid-Holocene (Pausata Roberts et al., 2017). Second, modern climate models forced by et al., 2016). Specifically, several studies found that vegetation early- to mid-Holocene insolation (with up to 7% precession-driven feedbacks and the recycling further north of moisture by increasing increase in boreal summer insolation) tend to underestimate Saha- the area covered by water bodies amplify the precipitation re- ran precipitation relative to proxy reconstructions (Joussaume et sponse to orbital forcing (Claussen and Gayler, 1997; Patricola and al., 1999; Claussen et al., 2017), even when ocean and land feed- Cook, 2007; Krinner et al., 2012;Swann et al., 2014), which may then be further amplified by dust feedbacks (Pausata et al., 2016; backs are included (e.g., TEMPO, 1996;Kutzbach and Liu, 1997). Gaetani et al., 2017). However, vegetation feedbacks were included Third, wet Saharan episodes are suggested to have existed during in the earlier generation of PMIP simulation (PMIP2) but were not intervals of relatively low boreal summer insolation (e.g., Lézine found to significantly amplify the precipitation response (Harrison and Casanova, 1991;Castañeda et al., 2009). et al., 2014). More generally, these feedbacks were only partially Here we examine how the regional precipitation and at- successful in producing the needed latitudinal shift in the rainbelt mospheric energy balance are affected by mid-Holocene orbital and in producing the needed change in precipitation intensity. changes in models participating in the third phase of the Paleo In this study, rather than assessing the performance of the Model Intercomparison Project (PMIP3, Braconnot et al., 2011), models, we examine the variation across models in an attempt which is the most extensive modeling study of mid-Holocene to provide constraints on the relation between of orbital forcing conditions to date.1 Our analysis sheds light on the discrepancy and African monsoon variations (cf. Allen and Ingram, 2002). This between models and reconstructions of the greening of the Sa- approach has its limitations, since PMIP models both underesti- hara and provides a regional perspective on how orbital variations mate the range of climate variations (Harrison et al., 2014) and, may drive African monsoon climate variations. In particular, our re- as mentioned above, misrepresent key physical processes. Never- sults suggest that, in addition to the commonly invoked differential theless, we find that the variation across models is sufficient for heating of the hemispheres, equatorial heating of the atmosphere analyzing the general relation between the African monsoon and is an important factor controlling African monsoon variations. the atmospheric energy budget.

2.2. Insolation 1 It is important to note, however, that PMIP3 models suffer from severe biases related to misrepresentation of land surface feedbacks, which are critical for cap- The orbital forcing in the PMIP3 simulations is set to mid- turing monsoonal climate variations. Therefore, while PMIP3 models represent the most extensively coordinated simulation of mid-Holocene conditions to date, they Holocene conditions (6 kya). Note, however, that boreal summer are not free from known biases. (defined here as the months July–September) insolation and the 22 O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29 greening of the Sahara peak much earlier, around 9 kya (Marcott et al., 2013). During the mid-Holocene, peak boreal summer inso- lation was about 5% greater than today, primarily due to Earth’s precession – at perihelion, the northern hemisphere faced the sun, rather than the as today. In addition, obliq- ◦ uity was 0.6 higher, and eccentricity was 12% larger. The insola- tion variations shown below are calculated following the methods described in Huybers and Eisenman (2006).

2.3. Position indices

In the zonal mean, the intertropical convergence zone (ITCZ), Fig. 2. A depiction of atmospheric energy transport (AET) and its relation to the identified as the of peak tropical precipitation, approxi- energy flux equator (EFE), cross-equatorial AET and equatorial heating (I0, given by mately coincides with the latitude where the zonal-mean northern the slope of the meridional component of AET at the equator). and southern Hadley cells meet. However, regional manifestations of the ITCZ, for example, in the African sector, are not clearly re- lated to the Hadley circulation (see Nicholson, 2018 for a review). Nevertheless, for simplicity, we refer to the latitude of peak African precipitation as a ‘regional ITCZ’. Following Adam et al. (2016a), the latitude of peak zonal- or sector-mean precipitation is estimated as  ◦ 30 N 30 30◦S φ(cos(φ)P ) dφ φmax =  ◦ (1) 30 N 30 30◦S (cos(φ)P ) dφ ◦ where φ denotes latitude and P denotes precipitation. The 30 S– ◦ 30 Nintegration boundaries and power of 30 were found to cap- ture the position of the precipitation maximum well, while re- ducing grid dependence (see Adam et al., 2018b for a sensitivity analysis of Eq. (1)).

3. The energy flux equator

The ITCZ marks the location of maximal surface mass con- vergence, and therefore, by mass conservation, the location of the rising branch of the mean meridional overturning circulation (Schneider et al., 2014). Since energy transport in the deep trop- Fig. 3. The sensitivity of the position of the EFE (φEFE) to increasing differential ics is dominated by the mean meridional overturning circulation, heating, resulting in an anomalous cross-equatorial energy fluxes (AET0), and to increasing equatorial heating (I , the meridional AET divergence at the equator; the ITCZ also approximately marks the latitude of the atmospheric 0 the red and blue arrows indicate longwave and shortwave radiation), according to energy flux equator (EFE), where total atmospheric energy trans- Eq. (2). The EFE shifts toward the differentially heated hemisphere. Since differen- port (AET) vanishes and diverges (i.e., flows away from) (Kang et tial heating of the northern hemisphere is balanced by southward energy fluxes al., 2009; Schneider et al., 2014;Adam et al., 2016a). across the equator, AET0 is negative in the depicted examples. Increased equatorial heating dampens the migration of the EFE toward the differentially heated hemi- To first order, the position of the EFE (φ ) is proportional to EFE sphere. Therefore, in the depicted examples, the EFE is nearest to the equator in cross-equatorial AET (AET0, Fig. 2a) and inversely proportional to panel c where AET0 is weakest and I0 is largest, and is farthest from the equator a measure of equatorial heating I0 (Bischoff and Schneider, 2014; in panel b where AET0 is strongest and I0 is weakest. In panels a and d the EFE is Schneider et al., 2014;Adam et al., 2016b), equally displaced because the fractional changes in AET0 are balanced by the frac- tional changes in I0. 1 AET0 φEFE =− . (2) a I0 The relation of the meridionally varying AET to the EFE, AET0 and I0 are depicted in Fig. 2. The sensitivity of the position of the Here a denotes Earth’s radius, the subscript ( )0 indicates equa- EFE (and hence the ITCZ) to variations in AET0 and I0 is depicted torial values, and φEFE, AET0, and I0 can vary in longitude. Iis in Fig. 3. By assuming that changes in the spatial distribution defined as the meridional AET divergence (see the Appendix for of tropical precipitation about its peak are small relative to the details). It is given by the combination of vertical atmospheric net changes associated with meridional migrations of the rain band, energy input (NEI), energy storage in the atmosphere (ES), and the EFE framework simplifies the analysis of variations in the trop- zonal energy input (ZEI), ical rain belt to two key parameters: (1) cross-equatorial energy vertical energy input energy storage zonal energy input transport (AET ), which is associated with inter-hemispheric dif-    0 ferential heating, and (2) equatorial heating (I ). I ≡ NEI − ES − ZEI . (3) 0 The net effect of orbital variations on the atmospheric energy Here, NEI is given by the sum of net radiative input at the top of budget is given by the changes in the incoming solar radiation at the atmosphere, and net radiative, sensible, and latent heat fluxes the top of the atmosphere and by the response of the climate sys- at the surface; ES is given by the rate of change over time of tem to these changes. The response is composed of changes in the column-integrated atmospheric sensible and latent heat, and ZEI is net radiative balance at the top of the atmosphere, the surface en- given by the zonal gradient of the column-integrated moist static ergy budget, energy storage in the atmosphere, and zonal energy energy flux. See the Appendix and Adam et al. (2016a, 2016b)for fluxes. It is therefore not clear a priori how, or if at all, orbital vari- the derivation of Eqs. (2) and (3) and for more details on the en- ations affect changes in the zonal-mean or regional differential and ergetic quantities. equatorial heating, which drive the shifts of the tropical rain belt. O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29 23

Fig. 4. Zonal-mean changes in the PMIP3 ensemble-mean atmospheric energy bud- get during boreal summer (July–September) for mid-Holocene and preindustrial conditions. (a) Changes in zonal-mean top-of-atmosphere (TOA) incoming solar ra- diation (black) and net atmospheric energy input (NEI = Iin the zonal mean; green, shading indicates ±1 standard deviation of inter-model spread). (b) Zonal-mean AET during mid-Holocene (red) and preindustrial conditions (blue). The intercept of AET at zero latitude is AET0, and its zero is the EFE.

Fig. 4a shows the zonal-mean changes in insolation and in net atmospheric energy input (which, in the zonal mean equals the Fig. 5. Historic changes in boreal summer insolation. (a) Change from present-day meridional AET divergence I) between mid-Holocene and prein- conditions in zonal-mean insolation since the last glacial maximum (LGM). (b, c) Fractional changes (from present day) in differential and equatorial heating since dustrial conditions in PMIP3 models. The importance of balanc- the LGM and during the Quaternary (past 2.5 million years). The insolation vari- ing climatic feedbacks is evident by the lack of a clear rela- ations are calculated following the methods described in Huybers and Eisenman tion between the orbital forcing (i.e., the change in insolation) (2006). and the net response. Nevertheless, as expected, net atmospheric energy input in the northern hemisphere increases on average. 4. Regional energetic constraints In addition, equatorial heating is slightly elevated on average. As shown in Fig. 4b, the increased differential heating of the As expected, a clear northward shift of the annual-mean pre- northern hemisphere is balanced by the increase in southward cipitation over Africa is captured in the ensemble mean of PMIP3 cross-equatorial energy flux. This is seen as a negative shift in models (Fig. 1a; see Harrison et al., 2015;Chevalier et al., 2017 AET in the , which decreases AET0 and shifts the zonal- for a detailed analysis). However, all models severely underes- mean EFE (and with it the ITCZ) northward. However, both AET0 timate reconstructions of the minimal increase in precipitation and the slope of AET near the equator (i.e., I0) vary between required to sustain a Saharan steppe: simulated changes do not − the mid-Holocene and preindustrial conditions. Therefore, from exceed 100 mm yr 1 in the mid Sahara, in disagreement with evi- Eq. (2), changes in both AET0 and I0 (i.e., in both differential dence of widespread grasslands that require an increase of at least − and equatorial heating) may have played a role in driving shifts 200–300 mm yr 1 (Fig. 1b; Jolly et al., 1998; Joussaume et al., in the locations of the African EFE and ITCZ during the mid- 1999) or lacustrine environments (Lézine et al., 2011). Similarly, Holocene. the simulated mean position of the African ITCZ during boreal As shown in Fig. 1a, the ensemble-mean shift of the boreal summer is shifted by about 1 degree, compared with a minimal summer ITCZ over Africa, induced by mid-Holocene orbital forc- shift of about 5 degrees required to maintain a Saharan steppe ing, goes along with a shift in the regional EFE,2 although the (Fig. 1b; i.e., assuming the changes in precipitation are caused pri- EFE and ITCZ do not exactly coincide. This covariance of the ITCZ marily by changes in the position of the ITCZ, not its intensity or and EFE over Africa is also found in observations (Adam et al., the duration of the ). Consistent with reconstructions, 2016b) and simulations (Shekhar and Boos, 2016). Therefore, vari- most models simulate a wetter eastern Africa (see Fig. S1 for spe- ations in the position of the African ITCZ can be quantitatively cific models); however, the wetter conditions are not as extensive linked to variations in the atmospheric energy budget induced in equatorial Africa as empirical estimates suggest (Gasse, 2000; by, for example, orbital forcing, ocean variability, or regional feed- Tierney et al., 2011). backs (Schneider et al., 2014;Adam et al., 2016b). In what follows, From Eq. (2), fractional changes in the position of the EFE (and we examine how large regional shifts of the EFE and ITCZ may hence the ITCZ) are given by (Schneider et al., 2014;Bischoff and arise. Schneider, 2014;Bischoff et al., 2017)

shifts caused by shifts caused by differential  heating equatorial heating 2 Here the latitude of the EFE is calculated as the latitude of zero crossing of AET. The exact latitude of zero crossing is calculated using a linear interpolation of the φEFE AET0 I0 = − (4) two grid points on either side of the zero crossing (Adam et al., 2018b). ¯ ¯ φEFE AET0 I0 24 O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29

Fig. 6. Change in atmospheric energy input during boreal summer (July–September) between mid-Holocene and preindustrial conditions in the African sector. (a) Total atmospheric energy input (I, Eq. (3)). (b) Vertical atmospheric net energy input (NEI), composed of top-of-atmosphere (TOA) net radiative input and surface energy fluxes into the atmosphere. (c) Atmospheric energy storage (ES, the rate of decrease in column-integrated atmospheric sensible and latent heat). (d) Zonal column-integrated ◦ ◦ energy input (ZEI, the zonal gradient of the atmospheric energy flux). Side panels show zonal means in the African sector (20 W–40 E), with shaded confidence bounds of 1 standard deviation of intermodel spread. where  denotes changes between mid-Holocene and preindus- energy input are dominated by the vertical component (NEI0) near trial conditions and the overbar denotes preindustrial conditions. the equator, i.e., The first term on the rhs of Eq. (4)represents the fractional shift due to changes in cross-equatorial AET induced by inter- I0 ≈ NEI0. (5) hemispheric differential heating. The second term depends on the regional equatorial energy balance (i.e., equatorial heating), which Thus, by neglecting the regional dynamics associated with changes is generally independent of inter-hemispheric differential heating in zonal energy transport and energy storage, Eq. (4) simplifies (Adam et al., 2016b, 2018a; Bischoff et al., 2017) (see section 1 of considerably. Specifically, fractional variations of NEI0 can be cal- the SI for a derivation of Eq. (4)). culated or estimated from variations in the top-of-atmosphere and surface equatorial energy budget, associated with, for example, or- Neglecting the equatorial heating term and assuming regional bital forcing, surface radiative and heat fluxes, and cloud feedbacks. variations in AET0 follow the zonal mean, Eq. (4)suggests that More generally, variations in NEI dominate the variations in equa- the ∼5% increase in mid-Holocene boreal summer insolation (and 0 torial heating throughout the tropics in PMIP3 models (Fig. S2) and similar decrease in the southern hemisphere, Fig. 5a, b) leads to ◦ under present-day conditions (Adam et al., 2018a). This suggests a ∼10% (∼1 ) shift of the ITCZ position — in accordance with that the approximation (5)can be applied more generally to sea- the simulated shifts. More generally, as shown in Fig. 5c, dur- sonal and regional variations of the EFE and ITCZ also outside the ing the Quaternary, changes in differential heating during boreal African sector. Next we examine the relation of differential and summer rarely exceed more than 10% relative to preindustrial con- equatorial heating to zonal- and sector-mean shifts of the EFE and ditions. This implies that, in contrast to what is widely assumed, ITCZ in PMIP3 models. in the absence of amplifying feedbacks, orbital differential heat- ing is a weak driver of monsoon climate (e.g., Gaetani et al., 2017; 5. Zonal- and sector-mean variations in PMIP3 models Liu et al., 2017). The lack of regional feedbacks in the African sec- tor likely accounts for the underestimated ITCZ shifts in PMIP3 A comparison of the zonal-mean and African sector-mean frac- models, in which identical surface conditions are used in sim- tional changes in AET and NEI is shown in Fig. 7. Even though ulations of both the mid-Holocene and preindustrial conditions. 0 0 regional cross-equatorial energy fluxes are not clearly related to However, the typical fractional changes in solar equatorial heat- the zonally-uniform orbital forcing, the zonal-mean and sector- ing during the Quaternary are of relatively larger magnitude than mean fractional changes in AET0 are strongly correlated (Fig. 7a, those in differential heating (Fig. 5c) and may therefore also be R = 0.81). As expected from orbital forcing, the mean fractional important for understanding poleward shifts of the ITCZ. We there- change in the zonal-mean AET0 is 10%, compared with 15% in fore now turn to examine the potential effects of variations in the African sector. However, these changes vary significantly across equatorial heating. models, with maximal and minimal changes of 33% and 2%. Unlike As shown in Fig. 6, the contributions of regional energy stor- AET0, only a weak agreement exists between sector- and zonal- age and zonal energy fluxes to the difference in meridional AET mean fractional changes in NEI0 (R = 0.49). The variation across divergence (I) between the mid-Holocene and preindustrial condi- models in sector-mean NEI0 is also 5 times larger than in the zonal tions are negligible. Therefore, fractional changes in atmospheric mean. Both zonal- and sector-mean fractional changes in NEI0 are O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29 25

◦ ◦ Fig. 7. A comparison of zonal-mean vs. African sector-mean (20 W–40 E) fractional Fig. 8. The relation of the fractional changes between the mid-Holocene and prein- changes between the mid-Holocene and preindustrial control conditions during bo- ◦ ◦ real summer (July–September) in (a) cross-equatorial atmospheric energy transport dustrial conditions over Africa (20 W–40 E) in the positions of boreal summer (July–September) EFE (purple) and ITCZ (green), and (a) African sector-mean cross- (AET0) and (b) equatorial atmospheric net vertical energy input (NEI0). The identity line is shown in gray. Models are numbered as in Fig. 1. equatorial atmospheric energy transport AET0, and (b) equatorial atmospheric net vertical energy input (NEI0). Models are numbered as in Fig. 1. Reference slope lines of +1 and −1 are shown in gray. generally positive. Therefore, from Eq. (4), NEI0 changes in PMIP3 models damp the poleward shift of the EFE and ITCZ. The relations of fractional changes in the positions of the African boreal summer EFE and ITCZ and fractional changes in α0 = 0.4 ± 0.1 and α1 =−0.5 ± 0.2(R = 0.62), where the un- African sector-mean AET and NEI are shown in Fig. 8. Consistent 0 0 certainty indicates one standard deviation of these coefficients us- with Eq. (4), fractional changes in the positions of both the EFE ing a leave-one-out cross validation approach. This suggests that and ITCZ are positively correlated with fractional changes in AET0 = ∗ PMIP3 models are generally equally sensitive to fractional changes (R 0.77 and 0.31 , respectively; the asterisk indicates p-values 4 3 in both NEI0 and AET0. From Fig. 1, since the mean position of larger than 0.05.), and negatively but weakly correlated with frac- ◦ ∗ ∗ ∼ tional changes in NEI (R =−0.34 and −0.48 , respectively). The the African ITCZ is 10 , for the ITCZ to shift by approximately 0 ◦ ∼ variance explained by combining the fractional changes of both 5 during the AHP a fractional change of 50% is required. Given AET and NEI is 78% for the regional EFE and 36% for the ITCZ. that the expected fractional change in AET0 associated with or- 0 0 ∼ Furthermore, the African EFE and ITCZ positions are well corre- bital forcing is 10%, this shift is likely associated with a decrease lated with each other (R = 0.77). We therefore conclude that both in NEI0, as opposed to the increased NEI0 seen in PMIP3 simula- =− AET0 and NEI0 are important predictors of the positions of the EFE tions. Quantitatively, in PMIP3 simulations (i.e., for α1 0.5) this −2 and ITCZ in the African sector. corresponds to a 70% (or ∼15 W m , cf. Fig. S7) mean decrease The above results suggest that an empirical model that relates in NEI0. This prediction is consistent with the paleo precipitation climatic variations of the mean position of the ITCZ on seasonal or model of Bischoff et al. (2017), which, as in Eq. (6), relates the po- longer timescales to the atmospheric energy budget can be written sition of the ITCZ to orbital variations, but also includes the effect as (Bischoff et al., 2017) of tropical heating on moisture availability (Merlis et al., 2013). Us- ing this model, a 50% reduction in NEI0 produced an annual-mean δφITCZ = α0δAET0 + α1δNEI0 + . (6) difference between the mid-Holocene and preindustrial conditions Here δ denotes fractional changes and  denotes residual contribu- similar to that found by Jolly et al. (1998)(Fig.1b). Addition- tions that are uncorrelated with differential and equatorial heating. ally, unlike AET0, relatively large fractional changes in NEI0 do not require large absolute variations. Since NEI is the net of large The empirical constants α0 and α1 account for the regional charac- 0 teristics of the relation of the EFE and ITCZ, which may vary across balancing terms, large fractional changes of NEI0 (i.e., above 50%) models, climates, regions, and seasons (Donohoe et al., 2013; require only relatively small changes in, e.g., cloud effects or sur- Donohoe and Battisti, 2013; Schneider et al., 2014; Adam et al., face latent heat fluxes (cf. section 2 of the SI). 2016a, 2016b). Using the inter-model spread in fractional changes between the mid-Holocene and preindustrial conditions over Africa, we find 4 The values of α0 and α1 are sensitive to the meridional extent of the equatorial averaging of AET0 and NEI0. On average, α0 ≈ 0.5and α1 ≈−0.5for equatorial ◦ ◦ ◦ ◦ averaging of AET0 and NEI0 ranging between 5 S–5 Nto 20 S–20 N. In addition, 3 Omitting the MPI-ESM-P model (model 3) strengthens the correlation of the the value of α1 is particularly sensitive to the MPI-ESM-P model (data point 3 in ITCZ and EFE with AET0 and weakens the relation with NEI0. Figs. 7 and 8), and reduces to −0.1in its absence. 26 O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29

Table 1 Summary of the correlation coefficients related to Figs. 7 and 8 and to the regression model Eq. (6). The asterisk indicates p-values above 0.05. Description R

Correlation between zonal- and African sector-mean fractional changes in AET0 0.81 Correlation between zonal- and African sector-mean fractional changes in NEI0 0.49 Correlation between the EFE and ITCZ over Africa 0.77 Correlation between the EFE and fractional changes in AET0 over Africa 0.77 ∗ Correlation between the ITCZ and fractional changes in AET0 over Africa 0.31 ∗ Correlation between the EFE and fractional changes in NEI0 over Africa −0.34 ∗ Correlation between the ITCZ and fractional changes in NEI0 over Africa −0.48 Correlation between the EFE and the linear regression model (Eq. (6)) 0.88 Correlation between the ITCZ and the linear regression model (Eq. (6)) 0.62

6. Discussion and conclusions

African monsoon variations are commonly conceptualized as meridional shifts of the continental ITCZ in the African sector. Based on the tendency of the ITCZ to migrate toward the dif- ferentially warming hemisphere, these shifts and similar shifts in the tropical rain belt outside the African sector are tradition- ally associated with changes in the inter-hemispheric differential heating of Earth. We have shown that changes in the equatorial heating of the atmosphere, which modulates both precipitation intensity by affecting moisture availability (Merlis et al., 2013; Bischoff et al., 2017) and the extent of ITCZ migrations (Eq. (4)), contributes to ITCZ variations comparable to those induced by differential heating alone. Incorporating this important and often overlooked driver of African monsoon variations therefore seems important for understanding wet Saharan episodes. The relation of the regional ITCZ position and the atmospheric energy budget can be understood in the framework of the co- variance of the ITCZ with the regional energy flux equator (EFE, Fig. 1). To first order, variations of the regional ITCZ on timescales Fig. 9. Energy fluxes controlling ITCZ shifts in the African sector. Variations in cross- of a season or longer are proportional to cross-equatorial atmo- equatorial atmospheric energy transport (AET), associated with inter-hemispheric differential heating, force the ITCZ to migrate towards the differentially heated spheric energy transport (AET0) associated with inter-hemispheric differential heating, and inversely proportional to equatorial heat- hemisphere. Atmospheric equatorial heating modulates precipitation intensity and the extent of the ITCZ migrations. The net equatorial heating is determined primar- ing (Eq. (2); for a review see Schneider et al., 2014). However, as ily by the radiative balance at the top of the atmosphere and ocean heat uptake, shown in Figs. 4 and 7, due to climatic feedbacks, the effect of or- composed of longwave and shortwave radiative fluxes (red and blue arrows, respec- bital forcing on zonal-mean or regional energy fluxes cannot be tively) as well as sensible and latent heat fluxes near the surface. accurately inferred from the associated variations in incoming so- lar radiation. tended regression model (Fig. 8), implying that processes other In PMIP3 models, differential heating and the associated cross- than meridional shifts of the ITCZ cannot be neglected. Indeed, ex- equatorial AET only partly account for the meridional shifts of the tensive literature exists on regional mechanisms associated with ITCZ. Equatorial heating, which modulates the extent of seasonal the AHP (for a review see Claussen et al., 2017). Prime examples ITCZ migrations (Eq. (4); Bischoff and Schneider, 2014; Schneider include the ‘monsoon-desert mechanism’, which links the invigora- et al., 2014;Adam et al., 2016a;Bischoff et al., 2017), is found to tion of the Indian monsoon with enhanced descent and hence sup- be an additional important driver of ITCZ shifts. Given the sensitiv- pressed precipitation over northern Africa (Rodwell and Hoskins, ity of the regional position of the ITCZ to regional feedbacks, which 1996); the ‘ventilation mechanism’, in which increases in precipita- may also vary in their representation across climate models, an tion are limited by the decreased relative humidity of the warming empirical regression model which includes the effects of both dif- African continent relative to the Atlantic (Su and Neelin, 2005); the ferential and equatorial heating is presented in Eq. (6). The contri- dynamics of moisture fluxes from the Atlantic and the Mediter- bution of equatorial heating variations (δNEI0) to variations in the ranean (e.g., Rowell, 2003; Zhao et al., 2005); dynamical effects of position of the ITCZ has been ignored in most previous empirical the African heat low (e.g., Lavaysse et al., 2009;Nicholson, 2013; studies of the ITCZ position, where the relation of the ITCZ position Shekhar and Boos, 2017); and vegetation and dust feedbacks (e.g., to the atmospheric energy budget is assumed to be solely depen- TEMPO, 1996; Gaetani et al., 2017). dent on AET0 (i.e., assuming α1 = 0in Eq. (6); e.g., Frierson et Nevertheless, the extended theory (Eq. (6)) captures over 30% al., 2013; Hwang and Frierson, 2013; Donohoe and Battisti, 2013; of the variance of the ITCZ in PMIP3 models, compared with about Donohoe et al., 2014). By including processes which are not related 20% when only differential heating is considered. Moreover, since to differential heating, Eq. (6) provides a simple yet critical gener- equatorial heating modulates the extent of seasonal ITCZ migra- alization of empirical models of ITCZ migrations. A summary of the tions (Eq. (2), Fig. 3), variations in equatorial heating provide a correlation coefficients pertaining to the relation of the EFE and conceptual framework for understanding hemispherically symmet- ITCZ to differential (AET0) and equatorial heating (NEI0) in PMIP3 ric variations in precipitation (Adam et al., 2016c), such as those models (Figs. 7 and 8) and to the regression model (Eq. (6)) is pre- observed over Africa since the last glacial maximum (Collins et sented in Table 1. al., 2011) and in the Pacific during the Little Ice Age (Yan et An appreciable amount of the variance in the position of al., 2015). Furthermore, the unique shape of the African conti- the African boreal-summer ITCZ is not accounted for by the ex- nent makes the African monsoon (in particular the West African O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29 27 monsoon) sensitive to variations in the equatorial eastern Atlantic. div(AET) = NEI − ES (7) The net equatorial heating over the eastern Atlantic is regulated where div(·) denotes the divergence. Atmospheric energy stor- by cloud feedbacks, which are not well understood, but which age (ES) is generally negligible on annual or longer timescales a growing body of evidence suggests may be positive (i.e., low- (Donohoe and Battisti, 2013), but it can be significant on seasonal cloud cover increases as the surface cools, Clement et al., 2009; timescales and is therefore included in our analysis. Boucher et al., 2013). Tropical low-cloud radiative cooling ex- − ceeding 10 W m 2 (i.e., the amount required to shift the African In order to allow for regional variations, zonal energy input into ITCZ by several degrees) is well within the potential radiative the African sector must also be considered. As mentioned in the response by clouds to the equatorial surface cooling during the Discussion, the zonal overturning circulation in the African sec- AHP that is suggested by reconstructions and models (Fig. S5; tor also contributes to the mean precipitation. However, given the Boucher et al., 2013; Lohmann et al., 2013) (though estimates of zonal orientation of the ITCZ in the African sector (e.g., Fig. 1), the temperature trends in the equatorial eastern Atlantic remain it is reasonable to assume predominantly Hadley-like circulation inconclusive (Molfino and McIntyre, 1990;Weldeab et al., 2007; (Fig. 9) in the African sector. The energy balance equation can be Lohmann et al., 2013)). However, modern climate models signifi- written as (Adam et al., 2016b), cantly underestimate low-cloud cover and overestimate the equa- torial heating in the eastern Atlantic during boreal summer (Adam ∂yAET = NEI − ES − ∂xAET (8) et al., 2018a). In the models, this reduces the fractional changes where −∂ AET denotes column-integrated zonal energy input in equatorial heating during the mid-Holocene, thus dampening x across atmospheric columns (this term vanishes in the zonal the response of the African monsoon to orbital variations (Eq. (4), mean). (Cartesian notation is used here only for simplicity; the Figs. 7 and 8). calculations presented in the results are done in spherical coordi- Similarly, vegetation and dust feedbacks (which are not present nates.) The rhs of Eq. (8) therefore describes the local net energy in PMIP3 models), alter the surface radiative and latent heat fluxes input into the atmosphere, defined here as I ≡ ∂ AET. (Patricola and Cook, 2007; Gaetani et al., 2017) and may amplify y regional cross-equatorial AET and hence ITCZ shifts. The regional Using Eq. (8), a first-order Taylor expansion of AET about the surface fluxes may also enhance the differential heating of the equator gives (Bischoff and Schneider, 2014) African continent relative to the Atlantic (cf. the land-sea con- AET ≈ AET + I · a (9) trast in Fig. 6) during periods of enhanced boreal insolation, thus 0 0 ( φ) amplifying the contribution of zonal moisture fluxes from the At- where ( )0 denotes equatorial average. It is therefore readily shown lantic (Kutzbach, 1981;Clement et al., 2004; Battisti et al., 2014; that the latitude where AET vanishes (i.e., the latitude of the en- Boos and Korty, 2016)— an aspect not covered by the framework ergy flux equator EFE) is given by Eq. (2). presented here. From Eq. (8), the difference in the net heating between the The factors controlling ITCZ shifts in the African sector are de- hemispheres is equal to the zonal-mean cross-equatorial AET. In picted in Fig. 9. The ITCZ shifts towards the differentially heated addition, since ITCZs are located in regions of strong surface con- hemisphere, inducing regional cross-equatorial AET (AET0), which vergence and upper-level divergence, it is useful to consider only balances the differential heating. The precipitation intensity and the divergent component of the energy flux. Therefore, regional the extent of the ITCZ migrations are modulated by equatorial cross-equatorial AET, denoted here as AET0, is calculated as the ◦ ◦ heating (NEI0). The equatorial heating is dominated by net radia- average between 5 S–5 Nof the divergent component of AET, de- tive input at the top of the atmosphere (TOA, Fig. S3) and surface rived from Eq. (7)(Adam et al., 2016b). In the zonal mean, AET0 energy fluxes into the atmosphere (Figs. S4-5), which are both can be directly calculated from the imbalance in I between the strongly coupled to cloud feedbacks.5 hemispheres. Similarly, local equatorial heating, denoted here as I0, ◦ ◦ The regional framework presented here challenges the general is calculated as the equatorial average (10 S–10 N) of I. The results conception that orbital modulation of inter-hemispheric differen- were found to be qualitatively insensitive to the meridional extent ◦ tial heating is the primary driver of AHPs; instead, it points to the of the equatorial average if it does not exceed 20 . See Adam et important role of equatorial heating and offers a more nuanced al. (2016b)for specific details on the calculation of the energetic picture that incorporates the regional characteristics of the African quantities. sector. Since vertical atmospheric energy input dominates the at- mospheric energy budget also outside the African sector (Figs. Appendix B. Supplementary material S3-5), the regional approach presented here may be generalizable to other sectors. Supplementary material related to this article can be found on- line at https://doi .org /10 .1016 /j .epsl .2019 .06 .019. Acknowledgements References The PMIP3 data was downloaded from Earth System Grid Feder- ation (ESGF). Ori Adam acknowledges support by the Israel Science Adam, O., Bischoff, T., Schneider, T., 2016a. Seasonal and interannual variations of Foundation grant 1185/17. the energy flux equator and ITCZ. Part I: zonally averaged ITCZ position. J. Cli- mate 29, 3219–3230. https://doi .org /10 .1175 /JCLI -D -15 -0512 .1. Appendix A Adam, O., Bischoff, T., Schneider, T., 2016b. Seasonal and interannual variations of the energy flux equator and ITCZ. Part II: zonally varying shifts of the ITCZ. J. Climate 29, 7281–7293. https://doi .org /10 .1175 /JCLI -D -15 -0710 .1. The variations in the heating of the atmosphere (NEI), which Adam, O., Schneider, T., Brient, F., 2018a. Regional and seasonal variations of the ultimately drive the shifts of the African monsoon, are related to double-ITCZ bias in CMIP5 models. Clim. Dyn. 51, 101–117. https://doi .org /10 . atmospheric energy transport (AET) by the energy balance equa- 1007 /s00382 -017 -3909 -1. Adam, O., Grise, K.M., Staten, P., Simpson, I.R., Davis, S.M., Davis, N.A., Waugh, tion of the atmosphere (e.g., Adam et al., 2016b) D.W., Birner, T.T., Ming, A., 2018b. The TropD software package (v1): standard- ized methods for calculating tropical-width diagnostics. Geosci. Model Dev. 11, 4339–4357. https://doi .org /10 .5194 /gmd -11 -4339 -2018. 5 In addition, due to the low continental heat capacity, changes in surface energy Adam, O., Schneider, T., Brient, F., Bischoff, T., 2016c. Relation of the double-ITCZ fluxes over land are negligible relative to changes in ocean heat uptake (Schneider bias to the atmospheric energy budget in climate models. Geophys. Res. Lett. 43, et al., 2014). 7670–7677. 28 O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29

Allen, M.R., Ingram, W.J., 2002. Constraints on future changes in climate and the Huybers, P., Eisenman, I., 2006. Integrated summer insolation calculations, hydrologic cycle. Nature 419, 224–232. NOAA/NCDC paleoclimatology program. Available online at ftp://ftp .ncdc .noaa . Armitage, S.J., Drake, N.A., Stokes, S., El-Hawat, A., Salem, M.J., White, K., Tunrer, gov/pub /data /paleo /climate _forcing /orbital _variations /huybers2006insolation/ P., McLaren, S.J., 2007. Multiple phases of North African humidity recorded in huybers2006b .txt. lacustrine sediment from the Fezzan Basin, Libyan Sahara. Quat. Geochronol. 2, Hwang, Y.-T., Frierson, D.M.W., 2013. Link between the double-Intertropical Conver- 181–186. gence Zone problem and cloud biases over the Southern Ocean. Proc. Natl. Acad. Battisti, D.S., Ding, Q., Roe, G.H., 2014. Coherent pan-Asian climatic and isotopic re- Sci. 110, 4935–4940. https://doi .org /10 .1073 /pnas .1213302110. sponse to orbital forcing of tropical insolation. J. Geophys. Res., Atmos. 119 (21), Jolly, D., Harrison, S.P., Damnati, B., Bonnefille, R., 1998. Simulated climate and 11,997–12,020. https://doi .org /10 .1002 /2014JD021960. biomes of Africa during the late Quaternary: comparison with pollen and lake Bischoff, T., Schneider, T., 2014. Energetic constraints on the position of the in- status data. Quat. Sci. Rev. 17, 629–6657. tertropical convergence zone. J. Climate 27, 4937–4951. https://doi .org /10 .1175 / Joussaume, S., Taylor, K.E., Braconnot, P., Mitchell, J.F.B., Kutzbach, J.E., Harrison, JCLI -D -13 -00650 .1. S.P., Prentice, I.C., Broccoli, A.J., Abe-Ouchi, A., Bartlein, P.J., Bonfils, C., Dong, Bischoff, T., Schneider, T., Meckler, A.N., 2017. A conceptual model for the response B., Guiot, J., Herterich, K., Hewitt, C.D., Jolly, D., Kim, J.W., Kislov, A., Kitoh, A., of tropical rainfall to orbital variations. J. Climate 30 (20), 8375–8391. https:// Loutre, M.F., Masson, V., McAvaney, B., McFarlane, N., de Noblet, N., Peltier, W.R., doi .org /10 .1175 /JCLI -D -16 -0691.1. Peterschmitt, J.Y., Pollard, D., Rind, D., Royer, J.F., Schlesinger, M.E., Syktus, J., Boos, W.R., Korty, R.L., 2016. Regional energy budget control of the intertropical con- Thompson, S., Valdes, P., Vettoretti, G., Webb, R.S., Wyputta, U., 1999. Monsoon vergence zone and application to mid-Holocene rainfall. Nat. Geosci. 9, 892–897. changes for 6000 years ago: results of 18 simulations from the Paleoclimate https://doi .org /10 .1038 /ngeo2833. Modeling Intercomparison Project (PMIP). Geophys. Res. Lett. 26, 859–862. Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, Kang, S.M., Frierson, D.M.W., Held, I.M., 2009. The tropical response to extratropical V.-M., et al., 2013. Clouds and aerosols. In: 2013: The Physical thermal forcing in an idealized GCM: the importance of radiative feedbacks and Science Basis. Intergovernmental Panel on Climate Change, Ch. 7. convective parameterization. J. Atmos. Sci. 66, 2812–2827. Braconnot, P., Harrison, S.P., Otto-Bliesner, B., Abe-Ouchi, A., Jungclaus, J., Peter- Krinner, G., Lézine, A.-M., Braconnot, P., Sepulchre, P., Ramstein, G., Grenier, C., Gout- schmitt, J.Y., 2011. The paleoclimate modeling intercomparison project contri- tevin, I., 2012. A reassessment of lake and wetland feedbacks on the North bution to CMIP5. In: CLIVAR Exchanges, vol. 16. International CLIVAR Project African Holocene climate. Geophys. Res. Lett. 39 (7). https://doi .org /10 .1029 / Office, Southampton, United Kingdom, pp. 15–19. 2012GL050992. Castañeda, I.S., Mulitza, S., Schefuß, E., Lopes dos Santos, R.A., Sinninghe Damsté, J.S., Kuper, R., Kröpelin, S., 2006. Climate-controlled Holocene occupation in the Sahara: Schouten, S., 2009. Wet phases in the Sahara/Sahel region and human migra- motor of Africa’s evolution. Science 313 (5788), 803–807. https://doi .org /10 . tion patterns in North Africa. Proc. Natl. Acad. Sci. USA 106 (48), 20159–20163. 1126 /science .1130989. http://science .sciencemag .org /content /313 /5788 /803 .full . https://doi .org /10 .1073 /pnas .0905771106. http://www.pnas .org /content /106 /48 / pdf. 20159 .full .pdf. http://www.pnas .org /content /106 /48 /20159 .abstract. Chevalier, M., Brewer, S., Chase, B.M., 2017. Qualitative assessment of PMIP3 rain- Kutzbach, J.E., 1981. Monsoon climate of the early Holocene: climate experiment fall simulations across the eastern African monsoon domains during the mid- with the earth’s orbital parameters for 9000 years ago. Science 214 (4516), Holocene and the last glacial maximum. Quat. Sci. Rev. 156, 107–120. https:// 59–61. https://doi .org /10 .1126 /science .214 .4516 .59. doi .org /10 .1016 /j .quascirev.2016 .11.028. Kutzbach, J.E., Liu, Z., 1997. Response of the African monsoon to orbital forcing and Chiang, J.C.H., Friedman, A.R., 2012. Extratropical cooling, interhemispheric thermal ocean feedbacks in the middle Holocene. Science 278, 440–443. gradients, and tropical climate change. Annu. Rev. Earth Planet. Sci. 40, 383–412. Lavaysse, C., Flamant, C., Janicot, S., Parker, D.J., Lafore, J.-P., Sultan, B., Pelon, J., 2009. Claussen, M., Gayler, V., 1997. The greening of the Sahara during the mid-Holocene: Seasonal evolution of the west African heat low: a climatological perspective. results of an interactive atmosphere-biome model. Glob. Ecol. Biogeogr. Lett. 6, Clim. Dyn. 33 (2), 313–330. https://doi .org /10 .1007 /s00382 -009 -0553 -4. 369–377. https://doi .org /10 .2307 /2997337. Levis, S., Bonan, G.B., Bonfils, C., 2014. Soil feedback drives the mid-Holocene north Claussen, M., Dallmeyer, A., Bader, J., 2017. Theory and modeling of the African hu- African monsoon northward in fully coupled CCSM2 simulations with a dynamic mid period and the green Sahara. In: Oxford Research Encyclopedia of Climate vegetation model. Clim. Dyn. 23, 791–802. https://doi .org /10 .1007 /s00382 -004 - Science. 0477 -y. Clement, A.C., Hall, A., Broccoli, A.J., 2004. The importance of precessional signals in Lézine, A.-M., Casanova, J., 1991. Correlated oceanic and continental records demon- the tropical climate. Clim. Dyn. 22, 327–341. strate past climate and hydrology of North Africa (0-140 ka). Geology 19 (4), Clement, A.C., Burgman, R., Norris, J.R., 2009. Observational and model evidence for 307–310. positive low-level cloud feedback. Science 325, 460–464. Lézine, A.-M., Hély, C., Grenier, C., Braconnot, P., Krinner, G., 2011. Sahara and Sahel Collins, J.A., Schefuß, E., Heslop, D., Mulitza, S., Prange, M., Zabel, M., Tjallingii, R., vulnerability to climate changes, lessons from Holocene hydrological data. Quat. Dokken, T.M., Huang, E., Mackensen, A., Schulz, M., Tian, J., Zarrieß, M., Wefer, Sci. Rev. 30 (21), 3001–3012. https://doi .org /10 .1016 /j .quascirev.2011.07.006. G., 2011. Interhemispheric symmetry of the tropical African rainbelt over the Liu, X., Battisti, D.S., Donohoe, A., 2017. Tropical precipitation and cross-equatorial past 23,000 years. Nat. Geosci. 4, 42–45. https://doi .org /10 .1038 /ngeo1039. ocean heat transport during the mid-Holocene. J. Climate 30 (10), 3529–3547. deMenocal, P., Ortiz, J., Guilderson, T., Adkins, J., Sarnthein, M., Baker, L., Yarusin- https://doi .org /10 .1175 /JCLI -D -16 -0502 .1. sky, M., 2000. Abrupt onset and termination of the African Humid Period: rapid Liu, Z., Harrison, S.P., Kutzbach, J., Otto-Bliesner, B., 2004. Global in the climate responses to gradual insolation forcing. Quat. Sci. Rev. 19, 347–361. mid-Holocene and oceanic feedback. Clim. Dyn. 22 (2), 157–182. https://doi .org / Donohoe, A., Battisti, D.S., 2013. The seasonal cycle of atmospheric heating and tem- 10 .1007 /s00382 -003 -0372 -y. perature. J. Climate 26, 4962–4980. Liu, Z., Zhu, J., Rosenthal, Y., Zhang, X., Otto-Bliesner, B.L., Timmermann, A., Smith, Donohoe, A., Marshall, J., Ferreira, D., McGee, D., 2013. The relationship between R.S., Lohmann, G., Zheng, W., Timm, O.E., 2014. The Holocene temperature co- ITCZ location and cross-equatorial atmospheric heat transport: from the sea- nundrum. Proc. Natl. Acad. Sci. 111, E3501–E3505. https://doi .org /10 .1073 /pnas . sonal cycle to the last glacial maximum. J. Climate 26, 3597–3618. 1407229111. Donohoe, A., Marshall, J., Ferreira, D., Armour, K., McGee, D., 2014. The interannual Lohmann, G., Pfeiffer, M., Laepple, T., Leduc, G., Kim, J., 2013. A model-data compar- variability of tropical precipitation and interhemispheric energy transport. J. Cli- ison of the Holocene global sea surface temperature evolution. Clim. Past 9 (4), mate 27, 3377–3392. 1807–1839. https://doi .org /10 .5194 /cp -9 -1807 -2013. Drake, N., Blench, R.M., Armitage, S.J., Bristow, C.S., White, K.H., 2011. Ancient wa- Manning, K., Timpson, A., 2014. The demographic response to Holocene climate tercourses and biogeography of the Sahara explain the peopling of the desert. change in the Sahara. Quat. Sci. Rev. 101 (Supplement C), 28–35. https:// Proc. Natl. Acad. Sci. USA 108, 458–462. doi .org /10 .1016 /j .quascirev.2014 .07.003. http://www.sciencedirect .com /science / Frierson, D.M.W., Hwang, Y.-T., Fuckar, N.S., Seager, R., Kang, S.M., Donohoe, A., article /pii /S0277379114002728. Maroon, E.A., Liu, X., Battisti, D.S., 2013. Contribution of ocean overturning cir- Marcott, S.A., Shakun, J.D., Clark, P.U., Mix, A.C., 2013. A reconstruction of regional culation to tropical rainfall peak in the northern hemisphere. Nat. Geosci. 6, and global temperature for the past 11,300 years. Science 339, 1198–1201. 940–944. https://doi .org /10 .1038 /ngeo1987. Gaetani, M., Messori, G., Zhang, Q., Flamant, C., Pausata, F.S.R., 2017. Understand- Marsicek, J., Shuman, B.N., Bartlein, P.J., Shafer, S.L., Brewer, S., 2018. Reconciling di- ing the mechanisms behind the northward extension of the West African vergent trends and millennial variations in Holocene temperatures. Nature 554, monsoon during the mid Holocene. J. Climate 30 (19), 7621–7642. https:// 92–96. https://doi .org /10 .1038 /nature25464. doi .org /10 .1175 /JCLI -D -16 -0299 .1. Merlis, T.M., Schneider, T., Bordoni, S., Eisenman, I., 2013. Hadley circulation re- Gasse, F., 2000. Hydrological changes in the African tropics since the Last Glacial sponse to orbital precession. Part I: aquaplanets. J. Climate 26, 740–753. Maximum. Quat. Sci. Rev. 19, 189–211. Molfino, B., McIntyre, A., 1990. Precessional forcing of nutricline dynamics in Harrison, S.P., Bartlein, P.J., Brewer, S., Prentice, I.C., Boyd, M., Hessler, I., Holmgren, the equatorial Atlantic. Science 249 (4970), 766–769. https://doi .org /10 .1126 / K., Izumi, K., Willis, K., 2014. Climate model benchmarking with glacial and mid- science .249 .4970 .766. Holocene climates. Clim. Dyn. 43 (3), 671–688. https://doi .org /10 .1007 /s00382 - Nicholson, S.E., 2013. The West African Sahel: a review of recent studies on the rain- 013 -1922 -6. fall regime and its interannual variability. ISRN Meteorol. 2013, 453521. https:// Harrison, S., Bartlein, P., Izumi, K., Li, G., Annan, J., Hargreaves, J., Braconnot, P., doi .org /10 .1155 /2013 /453521. 32 pp. Kageyama, M., 2015. Evaluation of CMIP5 palaeo-simulations to improve climate Nicholson, S.E., 2018. The ITCZ and the seasonal cycle over equatorial Africa. Bull. projections. Nat. Clim. Chang. 5, 735–743. https://doi .org /10 .1038 /nclimate2649. Am. Meteorol. Soc. 99 (2), 337–348. https://doi .org /10 .1175 /BAMS -D -16 -0287.1. O. Adam et al. / Earth and Planetary Science Letters 522 (2019) 20–29 29

Patricola, C.M., Cook, K.H., 2007. Dynamics of the west African monsoon under mid- Shekhar, R., Boos, W.R., 2016. Improving energy-based estimates of monsoon lo- Holocene precessional forcing: regional climate model simulations. J. Climate 20, cation in the presence of proximal deserts. J. Climate 29, 4741–4761. https:// 694–716. https://doi .org /10 .1175 /JCLI4013 .1. doi .org /10 .1175 /JCLI -D -15 -0747.1. Pausata, F.S., Messori, G., Zhang, Q., 2016. Impacts of dust reduction on the north- Shekhar, R., Boos, W.R., 2017. Weakening and shifting of the Saharan shallow merid- ward expansion of the African monsoon during the green Sahara period. Earth ional circulation during wet years of the West African monsoon. J. Climate 30, Planet. Sci. Lett. 434, 298–307. https://doi .org /10 .1016 /j .epsl .2015 .11.049. http:// 7399–7422. https://doi .org /10 .1175 /JCLI -D -16 -0696 .1. www.sciencedirect .com /science /article /pii /S0012821X15007530. Su, H., Neelin, D., 2005. Dynamical mechanisms for African monsoon changes during the mid-Holocene. J. Geophys. Res. 110, D19105. Quade, J., Dente, E., Armon, M., Dor, Y.B., Morin, E., Adam, O., Enzel, Y., 2018. Swann, A.L.S., Fung, I.Y., Liu, Y., Chiang, J.C.H., 2014. Remote vegetation feedbacks Megalakes in the Sahara? A review. Quat. Res. 90 (2), 253–275. https://doi .org / and the mid-Holocene green Sahara. J. Climate 27, 4857–4870. https://doi .org / 10 .1017 /qua .2018 .46. 10 .1175 /JCLI -D -13 -00690 .1. Renssen, H., Seppä, H., Crosta, X., Goosse, H., Roche, D., 2012. Global characteri- TEMPO, 1996. Potential role of vegetation feedback in the climate sensitivity of zation of the Holocene thermal maximum. Quat. Sci. Rev. 48 (Supplement C), high-latitude regions: a case study at 6000 years b.p. Glob. Biogeochem. Cy- 7–19. https://doi .org /10 .1016 /j .quascirev.2012 .05 .022. http://www.sciencedirect . cles 10 (4), 727–736. https://doi .org /10 .1029 /96GB02690. com /science /article /pii /S0277379112002168. Tierney, J.E., Lewis, S.C., Cook, B.I., LeGrande, A.N., Schmidt, G.A., 2011. Model, Roberts, W.H.G., Valdes, P.J., Singarayer, J.S., 2017. Can energy fluxes be used to in- proxy and isotopic perspectives on the East African humid period. Earth Planet. terpret glacial/interglacial precipitation changes in the tropics? Geophys. Res. Sci. Lett. 307 (1), 103–112. https://doi .org /10 .1016 /j .epsl .2011.04 .038. http:// Lett. 44, 6373–6382. https://doi .org /10 .1002 /2017GL073103. www.sciencedirect .com /science /article /pii /S0012821X11002652. Rodwell, M.J., Hoskins, B.J., 1996. Monsoons and the dynamics of deserts. Q. J. R. Weldeab, S., Lea, D.W., Schneider, R.R., Andersen, N., 2007. 155,000 years of West Meteorol. Soc. 122, 1385–1404. African monsoon and ocean thermal evolution. Science 316 (5829), 1303–1307. https://doi .org /10 .1126 /science .1140461. Rowell, D.P., 2003. The impact of Mediterranean SSTs on the Sahelian rainfall season. Yan, H., Wei, W., Soon, W., Zhisteng, A., Zhou, W., Liu, Z., Wang, Y., Carterm, R., 2015. J. Climate 16 (5), 849–862. https://doi .org /10 .1175 /1520 -0442(2003 )016<0849 : Dynamics of the intertropical convergence zone over the western Pacific during TIOMSO >2 .0 .CO ;2. the Little Ice Age. Nat. Geosci. 8, 315–320. Schneider, T., Bischoff, T., Haug, G.H., 2014. Migrations and dynamics of the Zhao, Y., Braconnot, P., Marti, O., Harrison, S., Hewitt, C., Kitoh, A., Liu, Z., Mikolajew- intertropical convergence zone. Nature 513, 45–53. https://doi .org /10 .1038 / icz, U., Otto-Bliesner, B., Weber, S., 2005. A multi-model analysis of the role of nature13636. the ocean on the African and Indian monsoon during the mid-Holocene. Clim. Shanahan, T.M., McKay, N.P., Hughen, K.A., Overpeck, J.T., Otto-Bliesner, B., Heil, C.W., Dyn. 25 (7), 777–800. https://doi .org /10 .1007 /s00382 -005 -0075 -7. King, J., Scholz, C.A., Peck, J., 2015. The time-transgressive termination of the African Humid Period. Nat. Geosci. https://doi .org /10 .1038 /NGEO2329.