Last Glacial Maximum dune activity in the Kalahari Desert of southern Africa: observations and simulations

Quaternary Science Reviews 28 (2009) 301–307

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Last Glacial Maximum dune activity in the Kalahari Desert of southern Africa: observations and simulations Brian M. Chase a, b, *, Simon Brewer c a

School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK Department of Environmental and Geographical Science, University of Cape Town, Rondebosch 7701, South Africa c ˆ t, B-4000 Lie`ge, Belgium Institut d’Astrophysique et de Ge´ophysique Universite´ de Lie`ge, Bat. B5c, 17 Alle´e du Six Aou b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 February 2008 Received in revised form 8 October 2008 Accepted 10 October 2008

It has long been understood that as ephemeral landscape features sand dunes are highly sensitive to environmental change, and thus their distribution and the timing of their development may provide clues to past climate dynamics. The relationship between climate and dune activity, however, is neither simple nor straightforward, with a range of controls affecting the balance between erodibility (the availability of sediment for deflation) and erosivity (the potential for sediment transport). To explore such complex systems over large spatial and temporal scales, a number of dune activity indices (DAI) have been created that incorporate wind speed and moisture balances to calculate the potential for, and degree of dune mobilisation. Using modern weather station data, these indices have generally been shown to provide reasonable indications of dune activity potential. Until recently, however, the detailed quantitative data required to inform these equations has not been available for past climate scenarios, and attempts to determine the relative importance of the various controls of dune activity have relied on rough estimations of climatic parameters. This paper combines data from monthly general circulation model (GCM) outputs from the coupled Ocean-Atmosphere GCMs for 21 ka with the most detailed DAI equation presently available to calculate the potential for dune reactivation in southern Africa during the Last Glacial Maximum (LGM, 18–24 ka). Based on these data and calculations it is indicated that there was significantly less potential for dune activity across southern Africa at 21 ka. When compared to the aeolian sediment records from the region, this study poses serious and fundamental questions about: 1) the reliability of the model outputs, 2) the degree to which DAIs are able to account for the complexity and dynamics of aeolian systems, and/or 3) the interpretation of dune records as palaeoclimatic proxies at millennial time scales. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction At decadal to multi-millennial time scales, the presence of active inland sand dunes have been taken to be indicators of arid conditions (e.g. Sarnthein, 1978; Stokes et al., 1997b; Thomas and Shaw, 2002; Hesse and Simpson, 2006). The potential for this dune development and activity is determined broadly by sediment supply, sediment availability (the susceptibility of sand to be deflated, determined by vegetation, soil moisture, and the position of the water table amongst other factors), and the transport capacity of the wind (c.f. Kocurek, 1998; Kocurek and Lancaster, 1999). To explore the interactions of these parameters, a variety of equations have been proposed to calculate dune activity/mobility

* Corresponding author. School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK. Tel./fax: þ44 27 21 788 3434. E-mail address: [email protected] (B.M. Chase). 0277-3791/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.quascirev.2008.10.008

indices and analyse the relative effects of variations in transport capacity and climate variables such as precipitation and potential evaporation (e.g. Fryberger, 1979; Talbot, 1984; Lancaster, 1988; Bullard et al., 1997; Thomas et al., 2005). These indices have been shown to perform reasonably well at regional scales (Lancaster and Helm, 2000; Wang et al., 2007), and using data derived from GCM outputs, the potential for dune reactivation under future climate scenarios has been investigated (e.g. Muhs and Maat, 1993; Thomas et al., 2005). In considering late Pleistocene climate scenarios, only speculative attempts at dune activity index (DAI) calculations have been possible as much of the available evidence is qualitative in nature, and very little quantitative data exists to inform the DAIs (e.g. Talbot, 1984; Lancaster, 1989). A long and firmly held hypothesis is that active dunefields were much more extensive during the LGM in the Kalahari region of central southern Africa, and that this activity was primarily the result of significant decreases in precipitation on the order of 30–80% (Sarnthein, 1978; Lancaster,

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1989; Thomas and Shaw, 2002). Direct luminescence dating of aeolian sediments does indicate that all of the Kalahari’s major dunefields, including the presently dormant dunefields in the northern and northeastern sectors, were active to some degree at w21 ka (O’Connor, 1997; Stokes et al., 1997a,b; Blu¨mel et al., 1998; Munyikwa et al., 2000; Thomas et al., 2000; Telfer and Thomas, 2007), but it is unclear how these indications of dune development correlate with other proxies from the region (Thomas and Shaw, 2002; Thomas et al., 2003), many of which indicate coeval increases in effective moisture (c.f. Chase and Meadows, 2007 and references therein). To explore these apparent contradictions, this paper combines the DAI outlined by Thomas et al. (2005) with output from a set of coupled ocean-atmosphere GCMs taken from the second phase of the Paleoclimate Modelling Intercomparison Project (PMIP, Braconnot et al., 2007) to describe the general climatic parameters and potential for dune activity during this portion of the LGM in southern Africa.

Table 1 List of coupled Ocean-Atmospheric GCMs used in current study. Model

Atmospheric resolution Ocean resolution (levels) (levels)

Reference

ECBILT-CLIO

T21(3)

3  3 (20)

FGOALS-1.0g HadCM3M2 ISPL-CM4V1-MR MIROC3.2

2.8  2.8 (26) 3.75  2.5 (19) 3.75  2.5 (19)

1  1 (33) 1.25  1.25 (20) 2  0.5 (31)

de Vries and Weber (2005) Yu et al. (2002) Gordon et al. (2000) Marti et al. (2004)

T42 (20)

1.4  0.5 (43)

K-1 model developers (2004)

LGM run (e.g. Kaplan et al., 2003), and then added to the CRU CL 2.0 modern climatology. Potential evapotranspiration (PET) data were calculated for both modern and LGM using the Priestly–Taylor equation (Priestly and Taylor, 1972).

2. Methods

3. Results

While many of the early indices calculate annual dune activity potential, more recent studies have indicated that this approach may not accurately represent the variations in potential activity that may exist on intra-annual time scales, and that monthly calculations are required for adequate assessments (Knight et al., 2004; Thomas et al., 2005). These monthly activity values are calculated as:

On aggregate, data derived from the models indicate that contrary to the conclusions of many recent researchers (e.g. Stokes et al., 1997b; Partridge et al., 1999; Thomas and Shaw, 2002) conditions were humid at 21 ka, both as a result of increased precipitation and reduced PET (Fig. 1, Table 2). These findings are, however, in broad accord with the available evidence as synthesised by Chase and Meadows (2007), which draws on data from a range of Kalahari archives from 21 1 ka including lacustrine systems (Lancaster, 1979; Cooke, 1984; Rust, 1984; Shaw and Cooke, 1986; Thomas et al., 2003; Huntsman-Mapila et al., 2006), macrofaunal remains (Klein, 1986), fossil pollen (Scott, 1987), speleothems (Holmgren et al., 1995) and groundwater (Stute and Talma, 1998), all of which indicate slightly to significantly wetter conditions relative to present day. While the simulated changes in the amount of rainfall vary, the results suggest that this increase occurred not only during the rainy season but also during the latter part of the dry season. On a cursory level, the GCM data do appear to underestimate LGM temperature reductions by between 1 and 4  C (Fig. 1, Table 2), in comparison to evidence from the Stampriet Aquifer in southeastern Namibia, the Letlhakeng Aquifer in the central Kalahari, and Cango Cave and the Uitenhage Aquifer on the south coast that indicate reductions of 5.3  C (Stute and Talma, 1998), 5.2  C (Kulongoski and Hilton, 2004), 5.5  C (Heaton et al., 1986) and 6  C (Talma and Vogel, 1992) respectively. While more realistic temperature reconstructions would serve to increase effective precipitation through reduced potential evaporation, they may also affect the distribution of precipitation in terms of both amount and seasonality/source. This combination of increased precipitation and decreased temperatures led to a slight decline in PET of around 100 mm yr1. Simulations of wind speeds at 21 ka generally show little change (model average of 4% for maximum month) from modern conditions (Fig. 1, Table 2). These data are inconsistent with marine records from the southeast Atlantic that indicate substantially increased upwelling (Little et al., 1997) and aeolian sediment deposition (Stuut et al., 2002), and it may be that wind speed is underestimated in the simulation. Based on DAIs calculated from present day precipitation, potential evaporation and wind speed data, it is clear that much of southern Africa exhibits very low potential for dune activity (Fig. 2). Even the driest portions of the southwestern Kalahari are predicted to experience only limited activity, and for only a portion of the year; at the end of the dry season in spring and early summer when wind speed and potential evaporation increase, but before the late summer rains arrive. These calculations are generally consistent with the modern observations of dune activity (Lancaster, 1988; Bullard et al., 1997).

  A ¼ U 3 = Plag =Ep;lag þ Prainy =Ep;rainy U3 is the mean wind speed cubed, Plag/Ep,lag is the residual effect of recent precipitation and potential evaporation, and Prainy/Ep,rainy is the effect of rainy season precipitation and potential evaporation on soil moisture. Plag and Ep,lag are (P1 þ P0)/2 and (Ep,1 þ Ep,0)/2 respectively, where P1 and Ep,1 are precipitation and potential evaporation in the previous month and P0 and Ep,0 are precipitation and potential evaporation in the current month. Prainy and Ep,rainy are the average precipitation and potential evaporation values for those concurrent months wherein rainfall is greater than onetwelfth the annual average. Results from this DAI have been divided into four classes of dune activity, with A < 70 indicating inactive dunes with vegetation across the whole dune; A ¼ 70–160 indicating dune activity limited to crests, with the dune flanks vegetated; A ¼ 160–700 indicating significant dune activity, with moderate interdune vegetation cover; and A > 700 indicating a highly active dune landscape with sparsely vegetated interdunes (Thomas et al., 2005). Validation of this index was achieved using climatic data from 1960 to 2000 from the southwest Kalahari at Twee Rivieren, empirical vegetation data for September 1999 and June 2000, dune surface activity data based on repeat dry-season measurements from 1992 to 2000, photographic evidence from 1983 to 2000 and remote sensing, and fieldbased evidence of vegetation cover in the region (Thomas et al., 2005). Present day climate was taken from the high resolution climatology dataset CRU CL 2.0 (New et al., 2000), upscaled to a resolution of 0.5 degrees. In order to account for possible variations in the simulated glacial climate, output from five GCMs was used (Table 1), taken from the PMIP2 website.1 According to the PMIP2 protocol, each GCM was run using the same set of LGM forcing conditions (ice sheets, atmospheric CO2 concentrations, orbital changes). In order to obtain an LGM climatology for the study region, anomalies were calculated between the control run and the

1

http://pmip2.lsce.ipsl.fr/.

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Fig. 1. Comparison of mean annual precipitation, temperature, potential evapotranspiration, precipitation minus potential evapotranspiration and maximum month wind speed data for present day and model simulations for 21 ka.

125% 99% 113% 13% 250% 49% 106% 83% 125% 49% 100% 60% 3.75 268.03 3.01 107.38

124% 250%

3.54 110.75

118% 103%

2.98 71.94

99% 67%

3.02 64.68

3.76 52.51

46% 6% 3% 13% 123% 82% 91% 116% 64% 75% 86% 101% 188% 89% 95% 137% 188% 78% 86% 137% 129% 83% 91% 117% 117.78 18.01 1510.18 1392.40

SW Mean annual precipitation (mm) Kalahari Mean annual temperature ( C) Mean annual potential evapotranspiration (mm) Mean annual precip. – pot. evapotranspiration (mm) Maximum month wind speed (m/s) Maximum month dune activity index value

183.65 20.29 1585.90 1402.26

64% 89% 95% 101%

179.38 15.20 1442.72 1263.34

98% 75% 91% 111%

250.80 17.51 1472.15 1221.36

137% 86% 93% 115%

237.21 16.94 1437.98 1200.77

345.48 15.81 1367.34 1021.86

213% 92% 122% 51% 755% 59% 210% 305% 100% 71% 106% 92% 4.41 37.72 2.07 5.00

213% 755%

1.90 2.92

92% 59%

2.03 3.69

98% 74%

2.19 4.59

2.07 3.57

25% 5% 4% 47% 110% 85% 90% 143% 75% 78% 84% 91% 134% 91% 95% 211% 134% 78% 84% 211% 118% 83% 91% 140% 482.92 20.17 1461.73 978.81

N Kalahari Mean annual precipitation (mm) Mean annual temperature ( C) Mean annual potential evapotranspiration (mm) Mean annual precip. – pot. evapotranspiration (mm) Maximum month wind speed (m/s) Maximum month dune activity index value

644.29 22.15 1537.88 893.59

75% 91% 95% 91%

604.55 18.50 1426.66 822.11

94% 84% 93% 109%

825.44 19.33 1365.72 540.29

128% 87% 89% 165%

760.86 18.49 1399.82 638.97

866.24 17.29 1290.50 424.26

133% 86% 101% 20% 220% 40% 92% 74% 90% 40% 105% 89% 3.38 38.87 2.53 17.68

133% 220%

2.19 9.54

86% 54%

2.28 9.98

90% 56%

2.66 15.78

2.27 7.15

22% 5% 4% 30% 109% 84% 90% 131% 80% 77% 85% 95% 131% 91% 96% 172% 131% 77% 85% 172% 731.31 16.83 1298.21 566.90 120% 83% 91% 134% 668.17 17.97 1395.98 727.81 444.26 19.85 1468.90 1024.64

NE Kalahari Mean annual precipitation (mm) Mean annual temperature ( C) Mean annual potential evapotranspiration (mm) Mean annual precip. – pot. evapotranspiration (mm) Maximum month wind speed (m/s) Maximum month dune activity index value

558.56 21.76 1534.74 976.18

80% 91% 96% 95%

517.58 18.41 1391.07 873.49

93% 85% 91% 112%

688.51 18.89 1365.83 677.31

123% 87% 89% 144%

% of modern ECBILTCLIO

% of modern

FGOALS1.0g

% of modern

MIROC3.2 % of modern

IPSL-CM4-V1MR

HADCM3M2 % of modern

Max. Min. Avg. Stdev.

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Modern

Table 2 Comparison of mean annual precipitation, temperature, potential evapotranspiration, precipitation minus potential evapotranspiration, maximum month wind speed data and maximum month dune activity index values for present day and model simulations for 21 ka.

304

DAI calculations using model data for 21 ka generally indicate less potential for dune activity than present day (Fig. 2). The limited increase in erosivity resulting from variations in wind speed is significantly outweighed by changes in the P–Ep balance as a result of increased precipitation and reduced temperatures. Exceptions are the ECBILT-CLIO and FGOALS-1.0g models, which indicate the potential for dune activity in the southwestern Kalahari being slightly greater than or similar to present day. Data from the MIROC3.2, IPSL-CM4-V1-MR and HADCM3M2 indicate inactivity at all of the sites throughout the year. Under all modelled scenarios, the dunefields of the north and northeastern remain inactive throughout the year. According to these data and calculations, under 21 ka environmental scenarios only the southwest Kalahari is indicated as possibly experiencing significant dune activity, and in the northern Kalahari even very limited activity is unlikely to have occurred. These findings are clearly at odds with evidence from the sedimentary records, where evidence for dune development exists in the northeastern (Stokes et al., 1997b; Munyikwa et al., 2000), northern (O’Connor and Thomas, 1999; Thomas et al., 2000) and southwestern Kalahari dunefields (Stokes et al., 1997a; Blu¨mel et al., 1998; Bateman et al., 2003; Telfer and Thomas, 2007), despite maximum month DAI values of only 9–20, 3–9, and 53–202 respectively. 4. Discussion Considering either wind speed, precipitation or potential evaporation alone, substantial changes need to be made to the model data if the DAI equations used here are to indicate the significant dune activity that has been inferred from the sedimentary record. To activate the northern Kalahari dunefields either precipitation would need to be decreased by 96–98%, or wind speed would need to be increased by 280–380% to activate the crests (>70) and flanks (>160) respectively. While this is considered to be an unrealistic decrease in precipitation given the data from both the GCM and other palaeoenvironmental archives from the region, daily fluctuations in wind speed in the southwest Kalahari have been shown to be regularly between 200 and 300% of the monthly mean (Knight et al., 2004). The calculation of U3 in the DAI is intended to account for this intra-monthly variability, but it is possible that it may still underestimate potential transport capacity (Knight et al., 2004). To address the potential that wind speed variability may have been greater than present at 21 ka, the standard deviation of monthly and daily wind speeds have been calculated, but in both cases variability is reduced at 21 ka in the region of the dune sites, and thus according to the model data it is unlikely that a greater number of extreme wind events or ‘storminess’ is responsible for the increased activity inferred from the sedimentary record. Important variations in sediment transport potential can also exist at decadal time scales (Bullard et al., 1997), and multi-decadal averages of climatic parameters may similarly underestimate the potential for dune activation. This means that while conditions in the past may have been generally wetter than present day, more pronounced variability may have resulted in dune development. This is a fundamental problem with using dune sediments to explore questions of climate change on multi-millennial time scales. As landscape elements that are highly sensitive to changes in climate, there is no clear way to determine whether the sediment recovered was deposited during extreme events at monthly/ annual/decadal time scales or is the result, as inferences often suggest, of larger long-term fluctuations in climate. It is also possible that the DAI is not adequately representing dune activity because it does not factor in other important factors affecting dune development such as external sediment supply

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305

Fig. 2. Maximum month DAIs calculated for present day and 21 ka model simulations. Sites where dune activity within errors of 21 ka has been reported are indicated, as are isolines for DAI zones.

(Rendell et al., 2003) and/or the effect of CO2 on vegetation (Hesse et al., 2003). Sediment supply is a very difficult variable to adequately quantify at these time scales, and suitable sedimentary records have yet to be identified and analysed from the region. It could be considered that increased precipitation would result in increased fluvial sediment supply, especially within the dense drainage network surrounding the northern Kalahari dunefields, but this would largely depend on the regularity of the precipitation and the effect it has on vegetation. In general, the precipitation data for the northern Kalahari indicates that the length of the rainy season would be similar to or shorter than today, with notable reductions occurring in the Angolan and Zimbabwean highlands at the headwaters of the rivers that may provide sediment to the dunefields. This may have resulted in ‘flashier’ fluvial systems, and the potential for greater sediment transport, but the resolution and nature of these data make more detailed inferences difficult. Considering the potential impact of reduced atmospheric CO2, Hesse et al. (2003) suggested that dune activity in the Blue

Mountains of Australia dating to w21 ka could not credibly be attributed to variation in P–Ep, and they envisioned that CO2 would result in more extensive grasslands, which when disturbed by fire would leave the sediment prone to deflation. However, attempts at modelling African LGM vegetation have shown that, despite lower CO2 levels, forests may have been more extensive across the northern Kalahari as a result of more humid conditions (Cowling et al., 2008), confirmed here by the simulated higher P–EP. However, Bond et al. (2003) have suggested that increased fire frequency and slow tree recovery rates may have resulted in their virtual elimination from at least some part of the southern African landscape. While this hypothesis may explain the development of some apparently anomalous phases of glacial age dune development, it does not explain the phases of dune development that occurred during what appear to have been more humid conditions after 11 ka (Chase, in press), when CO2 had reached near preindustrial levels. Given the complexity of the dune-climate relationships discussed earlier, it is clear that substantial work is

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required on the interactions between vegetation, sediment and wind strength dynamics over multi-millennial time scales before the relative importance of these variables can be understood and we can accurately interpret aeolian archives.

calculations, and two anonymous reviewers for their constructive comments.

5. Conclusions

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While the sedimentary record from southern Africa indicates that all of the major Kalahari dunefields were active at 21 ka, DAI calculations based on model data indicate significantly reduced potential for dune development compared to the present day. A number of possible explanations may exist for this apparent contradiction.  Issues with resolution and accuracy of GCM data. While palaeoprecipitation seems to be relatively well-represented by the model data as a whole, proxies indicating reduced LGM temperatures and increased wind strength suggest that these may be underestimated in the GCM outputs.  Issues of reliability of DAIs as indicators of potential dune activity. In simplifying complex sedimentary systems to a selection of basic parameters, accuracy is to some degree sacrificed in order to obtain a reasonable level of functionality. While studies suggest that DAIs perform well at the type of regional scales considered here (Lancaster and Helm, 2000; Wang et al., 2007), more spatially/temporally extensive observations of dune activation under a wider range of environmental conditions will allow for more accurate DAI calibrations, and more reliable calculations.  Knight et al. (2004) suggest that macro-scale studies, covering large regions over long time scales, are well suited to GCM and DAI analysis, but it is recognised that significant variability can exist within a dunefield, and that extreme sand-transporting events occurring at high temporal resolutions are especially important when considering dunefield dynamics and preserved sedimentary records. While the aggregate palaeoenvironmental record seems to broadly support the model simulations, it may be that increased inter-annual/decadal environmental variability allowed for pulses of dune development to occur under what was, at the millennial scale, a generally wetter period. This question of temporal scale affects many of the discontinuous records from dryland regions, including many of the archives that indicate increased humidity, and it may be that together with the dune records they are in fact reflecting fluctuations in precipitation and wind speed that cannot be adequately resolved within the existing chronological frameworks. This calls into question the reliability of aeolian archives as indicators of environmental change on millennial time scales, and may provide some explanation for those instances in which interpretations of palaeo-aridity based on dune sediments appear to conflict with evidence from other proxies for more humid conditions.

Acknowledgements We acknowledge the international modeling groups for providing their data for analysis, the Laboratoire des Sciences du Climat et de l’Environnement (LSCE) for collecting and archiving the model data. The PMIP2/MOTIF Data Archive is supported by CEA, CNRS, the EU project MOTIF (EVK2-CT-2002-00153) and the Programme National d’Etude de la Dynamique du Climat (PNEDC). The analyses were performed using version 02/22/08 of the database. More information is available on http://pmip2.lsce.ipsl.fr/ and http://motif.lsce.ipsl.fr/. We would also like to acknowledge Giles Wiggs for valuable discussions regarding dune activity index

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