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Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand

2014; Wiley; Volume: 119; Issue: 5 Linguagem: Inglês

10.1002/2013jf003009

2169-9011

Ann V. Rowan, S. H. Brocklehurst, David M. Schultz, Mitchell A. Plummer, Leif S. Anderson, Neil F. Glasser,

Climate change and permafrost

Journal of Geophysical Research: Earth SurfaceVolume 119, Issue 5 p. 1064-1081 Research ArticleFree Access Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand Ann V. Rowan, Corresponding Author Ann V. Rowan Centre for Glaciology, Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UK Now at British Geological Survey, Environmental Science Centre, Nottingham, UK Correspondence to: A. V. Rowan, [email protected]Search for more papers by this authorSimon H. Brocklehurst, Simon H. Brocklehurst School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, UKSearch for more papers by this authorDavid M. Schultz, David M. Schultz School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, UKSearch for more papers by this authorMitchell A. Plummer, Mitchell A. Plummer Idaho National Laboratory, Idaho Falls, Idaho, USASearch for more papers by this authorLeif S. Anderson, Leif S. Anderson Institute of Arctic and Alpine Research, and Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USASearch for more papers by this authorNeil F. Glasser, Neil F. Glasser Centre for Glaciology, Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UKSearch for more papers by this author Ann V. Rowan, Corresponding Author Ann V. Rowan Centre for Glaciology, Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UK Now at British Geological Survey, Environmental Science Centre, Nottingham, UK Correspondence to: A. V. Rowan, [email protected]Search for more papers by this authorSimon H. Brocklehurst, Simon H. Brocklehurst School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, UKSearch for more papers by this authorDavid M. Schultz, David M. Schultz School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, UKSearch for more papers by this authorMitchell A. Plummer, Mitchell A. Plummer Idaho National Laboratory, Idaho Falls, Idaho, USASearch for more papers by this authorLeif S. Anderson, Leif S. Anderson Institute of Arctic and Alpine Research, and Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USASearch for more papers by this authorNeil F. Glasser, Neil F. Glasser Centre for Glaciology, Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UKSearch for more papers by this author First published: 11 April 2014 https://doi.org/10.1002/2013JF003009Citations: 21AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Glaciers respond to climate variations and leave geomorphic evidence that represents an important terrestrial paleoclimate record. However, the accuracy of paleoclimate reconstructions from glacial geology is limited by the challenge of representing mountain meteorology in numerical models. Precipitation is usually treated in a simple manner and yet represents difficult-to-characterize variables such as amount, distribution, and phase. Furthermore, precipitation distributions during a glacial probably differed from present-day interglacial patterns. We applied two models to investigate glacier sensitivity to temperature and precipitation in the eastern Southern Alps of New Zealand. A 2-D model was used to quantify variations in the length of the reconstructed glaciers resulting from plausible precipitation distributions compared to variations in length resulting from change in mean annual air temperature and precipitation amount. A 1-D model was used to quantify variations in length resulting from interannual climate variability. Assuming that present-day interglacial values represent precipitation distributions during the last glacial, a range of plausible present-day precipitation distributions resulted in uncertainty in the Last Glacial Maximum length of the Pukaki Glacier of 17.1 km (24%) and the Rakaia Glacier of 9.3 km (25%), corresponding to a 0.5°C difference in temperature. Smaller changes in glacier length resulted from a 50% decrease in precipitation amount from present-day values (−14% and −18%) and from a 50% increase in precipitation amount (5% and 9%). Our results demonstrate that precipitation distribution can produce considerable variation in simulated glacier extents and that reconstructions of paleoglaciers should include this uncertainty. Key Points Glaciers in the Southern Alps are sensitive to precipitation distribution The New Zealand Last Glacial Maximum was 8.25°C to 5.5°C cooler than present Glacier models should estimate an envelope of paleoclimate variability 1 Introduction Glacial geology is an important terrestrial record of past climate change [e.g., Kaplan et al., 2010; Putnam et al., 2010]. Paleoclimate conditions can be inferred from this record using equilibrium line altitude (ELA) reconstructions based on mapping of paleoglacier shape [e.g., Porter, 1975] or using ice flow models that determine glacier volume [e.g., Anderson and Mackintosh, 2006; Doughty et al., 2013; Kaplan et al., 2013]. While both methods have advantages and disadvantages, the accuracy of the inferred paleoclimate is limited by the challenge of representing mountain meteorology in glacier models. Describing the spatial and seasonal variations of an essentially unchanging climate and the temporal changes in climatic conditions that are likely to affect the glacier balance also presents potential difficulties to model applications. Near-surface air temperature and precipitation rates are typically assumed to have a linear relationship with altitude, but the interaction of air masses with high topography modifies the distribution of precipitation. Reconstructions of glaciers located in the temperate, westerly dominated midlatitudes—for example, the Patagonian Andes [Glasser et al., 2005; Kaplan et al., 2008] and the Southern Alps of New Zealand [Anderson and Mackintosh, 2006; Rother and Shulmeister, 2006; Golledge et al., 2012; Rowan et al., 2013]—reveal compelling evidence for glacier sensitivity to both temperature and precipitation distribution. The interaction between rugged, evolving topography and variable air circulation patterns is complex, and the distribution of precipitation in mountainous regions is often difficult to predict. Precipitation peaks do not correlate with the highest topography [Henderson and Thompson, 1999; Schultz et al., 2002; Steenburgh, 2003; Roe, 2005; Anders et al., 2006], and observations are scarce, as high-elevation rain gauges are frequently lacking, and these data typically only represent short time spans [Groisman and Legates, 1994]. Moreover, precipitation amount, spatial distribution, temporal distribution, and phase will vary with climate change, so present-day precipitation data may not represent conditions during a glacial. As a result, the representation of precipitation in glacier models may be unsatisfactory and could result in unaccounted-for uncertainties in paleoclimate reconstructions [Rother and Shulmeister, 2006]. Seasonality in lapse rate [Doughty et al., 2013], air temperature, and precipitation amount [Golledge et al., 2010] may also modify mass balance. Many glacier modeling studies use summer-winter climatologies that do not consider the detail of these seasonal variations in meteorological variables. Furthermore, many glacier-modeling studies assume that glaciers were in equilibrium with the long-term mean values of temperature and precipitation amount. However, because glaciers also respond to interannual climate variations (climate noise), this assumption is likely to be invalid [Anderson et al., 2014]. The purpose of this paper is to quantify variations in the extents of reconstructed glaciers resulting from a realistic range of precipitation distributions for the Southern Alps of New Zealand. We apply two glacier models to the eastern Southern Alps to demonstrate the need for more realistic representations of orographic patterns of rain and snowfall. We consider whether these glacier reconstructions are precise indicators of past climate change, or if a more realistic approach is to quantify glacier sensitivities to climate and use these to infer an envelope of likely paleoclimate change—an approach previously used by Anderson and Mackintosh [2006] and Golledge et al. [2012] for temperature and precipitation amount. Our paper builds on these previous studies to quantify uncertainties in simulated glacier extents due to precipitation distribution, precipitation phase, interannual climate variability, and seasonality. The Southern Alps The Southern Alps of New Zealand (Figure 1) are an excellent location to explore the climate sensitivity of glaciers [Oerlemans, 1997; Anderson and Mackintosh, 2006; Anderson et al., 2010; Putnam et al., 2010; Doughty et al., 2013]. This 400 km long, ~120 km wide, northeast-southwest trending mountain range has summit elevations exceeding 3000 m [Tippett and Kamp, 1995; Willett, 1999]. The axial trend of the range is perpendicular to the prevailing westerly winds, resulting in a steep west-east precipitation gradient. The central Southern Alps experience extremely high precipitation of up to 14 m per year on the western (upwind) side of the range, which decreases rapidly to the east [Henderson and Thompson, 1999; Wratt et al., 2000]. The trend in ELA is strongly influenced by this precipitation gradient [Chinn, 1995], suggesting that orographic precipitation exerts a primary control on glaciation [Porter, 1975]. Figure 1Open in figure viewerPowerPoint (a) Location of the study area in the Southern Alps of New Zealand, showing the model domain used in the 2-D experiments (red-shaded area), the maximum glaciated extent during the LGM (white shading), and location of the precipitation transect shown in Figure 3 (blue line). Glacier volumes simulated under (b) the present-day (ΔT = 0°C) scenario (the baseline model), and (c) the Double Hill scenario (ΔT = −4.5°C), overlain on a shaded relief map of the model domain. Catchment boundaries (solid red lines; dashed sections indicate interpretation over areas of low relief) and the flowlines used to measure length of the Pukaki and Rakaia Glaciers (solid green lines) are shown. New Zealand is one of few landmasses in the southern midlatitudes, and paleoclimate reconstructions from New Zealand are important for comparison with global records [e.g., Kaplan et al., 2010; Putnam et al., 2012]. The Last Glacial Maximum (LGM) occurred in New Zealand between 24 and 18 ka [Putnam et al., 2013b]. The late Quaternary geology is well preserved and records frequent and rapid climate change [Alloway et al., 2007; Barrell et al., 2011]. There is little regional variation in bedrock lithology [Cox and Barrell, 2007], so glaciers are unlikely to be modified by their geological setting. Numerical simulations of the Southern Alps icefield [Golledge et al., 2012], the Ohau [Putnam et al., 2013b], the Pukaki [McKinnon et al., 2012], and the Rakaia [Rowan et al., 2013] Glaciers (Figure 1) demonstrated that LGM mean annual air temperature was between 6°C and 8°C cooler than present-day values and may have been accompanied by a reduction of up to 25% in precipitation. Applications of Glacier Models in the Southern Alps Previous glacier modeling studies in the Southern Alps have focused on the Franz Josef Glacier (Figure 1) to examine the climate sensitivity of this glacier [Oerlemans, 1997] and the drivers of the advance to the well-preserved Waiho Loop moraine [Anderson and Mackintosh, 2006; Anderson et al., 2008; Alexander et al., 2011]. Oerlemans [1997] and Anderson and Mackintosh [2006] demonstrated that differences in temperature rather than precipitation amount were the major control on the length of this glacier, but that high precipitation values enhanced temperature sensitivity; Oerlemans [1997] showed that the Franz Josef Glacier receded 1.5 km per °C of warming, whereas Anderson and Mackintosh [2006] showed that the glacier advanced at a rate of 3.3 km per °C of cooling. This difference was attributed to Oerlemans' unrealistically low precipitation values [Tovar et al., 2008; Shulmeister et al., 2009; Alexander et al., 2011]. Energy-balance calculations for the Brewster Glacier indicated high temperature sensitivity; a 50% change in precipitation amount was required to offset a temperature difference of 1°C [Anderson et al., 2010]. The Pukaki Glacier has a greater temperature sensitivity than the Brewster Glacier; an 82% increase in precipitation amount is required to offset a temperature difference of −1°C [Anderson and Mackintosh, 2012], probably due to the difference in the hypsometry of these glaciers. There may be uncertainty in simulated glacier extents due to bed geometry and subglacial erosion independent of climate change. A flowline model of the Pukaki Glacier indicated that variations in the bed topography could have forced kilometer-scale variation in glacier length to form the two distinct LGM moraine sequences in this valley [McKinnon et al., 2012]. The poor fit of some simulated glaciers to mapped moraines, particularly when the model features more than one glacier, indicates the need to quantify the uncertainties associated with the application of glacier models to avoid misleadingly precise paleoclimate estimates. In a model reconstruction of glaciers in the eastern Southern Alps, the Rakaia Glacier was underrepresented by the LGM simulation that provided the best fit to the geological data compared to the neighboring Rangitata and Ashburton Glaciers [Rowan et al., 2013]. A model reconstruction of the Southern Alps LGM icefield generally provided a good fit to the glacial geology, although for some glaciers including the Rakaia, this simulation underestimated the LGM terminus positions [Golledge et al., 2012]. Increasing the simulated length of the Rakaia Glacier to reach the LGM extents required either further cooling of −0.25°C from an LGM simulation with a temperature difference of −6.5°C and no change in precipitation amount from present-day values [Rowan et al., 2013], or further cooling of −1.75°C from an LGM simulation with a temperature difference of −6.25°C and a 25% reduction in precipitation amount from present-day values [Golledge et al., 2012]. 2 Methods The 2-D and 1-D Glacier Models We applied a 2-D energy-mass balance and ice flow model implementing the shallow-ice approximation [Plummer and Phillips, 2003] and a 1-D shallow-ice approximation flowline model [Roe and O’Neal, 2009] to catchments in the eastern Southern Alps (Figure 1). We used the 2-D model to investigate how temperature and precipitation modify the energy balance and extents of these glaciers, while the 1-D model was used for experiments investigating fluctuations in glacier length forced by interannual climate variability [cf. Anderson et al., 2014]. The 2-D glacier model has previously been applied to glaciers in the USA [Plummer and Phillips, 2003; Laabs et al., 2006; Refsnider et al., 2008] and New Zealand [Rowan et al., 2013; Putnam et al., 2013a]. These glacier models are based on the shallow-ice approximation developed for large ice sheets with shallow bed topography [Hutter, 1983], which is unsuitable for glaciers with dominantly steep bed topography [Le Meur et al., 2004]. Previous studies have successfully applied the shallow-ice approximation to glaciers in New Zealand [Anderson and Mackintosh, 2006; Rowan et al., 2013] and elsewhere [Oerlemans et al., 1998; Kessler and Anderson, 2006; Refsnider et al., 2008], and we consider this approximation valid for the large, low-angle valley glaciers that occupied the eastern Southern Alps. The model domain includes the Rakaia to the Pukaki valleys (Figure 1). The Land Information New Zealand (LINZ) 50 m digital elevation model (DEM) was resampled to a 200 m grid spacing to describe topography (Table 1). Present-day ice volumes were removed from the DEM before applying the glacier models following the method of Golledge et al. [2012] using glacier outlines defined by LINZ and assuming a uniform basal shear stress (τb) of 150 kPa (1)where H is ice thickness, ρ is the density of pure glacier ice (917 kg m−3) [Cuffey and Paterson, 2010], g is acceleration due to gravity (9.81 m s−1), and α is the glacier surface slope taken from the resampled DEM. Model parameter values followed Rowan et al. [2013] for the Rakaia-Rangitata Glaciers (Tables 1 and 2). After an initial simulation for a particular temperature difference, the simulated glaciers were added to the DEM to iteratively recalculate mass balance across the glacier allowing for the increase in surface elevation with greater ice volume. Calculated mass balance and DEM topography were used as inputs to the ice flow model to calculate ice thickness. Results from the ice flow model were considered acceptable when the integrated mass balance (the difference between accumulation and ablation across the entire glacier) was within 5% of steady state. Table 1. Two-Dimensional Glacier Model Parameter Values Used in the Simulations Described in This Paper Values Model Domain Description Native horizontal grid spacing of LINZ DEM (m) 50 Vertical precision of LINZ DEM (m) 1 Cell size of model domain (m) 200 Model domain grid (number of cells) 571 × 528 Glaciological parameters High albedo 0.74 Low albedo 0.21 Maximum slope that can hold snow (degrees) 30 Slope increment for avalanching routine (degrees) 12 Minimum new snow for avalanching to occur (m SWE) 0.1 Deformation constant (yr−1 kPa−3) 2.10 × 10−7 Table 2. Variables Used in the Simulations Described in This Paper Following Rowan et al. [2013] Climatological Variables Annual Summer Winter Monthly sea level temperature range (°C) 5.6–15.8 10.7–15.8 5.6–11.2 Standard deviation of daily temperature (°C) 2.9 3.1 2.7 Lapse rate (°C km−1) −6 Critical temperature for snowfall (°C) 2 NIWA annual rainfall maximum (mm) 8450 NIWA annual rainfall minimum (mm) 645 NIWA annual rainfall mean (mm) 1602 Wind speed (m s−1) 3.2 3.6 2.8 Base wind speed elevation (m) 457 Multiplier for wind speed increase with elevation 0.0008 Cloudiness (fraction of sky obscured) 0.7 Relative humidity (%) 77 75 79 Turbulent heat transfer coefficient (−) 0.0015 Ground heat flux (W m−2) 0.1 We tested the variability in glacier volume from a baseline model of the present-day climate resulting from; uniform differences in mean annual air temperature (hereafter referred to as temperature), for example, present-day mean annual air temperature minus 1°C (hereafter ΔT); and multiplicative differences in precipitation amount, for example, 75% of present-day precipitation amount (hereafter P). Temperature difference is defined here as an increase or decrease in temperature calculated as 30 year means from daily measurements. Elsewhere in the glaciological literature, temperature difference may be referred to as “temperature change,” implying variation in temperature throughout each simulation. Climatological Data The climate inputs to our baseline model (Tables 1 and 2) were based on 123 automatic weather stations (AWS) in the national climate database CliFlo (http://cliflo.niwa.co.nz/). We used 30 year (1971–2000) monthly mean and daily standard deviation values for temperature, monthly means for relative humidity and wind speed, and 30 year mean annual values for cloudiness. Although interannual variability was observed in the meteorological data, we used 30 year mean values as input to the 2-D model, as variations in climate with a shorter period than the glacier's response time are unlikely to produce the magnitude of length fluctuations we are examining (10–80 km length fluctuations). In the baseline model, precipitation distribution was defined using the National Institute of Water and Atmospheric Research (NIWA) 500 m gridded data [Tait et al., 2006]. Comparison with river flow measurements indicated that the NIWA data are within 25% of the total water input to the catchments in question [Tait et al., 2006], and probably record most rainfall but only some snowfall due to the limitations of standard precipitation gauging techniques [Goodison, 1978; Yang et al., 1998]. We applied the method of Yang et al. [1998] for a standard rain gauge to estimate the proportion of both precipitation phases that are not recorded by these gauges. The difference in amount between the gauge-estimated values and the modeled precipitation for the model domain was 11% for rainfall and 49% for snowfall, implying that the total annual precipitation amount was 144% of that recorded. To reflect this estimate of gauge undercatch, we increased the precipitation input to the 2-D baseline model by these ratios and again simulated glacier lengths. After increasing precipitation amount in line with this estimate, the LGM Rakaia Glacier simulated under the same ΔT (−6.5°C) was 4.1 km (10%) longer, which equated to a ΔT of less than −0.5°C. Experimental Design Glacier sensitivity to temperature, precipitation amount and distribution, interannual climate variability, temperature seasonality, precipitation seasonality, and precipitation phase was investigated for the eastern Southern Alps. We considered glacier sensitivity to climate change in terms of both change in mass balance and change in glacier length (volume) [cf. Oerlemans, 1997]. We performed five sets of experiments, each comprising multiple model simulations, to quantify uncertainty in simulated glacier extents resulting from the following: Experiment 1: Differences in temperature (ΔT) from the baseline model describing present-day climate. Experiment 2: Differences in precipitation amount (P) from the baseline model within a plausible worldwide present-day range. Experiment 3: Precipitation distribution using five estimated precipitation distributions and three statistical approximations of precipitation for the central Southern Alps (Table 3). Experiment 4: Interannual climate variability defined by the present-day standard deviation of mean melt season (December–February) temperature and annual precipitation amount. Experiment 5: Change in seasonality (S), defined here as an increase in monthly summer (October–March) temperatures of up to 3°C, while winter temperatures remain unchanged, combined with change in winter and summer monthly precipitation amounts relative to present-day values. Table 3. Precipitation Data and the Change in Simulated ELA Resulting From the Use of Different Precipitation Distributions Under Present-Day (ΔT = 0°C) and Double Hill (ΔT = –4.5°C) Scenarios Precipitation Data Annual Precipitation Within Model Domain (mm) ELA (m) at ΔT = 0°C ELA (m) at ΔT = −4.5°C ELA (m) Relative to NIWA Results Maximum Minimum Mean (± 1σ) ΔT = 0°C ΔT = −4.5°C NIWA 8450 645 1602 ± 1129 2201 ± 157 1528 ± 148 0 0 CliFlo 5070 580 2170 ± 1740 2239 ± 202 1650 ± 105 38 ± 180 122 ± 127 Griffiths and McSaveney 8015 868 1512 ± 775 2505 ± 143 1613 ± 113 304 ± 150 85 ± 131 Wratt et al. 5705 444 1134 ± 616 2380 ± 199 1620 ± 117 179 ± 178 92 ± 133 Henderson and Thompson 5373 788 1231 ± 776 2534 ± 108 1682 ± 70 333 ± 133 154 ± 109 Linear function 1769 825 1110 ± 190 2101 ± 102 1419 ± 151 −100 ± 130 −109 ± 150 NIWAmean 2141 2141 2141 ± 0 1763 ± 251 1317 ± 137 −438 ± 204 −211 ± 143 NIWAmedian 4854 4854 4854 ± 0 1750 ± 119 1043 ± 173 −451 ± 138 −485 ± 134 Despite the possible reduction in LGM precipitation amount of up to 25% indicated by previous glacier modeling [Golledge et al., 2012], all experiments used the same values for P in the present-day and LGM simulations, apart from those simulations where P was explicitly varied. This approach allowed us to isolate the sensitivity to P and to compare this directly to differences in temperature and precipitation distribution over a range of climate scenarios. Experiments 1 and 2 tested a range of plausible values of ΔT and P during the glacial. Experiments 3 and 5 were designed to simulate specific climate scenarios: (1) the present-day climate applied to the study area by Rowan et al. [2013] (Tables 1 and 2); (2) a Late Glacial paleoclimate indicated by the advance of the Rakaia Glacier to produce the Prospect Hill moraine at 16.25 ± 0.34 ka, equivalent to ΔT = −3.0°C [Putnam et al., 2013a]; (3) a Late Glacial paleoclimate indicated by the Rakaia Glacier advance to the Double Hill moraine at 16.96 ± 0.37 ka, equivalent to ΔT = −4.5°C [Putnam et al., 2013a]; and (4) a paleoclimate representing the LGM at ~21 ka, equivalent to ΔT = −6.5°C [Golledge et al., 2012; Rowan et al., 2013]. Experiment 4 simulated two scenarios: (1) a Late Glacial advance resulting in a reduction in ELA of ~100 m around ~11 ka [Kaplan et al., 2013] equivalent to ΔT = −1.25 °C and (2) the LGM scenario. Our 2-D glacier model calculated snowfall using the number of days per month for which the daily air temperature in each cell was below a critical value for rain-snow partitioning, using the mean monthly air temperature and its daily standard deviation [Plummer and Phillips, 2003]. The value used for the critical temperature at which precipitation falls as snow varies between modeling studies. We tested a range of critical temperatures from 0 to 3°C (results not presented here) which resulted in a 1.9 km (7%) uncertainty in the length of the Pukaki Glacier under present-day climate and a 1.1 km (2%) uncertainty in the glacier length under the Double Hill scenario. The critical temperature was set to 2°C for all simulations reported in this paper. The proportion of annual precipitation falling as snow across the model domain was 7% under the present-day scenario, 46% under the Prospect Hill scenario, 86% under the Double Hill scenario, and 92% under the LGM scenario. 2.3.1 Experiment 1: Temperature Variations in glacier extent due to ΔT were tested for the Rakaia-Rangitata catchments from −9.0°C to 0°C in 0.5°C increments to find an ELA equivalent to the LGM (799 ± 50 m) and the Prospect Hill advance (1540 ± 50 m). Results are presented in section 3.1. 2.3.2 Experiment 2: Precipitation Amount P was varied from 25% to 400% of present-day values in 10% or 25% increments to investigate glacier sensitivities in the Rakaia-Rangitata catchments. ELAs were calculated and glacier length simulated under each of the four climate scenarios. Results are presented in sections 3.1 and 3.2. Results from Experiments 1 and 2 (Figure 2) are compared to those produced for the Franz Josef Glacier [Anderson and Mackintosh, 2006] and for the Irishman Glacier [Doughty et al., 2013]. Figure 2Open in figure viewerPowerPoint Parameter sets (ΔT and P) for the advance of the Rakaia Glacier (RG) to Prospect Hill (green-dotted shading) and the LGM limit (purple diagonal-hatched shading), compared to results for the advance of the Franz Josef Glacier (FJG) to the Waiho Loop moraine (grey horizontal lined shading) from Anderson and Mackintosh [2006], and for the Late Glacial advance of the Irishman Glacier (red cross-hatched shading) from Doughty et al. [2013]. The present-day interannual precipitation variability at the FJG (blue shading) and the present-day worldwide precipitation maximum (blue dashed line) are shown [Henderson and Thompson, 1999]. As any change in LGM precipitation amount is unlikely to have exceeded the present-day worldwide maximum, the change in climate for these advances probably lies within the blue-shaded area. 2.3.3 Experiment 3: Precipitation Distribution Orography regulates precipitation distribution over the Southern Alps as the range axis trends perpendicular to the prevailing westerlies. Therefore, the distribution of precipitation is primarily a function of the distance from the west coast of the South Island rather than a function of elevation [Griffiths and McSaveney, 1983; Sinclair et al., 1997; Henderson and Thompson, 1999; Ibbitt et al., 2001; Tait et al., 2006] (Figures 3b and 3c). When plotted across the range, rain-gauge data show a rather wet region upwind on the western side of the range (3–4 m per year) compared to a much drier region east of the range (less than 2 m per year) (Figure 3a). The scarcity of rain gauges in the high-elevation region 20–60 km downwind of the west coast with which to document this dramatic precipitation gradient leaves open the possibility that glacier simulations could be highly sensitive to the peaks and distribution of precipitation in this region. We assume that precipitation during the LGM was unlikely to have increased beyond the present-day worldwide maximum, equivalent to an 85% increase in Southern Alps precipitation [Henderson and Thompson, 1999]. As the last glacial precipitation distribution is unknown, we instead experiment with present-day precipitation distributions for the region and acknowledge that there is unresolved uncertainty when using these to represent last glacial precipitation. Figure 3Open in figure viewerPowerPoint Precipitation amount along a 1-D transect through the center of the model domain orientated at 130°. (a) Annual 30 year mean precipitation data collected from rain gauges