On the accuracy of noble gas recharge temperatures as a paleoclimate proxy

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114,, doi: /2008jd010438, 2009 On the accuracy of noble gas recharge temperatures as a paleoclimate proxy Bradley D. Cey 1 Received 16 May 2008; revised 30 November 2008; accepted 17 December 2008; published 21 February [1] Dissolved noble gases in groundwater are an important terrestrial temperature proxy for the last glacial maximum (LGM). Noble gas temperatures (NGT) provide a record of long-term mean water table temperature (WTT) during groundwater recharge. For NGT to accurately represent surface air temperatures (SAT), the difference between mean annual air temperature (MAAT) and WTT must be known through time. Many paleoclimate studies reference NGT without articulating the potential difference between NGT and air temperature. Recognizing the array of climatic changes that have occurred since the LGM, it is possible some of these changes have altered the relationship between WTT and MAAT in groundwater recharge zones. The coupling of WTT and MAAT was evaluated in numerical modeling experiments that examined WTT sensitivity to changes in (1) precipitation amount, (2) water table depth, and (3) air temperature. Moderate changes in precipitation amount (±20%) and water table depth (1 2 m) caused WTT-MAAT decoupling of 0.2 C. Varying air temperature, either MAAT or annual amplitude, changed the duration of snow cover which caused seasonal decoupling of WTT from SAT. Assuming SAT was actually 5 7 C cooler at the LGM than at present, these modeling experiments suggest that errors associated with WTT-MAAT decoupling at snow-free sites are insignificant given the precision of NGT. However, results indicate that WTT-MAAT decoupling could cause an underestimation of the actual SAT change by 1.4 C at sites having seasonal snow cover. Citation: Cey, B. D. (2009), On the accuracy of noble gas recharge temperatures as a paleoclimate proxy, J. Geophys. Res., 114,, doi: /2008jd Introduction [2] Anthropogenically induced climate change has stimulated paleoclimate research, including the climate of the last glacial maximum (LGM, ka before present) [e.g., Hargreaves et al., 2007; Schneider von Deimling et al., 2006]. Insight into past climate is drawn from various different proxies (e.g., alpine snowlines, d 18 O, pollen, coral, foraminifera Mg:Ca, alkenones, tree rings, ice cores, speleothems). Noble gas temperatures (NGT) are another common paleotemperature proxy for the LGM [Stute and Schlosser, 1993]. [3] The NGT technique is based on the temperature dependence of noble gases dissolved in groundwater during recharge [Kipfer et al., 2002]. The heaviest noble gases (i.e., Xe, Kr) provide the most information about recharge temperature because their solubility is strongly temperature dependent. After correcting for gas dissolved from entrapped bubbles of soil gas (termed excess air ), the recharge temperature is calculated from the equilibrium dissolved gas concentration with respect to atmospheric pressure (using Henry s Law). The groundwater is most commonly dated 1 Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, Texas, USA. Copyright 2009 by the American Geophysical Union /09/2008JD using 14 C[Stute and Schlosser, 2000]. Dispersion restricts the ability of NGT to determine high frequency changes; however, NGT response to the LGM temperature signal is normally easily identifiable [Stute and Schlosser, 1993]. [4] NGT are especially significant because there are few other quantitative, terrestrial, low elevation paleotemperature proxy records of the LGM [Farrera et al., 1999]. Stute et al. [1995] found a 5 C NGT cooling in tropical Brazil during the LGM. These results are important for unraveling tropical LGM conditions, especially considering the seemingly inconsistent results from marine proxy data (coral 5 C cooling [Guilderson et al., 1994]; alkenone 2 C cooling [Rosell-Melé et al., 2004]). Many studies have used NGT in midlatitudes as well (see references in the work of Kipfer et al. [2002]); however, the absence of groundwater recharge beneath continental ice may limit NGT applicability at high latitudes [Beyerle et al., 1998]. [5] It is generally accepted that NGT are an accurate indicator of long-term mean water table temperature (WTT) [e.g., Kipfer et al., 2002]. Stute and Schlosser [2000] plotted NGT data from 16 sites illustrating the equivalence of NGT and soil temperature; however, studies investigating paleoclimate are primarily concerned with surface air temperatures (SAT) rather than subsurface temperatures. The developers of the NGT technique understand that NGT data is a measure of WTT, not necessarily SAT [Stute and Schlosser, 1993, 2000]. Unfortunately, discussions of 1of9

2 NGT data in some paleoclimate studies do not always clearly articulate the important distinction between WTT as measured by NGT and mean annual air temperature (MAAT) [e.g., Beerling and Mayle, 2006; Condesso de Melo et al., 2001; Kim et al., 2003; Wang et al., 2006]. The value of NGT data to paleoclimate studies relies on the temporal constancy of the coupling between WTT and MAAT. The use of NGT as a proxy of paleoair temperature is compromised when WTT does not track MAAT. [6] Subsurface temperature is affected by numerous interrelated processes near the land-atmosphere interface. Decoupling of WTT-MAAT could result from changes in: climate seasonality, snow cover, precipitation, soil water content, groundwater recharge, water table depth, vegetation, or any other factor that is involved in heat or mass transfer near the land-atmosphere interface. Arguably the most important of these for noble gas paleothermometry is snow cover [Farrera et al., 1999]. Snow insulates the subsurface from the coldest temperatures; therefore, increased snow cover (amount and/or duration) will result in warming of the subsurface relative to air [Zhang, 2005]. Not accounting for WTT-MAAT decoupling caused by changes in snow cover since the LGM would result in underestimation of atmospheric warming since the LGM. [7] Despite obvious major climatic/hydrologic changes since the LGM, little attention has been given to WTT- MAAT (de)coupling. Stute and Sonntag [1992] discussed the relationship between MAAT and subsurface temperature and noted the importance of vegetation on subsurface temperature. Beyerle et al. [2003] suggested WTT-MAAT coupling may not exist over glacial-interglacial periods. They measured NGT from Niger, west Africa and suggested that the majority of observed NGT warming since the late glacial/early Holocene (10 14 ka before present) is the result of changes in WTT-MAAT difference rather than changes in atmospheric warming (3.5 C attributed to WTT- MAAT difference versus 2 C attributed to atmospheric warming). Edmunds et al. [2006] also suggested that WTT-MAAT decoupling over glacial-interglacial climate changes may explain observed NGT. [8] Many studies have addressed the coupling of ground surface temperature (GST) to MAAT using borehole temperature profiles to deduce past air temperatures [González- Rouco et al., 2008, and references therein]. Field evidence exists to support the coupling of GST-MAAT during the last half century [Baker and Ruschy, 1993], while synthetic subsurface temperature profiles generated from air temperature data over the last century compared favorably with measured borehole temperature profiles [Chapman et al., 1992; Harris and Gosnold, 1999]. Mann and Schmidt [2003] questioned the validity of GST-MAAT coupling over centuries based on results from general circulation model simulations; however, González-Rouco et al. [2003] concluded that variations in deep soil temperature and MAAT are almost indistinguishable from each other over that time span based on global climate model simulation results. [9] Studies on GST-MAAT coupling are clearly relevant to the question of WTT-MAAT coupling. For example, Beltrami and Kellman [2003] presented data from multiple sites showing a correlation between soil-air temperature difference and snow cover duration. Smerdon et al. [2006] examined data from three sites to identify the main meteorological changes that lead to differences between SAT and GST. They associated measured GST-SAT differences to differences in the annual amplitudes of GST and SAT caused by evapotranspiration differences in summer and snow cover differences in winter. Lin et al. [2003] used numerical modeling experiments to explore the possibility of GST-MAAT decoupling as a result of changes in the amount, intensity, and timing of precipitation. While these studies are germane, they do not specifically discuss WTT under recharging conditions. This study builds on these related studies by specifically addressing WTT while considering multiple conditions that may affect WTT-MAAT coupling. Furthermore, this study considers glacial-interglacial climatic changes which are more pronounced than the climatic changes relevant to borehole temperature profile studies. [10] The goal of this study was to critically evaluate the precision and applicability of NGT records spanning glacial-interglacial cycles. The specific objectives of the study were to evaluate WTT-MAAT coupling as impacted by changes in (1) precipitation amount, (2) water table depth, and (3) air temperature. This study was not meant to be an exhaustive exploration of the precision of NGT. However, it was designed to evaluate some of the potential uncertainties associated with NGT use in paleoclimatology. 2. Methods 2.1. Model Description [11] The impact of climate variables on WTT-MAAT coupling was evaluated through numerical modeling experiments using the simultaneous heat and water (SHAW) model [Flerchinger, 2000; Flerchinger and Saxton, 1989a]. SHAW can simulate heat, water, and solute transport through a one dimensional profile containing plant cover, snow, plant residue, and soil. SHAW is well suited for this study because it can simulate snow accumulation and melt, soil freezing and thawing including freezinginduced moisture migration, and frozen soil related runoff. SHAW has been used in a variety of studies examining coupled heat and water flow in the unsaturated zone [Flerchinger and Cooley, 2000; Flerchinger et al., 1996; Flerchinger and Saxton, 1989b]. The plant canopy, snowpack, and soil are each discretized into multiple layers. Energy and moisture fluxes are computed between layers for each time step, and balance equations for each layer are written in implicit finite difference form [Flerchinger, 2000]. SHAW uses the water retention function of Campbell [1974], which is a modification of the Brooks and Corey [1966] function in which the residual water content is zero. The Burdine [1953] hydraulic conductivity function is used to calculate unsaturated hydraulic conductivity. Soil thermal conductivity is calculated using the method detailed by de Vries [1963]. A detailed description of SHAW is given by Flerchinger [2000]. [12] The system considered in this study consisted of a 0.02 m plant residue layer overlying 20 m of soil. The soil profile was divided into 47 layers ranging in thickness from to 2.0 m. Two soil types were considered, loam and sand. Soil properties were uniform with depth for both soil types considered (Table 1 and Table 2). Soil hydraulic properties were taken from Rawls and Brakensiek [1989]. 2of9

3 Table 1. Soil Properties Used in SHAW Model a Soil b Saturated Volumetric Water Content (m 3 m 3 ) Saturated Hydraulic Conductivity (m s 1 ) Bulk Density (kg m 3 ) Air Entry Potential (m) Campbell s Pore Size Distribution Index Sand (%) Silt (%) Clay (%) Loam Sand a From Rawls and Brakensiek [1989]. SHAW is simultaneous heat and water model. b United States Department of Agriculture soil texture class. [13] The region represented in this study is midcontinent North America (the Great Plains). Reasons for simulating conditions representative of this region are (1) the region has seasonal snow cover which would have increased during the LGM, (2) a strong east-west precipitation gradient presently exists in the region, making local precipitation changes more likely during periods of climate change, and (3) studies examining NGT changes between the LGM and present were conducted in the region [Clark et al., 1998; McMahon et al., 2004]. Although conditions representative of the Great Plains region were used, this study did not attempt to simulate conditions of any particular site. [14] A simplified system exclusive of vegetation was used in this study. Although beyond the scope of this study, it is anticipated that inclusion of vegetation would generally lower mean annual GST and WTT [Hillel, 2004; Stute and Sonntag, 1992]; however, the response of vegetation to climatic variations is very complex. The goal of future work is to assess the sensitivity of MAAT-WTT coupling to vegetation changes (type, density, etc.). [15] The duration of all model simulations was 40 years, with each simulation using one year of input data repeatedly for 40 consecutive years. In every case, model simulation results were checked to confirm that the soil reached equilibrium with the input forcings (i.e., annual averages of soil water content and soil temperature were no longer changing). Reported output is from the last full year simulated. [16] Modifications were made to SHAW to allow zero heat flux as a lower boundary condition. Lower boundary conditions were constant head and zero heat flux for all simulations. The soil zone was made sufficiently deep to ensure that the lower thermal boundary condition did not affect seasonal temperature fluctuations [Smerdon and Stieglitz, 2006]. The 20 m soil depth was sufficient to meet this requirement, consistent with the findings of Lin et al. [2003]. Stevens et al. [2007] discussed the effect of lower boundary position (i.e., soil thickness) on subsurface heat storage in climate models, and showed that a soil thickness of 20 m is unable to represent subsurface heat storage changes occurring during glacial-interglacial climatic transitions. However, the modeling approached used here does not consider subsurface heat storage changes during climatic transitions because all simulations are run until the soil profile is in equilibrium with the input forcings. Therefore, the 20 m soil profile is appropriate given the simplifications employed in this study Numerical Experiments [17] A Base Case scenario was defined to establish a consistent benchmark against which parameter sensitivity was measured. Hourly input data used for the Base Case were taken from the North American Land Data Assimilation System (NLDAS; year 2004 at 38.5 N, 97 W) [Cosgrove et al., 2003] (Figure 1 and Table 3). Data from Table 2. Soil Thermal Properties Used in SHAW Model Soil a Saturated Volumetric Water Content (m 3 m 3 ) Saturated Thermal Conductivity (W m 1 K 1 ) Sand b (%) Silt c (%) Clay c (%) Loam Sand a United States Department of Agriculture soil texture class. b Thermal conductivity is 8.80 W m 1 K 1. c Thermal conductivity is 2.92 W m 1 K 1. Figure 1. Input forcing data used in the Base Case scenario. (a) Hourly temperature data and (b) daily precipitation totals (hourly precipitation was used as input). Data taken from the North American Land Data Assimilation System (NLDAS) [Cosgrove et al., 2003], year 2004 at 38.5 N, 97 W. 3of9

4 Table 3. Meteorological Forcing Data Used in the Base Case Scenario Input Parameter Value Mean Annual Air Temperature, MAAT ( C) Total Annual Precipitation (mm of rain equivalent) 710 Median Wind Speed (m s 1 ) 4.6 Median Relative Humidity (%) were chosen because 2004 precipitation data (yearly total and seasonal amounts) were representative of longterm values in the area. Mean annual water table depth in the Base Case was 3 m below ground surface. [18] At present, SHAW only allows specified temperature or zero heat flux lower boundary conditions. Recognizing that a zero heat flux lower boundary condition is physically unrealistic, the impact of allowing heat flow at the lower boundary was tested. Two alternative lower thermal boundary conditions were compared: (1) zero heat flux and (2) specified temperature such that mean upward heat flux was 50 mw m 2. These alternatives were simulated using the Base Case input forcings. [19] Sensitivity analysis was completed by varying three parameters in this study: (1) precipitation amount, (2) water table depth, and (3) air temperature. These parameters were varied independently relative to the Base Case. Precipitation was varied by uniformly increasing or decreasing each day s precipitation by a scaling factor. The precipitation scaling factor range was times that of the Base Case scenario. Mean annual water table was raised 1 m and lowered 2 m from the Base Case scenario (i.e., 2 5 m). Input air temperature was modified in two different ways: (1) varying MAAT from 13 C below to 7 C above the Base Case scenario (i.e., C) and (2) varying the amplitude of the annual temperature fluctuation (±4 C) while keeping MAAT constant (amplitude was varied by adding or subtracting temperatures taken from a sinusoidal curve fit to the full year of hourly temperature data). 3. Results 3.1. Lower Boundary Condition [20] The effect of the zero heat flux lower boundary condition was tested. In this experiment, the specified temperature lower boundary condition was set to produce a mean upward heat flux of 50 mw m 2. The zero heat flux condition resulted in lower WTT for both the loam and sand compared to the specified temperature condition. The higher WTT for the specified temperature condition was expected because of the imposed heat flux through the soil column. The water table temperature difference was consistent throughout the year for both loam and sand (difference in mean daily water table temperature was 0.08 ± 0.01 C for every day of the year). The uniform water table temperature difference through time between the two boundary conditions demonstrates that temperatures in the shallow soil zone respond equivalently to the input forcings for both boundary conditions. These results suggest that the simplification of using a zero heat flux lower boundary condition is acceptable for this study Base Case [21] Both loam and sand had GST and WTT greater than MAAT for the Base Case scenario. Mean annual WTT for loam and sand were C and C, respectively. The mean soil temperatures are consistently greater than MAAT for two reasons: (1) insulating behavior of snow during winter and (2) advective heat flow. First, seasonal snow cover insulates soil during the coldest months, resulting in a net warming of soil relative to air. Second, the system considered is a recharge zone; therefore, advective heat flow into the soil zone occurs. The majority of recharging water infiltrates during the warmest months because (1) soil freezing during winter inhibits infiltration and (2) precipitation falls predominantly during the warmest months (41% during summer, i.e., June, July, and August). The infiltrating water carries heat into the soil, contributing to elevated soil temperatures relative to MAAT. These results are consistent with field data showing that shallow soil temperatures are commonly 1 3 C greater than MAAT [Smith et al., 1964]. The higher hydraulic conductivity of sand permitted greater advective heat flow, contributing to mean annual WTT of sand being greater than that of loam. [22] Evaporation causes soil cooling. This also contributed to the loam being cooler than sand. Differences in water retention functions between the two soil types caused the loam to have a higher average water saturation (Figure 2). The greater soil water content of the loam resulted in higher latent heat flux (and lower sensible heat flux) than in the sand. [23] Soil temperatures simulated in the Base Case showed increased damping and lag with depth (Figure 3a). Precipitation events caused high frequency perturbations of both temperature and water content (Figure 3). For example, the day of greatest precipitation (59 mm on day 64) caused a pronounced increase in water saturation and led to peak annual recharge flux on day 86 in loam and on day 71 in sand. The differing hydraulic conductivities of the soils explain the delayed recharged response in the loam as well as the more pronounced water table fluctuations in the loam (Figure 4). Short-term (daily to weekly) temperature and soil water content fluctuations caused by precipitation Figure 2. Mean annual saturation profiles for the Base Case (mean annual depth to the water table was 3 m). 4of9

5 Figure 4. Water table fluctuations of the Base Case scenarios (mean annual water table depth of 3 m). Figure 3. Modeled time series of (a) temperature and (b) saturation at various depths, and (c) snow depth, (d) sensible heat flux, and (e) latent heat flux at ground surface for the loam soil Base Case scenario. Depths given in Figures 3a and 3b are in meters. Positive heat flux values indicate flow into the soil. precipitation resulted in an increase in latent heat flux and a decrease in sensible heat flux. Increased precipitation caused a small increase in snow cover days which warmed the soil in winter; however, this effect was minor relative to summertime changes (Figure 6). The primary reason for soil cooling (warming) associated with increased (decreased) precipitation was increased (decreased) evaporation from the wetter (drier) soil. The greatest impact on temperature occurred during summer/fall and the least impact occurred in winter (December, January, February) (Figure 6). [26] Loam has a greater available water capacity (i.e., the difference in soil water content between field capacity and permanent wilting point), which leads to larger changes in soil water content for a given change in precipitation. Therefore, the impact of precipitation on soil temperatures was much more pronounced for loam (Figure 5). For loam, decreasing precipitation by 40% caused a warming of the water table relative to MAAT of 0.48 C while an increase of 40% caused a cooling of 0.19 C. For sand, a 40% precip- were largely damped out within the upper meter of soil (Figure 3). The seasonal temperature cycle caused temperature fluctuations much deeper in the soil profile, well below the water table. The seasonal range of WTT was 7.50 C for loam and 6.81 C for sand. [24] The snow properties were essentially the same for the two soil types. Snow cover was present for 33 days with a maximum snow depth of 0.23 m occurring on day 33 (Figure 3c). The most significant period of continuous snow cover was from day 25 to Precipitation [25] Precipitation was varied by scaling the daily precipitation amount relative to the Base Case. An increase in precipitation caused a cooling of the water table relative to the MAAT for both soil types (Figure 5). Summer is the season of greatest precipitation and therefore precipitation changes had the greatest impact on soil water content during summer. Increased soil water content associated with greater Figure 5. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in precipitation amount. 5of9

6 Figure 6. Seasonal response of ground surface temperature (GST) to changes in precipitation amount. Figure 7. Response of WTT relative to MAAT to changes in mean annual depth of water table below ground surface. Mean annual water table depth was 3 m in the Base Case scenario. itation reduction caused a warming of 0.36 C and a 40% precipitation increase caused a cooling of 0.11 C. [27] Lin et al. [2003] conducted a study similar to the precipitation experiments reported in this study, except that their model included grassy vegetation and excluded snow cover. Their results for scaling precipitation amount show a similar trend and magnitude of soil temperature change results presented here Water Table Depth [28] The second series of experiments involved varying the mean annual water table depth. Water table depth was not fixed in any simulation; rather the specified head condition at the lower boundary was adjusted to achieve the desired mean water table depth (Figure 4). [29] For both loam and sand, increasing water table depth caused a slight increase in WTT relative to MAAT (Figure 7). Decreasing the water table depth from 3 m to 2 m resulted in a cooling of the water table relative to MAAT of 0.04 C for loam and 0.08 C for sand. Increasing the water table depth from 3 m to 5 m caused a water table warming relative to MAAT of 0.05 C for loam and 0.15 C for sand. [30] Although WTT varies with water table depth, soil temperatures in the uppermost 0.1 m were insensitive to changes in water table depth with one exception; shallow soil temperatures in the 2 m water table depth in sand scenario were 0.02 C warmer than for the deeper water table scenarios. Similar to shallow soil temperatures, shallow soil saturation profiles and surface heat fluxes were effectively insensitive to changes in water table depth for both sand and for loam. Unsaturated zone temperature gradients for each of the loam scenarios were similar and for each of the sand scenarios were similar. Consequently, WTT for deeper water tables were warmer. The impact of water table depth on WTT was greater for sand than for loam (Figure 7) in part because of the steeper temperature gradient in the relatively drier unsaturated zone in sand (Figure 2) Temperature [31] The last two series of experiments investigated the impact of air temperature changes on WTT-MAAT coupling. In the first of these experiments, MAAT was varied while maintaining all other inputs constant. When MAAT was increased, the difference between WTT and MAAT decreased (Figure 8). The change in WTT-MAAT was similar for both soil types and strongly correlated with snow cover duration. As MAAT decreased, more precipitation occurred as snow rather than rain and the duration of the resulting snowpack increased (Figure 9). Snow insulates soil from the coldest temperatures resulting in wintertime soil warming relative to air temperature. The importance of snow cover is shown by the seasonal differences in GST. Relative to MAAT, winter GST was strongly affected whereas summer GST change was minimal (Figure 10). [32] The impact of changing MAAT on WTT-MAAT coupling was much greater than the impact of changes to either precipitation amount or water table depth. When considering a MAAT cooling of 7 C from the Base Case, WTT warms 1.38 C for loam and 1.42 C for sand relative to MAAT. For a unit change in MAAT, the corresponding change in WTT-MAAT ranged from 0.37 to 0.02 C for Figure 8. Response of WTT relative to MAAT to changes in MAAT. MAAT was C in the Base Case scenario. 6of9

7 Figure 9. Changes in snow depth and duration for various MAAT conditions for the loam soil. Snow was not present in any simulations during the excluded time interval. MAAT was C in the Base Case scenario. loam and from 0.36 to 0.03 C for sand, with the larger changes occurring at lower MAAT (Figure 8). [33] The impact of changing annual air temperature amplitude on WTT-MAAT coupling was also examined. Increases in annual air temperature amplitude caused warming of the water table (Figure 11). The cause of this result is similar to that of changes in MAAT discussed previously. Increased amplitude means warmer summers and colder winters, and colder winters produce more snow and a longer period of snow cover. Snow cover driven soil warming was the major process causing changes in WTT-MAAT as shown by the strong correlation between snow cover and WTT-MAAT (Figure 11). For a unit change in temperature amplitude, the corresponding change in WTT-MAAT was C for loam and C for sand, with the larger changes occurring at larger amplitudes. [34] Both types of air temperature variation, MAAT and annual amplitude, caused decoupling of WTT from MAAT. There was little difference between loam and sand in the Figure 10. for loam. Seasonal response of GST relative to MAAT Figure 11. Response of WTT relative to MAAT to changes in the annual amplitude of MAAT. magnitude of WTT-MAAT decoupling caused by air temperature changes. This differs from precipitation and water table depth experiments in which the magnitude of WTT- MAAT decoupling varied with soil type. 4. Discussion [35] Climate simulations and, where available, proxy data rarely give a consistent picture of paleoclimate conditions. Therefore, the sensitivity analysis presented in this study isolated one variable per experiment to identify the impact of individual climatic/hydrologic variables. Precipitation amount was examined and its impact on WTT-MAAT coupling was only a few tenths of a degree or less for moderate precipitation changes (20%), which is consistent with results of Lin et al. [2003]. Varying mean annual water table depth by 1 2 m also caused changes to WTT-MAAT of only a few tenths of a degree or less. Impacts of both precipitation and water table depth on WTT differed between the two soil textures, with loam impacted more than sand. [36] While precipitation results are consistent with the findings of Lin et al. [2003], ranges of precipitation and water table depth changes evaluated are not necessarily representative of LGM conditions. Actual changes in precipitation and water table depth since the LGM are likely greater than parameter ranges examined in this study [e.g., Farrera et al., 1999]. Therefore, it is difficult to assess the magnitude of error caused by precipitation or water table changes on paleo-sat inferred from NGT. However, these results suggest that moderate precipitation or water table changes are not sufficient to introduce significant error in paleo-sat inferred from NGT. [37] Results indicate that air temperature changes have the potential to cause WTT-MAAT decoupling, which causes errors in paleotemperature interpretations based on NGT data. Assuming the LGM SAT was actually 5 7 C cooler than present [Kipfer et al., 2002], these results suggest that decoupling of WTT from MAAT underestimates the actual SAT shift by 1.4 C. The primary cause of this effect is snow acting as a seasonal insulator [Zhang, 2005]. Therefore, it is reasonable to expect the largest errors related to decoupling to be in regions that have seasonal 7of9

8 snow cover, which is common in the midlatitudes. Many studies suggest the temperature difference between the LGM and present was larger during winter than summer [e.g., Denton et al., 2005; Wright et al., 1993]. Such an increase in annual temperature amplitude would increase the error of underestimating the SAT shift since the LGM. [38] These results suggest that only minor decoupling is likely at sites without snow cover. The resulting error in glacial-interglacial atmospheric temperature change inferred from NGT is less than NGT uncertainty itself [Stute et al., 1995]. However, the relative importance of different climatic/hydrologic factors is uncertain because of uncertainty in precipitation and water table changes since the LGM. [39] The results presented here are consistent with the results of Smerdon et al. [2006]. In evaluating data from multiple sites, they found that either summer or winter decoupling of GST from SAT could largely explain differences between mean annual GST and MAAT. Furthermore, they noted the importance of snow cover in accounting for differences between GST and MAAT at a site in the Great Plains. [40] WTT and GST output have a consistent 1:1 correlation when considering all modeled scenarios (plot not shown). These results are consistent with the relationship between NGT and soil temperature shown by Stute and Schlosser [2000]. [41] Borehole temperature profile related studies have investigated SAT-GST coupling and found decoupling is generally insignificant and therefore unlikely to bias calculations of past air temperatures inferred from borehole temperature profiles [González-Rouco et al., 2008]. Such borehole temperature profile studies provide temperatures for the past 0.5 ka, whereas noble gas paleothermometry provide temperature data for the past 20 ka. While decoupling of SAT-GST may be insignificant over the past few hundred years, clearly climatic changes since the LGM (18 23 ka before present) can result in SAT-GST decoupling (which causes WTT-MAAT decoupling). Therefore, WTT-GST decoupling is not necessary to cause WTT- MAAT decoupling. [42] This study contributes to our understanding of factors affecting WTT-MAAT (de)coupling; however, it has several limitations. The site considered, while based on actual data from the Great Plains, was highly idealized. For example, soil properties were constant throughout the soil profile. Potentially more significant was the absence of vegetation. Vegetation clearly influences water and heat flow across the land-atmosphere boundary, and vegetation changes in response to climatic changes are well documented [e.g., Williams, 2003]. Excluding vegetation is more likely to affect the outcome of experiments involving precipitation and water table depth because in these experiments the soil water content/soil textural differences were more critical. In contrast, soil texture was relatively unimportant in air temperature experiments because the dominant change was heat conduction as affected by snow cover. Modeling necessarily requires simplifications due to the complexity of land-atmosphere processes; however, simplifications made in this study do not negate the usefulness of these results in quantitatively examining WTT-MAAT decoupling. [43] This study examined several key parameters that can affect WTT-MAAT coupling. However, further investigation into WTT-MAAT decoupling would be beneficial. Recommendations for further work are (1) inclusion of vegetation, (2) examination of other parameters (e.g., precipitation intensity and timing), (3) simultaneous examination of multiple parameters (e.g., precipitation and MAAT), and (4) sensitivity analyses based on actual site data. 5. Conclusions [44] Numerical modeling experiments suggest only modest WTT changes (tenths of a degree) in response to moderate changes in precipitation amount (20%) and water table depth (1 2 m). Soil texture differences were more significant for experiments involving precipitation amount and water table depth than for those involving air temperature. Results of simulations varying air temperature suggest that changes since the LGM can lead to 1 C decoupling of WTT from MAAT. This decoupling is primarily the result of changes in snow cover. This decoupling would cause an underestimation of the atmospheric warming since the LGM as inferred from dissolved noble gas data. [45] The usefulness of noble gas paleothermometry lies in its ability to quantify low-frequency changes in mean annual (water table) temperature. This study suggests that snow cover changes caused by glacial-interglacial atmospheric temperature change results in an underestimation of that atmospheric temperature change as inferred from dissolved noble gas data. The complexities of land-atmosphere interactions and site specific heterogeneities preclude a simple correction to account for WTT-MAAT decoupling. Therefore, caution is necessary when deducing atmospheric temperature changes from dissolved noble gas data, especially in areas with seasonal snow cover. [46] Acknowledgments. This research was supported by the Jackson School of Geosciences at the University of Texas at Austin. Thanks to Gerald N. Flerchinger for use of the SHAW modeling code, to Bridget R. Scanlon and Chris M. 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