Thermal property measurements for ecoroof soils common in the western U.S.

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Energy and Buildings 40 (2008) 1246–1251 www.elsevier.com/locate/enbuild

Thermal property measurements for ecoroof soils common in the western U.S. D.J. Sailor *, D. Hutchinson, L. Bokovoy Department of Mechanical and Materials Engineering, Portland State University, PO Box 751-ME Portland, OR 97207, USA Received 9 November 2006; accepted 12 November 2007

Abstract To model the impacts of ecoroofs on building envelope heat transfer accurately, thermal property data for ecoroof soils are needed. To address this need we have measured thermal conductivity, specific heat capacity, thermal emissivity, short wave reflectivity (albedo) and density for ecoroof soil samples over a range of moisture states. To represent a wide range of commonly used ecoroof soils we created eight test samples using an aggregate (expanded shale or pumice), sand, and organic matter in varying volumetric composition ratios. The results indicate significant variability in properties as a function both of soil composition and soil wetness. Thermal conductivity ranged from 0.25 to 0.34 W/(m K) for dry samples and 0.31–0.62 W/(m K) for wet samples. Specific heat capacity ranged from 830 to 1123 J/(kg K) for dry samples and 1085–1602 J/(kg K) for wet samples. Albedo was consistently higher for dry samples (0.17–0.40) decreasing substantially (0.04–0.20) as moisture was added. Thermal emissivities were relatively constant at 0.96  0.02 regardless of soil type or moisture status. These results are discussed in the context of their impacts on building energy consumption and the importance of including daily and seasonal property variation within models of the ecoroof energy balance. # 2007 Elsevier B.V. All rights reserved. Keywords: Ecoroofs; Green roofs; Building energy; Thermal properties

1. Introduction and background Ecoroofs (vegetated or green roofs) have become increasingly popular in recent years due in large part to the wide range of environmental benefits that they provide. Ecoroof soils absorb stormwater, significantly altering the magnitude and timing of peak runoff—an important consideration in many cities, especially those with combined stormwater and sewage systems [1]. Ecoroofs also positively impact ambient air quality through their impact on air temperatures and particulate deposition [2]. If many ecoroofs are installed throughout a city, the air temperature reductions resulting from cool roof surfaces may combine to produce a measurable reduction in the summertime urban heat island magnitude [3]. Additional benefits of ecoroofs include the generation of habitat and their aesthetic appeal. While the benefits listed above are important, the focus of the present paper is on the ability of ecoroofs to reduce building

* Corresponding author. Tel.: +1 503 725 4265; fax: +1 503 725 8255. E-mail address: [email protected] (D.J. Sailor). 0378-7788/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2007.11.004

energy usage for heating and cooling. Specifically, ecoroofs have a significant impact on the building energy balance through the combined effects of soil insulation, evapotranspiration, convective shielding, and radiative shading of the plant canopy (see Fig. 1). This energy balance, while complicated, is similar in many respects to the energy balance above any forest or agricultural ecosystem. As a result, there are a variety of existing models that may be applicable to ecoroofs when suitably modified to accommodate differences in the lower boundary condition [4,5]. Recent research has investigated the building energy impacts of ecoroofs either through direct measurement of roof heat fluxes [6–8], or through modeling [9,10]. The measurement-based approaches generally use heat flux data to estimate the overall resistance to conduction heat transfer (Rvalue) for the ecoroof construction. While this approach is useful, any model that is to capture the transient nature of heat transfer in an ecoroof system must account for the thermal conductivity, density, and specific heat capacity of the soil itself. Otherwise, the heat storage in the soil – which may be significant in ecoroof constructions – cannot adequately be represented. While detailed thermal property data for naturally occurring soils are generally available, there is no information in the peer-

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Fig. 1. Simplified representation of the energy balance in an ecoroof canopy.

reviewed literature regarding the thermal properties of common ecoroof soils. As the composition of ecoroof soils is substantially different from naturally occurring soils, it is important that such information be made available so that models of ecoroof energy balances can more accurately represent the relevant heat transfer processes. Also, as these thermal properties vary significantly with moisture content, it is important to measure ecoroof soil thermal properties under multiple soil moisture conditions. 2. Methods The goal of this study was to develop a database of thermal properties for many common ecoroof soils under a wide range of moisture conditions. The properties measured as part of this study were thermal conductivity (k), specific heat capacity (Cp), short wave reflectivity (rsw), and long wave emissivity (esurf). 2.1. General experimental design The first task in the experimental design process was to determine which ecoroof soil samples would be tested. Ecoroof soils typically consist of three components: a lightweight inorganic aggregate, compost, and sand. Opinions vary widely as to the appropriate proportions, aggregate type, and compost type, but the general consensus is that the appropriate soil composition will vary by region and ecoroof design [11–13]. Aggregate selection is largely determined by local availability and cost. Manufactured aggregates include expanded slate in the eastern U.S., expanded clay in the mid-western and eastern U.S., and expanded shale in the western U.S. [13–17]. In areas where pumice is readily available, such as the U.S. Pacific Northwest, it is often used as the main aggregate. Aggregate typically makes up 50–80% by volume of most ecoroof soils. For the scope of this study, aggregate choice was limited to those commonly used in the western U.S.: pumice and expanded shale. Samples tested included either 50 or 75% ‘‘quarter-minus’’ (nominal size less than 0.25 in. or 0.64 cm) aggregate by volume. Appropriate compost selection for ecoroof applications has been a source of debate. Since a significant consideration in

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ecoroof design is the ability to reduce stormwater runoff without degrading water quality, composts high in nutrients such as animal manure are typically deemed inappropriate as they have been found to produce runoff with unacceptable levels of nitrogen and phosphorus [12]. The use of organic materials in ecoroof applications poses two additional problems as a result of their decomposition: the reduction in soil volume and the development of an organic sludge that interferes with roof drainage, particularly in humid regions. To minimize these problems, industry has begun to reduce compost content in ecoroof soils. In the past, compost typically comprised 10–20% of ecoroof soil. In most recent applications this has been reduced to 0–15%, depending on the climate and soil thickness [12,13]. In addition, high lignin composts are gaining popularity. These composts are produced from peat, bark, sawdust, coconut pith, recycled paper, or yard waste [18]. For this study, an aged yard waste compost was used as large quantities are available in the western U.S. and it is environmentally sustainable. Following the current design trend of low proportions of organic matter, samples tested in this study included 0 and 10% compost, by volume. The sand component of ecoroof growing media is generally specified as USGA sand. Recommended proportions vary from 0 to 50% depending on the region and ecoroof design [13,15]. In this study, samples included 15, 25, 40, and 50% sand by volume. The next task was to determine the moisture levels to be tested. To represent a reasonable range of soil moisture states each of the samples described in Table 1 was tested at four moisture levels ranging from ‘‘very dry’’ to ‘‘wet’’. As the raw materials used in creating samples were not completely dry when received from suppliers the ‘‘very dry’’ state was achieved by first thoroughly drying the samples in thin layers using a combination of heating pads and heat lamps. The three additional moisture levels were then created by adding fixed quantities of water: 42, 85, and 225 g water per liter of dry soil for the ‘‘dry’’, ‘‘moist’’, and ‘‘wet’’ states, respectively. In all, eight ecoroof soil samples were mixed and tested. Four of the samples used pumice as the aggregate and the others used expanded shale. As noted above, for each aggregate four variations of sand and organic matter volume fractions were tested. The sample compositions and corresponding moisture capacities are summarized in Table 1. Table 1 Composition (% by volume) and moisture capacity of ecoroof soil samples Sample no.

Pumice (%)

Expanded shale (%)

Compost (%)

Sand (%)

Moisture capacity (g/g)

DH01 DH02 DH03 DH04 DH05 DH06 DH07 DH08

50 50 75 75 0 0 0 0

0 0 0 0 50 50 75 75

10 0 0 10 10 0 0 10

40 50 25 15 40 50 25 15

0.32 0.30 0.43 0.44 0.23 0.23 0.22 0.24

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The moisture holding capacity of each soil sample was subsequently tested using three replicate measurements following the ASTM D2216-05 standard [19]. Despite the relatively high accuracy possible in the ASTM standard, the natural variability in the composition of samples of our ecoroof soils leads to an uncertainty of 8%. These results are reported as the final column in Table 1. 2.2. Thermal conductivity and specific heat capacity

4 4 ¼ 0:95sTrad Qrad ¼ erad sTrad

Thermal conductivity and specific heat capacity were measured using a dual needle probe system that is a variation on the transient line heat source methods published in IEEE 442-1981 [20] and ASTM D5334 [21]. The underlying approach involves using a needle probe to provide a line source of heat in a semi-infinite medium. The emitted heat pulse results in a temperature elevation measured at both probe locations as a function of time. The governing one-dimensional transient heat conduction equation can be solved with the resulting Bessel function solution depending upon both thermal conductivity (k) and thermal diffusivity (D). For a needle probe with heat rate q, length 2b and radius a, the temperature at a radial distance r from the centerline is given by Z 1 q Tðr; tÞ ¼ T 0 þ R1 exp ðRÞ 4pk r2 =4Dt   2     pffiffiffi  1 2aR b R exp  R I0 erf dR (1) r r r Here I0() is the zero order Bessel function and erf is the error function. The two measurement locations associated with the two-probe system allow for simultaneous solution of both properties. The thermal diffusivity is then related to the specific heat capacity by Cp ¼

k rD

Specifically, the emissivity of the radiometer was set to a constant value of 0.95 and differences between surface temperatures measured using redundant surface-mounted thermocouples (Tsurf) and inferred by the radiometer (Trad) were used to assess the actual surface emissivity (esurf). The long wave radiation intercepted by the radiometer can be determined by using the preset (assumed) surface emissivity (erad) and the temperature recorded by the radiometer: (3)

This calculated irradiation must also be equal to that actually emitted by the surface based on the actual surface emissivity and the directly measured surface temperature: 4 Qrad ¼ esurf sTsurf

(4)

With temperatures specified in units of Kelvin, these equations for irradiation can be solved for surface emissivity:  4 T rad esurf ¼ 0:95 (5) T surf Using this approach the measured emissivity was found to be insensitive to soil composition and moisture content. For all readings taken (two replicates of each of the eight soils at four moisture levels) the mean emissivity was 0.96  0.02. Due to the uniformity in measurement values for emissivity individual values are not given here. Rather, based on our measurements, it is reasonable to assume a constant value of 0.96 for thermal emissivity of the ecoroof soils tested. These values are generally in line with those presented in the literature for naturally occurring soils. For example, several textbooks put dry and moist soil emissivities in the range of 0.93–0.96 [24,25]. On the other hand, as pointed out by Balick et al. [26] and Taylor [27] thermal emittance for mineral soils can be substantially lower.

(2)

This method was implemented using a commercial probe (Decagon KD2 Pro) which automates the process of determining k and D from a set of temperature measurements taken at 1 s intervals over a 30 s heating period and a 30 s cooling period. Specifically, a microcontroller approximates the solution to the governing equation by fitting measurements with exponential integral functions using a nonlinear least squares procedure. A linear drift term corrects for temperature changes of the sample during the measurement, to optimize the accuracy of the readings. While these methods are capable of measuring thermal conductivity and specific heat capacity to within 5% [22], the natural variability in our ecoroof soil samples as evidenced in our replicate measurements (six for each sample and moisture level) yielded an uncertainty of 10% in both conductivity and specific heat capacity. 2.3. Thermal emissivity Thermal emissivity was measured using a variation of the contact thermometer method described in ASTM E1933 [23].

2.4. Albedo Albedo was measured using the technique described by Sailor [28]. These measurements involved placing a shallow sample (2–3 cm) of the soil within a 1 m diameter, 0.20 m tall isolation shade ring. Two ISO 2nd class pyranometers (CM3 from Kipp and Zonen) – one facing up and one facing down – were suspended in the center of the ring such that the downfacing pyranometer received reflected short wave radiation only from the test sample and the shade ring. The apparatus was placed in natural sunlight conditions in a rooftop location with a high sky view factor (>90%). In addition to recording downward and upward values of shortwave radiative fluxes the ratio of diffuse to total sky radiation was measured and found to range from 8 to 9%. Albedo was then calculated, accounting separately for diffuse and beam components of solar radiation and the view factors between the downward-facing pyranometer and the surfaces it sees—the shaded and unshaded sample and shade ring. Using the analysis method outlined by Sailor [28] the resulting albedo values were estimated to be accurate to within 10%. Using this method all eight samples

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were tested in the mid-afternoon on several cloud-free days (26–29 September 2006) at each of the four moisture levels. 3. Results and discussion The data gathered in this study are summarized in Table 2. This table illustrates the wide range of thermal properties that may occur as a function of soil composition and moisture status. The ways in which soil composition and moisture status impact these properties and the consequent implications for the rooftop energy balance are discussed in the following sections. 3.1. Role of soil composition The thermal property data for the dry soils indicate that soils using shale as the aggregate had higher values of thermal conductivity than those using pumice. The peak conductivity for dry shale- and pumice-based soils tested was 0.21 and 0.18 W/(m K), respectively. Both of these peaks occurred for soils involving an even mix of aggregate and sand with no organic matter. Increasing the ratio of aggregate to sand resulted in a slight reduction in thermal conductivity of 0.01 W/ (m K) in both cases. The addition of organic matter in the soil mixes resulted in a similar decrease in thermal conductivity. Table 2 Thermal property data summary for soil samples at multiple moisture levels Sample no.

Moisture (% sat)

Density (g/ml)

k (W/(m K))

Cp (J/(kg K))

Albedo

DH01 DH01 DH01 DH01 DH02 DH02 DH02 DH02 DH03 DH03 DH03 DH03 DH04 DH04 DH04 DH04 DH05 DH05 DH05 DH05 DH06 DH06 DH06 DH06 DH07 DH07 DH07 DH07 DH08 DH08 DH08 DH08

0 13 26 62 0 13 26 64 0 11 22 55 0 13 25 59 0 14 27 69 0 13 26 67 0 18 35 87 0 17 33 82

1.02 1.02 1.02 1.14 1.13 1.08 1.07 1.17 0.88 0.87 0.87 0.94 0.76 0.76 0.77 0.87 1.36 1.35 1.35 1.42 1.40 1.37 1.39 1.47 1.17 1.20 1.20 1.29 1.06 1.06 1.06 1.15

0.17 0.25 0.28 0.45 0.18 0.30 0.31 0.48 0.17 0.20 0.24 0.34 0.14 0.20 0.21 0.31 0.20 0.34 0.38 0.57 0.21 0.33 0.46 0.62 0.20 0.31 0.34 0.46 0.18 0.26 0.30 0.41

1093 1090 1181 1356 1032 1084 1149 1375 1227 999 1217 1388 1251 1123 1284 1602 887 952 884 1259 890 830 1005 1125 966 961 978 1085 1093 1035 1144 1151

0.28 0.17 0.11 0.06 0.41 0.23 0.15 0.16 0.38 0.26 0.21 0.09 0.39 0.29 0.22 0.21 0.17 0.12 0.06 0.04 0.19 0.10 0.06 0.05 0.18 0.13 0.09 0.07 0.18 0.13 0.08 0.06

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Specific heat capacity was higher for the pumice-based soils with a peak value of 1251 J/(kg K) as compared with 1093 for the shale-based soil. In both cases the specific heat capacity was highest for soils with high volume fractions of the aggregate. Albedo differences between the two aggregate choices were significant. The dry pumice-based soils had albedo values ranging from 0.28 to 0.41. The dry shale-based soil albedos were about half as high with values ranging from 0.17 to 0.19. The relative insensitivity of shale-based soil albedo to composition suggests that the albedo of each constituent (shale, sand, and compost) was similar. On the other hand, the pumice-based soils had albedo near 0.40 for all compositions except DH01, which had the lowest volume fraction of pumice combined with the highest volume fraction of compost. The dry albedo of DH01 was only 0.28, suggesting that the albedo of pumice is substantially higher than that of the compost. As the albedo is a surface property, it is important to note that the way in which the soil settles over time will impact the albedo. Specifically, it is likely that, over time, the albedo of the surface layer will be dominated by the relatively coarse aggregate. 3.2. Role of moisture state As is clear from the moisture capacitance data presented in Table 1 the soils using pumice as the aggregate could hold significantly more moisture than those using shale. In fact, for the soil samples with 75% aggregate by volume, the pumice soils could hold nearly twice as much moisture. The qualitative relationships between thermal conductivity and soil composition described in the previous section were not altered under conditions of added moisture. In general the thermal conductivity of all samples increased at a rate of approximately 0.038 W/(m K) per 0.1 increase in fractional soil saturation. This trend is illustrated in Fig. 2 which shows thermal conductivity as a function of fractional moisture saturation for all eight soils tested. A similar relationship was evident for specific heat capacity which increased at a nominal rate of 32 J/(kg K) per 0.1 increase in fractional soil saturation (Fig. 3). Albedo, on the other hand showed an exponential decay with increased moisture levels. This relationship, shown in Fig. 4, exhibits a much higher variability for dry soils where albedo ranged from less than 0.20 to more than 0.40. At 60% soil moisture capacity most soils had lost more than half of their dry-state albedo. Most pumice-based soil albedos were reduced from about 0.40 to about 0.20, while the shale-based soil albedos were reduced from about 0.18 to about 0.07. 3.3. Implications for building energy modeling Past efforts to model the effect of ecoroofs on the building energy balance have generally relied on a simplified representation of the ecoroof, ignoring the effects of plants, the transient nature of energy storage in a thick soil layer, or both [10,29]. In all cases, however, energy models have relied on crude estimates of soil thermal properties, generally assuming that ecoroof soil properties are similar to those

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Fig. 2. Soil thermal conductivity as a function of fractional moisture saturation level for all eight soil samples tested (four moisture levels each). The trendline has a slope and intercept of 0.38 and 0.20 W/(m K), respectively.

Fig. 3. Soil specific heat capacity as a function of fractional moisture saturation level for all eight soil samples tested (four moisture levels each). The trendline has a slope and intercept of 322 and 1027 J/(kg K), respectively.

published in the literature for naturally occurring soils. As noted earlier the ecoroof soil thermal properties presented here differ substantially from those available in the literature. The measured thermal conductivity of ecoroof soils is in the nominal range of 0.2–0.4 W/(m K). The literature has widely varying estimates of this property for wet and dry soils. For example, Cengel [25] reports dry and wet soil thermal conductivities of 1.0 and 2.0 W/(m K), respectively—a factor of 5 higher than the measurements reported here. On the other hand, Hiraiwa and Kasubuchi [30] report thermal conductivities ranging from 0.2 to 1.0 W/(m K). Data given by Arya [31] for sandy, clay, and peat soils indicate thermal conductivities of 0.30, 0.25, and 0.06 W(m K), respectively. This wide range of reported values – from 0.06 to 2.0 W/(m K) – is likely the result of the wide range of soil types and conditions found in the natural environment. In general, ecoroof soils differ from

naturally occurring soils in that they are composed primarily of lightweight aggregate combined with varying amounts of sand and relatively little organic matter. From the data reported here we suggest that the variation of thermal conductivity across different ecoroof formulations is small compared with the wide range of values found in the literature for naturally occurring soils. So, in the absence of detailed thermal conductivity data for a specific ecoroof soil formulation we would suggest that the average values represented by the trendlines in Fig. 2 be used. Furthermore, the thermal conductivity of ecoroof soil is likely to vary substantially both seasonally and diurnally as a function of moisture status. As a result, it is important for building energy analyses to track the moisture status of ecoroof soil and the corresponding impact on thermal conductivity. The literature also reports a wide range of values for specific heat capacity of naturally occurring soils. Cengel gives specific heat values ranging from 1900 to 2200 for dry and wet soils, respectively [25]. These values are approximately double than those measured and reported here for ecoroof soils. Arya on the other hand, reports specific heat capacity values that range from 800 J/(kg K) for sandy soils to 1920 J/(kg K) for peat soils [31]. The ability of an ecoroof to store thermal energy is proportional to its specific heat capacity. The resulting diurnal cycle of heat storage in and release from ecoroof soils is an important aspect of the rooftop energy balance. Thus, it would be inappropriate to treat an ecoroof as an equivalent thermal resistance (R-value) in a steady-state energy balance model. Rather, the ecoroof soil layer must be treated using the transient form of the heat conduction equation with specific heat capacity that varies with soil moisture status. In contrast to the other measured properties the albedo measurements for ecoroof soils were comparable to values reported in the literature for naturally occurring soils. For example, Oke [32] reports the albedo range for wet and dry soils to be from 0.05 to 0.40, which is the same as the range measured in the present work for ecoroof soils. This significant dependence of albedo on soil moisture is of great importance in the building rooftop energy balance. When there is moisture input to the ecoroof – either from precipitation or from irrigation – the surface of the soil will initially be very wet with a correspondingly low albedo. It will consequently absorb a relatively high fraction of solar radiation. As the top layer of the soil dries out, however, the albedo will increase and a lower fraction of solar radiation will be absorbed. The potential for soil albedo to vary by a factor of 5 makes it particularly important that any model of the ecoroof energy balance tracks surface moisture and the corresponding variation of albedo. 4. Conclusions

Fig. 4. Albedo as a function of fractional moisture saturation level for all eight soil samples tested (four moisture levels each). The exponential trendline has an intercept and exponent of 0.22 and 1.67, respectively.

The data presented here for eight variations of ecoroof soils consisting of two different aggregates, sand, and organic matter represent the first time that detailed thermal property data for ecoroof soils have been gathered and presented in the archival literature. These data are important to the building energy modeler as they illustrate the significant thermal property variability arising from ecoroof soil composition and moisture

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saturation levels. They also demonstrate that thermal property data from naturally occurring soils are not representative of ecoroof properties. Also, since ecoroof soil moisture varies on both diurnal and seasonal scales, and in direct response to both precipitation and irrigation inputs, it is clear that building energy modeling of ecoroofs must capture these variations and the associated variations in thermal properties. Furthermore, noting that albedo is highly dependent upon the moisture content at the surface of the ecoroof soil, it is evident that soil settling and the diurnal variation in surface moisture could dramatically impact the surface energy budget. Hence, any representation of ecoroofs must track not only the bulk soil moisture and associated thermal properties, but also the surface moisture and corresponding albedo. The measurements presented here are not comprehensive. Specifically, it would be useful to extend these measurements for soils in which the primary aggregate is expanded slate (common in the eastern U.S.) or expanded clay (common in the midwestern and eastern U.S.). Furthermore, only one type of organic matter was used in the soils tested here. While typical implementations of ecoroofs have relatively little organic matter by volume (less than 20%), it would be of value to gather thermal property data for soils with other organic matter choices. Finally, the focus of this work has been on the thermal properties of ecoroof soils at varying moisture levels. Further measurements to explore moisture transport properties in these soils would be important to understanding the vertical variability of soil moisture and properties within an ecoroof soil layer, and the consequent implications for the rooftop energy balance. Acknowledgements The authors wish to thank Abhishek Bhatnagar, Alamelu Brooks, Melissa Hart, and Vishaldeep Sharma for their assistance with the measurements. This research was funded in part by the Ecoworks Foundation, the City of Portland’s Office of Sustainable Development, Gerding Edlen Development, and the U.S. EPA under contract EP-D-06-054. References [1] T. Liptan, Integrating stormwater with site, street, and architectural design, in: Proceedings of the 8th Biennial Watershed Management Council Conference on Managing Watersheds in the New Century, American Society of Civil Engineers, Pacific Grove, CA, United States, (2000), p. 147. [2] D.J. Nowak, G. Sistla, C.J. Luley, D.E. Crane, K.L. Civerolo, S.T. Rao, A modeling study of the impact of urban trees on ozone, Atmospheric Environment 34 (2000) 1601–1613. [3] D.J. Sailor, Ecoroofs and the urban climate, in: Proceedings of the Second Annual Greening Rooftops for Sustainable Communities Conference, Portland, OR, 2004. [4] R.E. Dickinson, A. Henderson-Sellars, P.J. Kennedy, Biosphere-Atmosphere Transfer Sheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model, in: Climate and Global Dynamics Division of National Center for Atmospheric Research, Boulder, CO, 1993.

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