The fifth-generation PSU-NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the U.S. soil temperature and soil moisture annual cycle and interannual variability is evaluated by comparing the 1982–2002 continuous integration driven by the NCEP-DOE AMIP II reanalysis (R-2) with observations, the R-2 derivatives and North American Land Data Assimilation System (NLDAS) products. For the annual cycle, the CMM5 produces more realistic regional details and overall smaller biases than the driving R-2 and NLDAS outputs. The CMM5 also faithfully simulates interannual variations of soil temperature over the central United States and soil moisture in Illinois and Iowa, where observational data are available. The existing CMM5 differences from observations in soil temperature (moisture) cannot be fully explained by model biases in surface air temperature (precipitation). Inconsistencies between measurements taken under short grass versus model representations beneath other land cover types may play an important role. In particular, such measurements overestimate soil temperature in summer and fall while generating a 1-month phase lead in the soil moisture annual cycle with respect to croplands in the model. The result emphasizes the need for more comprehensive study on model evaluation and bias understanding of soil temperature and soil moisture.
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 Soil temperature and soil moisture are important factors which influence the climate by modulating sensitive and latent heat fluxes, and hence energy and water exchanges or interactions between the atmosphere and the land surface. Atmosphere-land interactions can trigger the mesoscale circulation [Weaver and Avissar, 2001], change the local planetary boundary layer [Betts and Ball, 1998], and regulate regional water recycling [Dirmeyer and Brubaker, 1999]. Weather forecast models have especially large deficiency in simulating precipitation, including location, amount and type during the warm season (from late spring to early fall) and this deficiency has been linked to interactions between the atmosphere and underlying land surface [Robock et al., 2003]. For climate simulation and prediction, the land surface processes play an even more important role. The pioneering studies of Charney et al.  and Walker and Rowntree  demonstrated the significant impacts of land surface changes on climate simulations. Vinnikov and Yeserkepova  showed that, although the simulated soil moisture exhibits a high correlation between different general circulation models (GCMs), all models underestimate soil moisture, especially in summer and fall. They speculated that such underestimation might be caused by deficiency in parameterizations of the hydrological processes or by erroneous representations of the atmospheric fields (precipitation, temperature, net radiation) that drive the soil moisture calculations. On the other hand, both observational and modeling studies showed that soil moisture exerts considerable influence on precipitation, air temperature, and atmospheric circulation on a wide range of time scales [e.g., Namias, 1959; Walker and Rowntree, 1977; Koster and Suarez, 1995]. Nonetheless it is well established that improved soil moisture and other surface land processes enable more realistic simulations of regional climate, especially precipitation and temperature [Huang et al., 1996; Yang and Lau, 1998; Dirmeyer, 2000].
 The land surface is considered as an integral part of the coupled climate system, as atmosphere-surface interactions play a key role in convection formation and precipitation distribution [Robock et al., 2003]. However, validation studies on soil temperature and moisture simulations are rare due to the lack of high-quality observations and the existence of great temporal and spatial variability. To our knowledge, very few studies have compared GCMs' simulations of soil moisture with observations [Vinnikov and Yeserkepova, 1991; Robock et al., 1998; Entin et al., 1999; Srinivasan et al., 2000], while Pan et al. [2001a, 2001b] was the only one that evaluated the performance of soil moisture in a regional climate model (RCM). As one reviewer pointed out, the rarity of such a comparison is partially because of model simulations of soil moisture have been generally poor and not worth publishing. No comparison on GCM or RCM performance in simulating soil temperature has been published in literature. The primary goal of this study is to evaluate both soil temperature and soil moisture from a 1982–2002 baseline integration of the CMM5 (MM5-based regional climate model [Liang et al., 2004b]) driven by the R-2 (NCEP-DOE AMIP II reanalysis [Kanamitsu et al., 2002]). We compare the CMM5 result with in situ measurements of soil temperature over a domain east of the Rockies and soil moisture in Illinois and Iowa. Given the lack of coincident soil moisture measurements, a direct comparison between the simulation and observations is not possible. Spatial averaging has been used in previous studies to avoid this problem [Robock et al., 1998; Entin et al., 1999; Srinivasan et al., 2000]. We compare the CMM5 result with the driving R-2 and NLDAS (North American Land Data Assimilation System) outputs, in both spatial averaging and geographic pattern, to examine the RCM downscaling skill.
2. Observations and Simulations
2.1. In Situ Measurements
 Observations of soil temperature from National Climatic Data Center (NCDC) over the contiguous U.S. [Hu and Feng, 2003] and soil moisture in Illinois and Iowa [Hollinger and Isard, 1994; Robock et al., 2000] are used for this study. There are 337 stations with soil temperature measurements during 1982–2001 [Hu and Feng, 2003]. Data are available at the depth of 10 cm and 100 cm, with a much smaller number of stations in the latter (see Figure 1 for the station distribution). For a direct comparison, these station data are mapped onto the CMM5 grid mesh using a bilinear spatial interpolation. This study focuses on the sub-domain where sufficient data are available (inside boxes 1 and 2 in Figure 1 for soil temperature at 10 cm and 100 cm, respectively). Soil moisture measurements include 18 stations distributed over Illinois, all under short grass, from 1984 to 2002 [Hollinger and Isard, 1994] and 6 sites in Iowa near (41.2°N, 95.6°W) for the growing season (April–November) during 1982–1994. Evapotranspiration data in Illinois were available during 1997–2002 [Hollinger et al., 1994; Meyers and Hollinger, 2004]. The analysis of this study is based on monthly means averaged from these data.
2.2. NLDAS Assimilations
 The NLDAS is designed to generate comprehensive and physically consistent land surface information through data assimilation [Robock et al., 2003; Mitchell et al., 2000, 2004]. It currently includes four land surface models (LSMs), all driven by the same meteorological forcing and assimilated with observed precipitation data [Schaake et al., 2004]. The NLDAS outputs using the Mosaic LSM [Schaake et al., 2004] are now available hourly at 1/8th degree latitude-longitude grid spacing over the continental United States bounded within 25–53°N and 125–67°W from 1997 onward (http://ldas.gsfc.nasa.gov/). Here we use the soil moisture for the top 1-m and 2-m layers during 1997–2002.
2.3. R-2 and CMM5 Simulations
 The R-2 is a model result that assimilates all atmospheric measurements (temperature, humidity, wind, pressure) to produce a physically consistent representation of the observed 3-D circulation structure [Kanamitsu et al., 2002]. The resulting variables that have direct measurements are used to construct CMM5 lateral boundary condition (LBCs). The derivative fields without direct measurements, soil temperature (available at 10 cm) and soil moisture (available in top 2-m layer), are also used to study the CMM5 downscaling skill.
 The CMM5 simulation is a continuous integration during 1982–2002 as driven by the R-2 LBCs [Liang et al., 2004b]. The CMM5 is an improved version of the RCM developed by Liang et al.  from the MM5 version 3.3 [Dudhia et al., 2000]. Important modifications include incorporation of more realistic surface boundary conditions and cloud cover prediction. It has been demonstrated that the CMM5 has a pronounced downscaling skill in precipitation annual cycle [Liang et al., 2004b] and diurnal cycle [Liang et al., 2004a].
 Details of the CMM5 model dynamic and physical configurations are referred to [Liang et al., 2004b]. The most relevant component to this study is the updated Oregon State University LSM [Chen and Dudhia, 2001]. The LSM predicts soil temperature and soil moisture in four layers, with the thickness of 0.1, 0.3, 0.6, and 1.0 m from the surface downward. The total soil depth is 2 m, with the root zone in the upper 1 m and gravity drainage (acting as a reservoir) in the lower 1 m. The root depth is specified as a function of 24 U.S. Geological Survey vegetation types.
3. Soil Temperature Evaluation
3.1. Geographic Distributions
Figure 2 compares seasonal mean geographic distributions of the observed, CMM5 and R-2 simulated soil temperatures. The R-2 has obvious cold biases in all seasons, especially in summer and fall. The largest cold biases are in the eastern half of sub-domain 1. The CMM5 driven by the R-2 produces much more realistic distributions. In particular, the simulated soil temperatures over the central U.S. are close to observations. However, the cold biases in fall and partially in summer over the coast states still exist. The major cold biases are found during winter in the north and during summer in the south and warm biases are found during summer in the north. These CMM5 cold biases are generally identified with more severe ones in the driving R-2. Observations show the largest seasonal change from spring to summer with the 20°C contour jumping northward from the Gulf of Mexico (30°N) to the Canada border (49°N). The CMM5 realistically simulates this seasonal pattern change. In particular, the overall north-south gradient changes per 1000 km are 6.5, 6.5, 5.2 and 6.9°C for winter, spring, summer and fall in observations, while they are 7.8, 6.5, 2.6 and 6.9°C in the CMM5 and 7.5, 7.0, 2.0 and 6.6°C in the R-2. The summer gradients are much smaller than observations in both CMM5 and R-2 mainly because of the existence of the cold bias in the driving R-2 over the south.
 The patterns of CMM5 biases in soil temperature would be expected to be related with those in surface air temperature. The sub-domain 1 mean biases in winter, spring, summer and fall are, respectively, −0.9, 0.3, −1.7, −2.5°C for 10-cm soil temperature and 1.1, 2.4, 0.8, 1.3°C for surface air temperature. The warm biases of surface air temperature prevail over the whole domain during all seasons except for the north in winter. In general, warmer surface air temperature is related to less cold biases of soil temperature. The spatial correlation between soil and air temperature biases over sub-domain 1 illustrates that their correspondences are strong in winter, but weak in spring and summer, and even opposite in fall. This indicates that soil temperature biases are caused by deficiencies in model representations of not only atmospheric (heat exchange, radiation budget) but also soil (heat capacity, storage) processes.
3.2. Annual Cycle
Figure 3a shows the annual cycle of soil temperature at 10 cm averaged over sub-domain 1. The CMM5 simulation follows observations closely from January to June, while producing colder temperatures during the rest of the year. The cold bias is about 2°C in summer and fall. The R-2 soil temperature is systematically colder than observations throughout the year, especially in summer with the bias exceeding 3°C. The R-2 is generally colder than CMM5 except for a slightly warmer winter. As discussed above, the biases in soil temperature do not have direct correspondences in surface air temperature. The CMM5 produces 2°C warmer air temperature than observations during January–August while small biases in the remainder of the year (Figure 3b). The R-2 has relatively smaller warm biases in March–June and September–October. The air temperature bias reduction in the R-2 as compared with the CMM5 is a result of in situ data assimilation.
 The soil temperature difference between CMM5 and observations can be identified with the difference in surface boundary conditions. Figure 4 compares the CMM5 annual cycle of soil temperature at 10 cm between two neighboring areas under cropland and grassland. The two areas are selected in the southwest corner of Illinois, each containing 3 × 3 30-km grids. These two areas have almost identical soil temperature in both winter and spring. However, they are quite different in summer and fall, where soil temperature under grassland is about 1∼2°C warmer than that under cropland. In the Midwest, the measurement is taken under short grass while the surface is specified mostly as cropland in the model. During winter, the land becomes bare after crop harvest, while in spring and early summer, growing crops are short, all of which does not represent a big difference in soil temperature from grass cover in those seasons. During summer and fall, the height of the crop canopy is much larger than grass and the effective coupling between the free atmosphere and the soil surface is less. This may partially explain why soil temperature is simulated better during winter and spring than summer and fall. For the same reason, the CMM5 result likely represents the actual soil temperature of croplands.
3.3. Interannual Variations and Trends
Figure 5 compares the observed, CMM5 simulated and R-2 produced 1982–2001 variations of annual mean soil temperature at 10 and 100 cm, both averaged over sub-domain 2. The interannual variations of soil temperature are well simulated by the CMM5, with an overall cold bias of 0.5°C at 10 cm and a warm bias of 2.1°C at 100 cm. One exception is that larger CMM5 biases are found for 1987–88 and 1990–91 at both 10 cm and 100 cm depths. These years are identified with even greater biases in the R-2 at 10 cm depth, which may partially explain the CMM5 poor performance relative to other years. The reason for the reversal of the CMM5 biases between 10 and 100 cm is not known. The correlation coefficient for annual mean between observations and CMM5 is 0.83 at 10 cm and 0.79 at 100 cm, both of which are larger than the statistical significance threshold of 0.56 at the 99% confidence level. The R-2 has a larger cold bias of 0.9°C at 10 cm with a lower correlation of 0.51. For seasonal means at 10 cm (Figure 6), the correlation coefficients with observations are 0.86, 0.76, 0.59 and 0.79 for the CMM5, and 0.80, 0.84, 0.49 and 0.89 for the R-2 in winter, spring, summer and fall, respectively. The CMM5 has less cold biases than the R-2 in spring and summer, whereas the difference is small in winter and fall. Among the seasons, the worst correlation skill is found in summer, where the CMM5 is better than the R-2.
 The observed linear trends of soil temperature at 10 cm averaged over sub-domain 1 are 0.96, 0.28, −0.10 and 0.38°C per decade for winter, spring, summer and fall during 1982–2001. The corresponding trends are 0.42, 0.09, −0.03 and 0.10°C for the CMM5, and 0.28, −0.02, 0.23 and 0.20°C for the R-2. The CMM5 reproduces the same signals as observations in all seasons, with weaker magnitudes, especially in spring and summer. In contrast, the R-2 generates a wrong sign in both spring and summer and much weaker winter trend.
4. Soil Moisture Evaluation
4.1. Spatial Patterns Compared With the NLDAS Products
 Soil moisture, although crucial to climate, has even less observed data than soil temperature suitable for model validation. In particular, representative data for geographic distributions over the U.S. are lacking. Here we compare the CMM5 simulation with the NLDAS products. Schaake et al.  demonstrated that total soil water storage from each of the four NLDAS LSMs is highly correlated with observations at a few limited regions with available data, although inter-model differences are substantial.
Figure 7 presents seasonal mean distributions of soil moisture in the top 1 m simulated by the CMM5 and NLDAS/Mosaic averaged during 1997–2002. Since the Mosaic soil moisture is systematically wetter than the CMM5, the annual mean difference (221 minus 214 mm) between the Mosaic and CMM5 averaged over the entire land domain is removed from the Mosaic patterns in Figure 7, emphasizing the comparison in spatial patterns and seasonal variations. In both models, the general seasonal mean soil moisture patterns are dry over the Rockies/Great Plains and Florida while wet over the rest of the U.S., especially in the Northeast, Northwest, and Midwest. The wettest conditions are simulated in spring, which may be attributed to the integrated excess of precipitation over evapotranspiration during the cold season. The driest conditions are produced in fall, which results primarily from accumulated high evapotranspiration rates during the warm season.
 A good spatial agreement between the CMM5 and Mosaic is found over most regions, except where the latter is wetter than the former over the Ohio River basin and lower Mississippi River basin, especially in spring. Note that the Mosaic soil moisture is a product of the NLDAS driven by observed meteorological forcing and precipitation over the entire domain, while the CMM5 values result from the dynamic relaxation of meteorological LBCs only within the narrow domain edges. One may expect that the CMM5 and Mosaic soil moisture differences are caused by CMM5 precipitation biases. Our analyses show that such a correspondence is not robust. For example, the CMM5 simulates larger (smaller) amounts of precipitation than observations over the Midwest (eastern U.S.) during spring (fall) [Liang et al., 2004b]; in contrast, the CMM5 soil moisture is drier (wetter) than the Mosaic over the respective regions. We speculate that deficiencies in LSM representations of both the CMM5 and NLDAS may likely be responsible for the result differences.
4.2. Temporal Variations in Illinois and Iowa
Table 1 compares the general statistics of the soil moisture annual cycles in Illinois and Iowa between observations and simulations by the CMM5, NLDAS/Mosaic and R-2. The Illinois annual means for the Mosaic, CMM5 and observations are, respectively, 334, 272 and 296 mm in the top 1-m layer and 596, 559 and 686 mm in the top 2-m layer. The amplitude of the annual cycle produced by the Mosaic is greater than the CMM5 and observations. The corresponding standard deviations are 39, 27 and 33 mm in the top 1-m layer and 76, 40 and 38 mm in the top 2-m layer. The R-2 has a much drier annual bias and smaller seasonal variation than others, producing an annual mean of only 501 mm with a standard deviation of 18 mm in the top 2-m layer. The comparisons in Iowa are qualitatively similar.
Table 1. Comparison of Annual Cycle of Soil Moisture (mm) Simulated by the CMM5, Mosaic, and R-2 Outputs With Observationsa
All are averaged during 1984–2002 for Illinois and 1982–1994 for Iowa except for the Mosaic in 1997–2002.
Figure 8 compares the annual cycles of soil moisture in the upper 1-m and 2-m layers and precipitation averaged over Illinois and Iowa between observations, CMM5 simulations, NLDAS/Mosaic outputs, and R-2 products. The annual (April–November) means of soil moisture for both layers listed in Table 1 are removed from the respective cycle in Illinois (Iowa). In Illinois, observations showed that soil moisture in both layers reaches the highest in spring and the lowest in fall, while winter is relatively wet and summer is relatively dry. This seasonality of soil moisture is associated with snow accumulation in winter and subsequent melting in spring, enhanced evapotranspiration by growing vegetation in summer, and decreasing rainfall in fall. The CMM5 realistically simulates this annual cycle except for a systematic dry bias. This dry bias cannot be fully explained by the CMM5 precipitation which is greater than observations on an annual basis. It may partially be attributed to soil moisture overestimation by monitoring sensors that tend to include the contribution from emissions of non-water substances (such as organic material). On the other hand, the annual cycle is relatively flat in the R-2 and much sharper in the Mosaic; both of which are worse than the CMM5 as compared with observations.
 Another possible explanation for the CMM5 soil moisture deficiency may be the model difference in evapotranspiration from observations. Direct measurements of actual evapotranspiration are rare. Such measurements have been continuously conducted since 1997 on a crop field near Champaign, Illinois, with a 2-yr rotation of corn and soybean [Meyers and Hollinger, 2004]. In addition, there are estimates for potential evapotranspiration, following Rosenberg et al. , from surface radiation, temperature, humidity and wind measurements at 19 stations of the Illinois Climate Network (ICN [Hollinger et al., 1994]). Most ICN stations are also soil moisture monitoring sites. A scaling coefficient for each calendar month is first calculated from the actual versus potential evapotranspiration data near Champaign, as averaged during 1997–2002, and then applied identically to all other ICN stations to obtain the approximation for the observed evapotranspiration over Illinois. Figure 9 compares the 1997–2002 annual cycle difference of the CMM5 simulated from the observed evapotranspiration as well as those of concurrent soil moisture in the top 1-m layer, precipitation, and evapotranspiration minus precipitation. Clearly, the soil moisture bias is not well explained by the deficiency in either evapotranspiration or precipitation alone, but more attributable to that in evapotranspiration minus precipitation with a strong positive correlation except for June–August when crops are fully developed. More comprehensive data and analysis are needed to understand this summer breakdown of the relationship.
 The CMM5 soil moisture annual cycle in Illinois, after removing the annual mean bias, has an approximate one month delay in relation to observations. This delay may likely be caused by the surface land cover difference where the measurements were taken under short grass while the CMM5 values are represented by the dominant cropland. At the planting and initial growing stages in April–May, croplands have more extensive area of bare ground and smaller leaf coverage, thus less evapotranspiration to keep more moisture in soil than grasslands. On the other hand, developed crops (mainly soybeans and corns) have deeper roots, taller canopies with more leaves, all of which produce greater evapotranspiration and thus more loss of soil moisture than grasses. These two factors collectively lead to the delayed cycle and dry biases under croplands relative to observations under short grass. Figure 10 compares the annual cycle of the CMM5 soil moisture at the top 1-m under cropland and grassland averaged over the two neighboring areas identical to those for soil temperature discussed previously (Figure 4). From March to May, soil moisture in cropland is slightly higher than that in grassland, but much lower during the rest of the year. Thus, the CMM5 result likely represents the actual soil moisture of croplands.
 Compared to the result of Illinois, the CMM5 performed better in Iowa, where the observed soil moisture variations during April–November are relatively weak in both the top 1-m and 2-m layers. The CMM5 captures this characteristic well but with about one month delay in fall. The Mosaic is too wet and has a larger deviation with a delay of about 1 and 2 months in the top 1-m and 2-m layers, respectively. On the other hand, the R-2 is too dry with a much smaller seasonal variation in the top 2-m layer.
 The differences between the NLDAS/Mosaic and others in the annual mean (Table 1) and the annual cycle (Figure 8) of soil moisture are not likely caused by the different averaging periods. During the common period 1997–2002, the CMM5 simulated soil moisture in Illinois has an annual mean (deviation) of 279 (21) and 561 (33) mm in the top 1-m and 2-m layer, respectively; these differences from the 1984–2002 averages are relatively small as compared with the Mosaic values. A similar conclusion is reached by comparing CMM5 averages between 1997–2002 and 1982–1994 in Iowa. The result implies that the Mosaic product contains soil moisture biases likely inherent from its LSM deficiency.
Table 2 lists the general statistics and Figure 11 illustrates the time evolution of interannual variations in Illinois and Iowa, where the respective monthly means of the soil moisture annual cycle for both layers (Figure 8) are removed from all quantities. In Illinois, the correlation coefficients between the CMM5 and observations during 1984–2002 are 0.52 and 0.58 in the top 1-m and 2-m layers, respectively. The precipitation is realistically simulated with an even higher correlation of 0.76 for the same period. The observed and CMM5 simulated interannual deviations of soil moisture are, respectively, 14 and 21 mm in the top 1-m layer and 19 and 36 mm in the top 2-m layer, while the R-2 value in the top 2-m layer is 27 mm.
Table 2. Comparison of Interannual Variations of Soil Moisture (mm) Simulated by the CMM5 and R-2 as Well as Their Correlations With Observations
Illinois (1984–2002 Annual)
Iowa (1982–1994 Apr–Nov)
 The CMM5 simulated Iowa soil moisture in the top 1-m and 2-m layers are close to observations during the 1982–1994 April–November. The R-2 soil moisture in the top 2-m layer follows the observed interannual variations well, albeit with a much drier annual bias. The difference between the R-2 and CMM5 is obvious from 1996 onward, during which no observation was available. Although the CMM5 has a high precipitation correlation (0.63) with observations, the simulation of soil moisture in Iowa is not as good as in Illinois.
 Soil moisture in the upper layer seems to be more closely related with precipitation than the lower layer. This is reflected in both the annual cycle and interannual variation. The positive precipitation biases in spring and summer may account for the less dry biases of soil moisture while negative precipitation biases in fall may cause larger dry biases (Figure 8). The interannual correlation coefficients between precipitation and soil moisture for observations and CMM5 are, respectively, 0.54 and 0.80 in the top 1-m layer, which are greater than 0.33 and 0.76 in the top 2-m layer.
5. Concluding Remarks
 A continuous integration during 1982–2002 driven by the R-2 reanalysis is analyzed to determine the CMM5 capability in reproducing the observed annual cycle and interannual variability of U.S. soil temperature and soil moisture, and to better understand the causes for model biases. It is demonstrated that the CMM5 produces more realistic regional details and overall smaller biases than the driving R-2 and the NLDAS/Mosaic assimilation product, indicating a pronounced downscaling skill. In particular, the CMM5 well simulates interannual variations of soil temperature over the central U.S. and soil moisture in Illinois and Iowa, where observational data are available.
 However, important biases exist in the CMM5. For example, the CMM5 has systematic cold biases in soil temperature at 10 cm over the central U.S., especially large in summer (−1.7°C) and fall (−2.5°C). These biases are not explained by atmospheric forcing errors, where, in contrast, surface air temperature is generally overestimated. Similarly, soil moisture biases are not fully attributed to precipitation errors. The most challenging issue is then how to explain and ultimately remove the model differences from observations. The lack of high-quality observations is one important limiting factor for a rigorous model validation of soil temperature and soil moisture. Another problem is that the land cover types, under which soil variables are calculated in the model and measured in observations, are often different. For example, measurements are taken under short grass, which may cause warmer soil temperature in summer and fall and soil moisture annual cycle phase lead as compared with the croplands in the model. Therefore, validation studies using the soil variables that are consistently defined between the model and observations are warranted.
 We appreciate Kenneth Kunkel, Steve Hollinger, and Robert Scott for constructive discussions and providing Illinois observational data. We thank two anonymous reviewers for their valuable comments. We acknowledge FSL/NOAA and NCSA/UIUC for the supercomputing support, NCAR for access to the R-2 data, NCDC for the soil temperature data, and NASA/GSFC for the NLDAS/Mosaic soil moisture product. This study was supported in part by the U.S. Environmental Protection Agency award RD-83096301-0. The views expressed are those of the authors and do not necessarily reflect those of the sponsoring agencies or the Illinois State Water Survey.