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  • View in gallery

    The geographic distribution of DBED.

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    The geographic distributions of SAND and CLAY (%) for the (a), (b) the first (0–1.75 cm) and (c), (d) the eighth (82.89–138.28 cm) CLM layers.

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    The geographic distribution of LCC, with only 18 categories occurring over the RCM domain. Outlined are five key regions of interest, each with a predominant category: Texas (grassland), the Southwest (shrubland), the Midwest (dryland cropland and pasture), the Southeast (evergreen needleleaf forest), and the Northeast (deciduous broadleaf forest) United States.

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    The geographic distributions of FVC derived using the NDVI data from (a) the AVHRR (Apr 1992–Mar 1993) and (b) the scaled MODIS (Jan 2000–Dec 2003).

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    The geographic distributions of Apr and Jul mean LAI based on the original data of (a),(b) the AVHRR (1981–99) and (c),(d) the MODIS (2000–03).

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    The annual cycle of the LAI climatologies for the predominant LCC types over the five key regions outlined in Figure 3, as derived from the original AVHRR (1981–99; thin solid) and MODIS (2000–03; thick solid).

  • View in gallery

    Interannual variations of LAI averaged over the five key regions outlined in Figure 3 for the respective predominant LCC types 7 (Texas), 8 (Southwest), 2 (Midwest), 14 (Southeast), and 11 (Northeast) as derived from the original AVHRR (1981–99; thin solid) and MODIS (2000–03; thick solid) and their bias-corrected correspondences (thin, thick dashed).

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    The comparison of LAI (Midwest dryland cropland and pasture) based on AVHRR 8-km (dot) and 16-km (solid) data, and MODIS 1-km (dash) data with Illinois soybean/corn field measurements (spot) during Jan 1999–May 2001.

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    The geographic distributions of the Oct 2002 mean MODIS SST differences (°C) from (a) RTG and (b) OI and the frequency distributions of the differences at (c) raw data pixels and (d) 30-km RCM grids, and (e) the mean and absolute differences of MODIS, RTG and OI SSTs from the hourly observations at 69 buoy stations [dot marks in (b)] uniformly distributed over U.S. coastal oceans and the Great Lakes in Jan, Apr, Jul, and Oct.

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    (a) The geographic distributions of diurnal (night–day) differences of MODIS SST (Jul 2002). Two column charts indicate the distributions of (b) diurnal (night–day) differences (°C) of MODIS SST and Buoy data and (c) differences (°C) of two SST (MODIS-Buoy) for daytime and nighttime at individual stations, respectively.

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Surface Boundary Conditions for Mesoscale Regional Climate Models

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  • 1 Illinois State Water Survey, University of Illinois at Urbana–Champaign, Champaign, Illinois
  • | 2 Research Center for Remote Sensing and GIS, Beijing Normal University, Beijing, China
  • | 3 Department of Physics and Astronomy, Howard University, Washington, D.C
  • | 4 National Oceanic and Atmospheric Administration/Air Resources Laboratory, Silver Spring, Maryland
  • | 5 Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Champaign, Illinois
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Abstract

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (land-cover category, and static fractional vegetation cover and varying leaf-plus-stem-area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe.

* Corresponding author address: Dr. Xin-Zhong Liang, Illinois State Water Survey, University of Illinois at Urbana–Champaign, 2204 Griffith Dr., Champaign, IL 61820–7495. xliang@uiuc.edu

Abstract

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (land-cover category, and static fractional vegetation cover and varying leaf-plus-stem-area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe.

* Corresponding author address: Dr. Xin-Zhong Liang, Illinois State Water Survey, University of Illinois at Urbana–Champaign, 2204 Griffith Dr., Champaign, IL 61820–7495. xliang@uiuc.edu

1. Introduction

Mesoscale regional climate models (RCMs) are recognized as an increasingly important tool to address scientific issues associated with climate variability, changes, and impacts at local–regional scales (Giorgi and Mearns 1999; Giorgi et al. 2001; Leung et al. 2003). Numerous RCMs have been developed, applied, and intercompared, demonstrating important downscaling skills, but also model deficiencies yet to be resolved (Takle et al. 1999; Leung et al. 1999; Roads et al. 2003; and references therein). The most widely used RCM has been the second-generation regional climate modeling system (RegCM2), developed by Giorgi et al. (1993a; Giorgi et al. 1993b) based on the fourth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM4; Anthes et al. 1987). Over the years, the hydrostatic MM4 was significantly improved and eventually replaced by the nonhydrostatic MM5 (Grell et al. 1994; Dudhia et al. 2000). Several RCMs built upon the MM5 then emerged to address a wide range of applications (Leung and Ghan 1999; Liang et al. 2001; Liang et al. 2004a; Liang et al. 2004b; Wei et al. 2002). Meanwhile, the next-generation Weather Research and Forecasting (WRF) model has been developed (Klemp et al. 2000; Michalakes 2000; Chen and Dudhia 2000; more information available online at http://www.wrf-model.org/index.php) to supersede the MM5. Accordingly, the Illinois State Water Survey then initiated an effort to develop a climate extension of the WRF (CWRF) by implementing numerous crucial improvements, including surface–atmosphere interaction, convection–cloud–radiation interaction, and system consistency throughout all process modules (see Liang et al. 2005 for an introductory overview). This extension inclusively incorporates all WRF functionalities for numerical weather predictions while enhancing the capability for climate applications.

For all RCMs, one essential component is the representation of surface–atmosphere interactions, which generally requires specification of surface boundary conditions (SBCs) over both land and oceans. However, there is no universal, complete set of SBCs that satisfies all models. For example, the WRF release version 2 included the six-layer Rapid Uptake Cycle (RUC; Smirnova et al. 2000) and the four-layer Noah (Chen and Dudhia 2001; Ek et al. 2003) land surface models (LSMs), while the CWRF added the 11-layer Common Land Model (CLM; Dai et al. 2003; Dai et al. 2004). Over oceans, observed daily sea surface temperature (SST) variations have been incorporated into the CWRF, integrated with all surface modules. Only the CLM predicts water temperature profiles for inland shallow and deep lakes (Bonan 1995). The RUC and Noah require the soil texture category to define static soil properties uniformly distributed throughout all layers and assume that bedrock is below the bottom layer everywhere, both of which are unrealistic. By contrast, the CLM needs soil sand and clay fraction profiles to specify soil properties in individual layers and bedrock depth to set the soil bottom impermeable to water. Meanwhile, all modules use the land-cover category (LCC) to define static canopy (morphological, optical, physiological) properties, which are more comprehensive in the CLM. With regard to these aspects, the CLM approach, albeit more demanding, is physically more appropriate and, fortunately, viable with current data availability.

A more troublesome issue is that the same SBC field may be inconsistently defined, used, or specified by different surface modules. For example, to distinguish canopy versus bare soil contributions, all modules require the fraction of the vegetated area in an RCM grid box. The RUC and Noah prescribe this fraction by monthly climatological means (Gutman and Ignatov 1998), but presently kept fixed at the initial condition. In accounting for the leaf density effect on canopy resistance, the Noah introduces the leaf area index (LAI), which is set to a universal and fixed value of 4 over the globe. In contrast, the CLM uses the combination of a static fractional vegetation cover (FVC) and time-varying LAI to describe dynamic canopy variations. A relevant question then is which parameter (FVC or LAI, or possibly both) should carry the information about the time variations of terrestrial vegetation phenology. Data for the global distribution at fine spatial and temporal scales can only be determined by means of remote sensing, such as the satellite product of normalized difference vegetation index (NDVI). Sellers et al. (Sellers et al. 1996) incorporated all geographic and seasonal variations of NDVI into LAI distributions. Gutman and Ignatov (Gutman and Ignatov 1998) revealed that the limited information contained in NDVI precludes construction of seasonal variations for both FVC and LAI, and thus derived time-varying FVC while prescribing a constant LAI. Recently, Zeng et al. (Zeng et al. 2000) argued that the assumption of a static FVC and varying LAI is more realistic from a modeling perspective and the model implementation of this assumption is made feasible by current data availability. As such, FVC is determined by distinct vegetation categories and long-term edaphic and climatic controls, whereas LAI includes all dynamic canopy variability. This study concurs with Zeng et al. and parameterizes, as in the CWRF CLM, the 3D canopy effects by the combination of the static FVC for the fractional area of vegetation covering a model grid (horizontal extent) and varying LAI for the abundance of green leaves of the vegetated area (vertical density).

It is advantageous that data sources of unprecedented scope are currently available to specify the SBC fields discussed above. The data quality problem, however, is often overlooked. No single data source can provide a long-term continuous record of a specific field, nor can multiple sources ensure consistency between fields. The goal of this study is to develop a coherent, realistic set of SBCs that are most suitable for mesoscale climate and hydrology modeling. This study focuses on those fields that have multiple data sources but contain significant uncertainty, inconsistency, and incompleteness. This requires both objective procedures and manually intensive efforts to process data. Although the data processing is specific to the CWRF, the resulting SBCs by design can be incorporated into all RCMs for climate studies in North America and the procedures are more generally applicable over the globe.

2. General consideration

The SBCs data quality and value representation largely depend on the RCM computational domain and grid resolution. For our simulations of U.S. climate, the domain is centered at (37.5°N, 95.5°W) using the Lambert Conformal Conic map projection and 30-km horizontal grid spacing, with total points of 196 (west–east) × 139 (south–north), covering most of North America. The domain has been demonstrated to facilitate skillful simulations of temporal and spatial variations of precipitation over North America (Liang et al. 2001; Liang et al. 2004a; Liang et al. 2004b). In this study, all SBCs are constructed and displayed on this RCM domain, suitable for U.S. applications. For convenience, the geographic location of a point is hereafter referred to as a “pixel” for raw data and a “grid” for the RCM result. A given value at a pixel or grid represents the area surrounding the point as defined by its respective horizontal spacing.

A critical requirement in constructing the SBCs is that each field must be globally defined with no missing value and physical consistency must be maintained across all relevant parameters. Missing data, if any, must be appropriately filled. For mesoscale weather and climate modeling, the raw data should be available at the finest possible resolution. This will facilitate a more realistic representation of surface heterogeneity effects. When the data resolution is sufficiently finer than the RCM grid, the subgrid effects can be further incorporated using composite, mosaic, or statistical–dynamical approaches (Avissar and Pielke 1989; Koster and Suarez 1992; Dickinson et al. 1993; Giorgi 1997; Leung and Ghan 1998). Although many raw datasets collected in this study have adequate resolutions (as fine as 1 km) to account for the subgrid effects, this study presents the SBCs only on a given RCM grid where a single dominant surface type is assumed. Note that Masson et al. (Masson et al. 2003) developed a global database at 1-km resolution for several land surface parameters,1 but their data are insufficient for the CWRF applications, especially in terms of those fields discussed in this study.

The existing observational databases have various resolutions, finer or coarser than the RCM grid, a wide range of map projections and data formats, and often contain missing values or inconsistencies between variables. This presents significant challenges and requires labor-intensive efforts to process the data onto the RCM-specific grid mesh. Our objective procedures employ the Geographic Information System (GIS) software application tools2 to do horizontal data remapping (Liang et al. 2005). In particular, the GIS tools are used to first determine the geographic conversion information from a specific map projection of each raw data to the identical RCM grid system. The information includes location indices, geometric distances, or fractional areas of all input cells contributing to each RCM grid. It can then be applied to remap all variables of the same projection. The remapping is completed by a bilinear interpolation method in terms of the geometric distances if the raw data resolution is low or otherwise a mass conservative approach as weighted by the fractional areas. Some raw input data available only at coarse resolutions (e.g., 1°), especially those for land or ocean only, contain gaps along coastal regions and over islands that are resolved by the RCM. These gaps are filled by extrapolating from available adjacent values using the bilinear approximation. Even the relatively finer resolution (1 and 8 km) input data such as soil fraction and LAI have missing value pixels. They are filled by the average over the nearby data pixels having the same LCC within a certain radius around a missing point. Here the number of pixels and the range of radius used for filling depend on the resolution of the raw input data (see below).

3. Surface boundary conditions

Table 1 lists the key SBCs that are the focus of this study. The soil characteristic fields (DBED, SAND, CLAY) have for the first time ever been consistently introduced into climate or hydrology models. The vegetation characteristic fields (LCC, FVC, LAI, SAI) have been improved with data coherence and physical realism. The incorporation of daily SST variations is the minimal requirement enabling the CWRF for climate applications. Liang et al. (Liang et al. 2005) presented the details about the raw data sources and processing procedures for a comprehensive set of SBCs that have been developed for CWRF use. This study describes the scientific rationale, insights, and decisions that were made to develop the key SBCs in a manner that is reasonable and consistent with our understanding of the surface.

3.1. Soil characteristics

The CWRF CLM uses the bedrock depth to determine thermal and hydraulic properties in terms of the sand and clay fraction profiles for the soil layers above the bedrock or otherwise of rocks. The CLM also predicts water temperature profiles separately for shallow and deep lakes, distinguished by the lakebed depth. The CWRF dynamic ocean module needs specification of the seafloor depth to define the lower boundary of the water circulation, which can be integrated with the terrestrial hydrology module and a comprehensive routing scheme to predict the water level of major inland water bodies, including the Mississippi River and the Great Lakes. These features are important for regional climate simulations, although other RCMs have yet to incorporate them. This study thus develops static soil characteristics (DBED, SAND, CLAY) for general application in regional climate and hydrology models.

3.1.1. Bedrock, lakebed, or seafloor depth

The DBED consists of the bedrock, lakebed, and seafloor depth, and when combined, defines the bottom of all surface modules impermeable to water3 over the entire RCM domain. The lakebed depth is calculated by subtracting the lake topographic data from mean water surface elevations. Currently, only the Great Lakes topographic data are available at 2.56-km spacing from the National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory, whereas all others within the domain have no digital data available and are set to be 10 m deep. The seafloor depth is based on the global 2-min bathymetry data from National Geophysical Data Center (Smith and Sandwell 1997; Jakobsson et al. 2001).

The bedrock depth is defined as the depth of soil and/or unconsolidated material that lies between the land surface and the geologic substratum (Miller and White 1998). The data are a combination of the Continental United States Multi-Layer Soil Characteristics Dataset (CONUS–SOIL) and, outside of the United States, the Food and Agriculture Organization of the United Educational, Scientific, and Cultural Organization (FAO–UNESCO) Soil Map of the World. The FAO–UNESCO includes the global 5-min distribution of mapping units (FAO 1996), each containing 1–8 soil units among the 106 categories. For each mapping unit, all soil units are first assigned with their respective depth upper bounds (i.e., 10, 50, 100, 150, or 300 cm, provided by Dr. C. Reynolds of the U.S. Department of Agriculture Foreign Agricultural Service), and then integrated with their occurrence rates to estimate the mean bedrock depth. The CONUS–SOIL, developed from the State Soil Geographic Database (STATSGO), has a finer resolution of 1-km spacing (Miller and White 1998). About one-third of the data pixels, however, were coded as 152 cm, which generally indicates the maximum depth of soil data normally examined where bedrock was not actually encountered. A comparison showed that most regions with bedrock deeper than 152 cm in the CONUS–SOIL data are overlaid with certain FAO–UNESCO mapping units having soil depths of 150 cm. Given large uncertainties involved in these estimates, a uniform bedrock depth of 600 cm (deeper than the bottom of the last CLM soil layer) is subjectively reassigned to all CONUS–SOIL pixels with values of 152 cm and the corresponding FAO–UNESCO mapping units.

Figure 1 depicts the geographic distribution of DBED over the RCM domain. Sizeable areas of deep soils (bedrock below 300 cm) are found in the central United States extending into south-central Canada and along the Gulf and South Atlantic coasts. Many of the mountainous areas of the western United States and along the Appalachians as well as much of Canada and Mexico are characterized by shallow soils. The bedrock acts as a bottom lid that effectively prevents downward water flux. It raises the water table and limits moisture storage available in the soil column above the lid. Consequently, the bedrock controls the subsurface moisture dynamics, which in turn has significant impact on surface energy and water flux dynamics (Chen and Kumar 2001). Exposed and shallow bedrock keeps water close to the topographic surface and available for evaporation during wet periods while having a disproportionately large sensible heat but little to no evaporative flux under dry conditions (Spence and Rouse 2002). Local bedrock topography may be highly significant for runoff generation (Freer et al. 2002) and hillslope–riparian linkage (Katsuyama et al. 2005). Thus the bedrock geographic distribution can cause the terrestrial hydrology to exhibit considerable spatial variability, with greater soil moisture memory in deeper zones. General neglect of this variability in most LSMs likely results in unrealistic representation of the regional water recycling process. The deep nature of the Great Lakes is in clear contrast to other lakes as well as the large extent of shallow water along the Atlantic and Gulf Coast. Such contrast, when incorporated, will enable physically realistic simulations of processes related to the thermal inertia and vertical mixing of water.

3.1.2. Sand and clay fraction profiles

Increasing evidence shows the necessity to incorporate both horizontal and vertical soil heterogeneity effects for realistic hydrology modeling (Choi et al. 2005, unpublished manuscript). The key element in such modeling is the accurate specification of soil thermal and hydraulic properties, including specific heat capacity of dry soil, thermal conductivity of dry soil, porosity, saturated negative potential, saturated hydraulic conductivity, and the exponent B defined in Clapp and Hornberger (Clapp and Hornberger 1978). The CLM requires SAND and CLAY to parameterize these properties (Bonan 1996; Dai et al. 2003) following Cosby et al. (Cosby et al. 1984). Consistent with the bedrock depth, these profiles are determined by the combination of the CONUS–SOIL in the United States and FAO–UNESCO for the rest, and presented in terms of the CLM soil layer structure (Table 2). Similar profiles can be readily constructed for given layers of any LSM.

The FAO–UNESCO global 5-min distributions of sand and clay fractions for the two layers (0–30 and 30–100 cm) was produced by Reynolds et al. (Reynolds et al. 2000). The top layer data are uniformly assigned for the five CLM layers above 28.91 cm, while the bottom layer values are assigned for the remaining. Over the United States, they are replaced by the CONUS–SOIL 1-km distributions of sand and clay fractions in 11 standard layers, divided at 5, 10, 20, 30, 40, 60, 80, 100, 150, 200, and 250 cm (Miller and White 1998). Since the original values below 152 cm were likely not direct measurements, the raw data for 10th and 11th standard layers (150–250 cm) are discarded. The data in the top nine standard layers are interpolated with thickness weighting to the eight CLM layers above 138.28 cm, while those of the ninth standard layer are extended uniformly down to the remaining layers. When bedrock is located within a CLM layer, the averaging applies an additional thickness weight for the portion of the layer above bedrock. Some regions have soil texture classified as “organic material” without sand and clay data. They are mainly distributed in Florida, Minnesota, and several western states. These regions are assigned with a negative unit of sand or clay as an indicator to use the organic material properties in the CLM. The CONUS–SOIL also contains points with soil texture classified as “others,” giving no sand and clay data. Each missing point is filled by averaging over all nearby data pixels having the same land-cover category (see below) within a certain radius starting from 10 km (440 pixels) around the point and increasing until a minimum of 50 data pixels are obtained.

Figure 2 shows the geographic distributions of SAND and CLAY for the first and eighth CLM layers on the RCM domain. The central United States is characterized by low sand and rather moderate clay fractions, a combination that promotes high water-holding capacity and easy root penetration by plants. High sand fractions are found in southeast Canada and the southeast United States, reducing the water-holding capacity and increasing the likelihood of soil moisture stress. Although the variability is smaller in the vertical than horizontal, both spatial contrasts have been demonstrated to be important for modeling moisture movement in soil (Choi et al. 2005, unpublished manuscript).

3.2. Vegetation characteristics

Several vegetation characteristics are widely used in LSMs, mainly by prescription from observational proxies. There exist many data sources, which contain substantial biases in each and inconsistencies between. These deficiencies certainly affect model simulations, causing result uncertainty and incomparability. This study thus develops a consistent set of vegetation characteristics (LCC, FVC, LAI, SAI) for general application. In particular, the study elaborates on how to achieve the consistency between different data sources so that a coherent long-term record can be established to realistically represent the vegetation variability.

3.2.1. Land-cover category

This study adopts the U.S. Geological Survey (USGS) 24-category land-cover classification system, developed using the global 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) satellite-derived NDVI composites from April 1992 to March 1993. Within each RCM grid, the contributing area for each of the 24 land-cover categories is summed over all pixels of the same category. The majority category that contributes the largest area is chosen as the LCC for the grid. When the fractional area of water bodies (shallow or deep lakes, sea ice, or ocean) is less than 0.5 but dominates in a grid, the second major category is chosen as the LCC for the grid. Instead of the USGS system, some LSMs have used the International Geosphere Biosphere Program (IGBP) 17-category land-cover classification system (Belward 1996; Loveland et al. 2000). Obviously, the correspondence between the USGS and IGBP categories4 is not 1 to 1, but contains cross references. For a consistent conversion when needed (see below), the two raw land-cover distribution maps are intersected using the GIS tools to determine the fractional areas of all contributing categories within each RCM grid and thus the USGS–IGBP correspondences. Table 3 summarizes the total percentage coverage of each land-cover category in the RCM domain and over the globe for both the USGS and IGBP classification systems as well as their correspondences.

Figure 3 illustrates the USGS-based LCC geographic distribution. There is a general transition from deciduous and evergreen forests in the east and southeast United States to dryland cropland in the central United States to grassland and shrubland in the northern and western United States to forests in the northwest and mixed forests in southeast Canada. Model differences in energy budget partitioning and friction drag can be expected across the boundaries between regions because of differences in vegetative-cover fraction, roughness length, seasonal cycle, and root penetration. Note that the raw data do not contain categories 4 and 20 over the globe, and additionally 12, 17, and 23 within the RCM domain. Moreover, category 24 is not chosen as the majority type for LCC. Therefore, the final LCC includes only 18 land-cover categories over this RCM domain.

3.2.2. Fractional vegetation cover

The FVC is the one ecological parameter that determines the contribution partitioning between bare soil and vegetation for surface evapotranspiration, photosynthesis, albedo, and other fluxes crucial to land–atmosphere interactions. It is assumed to be time invariant or static, and derived following Zeng et al. (Zeng et al. 2000; Zeng et al. 2002), from the same global 1-km AVHRR satellite product as for LCC. The 10-day composites from April 1992 to March 1993 were used to determine the annual maximum NDVI (Np,max) for each land-cover category, minimizing the effect of cloud contamination on data quality. For each pixel, the vegetation cover is computed by
i1087-3562-9-18-1-e1
where Nc,υ is the NDVI value for a complete coverage of a specific USGS land-cover category over the pixel and Ns for bare soil. Zeng et al. (Zeng et al. 2000), using a commercial imagery database, determined Nc,υ by examining percentiles of the Np,max histogram for each IGBP land-cover category. To avoid redundant data processing, the Nc,υ value for each USGS land-cover category is calculated from those of all contributing IGBP categories as weighted by their corresponding fractional areas (Table 3). After Zeng et al. (Zeng et al. 2000), a uniform value of 0.05 is assigned to Ns for all USGS land-use categories.

There exist significant differences between the NDVI from the AVHRR and the most recent Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Gallo et al. (Gallo et al. 2004) compared the concurrent 16-day composite data during 2001 and showed that a linear relationship exists between the two. The regression intercept and slope values change with land-cover categories, but all are significantly different from 0 and 1, respectively. The MODIS has generally larger values than the AVHRR, causing Equation (1) to produce greater Cυ values. On the other hand, Zeng et al. (Zeng et al. 2002; Zeng et al. 2003) demonstrated that, using the same method with Equation (1), the Cυ derived from 8-km AVHRR NDVI during 1982–2000 (James and Kalluri 1994) is consistent with that from the 1-km data for April 1992–March 1993. Given the good agreement with field surveys and observational studies and the small interannual variability over areas with minimal anthropogenic impact, the FVC derived from the AVHRR NDVI was believed to be robust.

The MODIS is providing quality-controlled data for numerous variables that are necessary for terrestrial modeling, such as developing a new land surface albedo parameterization (Liang et al. 2004c). It is thus desirable to have a consistent FVC based on the MODIS data. One appealing approach is to scale Cυ from the MODIS toward AVHRR. For each USGS land-cover category, a scaling factor fp,υ is first defined to remove the systematic difference of MODIS from AVHRR in Np,max averaged over all pixels. Assuming the same Ns and multiplying Np,max by fp,υ in Equation (1), the corresponding Nc,υ is then estimated to minimize the Cυ difference between MODIS and AVHRR. Table 4 lists the resulting fp,υ and Nc,υ values as well as the correlation coefficients and root-mean-square (rms) differences between the Cυ based on the AVHRR and MODIS after scaling. The fp,υ ranges from 0.50 to 0.81, while the Nc,υ remains close to the respective AVHRR value except for category 19. The correlations are generally excellent, mostly above 0.5, around 0.4 for categories 18 and 22, but quite low (∼0.3) for categories 6 and 19. Nonetheless, the rms differences are small for all categories. It is noteworthy that the fp,υ correction has almost no impact on the correlations but reduces the rms differences. The low correlations reflect the poor correspondences between the raw MODIS and AVHRR Np,max data at 1-km resolution, for which no clear explanation can be given.

The final FVC is obtained by the area-weighted averaging of Cυ values for all pixels within each RCM grid. Figure 4 compares the FVC geographic distributions derived from the AVHRR and scaled MODIS data over the RCM domain. The two distributions are very similar, both in pattern and magnitude. This similarity is a direct result of the above described correction procedure; without this correction, the MODIS values are excessive almost everywhere. There are certain minor differences between the two. The values are somewhat lower in the MODIS compared to AVHRR for some western portion of the domain, most apparent in West Texas, New Mexico, and northern Mexico, whereas the opposite situation occurs for some eastern areas, particularly around Hudson’s Bay and Florida. These slight differences are unlikely to have a major impact on model simulations.

3.2.3. Leaf and stem area index

LAI and SAI are defined as the total one-sided area of all green canopy elements and stems plus dead leaves, respectively, over the vegetated ground area. They are constructed from the global monthly mean distributions of green vegetation leaf area index, based on the AVHRR NDVI data, during July 1981–December 1999 at 8-km spacing (Zhou et al. 2001; Buermann et al. 2002). There exist missing data zones in some regions with land cover of urban and built-up, permanent wetlands, marshes, tundra, barren, desert, or very sparsely vegetated area. These missing zones are filled by the average over nearby data pixels having the same land-cover category within a certain radius starting from 16 km (24 pixels) around a missing point and increasing until a minimum of three pixels are obtained. The product is denoted as Lraw.

Since Lraw is defined with respect to unit ground area, it is divided by local vegetation cover Cυ to define Lgv representing the green leaf area index with respect to vegetated area only (Zeng et al. 2002). Due to inconsistency between Cυ and Lraw data at individual pixels, some Lgv values are abnormally large, up to several hundreds. The inconsistency arises mainly because Cυ was derived based on the 24 USGS land-cover categories at 1-km spacing while Lraw was derived in terms of six alternative biomes with distinct vegetation structures at an 8-km interval. Zeng et al. (Zeng et al. 2002) determined Cυ at every point, while defining LAI for each IGBP land-cover category by a mean seasonal variation within a 10° latitude zone. Here the 1-km Cυ data is first integrated onto the 8-km Lraw map to compute initial estimates of Lgv and then a smoothing filter is applied to remove abnormal values. The filter is designed, through trial and error, by examining the frequency distribution of abnormal Lgv values and considering the canopy displacement height in the CLM for each USGS land-cover category. The point value that exceeds the filter threshold listed in Table 5 is filled by the average over nearby data pixels having the same land-cover category within a certain radius starting from 16 km (24 pixels) around the point and increasing until a minimum of three pixels are obtained. In addition, Lgv data contain large uncertainties in winter due to cloud contamination, especially for the USGS categories 13 and 14 (evergreen broadleaf and needleleaf forests). Following Zeng et al. (Zeng et al. 2002), Lgv values in winter months for these two categories are adjusted by
i1087-3562-9-18-1-e2
where correction coefficient c is 0.8 (0.7) for category 13 (14), and Lgv,max is the maximum Lgv. For the climatology, the maximum can be determined over all monthly values during the entire period, while for interannual variations it is taken in three consecutive years.
For each USGS land-cover category, SAI is then approximated as in Zeng et al. (Zeng et al. 2002) by
i1087-3562-9-18-1-e3
where m denotes mth month, SAImin the prescribed minimum SAI, and (1 − γ) the monthly removal rate of dead leaves. Both γ and SAImin are listed in Table 5. The resulting SAI for most land-cover categories reach the minimum in winter and the maximum in fall (October or November). This seasonal trend may not be appropriate for certain categories, especially those with croplands where nothing may remain on the field after crops are harvested in fall.

A serious concern is the systematic difference in the Lraw products based on the AVHRR (Zhou et al. 2001) and the recent MODIS (Knyazikhin et al. 1998; Myneni et al. 2002) data. The MODIS Lraw, available from February 2000 onward, has a finer resolution at 1-km spacing. Following the same procedure described above, the corresponding LAI and SAI can be constructed from the MODIS Lraw. Figure 5 depicts the April and July mean LAI distributions of the AVHRR and MODIS climatologies over the RCM domain, while Figure 6 presents seasonal variations of the five key regions outlined in Figure 3. The MODIS values are clearly smaller, which is not a result of long-term trends. Figure 7 compares AVHRR and MODIS monthly mean LAI variations averaged over five key regions for the respective predominant LCC types. These include Texas (grassland), the Southwest (shrubland), the Midwest (dryland cropland and pasture), the Southeast (evergreen needleleaf forest), and the Northeast (deciduous broadleaf forest) United States. Apparent discontinuities exist between the two datasets, where the MODIS values are systematically smaller, especially for the Midwest cropland. Analyses indicate that certain relationships exist, but vary greatly with regions. No physically sound and statistically robust adjustment can be made for consistency. A first-order correction is to obtain a same climatology (i.e., identical monthly means averaged over all years) while retaining the interannual variability at each grid. Such corrected regional mean time series using the AVHRR or MODIS climatology are also shown in Figure 7.

The question is which climatology, AVHRR or MODIS, is more realistic. While over 1000 published estimates during 1932–2000 at nearly 400 field sites over the globe have been compiled (Scurlock et al. 2001) and validation is under way, no direct intercomparison between these field measurements and satellite products is currently available. Figure 8 compares monthly mean LAI variations for the Midwest cropland based on the AVHRR 8-km (January–December 1999) and 16-km (January 1999–May 2001) and MODIS 1-km (February 2000–May 2001) data with filed measurements at a central Illinois soybean/corn site (June–September in 1999–2000; by courtesy of Dr. Steven Hollinger of the Illinois State Water Survey). The two AVHRR-based LAI estimates are in good agreement and well capture the peak values of the field observations. Clearly the MODIS-based estimates substantially underestimate the LAI of the growing season for the cropland.

Since the MODIS measurement is continuing and providing finer resolution and quality-controlled data with improved atmospheric correction and cloud screening (Justice et al. 1998) compared to the AVHRR (Goward et al. 1991), its LAI product is preferred. Until a comprehensive evaluation or validation is completed with improved products made available, it is suggested that the AVHRR LAI is corrected to have the same monthly mean climatology as the MODIS except for the cropland-related LCC categories (2–6), where the opposite correction is applied due to the obvious MODIS underestimation. The result is a long-term LAI dataset with continuation and consistency. This is particularly important when applied in an integrated model because, for example, the surface albedo parameterization developed from the MODIS data depends on LAI (Liang et al. 2004c).

3.3. Sea surface temperature

Given the lack of fine-resolution data, most mesoscale models have been using the weekly optimum interpolation SST (OISST) analysis at 1° spacing, a blend of multichannel AVHRR infrared measurements with in situ ship and buoy observations (Reynolds et al. 2002). Following Liang et al. (Liang et al. 2004b), daily variations are incorporated based on conservative spline fit from the weekly OISST data, available over the global oceans from November 1981 onward. Since daily SSTs interpolated directly from weekly values (treated as if they were at the middle of the week) do not conserve weekly means nor preserve the extremes (Taylor et al. 2000), an iterative spline-fit procedure was used to interpolate daily SST variations from the weekly data while conserving the weekly means. This procedure effectively preserves the extremes revealed in the original weekly data.

A new blended real-time global SST (RTGSST) analysis (Thiébaux et al. 2003) is now available with daily data at 0.5° spacing since 11 February 2001. The analysis ingests the most current (in the prior 24 h) observations, including NOAA-16 SEATEMP retrievals from the Naval Oceanographic Office Major Shared Resource Center, ship and buoy in situ SST reports from the Global Telecommunications System, and Special Sensor Microwave Imager sea ice concentrations. Most recently, the MODIS level-3 mapped SST data are available twice daily at 4-km spacing, derived from infrared brightness temperature measurements by Terra since 19 July 2000 or Aqua since 7 January 2003. The MODIS sensor was designed with higher sensitivity and lower signal-to-noise ratio than the predecessor AVHRR radiometers on board NOAA satellites. Despite the similarity in the analysis algorithm, the MODIS SST has potentially better accuracy and higher resolution than the OISST.

Figure 9 compares the OISST and RTGSST analyses with the MODIS data for October 2002 in terms of geographic variations and frequency distributions of the differences. To depict the resolution enhancement effect, both OISST and RTGSST data are mapped onto the MODIS pixel mesh using longitude–latitude bilinear interpolation. Approximately 41% and 64% (46% and 71%) of oceanic pixels in the RCM domain have SST differences from the MODIS within ±0.25° and 0.5°C for the OISST (RTGSST) analysis. About 15% and 4% (11% and 2%) of pixels contain absolute differences greater than 1° and 2°C. In particular, the MODIS data tend to be cooler than both OISST and RTGSST in the northeast Pacific, while warmer over the Great Lakes, the Gulf of Mexico, and the western coastal Atlantic Ocean. A monthly mean comparison with the hourly observations at 69 buoy stations uniformly distributed over these water bodies for January, April, July, and October of 2002 (Figure 9e) indicates that an overall better agreement is achieved by the RTGSST and MODIS data, while a relative larger error is identified with the OISST.

While the MODIS average compares rather well with buoy observations, the diurnal cycle of MODIS SSTs exhibits large magnitudes in certain areas. The spatial distribution of the July 2002 mean night–day difference (Figure 10a) shows large positive values exceeding 2°C in the eastern Pacific while negative values over 1°C near Baja California and other coastal areas of Mexico along with coastal areas of eastern Canada. A comparison with buoy data (Figures 10b,c) indicates that MODIS values are excessive and unrealistic in the eastern Pacific. Interestingly, the buoy data shows that night temperatures are cooler than day values by 0.2°–0.9°C over the Great Lakes, western Atlantic, and Gulf of the Mexico, whereas the opposite (0.1°–0.4°C) prevails in the eastern Pacific. The MODIS realistically captures the cooler night regions with a few exceptions, but overwhelmingly overestimates the warmer night areas in the eastern Pacific. More problematically, the MODIS tends to exhibit biased SSTs during day and night, compared to the buoy data by a similar magnitude [so the good agreement with buoy data is a cancellation of large errors] in the Great Lakes, northern Atlantic, and northern Pacific, while producing warmer nighttime (∼1°C) and cooler daytime (1°–2°C) temperatures (thus significantly exaggerating the diurnal magnitude) in the southern Pacific. The comparison clearly suggests that the MODIS data at present cannot be directly used to represent diurnal SST variations.

SSTs from infrared satellite retrievals are subject to varying degrees of residual cloud contamination, which is difficult to characterize, particularly in the absence of in situ observations. The cloud-masking results in cold biases for SST retrievals from single-view infrared sensor data; this is difficult to correct using the OI analysis in regions with sparse in situ data. In addition, the MODIS data, albeit twice daily, are available only over the scanning tracks along the satellite passes, and hence contain broad missing areas. Similar to the OISST and RTGSST analyses, the MODIS measurements must be blended with in situ ship and buoy observations to objectively fill all missing data and correct diurnal errors before their application. A reasonable approach at present is to incorporate daily SST variations using the iterative, conservative spline fit to the weekly OISST data or the daily RTGSST data from February 2001 onward.

4. Concluding remarks

This study focused on developing high-quality surface boundary conditions for general use in mesoscale regional climate models. This was motivated by our experiences with the limitations and inconsistencies of existing SBCs and the resulting uncertainties whether model–observation differences derived from model physics deficiencies or SBCs inaccuracies. Our primary effort is in the thoughtfulness given to the data quality and the product accuracy. This will benefit the continuing development of RCMs by permitting a more focused effort on model physics. The utility of this set of SBCs relies on the multitude of scientific justifications and quality-control procedures that underpin its creation. Although the SBCs are constructed on the 30-km CWRF domain, they can be readily incorporated into any RCM suitable for U.S. climate and hydrology simulations. The study also describes in detail the processing and validation procedures, by which SBCs (especially those derived from remote sensing data) can be constructed for any specific domain over the globe.

Although this study has striven for the best available quality data, comprehensive processing procedures, and the proper approaches to achieve consistency between alternative data sources, the SBCs so constructed carry uncertainties inherent in the raw data. Given the current understanding of physical processes and data uncertainties, this study concludes that 1) RCMs will likely demonstrate improved performance with the incorporation of 3D soil characteristics by integrating geographically varying bedrock depth with soil sand and clay fraction profiles, rather than using vertically constant quantities based on soil texture categories; 2) RCMs can combine the static fractional vegetation cover with varying leaf-plus-stem-area indices to represent spatial and temporal variations of vegetation, for which both MODIS and AVHRR must be corrected to create a long coherent record while removing the obvious errors and discontinuities; 3) RCMs need to prescribe daily SST variations using the most appropriate RTGSST data currently available or otherwise the iterative, conservative spline fit to the weekly OISST data, but the direct use of the new fine-resolution MODIS data is not appropriate because they suffer from large gaps and errors. Future model studies will be required to assess the qualitative or quantitative impacts of SBCs treatments (points 1–3). An upcoming paper will address the RCM climate sensitivity to these SBCs.

Acknowledgments

We acknowledge Dr. Xubin Zeng of The University of Arizona for constructive discussions on vegetation indices; Dr. Steven Hollinger of the Illinois State Water Survey for numerous instructions on soil properties and providing LAI field measurements for soybean and corn in Illinois; Dr. Curt Reynolds of the USDA Foreign Agricultural Service for valuable discussions on soil property derivation from the FAO–UNESCO data and providing the association pedon data between soil depth classes and soil units; and Dr. Liming Zhou of Georgia Tech for the original LAI data. This research was partially supported by the NOAA/HU NCAS Grant 634554172523 and the China National 973 Key Project Award G19990435. The data processing was conducted on the NOAA/FSL and UIUC/NCSA supercomputing facilities. The views expressed are those of the authors and do not necessarily reflect those of the sponsoring agencies or the Illinois State Water Survey.

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Figure 1.
Figure 1.

The geographic distribution of DBED.

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 2.
Figure 2.

The geographic distributions of SAND and CLAY (%) for the (a), (b) the first (0–1.75 cm) and (c), (d) the eighth (82.89–138.28 cm) CLM layers.

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 3.
Figure 3.

The geographic distribution of LCC, with only 18 categories occurring over the RCM domain. Outlined are five key regions of interest, each with a predominant category: Texas (grassland), the Southwest (shrubland), the Midwest (dryland cropland and pasture), the Southeast (evergreen needleleaf forest), and the Northeast (deciduous broadleaf forest) United States.

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 4.
Figure 4.

The geographic distributions of FVC derived using the NDVI data from (a) the AVHRR (Apr 1992–Mar 1993) and (b) the scaled MODIS (Jan 2000–Dec 2003).

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 5.
Figure 5.

The geographic distributions of Apr and Jul mean LAI based on the original data of (a),(b) the AVHRR (1981–99) and (c),(d) the MODIS (2000–03).

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 6.
Figure 6.

The annual cycle of the LAI climatologies for the predominant LCC types over the five key regions outlined in Figure 3, as derived from the original AVHRR (1981–99; thin solid) and MODIS (2000–03; thick solid).

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 7.
Figure 7.

Interannual variations of LAI averaged over the five key regions outlined in Figure 3 for the respective predominant LCC types 7 (Texas), 8 (Southwest), 2 (Midwest), 14 (Southeast), and 11 (Northeast) as derived from the original AVHRR (1981–99; thin solid) and MODIS (2000–03; thick solid) and their bias-corrected correspondences (thin, thick dashed).

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 8.
Figure 8.

The comparison of LAI (Midwest dryland cropland and pasture) based on AVHRR 8-km (dot) and 16-km (solid) data, and MODIS 1-km (dash) data with Illinois soybean/corn field measurements (spot) during Jan 1999–May 2001.

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 9.
Figure 9.

The geographic distributions of the Oct 2002 mean MODIS SST differences (°C) from (a) RTG and (b) OI and the frequency distributions of the differences at (c) raw data pixels and (d) 30-km RCM grids, and (e) the mean and absolute differences of MODIS, RTG and OI SSTs from the hourly observations at 69 buoy stations [dot marks in (b)] uniformly distributed over U.S. coastal oceans and the Great Lakes in Jan, Apr, Jul, and Oct.

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Figure 10.
Figure 10.

(a) The geographic distributions of diurnal (night–day) differences of MODIS SST (Jul 2002). Two column charts indicate the distributions of (b) diurnal (night–day) differences (°C) of MODIS SST and Buoy data and (c) differences (°C) of two SST (MODIS-Buoy) for daytime and nighttime at individual stations, respectively.

Citation: Earth Interactions 9, 18; 10.1175/EI151.1

Table 1.

The list of the primary SBCs incorporated into the CWRF.

Table 1.
Table 2.

The CLM soil layer thickness and depth (cm).

Table 2.
Table 3.

The comparison of the total percentage coverage and the NDVI value for a complete coverage (Nc,υ) of each USGS land-cover category in the RCM domain and over the globe as well as the correspondences of all contributing IGBP classification categories.

Table 3.
Table 4.

The estimated fp,υ and Nc,υ for Cυ based on MODIS NDVI (2000–03).

Table 4.
Table 5.

The ecological parameters in deriving LAI and SAI for each USGS land cover.

Table 5.

1

Among the variables listed in Table 1, Masson et al. only included FVC and LAI. Their FVC was parameterized as 1 − e−0.6LAI for crops and assigned with a constant for eight other vegetation types, while LAI was given as only a climatological mean. This study derives both FVC and LAI from satellite measurements and also includes interannual variations of LAI.

2

The GIS tools are Arc/Info and Arc/Map from the Environmental Systems Research Institute, Inc. In particular, IMAGEGRID and GRIDPOLY convert input data from the image to the ArcGIS raster grid and to the polygon coverage formats, respectively; PROJECT remaps the raw input data onto the CWRF grid projection; UNION and CLIP geometrically intersect polygon features of input data with the CWRF grid mesh and extract the fractional area of each pixel contributing to the grid; GRID DOCELL and IF statements conditionally merge, replace, or adjust different input datasets for an improved product.

3

Strictly speaking, water can penetrate through the bedrock between gaps of consolidated material. This penetration represents surface and groundwater interactions, an aspect that can be explored where detailed bedrock information is available.

4

This study prefers the USGS to IGBP classification system mainly because the former has been used as the basic land-cover identification in the MM5 and WRF and also contains more categories (24 versus 17) than the latter.

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