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    RegCM2 US90 domain and study area for analysis. BATS land cover types: 1—urban land, 2—agriculture, 3—range/grassland, 4—deciduous forest, 5—coniferous forest, 6—mixed forest and wet land, 7—water, 8—marsh or wet land, 9—desert, 10—tundra, 11—permanent ice, 12—tropical forest or subtropical forest, 13—savanna

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    Topography for the RegCM2 US90 domain. Elevation above sea level (in m). Contour interval = 200 m

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    Crop moisture index for (a) 25 Jun 1988 and (b) 29 Jun 1991 (from WWCB)

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    Mean Jun 1988 air temperature (T) in K at the lowest model level (σ = 0.997) simulated by (a) TCTRL run and (b) FINT run. Contour interval = 2 K

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    Mean difference in air temperature (in K) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for (a) Jun 1988 and (b) Jun 1991. Contour interval = 1 K. Areas shaded with a fine dot pattern have positive differences of 1 K or higher; nonshaded areas have positive differences between 0 and 1 K; and areas shaded with a coarse dot pattern have negative differences. Note that all negative differences are between 0 and −0.(9) K (except for local point minima)

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    Difference of the means between the FINT and TCTRL runs parametric time series tests for (a) Jun 1988 and (b) Jun 1991

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    Mean difference in mixing ratio (in g kg−1) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for (a) Jun 1988; and (b) Jun 1991. Contour interval = 1 g kg−1. Areas shaded with a coarse dot pattern have negative differences with values more negative than −1 g kg−1; nonshaded areas have differences between −1 g kg−1 and 1 g kg−1; and areas shaded with a fine dot pattern have positive differences of 1 g kg−1 or higher

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    Fig. 8a. Mean difference in air temperature (in K) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for Jul 1988. Contour interval = 1 K. Areas shaded with a fine dot pattern have positive differences of 1 K or higher; nonshaded areas have positive differences between 0 and 1 K; and areas shaded with a coarse dot pattern have negative differences. Note that all negative differences are between 0 and −0.(9) K (except for local point minima). Fig. 8b. Mean difference in mixing ratio (in g kg), at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for Jul 1988. Contour interval = 1 g kg−1. Areas shaded with a coarse dot pattern have negative differences with values more negative than −1 g kg−1; nonshaded areas have differences between −1 g kg−1 and 1 g kg−1; and areas shaded with a fine dot pattern have positive differences of 1 g kg−1 or higher

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    Fig. 9a. Mean difference in air temperature (in K) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for Jul 1991. Contour interval = 1 K. Areas shaded with a fine dot pattern have positive differences of 1 K or higher; nonshaded areas have positive differences between 0 and 1 K; and areas shaded with a coarse dot pattern have negative differences. Note that all negative differences are between 0 and −0.(9) K (except for local point minima). Fig. 9b. Mean difference in mixing ratio (in g kg−1) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for Jul 1991. Contour interval = 1 g kg−1. Areas shaded with a coarse dot pattern have negative differences with values more negative than −1 g kg−1; nonshaded areas have differences between −1 g kg−1 and 1 g kg−1; and areas shaded with a fine dot pattern have positive differences of 1 g kg−1 or higher

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    Vertical profiles of potential temperature, equivalent potential temperature, actual temperature, and mixing ratio for (a) Jun 1988; (b) Jul 1988; (c) Jun 1991; and (d) Jul 1991

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    (Continued)

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    Mean monthly maximum air temperature (°C) for Jun 1988

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    Mean monthly maximum air temperature (°C) for Jul 1988

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    Mean monthly precipitation (mm day−1) for Jun 1988

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    Mean monthly maximum air temperature (°C) for Jun 1991

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Investigating the Effect of Seasonal Plant Growth and Development in Three-Dimensional Atmospheric Simulations. Part II: Atmospheric Response to Crop Growth and Development

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  • 1 National Center for Atmospheric Research, Boulder, Colorado
  • | 2 The Pennsylvania State University, University Park, Pennsylvania
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Abstract

The authors examine the effect of seasonal crop development and growth on the atmospheric boundary layer in the warm season over the central Great Plains region of North America. They introduced daily crop development and growth functions into the Biosphere–Atmosphere Transfer Scheme (BATS) coupled to the National Center for Atmospheric Research Regional Climate Model version 2 (NCAR RegCM2). Coupled RegCM/BATS simulations were performed over the conterminous United States for a dry (1988) and favorably moist (1991) growing seasons at a spatial resolution of 90 km × 90 km. Largest differences between the control and interactive runs occurred in 1988, when up to 45% differences in surface latent and sensible heat fluxes were simulated in response to different Leaf Area Index (LAI) parameterizations employed by the models (in June and July, LAI was about 5 in the control cases and between 1 and 2 in the interactive cases). Two to four °C differences in air temperatures resulted in response to such changes in surface fluxes. Mixing ratio, lower atmospheric winds, and precipitation were also affected. These effects had a distinct diurnal pattern with the largest differences seen in midafternoon hours and smallest differences seen at night. The differences between the control and interactive simulations were largest near the surface and dampened with height. The boundary layer stratification (i.e., vertical profiles of equivalent potential temperature) produced with interactive runs was more stable compared to the control runs. Anemometer height maximum daily temperature and precipitation simulated in the interactive runs agreed better with observations compared to those of the control runs.

Corresponding author address: Elena Tsvetsinskaya, NCAR/ESIG, P.O. Box 3000, Boulder, Colorado 80307-3000.

Email: elena@ucar.edu

Abstract

The authors examine the effect of seasonal crop development and growth on the atmospheric boundary layer in the warm season over the central Great Plains region of North America. They introduced daily crop development and growth functions into the Biosphere–Atmosphere Transfer Scheme (BATS) coupled to the National Center for Atmospheric Research Regional Climate Model version 2 (NCAR RegCM2). Coupled RegCM/BATS simulations were performed over the conterminous United States for a dry (1988) and favorably moist (1991) growing seasons at a spatial resolution of 90 km × 90 km. Largest differences between the control and interactive runs occurred in 1988, when up to 45% differences in surface latent and sensible heat fluxes were simulated in response to different Leaf Area Index (LAI) parameterizations employed by the models (in June and July, LAI was about 5 in the control cases and between 1 and 2 in the interactive cases). Two to four °C differences in air temperatures resulted in response to such changes in surface fluxes. Mixing ratio, lower atmospheric winds, and precipitation were also affected. These effects had a distinct diurnal pattern with the largest differences seen in midafternoon hours and smallest differences seen at night. The differences between the control and interactive simulations were largest near the surface and dampened with height. The boundary layer stratification (i.e., vertical profiles of equivalent potential temperature) produced with interactive runs was more stable compared to the control runs. Anemometer height maximum daily temperature and precipitation simulated in the interactive runs agreed better with observations compared to those of the control runs.

Corresponding author address: Elena Tsvetsinskaya, NCAR/ESIG, P.O. Box 3000, Boulder, Colorado 80307-3000.

Email: elena@ucar.edu

1. Introduction

Regional and global climate models depend on surface vegetation models for simulation of surface fluxes of heat, moisture, and momentum to be used as a lower boundary condition for the atmosphere. These surface schemes differentiate between various land cover types but their treatment of a particular land cover type of interest, that is, agriculture, is rather crude. First, these models do not distinguish between different crops and agricultural practices (such as planting and harvesting schedules, fertilizer application, tillage practices), and second, within a given land cover type (i.e., crop) they do not address crop phenological development (i.e., the vegetative and reproductive stages crops go through during the growing season). Often the models treat plant growth as a simplistic function of accumulated heat units, which is the case for the BATS model employed in this study.

We were specifically interested in the extent to which the inclusion of an interactive physiologically based surface scheme, which models plant growth and development and thus represents plant seasonal and interannual variability, affects model simulations at the mesoscale level. Heat, moisture, and momentum fluxes over land strongly depend on the condition of surface vegetation. Experimental data indicate that some biophysical parameters (leaf area index, bulk canopy resistance, roughness length) vary significantly throughout the growing season, from early stages of development to peak growth. For example, Kim and Verma (1990) report fivefold changes in canopy conductance (Gs) in a midlatitude tall-grass prairie ecosystem associated with seasonal plant cover changes.

Adequate representation of plant growth proves important for simulations of seasonal variability in latentand sensible heat fluxes (Verma et al. 1992; Tsvetsinskaya 1999), the Bowen ratio (Lindroth 1993), surface daily maximum temperature (Schwartz and Karl 1990), and ultimately the height of the planetary boundary layer, PBL (Avissar and Pielke 1991) and precipitation and soil moisture (Betts et al. 1993). Viterbo and Beljaars (1995) found that simulation of strong precipitation in spring, which provides soil water storage reserves and makes water available for evapotranspiration later in the year, typical for midlatitude grasslands (e.g., FIFE region in the Konza Prairie United States), was possible only when the seasonal cycle of vegetation growth was represented in the ECMWF model. The inability of the earlier version of the ECMWF model to account for plant growth caused sharp differences between the simulated and observed precipitation amounts and seasonal patterns. The adequacy of surface flux parameterizations is particularly important when local and mesoscale atmospheric processes dominate over global atmospheric phenomena (e.g., during summer months in the Great Plains when the jet stream generally weakens and local convective processes dominate in determining precipitation patterns).

The condition of surface vegetation has also been suggested to act as a feedback mechanism for controlling drought intensity. For example, widespread plant wilt in response to the drought conditions of the summer 1988 has been shown to contribute to further drought intensification through a local soil moisture/precipitation feedback mechanism (Seth and Giorgi 1998; Dirmeyer 1994; Trenberth and Branstator 1992; Entekhabi et al. 1992). The inclusion of dormant vegetation into climate simulations during the spring and early summer 1988 greatly reduced evapotranspiration by eliminating transpiration, and the resultant effect on local and mesoscale climate intensified as summer progressed.

First attempts to incorporate interactive vegetation into surface models have been undertaken by Foley et al. (1996), Dickinson et al. (1998), and Lu et al. (2001). Interactive parameterizations resulted in generally higher temperatures and lower evapotranspiration and precipitation values compared to the control over the extratropical Northern Hemisphere in summer. The changes in climate resulted from the ability of the interactive models to simulate the effects of moisture and nitrogen stresses on leaf area index and stomatal conductance. None of these studies, however, explicitly addressed the issue of detailed representation of agroecosystems. Meanwhile, climate simulations over the central United States have been shown to come to better agreement with observations through the introduction of crop-specific leaf area index parameterizations (Xue et al. 1996). In our study we focus explicitly on agroecosystem simulations, and thus represent the actual landscapes of the central Great Plains of North America with a higher degree of realism. We focus on the detailed representation of crop seasonal changes associated with crop phenological development (from planting to harvest) and growth.

We incorporated seasonal crop growth and development functions into the Biosphere–Atmosphere Transfer Scheme. These functions are based on the CERES-Maize model. CERES-Maize (Jones and Kiniry 1986) is a quasi-physiologically based crop model that simulates crop development and growth as a function of cultivar-specific genetic parameters and environmental conditions (i.e., incoming solar radiation, temperature, precipitation, etc.). It operates on a daily time step. CERES-Maize calculates optimum crop growth and then adjusts it as a function of temperature, moisture, and nutrient stresses. Biomass allocation between various plant parts is growth-stage-specific and also is adjusted to account for environmental stresses. Introducing interactive crop development and growth functions into BATS has resulted in significant changes in the surface–atmosphere exchange of heat, moisture, and momentum described in the preceding paper (see Part I). In this paper we examine the effect of those changes on the seasonal climate simulated by RegCM2. We focused this research in the central Great Plains of North America, a distinct physiographic region with relatively homogeneous terrain, low relief, and land cover dominated by agricultural landscapes with rather homogeneous agricultural practices, to isolate the land surface forcing of climate.

2. The NCAR regional climate model

We used the National Center for Atmospheric Research Regional Climate Model version 2 (NCAR RegCM2; Giorgi et al. 1993a,b) to simulate the mesoscale climate over the domain of North America.

The dynamical component of the Regional Climate Model (RegCM2) is essentially the same as that of the standard Penn State–NCAR Mesoscale Model (MM4; Anthes and Warner 1978; Anthes et al. 1987). The MM4 is a hydrostatic, compressible, primitive equation, terrain following, σ vertical coordinate model. In our simulations model top is specified at 80 mb and the model has 17 vertical layers. The σ levels are defined to place 7 layers between the surface and 800 mb (corresponding to σ of 0.997–0.85), 3 layers between 800 and 300 mb (σ of 0.775–0.58), and 7 layers above 300 mb to the model top (σ of 0.48–0.02).

The radiative transfer scheme in RegCM2 is taken from Briegleb (1992). Cloud radiative properties are formulated based on the scattering and absorption parameterization of Slingo (1989), wherein optical properties of cloud droplets (extinction optical depth, single scattering albedo, and asymmetry parameter) are expressed in terms of the cloud liquid water content and effective droplet radius. The fractional cloud cover is defined as a function of relative humidity based on Slingo (1980). A cumulus parameterization employed in RegCM2 is based on Grell (1993).

The surface–atmosphere exchange fluxes are computed using the Biosphere–Atmosphere Transfer Scheme (BATS; Dickinson et al. 1993), which is described in the preceding paper (see Part I). BATS is coupled to the RegCM2 at its lowest model level. At this level, air temperature, humidity, pressure, winds, radiation, and precipitation are provided to BATS at each model grid point. For grid points designated as land, BATS computes surface radiative, sensible and latent heat, and momentum fluxes, and surface temperature based on the assigned vegetation and soil parameters.

The atmospheric boundary layer (ABL), or the lower part of the atmosphere directly affected by the surface, generally responds to surface forcings on a timescale of an hour or less, and is characterized by vertical wind shear and turbulent mixing. Over land the boundary layer thickness varies diurnally, mostly in response to the heating of the surface by solar radiation and nighttime radiative cooling. The effects of boundary layer turbulence and associated vertical transport are considered in the RegCM2 using a formulation developed by Holtslag et al. (1990) and Holtslag and Boville (1993). The boundary layer scheme of RegCM2 explicitly incorporates nonlocal effects (i.e., the transport of scalars, temperature, and water vapor, by deep convective plumes that can travel the entire depth of the ABL during daytime conditions).

3. Experimental design

The domain used in these experiments (hereafter referred to as US90 domain) includes the entire conterminous United States as well as portions of Canada and Mexico. The model grid resolution is specified at 90 km, with a domain of 61 × 42 grid points. The BATS land cover types employed for these simulations were derived from Loveland et al. (1991), see Fig. 1. The model topography is given in Fig. 2. The US90 domain is forced at its boundaries by analyses of observations from the European Centre for Medium-Range Weather Forecasts (ECMWF) at the beginning of the simulation and at 12-h intervals throughout the 6-month simulations. The analyses are interpolated from a Gaussian grid having a spatial resolution of approximately 300 km to the 90 km grid of the specified RegCM2 domain [for detailed description of ECMWF reanalyses see Trenberth (1992)]. In order to consider a variety of atmospheric conditions throughout the growing season, 6-month simulations were performed, with lateral boundary conditions derived from analyses of observations. To address the issue of interannual variability, two growing seasons (1988 and 1991) were considered. The performance of the RegCM2 is evaluated for the 1988 and 1991 growing seasons against observations. Previous analyses of RegCM2 over the central Great Plains were performed by Giorgi et al. (1998, 1996, 1994), Mearns et al. (1999, 2001, manuscript submitted to J. Climate), and Seth and Giorgi (1998).

The study area indicated in Fig. 1 with a box, is the focus of our analysis. It is composed of Nebraska, eastern Colorado, Kansas, Missouri, and Iowa, and is chosen for its vegetation characteristics and its lack of significant topography. Orographic effects are minimized in order to focus this study on the effects of vegetation and soil moisture on mesoscale climate. Soil moisture at the start of each simulation is assumed equal to 75% of field capacity.

For each of the growing seasons under consideration, 1988 and 1991, we performed a series of 6-month (1 April–30 September) simulations: 1) a control run (henceforth denoted as CTRL), where the standard unmodified Regional Climate Model version 2 (RegCM2) was used; 2) a tuned control run (henceforth denoted as TCTRL), where selected plant parameters in BATS (i.e., maximum and minimum LAI) were adjusted for the class of agroecosystems to those of maize; 3) an interactive run (henceforth denoted as INT), where CERES phenological development and growth functions were incorporated into RegCM2/BATS configuration for all grid cells with land cover type agriculture (see Fig. 1, where agriculture is denoted with 2); and 4) a fully interactive run (henceforth denoted as FINT), which is the same as INT with the addition of interactive calculation of canopy height. For INT and FINT simulations, all grid cells with land cover type agriculture (see Fig. 1) were treated as maize, and phenological development and growth functions from CERES were used to simulate crop seasonal changes. This is a considerable portion of the domain and does not represent the real spatial crop distribution patterns. We emphasize here that even in the central Great Plains study area depicted in Fig. 1 there are differences in crop distribution between states. For example, in 1991 Kansas was the number one wheat producer (predominantly winter wheat) in the nation; it also leads the country in the total area planted under wheat [47.7 × 103 km2; Agricultural Statistics (1994)]. During the same year, Iowa was the number one corn producer in the nation and lead the country in the total area planted under corn [50.6 × 103 km2; Agricultural Statistics (1994)]. In the future, realistic crop distribution patterns need to be represented.

Statistical tests were performed to determine the significance levels of differences between the simulations. We compared mean daily model-simulated air temperature and precipitation time series between the four sets of runs (CTRL, TCTRL, INT, and FINT), on a monthly basis. We used the parametric time series approach developed by Katz (1982), which accounts for temporal autocorrelation in the data. The test makes use of the innovation variance and autocorrelation structure of the series; see Mearns et al. (1995a,b) for details on the tests. In comparing results from the four runs, we calculate the test statistic across the entire US90 domain. The statistical problem of multiplicity in the presence of spatial autocorrelation is encountered when applying univariate statistical tests across a spatial field. The problem of multiplicity is raised when numerous tests are applied simultaneously. Essentially, for any given significance level (e.g., 10%) one would expect that over at least 10% of the grid points the null hypothesis would be rejected by chance. Here we use a similar methodology to Mearns et al. (1995a) and calculate the proportion of the domain where the null hypothesis is rejected (i.e., a statistically significant difference between control and interactive runs is found) and focus our analysis on months when the percentages are considerably higher than the univariate significance level selected.

It is essential for our analysis of the 90-km simulations to have an understanding of the large-scale atmospheric forcing and its evolution during the simulated months of April–September, 1988 and 1991. In the next two sections, we present some observations that outline the seasonal atmospheric conditions over the United States for the period of the simulations. We then evaluate the performance of our 90-km simulation, US90, and examine the differences among the control, tuned control, and interactive runs.

a. Overview of 1988 growing season

The Weekly Weather and Crop Bulletin (WWCB 1988) provides a useful summary of the weather over the United States for April–September 1988. During summer 1988, a strong La Niña event was under way, with below-normal sea surface temperatures (SSTs) in the eastern tropical Pacific. As shown by Trenberth and Guillemot (1996), in the summer of 1988 the jet stream and storm track were displaced northward and cyclonic disturbances over North America were weaker than normal and traveled north of the main region of influence of the Gulf of Mexico moisture source. Overall, in summer 1988, weak moisture transport from the Gulf of Mexico and northward displacement of the jet resulted in severely dry conditions. Precipitation totals for May–June–July (MJJ; three critical months for most central U.S. crops) over most of the central Great Plains study area (see Fig. 1) were under 65% of the climatological norm according to the National Climate Data Center (NCDC) monthly mean precipitation dataset based on the period of 1961–90. The drought conditions were especially apparent in the eastern portions of the central Great Plains study area. Analysis of the 6-month (April–September) Standardized Precipitation Index (SPI; McKee et al. 1993), and the Palmer Drought Severity Index (PDSI) shows that most of the Great Plains and the Midwest were under moderate to severe drought stress conditions. These dry conditions resulted in water-stressed crops in the region. Figure 3a shows crop water stress index for late June. Table 1 gives the corn crop condition for the states in the central United States study area.

Although the influence that the anomalous large-scale circulation had on the development of this extreme 1988 summer season has been recognized, debate is still ongoing on the importance of local processes, primarily associated with surface evaporation and sensible heat flux, for the maintenance and/or enhancement of the drought conditions. Surface sensible and latent heat fluxes have opposite effects on summer convection. On one hand, relatively dry soil conditions induce a decrease in evaporation, which results in smaller moisture source for convective storm systems. This leads to a decrease in precipitation and, for the drought periods, would provide a positive feedback mechanism capable of reinforcing the dry conditions. On the other hand, relatively dry soils induce an increase in sensible heat flux, which provides a source of buoyant energy that can enhance convection and deepen cyclonic systems. If sufficient atmospheric moisture is available, this process would increase precipitation, thereby providing a negative feedback that would alleviate the drought conditions.

b. Overview of 1991 growing season

During the summer of 1991, the jet stream position was close to its climatological mean and storm tracks and cyclonic disturbances over North America were close to normal; the role of the Gulf of Mexico as the main moisture source was well pronounced. Overall, in summer 1991, normal moisture transport from the Gulf of Mexico and close to normal position of the jet resulted in close to normal precipitation and temperature conditions for the central Great Plains and Midwest. Observed precipitation totals for MJJ over the continental United States indicate that, in contrast with 1988, most of the central Great Plains and the Midwest received over 300 mm rainfall in these three critical summer months, which is close to the climatological norm of 290 mm for the central Great Plains study area. Figure 3b shows the crop water stress index for late June 1991. Table 2 gives the corn crop condition for the states in the central United States study area. In all six states under consideration, much larger percentages of corn fall into the good and excellent categories compared to the same period for 1988.

4. Model performance and analysis

In this section, we examine the performance of RegCM2 for the two growing seasons, 1988 and 1991. Both horizontal and vertical fields of state variables are discussed, followed by comparisons of surface temperature and precipitation fields with observations.

a. State variables

Air temperature at the lowest model level (σ = 0.997) for June 1988 for TCTRL and FINT scenarios is shown in Figs. 4a,b, respectively. These are monthly integrations for June. We do not show results from the CTRL runs, but instead focus on TCTRL. The differences between these two sets of runs were rather small (see section 4b for details), with CTRL being consistently cooler and generally wetter than TCTRL. We also focus on the FINT runs and do not show results from the INT runs for the same reason of large similarity between the two. In both 1988 and 1991 (not shown here) there is a 1°–4°C increase in temperature over the central Great Plains in both INT and FINT cases compared to CTRL and TCTRL. The differences between TCTRL and FINT cases in both 1988 and 1991 (Figs. 5a,b, respectively) are mainly due to the fact that in the interactive scenario, it is still early in the growing season with leaf area index increasing throughout the month from around 0.5 to about 1–1.5. In the TCTRL scenario, LAI is high, in fact, close to its maximum value of 5 (or 6 for the CTRL case) for most of the month (since temperatures are consistently high and LAI in the control runs is solely a function of subsoil temperature). The differences between the control and interactive cases are larger in June 1988 compared to June 1991 due to significant moisture stress in the former case, which greatly impeded plant growth (thus the already low “early in the growing season” LAI is even lower). The differences in air temperature at the lowest model level (σ = 0.997) between the TCTRL and FINT cases in June 1988 are statistically significant at the 95% confidence level (p value <0.05) over most of the central Great Plains study area (Fig. 6a) except for the extreme western portions where the confidence levels drop to less than 90%. In June 1991, the differences in air temperature between the TCTRL and FINT cases are statistically significant at the 90% confidence level (p value <0.1) over about one half of the Great Plains study area (Fig. 6b), mainly in the northern and eastern portions. Note that the statistical tests were performed over the entire US90 domain, but only a subset for the central Great Plains is shown here to focus the analysis. Note also that the t statistic of ±2.57 corresponds to the p value of 0.01, t statistic of ±1.96 corresponds to the p value of 0.05, and t statistic of ±1.65 corresponds to the p value of 0.1. These are the only three intervals that we considered significant.

For both June 1988 and June 1991, the highest values of mixing ratio, q (not shown here), are seen in the southeastern United States (coastal plains around the Gulf of Mexico, Florida peninsula, and the southern portions of the U.S. east coast). The values of q there are anywhere between 14 g kg−1 and 17 g kg−1 in 1988 and between 16 g kg−1 and 18 g kg−1 in 1991. In the central Great Plains q is simulated to be between 10 g kg−1 and 12 g kg−1 in 1988 and between 10 g kg−1 and 14 g kg−1 in 1991. For both June 1988 and June 1991, the mixing ratio is higher in both of the control cases compared to the interactive cases mainly because LAI is higher in the control cases (close to the maximum value of 6 in CTRL case, and close to the maximum value of 5 in TCTRL case), and thus more moisture is being pumped into the atmosphere by plants through transpiration in the control cases (see Part I for details). This effect is strongest near the surface and decreases with height in the atmospheric column. This signal is strongest during the daytime when it extends up to the top of the boundary layer but is barely noticeable at night. The differences between CTRL and TCTRL scenarios are due mainly to different values of maximum LAI. The differences between INT and FINT cases are due to the addition of interactive canopy height parameterizations in FINT case that affect the momentum flux and thus vertical mixing of air in the lowest atmospheric levels. The differences between the TCTRL and FINT cases of 1–3 g kg−1 are observed for both June 1988 and June 1991 over the central Great Plains (Figs. 7a,b, respectively). These higher values of mixing ratio in TCTRL case compared to FINT (up to 25%) will later be related to higher precipitation amounts simulated by TCTRL case.

The differences between the control and interactive cases in both air and surface temperatures and mixing ratio are strongest early in the growing season (May and early June) when LAI in both interactive cases and canopy height in FINT case are at their lowest. The differences diminish but still persist later in the growing season. During the dry year, however (i.e., 1988), July temperature and mixing ratio deltas can be nearly as large as those early in the growing season due to extensive plant wilt and low LAI values associated with drought conditions and simulated by the interactive cases.

Difference plots for the TCTRL and FINT cases for July 1988 air temperature and mixing ratio, q, at the lowest model level (σ = 0.997) are shown in Figs. 8a,b, respectively. Both figures are monthly averages for July. As was the case for June 1988, in July air temperature is higher and mixing ratio is lower in both of the interactive cases compared to the control ones. The reason is somewhat different, however. By July 1988, drought conditions prevailed over most of the Midwest and eastern part of the Great Plains. Both of the interactive cases respond to this moisture deficit signal by simulating low LAI values. The control cases, on the other hand, do not. LAI in the interactive cases is anywhere between 0.8 and 2 throughout the central Great Plains, whereas it is close to its maximum (6 for CTRL and 5 for TCTRL) in the control cases. Thus, larger amounts of water are pumped into the atmosphere by vegetation in the control cases compared to the interactive cases, which is translated into lower temperature and higher mixing ratio values in the control runs compared to the interactive ones.

In contrast to 1988, the summer of 1991 was characterized as “favorably moist” over most of the central Great Plains (WWCB 1991). Generally plants were under little water stress in July; LAIs in both interactive runs were larger than in 1988 and closer to the values simulated by control runs (see Part I). As a result, the differences in air temperature and mixing ratio at the lowest model level between TCTRL and FINT cases in July 1991 are not nearly as large as in July 1988; see Figs. 9a,b. In July 1988, the differences in air temperature at the lowest model level (σ = 0.997) between the TCTRL and FINT runs are statistically significant at the 95% confidence level (p value <0.05) over about two-thirds of the central Great Plains study area (not shown). In contrast, in July 1991 the differences in air temperature at the lowest model level (σ = 0.997) between the TCTRL and FINT runs are not statistically significant (less that 90% confidence levels) over 95% of the Great Plains study area (not shown). The same statistical tests when performed on the precipitation differences between the runs (both occurrence and amount) were largely inconclusive (i.e., not statistically significant over most of the domain) and we do not discuss them any further.

As mentioned before, the differences between INT and FINT simulations arise from the inclusion of interactive canopy height parameterizations into the FINT runs. Note that in the INT runs, canopy height was set to 0.6 m throughout the entire time of simulations (the standard BATS approach), while in the FINT runs, the corn canopy height varied from zero at planting to some seasonal maximum (calculated as a function of climatic stresses) to zero again after harvest. As a result, near-surface wind speeds were generally higher in FINT runs early on in the simulation (interactively simulated canopy height was less than 0.6 m) until some time in July, when interactively simulated canopy height exceeded 0.6 m and FINT-simulated wind speeds became generally lower than those from the INT runs. The differences in mean monthly wind speeds between the two runs were generally under 0.5 m s−1. In both 1988 and 1991, the differences in wind speed, lower atmospheric temperature and mixing ratio, and precipitation between the INT and FINT runs appeared to be largely insignificant. We note here that more work, including multiple realizations (ensembles) for each type of run (INT and FINT), could shed more light on the issue of importance of interactively simulating canopy height for climate simulations.

b. Vertical profiles of state variables

In order to further explore the physical basis for the simulated differences between the control and interactive runs, we examined vertical profiles of state variables that influence the stability characteristics of the atmospheric boundary layer. Figures 10a–d show vertical profiles of potential temperature, equivalent potential temperature, actual temperature, and mixing ratio for CTRL, TCTRL, INT, and FINT cases. The variables are averaged over the central Great Plains domain and over time (for June and July). For all four variables, differences between the control and interactive cases are greatest near the surface and decrease with height. The differences are seen over several σ throughout the boundary layer. Overall, both of the interactive cases seem to be more stable than the control ones in 1988 whereas in 1991 all four scenarios are similar. The stable/unstable stratification of air column translates into precipitation and to some degree temperature patterns at the surface (e.g., in 1988 the more stably stratified interactive runs produce less precipitation compared to the control runs). We note here that in June and July in both years, the mean height of the lowest model level (σ = 0.997, pressure of about 960 mb) corresponds to the absolute height of about 600 m above sea level over the central United States study area (the actual heights varied somewhat between the years and between different parts of the study area). The mean height of the 900-mb level (σ = 0.935) corresponds to the absolute height of about 1150 m above sea level, and the mean height of the top of the boundary layer (σ = 0.775, pressure of about 760 mb) corresponds to the height of about 2700 km.

In June 1988, differences of up to 3 K are seen in the lowest model level air temperatures between the control and interactive cases. This difference in simulated actual air temperature translates into up to 3 K difference in potential air temperature (θ) at the lowest model level. Both actual and potential air temperatures for four cases converge at the top of the boundary layer, at σ of about 0.680. The difference of about 3 g kg−1 is seen in the lowest model level mixing ratio (q); it translates into the difference in equivalent potential temperature (θe) at the lowest model level of up to 6 K. The two control cases are clearly less stable compared to the interactive ones (Fig. 10a, θe), which may be one explanation as to why there is higher precipitation over the Central Great Plains in the control cases compared to the interactive ones. CTRL experiment seems to produce the least stable conditions. INT and FINT experiments are generally very close with INT being slightly more stable. In July 1988 the situation is generally similar to that of June with differences between the control and interactive cases being somewhat smaller compared to June ones (Fig. 10b).

In June 1991, the differences in air temperature at the lowest model level between the control and interactive cases are about 1–2 K, with CTRL case being the coolest and FINT case the warmest (Fig. 10c). The above difference in actual air temperature translates into a similar, 1–2 K, difference in potential temperature (θ) between the control and interactive cases. Both actual and potential air temperatures for four cases converge at σ of about 0.775 (compare to σ of 0.680 for June 1988, when air temperatures simulated by the interactive and control cases at the surface were farther apart). Mixing ratio at the lowest model level varies from about 12 g kg−1 in the FINT case to about 13.5 g kg−1 in the CTRL case. This difference in mixing ratio is the main reason for the differences in the equivalent potential temperature (θe) at the lowest model level. All four cases appear somewhat unstable with CTRL being the least stable and FINT the most stable.

In July 1991 (Fig. 10d), the differences between the control and interactive cases are the smallest compared to the other three months. There is hardly any difference in actual and potential air temperatures between the four cases throughout the entire vertical profile. TCTRL has somewhat lower mixing ratio at the lowest model level compared to the other three cases and seems most stable although the differences, again, are very small. In all four cases, LAI is close to its maximum values for the season and is almost uniformly over 4 by the end of the month.

c. Comparison of RegCM2 simulations with observed data

Using cooperative station data, a dataset of current daily climate (daily maximum and minimum air temperature, precipitation, and solar radiation) over the central Great Plains was created for 1983–93 and gridded on the 50-km horizontal grid of the RegCM2 used in other studies (Mearns et al. 1999; Easterling et al. 2000). About 440 cooperative stations in total were used for this study, which averaged to about three cooperative stations for each RegCM grid.

Figure 11 shows mean June 1988 observed daily maximum air temperature and daily maximum air temperature simulated by TCTRL and FINT runs. In 1988, June was the warmest of the summer months with the mean monthly maximum air temperature (Tmax) in excess of 30°C and mean monthly minimum air temperature (Tmin) under 14°C over 90% of the central Great Plains study area. Over half of the area Tmax was in excess of 32°C (Fig. 11); Tmin was between 4° and 12°C in the west and north and between 10° and 14°C in the south and east (not shown here). TCTRL generally underestimates Tmax over most of the region whereas FINT somewhat overestimates Tmax but is generally much closer to the observed patterns. FINT is also closer to the observed domain mean (Table 3). Both TCTRL and FINT overestimate Tmin for most of the region by up to 2°C (Table 3), which most likely is a result of unrealistically dense cloud cover simulated by RegCM2 (Giorgi et al. 1998). In July (Fig. 12) and August (not shown) 1988, Tmax was between 30° and 34°C throughout most of the region. Tmin was between 12° and 16°C in the west and 14° and 20°C in the central and eastern portions of the study area. Similarly to the June case, TCTRL somewhat underestimates Tmax whereas FINT is closer to observations, and both runs overestimate Tmin. We did not perform statistical tests on the differences between the observed and simulated temperature and precipitation fields due to the difficulties encountered when trying to aggregate/disaggregate daily data (Mearns et al. 1995b). We felt that the different spatial scales of the observed climate dataset (50-km grid) versus our simulated results (90-km grid) would compromise the robustness of a statistical test. For details on the issue of point versus grid box data analysis see Skelly and Henderson-Sellers (1996) and Osborn and Hulme (1997).

Figure 13 shows mean June 1988 observed daily precipitation amount and that simulated by TCTRL and FINT runs. June was the driest of the three summer months in 1988. Most of the central Great Plains study area received under 2.5 mm day−1 of rain throughout the month with less than 5% of the area receiving over 3 mm day−1. Both TCTRL and FINT reproduce the dry conditions of June 1988 over the central Great Plains with FINT producing a domain average (Table 3) and patterns closer to the observed compared to TCTRL (Fig. 13). July and August were still rather dry; in both months only about one-third of the region received over 2.5 mm day−1 of rain. Again, FINT produces closer domain averages to the observed compared with TCTRL (Table 3), and FINT precipitation patterns are closer to the observed as well. Such prolonged dry conditions of the summer of 1988 over the central Great Plains generally impeded crop growth and negatively affected crop yields. The severity of drought had a noticeable east–west gradient with the eastern portions of the study area (i.e., Iowa) most adversely affected.

In the summer months of 1991, FINT is again closer to the observed for Tmax than is TCTRL in terms of both area averages (Table 4) and spatial patterns over the region (the June case is shown in Fig. 14). TCTRL is closer to the observed for Tmin but the differences between TCTRL and FINT are small. In June 1991, both TCTRL and FINT cases underestimate the precipitation amount over the study area with FINT being somewhat closer to the observed mean (Table 4). In July both TCTRL and FINT cases overestimate precipitation amount over the region by about 35%; this is mostly due to the fact that the model simulates strong precipitation events in the far eastern parts of the study area that are not seen in the observed data. In August there is a fairly good agreement between both TCTRL and FINT cases with the observed data.

Overall, there is a better agreement between the RegCM2-simulated mean monthly maximum and minimum air temperatures from TCTRL run and the observed values in June, July, and August 1991 compared to the same months in 1988. One reason for this may be that the simulated LAI in June and particularly July and August 1991 of the TCTRL case was rather close to the observed values, whereas in 1988 there were major discrepancies between the simulated and observed values. As a result, in summer 1988 TCTRL run had too much available energy partitioned into latent heat flux, which tends to cool the lower atmosphere and often produce more rain.

An overall cold model bias for Tmax in TCTRL case can be seen from Tables 3 and 4. This cold bias has been mentioned previously in the literature (Giorgi et al. 1994, 1998); the 1998 paper looked specifically at RegCM2 performance over the central Great Plains region. The inclusion of plant growth and development functions into the RegCM2/BATS configuration resulted in a generally better agreement with observations of the simulated daily maximum air temperature and precipitation over the central Great Plains in both 1988 and 1991 but failed to improve the simulation of the diurnal range in temperature in either year. The failure to adequately simulate Tmin results from limitations of the RegCM2 cloud scheme and needs to be addressed in the future. Clearly, one limitation of this comparison between the observed and RegCM-simulated climate fields is the lack of statistical tests on both the temperature and precipitation fields.

5. Summary and conclusions

We investigated the effect of the interactively simulated LAI and canopy height on simulated mesoscale atmospheric circulations and ABL structure through conducting a numerical modeling experiment with a robust representation of seasonal changes in plant development and growth. We found 2°–4°C increases in surface and air temperatures in response to the inclusion of plant growth and development functions into the RegCM2/BATS configuration. Mixing ratio also changed by up to 25%. These changes diminished with height in the atmosphere but were observed up to the top of the boundary layer, σ of about 0.680. The inclusion of interactive vegetation generally improved atmospheric simulations in the summer of 1988 compared to the control runs. Differences between the control and interactive simulations during the summer of 1991 were smaller than those in 1988, with greater disparities in June due to the low LAI values of about 1 early in the growing season in the interactive cases and relatively high LAI values of about 5 in the control cases due to rather high temperatures during most of the month. We also found that lower atmospheric winds and to some extent ground and lower air temperatures were affected by the introduction of interactive plant height calculations. The issue of relative importance of LAI versus canopy height in mesoscale simulations was addressed by comparing INT and FINT runs. Interactive simulation of LAI had a much greater effect on lower atmospheric temperature and moisture than did interactive canopy height parameterizations. The main effect of the inclusion of interactive canopy height parameterizations was on the lower atmospheric winds with FINT generally producing higher wind velocities at the beginning of the simulations (April–mid-July) and end of the simulations (after harvest in mid-September). FINT had generally lower wind speeds compared to INT from mid-July to mid-September. Change in wind intensity affected the intensity of vertical mixing of air in the lower atmosphere, therefore changing temperature and mixing ratio values.

Our results are in general agreement with those of Xue et al. (1996), who found up to 3°C changes in lower atmospheric air temperature in response to different LAI curves (those of corn vs winter wheat) used to force their climate model. Our results are also in general agreement with those of Dickinson et al. (1998) and Lu et al. (2001), both studies indicating strong sensitivity of lower atmospheric air temperature and precipitation to the interactive LAI parameterizations employed by either climate model, NCAR CCM3 (coupled to BATS) in the former case and RAMS (coupled to LEAF) in the latter. In both studies, interactive parameterizations resulted in higher temperatures and lower evapotranspiration and precipitation values compared to the control over the extratropical Northern Hemisphere summer. The changes in climate variables resulted from the ability of the interactive models to simulate the effects of moisture and nitrogen stresses on LAI and stomatal conductance. The response of precipitation to LAI was highly nonlinear.

Significant limitations of this study associated primarily with unrealistic simplification of crop distribution and agricultural practices over the region as well as BATS inherent limitations decrease the degree of realism of our results. These issues should be addressed in the future. Acknowledging the limitations of this modeling study, several inferences can be made with respect to the importance of interactive representation of plant development and growth in mesoscale climate models. First, both point and regional simulations of the partitioning of available energy between the surface heat and moisture fluxes are greatly affected by the leaf area index development. These changes in H and LE translate into changes in regional air and surface temperature, mixing ratio, and precipitation. Momentum flux simulations are greatly affected by the canopy height calculations. Since production of both LAI and canopy height is a function of plant phenological stage and plant growth, which is determined by the environmental (PAR, temperature, soil moisture) conditions and genetic factors, adequate representation of both processes in mesoscale climate models is key to successful modeling of regional surface flux patterns and lower atmospheric fields. The proper representation of plant phenological development and growth in climate models can be particularly important for climate change studies. Crop response to the projected climate changes and likely changes in crop genetic makeup can be an important feedback to the atmosphere, changing the characteristics of the boundary layer. Through a more robust representation of crops in climate models, studies of the effects of land cover change (from potential to current, mostly agricultural, vegetation) on climate, such as those of Bonan (1997) and Chase et al. (1999), can be improved.

Acknowledgments

We wish to thank Dr. Filippo Giorgi for his useful insights for analyzing RegCM output and Christine Shields and Larry McDaniel of NCAR for their computing support. We also acknowledge Cynthia Hays of the Department of Agricultural Meteorology, University of Nebraska, for a major contribution to the compilation of the dataset of observed climate for the Great Plains used in this study. The National Center for Atmospheric Research is sponsored by the National Science Foundation.

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Fig. 1.
Fig. 1.

RegCM2 US90 domain and study area for analysis. BATS land cover types: 1—urban land, 2—agriculture, 3—range/grassland, 4—deciduous forest, 5—coniferous forest, 6—mixed forest and wet land, 7—water, 8—marsh or wet land, 9—desert, 10—tundra, 11—permanent ice, 12—tropical forest or subtropical forest, 13—savanna

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 2.
Fig. 2.

Topography for the RegCM2 US90 domain. Elevation above sea level (in m). Contour interval = 200 m

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 3.
Fig. 3.

Crop moisture index for (a) 25 Jun 1988 and (b) 29 Jun 1991 (from WWCB)

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 4.
Fig. 4.

Mean Jun 1988 air temperature (T) in K at the lowest model level (σ = 0.997) simulated by (a) TCTRL run and (b) FINT run. Contour interval = 2 K

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 5.
Fig. 5.

Mean difference in air temperature (in K) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for (a) Jun 1988 and (b) Jun 1991. Contour interval = 1 K. Areas shaded with a fine dot pattern have positive differences of 1 K or higher; nonshaded areas have positive differences between 0 and 1 K; and areas shaded with a coarse dot pattern have negative differences. Note that all negative differences are between 0 and −0.(9) K (except for local point minima)

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 6.
Fig. 6.

Difference of the means between the FINT and TCTRL runs parametric time series tests for (a) Jun 1988 and (b) Jun 1991

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 7.
Fig. 7.

Mean difference in mixing ratio (in g kg−1) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for (a) Jun 1988; and (b) Jun 1991. Contour interval = 1 g kg−1. Areas shaded with a coarse dot pattern have negative differences with values more negative than −1 g kg−1; nonshaded areas have differences between −1 g kg−1 and 1 g kg−1; and areas shaded with a fine dot pattern have positive differences of 1 g kg−1 or higher

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

i1520-0442-14-5-711-f08

Fig. 8a. Mean difference in air temperature (in K) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for Jul 1988. Contour interval = 1 K. Areas shaded with a fine dot pattern have positive differences of 1 K or higher; nonshaded areas have positive differences between 0 and 1 K; and areas shaded with a coarse dot pattern have negative differences. Note that all negative differences are between 0 and −0.(9) K (except for local point minima). Fig. 8b. Mean difference in mixing ratio (in g kg), at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for Jul 1988. Contour interval = 1 g kg−1. Areas shaded with a coarse dot pattern have negative differences with values more negative than −1 g kg−1; nonshaded areas have differences between −1 g kg−1 and 1 g kg−1; and areas shaded with a fine dot pattern have positive differences of 1 g kg−1 or higher

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

i1520-0442-14-5-711-f09

Fig. 9a. Mean difference in air temperature (in K) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for Jul 1991. Contour interval = 1 K. Areas shaded with a fine dot pattern have positive differences of 1 K or higher; nonshaded areas have positive differences between 0 and 1 K; and areas shaded with a coarse dot pattern have negative differences. Note that all negative differences are between 0 and −0.(9) K (except for local point minima). Fig. 9b. Mean difference in mixing ratio (in g kg−1) at the lowest model level (σ = 0.997) between FINT and TCTRL runs (FINT − TCTRL) for Jul 1991. Contour interval = 1 g kg−1. Areas shaded with a coarse dot pattern have negative differences with values more negative than −1 g kg−1; nonshaded areas have differences between −1 g kg−1 and 1 g kg−1; and areas shaded with a fine dot pattern have positive differences of 1 g kg−1 or higher

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 10.
Fig. 10.

Vertical profiles of potential temperature, equivalent potential temperature, actual temperature, and mixing ratio for (a) Jun 1988; (b) Jul 1988; (c) Jun 1991; and (d) Jul 1991

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 11.
Fig. 11.

Mean monthly maximum air temperature (°C) for Jun 1988

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 12.
Fig. 12.

Mean monthly maximum air temperature (°C) for Jul 1988

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 13.
Fig. 13.

Mean monthly precipitation (mm day−1) for Jun 1988

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Fig. 14.
Fig. 14.

Mean monthly maximum air temperature (°C) for Jun 1991

Citation: Journal of Climate 14, 5; 10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2

Table 1.

Corn crop condition (% in category) on 3 Jul 1988. (Source: Weekly Weather and Crop Bulletin, 6 Jul 1988)

Table 1.
Table 2.

Corn crop condition (% in category) on 7 Jul 1991. (Source: Weekly Weather and Crop Bulletin, 9 Jul 1991)

Table 2.
Table 3.

Mean monthly maximum and minimum air temperature and precipitation for Jun and Jul 1988

Table 3.
Table 4.

Mean monthly maximum and minimum air temperature and precipitation for Jun and Jul 1991

Table 4.
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