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  • View in gallery
    Fig. 1.

    Comparison of (top) Jun–Aug (JJA) observed precipitation (CMAP), (middle) ensemble mean simulated in the control case, and (bottom) the error for 1987 and 1988. Units are mm day−1, negative contours are dashed.

  • View in gallery
    Fig. 1.

    (Continued)

  • View in gallery
    Fig. 2.

    As in Fig. 1 for near-surface air temperature (observed = CAMS). Units are K, negative contours are dashed.

  • View in gallery
    Fig. 2.

    (Continued)

  • View in gallery
    Fig. 3.

    Comparison of initial root zone soil wetness between the (top) GSWP case and (middle) control case with (bottom) the difference for 1987 and 1988. Units are fraction of saturation, negative contours are dashed.

  • View in gallery
    Fig. 3.

    (Continued)

  • View in gallery
    Fig. 4.

    Comparison of the evolution of root zone soil wetness for individual ensemble members (light) and the ensemble mean (bold) for the control and GSWP cases during May–Aug 1987 at a grid cell over Illinois. Also shown is the top 1-m soil wetness averaged for 12 Illinois Soil Moisture Network stations. Units are fraction of saturation.

  • View in gallery
    Fig. 5.

    Aug minus May root zone soil wetness for (top) the GSWP case and (middle) control case with the difference shown at the bottom. Data from 1987 and 1988 are averaged together. Units are fraction of saturation, negative contours are dashed.

  • View in gallery
    Fig. 6.

    Control minus GSWP case JJA differences in (top) precipitation (mm day−1), (middle) near-surface air temperature (K), and (bottom) root zone soil wetness (fraction of saturation). Data from 1987 to 1988 are averaged together, negative contours are dashed. Shading indicates signal-to-noise ratio exceeding 1(light) and 2 (dark).

  • View in gallery
    Fig. 7.

    Grid cells where JJA errors in the control case exceed 2 mm day−1 for (top) precipitation, 2 K for (bottom) near-surface air temperature. Cells shaded black experience reduced error in the GSWP case, cells shaded gray do not.

  • View in gallery
    Fig. 8.

    1988 minus 1987 JJA precipitation; (top left) observed; (top right) control case; (bottom left) GSWP case, (bottom right) swapped case. Units are mm day−1, negative contours are dashed.

  • View in gallery
    Fig. 9.

    As in Fig. 8, for near-surface air temperature. Units are K.

  • View in gallery
    Fig. 10.

    (top) 1988 minus 1987 JJA SST (K), and (bottom) GSWP root zone soil wetness (fraction of saturation). Negative contours are dashed.

  • View in gallery
    Fig. 11.

    1988 minus 1987 JJA differences forced by interannual variations in (left) SST and (right) soil wetness, and (top) in precipitation (mm day−1) and (bottom) near-surface air temperature (K). Negative contours are dashed. Shading indicates signal-to-noise ratio exceeding 1 (light) and 2 (dark).

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Using a Global Soil Wetness Dataset to Improve Seasonal Climate Simulation

Paul A. DirmeyerCenter for Ocean–Land–Atmosphere Studies, Calverton, Maryland

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Abstract

Ensembles of boreal summer coupled land–atmosphere climate model integrations for 1987 and 1988 are conducted with and without interactive soil moisture to evaluate the degree of climate drift in the coupled land–atmosphere model system, and to gauge the quality of the specified soil moisture dataset from the Global Soil Wetness Project (GSWP). Use of specified GSWP soil moisture leads to improved simulations of rainfall patterns, and significantly reduces root-mean-square errors in near-surface air temperature, indicating that the GSWP product is of useful quality and can also be used to supply initial conditions to fully coupled climate integrations. Integrations using specified soil moisture from the opposite year suggest that the interannual variability in the GSWP dataset is significant and contributes to the quality of the simulation of precipitation above what would be possible with only a mean annual cycle climatology of soil moisture. In particular, specification of soil wetness from the wrong year measurably degrades the correlation of simulated precipitation and temperature patterns compared to observed.

Corresponding author address: Dr. Paul A. Dirmeyer, Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705-31060.

Email: dirmeyer@cola.iges.org

Abstract

Ensembles of boreal summer coupled land–atmosphere climate model integrations for 1987 and 1988 are conducted with and without interactive soil moisture to evaluate the degree of climate drift in the coupled land–atmosphere model system, and to gauge the quality of the specified soil moisture dataset from the Global Soil Wetness Project (GSWP). Use of specified GSWP soil moisture leads to improved simulations of rainfall patterns, and significantly reduces root-mean-square errors in near-surface air temperature, indicating that the GSWP product is of useful quality and can also be used to supply initial conditions to fully coupled climate integrations. Integrations using specified soil moisture from the opposite year suggest that the interannual variability in the GSWP dataset is significant and contributes to the quality of the simulation of precipitation above what would be possible with only a mean annual cycle climatology of soil moisture. In particular, specification of soil wetness from the wrong year measurably degrades the correlation of simulated precipitation and temperature patterns compared to observed.

Corresponding author address: Dr. Paul A. Dirmeyer, Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705-31060.

Email: dirmeyer@cola.iges.org

1. Introduction

Most weather and climate models consist of at least two complete component models coupled together: an atmospheric general circulation model (GCM) and a land surface scheme (LSS). The coupling may be accomplished in many ways, but usually the GCM and LSS exchange information regarding the values of state variables at the interface, as well as fluxes of energy and water. Information is usually exchanged at a time interval corresponding to the integration time step of the atmospheric model, which is typically on the order of minutes. This allows the simulation of the atmosphere and land surface to evolve in tandem with feedbacks between the two systems.

All numerical weather predictions or climate simulations must have a starting point. One of the most challenging problems is accurately determining the proper initial condition from which to begin a numerical integration. Operational weather prediction models require timely analyses of the ever-evolving state of the global atmosphere, which are created from a wide array of in situ and remotely sensed observations from a number of sources around the world that are assimilated into a GCM to produce a single, consistent representation of the global atmosphere at a particular point in time. A series of these analyses represent the best historical estimate of the evolution of the global atmosphere.

Real-time analyses of the land surface state (distributions of soil wetness, temperature, snow cover, and depth, and large-scale properties of the vegetation) have historically received less emphasis. There are two main reasons. First, there is a dearth of useful data collected on the land surface, and most data that are collected are not disseminated due to the lack of a well-developed real-time global network like that which exists for atmospheric observations. Also, many land surface characteristics vary on a range of scales, down to the order of meters (Kabat et al. 1997). Hundreds of measurements of soil wetness or snow water equivalent may be necessary to arrive at an accurate, representative estimation at the resolution of the models (typically 102–105 km2), which is typically near to the characteristic spatial scale of soil-moisture variations on monthly seasonal timescales (Vinnikov et al. 1996; Entin 1998). Second, most emphasis has historically been on the needs of short-term weather forecasting, where the initial atmospheric condition is the major determinant of the evolution of weather over periods of hours to a few days. For medium-range weather forecasting and climate simulation and prediction, the land surface and ocean boundary conditions play a more important role (U.S. National Research Council 1994).

Thus, modelers have struggled to develop adequate datasets of land surface conditions to initialize LSSs in weather and climate models. In addition, some land surface parameters are often specified or updated throughout an integration to prevent the coupled land–atmosphere system from drifting to an unrealistic climate. In particular, soil wetness has been shown to be important (e.g., Shukla and Mintz 1982; Dirmeyer 1994). The seasonal variations in soil wetness are large over many areas of the globe—use of a climatological annual cycle of realistic soil wetness to furnish initial conditions is much preferred to no initialization at all. There have been attempts to produce such datasets, typically derived from the application of simplified evaporation formulations to observed data (Mintz and Serafini 1981; Mintz and Walker 1993; Liston et al. 1993) There is also interannual variability in soil wetness. Huang et al. (1996) produced a multiyear dataset over the United States based on observational data and a simple evaporation formulation. More recently, data assimilation methods have been developed where an LSS driven by observed or analyzed atmospheric conditions can be integrated to produce time series of soil wetness (Bouttier et al. 1993; Houser et al. 1998).

To address the lack of consistent, high-quality datasets of soil wetness, and to understand how different LSSs determine soil wetness, the Global Soil Wetness Project (GSWP) was created (Dirmeyer et al. 1999). GSWP is an ongoing modeling activity of the International Satellite Land-Surface Climatology Project (ISLSCP), a contributing project of the Global Energy and Water Cycle Experiment. In the pilot phase of GSWP, a two-year global dataset of soil wetness, temperature, surface fluxes, and other land surface variables was produced using a dozen different LSSs and a common set of soil and vegetation parameters and meteorological forcing: the ISLSCP Initiative I dataset (Meeson et al. 1995). The land surface datasets produced by GSWP represent the current state of the art in global quality and consistency for quantities such as soil wetness, although there is still room for improvement.

These soil wetness data were used as boundary conditions in a set of seasonal climate simulations with the Center for Ocean–Land–Atmosphere Studies (COLA), GCM, and LSS, and compared to simulations from COLA’s dynamical seasonal predictability hindcasts (DSP; Shukla et al. 2000). The results suggested that the GSWP soil wetness data improved the simulation of boreal summer climate when applied as a specified boundary condition, and that interannual variability of soil wetness was an important factor in determining regional climate variations in many locations (Dirmeyer 1999). However, that experiment used small ensembles of integrations (4 members each), and applied a GSWP soil wetness product that, although produced with the same LSS as used in the COLA climate model, used very different distributions of soil and vegetation properties than those used in the DSP hindcasts. These model inconsistencies and small sample sizes cast some doubt on the significance of the results.

In this paper, the problem addressed by Dirmeyer (1999) is reexamined with a larger and more consistently developed set of experiments. The following two questions are raised.

  • Does the inclusion of GSWP soil wetness boundary conditions in a coupled GCM–LSS model significantly improve the simulation of climate?

  • Does the interannual variability in a multiyear soil wetness dataset contribute to interannual variability in climate simulations?

Section 2 describes the structure of the experiments in greater detail. The impact of GSWP soil wetness boundary conditions is examined in section 3. Section 4 appraises the impact of the interannual variability in the GSWP dataset on the simulation of climate. Conclusions are presented in section 5.

2. Experiment structure

The global atmospheric and land surface models are briefly described in this section. Also, the production and application of the GSWP soil wetness data are reviewed and the framework of the numerical integrations is detailed.

a. The models

The atmospheric GCM used is version 1.12 of the COLA GCM at a spectral resolution of R40 (approximately 1.8° lat × 2.8° long on the corresponding Gaussian grid) and 18 vertical levels. It is a research version of the global spectral model described by Sela (1980) with modifications as described by Kinter et al. (1988) and Schneider and Kinter (1994). This version of the model is very similar to that described by Kinter et al. (1997), with the principal differences being that this version uses a relaxed Arakawa–Schubert convection parameterization (DeWitt 1996), and a cloud radiation scheme based on that used in the Community Climate Model CCM3 (DeWitt and Schneider 1996). The observed sea surface temperatures (SST) specified in all integrations are interpolated from the weekly analysis of Reynolds and Smith (1994).

The LSS coupled to the GCM over land is a version of the simplified Simple Biosphere (SiB) model of Sellers et al. (1986) that is described by Xue et al. (1991), and referred to hereafter as SSiB. The soil moisture is modeled in three layers: a thin surface layer, a rooting layer that varies in depth according to vegetation type, and a deep recharge zone. In SSiB, vegetation is modeled explicitly, and controls on water uptake and transpiration are governed by moisture potentials and water and temperature stress thresholds (Xue et al. 1996). The version used in the coupled land–atmosphere integrations is modified to allow the input of independent two-dimensional grids of soil properties, and two-dimensional time-varying grids of vegetation cover fraction, greenness, and leaf area index (Dirmeyer and Zeng 1997).

An offline version of the same LSS was used for COLA’s participation in GSWP (Dirmeyer and Zeng 1999). The GSWP integrations are at a considerably higher resolution of 1° × 1° over all land points free of permanent ice (as specified by gridded data on the ISLSCP Initiative ICD-ROM; Meeson et al. 1995).

b. Soil wetness data

In GSWP, the ISLSCP Initiative I data (Sellers et al. 1996) were used to drive a number of LSSs in an offline mode on a 1° × 1° grid during the two-year period of 1987–88 (Dirmeyer et al. 1999). This involved the use of observed and analyzed meteorological forcings, including hybrid precipitation and radiation products that contain the temporal variability from analyses produced by operational global weather prediction models, but preserve the monthly means observed by surface or satellite measurements. GSWP specifies the use of a standard vegetation map and a set of soil and vegetation parameters to which all participating LSSs must adhere as closely as possible. SSiB was one of the participating LSSs, and global fields of soil wetness, soil and canopy temperatures, and the complete set of terms for the surface and subsurface energy and water balance were calculated with this model.

The soil wetness data from the offline GSWP simulation are used as boundary conditions for some of the GCM simulations described below. Data for each of the three soil levels are saved approximately every ten days, on the 1st, 11th, and 21st of each month at 0000 UTC. The data represent the time mean soil wetness index (SWI) over the previous interval. SWI is a normalized index of soil wetness where 0 represents the wilting point and 1 equals the field capacity of the soil layer. These limits vary according to soil layer depth, soil characteristics, and, in the case of wilting point, the properties of the overlying vegetation. Wilting point is the level of soil moisture at which all evapotranspiration ceases—field capacity is the maximum level of water that the soil can retain by capillary action against the draining force of gravity. This scale is somewhat different than that used for soil wetness, where 0 represents absolutely dry soil, and 1 is completely saturated (all soil pore space is filled with water). SWI can exceed 1 since field capacity is at a somewhat drier state than saturation. Likewise, SWI may be less than 0 where imposed base flow may reduce soil moisture below the wilting point.

For use in the lower-resolution coupled land–atmosphere integrations examined here, the GSWP soil wetness data have been aggregated and interpolated to a five-day time interval. Values of SWI greater than 1 have been reduced to 1 and negative values have been reset to zero. The SWI is then converted back to soil wetness based on the wilting points and field capacities of the vegetation and soils data as interpolated to the coarser GCM grid.

c. Setup of the integrations

All integrations are four months in duration, covering the period of May–August of 1987 and 1988. This is a period of general drying of soils in the Northern Hemisphere mid- and high latitudes, as evaporation increases markedly during summer. Typically an integration of the COLA GCM coupled to SSiB is begun from a set of initial atmospheric and land surface boundary conditions specified from analyses from one or more sources. The coupled system then evolves freely, with only SST specified as a boundary condition throughout the integration. This means that over land, atmospheric processes such as precipitation and surface quantities (e.g., soil wetness, surface temperature) evolve in tandem. With the full compliment of land–atmosphere climate feedbacks in place, errors in a quantity in one component model may impact the simulation of related quantities in the other component model. Similarly, if the strength of the feedback in the coupled model is either too strong or too weak, climate drift may result even when individual components of the coupled model system are performing properly. When this occurs to quantities that are involved in positive feedback mechanisms, the errors may grow significantly. As one example, a systematic bias by the GCM toward excessive rainfall over a location would necessarily lead to wetter soil than would be expected if there were no error in precipitation. This wetter soil could act as a stronger source of evaporation, contributing more moisture to the local atmosphere. The extra moisture available could enhance rainfall further, amplifying the error through positive feedback. Thus, positive feedbacks can exacerbate errors in both components of the coupled system.

Figure 1 shows the performance of the coupled land–atmosphere model in simulating precipitation during the boreal summer. Observed data come from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) dataset (Xie and Arkin 1997). The large-scale patterns are well simulated during both 1987 and 1988. However, it is clear from the error panels that there are significant errors in the simulation of precipitation. Many of the mid- and high-latitude land areas show excessive precipitation rates. There are also alternating wet and dry errors in the Tropics, suggesting that the position and extent of specific features in the seasonal rainfall may be amiss.

The similarity of the errors between 1987 and 1988 suggest they are very systematic. In fact, the root-mean-square (rms) of the 2-yr mean summer rainfall error calculated over all land points between 57°S and 75°N is 2.82 mm day−1. The rms of the error difference 1988 minus 1987 is only 1.34 mm day−1, suggesting that the interannual variability of the error is small compared to the mean error. Measured another way, the correlation coefficient between the terrestrial precipitation errors for 1987 and 1988 is 0.88. Over ocean, there is much more variability between years.

By no means are all of the terrestrial precipitation errors the result of problems with the LSS and its simulation of soil wetness. Inaccuracies no doubt exist in the representation of the atmospheric boundary layer, and the parameterization of many of the physical processes, such as convection and radiation. Any parameterization is by definition an approximation, and is difficult to perfect when it is part of a highly nonlinear dynamical system such as a climate model.

Figure 2 presents a comparison of the model-simulated near-surface air temperature to gridded observations from the Climate Anomaly Monitoring System (CAMS; Ropelewski et al. 1985). Again, the large-scale patterns are well simulated. Regionally there are discrepancies, as indicated by the last panel. Much of the extratropics has positive temperature errors. Cold biases exist predominantly over high terrain, desert areas of Africa and the Middle East, and much of South America.

Like the precipitation errors, there is little variation in the temperature error pattern between years. The rms temperature error averaged over land is 3.53 K, whereas the rms of the difference between 1987 and 1988 temperature errors is 1.41. As with precipitation, the correlation between the error fields between 1987 and 1988 is very high: 0.91. The errors appear to be linked to systematic local biases, which may arise from any of a number of reasons. At least some of the systematic error in precipitation and temperature may come from an incorrect representation of soil wetness, caused by systematic errors in precipitation amplified by feedback with the land surface.

To examine the role that soil wetness plays in the simulation of climate, three types of integrations for each year, 1987 and 1988, are performed. The first case, whose errors were displayed in Figs. 1 and 2, is a control case where soil moisture is initialized from the GSWP data as calculated offline at 1° × 1° resolution, and then interpolated to the GCM grid. In the second case, initial soil wetness is specified from the GSWP simulation described in section 2b, and then reset every five days throughout the integration to agree with the GSWP-calculated values. The third case is like the second case, except that GSWP soil wetness fields from the opposite year are specified (i.e., 1987 soil wetness is specified in the integrations for 1988, and vice versa). This“swapped” case is used to examine the role of interannual variability in soil moisture on climate in section 4. Table 1 outlines the boundary conditions used in each case.

There are differences in the initial state of the soil wetness between the control and the other two cases, as shown in Fig 3. In particular, the arid regions and much of the Southern Hemisphere have greater initial soil wetness in the control case. The differences are very consistent between the years. Although the same GSWP data were used in all cases, the method used to convert the soil wetness data from its original 10 day (1° × 1°)−1 resolution to a format compatible with its use in a climate model was different for the generation of standard GCM initial conditions, and the 5-day interval boundary conditions. Initial conditions for the GCM were calculated by taking GSWP soil wetness data at 1° × 1° resolution, averaging it to monthly mean values, and interpolating to the coarser GCM grid. The soil wetness boundary condition data were calculated from GSWP grids of SWI. The SWI was first interpolated to the GCM grid before being converted back to soil wetness, based on the wilting points and field capacities calculated for the vegetation and soils data on the GCM grid. Particularly because SSiB defines gridded vegetation based on the dominant type, SWI fields will interpolate differently than soil wetness.

Despite this discrepancy, the current set of experiments is more consistently designed than that of Dirmeyer (1999). In the previous study, integrations were performed with a version of the GCM that used a very different distribution of vegetation types, properties, and soils than those used in the offline GSWP simulations. The current experiment takes full advantage of the ISLSCP Initiative I data in specifying land surface properties within the GCM, making the GCM land surface highly consistent with GSWP. Also, ensembles of nine integrations are produced in all cases. This produces more stable statistics and a better sampling of the climate space than the experiment of Dirmeyer (1999), which relied on ensembles of only four members. Finally, the integrations are four months long, initialized at the end of April. This allows a full month for the ensemble members to differentiate themselves, giving a truer sampling of the model’s internal variability, or“noise,” for the estimation of signal significance.

In Fig. 4, a grid cell centered within the domain of the Illinois Soil Moisture Network (Hollinger and Isard 1994) has been chosen to show the time series of root zone soil wetness during the four-month integration in 1987 for the control ensemble, and the ensemble where GSWP soil wetness was used as a periodically updated boundary condition. An average of observed soil wetness in the top meter of soil from 12 stations that lie within 100 km of the grid box, taken from the Global Soil Moisture Data Bank (Robock et al. 1999), is also shown. In the control ensemble, there is a clear drying trend during the period. A spread quickly develops among the ensemble members, which expands greatly after the first month and a half. This spread is a result of the variations in the simulation of grid cell rainfall among the ensemble members. By the end of the period, most of the members have reached the wilting point. For the GSWP ensemble, the members do not stray very far from the observations, nor from one another. Indeed they cannot spread much, as soil wetnesses for all ensemble members are reset six times per month to the interpolated GSWP value. The greatest range of soil wetness attained in this case slightly more than 0.1, and is usually less than 0.04, whereas in the control case, the spread approaches 0.40 at one point. By the end of the integration, the difference between the mean root zone soil wetness of the two ensembles is 0.27, or nearly a factor of 2 (0.35 vs 0.62). Curiously, one ensemble member of the control integration remains high throughout the season, remaining close to the GSWP cases and the observations.

3. Impact on mean climate

The difference in soil wetness as specified from the GSWP offline simulations, and the soil wetness that evolves freely in the coupled land–atmosphere model can be quite large. Figure 5 contrasts the evolution in soil wetness from May to August in the control GCM integration with the difference stipulated from the GSWP simulation. In this figure, and throughout this section, results from the 1987 and 1988 ensembles are averaged together. In both cases, there are some similar features: the signature of drying in the Northern Hemisphere mid- and high-latitudes, drying after the rainy season in the Southern Hemisphere subtropics and Tropics, and increased soil wetness over the Northern Hemisphere monsoon regions of southern and eastern Asia, the Sahel, and Mesoamerica.

However, the difference between the control and GSWP integrations shows that the free-running climate model shows excessive drying almost everywhere that drying occurs. In addition, soil wetness over much of monsoon Asia does not increase as much as in the GSWP specification. These biases are consistent with systematic errors in the COLA GCM radiation, which exhibits too little cloud cover and too much absorbed solar radiation at the ground (DeWitt and Schneider 1996). Soil wetness increases too much over most of the remainder of monsoon Asia, as well as the other parts of the Tropics where the rainy season is underway. This reflects the systematic errors in rainfall shown in Fig. 1. Thus, the character of the unregulated coupled land–atmosphere model is to simulate the correct patterns of soil wetness change during boreal summer, but with a bias toward lower soil moisture.

Figure 6 shows the spatial distribution of the June–August mean differences in precipitation, near-surface air temperature, and soil wetness between the control and GSWP cases. The differences in temperature are highly correlated with the changes in soil wetness between cases. The correlation coefficient between the two is −0.66 over land. There appears to be a strong relationship between wetter–drier soils and cooler–warmer soils. One would expect where radiation is abundant in the Tropics and summer hemisphere, that the addition of water to the soil would retard warming by increasing evapotranspiration and soil heat capacity.

The correlation over land between changes in rainfall and soil wetness is also significant, but lower, at +0.24. Nonetheless, large-scale features are well correlated. For instance, the negative differences in precipitation throughout the mid- and high-latitudes of the Northern Hemisphere correspond to lower soil wetness there. There is also a good match over southern Asia. There is inherently a great deal of small-scale structure in model precipitation fields that may mask some of the correspondence. If a 25-point smoothing is applied to the precipitation and soil wetness difference fields on the 1.8° lat × 2.8° long grid, the correlation coefficient increases to +0.34. The correlation of the large-scale fields is consistent with Vinnikov et al. (1996) and Entin (1998).

So we come to the first question posed in the introduction: Do the GSWP soil wetness data improve the simulation of climate when applied as a boundary condition? Table 2 shows the correlations and rms errors in the simulation of precipitation and near-surface air temperature for selected regions of the globe in the control and GSWP cases. As a means to establish the significance of the impact of GSWP soil wetness, we estimate the inherent noise in the climate model system. This is found by calculating the variance of the differences in the correlations or rms errors among each of the nine pairs of integrations in each ensemble. The intra-ensemble variances for the control and GSWP ensembles are then averaged together, and the square root is taken to arrive at an estimate of the noise we can expect—the standard deviation calculated from the internal variability of the coupled land–atmosphere model. The signal is simply the difference in ensemble means (GSWP minus control) in either correlation with observations, or rms error. A ratio of 2 corresponds to a significance of nearly 98% on a single tail. This approach is also used to provide the significance shading in Fig. 6.

The results suggest that there is a net improvement, but it is not universal. For precipitation, there are significant improvements in the correlations with observations globally (57°S–75°N, land grid cells only). Africa and Australia show deteriorations in correlation. There is an especially large improvement over southern Asia, and smaller notable improvements over North America and Europe. However, the rms error in precipitation is 0%–15% worse over most areas; only over South Asia is there a marked improvement.

For near-surface air temperature, reductions in rms errors are widely apparent. The rms errors are decreased globally and over every region, from 3% to 23%. Correlations between model and observed temperatures are already very high in the control case, exceeding 0.9 in every region considered. Nevertheless, there are notable improvements globally, and over North America and Africa.

The response to changes in soil wetness appear to be more robust in the temperature field than in precipitation. Soil wetness directly affects surface temperature through changes in the partitioning of net radiant energy between latent and sensible heat fluxes, and changes in the soil heat capacity. Surface temperature exerts a strong control on the near-surface air temperature. The connection from soil wetness to precipitation is more tenuous, involving impacts of both surface heat and moisture fluxes, and is modulated by the character of the atmospheric boundary layer and the general circulation. In fact, it has been shown that land surface changes may induce either increased or decreased moisture flux convergence in the overlying atmosphere, depending on the relative strengths of the perturbations in latent and sensible heat flux, and the overlying circulation (e.g., Shukla and Mintz 1982; Dirmeyer and Shukla 1994, 1996).

A clearer picture emerges when the location of the improvements is investigated, particularly when the grid cells where errors are relatively small in either case are eliminated. Table 3 shows the number of grid cells where precipitation and near-surface air temperature errors were reduced. Various limits are applied to mask out grid cells where the errors lie below a certain threshold. Thresholds are set at 0, 1, 2, 4, and 6 mm day−1 for precipitation, and at 0, 2, 4, 6, and 8 K for temperature. For temperature, 63% of the land grid cells between 57°S and 75°N experience reduced errors during June–August when GSWP soil wetness is specified throughout the integration. That percentage increases to 74% when only cells where the temperature error exceeds 2 K are considered. The percentage tapers off slowly as the threshold is increased, but remains above the level when all points are considered. Figure 7b shows the spatial distribution of the points for the 2 K threshold. Large regions of Siberia, Canada, India, South America, and the central Sahara exhibit a reduction in surface temperature errors. Conterminous areas over Mongolia and Greenland show no improvement in their large errors.

Again, the precipitation response is less robust. When all land points are considered, there are actually more points experiencing an increase in precipitation errors than a decrease. However, as the minimum error threshold is raised, the number of grid cells experiencing a reduction of error quickly jumps above 50%, and peaks just under 60% for a threshold of 4 mm day−1. Figure 7a shows the spatial distribution for a threshold of 2 mm day−1. Most of the areas of improvement lie in Asia, particularly in the southern and eastern regions. The Tropics seem to be least remedied by the use of GSWP soil wetness.

4. Interannual variability

A comparison between the simulations of 1988 and 1987 reveals how the models respond to interannual variability in surface boundary conditions. Specifically, the simulated differences (1988 minus 1987) can be compared to observations to see how well the atmospheric response to year to year variations in SST and soil wetness is reproduced. In this section, the case where soil wetness is specified from the opposite year, that is, swapped with respect to the specified SSTs, is also examined. Thus, the second question is addressed:Does the interannual variability in a multiyear soil wetness dataset contribute to interannual variability in climate simulations?

Figure 8 shows the 1988 minus 1987 differences in precipitation during the three-month period from June to August. The upper left panel shows the observed precipitation differences with values over ocean masked out for clarity. The upper right panel shows the same difference field for the control simulation of the fully coupled land–atmosphere climate model. The lower left panel represents the difference when soil wetness is specified per the GSWP dataset. The last panel is from the case where GSWP soil wetness from the opposite year are specified over land. The first thing to notice is that all panels are similar in their representation of positive differences over much of tropical Africa, northern South America, and India, with negative differences over much of China, Southeast Asia, and Europe. The second conspicuous feature is that overall, the three model simulations are more similar to each other than any are to observations. This is a common feature of climate models.

Differences between the control and GSWP cases are small, but generally such that the GSWP case has better simulations of the differences. This is evident from Table 4, which shows the impact on the rms error and correlation with observations of the interannual difference by specification of GSWP and swapped soil wetness. Here again, signal to noise is estimated and used to determine the significance of GSWP soil wetness impacts. From the table, it is evident that GSWP soil moisture contributes to a reduction in rms error almost everywhere, including a global reduction of more than 12% for precipitation. The rms error reductions are particularly significant over North and South America. Correlation coefficients for precipitation are greatly increased over South America and southern Asia when GSWP soil wetness is used. Correlations are degraded over Africa, Europe, and northern Asia, where correlations were near zero to begin with. Global correlation increases from 0.26 to 0.33 when GSWP soil wetness boundary conditions are specified, and drops to 0.19 when specified soil wetness is swapped between the two years. The degradation is particularly strong over northern Asia, Australia, and North America. The impact over North America is especially visible in Fig. 8. Again, small-scale variations near the scale of the grid resolution degrade the correlations somewhat. If a 25-point smoothing is applied to the fields, the correlations increase to 0.36 for the control case, 0.48 for the GSWP case, and 0.31 for the swapped case.

Figure 9 presents a similar comparison for near-surface air temperature. The most striking trait is that the magnitude of the interannual variation in temperature is much larger in the observations than in the model simulations. This underrepresentation of temperature variability may be due in part to the soil temperature scheme used in SSiB: a two-layer force restore approach. Also, SSiB has a relatively shallow soil column for moisture storage. A heat-diffusion approach with collocated soil layers for heat and moisture, and a deeper total soil column would be physically more realistic, and probably provide the land surface with more realistic thermal inertia. Such a scheme will be implemented in future versions of the LSS. Ensemble averaging and GCM physics may also play a role in the weak variability seen in these simulations.

The lower half of Table 3 shows the global and regional correlation coefficients and rms errors for the model’s simulation of interannual temperature variability. Globally, the use of GSWP soil wetness improved the correlation from 0.22 to 0.33, and use of the swapped soil wetness reduces the correlation to 0.20. The increase in correlation is strongest over southern Asia, North America, South America, and Europe. As with precipitation, a 25-point smoothing serves to increase these correlations by approximately 0.1 in each case. GSWP soil wetness leads to significant improvements in correlations over all regions except Australia and northern Asia, whereas the swapped soil wetness actually causes improved simulation in those two regions while degrading correlations elsewhere, particularly over Europe and southern Asia. Use of GSWP soil wetness also reduces rms error everywhere except Australia.

In the mean (1987 and 1988 averaged together), even the swapped soil wetness case produces improvements in the simulation of precipitation and temperature comparable to those found for the GSWP case. Table 5 is like Table 2, but compares the swapped case rather than the GSWP case. The changes are remarkably similar between the two tables. It should be noted that on balance, the specified soil wetness improves rms errors in precipitation and temperature, regardless of whether data from the right or wrong year is used. This suggests that the climate of the fully coupled land–atmosphere system is drifting significantly relative to the cases where soil wetness is specified. However, interannual variation in the specified soil wetness seems to be important for the simulation of patterns of precipitation and temperature, as measured by the correlation coefficients. Correlations are improved globally when data from the right year is used, but diminish when data from the wrong year is applied. An analysis of the number of grid cells where rms error is reduced by use of the swapped soil wetness also produces similar results to those depicted in Table 3.

This apparent contradiction can be understood by comparing the difference between 1988 and 1987 GSWP soil wetness for the summer season (Fig. 10, also showing interannual differences in prescribed SST) with the soil wetness difference between GSWP and the control simulation (Fig. 6). The interannual variations are small and localized compared to the differences between the GSWP dataset and the soil wetness predicted by the coupled climate model. However, some of the local interannual variations are well positioned to affect the simulation of climate in those regions. Figure 11 shows the relative contributions of the interannual variations of ocean (SST) and land (soil wetness) to the simulated differences in precipitation and near-surface air temperature. These values are estimated by considering the two-by-two grid of ensembles as indicated in Table 1. There are only two aspects that are different in the simulations of 1988 versus 1987: SST and soil wetness. Initial atmospheric conditions are also different between the two years, specified from analyses from the appropriate year. However, the three-month period considered is a full month after the beginning of the integrations, and probably adequately remote so that the initial atmospheric conditions have no systematic effect on the later part of the simulation.

The relative signal contributed by variations in ocean and land conditions may be quantified as:
i1520-0442-13-16-2900-eq1
The above relationship is an approximation, based on the assumption that the mean effects of ocean and land can be separated in a linear fashion. Figure 11 shows these relative contributions for precipitation and temperature. The ocean signal in precipitation is stronger than the land signal, with a much greater area of statistical significance, but the two signals are largely collocated across the Tropics. The global rms (land points only) of the ocean signal is 0.99 mm day−1, while the land signal rms is 0.37 mm day−1. For temperature, the ocean signal is much more widespread than the land signal that is predominantly limited to India and central North America. These two locations are also located in the two regions where there was the greatest increase in correlation and largest decrease in rms error in Table 3. The global temperature rms of the ocean signal is 0.57 K, while the land signal rms is 0.30 K. So the response of the climate model to interannual ocean temperature variability is generally larger than the response to variability in soil wetness, but in key areas the variations in soil wetness can have important impacts on regional climate.

5. Conclusions

Ensembles of boreal summer integrations of a coupled land–atmosphere climate model are examined to determine whether the use of GSWP soil wetness as a specified boundary condition improves the simulation of climate (specifically precipitation and near-surface air temperature). The interannual differences between 1987 and 1988 in the GSWP soil wetness data have also been examined to determine whether they are large enough to impact the simulation of climate and its interannual variability.

GSWP soil wetness does improve the simulation of the pattern precipitation (as measured by correlation to observations) globally and regionally. Improvements are particularly large over monsoonal Asia. The simulation of surface temperature is also improved. The rms errors in temperature are reduced globally by 9% and over every region examined. The largest reductions in rms error occurred over Europe (23%) and North America (18%). Approximately two-thirds of the land grid cells between 57°S and 75°N experienced a reduction in rms error, including nearly three-quarters of points that had an error of 2 K or greater.

Even though the interannual variability in soil wetness in the GSWP dataset is small compared to the soil wetness differences between GSWP and control cases of the coupled climate model, they do have a definite impact on the climate simulations. The simulation of 1988 minus 1987 boreal summer differences in precipitation and near-surface air temperature are improved by the specification of GSWP soil wetness throughout the season. Globally and over most regions, correlations with observed 1988 minus 1987 differences is improved. Likewise, using GSWP soil wetness from the wrong year (swapped case) degrades correlations over most regions. A similar improvement is seen in the rms errors when GSWP soil wetness is used, although the swapped case usually maintains rms errors lower than the control case but higher than the GSWP case. This fact suggests that robust positive feedbacks between land and atmosphere components of the fully coupled climate model may drive precipitation and temperature away from observed values to an unrealistic climate balance. This is essentially a climate drift not unlike that often observed in coupled ocean–atmosphere model systems. The imposition of GSWP soil wetness every five days acts in the same manner as a flux correction, offsetting unrealistic trends in the LSS and GCM.

These results are consistent with those of Dirmeyer (1999), which had a smaller ensemble size (four vs nine members). The larger ensembles increases the reliability of the results by contributing to statistical stability. There is a positive impact on the simulation of climate when GSWP soil wetness calculated by the same LSS driven by observed meteorological forcings is applied as a boundary condition. In addition, there is a positive contribution to simulation from the interannually varying component of soil wetness. A longer time series of soil wetness calculated in the GSWP framework would be particularly useful for climate research.

Acknowledgments

Publication of this research is supported by National Aeronautics and Space Administration Grant NAG8-1526.

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Fig. 1.
Fig. 1.

Comparison of (top) Jun–Aug (JJA) observed precipitation (CMAP), (middle) ensemble mean simulated in the control case, and (bottom) the error for 1987 and 1988. Units are mm day−1, negative contours are dashed.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 2.
Fig. 2.

As in Fig. 1 for near-surface air temperature (observed = CAMS). Units are K, negative contours are dashed.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 3.
Fig. 3.

Comparison of initial root zone soil wetness between the (top) GSWP case and (middle) control case with (bottom) the difference for 1987 and 1988. Units are fraction of saturation, negative contours are dashed.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 4.
Fig. 4.

Comparison of the evolution of root zone soil wetness for individual ensemble members (light) and the ensemble mean (bold) for the control and GSWP cases during May–Aug 1987 at a grid cell over Illinois. Also shown is the top 1-m soil wetness averaged for 12 Illinois Soil Moisture Network stations. Units are fraction of saturation.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 5.
Fig. 5.

Aug minus May root zone soil wetness for (top) the GSWP case and (middle) control case with the difference shown at the bottom. Data from 1987 and 1988 are averaged together. Units are fraction of saturation, negative contours are dashed.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 6.
Fig. 6.

Control minus GSWP case JJA differences in (top) precipitation (mm day−1), (middle) near-surface air temperature (K), and (bottom) root zone soil wetness (fraction of saturation). Data from 1987 to 1988 are averaged together, negative contours are dashed. Shading indicates signal-to-noise ratio exceeding 1(light) and 2 (dark).

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 7.
Fig. 7.

Grid cells where JJA errors in the control case exceed 2 mm day−1 for (top) precipitation, 2 K for (bottom) near-surface air temperature. Cells shaded black experience reduced error in the GSWP case, cells shaded gray do not.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 8.
Fig. 8.

1988 minus 1987 JJA precipitation; (top left) observed; (top right) control case; (bottom left) GSWP case, (bottom right) swapped case. Units are mm day−1, negative contours are dashed.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 9.
Fig. 9.

As in Fig. 8, for near-surface air temperature. Units are K.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 10.
Fig. 10.

(top) 1988 minus 1987 JJA SST (K), and (bottom) GSWP root zone soil wetness (fraction of saturation). Negative contours are dashed.

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Fig. 11.
Fig. 11.

1988 minus 1987 JJA differences forced by interannual variations in (left) SST and (right) soil wetness, and (top) in precipitation (mm day−1) and (bottom) near-surface air temperature (K). Negative contours are dashed. Shading indicates signal-to-noise ratio exceeding 1 (light) and 2 (dark).

Citation: Journal of Climate 13, 16; 10.1175/1520-0442(2000)013<2900:UAGSWD>2.0.CO;2

Table 1.

Specified boundary conditions used in the ensembles.

Table 1.
Table 2.

Correlation coefficient and rms error for ensemble mean, seasonal mean (JJA) precipitation and temperature (1987 and 1988 combined). Only land points are considered over the following regions: global (57°S–75°N); North America (15°–75°N, 169°–55°W); South America (60°S–15°N, 90°–30°W); Australia (50°–10°S, 100°–160°E); Africa (50°S–35°N, 30°W–55°E); Europe (35°–75°N, 15°W–55°E); northern Asia (40°–80°N, 55°E–169°W); southern Asia (10°–40°N, 55°–140°E). Italics indicate significant improvement over the region, based on a signal-to-noise ratio greater than 1.0.

Table 2.
Table 3.

Grid cells improved (rms error reduced) by the use of GSWP soil wetness as a specified boundary condition in the simulation of ensemble mean, seasonal mean (JJA), climate (1987 and 1988 combined).

Table 3.
Table 4.

Correlation coefficient and rms error for ensemble mean, seasonal mean (JJA) precipitation, and temperature differences (1988 minus 1987). Only land points are considered over the regions, which are defined as for Table 2. The second number in some columns is the signal-to-noise ratio as defined in the text. For GSWP vs Obs, italics indicate significant improvement over the region compared to Ctl vs Obs. For Swap vs Obs, shading indicates a significant degeneration over the region compared to Ctl vs Obs. Significance is based on a signal-to-noise ratio greater than 1.0.

Table 4.
Table 5.

As in Table 2, but comparing the swapped case with the control case.

Table 5.
Save