Journal of the Meteorological Society of Japarn,Vol. 77, No. 1B, pp , Assessing GCM Sensitivity to Soil Wetness Using GSWP Data

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1 Journal of the Meteorological Society of Japarn,Vol. 77, No. 1B, pp , Assessing GCM Sensitivity to Soil Wetness Using GSWP Data By Paul Center for Ocean-Land-Atmosphere A. Dirmeyer Studies, Calverton, Maryland, U.S.A. (Manuscript received 28 February 1998, in revised form 6 September Abstract A new data set of global high-resolution soil wetness for has been prepared as part of the Global Soil Wetness Project (GSWP). To produce this data, the Simplified Simple Biosphere (SSiB) land surface process model (LSP) has been integrated oflline, driven by observed and assimilated meteorological data to produce a two-year global climatology of soil wetness at 1x1 resolution. GSWP data set has potentially higher quality data than those previously available. We are testing the impact of the GSWP data for climate simulations using the Center for Ocean-Land-Atmosphere Studies (COLA) general circulation model (GCM), coupled to the SSiB LSP. There are two principle questions which we will address with our preliminary GCM/LSP sensitivity experiments. First, does the inclusion of presumably more realistic GSWP soil wetness significantly improve the simulation and predictability of summer season climate? We use the GSWP product as a specified boundary condition in seasonal simulations (June August), and compared to existing GCM/LSP integrations, where soil wetness is initialized from operational analyses and allowed to evolve freely in the coupled system. In both sets of integrations, identical observed sea surface temperatures are specified. Results show that the GSWP soil wetness is significantly different from that of the coupled model's own climatology, and produces a better simulation of precipitation anomaly patterns over monsoon regions and the summer hemisphere extratropics. However, there is little improvement in the systematic error of the coupled model. Improvements can be attributed to changes in surface fluxes induced by the different soil wetness. Second, does the interannual variability in a multi-year soil wetness data set contribute to interannual variability in climate simulations? A parallel set of GCM/LSP integrations have been produced using specified GSWP soil wetness from the "wrong" (other) year (i. e., 1988 soil wetness applied in 1987 integrations, and vice versa). The use of soil wetness data from the wrong year significantly degrades the simulation of precipitation anomaly patterns. This indicates that interannual variability in soil wetness is important to climate. However, differences in precipitation due to SST variability generally dominated those apparently caused by soil wetness variations. 1. Introduction Corresponding author: Paul A. Dirmeyer, Center for Ocean-Land-Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, Maryland , U.S.A. dirmeyer@cola.iges.org (c)1999, Meteorological Society of Japan In GCMs, the land surface is represented in one of three ways. Most simply, it may be modeled using a simple "bucket" hydrology of one or more layers (Manabe, 1969). A more complex method is to implicitly include the effects of vegetation by using an evapotranspiration model with a root layer (Pan and Mahrt,1987; Noilhan and Planton, 1989). The most sophisticated methods explicitly include a vegetation model, where vegetation processes are parameterized. These can be water-stress models such as the Biosphere-Atmosphere Transfer Scheme (BATS; Dickinson et al., 1986) and the Simple Biosphere model (SiB; Sellers et al., 1986), or more biological models that simulate the carbon cycle like SiB2 (Sellers et al., 1996a), or the Land Surface Model (LSM) of Bonan (1996). This sequence of land surface parameterizations (LSPs) represents a spectrum from simple mass-balance schemes to sophisticated nonlinear models (Sellers et al., 1997). From the perspective of the atmospheric GCM, an LSP's role is to provide fluxes of heat, moisture and momentum at the lower boundary of the atmosphere over land. The principal state variable in the ocean is temperature (or more precisely, heat content), which influences surface fluxes more than any other characteristic of the ocean. Over land, the principal state variable is moisture content, that is: soil wetness. Since the heat capacity of the land surface is small compared to the ocean, but the moisture content is highly variable in space and time, 201

2 368 Journal of the Meteorological Society of Japan Vol. 77, No. 1B soil wetness is the dominant characteristic affecting land surface fluxes (Dirmeyer and Shukla, 1993). Despite its importance, soil wetness is an unknown, unmeasured quantity over much of the globe. There are very few locations where soil wetness is measured routinely, and those measurements are most often conducted in level agricultural fields which are not representative of the regional land surface (Vinnikov and Yeserkepova, 1991; Hollinger and Isard, 1994). Remote sensing techniques can achieve the spatial coverage that ground observations cannot, but remote sensing has other limitations. Microwave remote sensing techniques are only effective over sparsely vegetated areas, and cannot measure subsurface moisture (Choudhury, 1993). Gamma radiation remote sensing techniques show more promise, but currently only aircraftborne platforms are used, and subsurface measurements are possible only to about 20-cm depth (Peck et al., 1992). Derived wetness indices (e.g., from the Normalized Difference Vegetation Index; NDVI) also exist, but are highly dependent on vegetation and soil types, and have a slow response time since they measure the effect on vegetation of soil moisture variations (Davenport and Nicholson, 1993). The result of the limitations on measuring soil wetness is that there has been no global observed data set. Historically, most attempts to create a climatology of soil wetness have used computational methods. Observed or analyzed gridded data have been used to drive a simple LSP for a number of months or years, creating gridded soil wetness data on continental to global domains (Mintz and Walker, 1993; Liston et al., 1993; Huang et al., 1996). An alternative source of global soil wetness data would be the products of one of the reanalysis efforts (NCEP: National Centers for Environmental Prediction, Kalnay et al., 1996; ECMWF: European Centre for Medium-range Weather Forecasts, Gibson et al., 1997; NASA: National Atmospheric and Space Administration, Schubert et al. 1993). However, the accuracy of the surface hydrologic cycle may be more in question than offline model simulations, since observed rainfall is absent in the data assimilation for the reanalyses. The Global Soil Wetness Project (GSWP) is attempting to address this issue. 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 (GEWEX). The GSWP is charged with producing a two-year global data set of soil moisture, temperature, runoff, and surface fluxes by integrating one-way uncoupled land surface process models (LSPs) using externally specified surface forcings based on observed rainfall and analyzed near-surface meteorology, and standardized soil and vegetation distributions (namely, the ISLSCP Initiative-I CD-ROM data of Sellers et al., 1996b). There are approximately one dozen participating LSPs, covering all three levels of complexity described above. Each has taken the common ISLSCP forcing data to integrate over the period to generate global data sets. Please see IGPO (1995, 1998) for further details. The Center for Ocean-Land-Atmosphere Studies (COLA) is participating in the GSWP, developing and implementing a global two-dimensional offline version of the COLA LSP for use in the project. As a result, a global two-year data set of soil wetness at 1 resolution has been generated. Since this data set was generated with the same LSP as is used in the COLA GCM, it is likely to be more consistent than global soil moisture data sets from other offine model calculations or reanalyses. Furthermore, since the forcing data represent the highest quality and resolution data set from observations and remote sensing compiled to date, the GSWP product is probably also of better overall quality. The value of high-quality, model-consistent soil wetness data is clear. Any GCM simulation must start from a specified initial condition. The atmospheric and oceanic initial state can be taken from gridded analyzed observations. Lacking similar observations of soil wetness to initialize the LSP, a soil wetness field calculated with the same LSP driven by the same (or similarly analyzed) observations as used to initialize the GCM is the best substitute. The lack of global soil wetness observations also makes direct validation of the GSWP soil wetness product only partially attainable. An alternative method for assessing the quality of the GSWP product is to see how it effects coupled GCM/LSP simulations of climate when used as a boundary condition. We can pose this idea as a question:. Does the inclusion of the presumably more realistic GSWP soil wetness significantly improve the simulation of climate in a GCM? The soil wetness in a free-running coupled GCM/LSP will evolve differently than that in the offline LSP integration, since the offline LSP lacks feedbacks with the atmosphere. If these feedbacks in the coupled GCM/LSP system are too strong or too weak, climate drift will result. Since the GSWP data set covers more than one year, we can ask another question:. Does the interannual variability in a multi-year soil wetness data set contribute to interannual variability in climate simulations? It may be that the interannual variability in soil moisture in the GSWP product is small-perhaps smaller than the difference between GSWP soil wetness and soil wetness from the free-running 202

3 March 1999 P.A. Dirmeyer 369 GCM/LSP integrations, or too small to produce a significant deviation in the model climate. In that case, one could argue against the value of producing anything but a mean annual cycle Clmatology of soil wetness. The GCM and LSP used in these experiments, as well as the GSWP data and experiment design, are covered in Section 2. Section 3 describes how specified GSWP soil wetness effects seasonal climate simulations. Interannual variations in the GSWP data is discussed in Section 4, and conclusions are stated in Section Models and experiment design The atmospheric GCM used is version 1.11 of the COLA GCM at a spectral resolution of R40 (approximately 2.8 longitude by 1.8 latitude 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 by Kinter et al. (1988), and Schneider and Kinter (1994). This version of the model is very similar to that described by Schneider and Kinter (1994), with the principle difference being that this version uses a relaxed Arakawa-Schubert convection parameterization (DeWitt, 1996). The observed sea surface temperatures specified in all integrations are interpolated from the weekly analysis of Reynolds and Smith (1994). The LSP coupled to the GCM over land is the simplified version of the SiB described by Xue et al. (1991), and referred to hereafter as SSiB. The soil is represented by three layers: a thin surface layer, a rooting layer which varies in depth according to vegetation type, and a deep recharge zone. SSiB is like SiB in that vegetation is modeled explicitly, controls on water uptake and transpiration are governed by moisture potentials and water and temperature stress thresholds (Xue et al., 1996). An offline version of the same LSP is used for COLA's participation in GSWP. The GSWP integrations are at the considerably higher resolution of 1x1 over all land points free of permanent ice (as specified by gridded data on the ISLSCP Initiative- I CD-ROM). GSWP specifies the use of a standard vegetation map and a set of soil and vegetation parameters to which all participating LSPs must adhere as closely as possible. Although these parameters are very SiB-like, having been derived for use in SiB2, they are in a number of ways different from the settings historically used in SSiB for GCM integrations. The ISLSCP CD-ROM atmospheric forcing data used to drive the LSPs are derived from several sources. The temperature and specific humidity at screen height and wind speed at 10-m height were taken from ECMWF operational analysis calculated on T106 Gaussian grids (grid spacing close to 1.1). They are interpolated in space to the regular 1 grid. ECMWF analyzed surface pressure is used to compute vapor pressure from specific humidity. The reference height of surface forcings is assumed to be at 10 m above height of canopy top. Monthly and global downward solar/terrestrial radiative fluxes on 2.5 grids have been produced from International Satellite Cloud Climatology Project (ISCCP; Schiffer and Rossow, 1985) products. These were interpolated in space to 1 grids and a hybrid product was produced for ISLSCP to attain 6-hourly resolution. ECMWF operational analyses were used to impart variability on the radiation terms, while preserving the monthly means. The global 2.5 monthly Global Precipitation Climatology Project (GPCP; WCRP, 1990) analyses for 1987 and 1988 were the original source of the precipitation forcing. A 6-hourly temporal partitioning of the monthly GPCP precipitation amounts to match the other 6-hourly atmospheric forcings was accomplished in a manner similar to the radiation fluxes. The NCEP reanalysis project provided 6-hourly, global, 1 precipitation accumulations for 1987 and Both total and convective precipitation amounts were used to temporally partition the monthly GPCP amounts (see Sellers et al., 1996b for details). The precipitation forcing has no indicator for snowfall. Rather, a common air temperature threshold of 2C is used to determine when precipitation falls in a frozen form. Linear interpolation between successive longwave flux estimates, near surface meteorology, and precipitation is used to generate forcing data on the one-hour time step used by SSiB in GSWP. A method based on solar angle calculations was used to estimate hourly shortwave radiation fluxes from the 6-hourly estimates. The data produced by the offline integration of SSiB in the GSWP framework (hereafter referred to simply as the GSWP product) consist of decad (approximately 10-day) means and instantaneous values of soil wetness in each soil layer, as well as surface fluxes and other hydrologic and energy terms. Instantaneous fields are output at 0000 UTC on the 1st, 11th, and 21st of each month, and decad means are calculated over the period since the previous output time. The GCM/LSP is integrated for three months during 1987 and Integrations are conducted in ensembles of four, initialized at 0000 UTC on May, and integrated forward through the end of 31 August. Soil wetness is treated in one of two ways. In one pair of ensembles, soil wetness is initialized from operational ECMWF analysis values, adjusted to be consistent with SSiB (Fennessy and Shukla, 1998). This initial soil wetness is identical in all three layers, since the vertical structure of the operational ECMWF LSP lacked realism. The soil wetness is then allowed to evolve freely from the 203

4 370 Journal of the Meteorological Society of Japan Vol. 77, No. 1B Fig. 1. Schematic diagram of the 5-day update cycle for soil wetness in the GSWP GCM integrations. initial conditions in the coupled model. These integrations are actually a small subset of a large suite of seasonal ensembles conducted as part of a dynamical seasonal predictability (DSP) study. Thus, these ensembles are referred to as either "DSP or "free-running." Since there are some important differences in the geographical distribution of vegetation types, canopy properties, and soil attributes, we compared both the official GSWP parameters and the historical GCM-SSiB settings in the offline mode. Any difference found would compromise the results if the interannual variations in surface fluxes were exceeded by the difference between the cases with the different sets of parameters. We found that for decal means of key surface fluxes such as evapotranspiration and sensible heat flux, the root-mean-square (RMS) of differences between 1988 and 1987 fluxes was routinely 20-30% greater than the differences between cases with GSWP parameters and historical GCM-SSiB parameters. Differences between parameter sets seem to be less important than the interannual variability of the surface conditions. In the remaining ensembles, the 3-layer GSWP product is used as a specified soil wetness. This means that even the initial soil wetness will be different from the free-running integrations. Rather than specifying soil wetness at each time step, soil wetness is reset globally every 5 days to values interpolated from the decal mean GSWP product. This method is illustrated schematically in Fig. 1. This method prevents the model soil wetness from drifting very far from the specified values. Monthly and seasonal averages confirm that the drift of GCM soil wetness with a 5-day update cycle is not large, compared with the GSWP product. There are two sets of ensembles with specified GSWP soil wetness. One ensemble uses the temporally co-registered GSWP product as a boundary condition. This ensemble is referred to simply as GSWP in this paper. In the other ensemble, GSWP soil wetness from the same day and month, but the opposite year, is specified (i. e., 1988 for 1987 and vice versa). This ensemble is referred to as Reversed. The Reversed ensemble is used to examine the effect of interannual variability of soil wetness on climate simulations. It should be noted that the summers of 1987 and 1988 represent high contrast years in regional hydrology over many parts of the globe (Arkin, 1988; Ropelewski, 1988). The spring and summer of 1988 marked a significant drought event over much of the central and eastern United States was one of the wettest monsoons on record over India, while 1987 saw poor monsoon activity. Opposite anomalies were observed over parts of East, and Southeast Asia. The summer of 1988 was also one of the rare "normal" rainy seasons over the Sahel in recent years, while 1987 saw drought conditions. These two summers also fall within opposite phases of El Nino Southern Oscillation (ENSO), so interannual variability of SST is also strong between these two years. 3. Impact of GSWP soil wetness The seasonal mean root zone soil wetness from the GSWP ensemble is shown in Fig. 2a. The average of 1987 and 1988 is also shown. Arid regions show up clearly, as do the wet tropical monsoon regions. Much of the Northern Hemisphere midlatitudes is

5 March 1999 P.A. Dirmeyer 371 Fig. 2. JJA soil moisture in the root zone (fraction of saturation) averaged for 1987 and 1988; a) GSWP, b) DSP; c) GSWP 1988 minus Positive values are also contoured. undergoing a summertime drying during this period. The corresponding mean soil wetness from the freerunning integrations is given in Fig. 2b. It is apparent that the very dry desert soils cover a larger area in the DSP integrations. The Indian subcontinent is also drier in DSP, while parts of the northern United States and Canada are wetter. There are pronounced differences in precipitation between the GSWP and DSP integrations on the local scale. The two-dimensional structure of the precipitation changes are shown in Fig. 3. For comparison, Fig. 3c shows the average standard deviation of model precipitation within the ensembles (based on the internal variance of Eq. (2) presented 205

6 372 Journal of the Meteorological Society of Japan Vol. 77, No. 113 Fig. 3. JJA ensemble mean precipitation differences (GSWP minus DSP; mm d-1: a) 1987, b) 1988; c) Standard deviation of model precipitation (mm d-1). Positive values are also contoured. below). While precipitation is perhaps the most vitally important meteorological variable from a human standpoint, it is also among the most sensitive to model changes. Thus, there is rich structure over both land ocean in Fig. 3. The terrestrial differences are fairly consistent between 1987 and Surprisingly, so are many of the oceanic precipitation anomalies, particularly in the tropics, subtropics, and near coastlines. It appears that the changes in soil moisture between DSP and GSWP affect the Hadley, Walker, and local land-sea circulations in a systematic way. In the midlatitudes, atmospheric dynamics play a more dominant role in determining the distribution of precipitation, and precipitation variations are less connected to surface conditions. These changes in precipitation are triggered ulti- 206

7 March 1999 P.A. Dirmeyer 373 Fig. 4. JJA ensemble mean differences (GSWP minus DSP) averaged for 1987 and 1988; a) soil wetness of root zone (fraction); b) latent heat flux (W m-2); c) sensible heat flux (W m-2); d) surface temperature (C); e) net longwave radiation at the surface (W m-2); f) downward shortwave radiation at the surface (W m-2). Positive values are also contoured. mately by the differences in land surface conditions. Figure 4a shows the seasonal mean difference in root-zone soil wetness between the GSWP and DSP ensembles, averaged for both years. The GSWP ensemble has systematically wetter soil over arid and semi-arid regions throughout the globe. Areas of drier soil are largely confined to the midlatitudes, western China and central Africa. The differences between GSWP and DSP are quite similar during both 1987 and 1988, with temporal variations in the 207

8 374 Journal of the Meteorological Society of Japan Vol. 77, No. 1B Fig. 4. (Continued) difference generally much less than the mean difference between DSP and GSWP, suggesting that most of the error in the DSP integrations is systematic. In most areas, these changes in soil wetness have little effect on surface latent heat flux (Fig. 4b), but areas of pronounced increase do exist over western and southern Asia, and subtropical fringes of South America and Africa. There are only a few small regions of reduced terrestrial latent heat flux, the most prominent being over the northern Great Plains of the United States. The change in sensible heat flux (Fig. 4c) strongly mirrors that of latent heat flux, but with the opposite sign. This suggests that much of the soil wetness variations is manifest in the partitioning of available surface energy between latent and sensible 208

9 March 1999 P.A. Dirmeyer 375 heat flux. That is not to say that radiative fluxes are unchanged. Figure 4d shows the impact on surface temperature, which mimics the sensible heat flux changes. Anomalies in upward longwave radiation reflect the surface temperature changes, and dominate changes seen in the net longwave radiation balance (Fig. 4e). Overall, it appears that increasing soil wetness in some arid regions serves to accentuate evaporation and latent heat flux at the expense of other surface energy fluxes, cooling the surface in those areas. Over a smaller area, predominantly in the midlatitudes, the opposite effect is induced by a reduction of soil wetness. Over India, the Caucasus, Kazakhstan, and the northern Great Plains, the increase in latent heat flux appears to have a local effect on cloudiness, as indicated by the change in downward shortwave radiation at the surface (Fig. 4f). These changes likely reinforce thermal changes at the surface. The centers of activity seen in Fig. 4 are evident in the precipitation changes (Fig. 3), but there are numerous areas over land and ocean where precipitation fluctuations are not co-located with the differences in the state of the land surface. Again, the sensitive nature of precipitation is in evidence, as surface changes induce perturbations in regional circulation, altering the convergent wind fields enough to induce remote changes in precipitation within the model. Whereas many of the remote precipitation patterns appear in approximately the same place each year, they are not so systematically evident in the individual integrations, suggesting that internal variability within the climate model may be at play. A somewhat stronger picture emerges when we examine the simulation of interannual variability of precipitation calculated over large regions. When we examine the RMS error and correlation of minus-1987 ensemble-mean precipitation in the DSP and GSWP integrations, we see definite signals in the GSWP integrations. Table 1 shows the RMS error and normalized difference correlation coefficients (NDCC; analogous to an anomaly correlation coefficient, where the difference is defined as 1988 minus 1987, rather than a comparison against climatology) compared to observed differences in rainfall (merged analysis of precipitation from Xie and Arkin, 1996). That is: (1) where m87 and m88 are seasonal mean model rainfall for 1987 and 1988 respectively, the overbar denotes the ensemble mean, 087 and 088 are the observed seasonal mean rainfall for those two years, i denotes the points in the domain, and vis the internal model variance, or the variance of precipitation among all members within the ensembles, measured against the corresponding ensemble's mean, and is analogous to the noise in the coupled model system (cf. Schubert et al., 1992; Stern and Miyakoda, 1995): where m is the seasonal mean, the overbar denotes the ensemble mean, and the brackets indicate the average over all ensembles and years in this study. Likewise, vo is the variance of observed precipitation during the 19-year record of the observed data set, used as a proxy of internal variability which cannot be directly measured in the climate system: (3) This variance is likely too large of an estimate of internal variability, if calculations from model ensembles are any guide. Only points over land are considered. RMS error is seen to remain approximately constant on the global scale, with decreases of nearly 20% over the Northern Hemisphere continental regions. Increases of 12% and 6% are found in the Southern Hemisphere and tropics respectively. Marked effect can be seen in NDCC, with increases in most regions outside the Southern Hemisphere. Calculating the number of degrees of freedom, and thus the significance thresholds, in a field of global precipitation over land is not trivial. Vinnikov et al. (1996) estimates the spatial autocorrelation of precipitation in the midlatitudes of Russia to be 500 km. Spatial scales of autocorrelation in the tropics are much smaller, due to the convective nature of the precipitation. However, in the GCM the spatial scale of autocorrelation must be at least as large as the grid size, and in fact is usually larger. Applying the midlatitude value of 500 km globally, we arrive at an estimate of 171 degrees of freedom over all land points sans Antarctica. This estimate gives an NDCC of 0.15 that is significant at the 95% level, and for 90% confidence. In the DSP integrations, the global NDCC is The GSWP ensemble has a global NDCC of 0.17, an improvement of 20% in the simulation of seasonal mean patterns of precipitation. Still, the correlations are quite low. Over much of the Northern Hemisphere there are substantial improvements. This implies that poor correlations may be due in part to climate drift in the coupled model, at least in the warm season. There is some degradation of NDCC in regions of the Southern Hemisphere, where correlations are already relatively high. These areas are in winter when atmospheric dynamics can dominate land surface feedbacks, and also have a more maritime influence than the Northern Hemisphere land masses. 209

10 376 Journal of the Meteorological Society of Japan Vol. 77, No. 1B Table 1. RMS error of 1988 minus 1987 precipitation differences (mm d-1), and correlations of normalized 1988 minus 1987 precipitation compared to observed. All calculations are over land points only. To better understand the significance of the apparent improvement in simulated precipitation patterns, we can calculate the NDCC of each possible pair of the four ensemble members from 1987 and This results in a set of 16 distinct pairings. The NDCC for each member of this set is plotted in a scatter diagram in Fig. 5, with the NDCC value from the DSP integrations along the abscissa, and the GSWP values on the ordinate. The diagonal line represents x=y, and the dashed, dot-dashed, and dotted lines indicate the confidence thresholds at 90%, 95%, and 99% for each set, based on estimates of spatial degrees of freedom in the precipitation fields discussed earlier. thirteen of the sixteen points lie above the x=y line, indicating that the NDCC for the GSWP integrations is higher in those cases. In fact, more than half of the pairs show an improvement of at least 25%. This suggests that the improvement in NDCC may be robust. Of course, it would be preferable to investigate a longer series of years with larger ensembles. It is possible that a larger sample may help distinguish regions which truly have significant signal, particularly with respect to the effects of the GSWP soil wetness product. Plans to expand upon this study are mentioned in the final section. 4. Effect of interannual variability To test the effect of the interannual variation of the GSWP product on climate simulation, we compare the GSWP set of integrations with the Reversed set. If no significant difference is found between the climate of the two sets of integrations, we can conclude that this coupled GCM/LSP model is insensitive to the magnitude of interannual soil wetness variability that the same LSP predicts from observed data. Table 2 shows the impact on RMS error and NDCC of reversing the prescribed soil wetness between the years 1987 and Globally, and in virtually every region, simulation skill is lower in the Reversed integrations. In fact, it is generally worse than in the free-running DSP integrations. Asia is one of the areas that benefits the most from the specified GSWP soil moisture, and also loses the most when the Reversed soil moisture is prescribed. Indeed, it is clear from Fig. 4 that surface fluxes over South Asia in particular are greatly affected by the change to GSWP specified soil wetness. Europe and Australia both show an improvement in NDCC for either type of specified soil moisture. This suggests that the mean climatology of soil wetness in the freerunning GCM in these areas may have a negative consequence on the simulation of precipitation. The changes in soil moisture between the DSP and GSWP ensembles were quite large (see Fig. 4a), and have a definite effect on the simulated regional climate over many areas of the globe. The interannual variation of soil moisture in either set of cases is somewhat smaller. Do the interannual variations in soil moisture lead to significant changes in climate that are comparable to those caused by interannual soil moisture variations in SST? There has been some suggestion that this may be true in the case of the 1988 drought simulation with the COLA GCM (Fennessy and Shukla, 1998). The four ensembles of integrations with specified soil moisture form a two-by-two matrix with each combination of years for SST and soil wetness represented. We can gauge the effect of SST and soil wetness separately by grouping these ensembles appropriately. For example, SST impact can be gauged by examining the two pairs of ensembles with different SST (1988 versus 1987), but the same specified soil moisture. Likewise, soil wetness effects may be indicated by pairing the two ensembles with SST from the same year, but soil wetness from different years. Figure 6 shows this comparison in terms of the 210

11 March 1999 P.A. Dirmeyer 377 Fig. 5. Scatter plot of normalized difference correlation coefficients between model and observed 1988 minus 1987 precipitation for each possible pairing of 1988 and 1987 integrations, calculated over all ice-free land points. Table 2. RMS error of 1988 minus 1987 precipitation differences (mm d-1), and correlations of normalized 1988 minus 1987 precipitation compared to observed. simulated differences in precipitation between JJA 1988 and Differences are expressed both in mm d-l, and also normalized by the standard deviation of rainfall within ensembles-the square root of the internal variance from Eq. (2). It appears that SST variations between 1987 and 1988 have a large impact over much of the tropics, eastern China and Australia. There are also patchy areas in the subtropics and midlatitudes. Soil moisture records for these two years show almost no effect over the tropics, and a smaller total area of possible effects over the globe. The only coherent region of significant precipitation change between soil wetness cases over ice-free land is in southern Canada. The normalized rainfall differences, when a Gaussian distribution of anomalies about the mean is assumed, is two standard deviations at about the 95% significance level for a two-tailed Student's T-test. The lack of significant areas for soil wetness effects may be because the response to soil wetness variations is being modulated by the large changes in SST between the years, and their effects on seasonal climate. When individual pairs of ensembles are examined (i.e., looking at soil wetness patterns for 1987 or 1988 SST separately, and likewise for SST effects with 1987 or 1988 soil moisture), the net areas of significance are much more similar between the SST and soil wetness. Figure 7 shows the 1988 minus 1987 precipita- 211

12 378 Journal of the Meteorological Society of Japan Vol. 77, No. 1B Fig. 6. Impact of soil wetness variations (panels a and c) and SST variations (panels b and d) on simulated 1988 minus 1987 precipitation differences. Panels a) and b) are expressed in mm d-1, panels c) and d) are in standard deviations of the model's internal precipitation variability. Positive values are also contoured. Lion during JJA for all three experiments, as well as for observations (over land only, for clarity). From this figure, one can begin to see how the fluctuations found in Tables 1 and 2 come about. We see that in fact, GSWP simulations of rainfall over India and eastern Asia are improved, compared to the DSP simulations, but the improvements are largely undone in the Reversed case. Australia has high correlations in all experiments, as the negative precipitation difference in the west is present in all simulations. In fact, patterns which are systematically simulated in all experiments are probably the result of SST variations between the two years. It appears that much of the signal in the tropics (associated with the inter-tropical convergence zone) and Southern Hemisphere falls into this category (see Fig. 6). The DSP and GSWP integrations do a good job of simulating the signal in the Sahel, while the Reversed case gives a mixed signal. This may suggest that more than one factor is affecting precipitation 212

13 March 1999 P.A. Dirmeyer 379 Fig. 6. (Continued) anomalies in this region. The simulation of rainfall over northern Europe appears to improve in both the GSWP and Reversed cases, although it is difficult to attribute the improvement to any of the changes seen in Fig. 4. The model appears to have a difficult time simulating the North American drought, and the Reversed soil wetness exacerbates the problem. However, it should be noted that the 1988 hydrologic drought in fact peaked in June, and began to abate over much of the United States during July (Trenberth et al., 1988). The precipitation difference during June alone (not shown) is well simulated over the U.S. drought area, particularly in the GSWP integrations, As a final synthesis of the effect of interannual variations in the GSWP product, Fig. 8 shows the scatter diagram of NDCC like that in Fig. 5, but with pairs of Reversed cases also plotted. Here the degradation cased by the Reversed soil wetness in the simulation of precipitation is clear. Not only are the NDCCs of the Reversed case systematically lower than the GSWP case, but they are also lower than in the free-running simulations in 13 out of 16 pairings. Similar results are seen on the regional scale (not shown), but the clustering is not as tight as shown here. It is remarkable that the Reversed soil moisture can so systematically degrade the sim- 213

14 380 Journal of the Meteorological Society of Japan Vol. 77, No. 1B Fig. 7. JJA ensemble mean 1988 minus 1987 precipitation differences (mm d-1): a) observed; b) DSP; c) GSWP; d) Reversed. Positive values are also contoured. ulation of NDCC, when the magnitude of the interannual difference in GSWP soil moisture (Fig. 2c) is much smaller than the difference between GSWP and free-running GCM soil moisture (Fig. 4a). This fact makes it difficult to dismiss the role of soil wetness effects that are suggested to be so weak by Fig Conclusions For this particular coupled model, it appears that we can make the following set of statements: The GSWP soil wetness appears to improve the simulation o f the spatial patterns o f precipitation when used as a specified boundary condition. The GSWP product appears to steer the coupled GCM/LSP toward a more realistic simulation of precipitation anomaly patterns, although large systematic errors in the mean model climatology of precipitation persist. This implies two further results. First, it is clear that soil moisture simulation is not the only problem in the model. Second, the free-

15 March 1999 P.A. Dirmeyer 381 Fig. 7. (Continued) running coupled GCM/LSP appears to suffer from climate drift. Specifying soil wetness that is consistent with observed precipitation improves the model precipitation by removing some of the unrealistic feedbacks in the coupled system. This also suggests that the model coupling between land and atmosphere may be flawed as well.. Changes in soil wetness sometimes have a substantial effect on local latent heat fluxes. The differences in soil wetness between GSWP and DSP are widespread, but changes in surface fluxes were confined to a fraction of the affected areas. Fluxes appeared to be changed only in regions where soil moisture was near the midrange (changes apparent in Fig. 4b, for instance, correspond with soil wetness of in Fig. 2b). This is the domain where plant canopies can exert a great deal of control over the total evapotranspiration. Where soil moisture was already very high or low in the DSP cases, there was little sensitivity to the changes. One might want to look first in these areas of moderate soil wetness when trying to identify regions where the land surface variability exerts significant control over climate. 215

16 382 Journal of the Meteorological Society of Japan Vol. 77, No. 1B Fig. 8. As in Fig. 5, but also showing pairings for the Reversed case plotted against DSP. Substantial changes in latent heat fluxes impact other surface energy fluxes, and the local state of the atmosphere in fairly systematic ways. We see that where fluxes are altered, the pattern is usually the same. An increase (decrease) in soil wetness brings about an increase (decrease) in surface latent heat flux, with a corresponding decrease (increase) in sensible heat flux. The land surface is cooler (warmer), and both net longwave radiation and downward shortwave radiation are reduced (increased), although to a lesser extent than the changes in the heat fluxes. Substantial changes in surface fluxes also affect remote precipitation in fairly systematic manners, but these changes are on the same order as the apparently random perturbations in precipitation inherent in the coupled model system. We see fairly systematic changes in precipitation during both 1987 and 1988 over most of the tropical and subtropical land masses, in some of the temperate latitudes of the Northern Hemisphere, and even off the coasts of most continents. They may be triggered by the changes induced in the land surface. However, there are also many anomalies of similar magnitude which appear to be transient from one year to the next, mainly over the oceans and at high latitudes. Thus, the coupled system is not deterministic. The interannual variation o f soil wetness in the GSWP product is large enough to affect climate simulation in a GCM. We can see by specifying the soil wetness taken from the opposite year in the GCM/LSP system that the simulation of climate suffers measurably. This experiment gives perhaps the most convincing evidence of the importance of the quality of soil wetness in the coupled model. This fact combined with the improvements described in the first bullet suggests that there is indeed useful information in the GSWP product in the mean climatology and to a lesser extent the interannual variability. The small ensemble size has been a limitation in assessing the statistical significance in many of the comparisons presented in this paper. These experiments were performed as a quick attempt to investigate the impact of GSWP soil wetness, before planning a future phase of the GSWP that would cover ten or more years. A more thorough investigation with larger ensembles and consistent land surface parameters between GCM and offline versions of SSiB has been motivated by this work, and will be reported upon in the near future. It should be noted, however, that these and future results will contain an element of model dependency-application of a different GCM or LSP to this same experiment will lead to results that differ to some degree. Also, statistical significance alone does not imply cause and effect in any case. We suspect a priori that there might be a connection between soil moisture conditions and climate fluctuations based on previous experiments (e.g., Shukla and Mintz, 1982; Dirmeyer, 1994; Fennessy and Shukla, 1998). Given that 1987 and 1988 are such contrasting years in tropical SST, as there was a rapid transition from El Nino to LaNina conditions between the two years, 216

17 March 1999 P.A. Dirmeyer 383 it is particularly difficult to distinguish the impact of land surface conditions on interannual climate fluctuations between these two years. Examination of more years may reveal instances where the evidence for soil moisture impacts on climate is more pronounced. It should not be forgotten that the GSWP soil wetness is entirely the product of a LSP designed to be used in the coupled GCM framework. Although we should expect that variations of soil wetness in time should match well in sign those of any largescale observations, we do not expect the magnitude of the variations, or the mean climatology of soil wetness to agree with observations, or even that produced by other LSPs using identical forcing (Koster and Milly, 1997). Thus, the use of the GSWP soil wetness product calculated by SSiB in another LSP would be ill advised. 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