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  • View in gallery

    (a) Profiles of temperature (°C), dewpoint temperature (°C), and wind barbs (knots) used to illustrate adjustments made by default and modified KF2 parameterization. Pressure levels of the lifting condensation level (LCL), minimum θe (minθe), level of equilibrium temperature (LET), and cloud top are 934, 802, 219, and 136 hPa, respectively. Total adjustment by default (solid) and modified (dashed) KF2 of host model’s (b) potential temperature (K), (c) specific humidity (g kg−1), and (d) liquid and ice mixing ratio (g kg−1).

  • View in gallery

    Number of grid points exceeding precipitation thresholds of 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, and 7.0 cm h−1 vs the fraction of those grid points for which hourly 925–700-hPa thickness tendency is >|5| m h−1 for (a) 51- and (b) 17-km simulations.

  • View in gallery

    Tracks of centroids of mesoscale precipitation events for (a) D51, (b) M51, (c) D17, and (d) M17. Open circles are centered on the initial location; crosses are centered on the termination location.

  • View in gallery

    Number of mesoscale precipitation events by hour of day (UTC) in D17 (black) and M17 (gray).

  • View in gallery

    Diurnal cycle of (a) average precipitation rate (cm day−1) for station data (solid line), D51 (solid line with open squares), M51 (solid line with open circles), D17 (solid line with filled squares), and M17 (solid line with filled circles) and (b) average precipitation rate (cm day−1) factor separation terms f1 (M51; solid line with open circles), f2 (D17; solid line with filled squares), and f12 (M17; solid line with filled circles).

  • View in gallery

    Daily Hovmöller diagrams of average precipitation rate (cm day−1) for (a) station data, (b) D51, (c) M51, (d) D17, and (e) M17.

  • View in gallery

    Hovmöller diagram of average precipitation rate (cm day−1) factor separation terms (a) f1 (M51), (b) f2 (D17), and (c) f12 (M17).

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An Alternative Mass Flux Profile in the Kain–Fritsch Convective Parameterization and Its Effects in Seasonal Precipitation

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  • 1 Department of Agronomy, Iowa State University, Ames, Iowa
  • | 2 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
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Abstract

The authors have altered the vertical profile of updraft mass flux detrainment in an implementation of the Kain–Fritsch2 (KF2) convective parameterization within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). The effect of this modification was to alter the vertical profile of convective parameterization cloud mass (including cloud water and ice) supplied to the host model for explicit simulation by the grid-resolved dynamical equations and parameterized microphysical processes. These modifications and their sensitivity to horizontal resolution in a matrix of experimental simulations of the June–July 1993 flood in the central United States were tested.

The KF2 modifications impacted the diurnal cycle of precipitation by reducing precipitation from the convective parameterization and increasing precipitation from more slowly evolving mesoscale processes. The modified KF2 reduced an afternoon bias of high precipitation rate in both low- and high-resolution simulations but affected mesoscale precipitation processes only in high-resolution simulations. The combination of high-resolution and modified KF2 resulted in more frequent and more realistically clustered propagating, nocturnal mesoscale precipitation events and agreed best with observations of the nocturnal precipitation rate.

Corresponding author address: Christopher J. Anderson, NOAA/ESRL, R/GSD7, 325 Broadway, Boulder, CO 80305-3328. Email: candersn@iastate.edu

Abstract

The authors have altered the vertical profile of updraft mass flux detrainment in an implementation of the Kain–Fritsch2 (KF2) convective parameterization within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). The effect of this modification was to alter the vertical profile of convective parameterization cloud mass (including cloud water and ice) supplied to the host model for explicit simulation by the grid-resolved dynamical equations and parameterized microphysical processes. These modifications and their sensitivity to horizontal resolution in a matrix of experimental simulations of the June–July 1993 flood in the central United States were tested.

The KF2 modifications impacted the diurnal cycle of precipitation by reducing precipitation from the convective parameterization and increasing precipitation from more slowly evolving mesoscale processes. The modified KF2 reduced an afternoon bias of high precipitation rate in both low- and high-resolution simulations but affected mesoscale precipitation processes only in high-resolution simulations. The combination of high-resolution and modified KF2 resulted in more frequent and more realistically clustered propagating, nocturnal mesoscale precipitation events and agreed best with observations of the nocturnal precipitation rate.

Corresponding author address: Christopher J. Anderson, NOAA/ESRL, R/GSD7, 325 Broadway, Boulder, CO 80305-3328. Email: candersn@iastate.edu

1. Introduction

Mesoscale atmospheric dynamics, especially the development and propagation of mesoscale convective systems (MCSs), are primary driving mechanisms of warm season precipitation in the central United States (Rasmusson 1968; Fritsch et al. 1986; Higgins et al. 1997; Ashley et al. 2003; Sudradjat et al. 2003). MCSs result from upscale growth of convective elements O(1–10 km) into mesoscale circulations O(100 km). Although a general mechanism of upscale growth has not been identified, a common trait is mid- and upper-level virtual warming that establishes mesoscale pressure gradients aloft (Maddox et al. 1981; Cotton et al. 1989; Parker and Johnson 2000). Virtual warming occurs as thick mid- and upper-level clouds develop from cloudy air that exits convective updrafts. Idealized numerical experiments suggest that explicit simulation of this process of virtual warming and other precipitation processes in MCSs requires horizontal gridpoint spacing <1 km (Weisman et al. 1997; Bryan et al. 2003).

Present generation regional climate models (RCMs) use horizontal grid spacing that is typically 20 to 50 km. This grid spacing is much too coarse to explicitly represent convective precipitation processes, so that RCMs must implicitly represent the response of gridpoint variables to unresolved convective transport; that is, they must use convective parameterization. Results from recent RCM simulations for the central United States show a failure by RCMs to reproduce observed precipitation patterns associated with MCSs; some RCM results lack propagating nocturnal precipitation signals (Carbone et al. 2002; Davis et al. 2003; Liang et al. 2004; Zhang et al. 2003) and others fail to produce a nocturnal maximum of precipitation rate (Anderson et al. 2003). These errors may be attributed to parameterization deficiencies, model resolution, or some combination of both factors.

Traditional convective parameterization schemes compute tendencies of temperature and water vapor (Molinari and Dudek 1992). As horizontal grid spacing is reduced, Kreitzberg and Perkey (1977) and Zhang et al. (1994) suggest an alternative approach to convective parameterization might be needed in which the parameterization not only adjusts the host model’s thermal profiles but also interacts with parameterization of bulk microphysics. We have examined the simultaneous use of convective and bulk microphysics parameterization in the context of RCM simulations. In particular, we have altered the profile of feedback of cloud liquid water and cloud ice in the Kain–Fritsch2 (KF2; Kain 2004) parameterization to the grid-resolved variables acted upon by the bulk microphysics parameterization. We have tested the effects of these modifications and their sensitivity to horizontal resolution in a matrix of experimental simulations of the June–July 1993 flood in the central United States. During this period many MCSs were observed over the same region, and they occurred almost exclusively at night. Since observations of MCSs are too coarse and infrequent to resolve MCS mechanisms, we have exploited the clustering of MCSs in 1993 to study precipitation signals that can be attributed largely to MCSs to determine whether the experiments altered the realism of precipitation climatology in the model.

This paper is organized as follows. Section 2 describes the experimental design, including RCM configuration, modifications to KF2, and technique for identifying MCSs in the simulations. Results are presented in section 3, and discussion of results is given in section 4. Section 5 contains a concise list of the main results and conclusions inferred from the results.

2. Experimental design

a. RCM description

We used the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5) release 3-6-0 (Grell et al. 1993). We selected widely used parameterization options: Noah land surface parameterization (Chen and Dudhia 2001; Ek et al. 2003), Hong–Pan [Medium-Range Forecast (MRF)] planetary boundary layer parameterization [which allows nonlocal tranports (Hong and Pan 1996; Koren et al. 1999)], Dudhia cloud–radiation parameterization (Dudhia 1989), Reisner-I microphysics parameterization [mixed water phase microphysics (Reisner et al. 1998)], and KF2 convective parameterization (Kain 2004). We generated initial and lateral boundary conditions from National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis-II (R2) data (Kanamitsu et al. 2002). An advantage of using R2 data instead of NCEP–NCAR Reanalysis (R1) data (Kalnay et al. 1996) is that soil moisture in R2 is nudged by assimilation of observed precipitation rather than by prescribed climatological values as in R1. As a result, R2 more realistically reproduces interannual variations of soil moisture (Roads et al. 2002). Lateral boundary data were updated every six hours, using the default MM5 nudging zone. Time varying sea surface temperature (SST) lower boundary conditions, including the Great Lakes, were generated from the NOAA Optimum Interpolation SST V2 dataset (Reynolds et al. 2002).

We generated a control simulation using default KF2 settings and gridpoint spacing of 51 km, which is typical of contemporary RCMs. The grid configuration used a Lambert conic conformal map projection with the central grid point located at 36°N, 95°W. The grid contained 85 north–south and 131 east–west grid points, spanning 10°–55°N and 125°–65°W, and 23 vertical levels. We tested sensitivity to horizontal grid spacing by means of a nested, interactive grid. The nested domain was positioned with its southwestern corner at the coarse domain gridpoint I = 35, J = 50. The nested domain grid spacing was 17 km with 109 north–south, 118 east–west, and 23 vertical grid points. The nested grid spanned 32°–49°N and 105°–80°W.

b. Modifications to the Kain–Fritsch2 convective parameterization

A complete description of the KF2 convective parameterization is found in Kain and Fritsch (1993), Bechtold et al. (2001), and Kain (2004). The KF2 convective parameterization receives a profile of gridpoint values of temperature, specific humidity, vertical and horizontal wind speed, and pressure from the host model (here, MM5). Upon completion, the scheme returns tendencies applied to the gridpoint values of temperature, specific humidity, cloud liquid water, and cloud ice. Tendencies of rain and snow are controlled by a parameter that specifies the fraction of the precipitation mass to be transferred from KF2 to the host model. KF2 uses a Lagrangian model of a one-dimensional entraining–detraining steady-state plume with downdraft to compute convective tendencies such that convective available potential energy (CAPE) of the gridpoint profile is reduced by 90%.

The KF2 process we have modified is mass detrainment from the updraft. Mass detrainment in KF2 can occur by two mechanisms: 1) horizontal mixing of air internal and external to the plume and 2) mass outflow from the updraft that occurs to satisfy mass balance. For mechanism 1, KF2 uses a buoyancy sorting method that evaluates thermodynamic characteristics of mixtures of air external and internal to the updraft. The portion of the mixture that is buoyant joins the updraft mass flux, and the nonbuoyant portion is detrained. Mechanism 2 occurs in the layer over which updraft mass flux is decreased to zero, which we refer to as the outflow layer. The standard version of KF2 imposes a linear in pressure decrease of updraft mass flux from its value at the level of equilibrium temperature (LET) to zero at cloud top. We have introduced two modifications to the KF2 scheme. First, we have changed the bottom of the outflow layer from the LET to either the level of minimum gridpoint θe (minθe) or the melting level (ML), whichever is at a lower altitude, and we require this level to be above the LCL. This choice is motivated by observations of precipitating cumulonimbus, such as those reported in Knupp (1987), that show decreasing updraft mass flux above ML or minθe. Furthermore, there are precedents for using this criterion, or a similar measure of static stability, to establish the level of maximum mass flux in convective parameterizations (Moorthi and Suarez 1992; Zhang and McFarlane 1995; Sud and Walker 1999; Maloney and Hartmann 2001). Second, we have assumed updraft mass flux decreases linearly with the natural logarithm of pressure rather than with pressure. This is motivated by observed mass flux profiles that exhibit linear decrease of mass flux with height, which is proportional to the natural logarithm of pressure (Knupp 1987). Our adjustments have two effects: 1) mass outflow occurs over a deeper layer, and 2) the deepest portion of the outflow layer, and thus greatest mass outflow, occurs near cloud top. It is expected that by inducing outflow at a lower level the outflow air will contain higher densities of cloud liquid water and cloud ice that can be enacted upon by the bulk microphysics parameterization and that would otherwise have been converted to precipitation within the convective parameterization. Our modifications affect only the tendencies of cloud liquid water and cloud ice. The rain and snow feedback fraction is unchanged from its default setting of zero in all simulations.

In Fig. 1 we illustrate several features of the default and revised KF2 for a thermodynamic profile taken by a radiosonde balloon launched at Norman, Oklahoma, that is similar to profiles frequently observed in association with severe weather in the central United States. The level of minimum θe is unusually low in this profile, due to a mixing ratio minimum near 800 hPa. For this reason the profile dramatically illustrates the effects of the alternative mass flux profile. We have confirmed the impacts are similar though less severe in a large number of other thermodynamic profiles. The LCL of the plume is 934 hPa, plume top is 136 hPa, and LET is 219 hPa (Fig. 1a). Activation of the KF2 scheme reduces CAPE by replacing near-surface air with cooler and drier midtropospheric air by diabatic processes and subsidence warming aloft (Fig. 1b). The largest temperature change occurs near the tropopause, where subgrid subsidence in very stable air is induced by mass outflow above the LET.

The modifications had two main impacts. First, warming is reduced near the tropopause, because compensating subsidence occurs over a deeper layer (Fig. 1b). The modifications reduce a longstanding problem in KF2 scheme and its predecessors, which is the tendency to produce an unrealistic warming/cooling couplet near cloud top and too much warming and drying in lower and middle troposphere (Liu et al. 2001). Second, liquid and ice mixing ratios are increased in the middle portion of the troposphere (Fig. 1d). We have found in several one-dimensional tests that the modified KF2 produces maximum liquid water and ice mixing ratios that are as much as 6 times larger than the default KF2, and liquid water and ice are added through a much deeper layer. The additional liquid and ice are compensated mainly by a decrease of rainfall from the convective parameterization (reduction from 2.59 to 0.884 mm for sounding in Fig. 1).

c. Simulation experiments

We performed four 61-day simulations for the period 1 June–31 July 1993. A control simulation (referred to as D51 hereafter) uses the default MM5 settings and the 51-km grid described in section 2a. We simulated this period using three alternative formulations of the RCM as described in section 2a: default KF2 with the 17-km nested grid (D17), modified KF2 with 51-km grid spacing (M51), and modified KF2 with the 17-km nested grid (M17).

d. Identification of mesoscale precipitation events in RCM simulations

Techniques commonly used to identify MCSs in observations are not easily applied to model output. Nearly all MCS identification procedures use either radar or satellite measurements that are not directly comparable to model output (Laing and Fritsch 1997; Ashley et al. 2003). One approach for identifying mesoscale circulation in RCM output is to compute mesoscale perturbation of the wind field aloft (Takle et al. 1999). This approach is particularly useful for identifying isolated mesoscale events in simulation output. However, when mesoscale precipitation events are numerous or are embedded within vigorous subsynoptic- and synoptic-scale circulation, the technique produces a somewhat ambiguous measure of the mesoscale circulation.

For this study, we developed an empirical approach for identifying mesoscale precipitation events in the simulations that may be replicated and that are consistent with observations of MCS processes. We caution that our empirical approach does not ensure that the internal processes of mesoscale precipitation events in the simulations and observed MCSs are identical. Furthermore, it is motivated by observed features of midlatitude, continental MCSs: they produce 1) large rain rates (McAnelly and Cotton 1989) and 2) low-level cooling by melting snow and graupel and evaporation of rain, which promotes MCS propagation (Rotunno et al. 1988).

We first identified low-level cooling rates by examining histograms of 925–700-hPa thickness hourly changes and found that 99% were <|5| m. By scale analysis of the quasigeostrophic thickness tendency equation, it can be expected that quasigeostrophic dynamics should not cause changes exceeding |5| m h−1. Thus, we interpret 925–700-hPa thickness hourly change ≤−5 m as an indicator of low-level cooling by mesoscale and diabatic processes rather than by large-scale circulation. Next, we identified contiguous rain areas exceeding a given rain-rate threshold. We chose the rain-rate threshold carefully; for example, if the threshold were too low the results would contain contiguous rain areas that encompass multiple mesoscale precipitation events or rainfall created by other large-scale forcing. For each simulation, we extracted contiguous rain areas with hourly precipitation rates exceeding 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, and 7.0 cm h−1 (Fig. 2) and computed the fraction of the rain area that was overlapped by 925–700-hPa thickness hourly change ≤−5 m. We chose a value near the saddle point of the curve as the optimal precipitation rate threshold. This procedure resulted in thresholds of 1 cm h−1 for the 51-km simulations and 1.5 cm h−1 for the 17-km simulations. We chose a value near the saddle point to allow the procedure to be replicated. After viewing movies of precipitation fields, nearly identical results were obtained for the 17-km simulations when using thresholds between 1 and 2 cm h−1.

e. Factor separation technique

Stein and Alpert (1993) developed a factor separation technique that can be used to quantify the individual and joint effects of multiple changes made to a model. We have used their approach to quantify the effects of finer grid spacing and alternative KF2 formulation when applied individually and in tandem. Stein and Alpert (1993) viewed a model output field as the sum of a base condition when the model is used in its unaltered form and the change from the base condition caused by the new model formulation. Let Fo, F1, F2, and F12 represent an arbitrary field from the unaltered model, the model altered by factor 1, the model altered by factor 2, and the model altered by both factors 1 and 2, respectively. Let f1, f2, and f12 represent the change from the base condition that results when the model formulation is altered by factors 1, 2, and both 1 and 2, respectively. A system of three equations describes the changes from F0 by each model reformulation:
i1525-7541-8-5-1128-e1
i1525-7541-8-5-1128-e2
i1525-7541-8-5-1128-e3
The change f12 reflects the interaction between factors 1 and 2; that is, f12 is zero if changes produced by factors 1 and 2 are linearly additive. In our results, f1 is the change caused by using the modified KF2, f2 is the change caused by reduction of grid spacing, and f12 is the change caused by interaction of the revised scheme with grid spacing.

3. Results

a. Summary of mesoscale precipitation events

Mesoscale precipitation event centroid locations are computed as the arithmetic average of latitude–longitude locations of the contiguous rain area. For the entire simulation period, mesoscale precipitation events were more frequent in the 17-km simulations and most frequent in M17 (Fig. 3). In particular, there were 11 in D51, 9 in M51, 33 in D17, and 57 in M17. It is not surprising that fewer mesoscale precipitation events occur in the coarser simulations. Previous studies have shown that MCS organization tends to be enhanced when the system dimensions are comparable to or larger than the Rossby radius of deformation (e.g., Cotton et al. 1989). This radius (few hundred kilometers) is not much larger than the minimum resolvable wavelength of the model when using 51-km gridpoint spacing (about 200 km). It is reasonable to expect, therefore, that mesoscale precipitation events will be underrepresented due to the inability of the model to resolve all but the largest events. The climatology of MCSs from satellite data presented by Anderson and Arritt (1998) found a total of 36 large MCSs during this period. Note that the mesoscale precipitation event identification procedure used herein does not include the minimum size criterion that was imposed in the satellite climatology; thus, the number of MCSs in the satellite climatology provides a lower bound for the number of mesoscale precipitation events that can be expected in the simulations. The number of mesoscale precipitation events in the 51-km simulations is approximately one-fourth of this lower bound, an indication of the severity of the constraint placed by grid resolution on the model’s ability to represent the dynamics of mesoscale convective systems.

The tracks of mesoscale precipitation events were altered by the KF2 modifications and the use of finer resolution (Fig. 3). In both D51 and M51 very few centroid tracks overlaid the region of maximum 61-day accumulated precipitation, indicating that mesoscale precipitation events contributed a small fraction of the precipitation maximum. In contrast, mesoscale precipitation event tracks for the 17-km grid simulations suggested a more substantial contribution to the precipitation maximum. Centroid tracks in D17 were within, north, and west of the region of maximum precipitation; note the three southwest–northeast-oriented tracks that extend from Iowa into Wisconsin and the upper peninsula of Michigan. These three unusual centroid paths occurred when mesoscale precipitation events were embedded within northeastward-moving synoptic-scale systems. Many of the centroid track features in D17 were evident in M17, with the main difference being the density of tracks within and west of the maximum flood region (eastern Kansas through central Iowa). Centroid tracks in M17 had noticeably higher concentration in South Dakota, southern Minnesota, Iowa, southern Nebraska, and Kansas and were in closer agreement with the higher density of observed MCS tracks over the flood region relative to surrounding regions (Anderson and Arritt 1998).

A distinguishing characteristic of MCSs in the central United States is their tendency to occur at night. The relative nocturnal frequency of observed MCSs was larger than normal in 1993 (Anderson and Arritt 1998). Mesoscale precipitation events in M17 most frequently occurred between 0000 and 0800 UTC, corresponding to the period from a few hours before local sunset to shortly after local midnight (Fig. 4). In comparison, peak frequency in D17 was between 1400 and 1800 UTC, corresponding to late morning through local noon. It is unclear why MCSs in D17 had this morning preference. The broad evening peak in M17 is consistent with the natural mechanism of MCSs in which MCSs develop as afternoon convection grows and self organizes into an MCS. In summary, the observed spatial and temporal characteristics of observed MCSs for the simulation period, that is, spatial concentration of centroid tracks and high frequency of nocturnal occurrence, were replicated best by mesoscale precipitation events in M17.

b. Diurnal cycle of precipitation

We examined the diurnal cycle of precipitation within the greater Upper Mississippi River basin (GUMRB) as delineated by latitude–longitude boundaries of 37°–47°N and 99°–89°W. The location of maximum accumulated precipitation is within the GUMRB in observations and in all simulations. The average precipitation rate is the arithmetic average of precipitation at stations (or grid points) within the GUMRB region.

The diurnal cycle of precipitation rate illustrates time integration of precipitation processes (Fig. 5a). The diurnal cycle of station precipitation rate had a nocturnal maximum with a peak rate of 8.0 mm day−1 between 0600 and 1000 UTC, afternoon minimum of 4.2 mm day−1 at 2000 UTC, and gradual increase of precipitation rate between 2100 and 0400 UTC. Nocturnal maximum of precipitation is a climatological feature of the Midwest (Wallace 1975; Higgins et al. 1997), but the large magnitude of the nocturnal maximum in 1993 is unique (Kunkel et al. 1994; Higgins et al. 1997). The unusual spatial coherence of large nocturnal MCSs in 1993 contributed to unusually large nocturnal maximum of precipitation (Kunkel et al. 1994; Anderson and Arritt 1998), so that the spatial and temporal characteristics of the precipitation distribution are interrelated.

Results indicated that the default and modified KF2 produce different diurnal cycles of precipitation (Fig. 5a). A preference for afternoon rather than nocturnal precipitation was evident in all simulations, except M51 in which precipitation rate decreased from 1900 through 0300 UTC. Results in Liang et al. (2004) and Gochis et al. (2003) also contained an afternoon maximum of precipitation in simulations using the KF and MRF parameterizations. We speculate this combination of parameterizations may cause a unique error in afternoon precipitation in the following way. Because the trigger function for KF2 is related to both the low-level potential convective instability and wind convergence, errors in either boundary layer temperature or wind may adversely affect KF2. Zhang and Zheng (2004) found that the MRF boundary layer parameterization underestimated the diurnal variation of low-level wind during a 3-day period in which low-level jets (LLJs) were observed on each night, producing stronger than observed wind speed during the late afternoon. It is possible that similar low-level wind errors contributed to the incorrect afternoon precipitation maximum, since LLJs occurred frequently during the simulation period (Arritt et al. 1997). Unrealistically strong afternoon low-level wind speed within the confluent wind field of the quasi-stationary frontal zone could enhance afternoon convergence, causing KF2 to be invoked.

The KF2 modifications mitigated to some extent this unrealistic sensitivity. The smaller afternoon maximum in M17 was caused primarily by a reduction of parameterized precipitation rate. Between 1600 and 0100 UTC, the fraction of grid points that reported nonzero hourly parameterized precipitation was 20% larger in M17 compared to D17, but the parameterized precipitation rate was 30% less. The mean diurnal cycle of surface temperature had warmer afternoon temperature in M17 compared to D17, implying larger CAPE. The expected impact of larger CAPE in the default KF2 is an increase of upward mass flux and precipitation. Thus, while the modifications resulted in higher frequency of simulated convection in the afternoon, less rainfall was produced and water was instead detrained aloft.

Factor separation shows more clearly the impacts of grid resolution and KF2 modifications on the diurnal cycle of precipitation rate (Fig. 5b). The reduction of afternoon precipitation rate by the modified KF2 was apparent in the factor separation diurnal cycle for M51. The most dramatic improvement in the M17 simulation was the increase in nocturnal precipitation rate; a smaller increase of nocturnal precipitation rate was produced in D17. The two 17-km simulations showed similar changes in daytime precipitation rate, namely, a decrease from around sunrise through early afternoon and an increase in late afternoon.

In summary, the combination of decreased daytime precipitation and increased nocturnal precipitation results from the interaction of the modified KF2 scheme with decreased grid spacing and is not a simple additive effect of these two changes to the model formulation. It appears that when grid spacing is too coarse to properly resolve dynamical and thermodynamic processes relevant to MCSs, the model cannot appropriately process the additional cloud water and ice detrained onto the grid scale in the revised scheme.

c. Daily Hovmöller diagrams

The previous sections show evidence that the diurnal cycle of precipitation was improved in M17 by 1) emphasizing water detrainment rather than precipitation in the afternoon and 2) increasing the frequency of more slowly evolving mesoscale precipitation events. The result is in closer agreement with the observed diurnal cycle of precipitation. We examine further the precipitation propagation signal with daily Hovmöller diagrams. Hovmöller diagram cells are 2° longitude bands and 1-h increments. The cell average is the arithmetic average of precipitation from stations (grid points) that fell within the longitude time window.

The Hovmöller diagram for the station data revealed the complexity of the spatiotemporal precipitation patterns (Fig. 6a). The data showed an eastward shift of precipitation rate >0.4 mm h−1 between 0200 and 1800 UTC. The eastward shift of this precipitation region overnight is consistent with the propagation of nocturnal MCSs but is not entirely due to MCSs. In fact, unorganized nocturnal storms associated with a quasi-stationary frontal boundary (Anderson et al. 2003) created the local maximum of precipitation rate >0.4 mm h−1 at 0600 and 0700 UTC within 102°–92°W.

Hovmöller diagrams for simulated precipitation not surprisingly displayed less complexity (Figs. 6b–d). Generally, high precipitation rates were underestimated, which is a common trait of RCMs (see, e.g., Giorgi and Marinucci 1996; Anderson et al. 2003). In all simulations, maximum precipitation rate in the flood region occurred in the afternoon rather than at night, and a coherent, nocturnal eastward shift of high precipitation rate was not obvious in any of the simulations. However, factor Hovmöller diagrams reveal some improvement in M17 (Fig. 7c). A contiguous region of precipitation increase >0.5 mm day−1 between 100° and 96°W from 2000 to 1000 UTC is evident after removing the effects of D51 and D17. Its timing is coincident with the peak frequency of propagating precipitation events in M17 (cf. Fig. 4). Thus, although the precipitation signal is small, it is likely produced by the increase of eastward-propagating, nocturnal precipitation, and it represents an improvement of the precipitation climatology. Further observational and modeling work is needed to confirm that the nocturnal precipitation propagates eastward by observed MCS mechanisms.

4. Discussion

a. Generality of the findings

Although model changes described here improved the correspondence of spatial and temporal distributions of mesoscale precipitation events with observed MCSs, it is possible that simulations of precipitation in other periods or locations may be degraded, given that improvements of precipitation climatology were obtained by optimizing a parameterization for a specific (and highly anomalous) period. We have examined whether the results are unique to the simulation period by repeating the matrix of simulations for the Project to Intercompare Regional Climate Simulations (PIRCS) 1(a) period of 15 May–15 July 1988 (Takle et al. 1999), when an extreme drought affected the central United States. The results (not shown) exhibited similar sensitivity of default KF2 to insolation when grid spacing was decreased from 51 to 17 km. Precipitation rate in the Upper Mississippi River basin was reduced in the afternoon and was increased overnight when using the combination of 17-km grid spacing and modified KF2. Comparison with station data showed that afternoon precipitation in the 51-km default KF2 simulation was much greater than observed, so that the diurnal cycle in the 17-km modified KF2 simulation was in better agreement with observations. Furthermore, the 17-km modified KF2 produced as little as half as much precipitation as the 51-km default KF2 simulation over a wide area, including Iowa, Illinois, and Wisconsin, more accurately reproducing the observed spatial pattern of drought. Thus, the modified scheme improved precipitation climatology for two strongly contrasting seasonal precipitation anomalies in the central United States.

b. Improvements to precipitation simulation in RCMs

In light of the need to improve the representation of subdaily processes within RCMs [e.g., as discussed in the Third Assessment Report of the Intergovernmental Panel on Climate Change (Giorgi et al. 2001)], it is necessary to identify strategies that can be used to mitigate the limitations of convective parameterization. We note several possibilities. First, the need for convective parameterization could be removed altogether through explicit simulation of cloud processes. Davis et al. (2003) advocates this approach for short-term forecasts of MCSs, although Arakawa (2004) suggests the computational expense of this approach might preclude its use in climate simulations for decades to come. The “superparameterization” approach is a compromise in which a cloud-resolving submodel is implemented for grid cells where convection occurs (Grabowski and Smolarkiewicz 1999; Khairoutdinov and Randall 2001), although its utility in RCMs has yet to be examined. Second, an RCM could be configured for optimal performance in a particular application given the known limitations of its convective parameterizations, in essence “tuning” the model. An obvious disadvantage is that tuning may compromise the generality of the model formulation; for example, tuning a model for optimal performance in the current climate may degrade its performance for a future climate. Transferability experiments, in which multiple climate regions are simulated with either multiple RCMs or multiple configurations of a single RCM, can be used to examine whether general applicability of an RCM is improved by updating specific model components (Pal et al. 2005; Rockel et al. 2005). Our results suggest that in this context it would be informative to explore interactions among parameterizations, such as the interaction between convective and bulk microphysics parameterizations. Third, an ensemble approach could be used in which multiple RCM simulations are performed using different models, parameterizations, or parameter settings (Yang and Arritt 2002). Evidence to date indicates that the use of multimodel ensembles may provide improvements in summary measures of forecast skill but is not likely to be beneficial for analysis of precipitation diurnal cycles. For example, if the simulations in PIRCS-1(b) are viewed as a multimodel ensemble, the ensemble mean diurnal cycle would have very small amplitude with a broad peak extending from midday through early the following morning (Anderson et al. 2003), whereas the observed diurnal cycle contained a pronounced nocturnal peak. Finally, at grid spacings too coarse to explicitly resolve the dynamics of mesoscale precipitation systems it may be necessary to develop a convective parameterization in which the broader effects of convection, including the effects of MCSs, are parameterized.

5. Conclusions

A previous intercomparison of RCM simulations of the 1993 central United States flood revealed that MCS signals in accumulated precipitation were poorly simulated (Anderson et al. 2003), suggesting that substantial improvements in simulated precipitation fields could be achieved by better emulating the effects of MCSs. The present study examines sensitivity of accumulated precipitation to horizontal grid spacing and modifications of the KF2 convective parameterization. The mass flux detrainment in the KF2 scheme was altered so that detrainment began at a lower level and was linear in height rather than linear in pressure. We tested the impact of these changes when implemented separately from and in tandem with reduced grid spacing in a matrix of experimental simulations. The main results were the following:

  • Mesoscale precipitation events increased in frequency as horizontal gridpoint spacing was reduced from 51 to 17 km. They were more frequent at night within the region of heaviest precipitation in simulations that used the combination of 17-km grid spacing and modified KF2.
  • The modified KF2 reduced an afternoon bias of high precipitation rate in both 51- and 17-km grid simulations.
  • Nocturnal precipitation in the central United States increased when grid spacing was reduced. The best correspondence with the observed nocturnal maximum was obtained in the simulation that contained both 17-km grid spacing and modified KF2. This simulation produced nocturnal hourly precipitation rates in the flood region that were 50%–100% larger than in the control simulation. However, nocturnal maximum values still were only about 75% of those observed and afternoon precipitation was 40% greater than observed.
  • Only the simulation using 17-km grid spacing and modified KF2 made improvements to the control simulation (51-km grid spacing and default KF2) by producing a nocturnal, propagating precipitation signal in daily Hovmöller diagrams of factor separation terms. The implication is that sufficiently fine horizontal resolution is required in order to represent properly the effects of cloud water and ice detrained by the revised KF2 scheme.

From these results we conclude that errors in simulations of the diurnal cycle of precipitation in the central United States can be reduced by careful consideration of how precipitation processes are represented through coupling of parameterizations for subgrid convection and bulk microphysics rather than by relying on convective parameterization alone.

Acknowledgments

This research was funded by grants provided by the DOE/BER Program through the Great Plains Regional Center of NIGEC (DE-FC03-90ER61010), NSF (ATM-9911417), and NOAA (NA16GP1583). Computational resources were provided by the NCAR Climate Simulation Laboratory and the Mathematics and Computer Science Division at the Argonne National Laboratory. The authors acknowledge use of the Ferret program from analysis and graphics in this paper. Ferret is a product of NOAA’s Pacific Marine Environmental Laboratory (www.ferret.noaa.gov). We are grateful to James Correia, Jr. for assistance in plotting the skew-T diagram. Optimum Interpolation (OI) SST V2 data were provided by the NOAA–CIRES Climate Diagnostics Center, Boulder, Colorado, from their Web site at http://www.cdc.noaa.gov.

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Fig. 1.
Fig. 1.

(a) Profiles of temperature (°C), dewpoint temperature (°C), and wind barbs (knots) used to illustrate adjustments made by default and modified KF2 parameterization. Pressure levels of the lifting condensation level (LCL), minimum θe (minθe), level of equilibrium temperature (LET), and cloud top are 934, 802, 219, and 136 hPa, respectively. Total adjustment by default (solid) and modified (dashed) KF2 of host model’s (b) potential temperature (K), (c) specific humidity (g kg−1), and (d) liquid and ice mixing ratio (g kg−1).

Citation: Journal of Hydrometeorology 8, 5; 10.1175/JHM624.1

Fig. 2.
Fig. 2.

Number of grid points exceeding precipitation thresholds of 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, and 7.0 cm h−1 vs the fraction of those grid points for which hourly 925–700-hPa thickness tendency is >|5| m h−1 for (a) 51- and (b) 17-km simulations.

Citation: Journal of Hydrometeorology 8, 5; 10.1175/JHM624.1

Fig. 3.
Fig. 3.

Tracks of centroids of mesoscale precipitation events for (a) D51, (b) M51, (c) D17, and (d) M17. Open circles are centered on the initial location; crosses are centered on the termination location.

Citation: Journal of Hydrometeorology 8, 5; 10.1175/JHM624.1

Fig. 4.
Fig. 4.

Number of mesoscale precipitation events by hour of day (UTC) in D17 (black) and M17 (gray).

Citation: Journal of Hydrometeorology 8, 5; 10.1175/JHM624.1

Fig. 5.
Fig. 5.

Diurnal cycle of (a) average precipitation rate (cm day−1) for station data (solid line), D51 (solid line with open squares), M51 (solid line with open circles), D17 (solid line with filled squares), and M17 (solid line with filled circles) and (b) average precipitation rate (cm day−1) factor separation terms f1 (M51; solid line with open circles), f2 (D17; solid line with filled squares), and f12 (M17; solid line with filled circles).

Citation: Journal of Hydrometeorology 8, 5; 10.1175/JHM624.1

Fig. 6.
Fig. 6.

Daily Hovmöller diagrams of average precipitation rate (cm day−1) for (a) station data, (b) D51, (c) M51, (d) D17, and (e) M17.

Citation: Journal of Hydrometeorology 8, 5; 10.1175/JHM624.1

Fig. 7.
Fig. 7.

Hovmöller diagram of average precipitation rate (cm day−1) factor separation terms (a) f1 (M51), (b) f2 (D17), and (c) f12 (M17).

Citation: Journal of Hydrometeorology 8, 5; 10.1175/JHM624.1

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