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Keywords:

  • GCM;
  • CRM;
  • CSRM;
  • rainfall;
  • TRMM;
  • cascade

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

Global climate and weather models tend to produce rainfall that is too light and too regular over the tropical ocean. This is likely because of convective parametrizations, but the problem is not well understood. Here, distributions of precipitation rates are analyzed for high-resolution UK Met Office Unified Model simulations of a 10 day case study over a large tropical domain (∼20°S–20°N and 42°E–180°E). Simulations with 12 km grid length and parametrized convection have too many occurrences of light rain and too few of heavier rain when interpolated onto a 1° grid and compared with Tropical Rainfall Measuring Mission (TRMM) data. In fact, this version of the model appears to have a preferred scale of rainfall around 0.4 mm h−1 (10 mm day−1), unlike observations of tropical rainfall. On the other hand, 4 km grid length simulations with explicit convection produce distributions much more similar to TRMM observations. The apparent preferred scale at lighter rain rates seems to be a feature of the convective parametrization rather than the coarse resolution, as demonstrated by results from 12 km simulations with explicit convection and 40 km simulations with parametrized convection. In fact, coarser resolution models with explicit convection tend to have even more heavy rain than observed. Implications for models using convective parametrizations, including interactions of heating and moistening profiles with larger scales, are discussed. One important implication is that the explicit convection 4 km model has temperature and moisture tendencies that favour transitions in the convective regime. Also, the 12 km parametrized convection model produces a more stable temperature profile at its extreme high-precipitation range, which may reduce the chance of very heavy rainfall. Further study is needed to determine whether unrealistic precipitation distributions are due to some fundamental limitation of convective parametrizations or whether parametrizations can be improved, in order to better simulate these distributions. Copyright © 2012 Royal Meteorological Society


1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

Global climate models still have difficulty in simulating tropical climate, specifically rainfall (and associated circulation) patterns at many time scales ranging from interannual to diurnal. These simulated patterns are especially sensitive to details of convective parametrizations, which are used to approximate the cumulative effects of small-scale convection using information at the scale of the model grid cell (Slingo et al., 1996; Lin et al., 2006; Chen et al., 2007; Guilyardi et al., 2009; Turner and Slingo, 2009). Recent advances in computer power are beginning to allow atmospheric simulations large enough to represent large-scale tropical variability while explicitly simulating vertical motions associated with deep convection, thereby side-stepping the need to parametrize tropical rainfall. However, these large-domain high-resolution simulations are still too coarse to resolve convective clouds and motions properly at their observed scales, and they make many assumptions about unresolved processes at even smaller scales. It is vital to compare these simulations with both observations and lower-resolution models using parametrized convection in order to plan future modelling experiments, gain insights for improving parametrizations and improve our understanding of the climate system.

Many studies have shown that convective parametrizations generally lead to rainfall occurring at rates that are too small and over durations that are too long (Dai and Trenberth, 2004; Sun et al., 2006; Stephens et al., 2010), though there have been efforts to improve this behaviour (Wilcox and ???Donner, 2007). Observations over tropical regions show roughly power-law behaviour of precipitation distributions at lower rain rates, with faster, exponential decreases as very high rain rates are approached (DeMott et al., 2007; Field and ???Shutts, 2009; Peters et al., 2010). In this article, we compare different runs of the same model over different horizontal resolutions with both parametrized and explicit convection for a real case over a large domain covering the tropical Indian and Western Pacific oceans. We can then compare the model runs with observational data for the same case.

We compare the different models and observations averaged at a common grid spacing of 1° latitude/longitude and time interval of three hours in order to see how convective processes, either explicitly represented or parametrized, affect larger scales. Our hypothesis is that parametrizations, because of weaknesses in their representation of interactions between the convective plume scale and larger scales, will have deficiencies in representing large-scale convective organization and the statistics of precipitation in convective regions. We hope that understanding differences between parametrized and explicit convection will help lead to insights into improving parametrized convection so that it better reflects observations. This would benefit studies of present and future climate, for instance in narrowing climate-model predictions of future changes in precipitation extremes (O'Gorman and Schneider, 2009; Allan et al., 2010).

At the same time, we know of very few studies that use cloud-system-resolving models (CSRMs) for real case studies of tropical convection, especially over large domains. A few case studies using the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) on the Earth Simulator at resolutions as fine as 3.5 km, but more often at 7 km or 14 km, have shown large improvements of tropical phenomena such as the Madden–Julian Oscillation (MJO: Miura et al., 2007; Liu et al., 2009) and tropical cyclones (Oouchi et al., 2009; Taniguchi et al., 2010) compared with coarser resolution simulations. Since high-resolution models with explicit convection will undoubtedly be used more and more for weather prediction and climate studies on global scales, it is vital that these models are systematically tested and studied both to improve their representations of atmospheric processes and to inform our understanding of those processes and the physical mechanisms underlying them.

The simulations discussed in this article have been run and analyzed as part of ‘Cascade’, a UK consortium project funded by the Natural Environment Research Council (NERC). Cascade seeks to better understand the interaction between tropical convection at the cloud-system scale and larger-scale processes including the MJO, the diurnal cycle of convection over land (Love et al., 2011; Pearson et al., 2010), easterly waves and equatorially trapped waves. To achieve this goal, Cascade employs high-resolution CSRM simulations of the UK Met Office Unified Model (UM) over very large tropical domains and compares them with high-resolution observations, operational analyses provided for the Year of Tropical Convection (YOTC: Waliser and Moncrieff, 2008) and simulations of the same model using parametrized convection.

We describe the observational data and operational analyses in section 2 and explain the set-up of the model simulations in section 3. In section 4 we discuss the mean precipitation over the whole domain and time period of the case study and then present an analysis of probability density distributions of precipitation for the different simulations and observations. In section 5 we present analysis of environmental conditions and heating and moistening rates conditionally averaged by rain rate to explore possible reasons for differences in precipitation distributions. We then discuss these possible mechanisms and the implications of differences in precipitation distributions for the representation of tropical waves and circulation in section 6, before ending with a summary in section 7.

2. Data

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

We use Tropical Rainfall Measuring Mission (TRMM) 3B42 merged satellite rainfall data, with 0.25° latitude/longitude resolution and three-hour temporal resolution (Huffman et al., 2007). Unless otherwise noted, we restrict our analyses to points over the sea, which include over 80% of the total domain, to avoid more-complex behaviour over land (land points for all data analysis are defined as 1° grid boxes that contain more than 10% of land area as defined by the 4 km model land–sea mask). Because TRMM does not measure very small rain rates well (Huffman et al., 2007), we set any values of rainfall in both the observations and the model data (after being averaged on to the same 1° three-hour grid) that are below 0.04167 mm h−1 (1 mm day−1) to zero when we calculate probability densities and fractional contributions to rainfall in section 4.2. However, for the saturation deficit composite on rain rates in section 5.1 we do use lower rain rates for TRMM to represent nearly suppressed conditions, and therefore those composites on TRMM rainfall rates below the threshold rain rate should be viewed with more caution.

We have used Global Precipitation Climatology Project (GPCP: Huffman et al., 1997) daily average data and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN: Sorooshian et al., 2000) six-hourly data, averaged over the entire domain for this case-study time period, for comparison with TRMM.

European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses archived for YOTC are used for comparison with model simulations and also as lateral boundary conditions for the limited-area model runs. The analyses are at approximately 25 km grid spacing in the Tropics and values are output at 6 h intervals. To compare values of saturation deficit with model values and to compare column water vapour values with TMI data as discussed below, we interpolate 1° average specific humidity and temperature data from these forecast analyses to 3 h time intervals.

We use TRMM Microwave Imager (TMI) 0.25° precipitation and column water vapour data (Hilburn and ???Wentz, 2008) for comparison with TRMM merged data and ECMWF operational analyses. Our results do not change when we calculate the same precipitation distributions in section 4.2 with TMI data (not shown), which should be more accurate when and where it is available, rather than with the TRMM merged data. Although we do not compare other merged satellite datasets with TRMM in this article, except for domain-mean precipitation values, PERSIANN data analyzed in Dai et al. (2007) show slightly less-frequent and more-intense daily rainfall than TRMM over this region, which, if also true at smaller time scales, would show even more similarity to explicit convection models and less to the parametrized convection models in this study. We have compared the column water vapour of the ECMWF operational analyses with TMI column water vapour over 3 h means (interpolated from 6 h values for ECMWF) and 1° boxes where TMI data are available in the region over 10 days, and they agree fairly well, with a linear Pearson correlation coefficient of 0.97, a median absolute error of 1.8 mm, a mean absolute error of 2.2 mm and a difference of means (ECMWF − TMI), or bias, of −0.64 mm.

3. Model set-up

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

We use the limited-area mode of version 7.1 of the Met Office Unified Model (UM; Davies et al., 2005), which is semi-Lagrangian and non-hydrostatic; our limited-area runs are updated at the lateral boundaries by ECMWF operational analyses. The initial conditions also come from an ECMWF operational analysis (except for the UM sea surface temperature (SST) analysis, which is fixed at the initial value). The 40 km and 12 km horizontal grid model runs are updated directly from the ECMWF analyses every 6 h at the lateral boundaries via a ‘rim’ of 8 model grid points within which the prognostic fields are blended linearly between the interior model domain and the exterior analysis. The 4 km grid runs are updated every 30 min from lateral boundary conditions computed from the 12 km parametrized convection run. The rim points are excluded from the domain for the purposes of scientific analysis. The 12 km domain is approximately 21°S–21°N and 41°E–182°E, but all of the analyses here are performed on the shared domain of 19.625°S–19.375°N and 42.375°E–180.375°E at 1° grid spacing.

Within the rim, prognostic variables from the freely evolving inner domain are nudged toward the specified analysis lateral boundary conditions at the outer edge of the rim, with contributions from the two sources weighted linearly across the 8 points so that in the middle of the rim the two sides are weighted equally. This means that the lateral boundary conditions seen by the model at the outer edge of its interior domain partly depend on that domain itself, so that there can be feedback in which, for instance, greater low-level convergence and upper-level divergence in the model interior lead to greater low-level inflow and upper-level outflow in the rim itself and allow for greater moisture convergence and ventilation, which can help to sustain more large-scale heating and ascent in the interior. This can lead to different behaviour in different model runs even though they share the same lateral boundary conditions.

In addition to differences in horizontal grid spacing, there are more vertical levels in the 4 km runs (70 levels) than in the 12 km and 40 km runs (38 levels) with the model top around 40 km height in both cases. Vertical spacing between levels ranges from tens of metres in the boundary layer to around 250 m in the free troposphere for the 4 km models and approximately double this for the 12 km and 40 km models. The vertical levels are terrain-following hybrid heights, but for this article we consider profiles only over the sea so that hybrid height should be equivalent to height above sea level. The time step is 5 min for the 12 km param and 40 km runs, 75 s for the 12 km 3Dsmag run and 30 s for the 4 km runs.

The model physics settings differ among the runs as follows: the 12 km param model uses a modified Gregory–Rowntree convective parametrization (with convective available potential energy, or CAPE, as the basis for its closure: Gregory and Rowntree, 1990) with a 30 min CAPE relaxation time scale, as well as an adjustment to reduce this at very high vertical velocity in order to prevent grid-point storms. The standard boundary-layer scheme (Lock et al., 2000) is used for vertical subgrid mixing and there is no horizontal subgrid mixing. There is a single-moment mixed-phase microphysics scheme with two components: ice/snow and liquid water (Wilson and ???Ballard, 1999). The 40 km model has similar physics to the 12 km param model, although the convective parametrization closure lacks the vertical velocity adjustment. The 4 km 2Dsmag model uses a CAPE-limited version of the convective parametrization that asymptotes to the same 30 min CAPE time scale at zero CAPE but has a CAPE time scale that rapidly increases with increasing CAPE, such that for typical tropical values virtually all rainfall is generated explicitly (Roberts, 2003; Lean et al., 2008). This model version uses the standard boundary-layer scheme for vertical subgrid mixing but includes Smagorinsky-type subgrid mixing in the horizontal dimensions. The microphysics scheme now has prognostic rain in addition to the two components in the version above. The 4 km 3Dsmag model also uses the CAPE-limited convective parametrization but uses Smagorinsky mixing in all three dimensions, including the vertical, and therefore it does not use the boundary-layer scheme. The microphysics settings are the same as those in the other 4 km version. The 12 km 3Dsmag model uses the CAPE-limited convective parametrization and Smagorinsky mixing in all three dimensions as well; the microphysics settings are the same as those in the 4 km models.

4. Precipitation comparisons

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

4.1. Mean precipitation

Domain-mean precipitation rate (including land points, which are excluded in later analysis) over the 10 days starting on 6 April 2009 is shown in Figure 1. TRMM observations show little trend over the first five days and then an upward trend in the latter half of the period, with some diurnal-cycle variability evident among other oscillations. Both 4 km model versions have a significantly higher domain-mean precipitation rate than TRMM, whereas the 12 km param model (which has parametrized convection) averages only slightly higher than TRMM over the whole domain. The 4 km model versions also exhibit a strong spin-up from almost no precipitation at the start to a very strong maximum and then a partial reduction towards an equilibrium value within the first day. In the 12 km run with explicit convection (12 km 3Dsmag), the domain-mean precipitation rates are similar to the 4 km runs and even slightly larger. TRMM shows good agreement with two other satellite precipitation products averaged over this domain for this time period: daily-average GPCP and six-hourly PERSIANN both exhibit the same multi-day variability and vary by at most 0.04 mm h−1 from TRMM (not shown). Our results are not sensitive to the specific subperiod of time chosen within the case study; the analysis shown and discussed in the rest of the article excludes the first day because of spin-up.

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Figure 1. Domain-mean precipitation rates (including land points) in mm h−1 for four Cascade runs and TRMM merged precipitation data for 10 days starting on 6 April 2009.

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The explicit convection runs likely have more domain-mean rainfall because the explicit convective plumes are wider than typical convective plumes in nature (which usually have cores of a few hundred metres across: LeMone and Zipser, 1980), meaning, at least initially, that there is more build-up of CAPE and also perhaps less lateral fractional entrainment for a given plume. These explicit convection runs are able to maintain this higher domain-mean rainfall despite the same lateral boundary conditions largely because there is feedback between stronger rainfall and the ‘circulation’ through the outer model rim, as explained above in section 3. We have checked this by comparing the advection of moisture over the whole domain for the 4 km 3Dsmag model and the 12 km param model and we have found that this term dominates the differences. The average mass convergence measured around the inside of the shared inner domain shows increased convergence between 2–7 km in the 4 km 3Dsmag model. There are also contributions from slightly higher surface evaporation rates and a reduction of total column water vapour in the 4 km 3Dsmag run.

The models also exhibit a diurnal cycle that is too strong and too regular relative to TRMM (especially given that these are not local times but rather GMT). This is particularly true for the 12 km param model, which has an extremely regular diurnal cycle over the Maritime Continent land areas (Love et al., 2011).

Domain-mean precipitation rates over only sea points (not shown) are overall similar to Figure 1, especially for TRMM, which shows very little difference. The 12 km param model is about 0.01 mm h−1 larger on average over sea, whereas the explicit convection models are approximately 0.01 mm h−1 smaller over sea points. The shape of the diurnal cycle is a more-regular sinusoid over sea points for all models and, to a lesser extent, for TRMM as well. The fact that the parametrized and explicit convection models become slightly closer to each other in terms of mean precipitation when land points are excluded agrees with the findings in Stephens et al. (2010).

4.2. Precipitation distributions

Figure 2(a) shows probability densities of precipitation rates (excluding zero values as described in section 2) averaged onto a 1° grid with 3 h temporal averaging. The precipitation rate has been aggregated into logarithmic-spaced histogram bins (with just over 26 bins per decade) and the probability density in each rain-rate bin is calculated as

  • equation image(1)

where P(r) is the probability density in the bin with mean rain rate r, n(r) is the number of measurements in that bin, Δr is the bin width for that bin measured in mm h−1 and N is the total number of included measurements. Dividing by bin width is important because the bin width is not constant but rather increases in size logarithmically, so failure to normalize by bin width would result in a change in the slope of the plotted curve equivalent to adding −1 as compared with the same data plotted with equally spaced bins.

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Figure 2. Precipitation distributions: (a) probability densities in (mm h−1)−1, and (b) fractional rainfall amount densities in (mm h−1)−1 for two 12 km Cascade runs, two 4 km Cascade runs and TRMM merged precipitation data over sea points, on a 1° latitude/longitude grid and 3 h time averages for 9 days starting on 7 April 2009. Triangles indicate specific rain rates discussed in the article.

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TRMM probability densities show roughly power-law behaviour at lower rain rates with an exponent of approximately −1, before decreasing more rapidly at higher values. Probability densities are much higher at high rain rates for the 4 km model runs and TRMM, while below about 1 mm h−1 the 12 km param model probability densities are much higher than those of the observations and other models. The fact that the 12 km param model has far too much light rain agrees with many earlier analyses of weather and climate models using convective parametrizations (Dai and Trenberth, 2004; Sun et al., 2006; Wilcox and Donner, 2007; Stephens et al., 2010).

The differences discussed above are even clearer when the probability densities are multiplied by bin-average precipitation rate and divided by domain-mean precipitation rate, yielding a fraction of total precipitation per rain rate contributed by each bin:

  • equation image(2)

where M is the mean rain rate for all included values (over sea, > 0.04167 mm h−1) and R is the sum of all of these values, as shown in Figure 2(b). To aid interpretation of Figure 2, the values of {N, M} for the 12 km param, 12 km 3Dsmag, 4 km 2Dsmag, 4 km 3Dsmag and TRMM data are {186616, 0.44}, {52071, 2.11}, {120102, 0.88}, {64130, 1.56} and {73281, 0.90}, respectively, with M in units of mm h−1. The total number of possible values, including zero points, is 315 311 for each data source. These numbers are consistent with the discussion above regarding model differences, with more occurrences of rainfall at a fairly low average rain rate (near the apparent preferred scale) for the 12 km param model and fewer occurrences with higher average rain rates for the observations and the explicit convection models (with especially infrequent, heavy rain for the 4 km 3Dsmag and 12 km 3Dsmag models).

The rough power-law relationship of −1 for lower values of precipitation for both the 4 km runs and TRMM (Figure 2(a)) compares well with rain-rate distributions from several tropical observation sites (Peters et al., 2010, their figure 1). This value of the power law implies an equal contribution at all scales, which is evident in Figure 2(b). In contrast, the 12 km param model curve has a pronounced bump around 0.4 mm h−1 (10 mm day−1), implying a preferred rainfall rate (there are also less-pronounced peaks for the observations and the other model versions, particularly the 12 km 3Dsmag and 4 km 3Dsmag models, which could be slightly preferred rain rates as well). Precipitation distributions for a 40 km version of the model for the same case study and using parametrized convection (not shown) were very close to the 12 km param model distributions. This apparent preferred scale at low-to-moderate rain rates may be because the convective parametrization is constrained by the CAPE closure (with a 30 min relaxation time scale in this case) and a lack of mesoscale organization and its associated positive feedback between precipitation and local circulation/water vapour to produce steadier rainfall around the amount required by radiative cooling over the domain, given the convective area. Compare Figure 2(b) with figure 4 of DeMott et al. (2007), which shows scale-free rain amounts for observations (GPCP) and the superparametrized Multi-scale Modeling Framework (MMF) but not for the regular Community Atmosphere Model (CAM) in the western Pacific, which has a bump around 10 mm day−1 similar to our 12 km param distribution. A possible explanation for similarity in the preferred rain rate for the CAM in the western Pacific in DeMott et al. (2007) and our 12 km param model is that the parametrized convection in both models is too tightly constrained to balance the large-scale forcing. Note that over the Amazon basin, however, even the MMF and observations have a bump around this precipitation rate.

A mechanism suggested by DeMott et al. (2007) to explain the CAM behaviour relates to the fact that the parametrized plumes in that model's convective parametrization (Zhang and ???McFarlane, 1995) must detrain above the level of minimum saturated moist static energy. Since the deep convection scheme must consume CAPE, the entrainment values for the plumes are forced to be very small in order for the plumes to maintain buoyancy while satisfying this constraint on the height of detrainment. Therefore, deep convection forms too quickly before shallow convection has had a chance to moisten lower levels, causing rain to occur too soon, too often and too lightly, and there is a corresponding lower- and mid-level dry bias. This mechanism could theoretically explain the behaviour of the 12 km param model, which also rains in relatively dry tropospheric air; however, our 4 km 2Dsmag model has a large dry bias (see section 5.1) and still gets the rainfall distribution right, suggesting that there is perhaps something more universal that is allowed (or not forced to occur) in explicit convection, at least in the UM.

Note that the value of the preferred rain rate for the 12 km param model depends on horizontal averaging: when the model distributions are plotted using a 15 min averaging interval and the original 12 km param and 4 km grid size values, the 4 km 3Dsmag model again shows no preferred scale across a large range, while the 12 km param model has a peak that has shifted to values between about 2 and 4 mm h−1 (not shown). When the same 15 min data are plotted using 1° values (not shown), the plots look very similar to the 3 h average 1° grid, showing that this is a horizontal and not a temporal resolution dependence. A likely explanation for this is that, although on larger scales the average precipitation is constrained by radiative–convective equilibrium (and therefore agrees well with both M in the model and the preferred rain rate in the warm pool region in DeMott et al. (2007)), on smaller scales there is some mesoscale circulation feedback that allows some grid cells to rain at a consistently heavier rate at the expense of more-distant cells (typically, the heavier rain in the model occurs along lines of low-level convergence).

Field and ???Shutts (2009) found that instantaneous rain rates from UM operational forecasts with parametrized convection actually had too many instances of heavy rain relative to an idealized CSRM and satellite data. However, instantaneous rain rates from models will be sensitive to many parameters including model time step and, for parametrized convection, the number of times that the convection scheme is called as well as the relaxation time-scale for the closure; even instantaneous rain rates from observations imply a specific time scale. On the other hand, the 3 h accumulated rain-rate distributions from the parametrized convection model output in Field and ???Shutts (2009) have probability frequencies that are lower than or comparable to the CSRM instantaneous data at rain rates above 1 mm h−1 and higher at rain rates below this. Indeed, their dimensionless 3 h accumulated rain-rate distributions for the parametrized convection model output show a positive deviation from a power-law fit located at a rain rate of around 1 (the non-dimensionalized mean rain rate) similar to the findings in this article. To better compare our results with the plots in Field and ???Shutts (2009), we looked at the probability densities in Figure 2(a) using rescaled frequency (P(r) × M) versus dimensionless rain rate (r/M), as in their study, and found that our results are not affected, with the 12 km param model still having higher frequencies than the observations and other model versions for non-dimensionalized rain rates between 0.3 and 1.2 and significantly lower frequencies above 1.4 (not shown).

4.3. Sensitivity to subgrid-scale mixing

As discussed above, one of the 4 km model versions (the 4 km 3Dsmag model) uses 3-D Smagorinsky subgrid-scale mixing instead of using a traditional boundary-layer scheme for vertical mixing and only 2-D Smagorinsky (for horizontal mixing). This was motivated by results from smaller idealized convection runs showing that there is more-realistic moistening of the lower troposphere in 3-D Smagorinsky simulations. In the large-domain run, the 4 km 3Dsmag version has larger areas of convective organization (including a much better MJO simulation) and larger areas of suppressed convection than the 4 km 2Dsmag run (Holloway, Woolnough, and Lister, 2012; pers. comm.). In this respect, the 4 km 3Dsmag run is closer to observations than the 4 km 2Dsmag run. Also, the 4 km 3Dsmag run has fewer areas of very isolated showers. Figure 2 shows that this also corresponds to 4 km 3Dsmag precipitation distributions, which are somewhat too low at very low precipitation rates and slightly too high at higher rates relative to TRMM data. However, this might be expected for explicit convection with a grid size greater than 200 m or so, since large amounts of instability must build up until the entire grid cell begins to undergo convection, at which point rain rates can be quite strong; Khairoutdinov et al. (2009) found that small-scale water vapour and cloud properties did not approach convergence until at or below 200 m grid spacing for explicit convection simulations of tropical maritime convection ranging from 100–1600 m, although there were very similar equilibrium large-scale rain rates. Indeed, the 12 km 3Dsmag explicit convection run, also using 3-D Smagorinsky mixing, has a precipitation distribution shifted even further toward higher values. Zhou et al. (2007) also found that CSRM simulations over the South China Sea produced greater fractions of their rainfall from high rain rates than did satellite observations.

5. Vertical structure by precipitation rate

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

The tendency to have a preferred scale of rainfall rate and, relatedly, to have too much light rain and too little heavy rain compared with observations seems to be a consequence of parametrized convection over the Indo-Pacific warm pool. In this section, we analyze the vertical structure of the different model runs and analyses to compare different convective regimes and the processes operating within them. The goal is to suggest possible mechanisms generating the differences in precipitation distributions as well as the consequences for larger scale waves and dynamics of different distributions of precipitation and associated heating, moistening and circulation patterns.

5.1. Environmental conditions by rain rate

Many studies have found a strong relationship between tropospheric water vapour and precipitation in observations (Bretherton et al., 2004; Peters and Neelin, 2006; Holloway and Neelin, 2009) and CSRMs (Grabowski, 2003; Derbyshire et al., 2004; Bretherton et al., 2005). However, parametrized convection usually lacks this strong relationship; in particular, models with parametrized convection tend to have too much light rain falling in relatively dry tropospheric conditions (Thayer-Calder andRandall, 2009, their figure 4). The sensitivity of convection to free-tropospheric moisture has been shown to depend largely on entrainment within convective parametrizations and simple plume models, with larger entrainment rates yielding a greater, more-realistic sensitivity to moisture by helping to suppress deep convection in regions with drier free-tropospheric air (Derbyshire et al., 2004; Holloway and ???Neelin, 2009).

To investigate the distribution of subsaturation (the relevant quantity to assess the impact of entrainment drying on convective plumes), Figure 3 shows the saturation deficit, defined as qsq, where q is the specific humidity and qs is the saturation specific humidity, for the different model runs and ECMWF analyses at different precipitation rates. (Note that we use TRMM precipitation for the ECMWF panel, not the precipitation in the analyses themselves, since the satellite observations should be closer to truth than the operational analyses; ECMWF column water vapour agrees well with TMI column water vapour, as discussed above in section 2, and Holloway and ???Neelin (2009) showed that column water vapour is highly correlated with lower-free-tropospheric moisture in tropical radiosonde data). Standard-error values are nearly all below 0.1 g kg−1, with a few values reaching 0.6 in the highest precipitation bins because of the limited sample numbers. The 4 km 3Dsmag run shows significant subsaturation at mid-levels for light rain rates (below 1.0 E−1 mm h−1) but much lower values at moderate and high rain rates, which is overall very similar to the ECMWF analyses. In contrast, the 12 km param run shows much drier free-tropospheric air at low and moderate rain rates (below 3.0 E−1 mm h−1). On the other hand, the 4 km 2Dsmag run shows even drier lower-free-tropospheric air for nearly all precipitation rates. The 12 km 3Dsmag explicit convection run also shows a fairly dry lower free troposphere, although it has a fairly small gradient of saturation deficit with increasing precipitation rate at these levels, which agrees more with ECMWF.

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Figure 3. Saturation deficit composited on precipitation rate using 1° 3-h averages over sea for (a) 4 km 3Dsmag, (b) 4 km 2Dsmag, (c) 12 km 3Dsmag, (d) 12 km param and (e) ECMWF analyses (conditioned on TRMM precipitation) for 9 days starting on 7 April 2009. Triangles indicate specific rain rates discussed in the article.

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Since the 4 km 2Dsmag run still produces a precipitation distribution similar to observations, it seems unlikely that the sensitivity of convection to free-tropospheric moisture is the main process lacking in the 12 km param convection that would be needed to reproduce a realistic precipitation distribution. However, it is a likely candidate for other problems, shown in both the 12 km param and 4 km 2Dsmag runs but not the 4 km 3Dsmag run, in large-scale organization, including MJO propagation (Holloway, Woolnough, and Lister, 2012; pers. comm.). These composites of subsaturation are also important in understanding the implications of the different precipitation distributions for large-scale waves and circulations (discussed in section 6 below). One feature of Figure 3 is that the 12 km param model is even closer to saturation than ECMWF and all of the other models for rain rates greater than 2.0 E+0 mm h−1 and vertical levels in range 600–400 hPa; this unique behaviour in the parametrized convection run at high rain rates is discussed further below as an indicator of a possible mechanism for its lack of heavy rain.

Moist static energy (h) and saturation moist static energy (hs) profiles are shown in Figure 4, with corresponding profiles of saturation deficit and relative humidity shown in Figure 5, for the overall mean and five selected rain rates for the 12 km param, 4 km 3Dsmag and 4 km 2Dsmag models. Standard-error values are all below 0.2 kJ kg−1 for Figure 4 and below 0.1 g kg−1 and 1% for Figure 5, with values approaching 1 kJ kg−1, 0.5 g kg−1 and 7%, respectively, at 1.0 E+1. Note that we exclude the 12 km 3Dsmag model from further analysis for visual clarity and because that model was primarily run as confirmation of our hypothesis that explicit versus parametrized convection made a much bigger difference in behaviour than horizontal resolution.

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Figure 4. Moist static energy (dash–dotted lines) and saturated moist static energy (solid lines) composites for the mean and five rainfall bins using 1° 3 h averages over sea for 9 days starting on 6 April 2009.

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Figure 5. Saturation deficit (dash–dotted lines, lower axes) and relative humidity (solid lines, upper axes) composites for the mean and five rainfall bins using 1° 3 h averages over sea for 9 days starting on 6 April 2009.

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Since hs depends only on temperature and pressure, the h and hs curves together can be used to estimate the undiluted CAPE and convective inhibition for parcels lifted from various levels in the boundary layer. A lifted parcel will approximately conserve h, and once it has reached saturation its buoyancy is proportional to its difference from the hs curve; undiluted CAPE is proportional to the positive area between this curve and a straight vertical line drawn from a boundary-layer h value. However, this does not include the effects of entrainment mixing, which are likely to be very important where there are large saturation deficits.

Comparing the mean values for the three model versions, there is a significantly lower amount of h through much of the troposphere for the 4 km 3Dsmag model and 4 km 2Dsmag model, consistent with much drier air in the case of the 4 km 2Dsmag model and cooler, slightly drier air in the case of the 4 km 3Dsmag model (see Figure 5). This lower mean h may simply reflect the larger number of grid points with rain rates below 4.2 E−2 mm h−1 for the 4 km model runs, suggesting that these models are more able to produce suppressed convective regions. At upper-tropospheric levels, hs shows a slightly less-stable (more-vertical) profile for the 12 km param model. There is also a much shorter vertical distance before lifted undiluted near-surface parcels become buoyant for the 4 km 3Dsmag model (∼1 km compared with ≥2 km for the other models), possibly indicating more-realistic shallow convection. In addition, there is a sharper kink near the freezing level for the 12 km param model hs.

While mean relative humidity and saturation deficit are very similar for the 12 km param model and the 4 km 3Dsmag model, for the 4 km 3Dsmag model this deficit is steadily reduced with increasing rainfall, starting at lower levels and gradually being reduced more and more at upper levels (Figure 5). The 12 km param model has much larger saturation deficits than the 4 km 3Dsmag model at light rain rates (1.0 E−1 mm h−1 and below) and even slightly larger saturation deficits at moderate rates (3.0 E−1 mm h−1), although this model is extremely close to saturation at higher rain rates compared with the other models. The 4 km 2Dsmag version is even dryer than the 12 km param model in the boundary layer and lower troposphere at nearly all rain rates, consistent with the saturation deficit composite contours in Figure 3. This is likely due to insufficient mixing of the boundary-layer scheme for this version of the model. However, in the middle and upper troposphere at the two highest rain rates, both of the 4 km model versions are significantly drier than the 12 km param model.

While the 4 km 2Dsmag model is unable adequately to simulate large-scale variability such as the MJO, as mentioned above, it is instructive to compare thermodynamic profiles of this model with those of the 12 km param and 4 km 3Dsmag models to make inferences about why the explicit convection runs (the two 4 km runs in this case) are better able to simulate the observed precipitation distribution. For instance, there is a much more consistent temperature structure in the upper troposphere in the 4 km models across the different rain rates, with hs increasing somewhat linearly with height. In contrast, the 12 km param model has a more vertical hs profile in the upper troposphere, which becomes much warmer at high precipitation rates. One possibility is that heavy rainfall in the 12 km param model is only possible for very moist lower-tropospheric conditions where the entire region is fairly close to saturation, leading to less drying of convective plumes via entrainment and therefore warmer moist adiabats and increased convective heating which could then quickly stabilize the profile and reduce the likelihood of more heavy rain in that region.

Composites of vertical velocity on precipitation rate are shown in Figure 6 for four model versions (we do not include ECMWF operational analyses because their vertical velocity fields are likely to be more representative of the forecast model physics than the assimilated data). Standard-error values are all below 0.01 Pa s−1 for precipitation-rate bins below 2.0 E+0, with a few values reaching 0.7 for the highest precipitation bins. These velocities are the average on the 1° grid of the resolved vertical velocities. Parametrized convection yields no net vertical velocity on the resolved original grid scale, since convective updraughts are exactly balanced by downdraughts and subsidence. Explicit convection has no such imposed balance at any scale (other than mass continuity). Averaging on to the 1° grid should yield small net vertical motion associated with resolved convective motion (including compensating subsidence) in the explicit convection runs (particularly for the 4 km explicit convection runs, which have many more grid boxes in each 1° grid box). This is equivalent to the scale-separation assumption in convective parametrizations. Any differences between the 1° average vertical velocities of the parametrized convection and explicit convection models reflect weaknesses in that assumption or the effect of differently simulated convective processes on the large-scale circulation, not the averaging technique used.

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Figure 6. Pressure velocity composited on precipitation rate using 1° 3 h averages over sea for (a) 4 km 3Dsmag, (b) 4 km 2Dsmag, (c) 12 km 3Dsmag and (d) 12 km param, for 9 days starting on 7 April 2009. Contour intervals are 0.5 Pa s−1 (solid lines), 0.05 Pa s−1 (dotted lines) and 0.025 Pa s−1 (dashed lines), all starting from the zero contour (thick solid line), with positive region (downward motion) shaded. Triangles indicate specific rain rates discussed in the article.

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The 4 km 3Dsmag model and the 12 km 3Dsmag model are the only model versions to have upward velocity (at least at low levels) at all precipitation rates, suggesting explicit shallow convection at low rain rates. In fact, the 4 km 3Dsmag model and the 12 km 3Dsmag model both have percentages of rainfall contributed by the convective parametrization that range from less than 1% at and above rain rates of 1.0 E−1 mm h−1 to 5% and 3%, respectively, at 1.0 E−2 mm h−1. In contrast, the 4 km 2DSmag model, while still having less than 1% of all rainfall contributed by the convective parametrization, has about 4% of its rainfall at 1.0 E−1 mm h−1 contributed by convective parametrization and this increases to 36% as the rain rate goes down to 1.0 E−2 mm h−1, showing that much of the rain at very low rain rates is coming from the parametrization. The 12 km param model has over 99% of its rainfall contributed by the convective parametrization at all rainfall rates below 3.0 E+0 mm h−1, with this percentage dropping to as low as 86% within the range of highest model rain rates. The differences in the amount of parametrized convection at very low rain rates might also explain why there is relatively less light rain for the 4 km 3Dsmag and 12 km 3Dsmag models.

The 12 km param model tends to have relatively top-heavy upward velocity profiles for high precipitation values and subsidence at all levels for very low rain rates. For very heavy rainfall, the 12 km param model is more top-heavy than any of the explicit convection runs; we know from Figure 4 that it is warmer in the 12 km param run in the upper troposphere than for either of the 4 km runs for the highest rain rate, again suggesting that it may be very difficult to maintain heavy rain for long because of very strong stabilization effects. The different precipitation distribution in the 12 km param model relative to the other model runs (Figure 2) means that the overall pattern of resolved free-tropospheric vertical velocity in rainy regions will be much weaker than for the explicit convection runs, which will have implications for large-scale waves and circulation (see discussion in section 6 below). Another difference between the 12 km param model and the other model runs is that the 12 km param model has upward motion through most of the troposphere for rain rates greater than 2.0 E−1 mm h−1, including within the range of its preferred rain rate, whereas the other models have mid-tropospheric subsidence for rain rates below about 4.0 E−1 mm h−1.

5.2. Heating and moistening rates by rain rate

The temperature and moisture budgets for a given large-scale region can be illustrated by writing the total tendency and large-scale advection terms equal to the apparent heat source Q1, radiative heating QR and apparent moisture sink Q2 (Yanai et al., 1973; Ciesielski et al., 1999) as follows:

  • equation image(3)
  • equation image(4)

where θ is potential temperature, q is specific humidity, v is the vector horizontal velocity, w is the vertical velocity, cp is the specific heat of dry air at constant pressure, L is the latent heat of condensation, ρ is the density, Π is the Exner function defined as

  • equation image

R is the gas constant for dry air, p is the pressure and p0 = 1000 hPa is the reference pressure. {equation image} denotes the horizontal average at a single level and time over the ‘large scale’ (1° in this case). Although these equations are often used to describe the separation between resolved and parametrized processes, they can equally be used for any area average to distinguish between net advective fluxes into the area and net advective (vertical transport) fluxes, turbulent fluxes and physical processes occurring within the area. Here we apply this budget analysis to the 1° grid boxes. We will therefore use the terms ‘large-scale’ and ‘subgrid’ with this in mind, as described below. Note that we have defined QR as a separate term rather than including it as part of Q1, unlike Yanai et al. (1973) and some other articles. Q1 and Q2 can then be related to net condensation and vertical subgrid transport as

  • equation image(5)
  • equation image(6)

where c is condensation and e is evaporation of condensate; only liquid–vapour phase transitions are included in the equations for simplicity, although in the model calculations ice-phase transitions are also accounted for. {′} denotes the anomaly from the horizontal average, {equation image}, defined above.

Figures 7 and 8 show the total, subgrid-scale and large-scale heating and moistening averaged for all points below 4.2 E−2 mm h−1 and for three higher rain rates for the 12 km param and 4 km 3Dsmag runs. The total tendencies are represented by the first term in (3) and (4), with the large-scale advection represented by the second and third terms. The subgrid-scale terms (and radiation term for temperature) are shown on the right-hand side of (3) and (4), with the subgrid terms further defined by (5) and (6). Standard-error values are all below 0.5 K day−1 for Figure 7 and 0.3 g kg−1 day−1 for Figure 8.

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Figure 7. Average total temperature tendency and its three component terms (subgrid processes, large-scale advection and radiation) for four rainfall bins using 1° 3 h averages over sea for 9 days starting on 6 April 2009.

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Figure 8. Average total moisture tendency and its two component terms (subgrid processes and large-scale advection) for four rainfall bins using 1° 3 h averages over sea for 9 days starting on 6 April 2009.

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The subgrid terms in Figures 7 and 8, given by (5) and (6), are calculated by first adding the increments to temperature and moisture from the convective parametrization, boundary-layer/large-scale cloud scheme (including vertical subgrid turbulence mixing and surface temperature and moisture fluxes), large-scale rain scheme and horizontal subgrid turbulence mixing (which is very small and is not used for the 12 km param model). Note that ‘large-scale’ in ‘large-scale rain’ and ‘large-scale cloud’ refers to the original model grid scale and these schemes are considered ‘subgrid’ processes here. Additionally, the last term in (5) and (6), the vertical transport of heat and moisture from subgrid-scale motions, while partly included in the convective, mixing and surface-flux terms already accounted for in the model increments listed above, will also be partly represented by explicit vertical advection at each model's own grid scale. This is particularly true for the explicit convection runs. To account for this, we estimate these explicit subgrid fluxes from instantaneous anomalies every hour within each 1° large-scale grid box and then terms are averaged over three-hourly periods and added to the other subgrid terms listed above.

The large-scale advection terms are calculated by using the temperature and moisture increments from the advection scheme and then subtracting the explicit subgrid vertical transport term, calculated as described above. The radiation and total tendency terms come directly from model increments, with any small residuals in the budget added to the subgrid term so that the budget is balanced. All terms are averaged onto the 1° grid scale and three-hourly temporal scale.

For heating, the subgrid term for the 4 km 3Dsmag model is mainly made up of a combination of the large-scale rain scheme and the boundary-layer/large-scale cloud scheme, while for moisture the transport term is also important. The 12 km param subgrid heating and moistening is dominated by the convection scheme at all rain rates except in the boundary layer, where the boundary-layer/large-scale cloud scheme is also important.

In most cases, the subgrid terms nearly balance the large-scale advection, although radiation is sometimes important in the heating budget, particularly at lighter rain rates (also note that the subgrid and large-scale advection terms are plotted on a larger scale at high precipitation rates). At very low rain rates (below 4.2 E−2 mm h−1 or 1 mm day−1, where deep convection is likely suppressed), the two models are mostly in agreement, although there is slightly more warming and drying above the boundary layer for the 12 km param model in the total field increments. At light to moderate rain rates (1.0 E−1 and 3.0 E−1 mm h−1) the total increments show that the 4 km 3Dsmag run has significant drying and warming in the boundary layer (below 2 km) that is not seen in the 12 km param model, as well as more warming and slightly more moistening in the lower free troposphere. This is associated with subgrid increments with significantly more moistening in the free troposphere and drying in the boundary layer as well as mid-tropospheric cooling; these are likely related to more shallow convection and/or evaporation of stratiform rainfall (although the relationship between subgrid terms and total increments can be complicated to explain). The subgrid moistening profiles at rain rates of 1.0 E−1 mm h−1 and 3.0 E−1 mm h−1 for the 4 km 3Dsmag run are similar to the suppressed period CSRM integrations in (Woolnough et al., 2010, their figure 8), although the magnitude in the 12 km param model is more similar to those profiles at 1.0 E−1 mm h−1; the precipitation in the CSRMs compared in the suppressed period of that study ranges from 4.0 E−2 to 1.0 E−1 mm h−1. Interestingly, the lowest rain-rate bin in our Figure 8, with rain rates lower than those in the suppressed period CSRMs in Woolnough et al. (2010), shows strong moistening at levels below 2 km for both model versions, somewhat lower than shown by the CSRMs (at somewhat higher rain rates) in that study.

At large precipitation rates (2.0 E+0 mm h−1), both models show strong subgrid drying and warming throughout the troposphere, but the total increment for moisture shows significant low-level drying below 2 km and free-tropospheric moistening above 4 km in the 4 km 3Dsmag run, which is almost absent in the 12 km param run. The subgrid heating in the 12 km param model is stronger at upper-tropospheric levels (6–12 km) at the highest rain rate than in the 4 km 3Dsmag model, agreeing with the discussion in section 5.1 suggesting that more upper-tropospheric stabilization for heavy rainfall in the 12 km param model might be a cause of it being very difficult for that model to produce very heavy rain.

Note that there are obvious large differences in the vertically integrated drying and heating (which would be found by vertically integrating subgrid terms suitably weighted by density and multiplying by appropriate constants) for the two models, even at the same precipitation rate. Since the subgrid terms represent the effects of surface evaporation as well as conversion between liquid or solid condensate and water vapour, these are partly due to differences in surface evaporation rates and partly due to the advection of condensate and changes in the total condensate for a given rain rate: for instance, at 3.0 E−1 mm h−1 the 4 km 3Dsmag model has about 1.0 E−1 mm h−1 (one third) of the rain rate explained not by subgrid moisture tendencies but by the advection of liquid and ice condensate into the 1° box (7.0 E−2 mm h−1) and also a reduction in the condensate reservoir of the box (3.0 E−2 mm h−1), whereas these two terms are negligible in the 12 km param model. The mid-tropospheric cooling and moistening in the subgrid terms show the effects of evaporation of these falling hydrometeors.

The subgrid heating profiles at the highest rain rates are more top-heavy than some of the Tropical Ocean Global Atmosphere Program's Coupled Ocean Atmosphere Response Experiment (TOGA-COARE) estimates and Goddard Cumulus Ensemble (GCE) CSRM estimates in Shige et al. (2007), although their shapes are similar to TRMM estimates of latent heating in that study over various tropical convective regions. The top-heaviness of the heating profiles agrees with the overall conclusion of Lin et al. (2004) that MJO active events have more upper-tropospheric heating, relative to total heating, than mean tropical rainfall. However, it is difficult to compare these profiles directly with most other studies, since these are averages not over regions but rather over rainfall regimes, which occur over many locations and times.

For the subgrid heating and moistening terms, the models agree fairly well at the highest precipitation rate but are very different at the lower rain rate of 1.0 E−1 mm h−1. The 4 km 3Dsmag model exhibits cooling and moistening at mid-levels and warming and drying at low levels, while the 12 km param model shows much less, with perhaps slight warming and moistening at low levels for subgrid terms. Looking only at what convection does directly (the subgrid terms), the 4 km 3Dsmag model would seem to take longer in the transition to deeper convection, while the 12 km param model might transition to deep convection more quickly (especially given its tendency to rain at profiles with dryer mid-levels anyway, likely due to small entrainment rates) and therefore might consume CAPE more quickly and not maintain extended heavy precipitation because the CAPE never builds up, similar to arguments from DeMott et al. (2007). However, it is not as simple as arguing that rainfall occurs only in moister tropospheric conditions, since the 4 km 2Dsmag model still gets the rainfall distribution right. There may be other reasons why CAPE is able to build up for longer locally before heavy rain begins. However, there may also be reasons why rainfall in the 4 km models is able to release potential energy from larger regions via circulation feedback, which does not operate to the same degree in the 12 km param model.

6. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

The different heating and moistening profiles in the 12 km param and 4 km 3Dsmag model runs, as well as the differences in environmental conditions, have important implications for larger scales. This is true both because of the differences at any given rain rate and because of the differences in the rainfall distributions, meaning that the 12 km param model will have more regions dominated by conditions and increments typical of its preferred, fairly light, rain rate (represented in Figures 7 and 8 by the composites around 3.0 E−1 mm h−1). In the first instance, the discussion in section 5.2 above points out differences at the lightest rain rates (in relatively suppressed conditions) that would tend to cause more mid-level moistening and destabilization of the column in the 4 km 3Dsmag run relative to the 12 km param run, allowing for a transition to less convectively suppressed conditions.

The second consideration above stems from the fact that the 12 km param model has far less heavy rain than the 4 km 3Dsmag run. Even though both models have at least some drying throughout most of the lower troposphere at high rain rates, the 12 km param model has relatively few regions with these tendencies. As a result, it is likely more difficult for active convective regions to become drier and transition to more suppressed regions as they do in the MJO. Indeed, an upcoming paper (Holloway, Woolnough, and Lister, 2012; pers. comm.) will discuss the fact that the 12 km param model tends to maintain a single phase of the MJO rather than allowing it to propagate eastward.

The tendency for slow moistening at fairly light rain rates, drying at heavier rain rates and fairly small tendencies in between, is one possible mechanism to explain the preferred scale for the 12 km param model. However, this would be a stronger case to make for a model with a good sensitivity of convection to free-tropospheric moisture, which does not appear to be present in the 12 km param model based on Figure 3.

At the same time, in section 5.1 and section 5.2 it is argued that at high rain rates more heating at upper levels in the 12 km param model may lead to increased stabilization and a quick termination of any very heavy rainfall (although greater lower-level drying in the 4 km 3Dsmag model could do the same thing). The 12 km param model also has a much weaker pattern of resolved vertical velocity associated with nearly all rain rates (Figure 6): there is much less upward motion at higher rain rates than for any of the other models and less subsidence at very low rain rates than for the 4 km 3Dsmag or 12 km 3Dsmag models. This will be skewed even further toward areas of low vertical motion by the fact that the 12 km param model has a rainfall distribution more tightly distributed around low-to-moderate rain rates. At large rain rates, the 12 km param model has a more-top-heavy upward vertical velocity pattern as well, which agrees with relatively strong heating at upper-tropospheric levels. Taken together, these patterns suggest that the 12 km param model has a weaker overturning circulation between active and suppressed convective regions than the other models, with an added possibility that stabilization occurs more quickly at very high rain rates.

Although not shown, the increments for temperature and moisture in the 4 km 2Dsmag run at higher rain rates are broadly similar to those of the 4 km 3Dsmag run: much more lower-level drying and mid- to upper-level moistening in the total moisture increment and less-top-heavy warming in the subgrid temperature increment. Since we know that the 4 km 2Dsmag run has a very poor sensitivity of precipitation to moisture (Figure 3) and yet still produces a very realistic precipitation distribution (Figure 2), this is again a hint that these differences may be important for simulating precipitation distributions.

To show how the combination of differences in moistening rates at a given precipitation rate and differences in precipitation distributions gives important overall differences in moistening for the two models, Figure 9 shows the total moisture tendency for rainfall rates below 4.2 E−2 mm h−1 and the three terciles between this value and the maximum precipitation rate. Standard-error values are all below 0.05 g kg−1 day−1. The figure shows that the 4 km 3Dsmag model has significantly more low-level drying at all three rainy terciles than the 12 km param model, which must do virtually all of its drying in the suppressed regions. There is also more moistening in the upper terciles for the 4 km 3Dsmag model and it is relatively higher in altitude. Note that there are many more rainy values in the 12 km param model: 19.7% of the total values are in each tercile, with 40.9% in the suppressed, low-precipitation bin. For the 4 km 3Dsmag bin, these numbers are 6.8% and 79.6%, respectively. The mean moistening rate over all values, which is obtained by taking a weighted average of the four curves in Figure 9 using the population percentages given above as weights for each model, is small: about 0.07 g kg−1 day−1 in the lower free troposphere in the 12 km param model and 0.02 g kg−1 day−1 in the troposphere for the 4 km 3Dsmag model. The upper limits of the three terciles for the 12 km param and 4 km 3Dsmag models are {0.18, 0.46, 11.04} and {0.34, 1.53, 23.42}, respectively, and the mean rainfall in the low-precipitation bin and the three terciles for those two models are {0.0083, 0.10, 0.30, 0.93} and {0.0024, 0.14, 0.83, 3.71}, respectively, with all units in mm h−1.

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Figure 9. Average total moisture tendency for rainfall rates below 4.2 E−2 mm h−1 and the three terciles between this value and the maximum precipitation rate for two model versions using 1° 3 h averages over sea for 9 days starting on 6 April 2009.

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Comparing the terciles with the total tendencies in Figure 8, tercile 1 looks similar to the 1.0 E−1 bin for both models, as might be expected from the similar mean rain rates. Tercile 2 looks similar to the 3.0 E−1 bin for the 12 km param model and the mean rain rates are also the same, but for the 4 km 3Dsmag model tercile 2 is signficantly different from the 3.0 E−1 bin and has some similarities to the 2.0 E+0 bin, as might be expected from the fact that all of its values lie in between the means of those two bins. Tercile 3 looks similar to 2.0 E+0 for the 4 km 3Dsmag model, although its mean rain rate is significantly larger. For the 12 km param model, tercile 3 has a moistening profile that is lower in altitude and has significantly less low-level drying than the 2.0 E+0 bin, and its mean rain rate is only about half. Similar curves for the total temperature tendencies (not shown) are fairly similar to the three rainy bins in Figure 7 except that the tercile 3 heating rates look more similar to the 3.0 E−1 bin for the 12 km param model. Overall, it is clear that the moistening profiles and terciles are shifted to much lower rain rates for the 12 km param model than for the 4 km 3Dsmag model. These curves support the idea that it should be more difficult for rainy regions to dry out at lower levels and for suppressed regions to moisten above the boundary layer in the 12 km param model relative to the 4 km 3Dsmag model, perhaps leading to preferred rain rates and insufficient convective transitions.

In any event, the different distributions of rainfall lead to different distributions of heating and moistening, which will be important to large scales. Whether or not these are also a cause of the difference in rainfall distribution is a more difficult question.

7. Summary and conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

We compare limited-area simulations of the tropical atmosphere over a very large domain at several different horizontal resolutions and with both parametrized and explicit convection versions for a 10 day MJO case study in April 2009. We then compare these simulations with TRMM precipitation observations (as well as other fields taken from ECMWF operational analyses and TMI satellite data) for the same domain and time period. The main finding of this article is that the explicit convection simulations have precipitation distributions that are much more similar to observations than the simulations with parametrized convection. This is true even though at least one of the simulations with explicit convection has a relationship between rainfall and lower-tropospheric moisture that is arguably even worse than the run with parametrized convection (and has a worse MJO, as discussed in Holloway, Woolnough, and Lister, 2012; pers. comm.).

The precipitation distribution in the parametrized convection run (shown at 12 km grid spacing, but similar to a run performed at 40 km grid spacing) exhibits relatively too much rainfall at light and moderate rain rates and too little rainfall at high rain rates. Indeed, while all of the explicit convection runs have probability densities similar to a power-law distribution across most of their range before decreasing exponentially at very high rain rates (similar to recent observational studies; DeMott et al., 2007; Field and Shutts, 2009; Peters et al., 2010), the 12 km param model with parametrized convection has a positive deviation from this type of power-law distribution, suggesting a ‘preferred’ scale of rainfall centred around 0.4 mm h−1 (10 mm day−1). Rainfall rates around this value contribute more to the total rainfall in this model than higher or lower rates, unlike observations and explicit convection runs.

Although we do not show conclusive evidence for a specific mechanism that is lacking or incorrect in the parametrized convection run and would lead to this incorrect precipitation distribution, we do suggest several possible reasons for it. It is possible that the 12 km param model consumes CAPE and reaches radiative–convective equilibrium too quickly, preventing interactions between rainfall and circulations across many scales. For instance, Pearson et al. (2010) showed that explicit convection in a similar model configuration captured the observed progression of diurnal convection over West Africa from small to large scales, whereas parametrized convection models did not. There are also indications that the 12 km param model (compared with the explicit runs) lacks the ability to transition from suppressed to active conditions and vice versa, which would tend to maintain rainfall at an average, preferred rain rate.

Although the 4 km 3Dsmag run appears to have a better, more realistic relationship between precipitation and moisture than the 12 km param run, this does not seem to be the main factor in determining the ability to simulate observed precipitation distributions because, as mentioned above, the 4 km 2Dsmag run is able to simulate the observed precipitation distribution while having a very poor sensitivity of precipitation to moisture. Comparisons between the two 4 km runs and the 12 km param run suggest that there may be processes that require extremely high tropospheric humidity and/or result in very large upper-tropospheric heating and stabilization in order to produce very high rain rates for the 12 km param model, preventing those rates from occurring very often.

We hope that this study will encourage more comparisons between cloud-system-resolving simulations of large convective regimes, parametrized convection simulations and observations, to improve both parametrizations and high-resolution simulations with explicit convection.

Acknowledgements

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References

We have used ECMWF operational analyses from the YOTC online archive. We thank Adrian Matthews for discussions and for providing rainfall data in NetCDF format. Thanks to Thorwald Stein, Richard Allan and Jon Petch for useful comments. This work made use of the facilities of HECToR, the UK's national high-performance computing service, which is provided by UoE HPCx Ltd at the University of Edinburgh, Cray Inc and NAG Ltd, and funded by the Office of Science and Technology through EPSRC's High End Computing Programme. This research was carried out with funding from NERC grant NE/E00525X/1.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data
  5. 3. Model set-up
  6. 4. Precipitation comparisons
  7. 5. Vertical structure by precipitation rate
  8. 6. Discussion
  9. 7. Summary and conclusions
  10. Acknowledgements
  11. References
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