1 The Treatment of Convection in the Next
2 Generation Global Models: Challenges
3 and Opportunities
4
5 Samson Hagos*, Robert Houze*,# , Zhe Feng*, and Angela Rowe#
6 *Pacific Northwest National Laboratory
8
9
10 Corresponding Author Address
11 Samson Hagos
12 Pacific Northwest National Laboratory
13 902 Battelle Boulevard
14 Richland WA 99352
15 Email: [email protected]
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16 Abstract
17 Cloud-permitting global modeling is becoming a new reality, and rethinking of the
18 objectives, assumptions, and methods of convection parameterizations is already occurring.
19 Models must now represent sub-grid and resolved processes as part of the same continuum.
20 Scale separation, commonly used in the past, must be replaced by the stringent requirement of
21 "scale awareness." To this end, advances are needed in understanding of transitions in cloud
22 populations including boundary-layer evolution, deepening of initially shallow clouds,
23 formation of precipitation and cold pools, and modes of growth and aggregation of clouds to
24 form mesoscale units of convection, which have dynamical features larger than individual
25 clouds.
26 Such advances require appropriate observational data collection, processing, and
27 packaging strategies that include the development of merged datasets on convective and
28 microphysical processes along with the environmental context. In particular, concurrent ice-
29 phase microphysical processes and corresponding updraft and downdraft statistics are needed
30 but presently missing. These observational gaps need to be filled by increasingly advanced
31 remote-sensing techniques and research aircraft capable of penetrating intense convection at a
32 wide range of altitudes. This new information, provided on the scales most relevant to
33 parameterization, can be related to model output via advanced radar and satellite simulators that
34 convert model output to observable variables. Because no single observational method is self-
35 sufficient, field experiments that integrate as many instrument platforms as possible, including
36 emerging technologies, will remain a necessary avenue for model validation and hypothesis
37 testing. This holistic approach will accelerate parameterization development by allowing
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38 validation of a hierarchy of modeling frameworks using the multi-variable data collected in
39 similar environmental conditions.
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40 1. Introduction
41 For decades, the development of convective cloud parameterizations in global models
42 has implicitly or explicitly relied on a perceived spatio-temporal scale separation, in which the
43 resolved large-scale environment is in a statistical equilibrium with the unresolved cloud
44 processes. Such an approach, however, neglects the continuum of scales of motion shown by
45 field programs and satellite remote sensing to be present in convection. As shown in the satellite
46 image in Figure 1, convective cloud populations are a mix of cumulus, cumulonimbus, and
47 mesoscale convective systems. It is well known that the larger forms of these convective clouds
48 (e.g., mesoscale convective systems, MCSs) can evolve upscale from the smaller elements.
49 Furthermore, understanding and accurately representing the processes involved in the
50 transitions from shallow non-precipitating clouds to precipitating shallow clouds to deep
51 convection to MCSs and planetary scale phenomena, such as the Madden-Julian Oscillation
52 (MJO), is important for accurate representation of the mean state of the climate, its natural and
53 forced variability, and for the prediction of drought and extreme precipitation events.
54 The need to understand how convective processes interact across a continuum of scales
55 and with the atmospheric circulation and climate intersects with the current state of global
56 modeling. Rapid expansion of computational resources is pushing global model resolution into
57 a "gray zone," where some aspects of convection are partially resolved and the traditional scale-
58 separation argument no longer applies. The next generation of global models, here defined as
59 those with grid spacing of 1–10 km, urgently requires novel strategies of combining
60 parameterization and explicit representation of convection processes that represent convective
61 cloud populations and variability consistent with observations.
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62 The overview presented here is motivated by discussions at a U.S. Department of
63 Energy -supported workshop aimed at devising strategies for addressing these issues. The
64 complete workshop report from which much of the material discussed in this article is derived
65 will be available at http://asr.science.energy.gov/publications/program-docs. The premise of the
66 workshop was that a complete strategy for improving the treatment of convection in the next-
67 generation global models could be organized into three intersecting core themes:
68 • Basic understanding of cloud processes
69 • Parameterization
70 • Collection, processing, and analysis of observational data.
71 Equally important is that these three topics need to be approached in a coordinated way,
72 with particular attention to the integration of each of the three core themes with the others, as
73 depicted in Figure 2. With this framework in mind, this overview attempts to address the
74 following questions:
75 • What are the current challenges in each of the three core themes and their integration?
76 • What can be done in the short term (~3 years) using existing resources, and what new
77 capabilities and/or long-term (~10 years) investments are required to address these
78 challenges?
79 This article is intended to serve as a road map for integrated research and development
80 activities aimed at accurate treatment of convection in the next-generation (1–10 km grid
81 spacing, non-hydrostatic dynamical core) global models. In the remainder of the article,
82 convection-related issues in current global models are briefly reviewed, the challenges in the
83 three core themes and their integration are identified, and strategies for meeting these challenges
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84 are laid out. Finally, the article concludes with a summary of short- and long-term activities that
85 could improve the understanding and model representation of convective clouds.
86 2. Issues associated with convection in current global
87 models
88 As a primary mechanism of heat transport between Earth’s surface and upper atmosphere,
89 convection is a critical component of the climate system and its variability. As a result,
90 limitations in our understanding and representation of convection in global models are
91 manifested in the biases in the simulated climate. The list of convection-related biases in
92 present-day global models is extensive and covers all spatio-temporal scales. Highlighted below
93 are a few of the most prominent biases.
94 a) Diurnal cycle
95 Convection often initiates as shallow cumulus clouds in response to solar forcing. The
96 shallow clouds mostly constitute a field of transient clouds, each bubbling up then dying out
97 quickly, while a few last longer and may grow larger to individual congestus or isolated
98 cumulonimbus clouds in late afternoon if humidity in the lower free troposphere is sufficiently
99 high. Many of these taller clouds decay near the area of the clouds' initiation, while others
100 organize into MCSs, propagate over long distances, and precipitate well into the next morning.
101 Thus, the diurnal cycle of precipitation varies with the scale of the convective entity.. Figure 3
102 shows the diurnal cycle of precipitation over the Southern Great Plains (SGP) of the U.S. from
103 observations and CMIP5 models and seasonal cycle of surface temperature. Globa models such
104 as those in CMIP5 do not explicitly account for MCSs, which produce over half of the warm
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105 season precipitation (Fritsch et al. 1986; Nesbitt et al. 2006) and most often occur at night
106 (McAnelly and Cotton 1988; Carbone et al. 2002). As a result, nocturnal convective
107 precipitation is essentially absent in the CMIP5 models and many such models have peak
108 precipitation just before or after noon. The underestimated propagating nocturnal precipitation
109 has important implications for land-atmosphere interactions and the seasonal cycle of
110 temperature, probably contributing to the models' tendency to have a warm bias in summertime
111 near-surface temperatures on account of dry soil possibly due to low precipitation. This bias is
112 notable over the central part of the U.S. (Figure 3b).
113 b) Madden-Julian Oscillation
114 The MJO is a major component of tropical intra-seasonal variability with far-reaching
115 impacts on regional extremes such as tropical cyclone activity, atmospheric rivers, heat waves,
116 and floods (Zhang 2005, 2013). Despite extensive research over the last four decades,
117 fundamental understanding and accurate representation of MJO initiation, propagation, and
118 interaction with other regional processes remain as unmet challenges. This is mainly because of
119 the multiscale nature of the cloud processes involved and associated difficulties in
120 parameterizing these processes. Parameterizations are as yet unable to handle satellite-observed
121 multi-scale aspects of the convective population, which varies with MJO phase (Barnes and
122 Houze 2013; Yuan and Houze 2013). In particular, the cloud population is composed of
123 different proportions of shallow, deep, and mesoscale convection during each MJO phase. The
124 consequence of this problem is apparent in the comparison of MJO variance between CMIP
125 models and observations (Figure 4). While there have been improvements in CMIP5 over
126 CMIP3, the more recent models generally continue to underestimate the variance and have more
127 persistent precipitation over equatorial regions than is observed (Hung et al. 2013). Another
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128 aspect of the MJO that challenges global models is the “MJO prediction barrier” over the
129 maritime continent (Neena et al. 2014; Kim et al. 2009), where problems in the representation
130 of convection in the MJO are compounded by interactions with a pronounced diurnal cycle
131 (Johnson and Priegnitz 1981; Williams and Houze 1987) and affected by the complex
132 topography of the islands (Hagos et al. 2016).
133 c) South Asian Summer Monsoon
134 In general, precipitation issues in CMIP5 models are of two types: spread, which
135 pertains to large differences among the models that limits the confidence level in their
136 projections, and bias, which represents consistent deviations of the model results from
137 observations. These two forms of uncertainty are especially apparent in monsoon environments.
138 Consider, for example, the seasonal cycle of precipitation associated with the South Asian
139 monsoon. The solid black curve in Figure 5a shows the monthly mean precipitation averaged
140 over all of India; Figure 5b shows the same data normalized by the annual mean precipitation.
141 The CMIP5 ensemble has a very large spread in seasonal cycle amplitude, and when the
142 precipitation is normalized, it is also apparent that the onset of the monsoon is delayed in most
143 of the models.
144 3. Strategy of representing convection in the next
145 generation global models
146 Convection-related model biases in key features of climate variability are reviewed in
147 the last section. In this section, specific challenges in each of the three core themes depicted in
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148 Figure 2 that contribute to those biases are identified and strategies for addressing them in an
149 integrated way are proposed.
150 a) Improving the basic understanding of convective cloud processes
151 In order to highlight the gaps in our understanding of convection, we consider the full
152 lifecycle of convection as a starting point, which can be treated as a series of three transitional
153 processes:
154 1) Boundary layer variability and the development of precipitating shallow cumulus clouds
155 2) Transition to deep convection
156 3) Upscale growth from deep convective cells to form MCSs
157 These transitions occur under certain environmental conditions and involve feedback
158 mechanisms. The overarching challenge is determining what environmental conditions and
159 feedback processes control these transitions? Each of the above-listed transitions and the
160 specific scientific questions related to them are individually discussed below.
161 i. Boundary layer dynamics and the development of precipitating shallow cumulus
162 clouds.
163 The boundary layer contains internal instabilities that produce rolls, hexagonal cells, and
164 other features of enhanced convergence that organize clouds into patterns and make some of the
165 clouds more robust. Such dynamical transitions can occur without external forcing; however, in
166 some situations vertical wind shear plays a crucial organizing role. Highly sensitive radars
167 deployed during the AMIE field campaign have led to some advances in understanding these
168 processes over a tropical oceanic environment. Rowe and Houze (2015) have shown how the
169 boundary layer develops rolls (due to internal instability), which favor some clouds to grow
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170 deeper and precipitate. The resulting deep convection replaces boundary-layer air with
171 downdrafts on the scale of the precipitation, creating cold pools, which entirely change the
172 character of the boundary layer and dominate the formation of subsequent convection. Where
173 cold pools intersect, enhanced boundary-layer convergence often results in stronger secondary
174 convection (Feng et al. 2015), leading to a chain reaction as these intersecting cold pools trigger
175 ever-larger convective systems (Feng et al. 2015) and often leads to a cloud population
176 containing MCSs (Rowe and Houze 2015).
177 The studies of Rowe and Houze (2015) and Feng et al. (2015) were of oceanic convection;
178 similar studies are needed of boundary-layer evolution associated with cloud-field transitions
179 over continental regions. In addition to boundary-layer instabilities leading to rolls and other
180 patterns of convective initiation, the surface conditions, including land-use heterogeneity,
181 nearby water body conditions, and topographic features, also affect the formation and
182 development of cumulus clouds. Additionally, large-scale wind shear and thermodynamic
183 instability all lead to certain boundary layer dynamics affecting cloud patterns.
184 ii. Factors affect the transition to deep convection
185 One of the key challenges continuing to impede understanding of how convective clouds
186 deepen is the inability to determine the characteristics of convective updrafts and downdrafts
187 and their interactions with the environment (especially via entrainment) and microphysical
188 processes. Statistics of the intensity, size, and variation of drafts with the height and width of
189 convective clouds are inadequate to non-existent under many key environmental conditions. In
190 addition, little information exists on the internal turbulent characteristics of the drafts. As a
191 result, the factors determining the behavior of drafts in convective clouds at all stages of
192 development remain far from clear. Aircraft data (e.g., Zipser and LeMone 1980) and TRMM
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193 radar observations (Zipser et al. 2006; Houze et al. 2015) highlight the relationship between the
194 nature of precipitating convection and environmental conditions that differs between land and
195 ocean and from one climatic regime to another. For example, even though CAPE is often very
196 large over tropical oceans, observations from field programs show that undiluted ascent from
197 the boundary layer is extremely rare over tropical oceans (Zipser 2003). The most powerful,
198 nearly undiluted towers occur mainly over a few land areas (i.e., relatively dry regions near
199 major mountain ranges, Zipser et al. 2006).
200 iii. Upscale growth from deep convective cells to MCSs
201 The tendency toward upscale growth of convective entities to form mesoscale units
202 differs among open oceans, arid lands, rainforests, and monsoons (Houze et al. 2015), yet MCSs
203 are important rain producers over both land and ocean. About 30–70% of warm season rain over
204 the U.S. east of the Rocky Mountains (Fritsch et al. 1986; Nesbitt et al. 2006) and 50–60% of all
205 tropical rainfall (Yuan and Houze 2010) is produced by MCSs. An MCS often begins when
206 convective clouds rooted in the boundary layer aggregate into a unit that is 1–2 orders of
207 magnitude larger in area than an individual convective cloud. However, "elevated MCSs" that
208 develop with no connection to the boundary layer whatsoever are also important over
209 continental regions such as the U.S. (Marsham et al. 2011; Schumacher 2015). Whether MCSs
210 develop from boundary-layer-rooted convection or as elevated systems, net heating by the
211 aggregated convection eventually induces mesoscale circulations in the form of broad sloping
212 layers of up and downdraft circulations (Moncrieff 1992; Pandya and Durran 1996). The
213 induced mesoscale circulation is not generally connected to the boundary layer; rather, a lower
214 tropospheric layer up to several kilometers in depth feeds the sloping updraft, and the sloping
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215 downdraft begins in the mid troposphere. No parameterization scheme yet addresses the nature
216 of overturning induced by MCSs.
217 A key question is what determines the scale of MCSs? More specifically, how does a
218 non-uniform grouping of convective cells begin growing? One popular theory is that of "self-
219 aggregation" (e.g., Wing and Emanuel 2013) whereby small convective clouds initially
220 randomly dispersed over a broad area concentrate into a mesoscale unit that intensifies through
221 radiative/dynamic feedback. The resulting mesoscale cloud unit can then develop the mesoscale
222 dynamical circulation of an MCS. Houze et al. (2015) have noted that observations of
223 precipitating cloud populations seen by the TRMM radar suggest that conditions are especially
224 suitable for self-aggregation over tropical oceans; it is not yet clear whether self-aggregation
225 occurs in the same way over land, where there is significant diurnal variation in surface forcing
226 and a variety of land-surface and topographic conditions come into play. In realistic
227 environments over both land and ocean, the growth and propagation of MCSs is affected by
228 vertical wind shear that modifies the baroclinic generation of vorticity by the horizontal gradient
229 of convective heating (Moncrieff 1992). Additionally, the heating by aggregated convection in
230 an MCS induces a gravity wave response in the form of the intertwined layers of mesoscale
231 ascent and descent upon which convective-scale elements continue to be superimposed (Pandya
232 and Durran 1996).
233 As introduced in Section 3.a.i, another factor that plays a role in upscale growth of
234 convection to form MCSs is cold pools. As the mode of communication between precipitating
235 convective elements (e.g., Johnson and Houze 1987), cold pool dynamics and evolution need to
236 be understood within the context of the precipitating systems and their environment. Cold pools
237 are affected both by environmental shear and thermodynamic profiles and internal cloud
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238 microphysics, which in turn feed back on the precipitating system. For example, the horizontal
239 extent of an MCS depends in part on the fallout trajectories of ice particles in the overtopping
240 layer of stratiform cloud. As these fall, cooling by evaporation and melting of hydrometeors
241 determine the intensity, frequency (Hagos et al. 2014), and subsequent extent of cold pools
242 (Feng et al. 2015). More specifically, cold-pool depth partially controls gust-front speed
243 (Wakimoto 1982) and subsequent updraft formation (Feng et al. 2015). As precipitating
244 convective cells deposit cold pools in the boundary layer, they trigger new convection in the
245 vicinity of aging convection, contributing to the development, propagation, and longevity of
246 MCSs depending, in part, on lower-tropospheric vertical shear (Thorpe et al. 1980; Rotunno et
247 al. 1988).
248 Several questions related to cold pools constitute remaining uncertainties. Among those
249 are: what determines whether or not convection is initiated at a cold pool boundary? How long
250 do cold pools last? How deep are they? How strong are the updrafts they induce? What is the
251 inter-relationship among the natures of primary updrafts, downdrafts, cold pools, and secondary
252 updrafts in the process of organization? What are the relative roles of microphysical processes
253 that determine cold pool dynamics (hydrometeor loading, melting, evaporation, riming, ice
254 multiplication, graupel-hail production)? The relative importance of these factors is partially
255 known in regard to microburst downdrafts (Srivastava 1985, 1987; Kessinger et al. 1988) but
256 not for broader cold pools associated with MCSs. What are the roles of dynamical-
257 microphysical feedbacks affecting vertical velocity, especially with regard to ice processes?
258 Concurrent observations of cold pool characteristics with kinematics, microphysics, and
259 environmental conditions are therefore required to address these important questions.
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260 In summary the key challenges in our understanding of evolving cloud populations are: 1)
261 how boundary-layer processes evolve in a way that leads to cloud populations containing deep
262 and mesoscale convection including cold pool dynamics; 2) Size, intensity, and internal
263 variability of convective drafts; 3), microphysical feedbacks; 4) aggregation of convection; 5)
264 inducement of mesoscale circulation, especially gravity wave response to aggregated
265 convective elements.
266 b) Paths toward improving the treatment of convection in high-resolution
267 global models
268 As noted in the introduction, the representation of convection in traditional global
269 models, with grid spacing ~100 km, implicitly or explicitly relies on the assumption that the
270 combined area covered by convective drafts is much smaller than those of a grid column. In that
271 case, scale-separation and statistical quasi-equilibrium are assumed between the resolved
272 circulation, which may destabilize an atmospheric column, and the aggregate of convective
273 drafts, which work to stabilize the column (Arakawa and Schubert 1974). Importantly, such
274 parameterizations implicitly require the grid column to be large compared to the mean distance
275 between updrafts in order for the column to contain a meaningful sample size of updrafts.
276 However, it has been known since the Global Atmospheric Research Program’s Atlantic
277 Tropical Experiment (GATE, Houze and Betts 1981) that long-lasting MCSs are important, and
278 scale-separation is not present in either time or space, even in traditional models. The
279 breakdown of the mean-distance assumption means that stochastic effects start to be relevant
280 (e.g., Plant and Craig 2008). The breakdown of the area assumption means that convection
281 enters a so-called “gray zone,” further discussed below. Additionally convection is often
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282 assumed to respond to forcing almost instantaneously and deterministically with little memory
283 or internal variability of its own.
284 Advances in computational resources have made possible operational global weather
285 and experimental climate models with spatial resolution ≤ 10 km, which allows the larger
286 aspects of circulations associated with convection to be partially resolved. As a result, we need
287 a fundamental rethinking of the objective of, approach to, and assumptions in convection
288 parameterizations. First of all, simulations at scales ≤ ~10 km could easily have MCSs with
289 areas of updrafts and downdrafts comparable to and even greater than the grid spacing and
290 could last longer than the often assumed adjustment time-scale of cumulus convection. In such
291 cases, convection cannot be treated as an aggregate response to the environment but is partially
292 included in the resolved dynamics. This partial resolution of mesoscale convection does not
293 obviate parameterization; instead it gives it new purpose and definition. Parameterizations must
294 represent the sub-grid states, processes and transitions discussed in the last section, and their
295 interactions with resolved dynamic and thermodynamic processes, which include mesoscale
296 processes. Thus, the resolved dynamics, sub-grid states, and transitions are parts of a continuum,
297 and the scale separation is arbitrarily imposed by the model grid spacing. As scale-separation
298 (i.e., the foundation of the statistical quasi-equilibrium assumption) disappears, an important
299 and stringent constraint emerges: the requirement for resolution awareness. That is, model
300 results should be insensitive to arbitrary changes in grid spacing, especially when grid spacing
301 varies across a model domain. Parameterizations must be aware of the processes that are
302 unresolved, partially resolved, or fully resolved and adjust their operations accordingly.
303 Resolution awareness means that the sums of resolved and parameterized parts of key quantities
304 (e.g., mass flux) should not vary with resolution for the range of resolutions of interest.
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305 In the absence of scale separation, the need for resolution awareness cannot be treated as
306 an afterthought, which can be addressed by tuning some parameters in order to produce a
307 desired outcome at a given resolution; rather (like statistical quasi-equilibrium before it),
308 resolution awareness must be built-in as a fundamental constraint on the design of robust
309 parameterizations.
310 With the increase of the resolution of models, attempts at resolution awareness have
311 been progressing along several lines, each with its unique strengths and challenges. These
312 methods are briefly summarized below.
313 i. Modifying quasi-equilibrium mass flux schemes
314 As mentioned above, one of the key assumptions in statistical, quasi-equilibrium-based
315 mass flux schemes is that updraft area fraction, σ , satisfies σ <<1 and the compensating
316 subsidence area fraction is . This assumption has never been accurate in the presence of
317 MCSs, and with increased resolution it breaks down even for convective-scale drafts. Arakawa
318 et al. (2011) proposed an approach to address this issue by requiring that the parameterized
319 mass flux be rescaled as . Thus, when σ 1 , matches the quasi-
320 equilibrium adjustment value M adj and it gradually decreases to zero for situations when the
321 mass flux is fully resolved, i.e., σ=1. Implementation and evaluation of quasi-equilibrium
322 parameterizations with this and similar methods are currently actively being pursued (Grell and
323 Freitas 2013; Wu and Arakawa 2014; Liu et al. 2015; Xiao et al. 2015). Key issues arising for
324 this approach are how one should actually diagnose the value of σ within partially resolved
325 convection, and moreover, how one should diagnose M adj given that the grid-scale atmospheric
326 state traditionally supplied input to a scheme cannot by construction be considered as
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327 representative of a quasi-equilibrium state. While, by design, such models attempt to account
328 for the changes in the overall mass flux with resolution, they do not address underlying issues
329 related to the resolution dependence on the nature of the interactions between the environment
330 and convection, or on the differences (physical and dynamical) in the resolved and unresolved
331 components of convective clouds. Essentially, this approach attempts to treat the issue of spatial
332 scale separation but not the breakdown of temporal separation, as current implementations of
333 Arakawa’s scaling of the mass flux still assume convection responds fully to the environment
334 within the model time step.
335 ii. Prognostic parameterization of processes
336 The quasi-equilibrium-based approach discussed above is essentially diagnostic. A
337 prognostic approach aims to account for physical processes involved in convection, such as
338 convective up- and downdrafts, and the sub-grid circulations associated with them, such as cold
339 pools. Park (2014) has proposed a methodology of this type, which incorporates the dynamics
340 of multiple convective plumes within a grid column; predicts the initiation, evolution, and
341 advection of plume and environmental properties; allows convection to propagate; and includes
342 aspects of cold pools. This approach is scale adaptive in that it represents only sub-grid scale
343 motion with respect to the resolved motions, thus guaranteeing that the parameterized mass flux
344 vanishes as approaches 1. Currently neither this approach nor quasi-equilibrium approaches
345 include convection originating from above the boundary layer or the impacts of wind shear on
346 convection, both important factors in MCS dynamics (Rotunno et al. 1988; Moncrieff 1992;
347 Marsham et al. 2011; Schumacher 2015). Furthermore, the effects of the parameterized sub-grid
348 circulations on surface fluxes are not included.
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349 Other studies that have experimented with prognostic approaches include Pan and
350 Randall (1998) (recently revisited by Yano and Plant 2012), Gerard et al. (2009), and Grandpeix
351 and Lafore (2010). A key issue in this area is to establish which quantities need to be treated as
352 explicitly prognostic in order to capture the relevant dynamical effects, and which quantities
353 may continue to be handled diagnostically. In addition, none of these schemes represent the
354 unique dynamics of an MCS (viz., sloping layered ascent and descent, as described by
355 Moncrieff 1992 and Kingsmill and Houze 1999). Those features would need to be explicitly
356 predicted as part of the resolved dynamics.
357 iii. PDF-based turbulent schemes
358 The PDF-based approach (Golaz et al. 2002; Larson and Golaz 2005) involves a three-
359 step process beginning with the second-order turbulence equations that solve for the second-
360 order moments (correlations) of vertical velocity, moisture, and potential temperature, as well as
361 their covariances. Predicted moments are used to construct PDFs of model variables. The PDFs
362 are then used to calculate the third-order moments (triple correlations), which are necessary to
363 bring the method to closure. The current version of this parameterization uses a family of
364 double Gaussian PDFs. This approach has been successfully implemented in GCMs: CAM and
365 ACME with the Zhang and McFarlane (1995) deep convection scheme (Bogenschutz et al. 2013)
366 and GFDL AM3 with the Donner (1993) deep convection scheme. The approach performs well
367 for shallow cumulus and stratocumulus clouds (Guo et al. 2014). Its development into a unified
368 scheme that does not specifically refer to convective cloud categories (i.e., shallow vs. deep) is
369 in progress. However, it is computationally expensive, and the strategies for implementing
370 organization mechanisms (such as shear and cold pool dynamics) in such a scheme are only
371 beginning to be developed (e.g., Storer et al. 2015; Griffin and Larson 2016). One feature of this
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372 approach is that it predicts largely prescribed probability distributions of vertical velocity,
373 temperature, water vapor, and hydrometeor mixing ratios. The hydrometeor concentrations in
374 deep convection are rarely directly obtained in observations, except for limited aircraft in-situ
375 measurements in field programs. However, useful statistics of closely related quantities can be
376 derived from polarimetric radars and possibly other remote sensors. An observation of these
377 variables concurrently with updraft/downdraft measurements, to the extent possible, is
378 necessary so that covariances can be derived. This requirement implies that some quantities
379 need to be measured at higher frequency than routine observations (esp., temperature, wind, and
380 water vapor content), and others such as vertical velocity may require as-yet unavailable
381 platforms or instruments. LES models can provide further information on the nature of PDFs
382 down to very small scales. Having empirical and high resolution simulation-based knowledge of
383 the PDFs will provide the natural forms of the PDFs used in these schemes. Gaining such
384 information empirically and from high resolution models is crucial for the PDF methods to
385 work.
386 iv. Explicit approaches: Superparameterization and global cloud permitting models
387 In the superparameterization approach, 2-D cloud resolving models are embedded in
388 model grid elements. The GCM provides the large-scale forcing and the CRM runs at ~1–4 km
389 resolution with periodic lateral boundary conditions within each grid element (Randall et al.
390 2013) to provide the cloud and radiative tendencies to the GCM. Evaluation and improvement
391 of this approach, including extending it into three dimensions (i.e., two perpendicular CRM
392 columns) is an active area of research. It has been shown to improve the progression of MCSs
393 over the central U.S. in comparison to the same model using traditional parameterizations
394 (Prichard et al. 2011; Kooperman et al. 2013; Elliott et al. 2016). Furthermore, a
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395 superparameterization version of NCAR’s CAM model (SPCAM) has been shown to have
396 better skill in representing the MJO than several other models (Kim et al. 2009) including the
397 conventional version of CAM (Benedict and Randall 2009). However, as is often the case with
398 changes in cumulus parameterizations (Kim et al. 2012), the improvement comes with biases in
399 the mean state and in the boundary-layer interactions. Lack of communication among the
400 embedded CRMs is a challenge for the superparameterization of convection and convective
401 organization. For instance, when MCSs are generated in the CRM grid elements within a GCM
402 column, they are confined to that GCM column due to the CRM’s periodic lateral boundary
403 conditions. Although MCSs can be generated across contiguous global model domains on the
404 parent global model grid as a result of the joint effects of latent heating and vertical shear,
405 circulation structure of the MCSs is compromised (Pritchard et al. 2011). The 2-D assumption is
406 also limiting because most real MCSs are not 2-D. Although 3-D CRMs can be utilized, it has
407 only been attempted in very small domains (e.g., Khairoutdinov et al. 2005) owing to the added
408 computational expense.
409 The most physically realistic and mathematically consistent approach to including
410 convection in a global model is to employ a global cloud-permitting model (GCPM). The
411 simulated mesoscale cloud systems are three-dimensional and not confined by periodic lateral
412 boundary conditions. The pioneering global CPM simulations conducted on Japan’s Earth
413 Simulator had 3.5-km, 7-km, or 14-km computational grids according to the length of the
414 simulation (e.g., Miura et al. 2005; Satoh et al. 2008). When compared to TRMM observations,
415 the 7-km grid spacing those Nonhydrostatic icosahedral atmospheric model (NICAM)
416 simulations successfully captured not only the MJO but also the clusters of MCSs within it
417 (Miyakawa et al. 2012). Additionally, while they are computationally extremely expensive,
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418 such models are now being run for short periods at cloud resolving sub-kilometer grid spacing
419 (Miyamoto et al. 2013).
420 v. Dynamically based parameterization for mesoscale convection
421 The main message from the foregoing sections is that we are at the crux of a new era of
422 cloud-permitting global weather and global models where we can no longer neglect mesoscale
423 convection. This situation points to the need to integrate mesoscale dynamics into
424 parameterizations where it is presently conspicuous only by its absence. Moncrieff (1992)
425 pointed out how a large class of MCSs can be characterized by a simultaneous adjustment to the
426 thermodynamic and wind shear profiles. Two parameterization developments are underway that
427 build on this idea: the multi-cloud model (Khouider and Majda 2006) and the slantwise-layer-
428 overturning model (Moncrieff 2010; Moncrieff and Waliser 2015). The multi-cloud model
429 represents the diabatic heating and the associated circulations as three cloud types observed to
430 dominate the diabatic heating in tropical convection: congestus, deep precipitating convection,
431 and precipitating stratiform cloud associated with MCSs (Johnson et al. 1999; Mapes 2006).
432 Slantwise layer overturning is a computationally efficient paradigm for the parameterization of
433 mesoscale convection based on multiscale coherent structures in a turbulent environment.
434 c) Observational data needs
435 The scientific and parameterization challenges discussed in the previous section
436 highlight the need for understanding how the size, intensity, and internal turbulent structure of
437 updrafts/downdrafts relate to one another, to boundary-layer processes, to microphysical and
438 cold pool processes, and to large-scale and mesoscale context. The corresponding observational
439 requirement includes continued advancement in methods of observation, further collection, new
21
440 analysis methods, and delivery of observational data in ways that will inform process studies
441 and parameterization. Datasets and analyses need to indicate how aspects of drafts relate to one
442 another to determine the interactive processes of convection. It is important, therefore, for
443 information to be examined, collected, and retrieved in the form of concurrent and collocated
444 observations/retrievals rather than isolated time series of single quantities. Some especially
445 pressing needs are discussed below, and some future short- and long-term observation strategies
446 and investments are suggested.
447 i. Merged products from existing data and infrastructure
448 Relationships between environmental water vapor and precipitation, precipitation type
449 and latent heating, cloud structure and radiative processes, and between microphysical processes
450 and cold pool formation have all been previously examined mostly individually—but they must
451 be obtained concurrently with up- and downdraft statistics in order to be most useful in
452 parameterization development. Field projects are the best venue for providing such information.
453 While many field experiments have been carried out, these projects have primarily documented
454 the synoptic and mesoscale environments of convective drafts with insufficient ability to
455 observe the drafts themselves. Further field efforts for obtaining draft statistics in highly
456 documented environmental settings remain a paramount need and objective.
457 Even with improved airborne capability, field programs have a major shortcoming,
458 which is their short duration—typically a few months or less. Statistically robust datasets over
459 longer time periods are needed. One possible solution is to use the DOE SGP site in a field-
460 experiment mode. For example, instead of passively obtaining measurements at the site with
461 standard scanning strategies of the radars, lidars, sounding launches, and other measurements, a
462 new approach would be to adjust the scan strategies and other measurement procedures (such as
22
463 sounding frequency) to the forecasted weather situation and real-time conditions. The default
464 pre-planned modes would be altered to ones best suited to sample properties of shallow clouds
465 when deep clouds are absent, to a deepening cloud population, and to expected MCSs
466 occurrence, depending on the forecast. This adaptive operation procedure would collect the
467 most relevant information for the type of weather that is occurring. Decisions could be made by
468 scientists monitoring the weather forecasts and the scientists could implement different
469 strategies quickly through online communication with engineers operating the instruments. This
470 mode would adapt a field campaign approach to a permanent observational facility to optimize
471 its ability to provide concurrent observations of the details of convective systems that are
472 necessary for parameterization development.
473 ii. Long-term improvement of observational infrastructure
474 Some of the most-needed measurements for parameterization development are not only
475 unavailable but also may be difficult or impossible to obtain with existing resources and
476 observational platforms. They require sustained, coordinated investments from interested
477 national and international agencies. There are some especially important directions for future
478 observational work that will support development of convective parameterization:
479 • Airborne platforms for in-situ measurements and radar technology for remote detection
480 of draft properties have been limited to date, resulting in a critically insufficient amount
481 of information on updraft/downdraft intensities, dimensions, and internal turbulent
482 characteristics, which need to be determined concurrently and statistically. Multi-
483 Doppler radar techniques are sometimes offered as a substitute for airborne
484 measurements; however, limitations of sampling, resolution, and uncertainty in
485 converting Doppler data to air motion velocities make Doppler radar inadequate by
23
486 themselves. Aircraft are not nimble because of flight planning restrictions, and other
487 logistics such as safety concerns. Nevertheless, there appears to be no substitute for the
488 need for in-situ targeting by suitable aircraft, with strong airframe, high-altitude
489 capability, and instrumentation to obtain information on drafts of all strengths,
490 turbulence, and cloud microphysics at multiple altitudes. A state-of-the-art convection-
491 penetrating research aircraft is needed in the atmospheric sciences community, and
492 multi-agency cooperation could allow it to be used in connection with the
493 aforementioned SGP observational program as well as in shorter-term field programs
494 using advanced radars, lidars, profilers, soundings, and other observations to provide the
495 environmental context.
496 • Flexible S-band dual-polarization scanning radars as dedicated research facilities are
497 critical to support aircraft measurements. Specifically, these long-wavelength radars are
498 the most important instrumentation to provide microphysical context. NEXRAD radars
499 operate in a pre-defined, full-volume scanning mode that is optimized for nowcasting
500 but does not provide sufficient vertical resolution to obtain precise distributions of
501 microphysical characteristics indicated by dual-polarization radar technology. Highly
502 sensitive S-band scanning radars such as NSF's S-Pol and NASA's NPOL are essential
503 to provide the necessary microphysical context because these radars are not constrained
504 to operational scanning strategies. They can provide increased vertical resolution
505 through frequent, adaptable Range Height Indicator (RHI) scan sectors, which is critical
506 because microphysical processes and updraft characteristics have a very fine-scale
507 variability with height (or temperature) that cannot be captured by routine operational
508 tilt-sequence scanning of NEXRAD radars. S-band information is also critical because
24
509 W, Ka-, X- and C-band scanning radars can be severely, and at times completely,
510 attenuated by heavy precipitation associated with MCSs, where the up- and downdrafts
511 are most intense, thus limiting the full spectrum of observations needed to understand
512 upscale growth or precipitating systems. For these reasons, it is very important to
513 continue carrying out field programs that employ S-band research radars with flexible
514 scanning strategies in concert with aircraft direct measurement of up- and downdraft
515 properties. The primary obstacle is that these radars are expensive to maintain and
516 deploy, so interagency cooperation might be needed to maintain or even expand the
517 number of S-band scanning radar facilities.
518 • Most of the vertical re-distribution of heat by convection occurs at low latitudes,
519 especially over the tropical warm oceans, the Maritime Continent, and monsoon regions
520 of Asia and Africa. Although GATE, TOGA-COARE, MONEX, and AMIE/DYNAMO
521 have provided critical information over the world's largest oceans, these projects did not
522 fully address the scientific questions discussed in foregoing sections because of limited
523 observational technology—especially aircraft unable to document up- and downdraft
524 statistics. Satellite-based radar reflectivity data indicate that the nature of convection
525 varies from one regime to another throughout low latitudes (Houze et al. 2015).
526 However, the satellite measurements are not capable of documenting dynamical
527 differences from one region to another (Maritime Continent, monsoons, western vs.
528 eastern Atlantic and Pacific ITCZs, and the South Pacific Convergence Zone). Field
529 campaign data will be needed ultimately to address the key science questions in order
530 for parameterizations to accurately distinguish among the various forms of tropical and
25
531 subtropical convection. Interagency, international programs will be needed to
532 accomplish this large challenge.
533 d) Integration
534 In the last three sections, key scientific and parameterization challenges, as well as
535 observational needs, have been discussed individually. However, even if the technical aspects of
536 process modeling, parameterization development, and observations are addressed, progress is
537 not guaranteed unless the challenges of effectively using observations to inform
538 parameterization development and validate modeling are met. For that, integration of
539 observations, improved process understanding, and model development will be required.
540 Among the many challenges for integration are:
541 1) Observed and modeled quantities not being the same,
542 2) Spatiotemporal scales represented by the measurements being different than what the
543 model represents, and
544 3) Uncertainties associated with observations not well quantified, thus introducing
545 additional uncertainties when evaluating model processes.
546 Two specific approaches for meeting these challenges are presented below.
547 i. Instrument simulators
548 Model variables are usually in the form of temperatures, mixing ratios, and wind
549 components, averaged over a grid-cell volume. Many instruments, especially remote sensors,
550 measure other types of atmospheric variables, such as radar reflectivity, light scattering, or
551 radiative flux. To connect the observations to model output requires simulators, which are
552 software designed to calculate the observable quantities from model output. Remotely sensed
26
553 quantities are generally electromagnetic or optical fields that respond to complex moments of
554 the particle distributions (cloud, precipitation, air molecules), but which are not computed
555 directly within models due to the differences in measurement volume to grid-cell volume and
556 assumptions due to attenuation, cloud and aerosol size distributions, and other instrument
557 specific technical details that generally are unknown when running a model. Nonetheless, model
558 output can be used to estimate the observable quantities via the simulators. The simulators
559 involve a range of physical assumptions, are challenging to design, and require further research.
560 Various investigators are in the process of developing simulators for GCMs using the Cloud
561 Feedback Model Inter-comparison Project (CFMIP) Observation Simulator Package (COSP)
562 framework (Bodas-Salcedo et al. 2011). However, much effort remains to produce the needed
563 wide range of simulators. Nonetheless, currently available simulators have begun to be used in
564 studies using CRMs, LES models, and GCMs (e.g., Varble et al. 2011; Hagos et al. 2014).
565 ii. Cross-scale and hierarchical approaches to modeling
566 High-resolution global atmospheric modeling is often thought of as the ultimate limit to
567 traditional global modeling. Alternatively, one can view a high-resolution global model as a
568 large-domain limit to highly resolved regional models. This latter perspective enables
569 evaluation of model physics in regional and variable resolution global modeling frameworks at
570 a fraction of the computational cost of global high-resolution models. The treatment of
571 mesoscale convection in the gray zone can advance by utilizing advances in knowledge of
572 physical and dynamical processes gained from improved observations and from LES and CRM
573 modeling, as discussed in preceding sections of this article. Thus, high-resolution global
574 modeling activities should be viewed as a hierarchy and designed whenever possible as integral
575 parts of the modeling continuum that includes LES, CRM, variable resolution models, as well
27
576 as operational global cloud permitting models. Consideration is required of what can be learned
577 through evaluation of one approach using a specific choice of observational data that can benefit
578 other approaches up and down the hierarchy where direct evaluation using that specific
579 observational data is not feasible.
580 4. Conclusion
581 The next generation of global models, with grid spacings as fine as 1–10 km, must be
582 able to represent the entire spectrum of convective clouds regardless of model resolution. Future
583 models will resolve certain features of clouds, while other aspects will remain parameterized,
584 even in the highest-resolution models, and the features parameterized will depend on the nature
585 of the cloud populations in relation to the model resolution. Thus, parameterizations will need to
586 operate seamlessly across all the involved scales and phenomena; they cannot be scale specific.
587 The overview presented here was motivated by discussions at a DOE-supported workshop
588 aimed at devising strategies for addressing these issues. The workshop concluded that accurate
589 representation of convection in global models requires advances in our basic understanding of
590 convection, specifically, the sequence of transitions in convective cloud populations from stable
591 boundary layer up to cloud population states that include mesoscale dynamics and
592 dynamical/microphysical interaction on a range of scales, from turbulent elements within
593 individual drafts to MCSs. Furthermore, high-resolution global modeling can benefit from a
594 hierarchical approach that takes full advantage of progress in other modeling frameworks
595 including LES, limited-area CRMs, variable-resolution, and operational high-resolution forecast
596 models.
28
597 In order to effectively test hypotheses and evaluate models, observations need to be
598 considered in terms of merged products that document concurrent and collocated
599 observations/retrievals of cloud variables as well as environmental context. Furthermore,
600 observational strategies can be proactively adapted in near-real time to effectively sample
601 prevailing cloud populations in near-real time. Based on forecast conditions, scanning
602 procedures and sounding launches can be scheduled to optimize instrument operations. Flexible
603 S-band radars designed for research should continue to be used to conduct specialized dual-
604 polarization scans that will support aircraft sampling. Aircraft platforms must be improved to
605 include robust convection-penetrating aircraft with sufficient altitude capability to study deep
606 convection. Field campaigns remain essential with advanced aircraft and radar instrumentation
607 that can explore all deep convective regimes, including Tropical Ocean, coastal zones, and
608 various types of land surface and topography.
609
29
610 Acknowledgement: Funding for the workshop was provided by the U.S. Department of
611 Energy’s Atmospheric Systems Research Program. We thank the ASR Program Managers,
612 Shaima Nasiri and Ashley Williamson, as well as Jerome Fast, ASR Science Focus Area (SFA)
613 Principal Investigator at PNNL, for the support and encouragement throughout the planning and
614 execution of the workshop. We also would like to thank Emily Davis and Alyssa Cummings
615 who provided logistical support to the workshop. Finally we would like to thank all the
616 workshop participants: Mitch Moncrieff (NCAR), Ed Zipser (University of Utah), Greg
617 Thompson (NCAR), Sungsu Park (Korean National University), Chidong Zhang (University of
618 Miami), Courtney Schumacher (Texas A and M), Russ Schumacher (Colorado State University),
619 Robert Plant (University of Reading), Daehyun Kim (University of Washington), Chris
620 Williams (NOAA), Sue van den Heever (Colorado State University), Yunyan Zhang (Lawrence
621 Livermore National Laboratory), Shaocheng Xie (Lawrence Livermore National Laboratory),
622 Scott Collis (Argonne National Laboratory), Jeff Trapp (University of Illinois Champaign-
623 Urbana ), Chris Golaz (Lawrence Livermore National Laboratory), Steven Rutledge (Colorado
624 State University), Angela Rowe (University of Washington), Jim Mather (Pacific Northwest
625 National Laboratory), Phil Rasch (Pacific Northwest National Laboratory), Jiwen Fan (Pacific
626 Northwest National Laboratory), Jerome Fast (Pacific Northwest National Laboratory), William
627 Gustafson (Pacific Northwest National Laboratory), Steve Klein (Lawrence Livermore National
628 Laboratory), Vince Larson (University of Wisconsin), and Tony Del-Genio (NASA GISS).
629 Pacific Northwest National Laboratory is operated by Battelle for the U.S. Department of
630 Energy under Contract DE-AC05-76RLO1830.
631
30
632 References
633 Arakawa A, and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-
634 scale environment Part I. J. Atmos. Sci.,. 31, 674–701.
635 Arakawa, A., J.-H. Jung, and C.-M. Wu, 2011: Toward unification of the multiscale modeling
636 of the atmosphere. Atmos. Chem. Phys., 11, 3731–3742.
637 Barnes, H. C., and R. A. Houze, Jr., 2013: The precipitating cloud population of the Madden-
638 Julian Oscillation over the Indian and West Pacific Oceans. J. Geophys. Res., 118, 6996-7023,
639 doi:10.1002/jgrd.50375.
640 Bodas-Salcedo, A., and Coauthors, 2011: COSP: Satellite simulation software for model
641 assessment. Bull. Amer. Meteor. Soc., 92, 1023–1043
642 Bogenschutz, P. A., A. Gettelman, H. Morrison, V. E. Larson, C. Craig, and D. P. Schanen,
643 2013: Higher-order turbulence closure and its impact on climate simulations in the Community
644 Atmosphere Model. J. Climate, 26, 9655-9676.
645 Chen, S. S., and R. A. Houze, Jr., 1997: Diurnal variation and life cycle of deep convective
646 systems over the tropical Pacific warm pool. Quart. J. Roy. Meteor. Soc., 123, 357-388.
647 Donner, L. J., 1993: A cumulus parameterization including mass fluxes, vertical momentum
648 dynamics, and mesoscale effects. J. Atmos. Sci., 50, 889–906.
649 Feng, Z., S. Hagos, A. K. Rowe, C. D. Burleyson, M. N. Martini, and S. P. de Szoeke, 2015:
650 Mechanisms of convective cloud organization by cold cools over tropical warm ocean during
651 the AMIE/DYNAMO field Campaign. J. Adv. Model. Earth System, 7, 357–381,
652 doi:10.1002/2014MS000384.
31
653 Fritsch, J. M., R. J. Kane, and C. R. Chelius, 1986: The contributionc of mesoscale convective
654 weather systemsmcws to the warm-season precipitationws0 in the United States. J. Climate
655 Appl. Meteor.,. 25, 1333-1345.
656 Gerard, L., 2015: Bulk mass-flux perturbation formulation for a unified approach of deep
657 convection at high resolution. Mon. Wea. Rev., 143, 4038–4063.
658 Gerard, L., J.-M. Piriou, R.J.-M., Brozkova, J.-F.R., Geleyn, J.-F. and D. Banciu, D., 2009:.
659 Cloud and precipitation parameterization in a meso-gamma-scale operational weather prediction
660 model., Mon. Wea. Rev., 137, 3960-3977.
661 Gerard, L., 2015: Bulk Mass-Flux Perturbation Formulation for a Unified Approach of Deep
662 Convection at High Resolution. Mon. Wea. Rev., 143, 4038–4063.
663 Golaz, J.-C., V. E. Larson, and W. R. Cotton, 2002: (2002a), A PDF-based model for boundary
664 layer clouds. Part I: Method and model description., J. Atmos. Sci., 59,(24), 3540–3551.
665 Grandpeix, J.-V., and J.-P. Lafore, J.-P., 2010: A density current parameterization coupled with
666 Emanuel’s convection scheme. Convection Scheme. Part I: The modelsModels. J. Atmos. Sci.,
667 67, 881–897.
668 Grell, G. A., and S. Freitas, 2013: A scale and aerosol aware stochastic convective
669 parameterization for weather and air quality modeling. Atmos. Chem. Phys. Discuss., 13,
670 23845-23893, doi:10.5194/acpd-13-23845-2013.
671 Guo, Z., M. Wang, Y. Qian, V. E. Larson, S. Ghan, M. Ovchinnikov, P. A. Bogenschutz, C.
672 Zhao, G. Lin, and T. Zhou, (2014:), A sensitivity analysis of cloud properties to CLUBB
32
673 parameters in the single-column Community Atmosphere Model (SCAM5).), J. Adv. Model.
674 Earth Syst., 6, 829–858, doi:10.1002/2014MS000315.
675 Hagos, S. M., Z. Feng, C. D. Burleyson, K. S. Lim, C. N. Long, D. Wu, and G. Thompson,
676 2014: Evaluation of convection-permitting model simulations of cloud populations associated
677 with the Madden-Julian Oscillation using data collected during the AMIE/DYNAMO field
678 campaign. J. Geophys. Res. Atmos., 119, 12052-12068, doi:10.1002/2014JD022143.
679 Hagos, S, C Zhang, Z Feng, C Burleyson, C De Mott, J Benedict, and M Martini. 2016. “The
680 Impact of Diurnal Cycle on the Propagation of MJO Across the Maritime Continent.” In Press.
681 Journal of Advances in Modeling Earth Systems.
682 Hirota, N., Y. N. Takayabu, M. Watanabe, and M. Kimoto, 2011: Precipitation reproducibility
683 over tropical oceans and its relationship to the double ITCZ problem in CMIP3 and MIROC5
684 climate models. J. Climate, 24, 4859-4873.
685 Houze, R. A., Jr., and A. K. Betts, 1981: Convection in GATE. Rev. Geophys. Space Phys., 19,
686 541-576
687 Houze, R. A., Jr., S. G. Geotis, F. D. Marks, Jr., and A. K. West, 1981: Winter monsoon
688 convection in the vicinity of north Borneo. Part I: Structure and time variation of the clouds and
689 precipitation. Mon. Wea. Rev., 109, 1595-1614.
690 Houze, R. A., Jr., K. L. Rasmussen, M. D. Zuluaga, and S. R. Brodzik, 2015: The variable
691 nature of convection in the tropics and subtropics: A legacy of 16 years of the Tropical Rainfall
692 Measuring Mission (TRMM) satellite. Rev. Geophys., 53, doi:10.1002/2015RG000488.
33
693 Hung, M.-P., J. Lin, W. Wang, D. Kim, T. Shinoda, and S. J. Weaver, 2013: MJO and
694 convectively coupled equatorial waves simulated by CMIP5 climate models. J. Climate, 26,
695 6185-6214.
696 Johnson, R. H., and D. L. Priegnitz, 1981: Winter monsoon convection in the vicinity of North
697 Borneo. Part II: Effects on large-scale fields. Mon. Wea. Rev., 109, 1615-1628.
698 Johnson, R. H., and R. A. Houze, Jr., 1987: Precipitating cloud systems of the Asian monsoon.
699 In Monsoon Meteorology (C.-P. Chang and T. N. Krishnamurti, Eds.), 298-353.
700 Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, and W. H. Schubert, 1999:
701 Trimodal characteristics of tropical convection. J.Clim., 12, 2397–2418.
702 Kessinger, C.J., Parsons, D.B., Wilson, J.W., 1988. Observations of a storm containing
703 misocyclones, downbursts, and horizontal vortex circulations. Mon. Weather Rev. 116, 1959–
704 1982.
705 Kikuchi, K., and B. Wang, 2008: Diurnal precipitation regimes in the global tropics, J. Clim.,
706 21, 2680–2696, doi:10.1175/2007JCLI2051.1.
707 Kooperman, G. J., M. S. Pritchard, and R. C. J. Somerville, (2013:), Robustness and
708 sensitivities of central U.S. summer convection in the super-parameterized CAM: Multi-model
709 intercomparison with a new regional EOF index., Geophys. Res. Lett., 40, 3287–3291.
710 Larson, V. E., and J.-C. Golaz, 2005: Using probability density functions to derive consistent
711 closure relationships among higher-order moments., Mon. Wea.Weather Rev., 133,(4), 1023–
712 1042.
34
713 Li, G. and S. P. Xie, S.P., 2014:. Tropical biases in CMIP5 Multimodel Ensemble: The
714 excessive Equatorial Pacific cold tongue and double ITCZ problems. J. Problems. Journal of
715 Climate, 27, (4), pp.1765-1780.
716 Liu, Y.-C., J. Fan, G. J. Zhang, K.-M. Xu, and S. J. Ghan, 2015:, Improving representation of
717 convective transport for scale-aware parameterization: 2. Analysis of cloud-resolving model
718 simulations., J. Geophys. Res. Atmos., 120, 3510–3532, doi:10.1002/ 2014JD022145.
719 Marsham, J. H., S. B. Trier, T. M. Weckwerth, and J. W. Wilson, 2011: Observations of
720 elevated convection initiation leading to a surface-based squall line during 13 June IHOP 2002.
721 Mon. Wea. Rev., 108, 322-336.
722 Miura, H., H. Tomita, T. Nasuno, S. Iga, M. Satoh, and T. Matsuno, 2005: A climate sensitivity
723 test using a global cloud resolving model under an aqua planet condition. Geophys. Res. Lett.,
724 32, doi:10.1029/2005GL023672.
725 Miura, H., M. Satoh, H. Tomita, A. T. Noda, T. Nasuno, and S. Iga, 2007: A short-duration
726 global cloud-resolving simulation with a realistic land and sea distribution. Geophys. Res. Lett.,
727 34, doi:10.1029/2006GL027448.
728 Miyakawa, T., Y. N. Takayabu, T. Nasuno, H. Miura, M. Satoh, and M. W. Moncrieff, 2012:
729 Convective momentum transport by rainbands within a Madden-Julian Oscillation in a global
730 nonhydrostatic model with explicit deep convective processes. Part 1: Methodology and general
731 results69, 1317-1338.
732 Moncrieff, M. W., 1992: Organized convective systems: Archetypical dynamical models, mass
733 and momentum flux theory, and parametrization. Quart. J. Roy. Meteor. Soc., 118, 819–850.
35
734 Moncrieff, M. W., 2010: The multiscale organization of moist convection and the intersection
735 of weather and climate, in Climate Dynamics: Why Does Climate Vary? Geophys. Monogr.
736 Ser., Vol. 189, Eds. D-Z. Sun and F. Bryan, pp. 3–26, doi: 10.1029/2008GM000838.
737 Moncrieff, M. W., and D. E. Waliser, 2015: Chapter15. Organized Convection and the YOTC
738 Project, Seamless Prediction of the Earth-System: From Minutes to Months, (G. Brunet, S
739 Jones, P.M. Ruti Eds.), WMO-No. 1156, ISBN 978-92-63-11156-2, Geneva, Switzerland,
740 http://library.wmo.int/pmb_ged/wmo_1156_en.pdf.
741 Moncrieff, M. W., D. E. Waliser, M. J. Miller, M. E. Shapiro, G. Asrar, and J. Caughey, 2012:
742 Multiscale convective organization and the YOTC Virtual Global Field Campaign. Bull. Amer.
743 MeteorMeteorol. Soc., 93,1171-1187, doi:10.1175/BAMS-D-11-00233.1.
744 Neena, J.M., J-Yi Lee, D. Waliser, B. Wang, and X. Jiang (2014), Predictability of the Madden
745 Julian Oscillation in the Intraseasonal Variability Hindcast Experiment (ISVHE), J. ofClimate,
746 27, 4531-4543.
747 Nesbitt, S. W., R. Cifelli, and S. Rutledge, 2006: A. Storm morphology and rainfall
748 characteristics of TRMM precipitation features. Mon. Wea. Rev. 134, 2702-2721.
749 Pan, D.-M. and D. A. Randall, D. A., 1998:. A cumulus parameterization with prognostic
750 closure. Quart, Q. J. Roy. MeteorMeteorol. Soc., 124, 949–981.
751 Pandya, R., Durran, D., 1996. The influence of convectively generated thermal forcing on the
752 mesoscale circulation around squall lines. J. Atmos. Sci. 53, 2924–2951.
753 Park, S., 2014: A Unified Convection Scheme (UNICON). Part I: Formulation. J. Atmos. Sci.,
754 71, 3902–3930.
36
755 Plant, R. S., and G. C. Craig, G. C., 2008:. A stochastic parameterization for deep convection
756 based on equilibrium statistics., J. Atmos. Sci., 65, 87-105.
757 Pritchard, M., M. W. Moncrieff, and R. C. J. Somerville, 2011: Orogenic propagating
758 precipitation systems over the US in a global climate model with embedded explicit convection.
759 J. Atmos. Sci., 68, 1821-1840.
760 Randall, D., M. Branson, M. Wang, S. Ghan, C. Craig, A. Gettelman, and J. Edwards, (2013:),
761 A community atmosphere model with superparameterized clouds, Eos Trans. AGU, 94,(25),
762 221–222..
763 Rasmussen, K. L., A. J. Hill, V. E. Toma, M. D. Zuluaga, P. J. Webster, and R. A. Houze, Jr.,
764 2015: Multiscale analysis of three consecutive years of anomalous flooding in Pakistan. Quart. J.
765 Roy. Meteor. Soc., 141, 1259–1276, doi:10.1002/qj.2433.
766 Romatschke, U., and R. A. Houze, Jr., 2010: Extreme summer convection in South America. J.
767 Climate, 23, 3761-3791.
768 Rowe, A. K., and R. A. Houze, Jr., 2015: Cloud organization and growth during the transition
769 from suppressed to active MJO conditions. J. Geophys. Res. Atmos., 120, 10,324–10,350,
770 doi:10.1002/2014JD022948.
771 Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, 2008: Nonhydrostatic
772 Icosahedral Atmospheric Model (NICAM) for global cloud resolving simulations. J. Comput.
773 Phys., 227, 3486-3514.
37
774 Schumacher, R.S., 2015: Sensitivity of precipitation accumulation in elevated convective
775 systems to small changes in low-level moisture. Journal of the Atmospheric Sciences, 72, 2507-
776 2524.
777 Srivastava, R.C., 1971. Size distribution of raindrops generated by their breakup and
778 coalescence. J. Atmos. Sci. 28, 410–415.
779 Srivastava, R.C., 1985. A simple model of evaporatively driven downdraft application to
780 microburst downdraft. J. Atmos. Sci. 42, 1004–1023.
781 Storer, R. L., B. M. Griffin, J.B. M., Höft, J. K.., Weber, E.J. K., Raut, V. E.., Larson, M. Wang,
782 and P. J. V. E., ... & Rasch, P. J. (2015:). Parameterizing deep convection using the assumed
783 probability density function method. Geoscientific Model Development, 8,(1), 1-19.
784 Thorpe, A. J., M. J. Miller, and M. W. Moncrieff, 1980: Dynamical models of two-dimensional
785 downdraughts. Quart. J. Roy. Metor. Soc., 106, 463-484.
786 Varble, A., A. M. Fridland, E. J. Zipser, A. S. Ackerman, J.-P. Chaboureau, J. Fan, A. Hill, S. A.
787 McFarlane, J.-P. Pinty, and B. Shipway, 2011: Evaluation of cloud-resolving model
788 Intercomparison simulations using TWP-ICE observations: Precipitation and cloud structure. J.
789 Geophys. Res., 116, doi: 10.1029/2010JD015180.
790 Varble, A.,J. Zipser, A. M. Fridlind, P. Zhu, A. S. Ackerman, J.-P. Chaboureau, S. Collis, J. Fan,
791 A. Hill, and B. Shipway, 2014: Evaluation of cloud-resolving and limited area model
792 intercomparison simulations using TWP-ICE observations: 1. Deep convective updraft
793 properties. J. Geophys. Res., 119, 13,891-13,918, doi:10.1002/2013JD021371.
38
794 Williams, M., and R. A. Houze, Jr., 1987: Satellite-observed characteristics of winter monsoon
795 cloud clusters. Mon. Wea. Rev., 115, 505-519.
796 Wing, A. A., and K. A. Emanuel, 2013: Physical mechanisms controlling self-aggregation of
797 convection in idealized numerical modeling simulations. J. Adv. Model. Earth. Syst., 5,
798 doi:10.1002/2013MS000269.
799 Wu, C.-M., and A. Arakawa, 2014: A Unified Representation of Deep Moist Convection in
800 Numerical Modeling of the Atmosphere. Part II. J. Atmos. Sci., 71, 2089–2103.
801 Xiao, H., W. I. Gustafson Jr., S. M. Hagos, C.-M. Wu, and H. Wan, (2015:), Resolution-
802 dependent behavior of subgrid-scale vertical transport in the Zhang-McFarlane convection
803 Parameterization., J. Adv. Model. Earth Syst., 7, 537–550, doi:10.1002/ 2014MS000356.
804 Yano, J.-I. and R. S. Plant, R. S., 2012:. Finite departure from convective quasi-equilibrium:
805 periodic cycle and discharge-recharge mechanism. Quart, Q. J. Roy. Meteor. Soc., 138, 626-637.
806 Yuan, J., and R. A. Houze, Jr., 2010: Global variability of mesoscale convective system anvil
807 structure from A-train satellite data. J. Climate, 23, 5864-5888.
808 Yuan, J., and R. A. Houze, Jr., 2013: Deep convective systems observed by A-Train in the tropical
809 Indo-Pacific region affected by the MJO. J. Atmos Sci., 70, 465–486.
810 Zhang, C., 2005: The Madden–Julian oscillation. Rev. Geophys., 43, RG2003,
811 doi:10.1029/2004RG000158.
812 Zhang, C., 2013: Madden-Julian Oscillation: Bridging weather and climate. Bull. Amer. Meteor.
813 Soc., 94, 1849-1870.
39
814 Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the
815 parameterization of cumulus convection in the Canadian Climate Centre general circulation
816 model. Atmos-Ocean, 33, 407-446.
817 Zhang, X., W. Lin, and M. Zhang, (2007), Toward understanding the double Intertropical
818 Convergence Zone pathology in coupled ocean-atmosphere general circulation models., J.
819 Geophys. Res., 112, D12102, doi:10.1029/2006JD007878.
820 Zipser, E. J., and M. A. LeMone, 1980: Cumulonimbus vertical velocity events in GATE. Part
821 II: Synthesis and model core structure. J. Atmos. Sci., 37, 2458-2469.
822 Zipser, E. J., D. J. Cecil, C. Liu, S. W. Nesbitt, and D. P. Yorty, 2006: Where are the most
823 intense thunderstorms on earth? Bull. Amer. Meteor. Soc., 87, 1057–1071.
824
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836
837 Appendix: Acronyms and Abbreviations
838 ACME Accelerated Climate Model for Energy
839 AMIE ARM MJO Investigation Experiment, Indian Ocean, 2011-12
840 ARM Atmospheric Radiation Measurement
841 ASR Atmospheric System Research program
842 CAPE Convective Available Potential Energy
843 CAM Community Atmospheric Model
844 COSP Cloud Feedback Model Intercomparison Project (CFMIP) Observation
845 Simulator Package (COSP)
846 CMIP5 Coupled Model Inter-comparison Project Phase 5
847 CPM Cloud Permitting Model
848 DOE U.S. Department of Energy
849 DYNAMO Dynamics of Madden-Julian Oscillation field campaign, Indian Ocean,
850 2011-2012
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851 ENSO El Niño Southern Oscillation
852 GATE Global Atmospheric Research Program’s Atlantic Tropical Experiment, 1974
853 GCM Global Climate Model
854 GFDL AM3 Geophysical Fluid Dynamics Laboratory Atmospheric Model 3
855 GoAmazon Green Ocean Amazon Field Campaign, 2014-2015
856 IOP Intensive Observing Period
857 ITCZ Inter-tropical Convergence Zone
858 LES Large Eddy Simulation
859 MCS Mesoscale Convective System
860 MONEX Monsoon Experiment, India and Malaysia, 1978-1979
861 PECAN Plains Elevated Convection At Night, Central U. S., 2015
862 PNNL Pacific Northwest National Laboratory
863 ROCORO Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF)
864 Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO)
865 SGP Southern Great Plains, ARM Observational site in Oklahoma
866 TRMM Tropical Rainfall Measurement Mission, U.S./Japan satellite with radar and
867 radiometers for precipitation measurement, in orbit 1997-2014
868 RHI Range Height Indicator, a radar display at constant elevation angle
42
869 SPA Storm Penetrating Aircraft
870 TOGA-COARE Tropical Ocean—Global Atmosphere Coupled Ocean Atmosphere Response
871 Experiment, western tropical Pacific, 1992-1993
872 WRF Weather Research and Forecasting Model
873
874
875
43
876 Figures
877
878 Figure 1. A photograph of convective clouds over Africa from the International Space Station
879 (photo credit NASA).
880
881
882
44
883
884
885 Figure 2. The core themes on which progress is required for accurate treatment of convection in
886 the next-generation global models.
887
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888
889
890 Figure 3. (a) Diurnal cycle of June-July-August precipitation from observations and
891 CMIP5 models (Courtesy of Chengzhu Zhang from Lawrence Livermore National
892 Laboratory) and (b) the annual cycle of surface temperature at the location of ARM’s
893 Southern Great Plains site (Adapted from Zhang et al. 2016). The gray lines represent
894 individual CMIP5 models.
895
46
896
897 Figure 4. Variance of the MJO mode along the equator averaged between (a) 15°N and
898 15°S and (b) 5°N and 5°S (Adapted from Hung et al. 2013). The different line styles
899 represent different CMIP models.
47
900
901
902
48
903 Figure 5. (a) Annual cycle of all-India rainfall derived from satellite observations (black) and
904 from 20 CMIP5 models (blue) and (b) same but normalized by the annual mean precipitation.
905 The dashed red curve represents the multi-model mean.
906
49