On the Identification of the Large-Scale Properties of Tropical Convection Using Cloud Regimes

Tan, Jackson; Jakob, Christian; Lane, Todd P.

doi:10.1175/jcli-d-12-00624.1

Cited by 49 publications

(80 citation statements)

References 47 publications

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“…In particular, Tan et al [] concluded that these cloud regimes are informative proxies for the various modes of convection and are empirical candidates for the building blocks of tropical convection. A further extension to these cloud regimes in Tan and Jakob [] was able to identify the major cloud regimes at a higher time resolution.…”

Section: Methodsmentioning

confidence: 99%

The consequences of a local approach in statistical models of convection on its large‐scale coherence

2015

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Key Points:• Observed convection has coherence beyond the grid box and the time step • A model local in space and time does not produce such coherence • Incorporating spatial and temporal memory recovered some of this coherence Abstract Organized tropical convection is a crucial mechanism in the climate system, but its representation in climate models through parametrization schemes has numerous shortcomings. One of these shortcomings is that they are deterministic despite the statistical nature of the relationship they are representing. Several attempts at devising a stochastic parametrization scheme have been made, many of which assume a local approach, that is, one in which the convection in a grid box is determined without consideration of the previous time steps and the surrounding boxes. This study seeks to explore the effect of this assumption on the coherence of convection using cloud regimes, which represent various modes of tropical convection. First, we analyze the coherence of observed convection beyond the typical size of a model grid box and time step. Then, we evaluate the consequences of the local assumption on this coherence in simple statistical models. Cloud regimes in the real world show high degrees of coherence, manifesting in their lifetimes, areas, and inter-regime relationships. However, in a local statistical model, they are too small, too short-lived, and have incorrect relationships between each other. This can be improved by incorporating time memory and spatial dependence in the modeling. Our results imply that a local approach to a statistical representation of convection is not viable, and a statistical model must account for nonlocal influence in order to have large-scale convective coherence that more closely resembles the real world.

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Section: Methodsmentioning

confidence: 99%

The consequences of a local approach in statistical models of convection on its large‐scale coherence

2015

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“…While cloud clusters are often identified by searching for large, contiguous cold cloud shields, more advanced techniques search for the combined signature of deep convection and extensive stratiform cloud and precipitation area. For example, higher stratiform rain fractions are associated with organized convection, which can be diagnosed from satellite precipitation radar data (Schumacher and Houze 2003), as are large optical thicknesses and low-cloud top pressures, which can distinguish a particular cloud regime (Tselioudis et al 2010;Tan et al 2013).…”

Section: Literature Review: Observational Studies Of Convective Organmentioning

confidence: 99%

“…Despite the fact that the occurrence of mesoscale organized convection makes up a small fraction of the total frequency of cloud/precipitation features in the tropics (\6%, Mapes and Houze 1993;Nesbitt et al 2000;Tan et al 2013), it contributes a significant proportion of total tropical cloudiness 1 and about half of total tropical precipitation.…”

Section: Literature Review: Observational Studies Of Convective Organmentioning

confidence: 99%

“…Mapes (1993) found that 43% of the total cold cloud coverage in the tropics was associated with large superclusters. 2 Tan et al (2013) found that the cloud regime associated with organized convection contributes 45% of total tropical rainfall. Nesbitt et al (2000) found that precipitation features that include an MCS contribute 38-55% of total rainfall in various regions of the tropics.…”

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confidence: 99%

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Observing Convective Aggregation

et al. 2017

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Convective self-aggregation, the spontaneous organization of initially scattered convection into isolated convective clusters despite spatially homogeneous boundary conditions and forcing, was first recognized and studied in idealized numerical simulations. While there is a rich history of observational work on convective clustering and organization, there have been only a few studies that have analyzed observations to look specifically for processes related to self-aggregation in models. Here we review observational work in both of these categories and motivate the need for more of this work. We acknowledge that self-aggregation may appear to be far-removed from observed convective organization in terms of time scales, initial conditions, initiation processes, and mean state extremes, but we argue that these differences vary greatly across the diverse range of 123Surv Geophys (2017) 38:1199-1236 https://doi.org/10.1007/s10712-017-9419-1 model simulations in the literature and that these comparisons are already offering important insights into real tropical phenomena. Some preliminary new findings are presented, including results showing that a self-aggregation simulation with square geometry has too broad distribution of humidity and is too dry in the driest regions when compared with radiosonde records from Nauru, while an elongated channel simulation has realistic representations of atmospheric humidity and its variability. We discuss recent work increasing our understanding of how organized convection and climate change may interact, and how model discrepancies related to this question are prompting interest in observational comparisons. We also propose possible future directions for observational work related to convective aggregation, including novel satellite approaches and a groundbased observational network.

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“…r has been chosen as a proxy for atmospheric moisture because Bretherton et al () showed an empirical exponential relationship between the variable and precipitation in the tropics. Guided by the work of Tan et al (), the modified k‐index

k i = \frac{1}{2} \cdot (T_{s f c} + T_{850}) - T_{500} + \frac{1}{2} \cdot (T_{D_{s f c}} + T_{D_{850}}) - (T_{700} - T_{D_{700}})

is used to describe the atmospheric instability. Here T p and

T_{D_{p}}

are the air and dew point temperatures at pressure level p .…”

Section: Simulation Of Tropical Coastal Convectionmentioning

confidence: 99%

Coastal Tropical Convection in a Stochastic Modeling Framework

Bergemann

Khouider

Jakob

2017

J Adv Model Earth Syst

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Recent research has suggested that the overall dependence of convection near coasts on large‐scale atmospheric conditions is weaker than over the open ocean or inland areas. This is due to the fact that in coastal regions convection is often supported by meso‐scale land‐sea interactions and the topography of coastal areas. As these effects are not resolved and not included in standard cumulus parametrization schemes, coastal convection is among the most poorly simulated phenomena in global models. To outline a possible parametrization framework for coastal convection we develop an idealized modeling approach and test its ability to capture the main characteristics of coastal convection. The new approach first develops a decision algorithm, or trigger function, for the existence of coastal convection. The function is then applied in a stochastic cloud model to increase the occurrence probability of deep convection when land‐sea interactions are diagnosed to be important. The results suggest that the combination of the trigger function with a stochastic model is able to capture the occurrence of deep convection in atmospheric conditions often found for coastal convection. When coastal effects are deemed to be present the spatial and temporal organization of clouds that has been documented form observations is well captured by the model. The presented modeling approach has therefore potential to improve the representation of clouds and convection in global numerical weather forecasting and climate models.

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On the Identification of the Large-Scale Properties of Tropical Convection Using Cloud Regimes

Cited by 49 publications

References 47 publications

The consequences of a local approach in statistical models of convection on its large‐scale coherence

The consequences of a local approach in statistical models of convection on its large‐scale coherence

Observing Convective Aggregation

Coastal Tropical Convection in a Stochastic Modeling Framework

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