The Year of Tropical Convection (YOTC) project recognizes that major improvements are needed in how the tropics are represented in climate models. Tropical convection is organized into multiscale precipitation systems with an underlying chaotic order. These organized systems act as building blocks for meteorological events at the intersection of weather and climate (time scales up to seasonal). These events affect a large percentage of the world's population. Much of the uncertainty associated with weather and climate derives from incomplete understanding of how meteorological systems on the mesoscale (~1–100 km), synoptic scale (~1,000 km), and planetary scale (~10,000 km) interact with each other. This uncertainty complicates attempts to predict high-impact phenomena associated with the tropical atmosphere, such as tropical cyclones, the Madden–Julian oscillation, convectively coupled tropical waves, and the monsoons. These and other phenomena influence the extratropics by migrating out of the tropics and by the remote effects of planetary waves, including those generated by the MJO. The diurnal and seasonal cycles modulate all of the above. It will be impossible to accurately predict climate on regional scales or to comprehend the variability of the global water cycle in a warmer world without comprehensively addressing tropical convection and its interactions across space and time scales.

Vastly improved satellite and in situ measurements, data assimilation, and modeling make possible a virtual field study of multiscale Earth system problems, such as convective organization and its interaction with larger-scale circulation.

Day-to-day weather variations and extreme rainfall events, as well as most components of climate in the tropics, are closely associated with moist convection, the process by which the relative abundance of heat and moisture near Earth's surface gets transported upward into the atmosphere. From there, the heat is radiated back to space and the moisture may condense and form clouds. Some of the condensate grows large enough to fall back to Earth's surface as precipitation. In this regard, moist convection plays a crucial role in the energy and water cycles of the tropics as well as the variability of the tropical climate system. In concert with its effects on the tropics per se, moist convection can generate planetary (Rossby) waves, which affect weather and climate around the globe. Therefore, the effects of tropical convection, its multiscale organization, and interaction across scales are crucial elements of weather and climate.

The organization of tropical convection is associated with a hierarchy of meteorological phenomena: (i) cumulus/cumulonimbus clouds, (ii) the mesoscale organization of fields of clouds into coherent structures, generically called mesoscale convective systems (MCSs); (iii) synoptic-scale disturbances such as tropical cyclones and easterly waves; (iv) ensembles of mesoscale systems (superclusters) embedded within planetary-scale phenomena, such as convectively coupled equatorial waves (Kiladis et al. 2009), the Madden–Julian oscillation (MJO; Madden and Julian 1972), the monsoons (Li 2010), and the El Niño–Southern Oscillation (ENSO; McPhaden 2004); and (v) the iconic long-term pattern of persistent convection known as the intertropical convergence zone (ITCZ). Elements of some of these phenomena are illustrated in Fig. 1 (top), and the complexity of their collective effects is apparent in the patterns of total atmospheric water (Fig. 1, bottom). Such patterns exhibit temporal variability across a wide range of scales, including the well-defined diurnal and annual cycles, and more broadband features such as synoptic time scales of a few days, and intraseasonal to interannual variability. This remarkable space–time continuum renders the distinction between weather and climate a seamless one when considering the role and impacts of tropical convection.

Fig. 1.

(top left). Photograph of a cumulonimbus cloud. (top right). Satellite image of a mesoscale convective system. The bubbly regions in the stratiform cloud deck are overshooting cumulonimbus clouds, showing the multiscale/upscale character of convective organization. (bottom). Snapshot of the global distribution of total precipitable water (TPW) derived from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) satellite data using the Morphed Integrated Microwave Imagery at the Cooperative Institute for Meteorological Satellite Studies (CIMSS)—i.e., MIMIC—product. The TPW ranges from 5 to 65 mm. Higher values are in red/brown hues, lower values in purple hues, and the mid-range in blue hues. High water content in the tropics (red hues) drops off rapidly in the extratropics. TPW maxima are the product of cloud systems organized on large scales (e.g., the MJO in the Indian Ocean). “Atmospheric rivers” of moisture flow poleward from the tropics associated with the incursion of midlatitude fronts, effects of planetary waves, and the extratropical transition of tropical disturbances. Courtesy of Tony Wimmers and Chris Velden, CIMSS, University of Wisconsin at Madison.

Fig. 1.

(top left). Photograph of a cumulonimbus cloud. (top right). Satellite image of a mesoscale convective system. The bubbly regions in the stratiform cloud deck are overshooting cumulonimbus clouds, showing the multiscale/upscale character of convective organization. (bottom). Snapshot of the global distribution of total precipitable water (TPW) derived from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) satellite data using the Morphed Integrated Microwave Imagery at the Cooperative Institute for Meteorological Satellite Studies (CIMSS)—i.e., MIMIC—product. The TPW ranges from 5 to 65 mm. Higher values are in red/brown hues, lower values in purple hues, and the mid-range in blue hues. High water content in the tropics (red hues) drops off rapidly in the extratropics. TPW maxima are the product of cloud systems organized on large scales (e.g., the MJO in the Indian Ocean). “Atmospheric rivers” of moisture flow poleward from the tropics associated with the incursion of midlatitude fronts, effects of planetary waves, and the extratropical transition of tropical disturbances. Courtesy of Tony Wimmers and Chris Velden, CIMSS, University of Wisconsin at Madison.

Inherent to the above meteorological phenomena is the interaction between moist convection and atmospheric dynamics—such that each impacts, and yet may arise from, the other. Within the wide range of organizing scales of tropical convection referred to above are many tangible, although far from obvious, connections among small-scale clouds (e.g., cumulus), mesoscale convective systems, and planetaryscale motion. As a nexus of chaotic order in Earth's atmosphere, convective organization raises new considerations and challenges. As noted above, one is the blurring of the traditional scale boundaries near the intersection of weather and climate, both fundamentally and from the practical viewpoint of prediction. Another requirement, stemming from the first, is for our research and prediction models to concurrently represent the small-scale clouds, the intermediate (mesoscale) systems, and the largest planetary scales of motion. Models with computational grids of 1 km explicitly represent (simulate) mesoscale organization, but global models with this level of resolution are several orders of magnitude beyond our current computer capabilities. A global model with a 100-m computational grid would render convective parameterization unnecessary, but that resolution will not be possible for a very long time.

In order to reduce the scale range, the presentday practice in climate modeling is to resolve scales on the order of 100 km and “parameterize” the smaller unresolved “subgrid” scales. This raises a third challenge of the wide range of scales that has to be reckoned with. Past parameterization developments and improvements (convection and other physical processes) have often relied upon field campaigns that deploy instruments on aircraft, buoys, balloons, etc., often in distant or remote areas. Such deployments are typically for short “intensive observation periods” of days to weeks and occasionally, in the case of large international programs, for a few months (Betts 1974; Webster and Lukas 1992). Field campaigns focus the community on selected periods and events of interest. While considerable gains have been made, this traditional approach is unable to adequately address tropical convection: it is impossible, logistically and financially, to span the enormous range of interacting scales. What is needed is a flexible and comprehensive approach that concurrently addresses the global-scale context, the intermediatescale building blocks (e.g., MCS), and various physical processes at significantly smaller scales.

In the past couple of decades, there has been a steady and widespread investment and implementation of observing system infrastructures that, when integrated together, can be marshaled to address the above challenge (e.g., Fig. 2, top). That includes the vast increase in global satellite observations, now including quantities such as atmospheric temperature, water vapor, sea surface temperature, salinity and height, and a plethora of wind, cloud, and radiation data. In addition, arrays of observing buoys are now deployed in all three tropical ocean basins, together with near-surface measurements from hundreds of drifting buoys and profiling floats, most recently in the Indian Ocean (McPhaden et al. 2009).

Fig. 2.

(Upper bar charts) Recent time history of the data types and instruments assimilated into the ECMWF IFS. The upper chart shows information in terms of the number of data sources for the given satellite platform. The lower bar chart shows the number of observations used per day, with the brown bars indicating the number of conventional (e.g., radiosonde, surface stations) and atmospheric wind vectors (AMVs) typically derived from geostationary-observed cloud motions, and the green bars showing the total, namely with the addition of all the satellite observations shown in the upper chart. (Lower global images) (left) Meteosat-9 Spinning Enhanced Visible and Infrared Imager (SEVIRI; channel 9 IR10.8) brightness temperature, 0000 UTC Sunday 25 May 2008. The gray scale for brightness temperature ranges from 15°C to −70°C. The whitest regions are a proxy for cold/high cloud tops and enhanced precipitation. (right) As at (left), but from the ECMWF IFS using the latest operational system as of Nov 2011. Courtesy of Peter Bauer, ECMWF, UK.

Fig. 2.

(Upper bar charts) Recent time history of the data types and instruments assimilated into the ECMWF IFS. The upper chart shows information in terms of the number of data sources for the given satellite platform. The lower bar chart shows the number of observations used per day, with the brown bars indicating the number of conventional (e.g., radiosonde, surface stations) and atmospheric wind vectors (AMVs) typically derived from geostationary-observed cloud motions, and the green bars showing the total, namely with the addition of all the satellite observations shown in the upper chart. (Lower global images) (left) Meteosat-9 Spinning Enhanced Visible and Infrared Imager (SEVIRI; channel 9 IR10.8) brightness temperature, 0000 UTC Sunday 25 May 2008. The gray scale for brightness temperature ranges from 15°C to −70°C. The whitest regions are a proxy for cold/high cloud tops and enhanced precipitation. (right) As at (left), but from the ECMWF IFS using the latest operational system as of Nov 2011. Courtesy of Peter Bauer, ECMWF, UK.

Considered in isolation, these observational resources help to characterize and advance our understanding of weather and climate. An even greater advance comes through the incorporation of various observational measurements into the quantitative framework of weather prediction models via data assimilation, which has progressively improved through scientific and technical developments and by virtue of concurrent increases in computer processing power. Data assimilation, the backbone for modern robust prediction systems, provides a consistent and quantitative characterization of the atmospheric state: “global analysis” at a physical resolution of tens of kilometers in space and hours in time (e.g., Fig. 2, bottom right).

THE VIRTUAL GLOBAL FIELD CAMPAIGN

The integration of global observations, numerical models, and data assimilation heralds tremendous advances in high-resolution prediction (e.g., Fig. 2, bottom)—a vision initiated a generation ago with the 1979 First Global Atmospheric Research Program (GARP) Experiment (FGGE). Global analysis provides the means to conduct a virtual global field campaign for tropical convection that not only has a global reach but also provides unprecedented information on atmospheric structure, thermodynamics, and dynamics. Developed over several decades, this remarkable “observing” system leverages billions of dollars of investment in measurement platforms, instruments, and infrastructure. This realization, which emerged at a 2006 workshop held in Trieste, Italy, led to the recommendation to “develop an internationally coordinated computational-observational laboratory,” abbreviated herein to a “virtual global field campaign” for the integrated study of tropical convection (see Moncrieff et al. 2007).

The virtual approach complements traditional f ield campaigns in ways that were not possible until recently. In a global context, and with high resolution in mind, the virtual approach is the only way to address tropical convection and its scale interactions in a costeffective, flexible and efficient manner.

  • It is cost effective because of the vast amount of in situ measurements (e.g., aircraft, balloons, buoys, drifters, ships, and surface stations) and satellite measurements already available and optimally assimilated into the global analysis.

  • It is flexible because the global analysis can be updated as data assimilation practices improve, as observation accuracy gets refined (e.g., via improved instrument calibrations and satellite retrievals), as the representation of the physical processes in numerical models become more reliable, and as increasing computer capacity enables ever higher resolution.

  • It is efficient because the preparation of the databases (global analysis) is an integral part of operational global weather prediction.

With the virtual approach firmly in mind, the Year of Tropical Convection (YOTC) project, whose three principal components are sketched in Fig. 3, calls for a radical improvement in how tropical convection is represented in our global climate models. The deliberate focus on organized tropical convection is partly motivated by the fact that convective organization is missing from contemporary climate models, affecting the type and distribution of precipitation in important ways.

Fig. 3.

The three components of the YOTC project: (i) Global analysis, forecasts, and subgrid tendencies from high-resolution global weather models; (ii) comprehensive measurements from instruments deployed on satellite, field campaign, and in situ platforms; and (iii) research programs, including diagnostic studies of meteorological events in high-resolution analyses and forecasts, cloud-system resolving numerical simulations on spatial scales up to global, idealized models and dynamical analogs for studies of multiscale convective organization, scale interaction, and the development of improved convection parameterizations.

Fig. 3.

The three components of the YOTC project: (i) Global analysis, forecasts, and subgrid tendencies from high-resolution global weather models; (ii) comprehensive measurements from instruments deployed on satellite, field campaign, and in situ platforms; and (iii) research programs, including diagnostic studies of meteorological events in high-resolution analyses and forecasts, cloud-system resolving numerical simulations on spatial scales up to global, idealized models and dynamical analogs for studies of multiscale convective organization, scale interaction, and the development of improved convection parameterizations.

The focus on convection is not meant to imply that global model problems are solely the fault of convection. All the subgrid physical processes (convection, planetary boundary layer, surface exchange, microphysics, radiative transfer, turbulence) have shortcomings. Improving parameterizations per se has remained an unsolved problem since the early days of numerical weather prediction, as noted by Randall et al. (2003). The basic problem of parameterization is to approximate the effects of subgrid processes as functions of the resolved variables. Unfortunately, there is no physical principle that states under what conditions such functional relationships may uniquely exist.

In the remaining sections of this paper, we provide more in-depth highlights of the motivating scientific/technological challenges, describe the YOTC project, and present a way forward by identifying some research imperatives, including near-term collaborative studies.

SCIENTIFIC AND TECHNICAL CHALLENGES

Our intellectual gaps in fully understanding tropical convection and its scale interactions lead to shortcomings that range from the forecasting of tomorrow's weather to the projection of future climate. In recent years there has been attention to the “seamless prediction” of weather and climate, for several reasons. A practical reason is that climate and weather models are on convergent paths in regard to resolution (i.e., 10-km computational grids). The seamless prediction of weather and climate is a many-faceted problem as articulated by Dole (2008), Hurrell et al. (2009), Palmer et al. (2009), Brunet et al. (2010), Shapiro et al. (2010), and Shukla et al. (2010). The YOTC project addresses seamless prediction in a focused way through the deliberate attention to tropical convection, multiscale convective organization, and scale interaction.

The shortcomings referred to above are starkly illustrated the highly simplified framework of aquaplanet (ocean everywhere) simulations using contemporary global climate models (see Fig. 4). The panels in this figure show the evolution of precipitation patterns near the equator, as a function of longitude, for an arbitrary 30-day period. While there is no true observational counterpart of an aquaplanet to compare with, the precipitation patterns would be similar among the models if the parameterizations of the subgrid processes were physically correct and consistent. That is far from the case. The tropical rainfall varies enormously among models in terms of its spatial distribution and variability. The difference between the shortcomings in Fig. 4 and the comparatively realistic representation in the bottom right panel of Fig. 2 is that the latter relies on an optimal blend of model and assimilation that utilizes the individual strengths of each. We have assumed that the predictions of convective organization in Fig. 4 are representative for each model. However, addressing the predictability of these organized structures would require performing an ensemble of simulations for each model.

Fig. 4.

Time–longitude plots of equatorial precipitation (mm day−1) averaged over the −5° to +5° latitude band from all models in the Aqua Planet Experiment (APE). This figure with further explanation and the models identified will appear in Blackburn et al. (2012) and Williamson et al. (2012). The plate in the top left-hand corner is from a global cloud-system-resolving simulation using the Japanese NICAM model with a 7-km computational grid. Courtesy of David Williamson (NCAR) and Michael Blackburn (University of Reading, UK).

Fig. 4.

Time–longitude plots of equatorial precipitation (mm day−1) averaged over the −5° to +5° latitude band from all models in the Aqua Planet Experiment (APE). This figure with further explanation and the models identified will appear in Blackburn et al. (2012) and Williamson et al. (2012). The plate in the top left-hand corner is from a global cloud-system-resolving simulation using the Japanese NICAM model with a 7-km computational grid. Courtesy of David Williamson (NCAR) and Michael Blackburn (University of Reading, UK).

We have difficulties with reliable representations of tropical convection in our global weather/climate models, partly because of the complex hierarchy of interacting scales, processes, and phenomena highlighted above. While convective organization is a natural component of the seamless prediction of weather and climate, and despite being identified decades ago as a shortcoming from an international field campaign (Houze and Betts 1981), very little attention has been paid to organized convection in global models.

Organized convection and global models. Organized tropical convection of much larger size (mesoscale: 20–2,000 km) than the 1–10-km cumulus/cumulonimbus clouds has long been observed around the world: first in highly focused field campaigns [e.g., West Africa (Hamilton et al. 1945), the central Pacific (Zipser 1969), and Venezuela (Betts et a l. 1976)] and subsequently as organized circulations and “gregarious” properties (Moncrieff 1992; Mapes 1993). The parameterization of the upscale effects associated with the evolution from cumulonimbus (upper left, Fig. 1) into mesoscale systems (upper right, Fig. 1), and subsequent interaction with large-scale dynamics, presents a formidable scientific and practical challenge.

Observations show that MCSs on the order of 100 km produce about half the total precipitation in the tropics (Schumacher and Houze 2003) and have distinctive vertical heating profiles (e.g., Houze 1993e.g., Houze 1997). Contemporary parameterizations are not designed to represent the upscale effects of convective organization, although a semblance of organization may emerge as a result of interactions between dynamics and parameterization in weather models (Moncrieff and Klinker 1997) and climate models (Mapes et al. 2008). Presently, no climate model incorporates a satisfactory parameterization of mesoscale organization, although attempts have been made to approximate some thermodynamic aspects (e.g., Donner 1993; Donner et al. 2001) and aspects of organization (Mapes and Neale 2011).

Moist convection interacts with the large-scale circulation both upscale and downscale (Fig. 5, upper panel). There are important differences between the tropics and the extratropics, with consequences for parameterization. In the extratropics, interactions among synoptic and mesoscale motion and the embedded convection are downscale controlled by the life cycle of extratropical cyclones associated with midlatitude baroclinic instability (Charney 1947; Eady 1949). In the extratropics parameterized convection in weather models has reasonable fidelity owing, in part, to the successful reproduction in these models of this downscale baroclinic control. Upscaling occurs in the midlatitude baroclinic zones as cumulonimbus develop into propagating mesoscale convective systems organized by the vertical shear.

Fig. 5.

(top) Temporal forcing scales of tropical convection (diurnal cycle, seasonal cycle) and the primary scales of convective organization (cumulonimbus, mesoscale convective systems, synoptic waves and superclusters, and the Madden–Julian oscillation) . The monsoon intraseasonal oscillation interacts strongly with the Asian-Australian monsoon. The organized systems exhibit hierarchical coherence: (i) mesoscale systems consist of families of cumulonimbus; (ii) cumulonimbus and MCS are embedded in synoptic waves; and (iii) the MJO is an envelope of cumulonimbus, MCS, and superclusters. The upscale effects of convective organization are not represented in traditional climate models. The mean atmospheric state exerts a strong downscale control on convective structure, frequency, and variability. The weather–climate intersection has connotations for lower-frequency climate variability (e.g., ENSO). (bottom) Mesoscale convective organization bridges the scale gap assumed in traditional convective parameterization. (i) LES resolves cumulus, cumulonimbus, and mesoscale circulations, but the computational domain is small (~100 km) and simulations short (~1 day). (ii) CSRMs simulate mesoscale circulations but not cumulus/cumulonimbus. Two-dimensional CSRMs in superparameterized global models permit MCS-type organization and mesoscale dynamics. (iii) High-resolution global numerical prediction models may crudely represent large MCS (superclusters). (iv) MCS, and other mesoscale dynamical systems, are conspicuously absent from traditional climate models—organized convection is not parameterized and the computational grid is too coarse.

Fig. 5.

(top) Temporal forcing scales of tropical convection (diurnal cycle, seasonal cycle) and the primary scales of convective organization (cumulonimbus, mesoscale convective systems, synoptic waves and superclusters, and the Madden–Julian oscillation) . The monsoon intraseasonal oscillation interacts strongly with the Asian-Australian monsoon. The organized systems exhibit hierarchical coherence: (i) mesoscale systems consist of families of cumulonimbus; (ii) cumulonimbus and MCS are embedded in synoptic waves; and (iii) the MJO is an envelope of cumulonimbus, MCS, and superclusters. The upscale effects of convective organization are not represented in traditional climate models. The mean atmospheric state exerts a strong downscale control on convective structure, frequency, and variability. The weather–climate intersection has connotations for lower-frequency climate variability (e.g., ENSO). (bottom) Mesoscale convective organization bridges the scale gap assumed in traditional convective parameterization. (i) LES resolves cumulus, cumulonimbus, and mesoscale circulations, but the computational domain is small (~100 km) and simulations short (~1 day). (ii) CSRMs simulate mesoscale circulations but not cumulus/cumulonimbus. Two-dimensional CSRMs in superparameterized global models permit MCS-type organization and mesoscale dynamics. (iii) High-resolution global numerical prediction models may crudely represent large MCS (superclusters). (iv) MCS, and other mesoscale dynamical systems, are conspicuously absent from traditional climate models—organized convection is not parameterized and the computational grid is too coarse.

Tropical convection differs significantly from this classical picture. Rather than being controlled by the large-scale motion, tropical convection is an integral part of that motion, organizing across scales and constructively interacting with tropical waves and their environment (see Fig. 5, upper panel). In sheared environments downstream of mountains, convection organizes into mesoscale systems during the warm season in the midlatitudes (Carbone et al. 2002) and throughout the year in the tropics (e.g., Laing and Fritsch 1997).

The distinction between the tropics and extratropics is clearly evident in satellite measurements of atmospheric water. The structurally complex transition zone in Fig. 1 (bottom) demarks the moist tropics from the relatively dry extratropics. On weather time scales, intermittent “atmospheric rivers” of moisture f low from the tropics to higher latitudes, causing disastrous floods (Zhu and Newell 1994; Ralph et al. 2011). The rivers are associated with the incursion of midlatitude fronts, the extratropical transition of tropical cyclones, and the remote effects of planetary waves emanating from the tropics. On seasonal time scales, the Asian-Australian monsoon and its intraseasonal variability strongly modulates the distribution of atmospheric water. Global models have great difficulty in reproducing such variability.

Scale gap and missing mesoscale in climate models. Traditional convective parameterizations view convective clouds as much smaller than the grid scale of the numerical model: a “scale gap” in the 10–100-km range is assumed to separate cumulus convection from the resolved scales. Early global models had grids of hundreds of kilometers so this assumption was reasonable, in the sense that the gap was bigger than the cumulus clouds being parameterized. In the real world, mesoscale systems fill the gap (Fig. 5, bottom panel). Mesoscale convective organization is structurally absent from current global climate models: convective parameterizations do not represent the important dynamics (e.g., shear effects) and the spatial resolution is too coarse to simulate the mesoscale circulations. In other words, the scale-gap assumption enforced in models does not have a reliable physical basis. This raises a formidable weather–climate intersection research challenge: a rethinking of convective parameterization for the next generation of climate models where the scale-gap assumption is not applicable.

For decades, MCSs have been observed in field campaigns, simulated by finescale numerical models, and dynamically modeled [see review articles of Houze (2004), Tao and Moncrieff (2009), and Moncrieff (2010)]. The lower panel of Fig. 5 shows the modern hierarchy of models: (i) Large-eddy simulation (LES), which explicitly represents organized convection and cumulus, albeit in small domains and for integrations lasting about a day; (ii) cloud-system-resolving models (CSRMs), which simulate mesoscale convective organization in computational domains up to global but do not resolve cumulus convection; (iii) high-resolution global weather models; and (iv) traditional climate models.

Observations and models show that convective organization is controlled by wind shear, yet convective parameterizations pay little attention to shear. The upper panel in Fig. 6 shows that slantwise convective overturning in a sheared environment (Moncrieff 1992, 2010) approximates the organized airflow within mesoscale convective systems (e.g., Houze 1993e.g., Houze 2004). The two dynamical forms of energy (kinetic energy of shear and propagation, and the work done by the horizontal pressure gradient) that are key to the organization of convection in shear are not represented by contemporary parameterizations. Organized overturning is relevant to climate because it is a statistically significant contributor to the large-scale dynamics. For instance, Fig. 6 (lower panel) shows that MCSs contribute about half of the rain and latent heating in regions where climate models have difficulty with the distribution, amount, and frequency of precipitation (e.g., over continents, in the monsoons, and in the ITCZ).

Fig. 6.

(top) Organized slantwise overturning mesoscale airflow through an MCS depicted by the red/blue trajectories of upflow/downflow relative to the propagating system (uc, where c is the propagation speed), represented by a dynamical model (Moncrieff 1992) superimposed on the Houze (2004) description of an MCS. Blue insets identify three forms of energy: convective available potential energy (CAPE), kinetic energy of shear and propagation, and the work done by the horizontal pressure gradient. (bottom) Fraction of rainfall from precipitation features ≥ 100 km in maximum size (i.e., MCS) measured by the TRMM Precipitation Radar (PR) for Jan 1998–Dec 2006 using the procedure of Nesbitt et al. (2006). Courtesy of Tao and Moncrieff (2009).

Fig. 6.

(top) Organized slantwise overturning mesoscale airflow through an MCS depicted by the red/blue trajectories of upflow/downflow relative to the propagating system (uc, where c is the propagation speed), represented by a dynamical model (Moncrieff 1992) superimposed on the Houze (2004) description of an MCS. Blue insets identify three forms of energy: convective available potential energy (CAPE), kinetic energy of shear and propagation, and the work done by the horizontal pressure gradient. (bottom) Fraction of rainfall from precipitation features ≥ 100 km in maximum size (i.e., MCS) measured by the TRMM Precipitation Radar (PR) for Jan 1998–Dec 2006 using the procedure of Nesbitt et al. (2006). Courtesy of Tao and Moncrieff (2009).

THE YOTC PROJECT

Recognizing the pervasive challenges associated with tropical convection highlighted above, the World Climate Research Program (WCRP) and the World Weather Research Program (WWRP), via their prominent research program The Observing System Research and Predictability Experiment (THORPEX), jointly coordinate the Year of Tropical Convection project. A complete description is given in the YOTC Science Plan (Waliser and Moncrieff 2008), available online at www.ucar.edu/yotc. As previously summarized in Fig. 3, the YOTC project is in the spirit of a virtual global field campaign. Phenomena highlighted for research include the Madden–Julian oscillation and convectively coupled equatorial waves, easterly waves and tropical cyclones, the monsoons, tropical– extratropical interaction, and the diurnal cycle.

As noted in the introduction, there has been a vast increase in the number and types of observations available for weather and climate studies. Figure 7 shows an important component: the Earth Observing System (EOS) Afternoon-Train (A-Train) constellation of satellites (Stephens et al. 2002), which includes the Aqua, Aura, PARASOL, CloudSat, and CALIPSO satellites. This constellation includes sensors that measure solar and infrared radiation, cloud structure and microphysical properties, temperature and water vapor, aerosols and chemical composition, near-surface wind speed, sea surface temperature, etc. The A-Train makes near-simultaneous measurements from a variety of sensors. Along with the Tropical Rainfall Measuring Mission (TRMM) and Terra satellites, the A-Train samples vertical structure, including temperature, moisture, clouds, precipitation, latent heating, and radiative heating.

Fig. 7.

Schematics of a key component of the YOTC satellite database. (top) The A-Train satellite constellation of the Earth Observing System, where “A” identifies the afternoon local equatorial crossing at 1:30 pm LT. (bottom) The comprehensive range of relevant observations for studying tropical convection, depicting a summary of a specific A-Train CloudSat-centric collocated dataset made available for YOTC at the CloudSat Data Processing Center (cloudsat.cira.colostate.edu).

Fig. 7.

Schematics of a key component of the YOTC satellite database. (top) The A-Train satellite constellation of the Earth Observing System, where “A” identifies the afternoon local equatorial crossing at 1:30 pm LT. (bottom) The comprehensive range of relevant observations for studying tropical convection, depicting a summary of a specific A-Train CloudSat-centric collocated dataset made available for YOTC at the CloudSat Data Processing Center (cloudsat.cira.colostate.edu).

Along with other low-Earth orbit and geostationary satellites flying passive (infrared, visible, and microwave) and active (radar and lidar) instruments, there is a veritable armada of spacecraft measurements for tropical convection. The most recent low-Earth orbit satellite, Megha-Tropiques, launched in October 2011, will give useful information on the diurnal variability of MCSs, MJOs, and the monsoons. Geostationary satellite measurements complement the above mix of low-Earth orbit measurements by providing near-global, visible and infrared imagery that observe convection and clouds on the mesoscale to planetary scales as well as the diurnal cycle (Rossow and Duenas 2004).

To facilitate efficient access and manipulation of data, the YOTC Giovanni System (YOTC-GS), a web-based application developed by the National Aeronautics and Space Administration (NASA), gives swath and/or gridded data from a number of low- Earth orbit sensors. The swath data are best suited to detailed process studies, and for exploiting high spatial resolution (in some cases ~1 km), such as the statistical evaluation of regional cloud-system-resolving model output. The gridded data are suited to the study of meteorological phenomena and processes on large to global scales, for comparisons with global analyses of quantities not assimilated in models, and with hindcast/prediction products. Giovanni's intuitive “one-stop-shop” for visualizing, analyzing, and accessing data for YOTC is described more fully online (at http://disc.sci.gsfc.nasa.gov/YOTC).

The ECMWF-YOTC database includes 6-hourly global analyses, multiday forecasts, and over 30 model-based subgrid tendencies from the European Centre for Medium-Range Forecasts (ECMWF) Integrated Forecasting System (IFS) on a 25-km grid (16 km for January 2010–April 2010) for the 2-yr period May 2008–April 2010. Subgrid tendencies spanning the globe in this way would be impossible to get from a real field campaign. As for a real field campaign, the 2-yr period focuses the community interest and concentrates resources on common meteorological events and phenomena (see Waliser et al. 2012). In much the same way as an atmospheric sounding network is a core element of regional field experiments, the ECMWF-YOTC database, and similar products from the National Centers for Environmental Prediction (NCEP) and the Global Modeling and Assimilation Office (GMAO), are the backbone of the virtual global field campaign approach. The YOTC global analysis complements global reanalysis, which is performed for longer time periods (decades) and at coarser resolution, typically 100–200-km grid length. More information on reanalysis can be found in Compo et al. (2011) and online at www.reanalysis.org.

For a “fast” process such as atmospheric convection, shortcomings in parameterizations can be diagnosed and addressed by short “hindcasts” of illustrative convective events. Figure 8 shows the bias in tropical precipitation for two climate models, those of the National Center for Atmospheric Research (NCAR) and the Geophysical Fluid Dynamics Laboratory (GFDL). For each model, the upper panel refers to composited 3-day weather hindcasts and the lower panel to a 20-yr climate integration. The precipitation bias in the hindcasts has a similar distribution to that in the climate integration, notably in the Indian Ocean, west Pacific, Africa, and the ITCZ where convective organization is widespread (Fig. 6, lower panel). Running climate models in weather prediction mode is part of the YOTC project. For example, the same modeling group at the Department of Energy's Program for Climate Model Diagnostics and Intercomparison (PCMDI), which helped develop the research framework illustrated in Fig. 8, utilizes the YOTC period as a test bed for diagnosing and improving the NCAR Community Climate Model.

Fig. 8.

Comparisons of weather and climate bias in terms of precipitation rate (mm day−1) for two global modeling systems, GFDL and NCAR. The weather bias is the difference between predicted and observed precipitation rate for composited 3-day forecasts for Dec–Feb (DJF), 1992–93. The climate bias is from a 20-yr integration with prescribed sea surface temperature for Dec 1992–Feb 1993. Courtesy of Steve Klein and Jim Boyle, Lawrence Livermore National Laboratory.

Fig. 8.

Comparisons of weather and climate bias in terms of precipitation rate (mm day−1) for two global modeling systems, GFDL and NCAR. The weather bias is the difference between predicted and observed precipitation rate for composited 3-day forecasts for Dec–Feb (DJF), 1992–93. The climate bias is from a 20-yr integration with prescribed sea surface temperature for Dec 1992–Feb 1993. Courtesy of Steve Klein and Jim Boyle, Lawrence Livermore National Laboratory.

Most of the remaining part of this paper will describe how the experimental framework described above, involving the YOTC global analysis and observational databases, are being utilized to study and improve the prediction of the MJO.

PROGRESS WITH THE MJO

As the leading mode of tropical intraseasonal variability (e.g., Zhang 2005; Kiladis et al. 2009) and unforced convective organization ranging from cumulus to planetary scales, with cloud clusters and superclusters on the intervening scales (Nakazawa 1988), the MJO influences a wide range of weather and climate phenomena (Lau and Waliser 2011). With external forcing in mind, it is not surprising that the MJO has for long seriously challenged global models (e.g., Slingo et al. 1996; Lin et al. 2006; Sperber and Waliser 2008; Sperber et al. 2011). Its intraseasonal time scale makes the MJO a leading source of potential predictability (e.g., Waliser et al. 2003a,b; Waliser 2006) and a natural bridge between medium-range weather forecasting and seasonal prediction. Despite the challenging nature of the MJO, significant progress is being made on several fronts, as summarized below.

Global weather models. The upper panel of Fig. 9 shows that over recent years the strength and character of the MJO at hindcast lead times of 15 days in the ECMWF IFS has improved from a barely detectable disturbance to a quite realistic propagating system (Bechtold et al. 2008). This advance is attributed to the combined effects of improved parameterizations, increased resolution (now 16-km grid), and the assimilation of vast volumes of data (up to 40 million per day from various platforms; see Fig. 2). It is not known how each of these three improvements individually affects the MJO prediction. The IFS can now maintain the amplitude of the MJO for more than 30 days (Vitart and Molteni 2010), although some problems remain (F. Vitart 2011, personal communication):

  • The MJO propagates too slowly in the medium and subseasonal time range, and in the seasonal/climate time range the MJO becomes almost stationary.

  • In seasonal hindcasts the peak MJO activity is at much lower frequency than the observed 30–60- day range.

  • The MJO dissipates too readily upon crossing the Maritime Continent.

  • The MJO tends to regenerate more frequently from a previous MJO than observations (e.g., Matthews 2007) suggest.

  • The genesis of an MJO is rarely predicted more than a week in advance, but it is not known whether this is a specific model problem or a fundamental predictability issue.

Fig. 9.

(top) Progress with the MJO in the ECMWF IFS shown by the Hovmöller diagrams of the averaged outgoing longwave radiation (OLR; a surrogate for precipitation) between −10° and +10° latitude from 29 Dec 1992 to 15 Feb 1993 obtained from daily forecasts with different cycles of the IFS. Red/yellow colors denote warm regions (e.g., SST) and blue/green colors deep convection. (left) The “control” according to the 40-yr ECMWF Re-Analysis (ERA-40); (middle) a barely detectable MJO in the IFS for Sep 2004; (right) the greatly improved MJO for Sep 2009. A more complete record of progress is in Vitart and Molteni (2010). Courtesy Frederic Vitart, ECMWF. (bottom) Snapshot of the global distribution of cloud simulated by NICAM at 7-km grid spacing. Courtesy of H. L. Tanaka and the NICAM Team.

Fig. 9.

(top) Progress with the MJO in the ECMWF IFS shown by the Hovmöller diagrams of the averaged outgoing longwave radiation (OLR; a surrogate for precipitation) between −10° and +10° latitude from 29 Dec 1992 to 15 Feb 1993 obtained from daily forecasts with different cycles of the IFS. Red/yellow colors denote warm regions (e.g., SST) and blue/green colors deep convection. (left) The “control” according to the 40-yr ECMWF Re-Analysis (ERA-40); (middle) a barely detectable MJO in the IFS for Sep 2004; (right) the greatly improved MJO for Sep 2009. A more complete record of progress is in Vitart and Molteni (2010). Courtesy Frederic Vitart, ECMWF. (bottom) Snapshot of the global distribution of cloud simulated by NICAM at 7-km grid spacing. Courtesy of H. L. Tanaka and the NICAM Team.

Global climate models. The shortcomings noted above for the MJO in weather models also plague climate models (e.g., Kim et al. 2009), attesting to their basic nature. The fact that MJO-like systems are simulated by cloud-system-resolving models in conditions of constant sea surface temperature indicates that atmosphere–ocean interaction is not basic to the MJO (e.g., Grabowski 2001). Climate models traditionally have weak MJOs. However, modified parameterizations have led to improvements (e.g., Wu et al. 2007; Zhou et al. 2011). While interaction with the ocean is seemingly not necessary for its existence, the MJO is considerably improved in coupled ocean–atmosphere models compared to atmosphere-only models (Subramanian et al. 2011). Unrealistic mean states can negatively affect the initiation of the MJO (Ray et al. 2011). This is a significant issue for climate models.

Global cloud-system-resolving models, an important recent development, simulate mesoscale circulations and also their interactions with the larger scales of motion. The lower panel of Fig. 9 is a snapshot of the distribution of cloud simulated by the world's first such model, the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), in this case with a 7-km grid, although the NICAM has also been run with a 3.5-km grid (Tomita et al. 2005; Nasuno et al. 2007; Satoh et al. 2008). These simulations give a visually plausible cloud distribution, including an MJO case by Miura et al. (2007). Much work remains to fully evaluate these complex models against observations, an area where the virtual global field campaign approach could be used to advantage.

Superparameterized models. In superparameterization, cloud-system-resolving models are used in place of conventional parameterizations of convection and the planetary boundary layer in each grid cell of the parent global model (e.g., Grabowski 2001; Khairoutdinov et al. 2005; Tao et al. 2009). Superparameterization has some immediate advantages over the traditional parameterization: (i) mesoscale convection is explicit, albeit two-dimensional and limited to periodic columns of the parent global model; (ii) the convective lifecycle has a built-in memory; (iii) microphysical processes interact more realistically with the model dynamics; and (iv) convection is initiated/maintained by explicit downdraft outflows instead of needing an implicit “trigger.” Superparameterization simulates MJO-like systems (e.g., Benedict and Randall 2009), although there is a tendency for the MJO amplitude to be exaggerated and the environment to be too moist compared to observations. A possible reason is that the two-dimensional MCS simulated by the cloud-system-resolving models are overly efficient processors of energy compared to nature. Superparameterization is hundreds of times more computationally expensive than traditional parameterization, so it is not suitable for operational weather forecasting, centuries-long climate change projections, and Earth-system modeling.

SOME RESEARCH IMPERATIVES

Some research imperatives are given below, mainly in the context of the MJO and organized tropical convection.

Fundamentals. It remains to be fully understood why the MJO is improved in models, and whether the MJO is reproduced in the same way as in nature. In this respect, insights into dynamical aspects, such as upscale and downscale transport, scale interaction, multiscale organization, and convective transport are important. Considerable progress is being made through insights gained from theoretical–dynamical models and idealized numerical simulations. Examples are the role of organized convective momentum transport in the structure and evolution of the MJO and convectively coupled tropical waves (Moncrieff 1992,Moncrieff 2004; Grabowski and Moncrieff 2001, 2004; Biello et al. 2007; Majda and Stechmann 2009a; Khouider et al. 2011), the MJO as a neutrally stable moisture mode on intraseasonal/planetary scales (Majda and Stechmann 2009b), multicloud parameterization (Majda 2007; Khouider and Majda 2008), and the effects of multicloud parameterization on the MJO in low-resolution global models (Khouider et al. 2011).

Near-term international collaborative research. A number of activities are underway in collaboration with WCRP, WWRP/THORPEX, the Global Energy and Water Cycle Experiment (GEWEX), and a recent field campaign:

  • Vertical Structure and Diabatic Processes of the MJO: A Global Model Evaluation Project seeks to improve physical parameterizations for global weather and climate models. The series of numerical experiments are 20-yr climate simulations, along with 2-day and 20-day initialized hindcasts of two MJO events that occurred during the October 2009–February 2010 period of the YOTC, when El Niño conditions prevailed. This activity is summarized by Petch et al. (2011), with more information available online at www.ucar.edu/yotc (see MJO Task Force section).

  • The Transpose-Atmospheric Model Intercomparison Project (T-AMIP) consists of hindcasts with climate models run with specified sea surface temperature for four MJOs that occurred during October 2008–July 2009 for the La Niña conditions of the YOTC. More information can be found online at hadobs.metoffice.com/tamip.

  • The Dynamics of the MJO (DYNAMO; www.eol.ucar.edu/projects/dynamo) is the U.S. component of the international Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011). The main objective is to investigate processes that initiate the MJO in the Indian Ocean during boreal winter. Some of the DYNAMO modeling will be collaborative with the above MJO Task Force activity.

Longer-term research. A number of physically and dynamically complex issues pertinent to the intersection of weather and climate need to be addressed, such as the following:

  • Characterization of the temperature, moisture, and multiscale cloud structures within the MJO lifecycle including the roles of diabatic heating, lower-tropospheric moistening, and organized convective momentum transport.

  • Initiation mechanisms for the MJO, including the relative importance of local influences (e.g., surface fluxes, diurnal cycle) and extratropical influences such as planetary waves, and wintertime cold surges emanating from the Asian continent.

  • Orographic and diurnal cycle effects of the Indonesian maritime continent on the amplitude and propagation of the MJO.

  • Generation mechanisms for planetary waves by the MJO and convectively coupled tropical waves.

  • Effects of intraseasonal variability on the life cycle of the Asian-Australian monsoon.

  • Effects of the MJO on ENSO and vice versa.

  • Interplay between parameterized convection and explicit convective organization in high-resolution global models.

  • Improved parameterizations of the physical processes in global models, including but not restricted to convection.

CLOSING REMARKS

The Year of Tropical Convection (YOTC) project began as a grassroots response to a recommendation arising from an international workshop. It has grown into a significant effort involving numerical prediction centers, research groups, and individual researchers around the world. The project has been promoted at numerous workshops and meetings, and recently at the 2011 YOTC International Science Symposium in Beijing, China, as reported by Moncrieff et al. (2012).

Tropical convection includes precipitation systems that display an underlying chaotic order, notably the organized mesoscale systems that are building blocks for larger-scale meteorological phenomena. The YOTC project calls for major improvements in how the tropics are represented in climate models, recognizing that it will be impossible to predict climate on regional scales or to comprehend the global water cycle in a warmer world without addressing tropical convection and its multiscale organization.

Mesoscale convective organization is a “missing process” in contemporary climate models because coherent circulations are not represented by parameterizations and the model resolution is too coarse to simulate them. The next generation of climate models (i.e., 10-km grids) will need to address convective organization and how it interacts with contemporary parameterizations. Fortunately, we have the observations, numerical models, and theoretical insights to embark upon this challenging new quest.

We deliberately focused the latter part of this paper on the MJO. However, many of the conclusions are relevant to other multiscale phenomena at the intersection of weather and climate that severely challenge global models: the monsoons, tropical–extratropical interaction, easterly waves and tropical cyclones, and the diurnal cycle.

Acknowledgments

We thank the WWRP/THORPEX and WCRP for their lasting support of the YOTC project, and NASA, NOAA, and NSF for supporting the YOTC Project Office through the U.S. THORPEX Executive Committee (USTEC). We thank the ECMWF for their major commitment to preparing the ECMWFYOTC database. We thank Brian Mapes for helpful comments on the manuscript. Duane Waliser's contribution was carried out on behalf of the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA

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Footnotes

*The National Center for Atmospheric Research is sponsored by the National Science Foundation.