Tropical Warm Pool - International Cloud Experiment (TWP-ICE)
21 January 2006 - 13 February 2006
Lead Scientist: Peter May
The Tropical Warm Pool – International Cloud Experiment (TWP-ICE) was an intense airborne measurement campaign conducted in the region near Darwin, Northern Australia in early 2006. The experiment was a collaborative effort between the US DOE ARM project, the Bureau of Meteorology, NASA, the European Commission DG RTD-1.2 and several United States, Australian, Canadian and European Universities. TWP-ICE aimed to describe the properties of tropical cirrus and the convection that leads to their formation. Cirrus are ubiquitous in the tropics and have a large impact on their environment but the properties of these clouds are poorly understood. A crucial product from this experiment was a data set suitable to provide the forcing and testing required by cloud resolving models and parameterizations in Global Climate Models (GCMs). This data set provided the necessary link between cloud properties and the models attempting to simulate them.
This experiment was undertaken over a four week period in early 2006. January/February corresponding to the wet phase of the Australia monsoon. This season was selected because, despite Darwin’s coastal location, the convection that occurs over and near Darwin at this time is largely of maritime origin with a large fetch over water. Based on previous experiments, the convection appears typical of maritime convection with widespread convection that has complex organization, but is not as deep or as intense as continental or coastal convection. Therefore, it was expected that the convection and cloud characteristics would be representative of conditions typical for wide areas of the tropics.
Observations of cloud properties were provided by an extensive set of in situ and remote sensing instruments. The Darwin ARM site provided continuous measurements of cloud properties through remote sensing retrievals. A second set of instruments was deployed on a ship in the Timor Sea, approximately 100 km northwest of Darwin. Added to these surface-based observations were several research aircraft including the NASA WB-57, the DOE Proteus, the DLR Falcon and the Geophysica, as well as three aircraft provided by Airborne Research Australia (ARA): the Egrett, King Air and Dimona. These aircraft fulfilled a variety of observational requirements to address the science goals. The strategy for utilizing these aircraft included obtaining in situ observations of cirrus properties combined with remote sensing observations obtained from above and below the clouds. In addition, a boundary layer aircraft was used to characterize radiative and turbulent fluxes in the boundary layer.
The boundary layer observations were part of the effort to characterize the convective environment in which the cirrus are formed. Other observational components geared toward describing the environment included a network of radiosonde stations encircling Darwin, a pair of precipitation radars, wind profilers, and surface flux stations. Darwin is a coastal site and it was important to characterize the oceanic region off the Australia coast. For this purpose, the CSIRO research vessel Southern Surveyor was stationed in the Timor Sea to the northwest of Darwin. This ship served as a launch site for sondes, to complete the ring around Darwin and also carried surface flux instruments. In addition to flux observations, remote sensing instruments were also planned for the ship. The addition of these instruments provided a complete second surface site. This capability increased the likelihood of obtaining good coordinated surface/aircraft observations.
The occurrence of tropical clouds in the form of shallow non-precipitating cumulus to deep precipitating cumulonimbus with extensive anvils have a significant and fundamental impact on the atmospheric energy balance. Understanding the properties of these clouds and parameterizing their impact within climate prediction models is one of the major challenges facing the international community. It is well recognized that the representation of these process is a major uncertainty in our ability to represent and predict climate. In that context modeling activities, including the application of Cloud Resolving and Single Column Models as well as a suite of Numerical Weather Prediction models, formed a major component of the experiment activities. Those activities included pre-experiment model evaluation, NWP support during experiment and a large post-experiment program that made use of the collected data in evaluating and improving the representation of cloud, convective and radiative processes in the models.
The ARM project takes long term observations to provide data to address the cloud characteristics and their radiative effects. TWP-ICE provided important in situ data to both verify the remote retrievals and provide high resolution in situ data that cannot be remotely sensed. The experiment also provided an invaluable description of the convective environment. Together, the observations of cloud properties and the atmospheric state make this a data set unique for improving cloud parameterizations in the tropics.
Tao W, T Iguchi, and S Lang. 2019. "Expanding the Goddard CSH Algorithm for GPM: New Extratropical Retrievals." Journal of Applied Meteorology and Climatology, , 10.1175/JAMC-D-18-0215.1. ONLINE.
Song F and G Zhang. 2018. "Understanding and Improving the Scale Dependence of Trigger Functions for Convective Parameterization Using Cloud-Resolving Model Data." Journal of Climate, 31(18), 10.1175/JCLI-D-17-0660.1.
Dolan B, B Fuchs, S Rutledge, E Barnes, and E Thompson. 2018. "Primary modes of global drop-size distributions." Journal of the Atmospheric Sciences, 75(5), 10.1175/JAS-D-17-0242.1.
Wong M and M Ovchinnikov. 2017. "A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection." Journal of the Atmospheric Sciences, 74(4), 10.1175/JAS-D-16-0146.1.
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Fan J, Y Wang, D Rosenfeld, and X Liu. 2016. "Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges." Journal of the Atmospheric Sciences, 73(11), 10.1175/jas-d-16-0037.1.
Tang S, S Xie, Y Zhang, M Zhang, C Schumacher, H Upton, M Jensen, K Johnson, M Wang, M Ahlgrimm, Z Feng, P Minnis, and M Thieman. 2016. "Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment." Atmospheric Chemistry and Physics, 16(22), 10.5194/acp-16-14249-2016.
Ovchinnikov M, K Lim, V Larson, M Wong, K Thayer-Calder, and S Ghan. 2016. "Vertical overlap of probability density functions of cloud and precipitation hydrometeors." Journal of Geophysical Research: Atmospheres, 121(21), 10.1002/2016jd025158.
Leung K. 2016. Stochastic Models for Precipitable Water in Convection [Thesis]. San Diego: San Diego State University.
Tao WK and X Li. 2016. "The relationship between latent heating, vertical velocity, and precipitation processes: The impact of aerosols on precipitation in organized deep convective systems." Journal of Geophysical Research: Atmospheres, 121(11), 10.1002/2015jd024267.
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Campaign Data Sets
|IOP Participant||Data Source Name||Final Data|
|Richard Austin||Twin Otter Radar||Order Data|
|Jason Beringer||Surface Flux Analysis||Order Data|
|Ann Fridlind||CRM Intercomparison||Order Data|
|Jorg Hacker||Airborne Surface Fluxes||Order Data|
|Hartmut Hoeller||Lightning Detection Network||Order Data|
|Christian Jakob||Radiosondes||Order Data|
|Yanluan Lin||AGCM Intercomparison||Order Data|
|Guosheng Liu||Liu cloud ice water||Order Data|
|Chuck Long||Surface Flux Analysis||Order Data|
|James Mather||PARSL||Order Data|
|Greg McFarquhar||Proteus Microphysics||Order Data|
|Mario Mech||Microwave Radiometer Profiler||Order Data|
|Peter Minnett||Maeri||Order Data|
|Patrick Minnis||visst||Order Data|
|R. Reynolds||Surface Flux - Ship||Order Data|
|Tim Tooman||Proteus||Order Data|
|David Turner||MWR Retrievals||Order Data|
|Jim Whiteway||Twin Otter Lidar||Order Data|
|Christopher Williams||S-band Radar||Order Data|
|Steven Wofsy||Proteus CO2 Mixing Ratios||Order Data|
|Shaocheng Xie||Constrained Variational Objective Analysis Data||Order Data|