A23D
 103 (TICC)
 Tuesday
 1400

Multiscale Organization of Tropical Convection: Year of Tropical Convection (YOTC) II


Presiding:  D E Waliser, Jet Propulsion Laboratory/Caltech, Pasadena; Y Takayabu; M Moncrieff

A23D-01

Status and Research Agenda of YOTC


Moncrieff, M W (moncrief@ucar.edu), CGD, NCAR, Boulder, CO, United States
Waliser, D E (duane.e.waliser@jpl.nasa.gov), JPL, NASA, Pasadena, CA, United States

Moist convection organizes into multiscale cloud systems of various kinds, a process with a dynamical basis and upscale connotations. While organized precipitation systems have been extensively observed, numerically simulated and dynamically modeled, our knowledge of their effects on the large-scale circulation is incomplete. Convective organization is absent de facto from contemporary climate models because the salient dynamics are not represented by parameterizations and climate model resolution is insufficient to represent them explicitly. High-resolution prediction systems, fine-resolution cloud-system models, and dynamical analogs comprehensively address this major challenge. As a key element in the seamless prediction of weather and climate on timescales up to seasonal, organized convection is the emphasis of an international project, the Year of Tropical Convection (YOTC) which is coordinated jointly by the World Weather Research Programme (WWRP)-THORPEX and the World Climate Research Programme (WCRP). This paper will review the scientific basis and progress towards the implementation of the YOTC project.

A23D-02

The ECMWF-YOTC database


Miller, M J (Martin.Miller@ecmwf.int), Research, ECMWF, Reading, United Kingdom

This talk will review recent developments in the ECMWF forecast system which contribute to the latest quality of the T799 and T1279 global analyses and 10-day forecasts which form the main part of the two-year YOTC dataset. As well as all the standard, routine operational fields there are many additional flux and tendency fields from all the physical parametrizations which help facilitate in-depth studies of the dynamics and physics of tropical phenomena on a wide-range of spatial and temporal scales. Some examples from the ECMWF system will illustrate what can be done and some outstanding problems still to be addressed. This dataset is freely available on the web and is intended to help YOTC studies worldwide. The WMO WWRP-THORPEX is also using this dataset to study amongst other things the influence of the Tropics on mid-latitudes.

A23D-03

Global nonhydorstatic model simulations of Typhoon 0806 Fengshen by NICAM


Satoh, M (satoh@ccsr.u-tokyo.ac.jp), Center for Climate System Research, the University of Tokyo, Kashiwa, Japan
Nasuno, T (nasuno@jamstec.go.jp), RIGC, JAMSTEC, Yokohama, Japan
Taniguchi, H (taniro@jamstec.go.jp), RIGC, JAMSTEC, Yokohama, Japan
Yamada, H (yamada@jamstec.go.jp), RIGC, JAMSTEC, Yokohama, Japan
Yanase, W (yanase@jamstec.go.jp), Ocean Research Institute, the University of Tokyo, Nakano, Japan

A23D-04

Tropical intraseasonal oscillations and convection-coupled waves in the western North Pacific during boreal summer


Sui, C (sui@ncu.edu.tw), Institute of Hydrological and Oceanic Sciences, National Central University, Jhongli, Taiwan

A23D-05

Structure and Variability of Diabatic Heating Associated with the MJO


Zhang, C (czhang@rsmas.miami.edu), RSMAS, University of Miami, Miami, FL, United States
Ling, J (jling@rsmas.miami.edu), RSMAS, University of Miami, Miami, FL, United States

This presentation describes recent diagnostic results of diabatic heating profiles associated with the MJO. Three latent heating estimates based on TRMM data and four diabatic heating (Q1) estimates based on global reanalyses were compared to seek common features in these products. A westward tilt in the heating profiles of the MJO is found in most of the products over the western Pacific, but in only few over the Indian Ocean. A apparent bi-modal structure exist in the MJO heating profiles. Dynamical implications of the results and caveats in the heating products are discussed.

A23D-06

Low-level Divergence Events and Associated Equatorial Westerly Flow around Sumatra during Passage of Kelvin Waves: Role of Stratiform Convection


Ridout, J A (james.ridout@nrlmry.navy.mil), Naval Research Laboratory, Monterey, CA, United States
Flatau, M K (maria.flatau@nrlmry.navy.mil), Naval Research Laboratory, Monterey, CA, United States

Analyzed wind profiles for the eastern Indian Ocean from COAMPS® for the summer of 2006 (June - September) and from the ECMWF forecast system for the summers of 2008 and 2009 show recurring episodes of low-level divergence along the equator to the west of Sumatra, followed by increased low-level negative relative vorticity to the west and north of the island. A case study for a particularly strong event in June 2006 suggests that the increased negative vorticity tends to result from horizontal advection stemming from the decay of the southerly cyclonic vortex of the vortex pair that is frequently observed in the region. TRMM rainfall data and Cloudsat data support the conclusion that these events tend to mark the stratiform phase of convection associated with passing Kelvin waves. The potential significance in regards to propagation of equatorial waves through the Maritime Continent will be discussed.

A23D-07

Role of Cloud Resolving Model on MJO simulation of Super Parameterized Community Atmospheric Model


Mukhopadhyay, P (mpartha@tropmet.res.in), Indian Institute of Tropical Meteorology, Pune, India
Waliser, D E (duane.e.waliser@jpl.nasa.gov), Jet Propulsion Laboratory, Pasadena, CA, United States
Xiang, X (xianan.jiang@jpl.nasa.gov), Jet Propulsion Laboratory, Pasadena, CA, United States
Tian, B (Baijun.Tian@jpl.nasa.gov), Jet Propulsion Laboratory, Pasadena, CA, United States
Goswami, B (goswami@tropmet.res.in), Indian Institute of Tropical Meteorology, Pune, India
Maloney, E D (emaloney@atmos.colostate.edu), Deaprtment of Atmospheric Science, Colorado State University, Fort Collins, CO, United States
Benedict, J (jim@atmos.colostate.edu), Deaprtment of Atmospheric Science, Colorado State University, Fort Collins, CO, United States
Khairoutdinov, M (mkhairoutdin@ms.cc.sunysb.edu), School of Marine and Atmospheric Science, Stony Brook University, Stony Brook, NY, United States

It is well established by previous work (Slingo etal. 2005, Lin etal. 2006, Zhang etal. 2006) that MJO are not well represented in global climate models. It is also established that the major source of climate model bias arise from cloud and convection parameterization. The knowledge of physical mechanism of genesis and life cycle of MJO also is not complete. In recent times the super parameterized convection framework or multi scale modeling framework (MMF) show promise (Khairoutdinov et al. 2008; Kim et al. 2009, James and Randal, 2009) in capturing realistic MJO. The success of superparameterization is attributed to better representation of clouds in the GCM through the cloud resolving model (CRM) embedded in each GCM grid. However, no attempt is made to actually analyse the CRM fields to bring out its role in modulating the large scale simulated by the GCM. An attempt is made to analyse the cloud water and cloud ice component of CRM embedded in CAM3.0. The data includes the coupled (slab ocean model) super parameterized CAM from September 2001 to August 2004. It is found that first mode of empirical orthogonal function of cloud dominantly represents the shallow mode and second and third mode represent the cloud in transition and the deeper mode respectively. The principal components (PCs) of the 1st, 2nd and 3rd mode when projected on to the GCM grids along with the (MJO) composite of filtered (20-100 days) OLR anomaly, it is distinctly found that PC mode 1 leads the maximum negative anomaly and PC mode 3 are collocated with the maximum of negative OLR anomaly and PC2 lies in between. The lag-lead correlation with the PC1, PC2 and PC3 with the filtered OLR anomaly further establishes the fact that in space and time, the PC1 of CRM fields leads the convection. This means that the lower level moisture preconditioning leading to build up of instability for the deep convection to set in subsequently, is actually driven by the CRM response in the Super parametrized framework. This answers the question why the SPCAM better simulates the MJO.

A23D-08

Vertical Moist Thermodynamic Structure of the MJO in AIRS Observations: An Update and a Comparison to ECMWF Interim Reanalysis


Tian, B (Baijun.Tian@jpl.nasa.gov), Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, United States
Waliser, D E (duane.e.waliser@jpl.nasa.gov), Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, United States
Fetzer, E (Eric.J.Fetzer@jpl.nasa.gov), Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, United States
Lambrigtsen, B (Bjorn.Lambrigtsen@jpl.nasa.gov), Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, United States
Yung, Y (yly@gps.caltech.edu), Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, United States

We have documented the large-scale vertical moist thermodynamic structure of the Madden-Julian Oscillation (MJO) using the first 2.5 years (2002-2005; 8 events) of atmospheric specific humidity and temperature profiles from the Atmospheric Infrared Sounder (AIRS) (Tian et al. 2006). Here, we further examine this issue using currently available 7-year AIRS data (2002-2009; 18 events) to test the robustness of our earlier results and their dependence on the AIRS data record and MJO event selection and compositing methods employed. Our results indicate a strong consistency of the large-scale vertical moist thermodynamic structure of the MJO between different AIRS data records (2.5 versus 7 years) and different MJO event selection and compositing methods (extended empirical orthogonal function method versus Wheeler and Hendon (2004) method). This demonstrates that our results are robust and show little sensitivity to the analysis methods examined here. Our earlier study also compared the large-scale vertical moist thermodynamic structures of the MJO between AIRS and NCEP/NCAR reanalysis, with the indication that the analysis was deficient in a number of areas, particularly in the Indian Ocean. In this study, we perform a similar comparison between AIRS and the newer ECMWF Interim reanalysis (ERA-Int). Our results indicate much better agreement between AIRS and ERA-Int, although AIRS humidity profiles are drier in moist regions (~10%) and moister in dry regions (10%) compared to ERA-Int. These results will provide a useful metric for climate model diagnostics.

A23D-09

The tropical biases in IPCC AR4 climate models


Lin, J (lin.789@osu.edu), Geography, The Ohio State University, Columbus, OH, United States

The tropical climate and variability play a key role in global weather predictions, climate predictions and climate change projections. However, they are not well simulated by the state-of-the-art general circulation models (GCMs), and the problems are generally referred to as the “tropical biases”. The most prominent tropical biases are the double-ITCZ (Intertropical Convergence Zone) problem, the El Nino/Southern Oscillation (ENSO) problem, and the Madden-Julian Oscillation (MJO) problem. These tropical biases have been persisting in the last several generations of GCMs. The major difficulties for understanding and alleviating these biases are twofold: (1) They all involve some forms of feedback, such as the ocean-atmosphere feedback and the wave-heating feedback, making it difficult to determine the real cause of the bias; and (2) The biases need to be traced back to specific model characteristics, such as certain aspect of the physical parameterizations, in order to provide useful guidance on how to improve the model simulations. We will present the results on the tropical biases in IPCC AR4 climate models, including double-ITCZ, ENSO, MJO and convectively coupled equatorial waves. The multi-model intercomparison is combined with feedback analysis to understand the physical reasons of the tropical biases and find systematic dependence of the biases on specific model characteristics.