Characterizing Clouds and Convection Associated with the MJO Using the Year of Tropical Convection (YOTC) Collocated A-Train and ECMWF Data Set

Transcription

1 Characterizing Clouds and Convection Associated with the MJO Using the Year of Tropical Convection (YOTC) Collocated A-Train and ECMWF Data Set Wei-Ting Chen Department of Atmospheric Sciences, National Taiwan University Coauthors at NASA JPL: D. Waliser, J.-L. F. Li, T. Kubar, B. Kahn, E. Fetzer, S. Lee, L. Pan, X. Jiang, P. von Allmen Apr. 3 rd, nd CCliCs Workshop on Climate Modeling, Taipei 2013

2 Image Credit: NASA The synergistic use of the A-Train measurements to understand clouds and convection The representation of clouds, convection and precipitation in global climate models remains a major source of uncertainty in climate projections and weather forecasts. The synergistic application of the multi-sensor measurements from the A-Train satellite constellation offers comprehensive info of the weather and climate systems, and new opportunities to evaluate and improve the key processes in atmospheric models. The A-Train

3 The YOTC CloudSat-centric, Multi-parameter A-Train and High-resolution ECMWF Analyses Data Set This data set has been developed to better quantify the dynamic, radiative, and microphysical properties of clouds and convections. As a contribution to the WMO Year of Tropical Convection (YOTC) research activity (covering the YOTC period: May, Apr, 2010) Developed an efficient and flexible nearest-neighbor finding algorithm (lead by von Allmen and Lee at JPL): Along CloudSat track, collocate selected parameters relevant for clouds and ambient environment from A-Train products and ECMWF analyses CloudSat footprint (target) AIRS footprint, co-located MODIS footprint, co-located AIRS footprint, not co-located MODIS footprint, not co-located

4 P (hpa) CALIPSO ( < ~3) Aerosol (p) Cloud (p) CERES TOA and SFC radiative fluxes MODIS Aerosol Opt Depth Cloud Top - T, p, Particle Size MLS (UTLS) T(p), IWC(p), CO (p), O 3 (p), HNO 3 (p) AIRS q(p), T(p), OLR CloudSat IWC(p) & IWP LWC(p) & LWP Cloud Type (p) Particle Size (p) Light Precip ECMWF (ERA-Int and YOTC high-res) (p), q(p), T(p) u(p), v(p) IWC(p), LWC(p) AMSR Precipitation SST Prec Water LWP Surf. Wind Speed

5 The CINDY2011/DYNAMO Campaign CINDY2011 & DYNAMO are international field programs to advance our understanding of MJO initiation processes and to improve MJO prediction: CINDY2011: in central equatorial Indian Ocean in Oct Mar 2012 DYNAMO: organizes the US interest of partaking in CINDY2011 (collaborative research of NSF, DOE, ONR, NOAA, NASA) Observational activities: sounding-radar array formed by research vessels and island sites and enhanced moorings; Modeling activities: forecast/hindcast at synoptic to seasonal time scales with operational/research models Scientific objectives of DYNAMO: Investigate three key processes for MJO, particularly during the initiation The role of moisture and moistening process The role of cloud population Air-sea interaction/coupling Develop process-oriented diagnostics for model evaluation CINDY = Cooperative Indian ocean experiment on intraseasonal variability in the year 2011 DYNAMO = Dynamics of the Madden-Julian oscillation

6 Previous works using A-Train data to investigate ambient thermodynamic structure and cloud population associated with MJO [Tian et al., Mon Wea Rev, 2010] AIRS water vapor AIRS temperature [Jiang et al., Clim Dyn, 2011] CloudSat cloud fraction associated with various cloud types

7 Characterizing cloud, convection during MJO events using the collocated A-Train data set Motivated by the DYNAMO scientific hypotheses, we d like to investigate the role of the moist layer and cloud populations for MJO initiation and propagation From the perspective of the new collocated multi-parameter data set, identify the characteristics and variability associated with the MJO phases for the ambient thermodynamics, cloud structure, and precipitation Select area over Indian Ocean (5N-10S, 65-85E) larger than the DYNAMO target areas so that enough statistics can be included DYNAMO area of interest (0-7S; 72-79E) Region to collect composite statistics in the present study (5N-10S; 65-85E) The shaded contours show the cloud top temperature on 21 UTC 12 Dec The square box is the DYNAMO area of interest, with key measurements planned at four primary observing sites (black dots) [credit: The DYNAMO Overview Document]

8 Composites in MJO Phases Based on the Multivariate MJO Index [Wheeler and Hendon, 2004] Period: Two winter MJO seasons covered by the collocated data set (Nov Apr, 2008 to 2010) Total=362 days; 131 days missing (CloudSat downtime) The phase diagram of the WH index for Oct-Dec, 2009 Collect composites for the eight MJO phases based on the multivariate MJO index (WH2004) For days with amplitude of WH index >1, label each day with its MJO phase WH index amplitude <1 = Non-MJO days (baseline for computing anomalies) Merge phases two by two to augment sample size Compute PDF, PDF anomalies, mean and anomalies profiles, and other diagnostics/statistics for parameters associated with ambient thermodynamics, clouds, and convection. [Credit: Bureau of Meteorology, Australia]

9 Composites in MJO Phases Based on the Multivariate MJO Index [Wheeler and Hendon, 2004] Composite AIRS OLR Anomalies in the Collocated Data Set (Nov-Apr, ) MJO Phases Non-MJO OLR (W/m 2 ) Region to collect composite statistics in the present study (5N-15S; 65-85E) Over Indian Ocean: Convectively Active: Phases 2 & 3 Convectively Suppressed: Phases 6 & 7 MJO phase Non-MJO # of days (13.4%) 25 (10.8%) 50 (21.6%) 57 (24.7%) 68 (29.5%) # of profiles 102,590 79, , , ,911

10 Probability Distribution of Moisture, Temperature, and Cloud AIRS RH at 700 hpa (Clear sky) AMSR-E Column Water Vapor (All sky) PDF PDF

11 Probability Distribution of Moisture, Temperature, and Cloud AIRS RH at 700 hpa (Clear sky) AMSR-E Column Water Vapor (All sky) ERA-Int RH at 700 hpa (All sky) AMSR-E SST CloudSat Cloud Top Pressure Convectively active phases are associated more frequently with high all-sky CWV, low clear-sky RH near BL top, and high cloud top. Higher SST in the build-up phases. Transitions of MJO phases are generally consistent among different sensors/products

12 Mean and anomaly vertical profiles AIRS RH mean profiles (Clear sky) AIRS RH anomaly profiles (Clear sky)

13 Mean and anomaly vertical profiles AIRS RH anomaly profiles (Clear sky) ERA-Int Vertical Velocity CloudSat Ice Water Content Convectively active phases are associated more frequently with low clear-sky RH near BL top, higher RH in mid-upper troposphere, updrafts, and high IWC

14 PC2 PC1 Variability of vertical profiles from EOF analysis CloudSat Total Cloud Frequency Mean and STD CloudSat Total Cloud Frequency 1 st and 2 nd EOFs 1 st and 2 nd PCs Grouped by MJO phase EOF1 (84.3%) Total CF CLDSAT (%) EOF2 (7.6%) N MJO phase N MJO phase

15 Co-variability among parameters: Coupled EOF analysis Total CF CLDSAT RH ERA ERA PCs Grouped by MJO phase EOF1 (66.8%) EOF2 (13.7%) EOF3 (4.6%) (0.1=17.9%) (0.1=29.2%) (0.1=0.03 Pa/s)

16 AIRS RH (clear sky) vs. AMSR-E rain rate Mean RH profiles sampled with rain rate ERA-Int RH (all sky) vs. AMSR-E rain rate Process-oriented diagnostics of MJO for GCM evaluation [Kim et al., 2009] ERA40 RH vs. GPCP precip RH vs precip in GCMs

17 Summary and Future Work The present work applies the YOTC collocated A-Train and ECMWF data set to characterize winter MJO over Indian Ocean, focusing on clouds and convection, and their relationships with large-scale environment Development of various statistics (mean/anomaly of PDF, profiles, EOF) and diagnostics (e.g. RH vs rain rate) to characterize the moisture structure and the evolution of cloud populations Ongoing Work Quantify uncertainty and biases associated with the sensitivity of instrument and/or algorithm, sampling issues, and resolution. (all suggestions are welcome!) Separate cases of different MJO types; extend to other regions (W. Pac)/time period (summer) Use cloud resolving model experiments to understand the feedback from the cloud populations to the large scale environment Continue developing process-oriented diagnostics/constraints for global model/regional model output for key processes associated with MJO initiation and propagation

18 To Download the Collocated A-Train and ECMWF Data Set The CSU Data Portal: or

19 Thank you for your attention. Questions?

20 CloudSat(IWC,cloud type) + AMSR-E(rain rate) + AIRS(T,q,RH) (Jul 2008) IWC q T RH 5-year radiosonde and precipitation gauge data at Nauru Island (Holloway and Neelin, 2009) Consistent trends: A strong association between rainfall and moisture variability in the free troposphere PPT (mm/hr) PPT (mm/hr) q RH

21 CloudSat(IWC,cloud type) + AMSR-E(rain rate) + AIRS(T,q,RH) (Jul 2008) IWC q T RH YOTC ECWMF model forecast (0.125x0.125 resolution, Jul 2008) 3hr-ave Prec. (mm/hr) IWC q T RH Less variability in low level moisture drying in heaviest precip. bins -- owing to parameterized subsidence??

22 Apply the collocated A-Train data set to study tropical deep convection clouds: A first look Over Tropical Ocean (20N-20S); July 2008 Choose deep convection clouds identified by CloudSat cloud class algorithm; collect and analyze statistics of CPR profiles with collocated retrievals from AIRS (q) and AMSR-E (rain rate) Collocated CloudSat(cloud type) + AMSR-E(rain rate) + AIRS(q) (Jul, 2008) 5-year radiosonde and precipitation gauge data at Nauru Island [Holloway and Neelin, 2009] PPT (mm/hr) Consistent trends: A strong association between rainfall and moisture variability in the free troposphere

23 Scientific Objectives of DYNAMO Three key-processes (particularly during the initiation) The role of moisture and moistening process The role of cloud population Air-sea interaction/coupling ERA40 RH vs GPCP precip Develop process-oriented diagnostics for model evaluation Potential improvements in model parameterization entrainment triggering stochastic component boundary layer rain evaporation boundary layer and large-scale cloudiness Example of process-oriented diagnostics of MJO for GCM evaluation proposed by the YOTC MJO Task Force [Kim et al., 2009]

24 Three Hypotheses to be Explored by DYNAMO Hypothesis I: Deep convection can be organized into an MJO convective envelope only when the moist layer has become sufficiently deep over a region of the MJO scale; the pace at which this moistening occurs determines the duration of the pre-onset state. Hypothesis II: Specific convective populations at different stages are essential to MJO initiation. Hypothesis III: The barrier layer, windand shear-driven mixing, shallow thermocline, and mixing-layer entrainment all play essential roles in MJO initiation in the Indian Ocean by controlling the upperocean heat content and SST, and thereby surface flux feedback. [DYNAMO Scientific Program Overview, 2010]

25 Topic 2 - Cloud populations in different MJO phase: Previous Works CloudSat IWC/LWC CloudSat Cloud Fraction associated with various cloud types [Jiang et al., 2011]

26 PDF of Moisture, Temperature in different MJO phases AIRS RH 700 Clear sky ERA-Int RH 700 Clear sky AMSR-E CWV All sky PDF ERA-Int, All sky PDF

27 PDF of Near-Surface Temperature AIRS Surface Air T ERA-Int Skin T AMSR-E SST PDF

28 PDF of Cloud Top, Optical Depth, and Precipitation CloudSat Cloud Top Pressure MODIS COD AMSR-E Rain Rate PDF

29 Mean RH profiles at different MJO phases AIRS mean RH ERA-Int mean RH AIRS RH anomaly ERA-Int RH anomaly

30 Anomalies of atmospheric water content and vertical velocity CloudSat IWC anomaly CloudSat LWC anomaly ERA-Int 500 anomaly

31 AIRS RH Mean RH profiles sampled with rain rate ERA-Int RH Process-oriented Diagnostics ofr MJO for GCM evaluation (Kim et al., 2009) ERA40 RH vs GPCP precip RH vs precip in GCMs