Madden-Julian Oscillation and Convectively Coupled Equatorial
Composite Behavior of Simulated Madden-Julian Oscillation ...
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REFERENCES Background & Motivation James J. Benedict 1 ([email protected] ), William D. Collins 1 , Michael S. Pritchard 2 1 Earth Science Division, Lawrence Berkeley National Lab; Berkeley, California 2 Department of Earth System Science, University of California-Irvine; Irvine, California Composite Behavior of Simulated Madden-Julian Oscillation Disturbances Based on Indian Ocean Dipole Phase [1] Zhang, C., 2005. Madden-Julian oscillation Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158. [2] Lin, J.-L., and Coauthors, 2006: Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models: Part I: Convective Sig- nals. J. Climate, 19, 2665–2690. [3] Wilson, E. A., A. L. Gordon, and D. Kim, 2013: Observations of the Madden-Julian oscillation during Indian Ocean Dipole Events. J. Geophys. Res. Atmos., 118, 2588–2599, doi:10.1002/jgrd.50241. [4] Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Na- ture, 401, 360-363. ACKNOWLEDGMENTS This research was supported by the Director, Office of Science, Office of Biological and Environmen- tal Research of the U.S. DOE under Contract No. DE-AC02-05CH11231 as part of their Earth System Modeling Program. The Madden-Julian Oscillation (MJO), a cyclic eastward-moving pattern of rainy and dry condi- tions in the tropical Indian and West Pacific Ocean regions, dominates atmospheric variability on in- traseasonal timescales (20-100 days) and is strongly modulated by air-sea interactions [1] . Many aspects of the MJO remain poorly understood, and most climate models struggle to reproduce the observed variability [2] . The MJO has profound impacts on mon- soons, ENSO, and midlatitude weather and precipitation ex- tremes [1] . Recent evidence [3] suggests a close connection be- tween the MJO and the Indian Ocean Dipole (IOD) [4] , a zonal dipole in SST. Here, we examine the modulation of the MJO based on amplified IOD patterns in the superparameterized Community Atmosphere Model (SP-CAM), a modified climate model able to realistically simu- late the MJO. Model Features: SP-CAM Observed MJO-IOD Connection Preliminary Model Results Summary & Future Work SP-CAM uses a unique approach to represent subgrid-scale cloud proc- esses: • A 2D cloud-resolving model (CRM) is embedded into each CAM grid cell to replace con- ventional cloud and atmos- pheric boundary layer parame- terizations • Strengths : Cloud system behavior explicitly resolved within CRM, clouds and radiation interact more naturally • Weaknesses : 2D and periodic CRM introduces artificial feedbacks; flat CRM topography; sensitivity to model configuration SP-CAM’s ability to represent multiscale cumulus clouds and their sen- sitivity to environmental humidity results in realistic MJO simulations. Image: NASA/GSFC/LaRC/JPL, MISR Team Image: Suppressed MJO phase; NRL Image: Active MJO phase; NRL SP-CAM: Each CAM grid cell (left) contains a 2D cloud- resolving model (right). SST anomalies during positive IOD events (+IOD) strongly impact the MJO (left). Interactions between the MJO and –IOD are less robust and we omit them here. During +IOD, we ob- serve: • Weakened background low-level west winds and vertical wind shear over the equatorial In- dian Ocean (not shown) • Drier conditions in the East Indian Ocean re- gion (not shown) • Weaker, disorganized MJO with disrupted eastward propagation (right). ‣ IOD-linked SST changes strongly impact the mean atmospheric circula- tion and moisture in ways that result in weaker and faster MJOs ‣ Preliminary evidence indicates that SP-CAM, which better captures subgrid-scale cloud processes and multiscale inter- actions, is able to reproduce the observed MJO-IOD connection ‣ Analyses of intraseasonal wind shear, moisture con- vergence, and moist static energy budget to under- stand why MJO changes occur is ongoing Suppressed MJO Active MJO We ran three 15-yr SP-CAM simulations using pre- scribed SSTs based on (a) long-term October mean; (b) “Case1”: October 2006 SST anomalies added to (a); and (c) “Case2”: October 1994 SST anomalies added to (a). The October 2006 and 1994 SST anomalies represent strong +IOD events with varying ENSO conditions. Poster: S54-999 SST anomalies for +IOD events. Source: JAMSTEC Lag day from peak Indian Ocean MJO convection Autumn OLR (color) and 200hPa Zonal Wind OLR anomaly MJO propagation during +IOD (left) and neutral IOD (right) conditions. From Wilson et al. (2013, JGR). (right) The October climatology and +IOD SST anoma- lies used to force the three simulations. Note the differ- ence in ENSO conditions between Case1 and Case2. (above) Simulated changes occur to October mean rainfall and low-level zonal winds and resemble observations (not shown). (above left) Simulated mean vertical wind shear (U200-U850) shows significantly reduced vertical shear in the east Indian Ocean and Maritime Conti- nent regions for the +IOD events. (above right) Both total and MJO-filtered variances are reduced over Indonesia for rainfall and winds. (left) Lagged correlations between rain and low-level wind anomalies indicate the MJO propagation and signal is faster and weaker during +IOD conditions vs. climatology.
Transcript of Composite Behavior of Simulated Madden-Julian Oscillation ...
REFERENCES
Background & Motivation
James J. Benedict1 ([email protected]), William D. Collins1, Michael S. Pritchard2
1 Earth Science Division, Lawrence Berkeley National Lab; Berkeley, California2 Department of Earth System Science, University of California-Irvine; Irvine, California
Composite Behavior of Simulated Madden-Julian OscillationDisturbances Based on Indian Ocean Dipole Phase
[1] Zhang, C., 2005. Madden-Julian oscillation Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158.[2] Lin, J.-L., and Coauthors, 2006: Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models: Part I: Convective Sig-
nals. J. Climate, 19, 2665–2690.[3] Wilson, E. A., A. L. Gordon, and D. Kim, 2013: Observations of the Madden-Julian oscillation during Indian Ocean Dipole
Events. J. Geophys. Res. Atmos., 118, 2588–2599, doi:10.1002/jgrd.50241.[4] Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Na-
ture, 401, 360-363.
ACKNOWLEDGMENTS This research was supported by the Director, Office of Science, Office of Biological and Environmen-tal Research of the U.S. DOE under Contract No. DE-AC02-05CH11231 as part of their Earth System Modeling Program.
The Madden-Julian Oscillation (MJO), a cyclic eastward-moving pattern of rainy and dry condi-tions in the tropical Indian and West Pacific Ocean regions, dominates atmospheric variability on in-traseasonal timescales (20-100 days) and is strongly modulated by air-sea interactions[1]. Many aspects of the MJO remain poorly understood, and most climate
models struggle to reproduce the observed variability[2]. The MJO has profound impacts on mon-soons, ENSO, and midlatitude weather and precipitation ex-tremes[1]. Recent evidence[3] suggests a close connection be-
tween the MJO and the Indian Ocean Dipole (IOD)[4], a zonal dipole in SST. Here, we examine the modulation of the MJO based on amplified IOD patterns in the superparameterized Community Atmosphere Model (SP-CAM), a modified climate model able to realistically simu-late the MJO.
Model Features: SP-CAM
Observed MJO-IOD Connection
Preliminary Model Results
Summary & Future Work
SP-CAM uses a unique approach to represent subgrid-scale cloud proc-esses:
• A 2D cloud-resolving model (CRM) is embedded into each CAM grid cell to replace con-ventional cloud and atmos-pheric boundary layer parame-terizations
• Strengths: Cloud system behavior explicitly resolved within CRM, clouds and radiation interact more naturally
• Weaknesses: 2D and periodic CRM introduces artificial feedbacks; flat CRM topography; sensitivity to model configuration
SP-CAM’s ability to represent multiscale cumulus clouds and their sen-sitivity to environmental humidity results in realistic MJO simulations.
Image: NASA/GSFC/LaRC/JPL, MISR Team
Image: Suppressed MJO phase; NRL Image: Active MJO phase; NRL
SP-CAM: Each CAM grid cell (left) contains a 2D cloud-resolving model (right).
SST anomalies during positive IOD events (+IOD) strongly impact the MJO (left). Interactions between the MJO and –IOD are less robust and we omit them here. During +IOD, we ob-serve:
• Weakened background low-level west winds and vertical wind shear over the equatorial In-dian Ocean (not shown)
• Drier conditions in the East Indian Ocean re-gion (not shown)
• Weaker, disorganized MJO with disrupted eastward propagation (right).
‣ IOD-linked SST changes strongly impact the mean atmospheric circula-tion and moisture in ways that result in weaker and faster MJOs
‣ Preliminary evidence indicates that SP-CAM, which better captures subgrid-scale cloud processes and multiscale inter-actions, is able to reproduce the observed MJO-IOD connection
‣ Analyses of intraseasonal wind shear, moisture con-vergence, and moist static energy budget to under-stand why MJO changes occur is ongoing
Suppressed MJO Active MJO
We ran three 15-yr SP-CAM simulations using pre-scribed SSTs based on (a) long-term October mean; (b) “Case1”: October 2006 SST anomalies added to (a); and (c) “Case2”: October 1994 SST anomalies added to (a). The October 2006 and 1994 SST anomalies represent strong +IOD events with varying ENSO conditions.
Poster: S54-999
SST anomalies for +IOD events. Source: JAMSTEC
Lag
day
from
pea
k In
dia
n O
cean
MJO
con
vect
ion
Autumn OLR (color) and 200hPa Zonal Wind
OLR anomaly
MJO propagation during +IOD (left) and neutral IOD (right) conditions. From Wilson et al. (2013, JGR).
(right) The October climatology and +IOD SST anoma-lies used to force the three simulations. Note the differ-ence in ENSO conditions between Case1 and Case2.
(above) Simulated changes occur to October mean rainfall and low-level zonal winds and resemble observations (not shown).
(above left) Simulated mean vertical wind shear (U200-U850) shows significantly reduced vertical shear in the east Indian Ocean and Maritime Conti-nent regions for the +IOD events.
(above right) Both total and MJO-filtered variances are reduced over Indonesia for rainfall and winds.
(left) Lagged correlations between rain and low-level wind anomalies indicate the MJO propagation and signal is faster and weaker during +IOD conditions vs. climatology.
