The Australian Summer Monsoon Aurel Moise, Josephine Brown, Huqiang Zhang, Matt Wheeler and Rob Colman Australian Bureau of Meteorology Presentation to WMO IWM-IV, Singapore, November 2017
Outline Australian summer monsoon represents the Southern Hemisphere counterpart of the Asian monsoon, penetrating southwards from the Maritime Continent over tropical Australia Characterised by seasonal reversal of easterly trade winds into deep and moist westerlies This presentation reviews selected recent research on this monsoon
Some features of the Australian Monsoon
Australian summer monsoon (a) Observed DJF rainfall (mm/day; shading) averaged over 1998-2013, from TRMM-3B43 satellite data. The vectors show the surface wind from ERA-Interim. The black contour lines indicate the surface air temperature with corresponding labels (in C) Note the expansive region north of Australia where T as > 28 C (b) Percent fraction of annual total rainfall occurring during DJF (shading) and climatology of surface wind from the atmospheric reanalysis ERA-Interim for January (pink vectors) and July (blue vectors) For northern Australia, the contribution of the monsoon to annual rainfall almost rivals that in India From Zhang and Moise (2016)
Highly variable rainfall during wet season Time series of 3-day running-mean rainfall (thin curves) for the period 1 Jul 2001 to 30 Jun 2004, area-averaged for the Top End of Australia (12.8 17.8 S, 130.8 136.8 E). Also shown are smoothed climatological annual cycles (thicker curves), computed using all daily data from 1948 to 2006. Each year is split at 1 Jul, and the yearly total rainfall for each July June period is provided on the right of each yearly panel (from Wheeler et al., 2009) total rainfall in a wet season can vary by factor of 2!
Monsoon bursts and breaks AWAP=Aust. Water Availability Project rainrate zonal wind +0.5σ -0.5σ (a, left) Area-averaged AWAP rainfall for the Australian monsoon region: (10 20S, 120 150E), for 1979 2010. The arithmetic mean is shown by the solid blue line, the harmonically smoothed climatology is shown by the dashed blue line, and the red lines show the harmonically smoothed climatological +/- 0.5 standard deviations. Vertical dashed black lines and labels at base of plot denote periods discussed in the text. (b, right) Example monsoon season bursts and breaks: as in (a), but from October 1988 to April 1989 and with the thin green line showing the 1000 500-hPa-layer mean pressure-weighted zonal wind from ERA-Interim at Darwin (12.5S, 131E; ms -1 ), the orange dashed lines denoting the dates of monsoon bursts. Some of the bursts accompanied by surges in the lower-tropospheric westerlies From Berry and Reeder (2016)
Monsoon bursts Lead up to the composite monsoon burst (day -8 to day 0) Bursts in the Australian monsoon are preceded by development of a well-defined extratropical wave packet in the Indian Ocean, which propagates toward the Australian continent in the few days leading up to the onset of heavy rainfall (shading=mslpa, vectors=925hpa anomaly) From Berry and Reeder (2016)
Monsoon bursts and the MJO There is a preferred progression of the MJO phase over the 20 days prior to the composite monsoon burst Approximately 20 days before the monsoon burst, the RMM is most frequently in phase 2 (convection centred in the central Indian Ocean, with suppressed convection over Australia); The RMM shifts to phases 3 and 4 (convection in the eastern Indian Ocean) 10 days before the burst; Reaches phase 5 and 6 (convection centred on Australian longitudes) at day 0. Occur. Freq. % This progression through RMM phases is consistent with the expected geographical progression of MJO convection, showing that there is a genuine connection with the occurrence of monsoon bursts, although the presence of the active phase of the MJO does not guarantee a monsoon burst will occur From Berry and Reeder (2016)
Monsoon bursts and the extra-tropics (1) Moisture and vorticity is coming into the monsoon area during bursts: Column-integrated moisture flux convergence vs columnintegrated absolute vorticity flux convergence (circulation tendency) for the day of bursts A positive flux is defined as into the monsoon region. The absolute vorticity flux is scaled by -1, so that a positive value is associated with an increase in cyclonic circulation in the monsoon region (2) Strong source through southern boundary: Absolute vorticity fluxes through the southern boundary are by far the most important influence on monsoon burst circulation changes, with only one-third of events more closely related to other influences including the MJO The occurrence of bursts in each month of the year, categorized by the direction of absolute vorticity flux with greatest influence on circulation tendency. Bursts with greatest influence from the south are referred to as SFinfluenced bursts (magenta), and similarly for NF- (yellow), EF- (blue), and WF-influenced (green) bursts From Narsey et al. (2017) anticyclonic cyclonic boundaries
Connection to East-Asia Winter Monsoon Global monsoon domain captured in ERA40 reanalysis (black contours) & CMAP data (red); The shadings in each zone represent annual range of precipitation (in units mm/d) From Wang and Ding (2008) When the EAWM is strong it is accompanied with more intense and southward penetration of dry and cold airflow originated from E. Asia Widespread southward flow in the Indo- China peninsula, South China Sea, and a large part of the maritime continent Linked to enhanced Australian summer monsoon westerlies Significant positive rainfall correlations in a large part of the northern and eastern Australia. January correlations; Wind index in each case COR(AMI with PR and U850) COR(EAWMI with PR and U850) From Zhang and Zhang (2010)
Predicting Australian Monsoon
Predicting Australian Monsoon Wet season onset prediction Using 50 years of hindcasts, POAMA is found to skillfully predict the variability of the onset of useful rainfall, despite a generally dry bias, with the percent correct exceeding 70% over about a third of Australia s Northern Territory POAMA - Predictive Ocean Atmosphere Model for Australia, version 2 Example: Probability of early wet season onset for the 2012/13 season, from the real-time forecast initialized on 1 Sep 2012 From Drosdowsky and Wheeler (2014)
Predicting Australian Monsoon Current onset product on the Australian Bureau of Meteorology website http://www.bom.gov.au/climate/rainfall-onset/#tabs2=outlook&tabs=rainfall-onset For 2017-18 wet season: Chances of an early or late northern rainfall onset roughly equal
Predicting Australian Monsoon: POAMA hindcast skill at Extended Range (<35 d) for monsoon OLR POAMA s RMSE is smaller than that of both persistence and climatology for the 2 35 day range, for both allseason and summer monsoon (DJFM) season RMSE of area-averaged OLR (in Wm -2, on a scale from 0 to 40 Wm -2 ) for POAMA ensemble-mean hindcasts (red curve), reference forecasts using a forecast of zero anomaly (i.e., climatology; green), and persistence of the initial daily anomaly (blue). Also shown is the ensemble spread (magenta) in Wm -2, and skill score as a percentage improvement of POAMA over climatology (orange) on a scale from 0 to 40%. (TOP) Is for all seasons, and (BOTTOM) for summer monsoon (DJFM) season only. The prediction of OLR over northern Australia should be more skilful when the MJO is strong in the initial conditions? However, there is no evident impact of the amplitude or phase of the MJO on the hindcast RMS error (not shown) From Drosdowsky and Wheeler (2017)
Predicting Australian Monsoon: POAMA realtime Extended Range forecast skill Real-time forecasts (area-average ensemble mean OLR) for all start dates from 4 February to 11 March 2013 Thick gray line shows the observed OLR; thin gray line is observed climatological seasonal cycle Verification of all available POAMA forecasts from August 2011 to June 2016 (red curves) compared with persistence (blue curves) and climatological forecasts (green curve) for the same period Real-time forecasts are constructed in a similar manner to the hindcasts, with the required bias correction obtained by interpolating the model seasonal cycle to give values at each real-time forecast start time Verification of all available forecasts from August 2011 through to June 2016 suggests that the hindcast skill relative to persistence and climatology is mostly maintained in completely independent forecasts From Drosdowsky and Wheeler (2017)
GCM representation of Australian Monsoon & climate change
GCM representation of Australian Monsoon OBS Merged Obs CMIP3 CMIP5 (a) Seasonal cycle of precipitation (mm day-1) averaged over 120-150 E, 20 S 10 S (land points only) over the period 1980-1999 simulated by 37 CMIP5 models. (b) As (a) but for averaged 850hPa zonal wind (ms-1). Comparison of DJF rainfall and 850hPa atmospheric circulation from observations and multi-model ensemble averages from 23 CMIP3 and 45 CMIP5 models. (a) The Australian Bureau of Meteorology high quality rainfall observation (AWAP) for the period of 1986-2005 (mm day-1); (b) 850hPa wind from ERA-40 together with the precipitation climatology averaged from three observational products to minimize the observational errors: the GPCP, GPCC) and CMAP; (c) CMIP3 model ensemble averages; (d) CMIP5 model ensemble averages. From Zhang and Moise (2017)
Model skill in Historical simulations Diurnal cycle of rainfall over tropical Australia (Ackerley et al, 2015): The diurnal cycle of the low-level (925 hpa) flow around the heat low is represented well by the models but the timing of precipitation is not (triggered too early) Evidence that representation of convection in these models is likely contributing to both precipitation and circulation errors over northern Australia. Seasonal Cycle of rainfall over tropical Australia (Jourdain et al, 2013): Multi model means (CMIP3 and CMIP5) fall within the observational uncertainty with considerable model spread (this is unlike the Indian monsoon, for example) Rainfall seasonality is consistent across observations and reanalyses, but most CMIP models produce either a too peaked or a too flat seasonal cycle, with CMIP5 models generally performing better than CMIP3 ENSO-rainfall teleconnections over tropical Australia (Jourdain et al, 2013): most models reproduce the observed Australian monsoon-enso teleconnection the strength of the relationship seems dependent on the strength of the simulated ENSO
Model projections of future monsoon change Time series for precipitation over Australian tropics (20S-0) for 1900 2100 as simulated in 40 CMIP5 models, relative to the 1950-2005 mean in historical & RCP8.5 The central line is the median; shading is 10-90%ile range of 20-year means (inner) and single year values (outer). The Bureau of Meteorology observations (AWAP) are shown in purple and projected series from a typical model (ACCESS1-0) are shown into the future From Zhang and Moise (2017) Summer monsoon rainfall averaged over 50-year periods and divided by the 1850 1899 mean for tropical Australia (LAUS). Error bars show the confidence interval at the 90 % level for the CMIP5 sub-set of 10 models Significant divergence (i.e. model uncertainty) in future projections From Jourdain et al (2013)
Model projections of future changes Change in DJF Australian monsoon precipitation (%K -1 ) vs change in DJF Western Equatorial Pacific SST (K) averaged over the domain 108S 108N, 1508 2008E for RCP8.5 (2070 99) minus Historical (1970 99) for all CMIP5 Strong inverse dependence of DJF rainfall change on WPac SST change (Gray circles are additional runs from CSIRO Mk3.6.0 model, and light blue circles are additional runs from CNRM-CM5 model.) Comparison of the group of models that simulate increased (WET) and decreased (DRY) future monsoon precipitation reveals that those models projecting drying tend to have a larger cold bias in the western equatorial Pacific (not shown), and therefore have less plausible future projections. Change in rainfall is strongly driven by spatial shifts in convection; the opposing effects on rainfall of increasing moisture ('thermodynamic') and slowing of circulation ('dynamic') are similar in size. Some additional impact over tropical Australia also from changes to relative humidity. From Brown et al (2016)
SST warming and Australian monsoon AMIP Experiment: (Zhang et al., 2017) Nine CMIP5 model-simulated global SST warming anomalies have been super-imposed onto the current SSTs simulated by the Australian climate model ACCESS1.3 SST warming explains only about 20 25% of the patterns of precipitation changes in each of the models in its rainfall projections over the oceans in the Indo-Pacific domain, but there are also a couple of models in which different SST warming explains little of their precipitation pattern changes. The influence is weaker again for rainfall changes over land. Need further research to tease out uncertainties caused by different SST warming patterns, different SST mean biases and different model physical/dynamical responses to the same underlying SST forcing
Model projections of future changes in variability Robust findings on changes in rainfall variability under enhanced warming (Brown et al, 2017): Example: daily variability Multimodel mean standard deviation of daily rainfall anomalies (mm/d) over land in HISTORICAL (1950 2000) simulations for all days in (a) DJFM and (b) JJAS and change in standard deviation of daily rainfall anomalies (%) from HIST (1950 2000) to RCP8.5 (2050 2100) in (c) DJFM and (d) JJAS. South Asian (SAS), East Asian (EAS), and Australian (AUS) monsoon domains are shown in the relevant wet season. Increase in rainfall variability on all time scales, here for the AUS box
Thank you Any questions? Please contact Dr Aurel Moise aurel.moise@bom.gov.au http://www.bom.gov.au/research/