Diagnosing potential changes in Asian summer monsoon onset and duration in IPCC AR4 model simulations using moisture and wind indices

Zhang, Huqiang; Ping, Liang; Moise, Aurel; Hanson, L.

doi:10.1007/s00382-012-1289-0

Cited by 28 publications

(25 citation statements)

References 44 publications

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“…Climate change may directly affect the monsoon in two compensating ways: 1-warmer SSTs enable more evaporation and tend to increase the monsoon strength, 2-SSTs warm more in the equatorial region than in the Tropics, which tends to weaken the monsoon circulation (Chung and Ramanathan 2006;Krishnan et al 2012). These two mechanisms are tightly linked to possible changes in large-scale modes of SST variability, in particular ENSO and the IOD (Shi et al 2008;Zhang et al 2012). Other factors such as, for instance, changes to upper tropospheric properties may also influence the monsoon .…”

Section: Introductionmentioning

confidence: 99%

The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations

et al. 2013

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A large spread exists in both Indian and Australian average monsoon rainfall and in their interannual variations diagnosed from various observational and reanalysis products. While the multi model mean monsoon rainfall from 59 models taking part in the Coupled Model Intercomparison Project (CMIP3 and CMIP5) fall within the observational uncertainty, considerable model spread exists. 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. Considering all North-Australia rainfall, most models reproduce the observed Australian monsoon-El Niño Southern Oscillation (ENSO) teleconnection, with the strength of the relationship dependent on the strength of the simulated ENSO. However, over the Maritime Continent, the simulated monsoon-ENSO connection is generally weaker than observed, depending on the ability of each model to realistically reproduce the ENSO signature in the Warm Pool region. A large part of this bias comes from the contribution of Papua, where moisture convergence seems to be particularly affected by this SST bias. The Indian summer monsoon-ENSO relationship is affected by overly persistent ENSO events in many CMIP models. Despite significant wind anomalies in the Indian Ocean related to Indian Ocean Dipole (IOD) events, the monsoon-IOD relationship remains relatively weak both in the observations and in the CMIP models. Based on model fidelity in reproducing realistic monsoon characteristics and ENSO teleconnections, we objectively select 12 ''best'' models to analyze projections in the rcp8.5 scenario. Eleven of these models are from the CMIP5 ensemble. In India and Australia, most of these models produce 5-20 % more monsoon rainfall over the second half of the twentieth century than during the late nineteenth century. By contrast, there is no clear model consensus over the Maritime Continent.

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Section: Introductionmentioning

confidence: 99%

The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations

et al. 2013

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“…The skill of these systems in predicting the ISM onset is still limited (Cherchi and Navarra 2003;Li and Zhang 2009;Alessandri et al 2015), although some progress has been made on prediction of June monsoon rainfall with monthly forecast systems (Vitart and Molteni 2009). Atmospheric GCMs generally tend to show an early mean onset of the monsoon (e.g., Wang et al 2004), while most coupled GCMs have delayed onsets (e.g., Kripalani et al 2007;Zhang et al 2012;Sperber et al 2013). Realistic initialization of the atmosphere has recently been shown to improve the simulation of early onset (Alessandri et al 2015), impacting the forecasts up to about a month.…”

Section: Introductionmentioning

confidence: 99%

Impact of springtime Himalayan–Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts

et al. 2016

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their Seasonal Forecasting System 4. The reforecasts were initialized on 1 April every year for the period 1981-2010. In these seasonal reforecasts, the snow is initialized "realistically" with ERA-Interim/Land Reanalysis. In addition, we carried out an additional set of forecasts, identical in all aspects except that initial conditions for snow-related land surface variables over the HTP region are randomized. We show that high snow depth over HTP influences the meridional tropospheric temperature gradient reversal that marks the monsoon onset. Composite difference based on a normalized HTP snow index reveal that, in high snow years, (1) the onset is delayed by about 8 days, and (2) negative precipitation anomalies and warm surface conditions prevail over India. We show that about half of this delay can be attributed to the realistic initialization of snow over the HTP region. We further demonstrate that high April snow depths over HTP are not uniquely influenced by El Nino-Southern Oscillation, the Indian Ocean Dipole or the North Atlantic Oscillation.

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“…Furthermore, if the model simulates more reduction of westerlies in the west of the warm pool region, then these models are likely to predict even larger delay of summer monsoon onset in the Asian monsoon region. Zhang et al [2012] conclude that simulated future changes to the monsoon circulation depend on the sensitivity of the GCM to either equatorial warming or changes in the zonal winds.…”

Section: Model Simulated Climate Changementioning

confidence: 84%

“…Note that there are many possible definitions of monsoon onset, retreat and duration [e.g., Drosdowsky , 1996; Zhang et al , 2012; Kajikawa et al , 2010]. The purpose of the current paper is not to investigate this aspect in detail, as only monthly mean changes are considered.…”

Section: Model Simulated Climate Changementioning

confidence: 99%

“…For the Asian component of the monsoon, most studies have found a slight increase in precipitation, but with a reduction in overall circulation strength [e.g., IPCC , 2007a, chapter 10], consistent with mechanisms proposed by Held and Soden [2006], Vecchi et al [2006] and Vecchi and Soden [2007] and Lu et al [2007]. Zhang et al [2012] found that changes in the Asian monsoon circulation seen in CMIP3 models are mainly related to the model response to changes in either Pacific equatorial sea surface temperatures (SSTs) or zonal winds with these patterns of anomalous SST and wind conditions likely linked to the weakening and westward shift of Walker circulation in the warm pool and maritime continent region. Increases in precipitable water associated with global (atmospheric) warming do not change Asian monsoon precipitation and circulation seasonality much but they can result in increased precipitation intensity once the summer monsoon is established.…”

Section: Introductionmentioning

confidence: 99%

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Behind uncertainties in projections of Australian tropical climate: Analysis of 19 CMIP3 models

2012

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[1] A systematic study is undertaken for projected changes in tropical Australian climate in 19 CMIP3 Coupled Models for the A2 scenario over the 21st century. While equatorial regions to the north of Australia are projected to have increased precipitation during austral summer (December to February) by the end of the 21st century, there is no significant change over northern Australia based on the model ensemble mean. There is a large spread in model simulations of precipitation change, with both large positive and negative anomalies. The ensemble mean change in the seasonal cycle of precipitation over tropical Australia is small, with precipitation increase during March and April, suggesting a prolonged Australian wet season. There is no model consensus on how interannual variability of tropical Australian precipitation will change in future climate, although more models simulate increased variability than decreased. Correlations between full wet season (October to April) precipitation and austral spring (September to November) NINO3.4 sea surface temperature anomalies show a slight weakening. The spread in projected precipitation seasonal cycle changes between simulations from the same model is larger than the inter-model range, indicating that there is large internal or natural variability in tropical Australian precipitation relative to the climate change signal. Zonal wind changes indicate an intensification of austral summer low level westerlies although combined with a weakening of upper easterlies. Low level westerlies also persist for longer, consistent with a delay in the monsoon retreat. All models simulate an increase in the land-ocean temperature contrast in austral summer, with a significant correlation between changes in land-ocean temperature contrast in the pre-monsoon (austral spring) and summer precipitation changes. Analysis of precipitation changes using regime-sorting techniques shows offsetting tendencies from thermodynamic changes associated with enhanced atmospheric moisture and dynamic changes associated with a weakened atmospheric circulation.

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Diagnosing potential changes in Asian summer monsoon onset and duration in IPCC AR4 model simulations using moisture and wind indices

Cited by 28 publications

References 44 publications

The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations

The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations

Impact of springtime Himalayan–Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts

Behind uncertainties in projections of Australian tropical climate: Analysis of 19 CMIP3 models

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