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

Abstract

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.

Plastic Pollution Patterns in Offshore, Nearshore and Estuarine Waters: A Case Study from Perth, Western Australia

“…This study found that offshore microplastics, introduced to the study site with the Leeuwin current, increased with strength of flow in May. Both the ITC and Australian Monsoon driving LC variability are susceptible to changes based on El Niño Southern Oscillation (ENSO) (Pearce and Phillips, 1988;Jourdain et al, 2013). Therefore, concentrations may be significantly higher during non-ENSO years and La Nina episodes (Pearce and Phillips, 1988).…”

Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble

“…In particular, the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations are used in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report for future climate projection (Flato et al 2013). However, the ability of GCMs to simulate the mean climate and climate variability over the Maritime Continent remains a modeling challenge (Jourdain et al 2013). As one of the main diabatic heat sources for regional and global circulation, biases in the mean state simulations of the Maritime Continent also affect tropical and extratropical variability and teleconnections (Neale and Slingo 2003;Wang et al 2014).…”

“…Several studies have evaluated the fidelity of CMIP5 models in simulating the Australian monsoon (Jourdain et al 2013;Ackerley et al 2014), the western Pacific monsoon (Brown et al 2013) and the Asian summer monsoon (Sperber et al 2013). These studies found that different models show varying ability at each aspect of monsoon simulation.…”

“…However, no single model in the CMIP5 ensemble best represents all aspects of the monsoon, either in an individual subregion or when considering all characteristics of the monsoon as a whole. Comparison studies between different phases of the CMIP multi-model ensemble found that generally there are improvements in the performance of CMIP5 over CMIP3 (Sperber et al 2013;Jourdain et al 2013) possibly due to increased horizontal and vertical resolution in the atmosphere and ocean, parameterization development and the improved representation of Earth-system processes in CMIP5 models. However, considerable systematic errors exist, suggesting that the models are still lacking good representations of the necessary physical mechanisms involved.…”

On the low-frequency component of the ENSO–Indian monsoon relationship: a paired proxy perspective

“…Understanding the nature of the ENSO-ISM relationship and specifically constraining the dynamics that have generated the recent change in the coupling between the systems is a problem ideally suited for general circulation models (GCMs) (e.g., Jourdain et al, 2013) where the response of the systems to various forcing mechanisms could be tested. However, systematic errors in simulating the mean state and variability of both ENSO and the ISM systems have hampered the ability to utilize this tool (Annamalai et al, 2007;Collins et al, 2010).…”