Using Global and Regional Model Simulations to Understand Maritime Continent Wet‐Season Rainfall Variability

Abstract

The Maritime Continent is a densely populated area of complex topography located between the Pacific and Indian Oceans. It is an area where model skill is particularly important but also difficult to obtain. In this study we examine interannual austral summer rainfall variability in the region and the teleconnection to the El Niño–Southern Oscillation (ENSO) in observation‐based data, reanalyses, and global and regional atmosphere‐only model simulations. We show that model ability to capture interannual rainfall variability is strongly related to model skill in reproducing the ENSO teleconnection to the region, despite strong spatial variability in the ENSO‐rainfall response in coastal areas. Model ability in capturing the spatial pattern of both the midtropospheric moisture and circulation response to ENSO is a strong predictor for model performance in capturing the ENSO‐Maritime Continent rainfall teleconnection. High‐resolution regional simulations and better performing models have opposing ENSO‐rainfall teleconnections between land and sea areas.

Pacific subsurface temperature as a long‐range indicator of El Niño, regional precipitation, and fire

“…Some of the regions and seasons where ENSO is known to play an important role in interannual precipitation variability are summarised as follows. Over the Maritime Continent, the precipitation and ENSO signal are anticorrelated throughout the year (As-Syakur et al, 2016;Yanto et al, 2016;King and Vincent, 2018); in Northeast Brazil, the precipitation and ENSO signal are anticorrelated in the wet season (MAM, see Folland et al, 2001;Andreoli and Kayano, 2006); in West Africa, the precipitation and ENSO signal are anticorrelated in the wet season (JJAS, see Losada et al, 2012;Srivastava et al, 2019); in South Africa, the precipitation and ENSO signal are anticorrelated in the wet season (DJFM, see Yuan et al, 2014;Hoell et al, 2017); in East Africa, the precipitation and ENSO signal are positively correlated in the so-called short rains season (OND) but have insignificant correlation in the so-called long rains season (MAM, see Mutai et al, 1998;Vigaud et al, 2017;Vellinga and Milton, 2018;Park et al, 2020).…”

“…Precipitation over the Maritime Continent ($$$ 9{5}^{\circ}\mathrm{E} $$$–$$$ 15{0}^{\circ}\mathrm{E} $$$, $$$ 1{2}^{\circ}\mathrm{S} $$$–$$$ {5}^{\circ}\mathrm{N} $$$) DJF (King and Vincent, 2018), Northeast Brazil ($$$ 4{7}^{\circ}\mathrm{W} $$$–$$$ 3{5}^{\circ}\mathrm{W} $$$, $$$ 1{0}^{\circ}\mathrm{S} $$$–$$$ {0}^{\circ } $$$) MAM (Folland et al ., 2001), Gulf of Guinea ($$$ 2{0}^{\circ}\mathrm{W} $$$–$$$ 1{0}^{\circ}\mathrm{E} $$$, $$$ {4}^{\circ}\mathrm{N} $$$–$$$ {8}^{\circ}\mathrm{N} $$$) and Sahel ($$$ 2{0}^{\circ}\mathrm{W} $$$–…”

“…Precipitation over the Maritime Continent (95 • E-150 • E, 12 • S-5 • N) DJF (King and Vincent, 2018), Northeast Brazil (47 • W-35 • W, 10 • S-0 • ) MAM (Folland et al, 2001), Gulf of Guinea (20 (Losada et al, 2012), South Africa (12.5 • E-42.5 • E, 35 • S-15 • S) December-March (DJFM) (Hoell et al, 2017), and East Africa (30 • E-41.25 • E, 15 • S-5 • N) October-December (OND) (Mutai et al, 1998) was correlated with the SubNiño4 index and, for benchmarking purposes, the WWV W anomaly and Niño 3.4 index for the 25 overlapping three-month periods leading to the relevant precipitation season. The analysis was also performed on the Maritime Continent over the remaining three main seasons MAM, JJA, and SON; on East Africa MAM; and on IITM-IMR JJAS.…”

Decadal variation of the rainfall predictability over the maritime continent in the wet season

“…As shown in Fig. 1b, the MC rainfall significantly varies over the two seasons (wet and dry), which is characterized by an annual cycle of typical wetness (one standard deviation larger than annual climatology) in winter and typical dryness (one standard deviation smaller than annual climatology) in summer, as in King and Vincent (2018) and Suwarman et al (2017). For comparison, Fig.…”

Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent

“…It is well-known that important differences arise in the relationship between convection and SST on intra-seasonal timescales between coupled and atmosphere-only simulations (see e.g. Figure 7 of Kim et al, 2010). In atmosphere-only models on intraseasonal timescales, convection tends to become in phase with SST as a result of the higher boundary layer moist static energy, whereas in coupled simulations the high SSTs are associated with periods of clear skies and low wind stresses, which lead to ocean warming.…”