A document by Pablo Zurita-Gotor. Click on the document to view its contents.

The TRACMIP ensemble includes slab-ocean aquaplanet control simulations and experiments with a highly idealized narrow tropical continent (0-45ºW; 30ºS - 30ºN). We compare the two setups to contrast the characteristics of oceanic and continental rain bands and investigate monsoon development in GCMs with CMIP5-class dynamics and physics. Over land, the rainy season occurs close to the time of maximum insolation. Other than in its timing, the continental rain band remains in an ITCZ-like regime akin deep-tropical monsoons, with a smooth latitudinal transition, a poleward reach only slightly farther than the oceanic ITCZ’s (about 10º), and a constant width throughout the year. This confinement of the monsoon to the deep tropics is the result of a tight coupling between regional rainfall and circulation anomalies: ventilation of the lower troposphere by the anomalous meridional circulation is the main limiting mechanism, while ventilation by the mean westerly jet aloft is secondary. Comparison of two sub-sets of TRACMIP simulations indicates that a low heat capacity determines, to a first degree, both the timing and the strength of the regional solsticial circulation; this lends support to the choice of idealizing land as a thin slab ocean in much theoretical literature on monsoon dynamics. Yet, the timing and strength of the monsoon are modulated by the treatment of evaporation over land, especially when moisture and radiation can interact. This points to the need for a fuller exploration of land characteristics in the hierarchical modeling of the tropical rain bands.

Key Points: • We examine Indian summer monsoon rainfall interannual variability and telecon-nections 1901-2020 • All-India Rainfall Index (AIRI) closely tracks spatial and temporal extent of wet anomalies • ENSO teleconnection projects onto AIRI but Indian Ocean Dipole onto a known tripole pattern Abstract We revisit long-standing controversies regarding relationships among the all-India rainfall index (AIRI), sub-India summer rainfall variations, El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) using 120-year sea surface temperature and high-resolution rainfall datasets. AIRI closely tracks with the spatial extent of wet anomalies and with the average across gridpoints in rainy day count. The leading rainfall variability mode is a monopole associated primarily with rainy day count and ENSO. The second mode is a tripole with same-signed loadings in the high-rainfall Western Ghats and Central Monsoon Zone regions and opposite-signed loadings in Southeastern India between. The IOD projects onto this tripole and, as such, is weakly correlated with AIRI. However, when the linear influence of ENSO is removed, the IOD rainfall regressions become quasi-homogeneously more positive, making the ENSO-residual IOD and AIRI time-series significantly correlated. Plain Language Summary The Indian summer monsoon generates copious rainfall each June through Septem-ber, but more in some years than others, and more in some Indian sub-regions than others. We use observation-based datasets spanning 1901-2020 to reconcile past disagreements about these fluctuations. There has long been concern that the rainfall rate averaged over the whole summer and whole of India-the All-India Rainfall Index-doesn’t necessarily track with the spatial extent of wet or dry anomalies within India which is more relevant for many societal purposes, but we show that the two measures vary closely with one another. There has been disagreement about how the all-India average relates to rainfall in fixed sub-regions of India, and we show using high-resolution rainfall data that the issue stems in part from the use of coarse datasets in the past. Finally, we reconcile disagreements about whether or not the Indian Ocean Dipole (IOD) teleconnec-tion influences all-India rainfall by showing that, when the El Niño-Southern Oscillation signal that influences both the IOD and the monsoon rains is removed, the IOD significantly influences the all-India average and with a distinct sub-India spatial pattern.