The double Intertropical Convergence Zone bias remains a persistent problem in coupled general circulation model simulations. Due to the strong sea surface temperature (SST)‐convection relationship in the tropics, precipitation biases are sensitive to background SST. Using historical simulations of 24 coupled general circulation models and an atmospheric general circulation model, we show that cold equatorial SST biases at least exacerbate double Intertropical Convergence Zone biases in the Pacific. A linear regression model is used to demonstrate that improved predictability of precipitation trends is possible with such model‐dependent information as mean‐state SST biases accompanying projected SST trends. These results provide a better understanding of the root of the double Intertropical Convergence Zone bias and a possible path to reduced uncertainty in future tropical precipitation trends.

A dry bias in climatological Central Indian rainfall plagues Indian summer monsoon (ISM) simulations in multiple generations of climate models. Here, using observations and regional climate modeling, we focus on a warm coastal Bay of Bengal sea surface temperature (SST) front and its impact on Central Indian rainfall. The SST front, featuring sharp gradients as large as 0.5 °C/100 km, is colocated with a mixed layer depth (MLD) front, in a region where salinity variations are known to control MLD. Regional climate simulations coupling a regional atmospheric model with an ocean mixed layer model are performed. A simulation with observed MLD climatology reproduces SST, rainfall, and atmospheric circulation associated with ISM reasonably well; it also eliminates the dry bias over Central India significantly. Perturbing MLD structure in the simulations, we isolate the SST front’s impact on the simulated ISM climate state. This experiment offers insights into ISM climatological biases in the coupled NCEP Climate Forecast System version-2. We suggest that the warm SST front is essential to Central Indian rainfall as it helps to sustain deep and intense convection in its vicinity, which may be a source for the vortex cores seeding the monsoon low-pressure systems.

The response of the hydrological cycle to anthropogenic climate change, especially across the tropical oceans, remains poorly understood due to the scarcity of long instrumental temperature and hydrological records. Massive shallow-water corals are ideally suited to reconstructing past oceanic variability as they are widely distributed across the tropics, rapidly deposit calcium carbonate skeletons that continuously record ambient environmental conditions, and can be sampled at monthly to annual resolution. Climate reconstructions based on corals primarily use the stable oxygen isotope composition (δ¹⁸O), which acts as a proxy for sea surface temperature (SST), and the oxygen isotope composition of seawater (δ¹⁸O_sw), a measure of hydrological variability. Increasingly, coral δ¹⁸O time series are paired with time series of strontium-to-calcium ratios ($\mathrm{Sr}/\mathrm{Ca}$ ), a proxy for SST, from the same coral to quantify temperature and δ¹⁸O_sw variability through time. To increase the utility of such reconstructions, we present the CoralHydro2k database, a compilation of published, peer-reviewed coral $\mathrm{Sr}/\mathrm{Ca}$ and δ¹⁸O records from the Common Era (CE). The database contains 54 paired $\mathrm{Sr}/\mathrm{Ca}$ –δ¹⁸O records and 125 unpaired $\mathrm{Sr}/\mathrm{Ca}$ or δ¹⁸O records, with 88 % of these records providing data coverage from 1800 CE to the present. A quality-controlled set of metadata with standardized vocabulary and units accompanies each record, informing the use of the database. The CoralHydro2k database tracks large-scale temperature and hydrological variability. As such, it is well-suited for investigations of past climate variability, comparisons with climate model simulations including isotope-enabled models, and application in paleodata-assimilation projects. The CoralHydro2k database is available in Linked Paleo Data (LiPD) format with serializations in MATLAB, R, and Python and can be downloaded from the NOAA National Center for Environmental Information's Paleoclimate Data Archive at https://doi.org/10.25921/yp94-v135 (Walter et al., 2022).

Monthly precipitation samples from Singapore were collected between 2013 and 2019 for stable isotope analysis to further our understanding of the drivers of tropical precipitation isotopes, in particular, ¹⁷O‐excess. δ¹⁸O ranges from –11.34‰ to –2.34‰, with a low correlation to rainfall (r = –0.31, p = 0.014), suggesting a weak amount effect. d‐excess is relatively consistent and has an average value of 10.89‰ ± 3.45‰. Compared to high‐latitude regions, ¹⁷O‐excess in our samples generally falls in a narrower range from 2 to 47 per meg with an average of 21 ± 11 per meg. Moreover, ¹⁷O‐excess shows strong periodic variability; spectral analysis reveals 3‐month, 6‐month, and 2.7‐year periodicities, likely corresponding to intraseasonal oscillations, monsoons, and the El Niño–Southern Oscillation (ENSO), respectively. In contrast, d‐excess shows no clear periodicities. Although spectral analysis only identifies 6‐month periodicity in the δ¹⁸O time series, δ¹⁸O tracks the Nino3.4 sea surface temperature variability; the average δ¹⁸O value (–5.2‰) is higher during El Niño years than ENSO neutral years (–7.6‰). Therefore, regional convection associated with monsoons and ENSO has different impacts on δ¹⁸O, d‐excess, and ¹⁷O‐excess. ¹⁷O‐excess and d‐excess are anticorrelated and do not relate to the relative humidity in moisture source regions. Extremely low humidity and drought conditions in moisture source regions would be required to account for high ¹⁷O‐excess. Processes during transport and precipitation likely modify these two parameters, especially ¹⁷O‐excess, which no longer record humidity conditions of moisture source regions. Our findings will be useful for further modeling studies to identify physical processes during convection that alter d‐excess and ¹⁷O‐excess.

To investigate stable isotopic variability of precipitation in Singapore, we continuously analysed the δ‐value of individual rain events from November 2014 to August 2017 using an online system composed of a diffusion sampler coupled to Cavity Ring‐Down Spectrometer. Over this period, the average value (δ¹⁸O_Avg), the lowest value (δ¹⁸O_Low), and the initial value (δ¹⁸O_Init) varied significantly, ranging from −0.45 to −15.54‰, −0.9 to −17.65‰, and 0 to −13.13‰, respectively. All 3 values share similar variability, and events with low δ¹⁸O_Low and δ¹⁸O_Avg values have low δ¹⁸O_Init value. Individual events have limited intraevent variability in δ‐value (Δδ) with the majority having a Δδ below 4‰. Correlation of δ¹⁸O_Low and δ¹⁸O_Avg with δ¹⁸O_Init is much higher than that with Δδ, suggesting that convective activities prior to events have more control over δ‐value than on‐site convective activities. The d‐excess of events also varies considerably in response to the seasonal variation in moisture sources. A 2‐month running mean analysis of δ¹⁸O reveals clear seasonal and interannual variability. Seasonal variability is associated with the meridional movement of the Intertropical Convergence Zone and evolution of the Asian monsoon. El Niño–Southern Oscillation is a likely driver of interannual variability. During 2015–2016, the strongest El Niño year in recorded history, the majority of events have a δ¹⁸O value higher than the weighted average δ¹⁸O of daily precipitation. δ¹⁸O shows a positive correlation with outgoing longwave radiation in the western Pacific and the Asian monsoon region, and also with Oceanic Niño Index. During El Niño, the convection centre shifts eastward to the central/eastern Pacific, weakening convective activities in Southeast Asia. Our study shows that precipitation δ‐value contains information about El Niño–Southern Oscillation and the Intertropical Convergence Zone, which has a significant implication for the interpretation of water isotope data and understanding of hydrological processes in tropical regions.

Rapid sea-level rise between the Last Glacial Maximum (LGM) and the mid-Holocene transformed the Southeast Asian coastal landscape, but the impact on human demography remains unclear. Here, we create a paleogeographic map, focusing on sea-level changes during the period spanning the LGM to the present-day and infer the human population history in Southeast and South Asia using 763 high-coverage whole-genome sequencing datasets from 59 ethnic groups. We show that sea-level rise, in particular meltwater pulses 1 A (MWP1A, ~14,500–14,000 years ago) and 1B (MWP1B, ~11,500–11,000 years ago), reduced land area by over 50% since the LGM, resulting in segregation of local human populations. Following periods of rapid sea-level rises, population pressure drove the migration of Malaysian Negritos into South Asia. Integrated paleogeographic and population genomic analysis demonstrates the earliest documented instance of forced human migration driven by sea-level rise.