Reef-building corals provide seasonally resolved records of past climate variability from the ocean via variations in their oxygen isotope composition (δ18O). However, a variety of non-climatic factors can influence coral δ18O including processes associated with coral biomineralization and post-depositional alteration of the coral skeleton, which add uncertainty to coral based paleoclimate reconstructions. These uncertainties are especially large in mean climate reconstructions developed from coral δ18O values due to the large variability that exists in mean skeletal δ18O signatures. We present a novel framework to minimize this uncertainty in mean coral δ18O records based on a regression model that uses four commonly measured properties in coral skeletons and associated coral δ18O records. We test the ability of the model to reduce noise in a Holocene climate reconstruction comprised of 37 coral δ18O records from Kiritimati in the equatorial Pacific. Up to 43% of the variance in the detrended Holocene dataset is accounted for by a combination of four predictors: (1) mm-scale variability in a coral δ18O record, (2) the physical extent of diagenetic alteration, (3) coral extension rate, and (4) the mean coral δ13C value. Once these non-climatic artifacts are removed from the reconstruction, the weighted variance of the Holocene dataset is reduced by 46% and the uncertainty in the trend of coral δ18O over time is reduced by 26%. These results have important implications for the climate interpretation of this Holocene data set. This framework has the potential to improve other paleoclimate records based on ensembles of coral δ18O records.

Coral oxygen isotopes (δ18O) from the central equatorial Pacific provide monthly-resolved records of El Niño-Southern Oscillation (ENSO) activity over past centuries to millennia. However, calibration studies using in situ data to assess the relative contributions of warming and freshening to coral δ18O records are exceedingly rare. Furthermore, the fidelity of coral δ18O records under the most severe thermal stress events is difficult to assess. Here, we present six coral δ18O records and in situ temperature, salinity, and seawater δ18O data from Kiritimati Island (2°N, 15°W) spanning the very strong 2015/16 El Niño event. Local sea surface temperature (SST) anomalies of +2.4±0.4°C and seawater δ18O anomalies of -0.19±0.02‰ contribute to the observed coral δ18O anomalies of -0.58±0.05‰, consistent with a ~70% contribution from SST and ~30% from seawater δ18O. Our results demonstrate that Kiritimati coral δ18O records can provide reliable reconstructions even during the largest class of El Niño events.

We evaluate the longitudinal variation in meridional shifts of the tropical rainband in response to natural and anthropogenic forcings using a large suite of coupled climate model simulations. We find that the energetic framework of the zonal mean Hadley cell is generally not useful for characterizing shifts of the rainband at regional scales, regardless of the characteristics of the forcing. Forcings with large hemispheric asymmetry such as extratropical volcanic forcing and meltwater forcing give rise to robust zonal mean shifts of the rainband, however the direction and magnitude of the shift varies strongly as a function of longitude. Even the Pacific rainband doesn't shift uniformly under any forcing considered. Forcings with weak hemispheric asymmetry such as CO and mid-Holocene forcing give rise to zonal mean shifts that are small or absent, but the rainband does shift regionally in coherent ways across models that may have important dynamical consequences.

General Circulation Models (GCMs) exhibit substantial biases in their simulation of tropical climate. One particularly problematic bias exists in GCMs’ simulation of the tropical rainband known as the Intertropical Convergence Zone (ITCZ). Much of the precipitation on Earth falls within the ITCZ, which plays a key role in setting Earth’s temperature by affecting global energy transports, and partially dictates dynamics of the largest interannual mode of climate variability: the El Nino-Southern Oscillation (ENSO). Most GCMs fail to simulate the mean state of the ITCZ correctly, often exhibiting a “double ITCZ bias”, with rainbands both north and south rather than just north of the equator. These tropical mean state biases limit confidence in climate models’ simulation of projected future and paleoclimate states, and reduce the utility of these models for understanding present climate dynamics. Adjusting GCM parameterizations of cloud processes and atmospheric convection can reduce tropical biases, as can artificially correcting sea surface temperatures (SSTs) through modifications to air-sea fluxes (i.e. “flux adjustment”). Here we argue that a significant portion of these rainfall and circulation biases are rooted in orographic height being biased low due to assumptions made in fitting observed orography onto GCM grids. We demonstrate that making different, and physically defensible, assumptions that raise the orographic height significantly improves model simulation of climatological features such as the ITCZ and North American rainfall as well as the simulation of ENSO. These findings suggest a simple, physically-based, and computationally inexpensive method that can improve climate models and projections of future climate.