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Abstract. Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century. 

Abstract. The response of the hydrological cycle to anthropogenic climatechange, 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 annualresolution. 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 ofseawater (δ18Osw), a measure of hydrological variability. Increasingly, coral δ18O time series are paired with time series of strontium-to-calcium ratios (Sr/Ca), a proxy for SST, from the same coral to quantify temperature and δ18Osw variabilitythrough time. To increase the utility of such reconstructions, we presentthe CoralHydro2k database, a compilation of published, peer-reviewed coral Sr/Ca and δ18O records from the Common Era (CE). The database contains 54 paired Sr/Ca–δ18O records and 125 unpaired Sr/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 useof the database. The CoralHydro2k database tracks large-scale temperatureand hydrological variability. As such, it is well-suited for investigationsof past climate variability, comparisons with climate model simulationsincluding 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). 

Abstract Scientific understanding of low-frequency tropical Pacific variability, especially responses to perturbations in radiative forcing, suffers from short observational records, sparse proxy networks, and bias in model simulations. Here, we combine the strengths of proxies and models through coral-based paleoclimate data assimilation. We combine coral archives ( δ 18 O, Sr/Ca) with the dynamics, spatial teleconnections, and intervariable relationships of the CMIP5/PMIP3 Past1000 experiments using the Last Millennium Reanalysis data assimilation framework. This analysis creates skillful reconstructions of tropical Pacific temperatures over the observational era. However, during the period of intense volcanism in the early nineteenth century, southwestern Pacific corals produce El Niño–Southern Oscillation (ENSO) reconstructions that are of opposite sign from those from eastern Pacific corals and tree ring records. We systematically evaluate the source of this discrepancy using 1) single-proxy experiments, 2) varied proxy system models (PSMs), and 3) diverse covariance patterns from the Past1000 simulations. We find that individual proxy records and coral PSMs do not significantly contribute to the discrepancy. However, following major eruptions, the southwestern Pacific corals locally record more persistent cold anomalies than found in the Past1000 experiments and canonical ENSO teleconnections to the southwest Pacific strongly control the reconstruction response. Furthermore, using covariance patterns independent of ENSO yields reconstructions consistent with coral archives across the Pacific. These results show that model bias can strongly affect how proxy information is processed in paleoclimate data assimilation. As we illustrate here, model bias influences the magnitude and persistence of the response of the tropical Pacific to volcanic eruptions. 

Reconstructing past climates remains a difficult task because pre‐instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi‐model ensembles produce lower error than reconstructions from single‐model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid‐ and high‐latitude ocean and sea‐ice regions. Furthermore, we find that multi‐model ensemble reconstructions outperform single‐model reconstructions that use covariance localization. We propose that multi‐model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables.