The future in the past: A major cause of uncertainties in climate projections is our imprecise knowledge of how much warming should occur as a result of a given increase in the amount of carbon dioxide in the atmosphere. Paleoclimate records have the potential to help us sharpen that understanding because they record such a wide variety of environmental conditions. Tierney et al. review the recent advances in data collection, statistics, and modeling that might help us better understand how rising levels of atmospheric carbon dioxide will affect future climate. Science , this issue p. eaay3701

Reproducible climate reconstructions of the Common Era (1 CE to present) are key to placing industrial-era warming into the context of natural climatic variability. Here we present a community-sourced database of temperature-sensitive proxy records from the PAGES2k initiative. The database gathers 692 records from 648 locations, including all continental regions and major ocean basins. The records are from trees, ice, sediment, corals, speleothems, documentary evidence, and other archives. They range in length from 50 to 2000 years, with a median of 547 years, while temporal resolution ranges from biweekly to centennial. Nearly half of the proxy time series are significantly correlated with HadCRUT4.2 surface temperature over the period 1850–2014. Global temperature composites show a remarkable degree of coherence between high- and low-resolution archives, with broadly similar patterns across archive types, terrestrial versus marine locations, and screening criteria. The database is suited to investigations of global and regional temperature variability over the Common Era, and is shared in the Linked Paleo Data (LiPD) format, including serializations in Matlab, R and Python.

Climate science is highly interdisciplinary by nature, so understanding interactions between Earth processes inherently warrants the use of analytical software that can operate across the disciplines of Earth science. Toward this end, we present the Climate Data Toolbox for MATLAB, which contains more than 100 functions that span the major climate‐related disciplines of Earth science. The toolbox enables streamlined, entirely scriptable workflows that are intuitive to write and easy to share. Included are functions to evaluate uncertainty, perform matrix operations, calculate climate indices, and generate common data displays. Documentation is presented pedagogically, with thorough explanations of how each function works and tutorials showing how the toolbox can be used to replicate results of published studies. As a well‐tested, well‐documented platform for interdisciplinary collaborations, the Climate Data Toolbox for MATLAB aims to reduce time spent writing low‐level code, let researchers focus on physics rather than coding and encourage more efficacious code sharing.

In April 2016, southeast Asia experienced surface air temperatures (SATs) that surpassed national records, exacerbated energy consumption, disrupted agriculture and caused severe human discomfort. Here we show using observations and an ensemble of global warming simulations the combined impact of the El Niño/Southern Oscillation (ENSO) phenomenon and long-term warming on regional SAT extremes. We find a robust relationship between ENSO and southeast Asian SATs wherein virtually all April extremes occur during El Niño years. We then quantify the relative contributions of long-term warming and the 2015–16 El Niño to the extreme April 2016 SATs. The results indicate that global warming increases the likelihood of record-breaking April extremes where we estimate that 29% of the 2016 anomaly was caused by warming and 49% by El Niño. These post-Niño Aprils can potentially be anticipated a few months in advance, and thus, help societies prepare for the projected continued increases in extremes.

The Mg/Ca ratio of planktic foraminifera is a widely used proxy for sea‐surface temperature but is also sensitive to other environmental factors. Previous work has relied on correcting Mg/Ca for nonthermal influences. Here, we develop a set of Bayesian models for Mg/Ca in four major planktic groups—Globigerinoides ruber (including both pink and white chromotypes), Trilobatus sacculifer, Globigerina bulloides, and Neogloboquadrina pachyderma (including N. incompta)—that account for the multivariate influences on this proxy in an integrated framework. We use a hierarchical model design that leverages information from both laboratory culture studies and globally distributed core top data, allowing us to include environmental sensitivities that are poorly constrained by core top observations alone. For applications over longer geological timescales, we develop a version of the model that incorporates changes in the Mg/Ca ratio of seawater. We test our models—collectively referred to as BAYMAG—on sediment trap data and on representative paleoclimate time series and demonstrate good agreement with observations and independent sea‐surface temperature proxies. BAYMAG provides probabilistic estimates of past temperatures that can accommodate uncertainties in other environmental influences, enhancing our ability to interpret signals encoded in Mg/Ca.