Monthly North Atlantic Sea Level Pressure reconstruction back to 1750 CE using Artificial Intelligence optimization

Main modes of atmospheric variability exert a significant influence on weather and climate at local and regional scales on all time scales. However, their past changes and variability over the instrumental record are not well constrained due to limited availability of observations, particularly over the oceans. Here we couple a reconstruction method with an evolutionary algorithm to yield a new 1° × 1° optimized reconstruction of monthly North Atlantic sea level pressure since 1750 from a network of meteorological land and ocean observations. Our biologically inspired optimization technique finds an optimal set of weights for the observing network that maximizes the reconstruction skill of sea level pressure fields over the North Atlantic Ocean, bringing significant improvements over poorly sampled oceanic regions, as compared to non-optimized reconstructions. It also reproduces realistic variations of regional climate patterns such as the winter North Atlantic Oscillation and the associated variability of the subtropical North Atlantic high and the subpolar low pressure system, including the unprecedented strengthening of the Azores high in the second half of the twentieth century. We find that differences in the winter North Atlantic Oscillation indices are partially explained by disparities in estimates of its Azores high center. Moreover, our reconstruction also shows that displacements of the summer Azores high center toward the northeast coincided with extremely warm events in western Europe including the anomalous summer of 1783. Overall, our results highlight the importance of improving the characterization of the Azores high for understanding the climate of the Euro-Atlantic sector and the added value of artificial intelligence in this avenue.This work was supported by the Ministerio de Economía y Competitividad del Gobierno de España through the PALEOSTRAT (CGL2015-69699-R) project. Jaume-Santero was funded by Grant BES-2016-077030 from the Ministerio de Ciencia e Innovación and the Ministerio de Universidades of the Spanish government. The authors thank Prof. Salcedo-Sanz for providing the core of the CRO algorithm and for helping translate it to C

North American regional climate reconstruction from ground surface temperature histories

Within the framework of the PAGES NAm2k project, 510 North American borehole temperature–depth profiles were analyzed to infer recent climate changes. To facilitate comparisons and to study the same time period, the profiles were truncated at 300 m. Ground surface temperature histories for the last 500 years were obtained for a model describing temperature changes at the surface for several climate-differentiated regions in North America. The evaluation of the model is done by inversion of temperature perturbations using singular value decomposition and its solutions are assessed using a Monte Carlo approach. The results within 95 % confidence interval suggest a warming between 1.0 and 2.5 K during the last two centuries. A regional analysis, composed of mean temperature changes over the last 500 years and geographical maps of ground surface temperatures, show that all regions experienced warming, but this warming is not spatially uniform and is more marked in northern regions

How many proxies are necessary to reconstruct the temperature of the last millennium?

Decades of scientific fieldwork have provided extensive sets of paleoclimate records to reconstruct the climate of the past at local, regional, and global scales. Within this context, the paleoclimate community is continuously undertaking new measuring campaigns to obtain long and reliable proxies. However, as most paleoclimate archives are restricted to land regions of the Northern Hemisphere, increasing the number of proxy records to improve the skill of climate field reconstructions might not always be the best strategy. By generating pseudo-proxies from several model ensembles at the locations matching the records of the PAGES-2k network, we show how biologically-inspired artificial intelligence can be coupled with reconstruction methods to find the set of representative locations that minimizes the bias in global temperature field reconstructions induced by the non-homogeneous distribution of proxy records. Our results indicate that small sets of perfect pseudo-proxies situated over key locations of the PAGES-2k network can outperform the reconstruction skill obtained with all available records. They highlight the importance of high latitudes and major teleconnection areas to reconstruct temperature fields at annual timescales. However, long-term temperature variations are better reconstructed by records situated at lower latitudes. According to our experiments, a careful selection of proxy locations should be performed depending on the targeted time scale of the reconstructed field

Selection of optimal proxy locations for temperature field reconstructions using evolutionary algorithms

In the Era of exponential data generation, increasing the number of paleoclimate records to improve climate field reconstructions might not always be the best strategy. By using pseudo-proxies from different model ensembles, we show how biologically-inspired artificial intelligence can be coupled with different reconstruction methods to minimize the spatial bias induced by the non-homogeneous distribution of available proxies. The results indicate that small subsets of records situated over representative locations can outperform the reconstruction skill of the full proxy network, even in more realistic pseudo-proxy experiments and observational datasets. These locations highlight the importance of high-latitude regions and major teleconnection areas to reconstruct annual global temperature fields and their responses to external forcings and internal variability. However, low frequency temperature variations such as the transition between the Medieval Climate Anomaly and the Little Ice Age are better resolved by records situated at lower latitudes. According to our idealized experiments a careful selection of proxy locations should be performed depending on the targeted time scale of the reconstructed field.This work was supported by the Ministerio de Economía y Competitividad del Gobierno de España through the PALEOSTRAT (CGL2015-69699-R) project. Jaume-Santero was funded by grant BES-2016-077030 from the Ministerio de Ciencia, Innovación y Universidades del Gobierno de España and the European Social Fund