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Juan C. Acosta Navarro, Pablo Ortega, Javier García-Serrano, Virginie Guemas, Etienne Tourigny, Rubén Cruz-García, François Massonnet, and Francisco J. Doblas-Reyes
Open access
Dragana Bojovic, Roberto Bilbao, Leandro B. Díaz, Markus Donat, Pablo Ortega, Yohan Ruprich-Robert, Balakrishnan Solaraju-Murali, Marta Terrado, Deborah Verfaillie, and Francisco Doblas-Reyes
Free access
Ed Hawkins, Pablo Ortega, Emma Suckling, Andrew Schurer, Gabi Hegerl, Phil Jones, Manoj Joshi, Timothy J. Osborn, Valérie Masson-Delmotte, Juliette Mignot, Peter Thorne, and Geert Jan van Oldenborgh


The United Nations Framework Convention on Climate Change (UNFCCC) process agreed in Paris to limit global surface temperature rise to “well below 2°C above pre-industrial levels.” But what period is preindustrial? Somewhat remarkably, this is not defined within the UNFCCC’s many agreements and protocols. Nor is it defined in the IPCC’s Fifth Assessment Report (AR5) in the evaluation of when particular temperature levels might be reached because no robust definition of the period exists. Here we discuss the important factors to consider when defining a preindustrial period, based on estimates of historical radiative forcings and the availability of climate observations. There is no perfect period, but we suggest that 1720–1800 is the most suitable choice when discussing global temperature limits. We then estimate the change in global average temperature since preindustrial using a range of approaches based on observations, radiative forcings, global climate model simulations, and proxy evidence. Our assessment is that this preindustrial period was likely 0.55°–0.80°C cooler than 1986–2005 and that 2015 was likely the first year in which global average temperature was more than 1°C above preindustrial levels. We provide some recommendations for how this assessment might be improved in the future and suggest that reframing temperature limits with a modern baseline would be inherently less uncertain and more policy relevant.

Open access
Pablo Ortega, Edward W. Blockley, Morten Køltzow, François Massonnet, Irina Sandu, Gunilla Svensson, Juan C. Acosta Navarro, Gabriele Arduini, Lauriane Batté, Eric Bazile, Matthieu Chevallier, Rubén Cruz-García, Jonathan J. Day, Thierry Fichefet, Daniela Flocco, Mukesh Gupta, Kerstin Hartung, Ed Hawkins, Claudia Hinrichs, Linus Magnusson, Eduardo Moreno-Chamarro, Sergio Pérez-Montero, Leandro Ponsoni, Tido Semmler, Doug Smith, Jean Sterlin, Michael Tjernström, Ilona Välisuo, and Thomas Jung


The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.

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