PeopleofSuperintelligence

Researcher

Fernando Iglesias-Suarez

Fernando Iglesias-Suarez is a climate and AI data scientist whose research applies machine learning to climate models and Earth-system science. After earning degrees at Universidad Pablo de Olavide in 2004 and the University of East Anglia in 2011, he completed a doctorate on stratospheric ozone at Lancaster University in 2017 and held research posts at the Spanish National Research Council in Madrid and DLR in Oberpfaffenhofen. At DLR, he developed neural-network cloud parameterizations for the ICON model, coauthored the ClimSim dataset that won a 2023 NeurIPS Outstanding Paper Award, and applied causal and stochastic deep learning to atmospheric processes. As of October 2026, he is a climate scientist at Predictia in Santander, where he spearheads a deep-learning cloud-forecasting component for Destination Earth.

Now at
Predictia

Timeline

  1. 2004–2011

    Degrees in Seville and Norwich

    He completes a BSc in Environmental Science at Universidad Pablo de Olavide in Seville in 2004. In 2011, he earns an MSc in Climate Change at the University of East Anglia in Norwich under supervisors Naomi Vaughan and Timothy Osborn, with a dissertation on geoengineering solutions to meet the 2ºC target.

  2. 2016–2017

    A PhD on stratospheric ozone

    At Lancaster University in 2016, he works as a Research Associate and PhD student, presenting his climate research on multi-decadal ozone variability and Pacific sea surface temperatures at the Royal Meteorological Society Student Conference in Manchester in July 2016. He completes a Lancaster doctorate in atmospheric science on stratospheric ozone in 2017 under supervisors Paul J. Young and Oliver Wild, with Lancaster Environment Centre and Data Science Institute affiliations.

  3. 2017–2021

    Madrid research and Pacific ozone

    From 2017 to 2019, he works as a Research Associate at the Spanish National Research Council in Madrid. A 2021 study he coauthors links multi-decadal variation in tropical stratospheric circulation and ozone to the Interdecadal Pacific Oscillation.

  4. 2022

    Neural networks for ICON clouds

    Joining DLR in Oberpfaffenhofen as a Research Associate from 2019 to 2024, he conducts ELLIS research on the project Machine learning for improving climate models. In 2022, he coauthors a DLR paper that trains neural networks to estimate sub-grid cloud cover from coarse-grained atmospheric data in the ICON modeling framework.

  5. 2023

    ClimSim wins at NeurIPS

    With collaborators, he coauthors ClimSim in 2023, a multi-scale dataset for hybrid ML-physics climate emulation. The paper wins a NeurIPS 2023 Outstanding Paper Award in the Datasets and Benchmarks track in December 2023.

  6. 2024

    Causal learning and digital twins

    In 2024, he coauthors Causally-Informed Deep Learning to Improve Climate Models and Projections, which applies causal discovery to atmospheric subgrid processes in a superparameterized climate model. He also contributes to Digital Twins of the Earth with and for Humans as a climate scientist at DLR, then continues climate work at Predictia in Santander as a Climate and AI Data Scientist in 2024.

  7. 2025

    Stochastic climate models

    His 2025 coauthored study, Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations, develops machine-learning methods for uncertainty in convection and turbulence.

  8. As of October 2026

    Climate scientist at Predictia

    As of October 2026, he is a climate scientist at Predictia in Santander and works on a deep-learning cloud-forecasting component for Destination Earth, presented at EGU26 in 2026, that models cloud-related fields up to 12 hours ahead.

Career

  1. NowPredictiaClimate Scientist
  2. 2017–2019Consejo Superior de Investigaciones CientíficasResearch Associate
  3. 2016–2017Lancaster UniversityResearch Associate
  4. German Aerospace CenterClimate Scientist

Education

  1. 2017Lancaster UniversityPhD
  2. 2011University of East AngliaMSc
  3. 2004Universidad Pablo de OlavideBSc

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Fernando Iglesias-Suarez. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. The United States employs an estimated 48% of people shaping AIIncludes Consejo Superior de Investigaciones Científicas. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Consejo Superior de Investigaciones Científicas. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  4. Google has the highest combined h-index among AI organizationsIncludes Consejo Superior de Investigaciones Científicas. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Fernando Iglesias-Suarez yet. Send us a free-licensed photo.

  1. About me figlesua.github.io. Checked 22 October 2026.
  2. PhD student speaks at RMetSoc Conference lancaster.ac.uk. Checked 22 October 2026.
  3. Stratospheric ozone in the Earth system research.lancaster-university.uk. Checked 22 October 2026.
  4. Tropical Stratospheric Circulation and Ozone Coupled to Pacific Multi-Decadal Variability research.lancaster-university.uk. Checked 22 October 2026.
  5. Fernando Iglesias-Suarez | European Laboratory for Learning and Intelligent Systems ellis.eu. Checked 22 October 2026.
  6. Deep Learning Based Cloud Cover Parameterization for ICON elib.dlr.de. Checked 22 October 2026.
  7. ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation papers.neurips.cc. Checked 22 October 2026.
  8. Announcing the NeurIPS 2023 Paper Awards blog.neurips.cc. Checked 22 October 2026.
  9. Causally-Informed Deep Learning to Improve Climate Models and Projections elib.dlr.de. Checked 22 October 2026.
  10. Digital Twins of the Earth for Actionable Science predictia.es. Checked 22 October 2026.
  11. Simulating Atmospheric Processes with Stochastic AI: A New Step Toward Smarter Climate Models predictia.es. Checked 22 October 2026.
  12. Tag - Machine learning learning.ecmwf.int. Checked 22 October 2026.
  13. An Early-Warning System and a DestinE component for cloud forecasting, at EGU26 predictia.es. Checked 22 October 2026.