Ana Patrícia Pires Marques Oliveira CTO for Space Applications, +Atlantic, CoLAB Atlantic, Portugal.

Title: From Citizen Science, Machine Learning and Earth Observation towards Urban Climate Services

Abstract: Climate change is reshaping patterns of human exposure to extreme weather, with profound consequences for health and urban resilience. Globally, rising temperatures are driving more frequent, and more intense heatwaves. In cities, these trends are further amplified by Urban Heat Island (UHI) effects, where modified urban surfaces intensify nocturnal heat retention, exacerbating thermal discomfort and contributing to excess mortality, increased energy demand, and wider socio-economic impacts. Understanding and anticipating these risks requires high-resolution environmental intelligence derived from Earth Observation (EO), climate modelling, reanalysis systems, and emerging Digital Twin of the Earth (DTE) capabilities. This work outlines how EO-driven innovation can strengthen climate–health risk assessment through three objectives: (1) assessing the role of EO in monitoring climate variables relevant to health; (2) exploring how DTE, reanalysis, and projections enhance understanding of climate–health linkages; and (3) demonstrating the application of these datasets in supporting adaptation and early warning. Within this context, CLIM4health—an ESA-funded project under the CLIMATE-SPACE activity—develops EO-informed, AI/ML-based Extreme Heat and Cold Climate–Health Risk Algorithms at 200 × 200 m resolution for four Functional Urban Areas (FUAs) in Portugal and Denmark. These indices integrate downscaled hazard exposure with health records to predict excess mortality and morbidity at sub-municipal scale. The methodology builds on a wider portfolio of initiatives. These include CLIM4cities, which demonstrated generalisable ML downscaling of reanalysis data across Denmark and the Urban Heat-Health Forecasting Service for ECMWF, which adapts data-driven downscaling for DestinE’s Extremes Digital Twin, as well as the Horizon Europe TerraDT project, which extends these methods to downscale climate projections and assess adaptation scenarios.

Wenyu Zhou Research Scientist, Pacific Northwest National Laboratory,(PNNL), USA.

Title: A theoretical index for understanding distinct land relative humidity trends in observations, reanalyses, and models

Abstract: Land surface relative humidity (RH) is a key variable in the coupled land-atmosphere system that profoundly influences terrestrial hydroclimate and ecosystems. Yet historical changes in land RH are not well understood due to limited observations, biased reanalyses, and the lack of a framework for interpreting RH changes under multiple influencing factors. Here we show that the spatiotemporal variability of land RH and its distinct historical trends among observations, reanalyses, and Earth system models are captured by a simple index based on the ratio of precipitation (P) to a modified potential evapotranspiration formulated independently of RH (PETo). The index provides a physical calibration of biased land RH in reanalyses and a quantitative framework for interpreting land RH changes. Over 1973-2024, land RH has decreased substantially, owing to the intrinsic rise in PETo with temperature and little increase in land precipitation. Reanalyses overestimate the observed RH decrease, consistent with their exaggerated surface warming and precipitation decline. The index captures this coherent bias and enables a calibration using observed precipitation and temperature. Models simulate a wide range of land RH trends, but nearly all runs underrepresent the historical drying. The index captures the model spread and discrepancy and attributes them to contributions of precipitation and PETo. Weaker land RH decreases in models arise mainly from weaker subtropical precipitation declines, linked to muted intensification of subtropical highs and biased subtropical climatology. The model-observation discrepancy is unlikely explained by internal variability, implying model underestimation of forced RH decrease and a drier land future than current projections.

Yilling Ma Phd Student, Karlsruhe Institute of Technology(KIT), Germany.

Title: mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for CMIP Simulations

Abstract: Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in common CMIP simulations, including pre-industrial, abrupt-4xCO2(Ma et al. 2026), historical and future Shared Socioeconomic Pathway (SSP) scenarios simulations. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models; the UK Earth System Model (UKESM), and the German ICOsahedral Nonhydrostatic (ICON) model. With meteorological variables and forcing data as inputs, mloz produces stable ozone predictions around 31 times faster than the chemistry scheme in UKESM, contributing less than 4% of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON in standard climate sensitivity simulations. This highlights mloz’s potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, where ozone trends and variability will significantly modulate atmospheric feedback processes.