Earth Observation Data Group

Abstract:

Limiting global warming to 1.5 °C above pre-industrial levels requires a rapid and sustained transition to renewable energy systems, with photovoltaic (PV) solar energy playing a central role due to its scalability and declining costs. However, PV power generation is inherently sensitive to atmospheric conditions such as aerosols, cloud cover, and temperature, which vary spatially and are expected to evolve under climate change. While global PV capacity has expanded rapidly, climate-related impacts on PV energy generation, particularly at the facility level, remain insufficiently quantified. Many existing assessments rely on generalized assumptions, overlooking the heterogeneity of PV deployment and local environmental conditions, which limits their relevance for integrated energy system modelling and planning.This study combines machine learning and satellite-based observations to improve the representation of PV systems and climate-related performance losses in global-scale assessments. A machine learning model is trained on diverse geospatial datasets to identify PV installations across a range of geographic and land-use contexts, including complex terrains. Facility-level PV data are then integrated with satellite and reanalysis products to quantify the influence of aerosols, cloud variability, and temperature on solar energy generation over the past decade.Results reveal pronounced regional variability in PV energy losses, driven by differences in atmospheric composition, cloud dynamics, and thermal stress. Elevated aerosol loads are associated with significant reductions in surface solar irradiance, while cloud variability affects both average generation and short-term reliability. Extreme temperatures further reduce PV efficiency in certain regions. These findings highlight the importance of incorporating site-specific climate sensitivities into energy system models to better assess performance, resilience, and trade-offs in renewable energy deployment.By shifting the focus from installed capacity to climate-related energy losses, this work contributes to integrated assessments of sustainable energy transitions. The approach provides actionable insights for system planning, model improvement, and policy development, supporting more robust and environmentally informed strategies for scaling solar energy within diversified and resilient energy systems.

Abstract:

This work assesses the impact that the assimilation of ozone profiles has on meteorological fields in NWP simulations of recent weather events that were influenced by stratosphere-troposphere interactions.We use the European Centre for Medium-Range Weather Forecasts (ECMWF) IFS model with CAMS configurations, focusing on Northern Hemisphere winters within the period 2020-2023. We investigate the impact of Microwave Limb Sounder (MLS) ozone profiles, as MLS on the Aura satellite has been providing essential observations of ozone for the stratosphere and the upper troposphere-lower stratosphere (UTLS) regions.Chemistry-dynamics interactions in these regions are key for winter weather and climate patterns, and for the coupling between troposphere and stratosphere. Our work highlights the capacity of MLS O3 to enhance weather forecasting and shows the need for alternatives once MLS is decommissioned.Our study also explores alternatives to be used after MLS data will stop being available. And it shows the need for future observation platforms similar to the ESA-CAIRT EE11 candidate instrument, to provide atmospheric composition measurements that would enable better representation of stratospheric and UTLS processes and enhance stratosphere-troposphere coupling in weather forecast systems and reanalyses.

Abstract:

The planetary energy imbalance depends on the amount of solar energy entering and leaving the system, as well as changes in greenhouse gas concentrations. Since the start of the 21st century, the Earth’s energy imbalance (EEI) is assumed to have doubled, linked to the reduction of solar radiation reflected back to space, due to atmospheric dimming. Rapid and responsive feedback mechanisms have contributed to the accumulation of excess heat within the global oceans. The ocean warming drives the positive change in EEI and impacts the hydrological cycle, becoming more intense. Such linkage disturbs well-established weather patterns and cause their alternation. To understand these phenomena, traditionally complex state-of-the-art coupled climate models would be used. However, the strength of simpler, energy balance climate models capturing large-scale features has shown to be an alternative approach in understanding the general state of climate.In this study, we utilise the ocean component of the newly developed novel energy balance climate model (nEBM) to examine the relationship between EEI and ocean warming. Our approach perturbs key hydrological cycle elements (e.g., precipitation, runoff, evaporation, etc) in addition to other forcing components (e.g., CO2) to show the resulting ocean response and the subsequent impacts on EEI. These results are compared to observational datasets to demonstrate the performance of the nEBM ocean model. The obtained results are compared to CMIP6, observations, and relevant literature. Finally, we discuss the ability of simpler climate models (e.g., nEBM) to quantify sensitivity in climate studies.

Abstract:

Advanced kilometer-scale resolution modeling offers unprecedented detail of atmospheric processes and properties, including of mineral dust. At kilometer-scale model resolutions, deep moist convective processes do not have to be parameterized any more, but can be represented explicitly at the grid resolution. These processes are very effective in transporting heat, moisture, and energy within the atmosphere and therefore have strong impacts on weather phenomena, such as wind storms. Mineral dust emission is a threshold process that depends non-linearly upon surface wind intensity, which means that the accuracy at which models represent surface winds, together with land-surface properties, is key to estimating dust emissions. A spectacular and intense type of dust storm, i.e. haboob dust storms, is caused by the cold pool outflow of moist convection. We therefore expect that the explicit representation of moist convection in kilometer-scale simulations is particularly beneficial for dust modeling. Determining whether kilometer-scale models can meet this expectation, demands in-depth evaluation against observations. This evaluation is now enabled through novel satellite missions, such as the Earth Cloud Aerosol and Radiation Explorer (EarthCARE). Here we present results of kilometer-scale simulations conducted with two models, ICON-ART and ICON-HAM-lite, both including an interactive dust representation. We investigate, for example, evaporative cooling and vertical velocities associated with moist convection as drivers of dust emission. We compare our results against observations from EarthCARE and ORCESTRA (Organized Convection and EarthCARE Studies over the Tropical Atlantic), and against results from other models in the framework of the EarthCARE-ORCESTRA Model Intercomparison Project (ECOMIP). Our results show fascinating detail of mineral dust processes, enabling novel insights into the mineral dust cycle, for example, a globally consistent characterization of haboob properties and impacts.

Abstract:

It is well known that scientific data have uncertainties and that it is crucial to take these uncertainties into account in any decision making process. Nevertheless, despite data producer’s best efforts to provide complete and rigorous uncertainty estimates alongside their data, users commonly struggle to make sense of uncertainty information. This is because uncertainties are usually expressed as the statistical spread in the observations (for example, as random error standard deviation), which does not relate to the intended use of the data.Put simply, data and their uncertainty are usually expressed as something like “x plus/minus y”, which does not answer the really important question: How much can I trust “x”, or any use of or decision based upon “x”? Consequently, uncertainties are often either ignored altogether and the data taken at face value, or interpreted by experts (or non-experts) heuristically to arrive at rather subjective, qualitative judgements of the confidence they can have in the data.In line with existing practices (e.g., the communication of uncertianties in the IPCC reports), we conjecture that the key to enabling users to make sense of uncertainties is to represent them as the confidence one can have in whatever event one is interested in, given the available data and their uncertainty.To that end, we propose a novel, generic framework that transforms common uncertaintiy representations (i.e., estimates of stochastic data properties, such as “the state of this variable is “x plus/minus y”) into more meaningful, actionable information that actually relate to their intended use, (i.e., statements such as “the data and their uncertainties suggest that we can be “z” % confident that…”). This is done by first formulating a meaningful question that links the available data to some events of interest, and then deriving quantiative estimates for the confidence in the occurrence of these events using Bayes theorem.We demonstrate this framework using two case examples: (i) using satellte soil moisture retrievals and their uncertainty to derive how confident one can be in the presence and severity of a drought; and (ii) how ocean temperature analyses and their uncertainty can be used to determine how confident one can be that prevailing conditions are likely to cause coral bleaching.