Earth Observation Data Group

Abstract:

Abstract The Earth’s middle atmosphere spans the deep region from the upper troposphere/lower stratosphere at around 10 km altitude to the mesosphere/lower thermosphere at around 100 km altitude. It is being increasingly recognized for its role in driving extreme surface weather and regional climate change. Climate models predict large ongoing and future changes in the middle atmosphere composition and circulation. However, the observations needed to detect, attribute and understand these changes and their impacts, to test predictions, and thereby to improve our models, are lacking. Here we show the capacity of infrared limb-imaging tomography to provide the needed observations. This evaluation is based on studies performed within a recent satellite mission concept – the Changing-Atmosphere Infrared Tomography Explorer, CAIRT. Observing thermal infrared emissions simultaneously from the middle troposphere at about 4 km up to the lower thermosphere at about 115 km altitude this technique provides observations of temperature and an extensive range of trace gases with unprecedented spatial resolution of about 50 by 50 km horizontally and about 1 km vertically. We show how these observations would (a) help to quantify the changing atmospheric circulation, (b) allow characterization and quantification of the gravity waves that are critical in driving this circulation, (c) reveal how variability in solar radiation and energetic particles propagate downward to affect regional climate at the surface, (d) detect how volcanic eruptions and wildfires impact the middle atmosphere and climate, and (e) resolve how stratosphere-troposphere exchange affects ozone and water vapor in the crucial and climate-relevant tropopause region.

Abstract:

Abstract UK summer heatwaves are dictated by the polar jet stream position and sea surface temperature (SST) variability, affecting the Summer North Atlantic Oscillation (SNAO) index. The SNAO can determine and influence the Central England Precipitation (CEP) and Central England Temperature (CET). A strong and significant negative correlation ( r  = −0.63) is found between the SNAO and CEP, but a weaker correlation for the CET. Summers with highly positive SNAO (i.e. 1976 and 2018) are among the driest and warmest on record. In this study, we highlight the roles of large‐scale atmospheric circulations and use of Met Office‐defined weather regimes in understanding UK heatwave characteristics.

Abstract:

The global energy transition depends on solar photovoltaic (PV) power displacing fossil fuels to deliver projected climate and air quality benefits. However, aerosol pollution from co-located coal plants actively suppresses PV energy production. Here a global, facility-level dataset shows that aerosols reduced global PV generation by 5.8% in 2023 (111 TWh). From 2017 to 2023, annual aerosol-induced PV energy losses from existing systems were, on average, equivalent to one-third of the energy added by new PV installations. In China, aerosols caused the largest PV energy losses worldwide, reducing national PV generation by 7.7% in 2023. The corresponding annual loss-to-growth ratio averaged 38% and frequently exceeded 50%. Despite continued coal expansion, PV energy losses have declined by 1.4% yr−1 since 2017 owing to stricter emission controls. By contrast, the USA, where co-location of solar and coal plants is limited, experienced only 3.1% aerosol-induced PV loss. Given the slow pace of global coal phase-out, these results reveal a constraint on solar performance that, if unaccounted for, could lead to a systematic overestimation of the transition’s contribution to climate and air quality goals.

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.