Earth Observation Data Group

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:

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:

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:

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:

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.