Universidad de Cantabria

SSc is characterized by micro- and macrovascular damage, increasing cardiovascular (CV) risk. The SCORE2 algorithm underestimates CV risk in systemic autoimmune diseases. Nailfold capillaroscopy (NFC) is used to assess SSc-related microvascular damage. We aimed to evaluate whether incorporating NFC findings into SCORE2/SCORE2-OP improves CV risk stratification in SSc. Retrospective multicentre study including 276 patients with SSc. Baseline 10-year CV risk was estimated using SCORE2 or SCORE2-OP. NFC at diagnosis was assessed. Cox regression identified NFC variables associated with major CV events (MCEs). Their adjusted hazard ratios (HRs) were applied to derive NFC-modified SCORE2 models. Discrimination, calibration and reclassification of original vs modified models were compared, with bootstrap internal validation. Over a median follow-up of 9.5 years, 45 (16.3%) patients experienced an MCE. In multivariable models, late NFC pattern (HR 4.056, P = 0.002) and avascular areas (HR 2.631, P = 0.039) were independently associated with MCEs, whereas microhaemorrhages were associated with reduced risk (HR 0.345, P = 0.017). Original SCORE2 showed modest discrimination for incident MCEs (AUC 0.687, 95% CI 0.574-0.801). NFC-modified models improved discrimination (AUC 0.784, 95% CI 0.674-0.894 for the NFC-findings model and 0.764, 95% CI 0.654-0.875 for the NFC-pattern model; both P < 0.05 vs SCORE2), with consistent gains in Harrell's C-index. Risk reclassification analyses showed classification of patients with MCEs, with categorical net reclassification indices of 0.31 and 0.36, respectively. Incorporating baseline NFC into SCORE2 improves CV stratification in patients with SSc, and may support more individualized CV prevention strategies in SSc.

Urban areas are becoming increasingly affected by the impacts of climate change, particularly through intensifying heat extremes that constitute a threat to public health and economic productivity. A key driver of urban heat stress is the Urban Heat Island (UHI) effect, a phenomenon in which nighttime cooling is reduced due to heat accumulation within the city structure.The UHI is usually defined as the temperature contrast between urban and rural areas; however, thermal discomfort is not solely determined by temperature: humidity, wind and radiation strongly modulate heat perception. Therefore, a purely temperature-based definition may underestimate its actual impact on the urban population. This study proposes a human-centered characterization of the UHI effect, introducing the concept of Urban Heat Stress Island (UHSI), by integrating several heat stress indices and assessing their complementarity.We analyze observations from ten meteorological stations in Paris and its surroundings for 1980-2017, within the CORDEX URB-RCC Flagship Pilot Study (Langendijk et al. 2024). The dataset includes subdaily records of air temperature, relative humidity, wind speed and radiation, from which widely used multivariable heat stress indices are computed. The UHSI effect is consequently defined as the urban-rural degree difference for each index, calculated at 3-hourly resolution and restricted to summer nights (from 21:00 to 06:00), when the phenomenon is strongest. Daily mean and maximum differences are considered to capture both average and extreme contrasts.We examine the UHI (for air temperature) and UHSI for each index independently, assessing complementarity and redundancy through correlation analysis and Kolmogorov-Smirnov distance, which quantify temporal co-variability and distributional similarity, respectively. We also evaluate the sensitivity of UHSI to key drivers, specifically temperature and humidity.Results show that the highest UHSI contrasts systematically occur for relatively cool rural nights, typically below the 20th percentile of rural temperature. Two highly redundant subgroups of heat stress indices emerge: one formed by air temperature and the Heat Index, and another dominated by humidity-sensitive indices such as the simplified WBGT, humidex and WBGT in the shade. In contrast, UTCI and Effective Temperature exhibit consistent independence from the rest, highlighting the added value of a multivariable UHSI approach over a solely temperature-based UHI definition by capturing complementary dimensions of urban thermal stress. Hence, the UHSI reframes the traditional UHI definitions offering a novel framework to quantify urban thermal risk beyond temperature, with implications for urban climate adaptation and public health.This work is part of Grant PID2023-149997OA-I00 (PROTECT) funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. C.R.R. acknowledges support from Grant PREP2023-001919 funded by MICIU/AEI/10.13039/501100011033 and by ESF+. Langendijk, G. S., et al. (2024). Towards better understanding the urban environment and its interactions with regional climate change—The WCRP CORDEX Flagship Pilot Study URB-RCC. Urban Climate, 58, 102165. https://doi.org/10.1016/j.uclim.2024.102165

Global burned area (BA) products are commonly available at a Non-Time Critical (NTC) basis, several months or even several years from the present date; i.e. they are unavailable for Near-Real Time (NRT) applications. The Copernicus Land Monitoring Service (CLMS) delivers the only global BA product in NRT, since recently, at very high accuracy, comparable to the most accurate non-CLMS NTC product (FIRECCIS311). However, global BA products are generated from coarse &gt;= 300 m reflectance observations. Despite the Sentinel-2 mission having been in operation since 2017, providing decadal resolution 10-50 m reflectance data every ~5 days, and despite the well-known benefits of using decadal resolution data to estimate BA, a global Sentinel-2 NRT BA algorithm does not exist. The purpose of this study is to adapt and apply the latest developments in NRT detection, as implemented in the CLMS, to Sentinel-2 L2A imagery. The mapping method uses a neural network (NN) with 2D convolutional layers, followed by a Long Short-Term Memory (LSTM) layer. The NN processes the time series of reflectance images on a per-pixel basis, with convolutional layers applied along the spectral and temporal dimensions. The time series of fractional BA maps, predicted by the NN, are combined with time series of spatio-temporal density of VIIRS active fire detections. Such a combination consists of a logistic model and allows the reduction of false positives (such as cloud shadows). The NN is trained on a sample dataset automatically generated from time series reflectance observations (Sentinel-2 data in this case), extracted over locations of VIIRS active fire detections across the Globe for the year 2020, and corresponding estimates of fractional BA, derived from physically-based radiative transfer modelling. The mapping method generates one BA map for each new Sentinel-2 image available (referred to as BAS2nrt0), which is updated with images from the following 5 days (referred to as BAS2nrt5) and the following 10 days (referred to as BAS2nrt10). The additional images available after the mapping day are expected to reduce false positives due to cloud shadows. The mapping method also generates an NTC BA map for each calendar month (referred to as BAS2ntc), with images available for a buffer of 45 days around the month. The algorithm results are validated against an independent global reference dataset for the year 2019, which includes long time series of Landsat-derived BA maps covering 105 sampling units distributed across the Globe. The analysis of the 2019 validation results shows that the accuracy of the proposed Sentinel-2 products is high regardless of estimation timeliness. As expected, (1) the accuracy of the NTC product, Dice coefficient (DC) of 87.2%, is higher than the NRT products, DC 82.7–85.4%, and (2) the accuracy of the NRT product is increased with each update. Such accuracy levels are remarkably high: the accuracy of NRT estimates is comparable to a precedent global non-CLMS NTC Sentinel-2 BA mapping (DC 81.8%).

Extreme fire weather (hot, dry, and windy conditions) has intensified globally, yet formally attributing this trend to anthropogenic climate change remains challenging. Here, we analyze global trends in extreme fire weather days (FWI95d, annual count of days with Fire Weather Index above the 95th percentile) over 1980–2023, using climate model ensembles, observational data, and fingerprint detection techniques. We find that the observed increase in extreme fire weather bears a clear externally forced signal, detectable at 99% confidence above natural variability and attributable to human-induced climate change. This emerging human-induced fingerprint on extreme fire weather highlights a benchmark for climate science and underscores the urgency of integrating these insights into wildfire risk management and adaptation strategies.

Climate change and the current biodiversity crisis are challenging the sustainability of human societies. Nature Based Solutions (NbS) strategically deployed in the landscapes could help reducing the impact of climate risks and help restoring and preserving biodiversity. The NBRACER Horizon Europe project has recently developed a new conceptual framework that connects regional climate risk assessments to the design of scalable networks of blue and green solutions. This framework synthesizes five core components:Climate Risk Impact Chains: Mapping hazard-to-risk propagation through environmental and social vulnerabilities, identifying critical intervention points where NbS can reduce exposure and enhance resilience.Landscape Functional Units &amp; Archetypes: Decomposing regions into functional units reflecting hydrological, ecological, and socio-economic processes, organized as recurring landscape archetypes. This approach links localized ecosystem functions to broader multi‑risk patterns.Meta–Ecosystem Perspective: Viewing interconnected ecosystems across spatial scales, enabling the evaluation of Blue Green Infrastructure (B–GI) networks that deliver cumulative ecosystem services across functional units.Ecosystem Service and Hazard Regulation Linkages: Demonstrating how targeted NbS interventions mediate water, energy, and material flows to attenuate hazard impacts and provide co–benefits.Network and Scaling Strategy: Moving beyond stand–alone projects that are functionally not linked, our framework supports systemic network solutions aligned with regional adaptation pathways, ensuring replicability and transferability across contexts.By integrating these elements, the developed conceptual framework guides practitioners and policymakers from risk–mapping to the strategic design of interconnected B–GI networks. It supports the identification of optimal intervention locations, the selection of NbS types suited to specific landscapes, and the assembly of strategies that build long–term resilience. The framework’s logic underpins subsequent developments focused on spatial mapping, scenario quantification, monitoring, and NbS implementation.This conceptual foundation paves the way for evidence–based, scalable NbS deployment, contributing to regional adaptation pathways and compliance with the EU Adaptation to Climate Change Mission objectives.