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Journal articleBoran I, Pettorelli N, Köberle AC, et al., 2024,
Making Global Climate Action work for nature and people: Priorities for Race to Zero and Race to Resilience
, Environmental Science and Policy, Vol: 159, ISSN: 1462-9011There is increasing recognition in science and policy that the current nature and climate change crises are highly intertwined, and that these need to be jointly addressed. Within the United Nations Framework Convention on Climate Change (UNFCCC), the Race to Zero (R2Z) and the Race to Resilience (R2R) campaigns foster climate action by cities, regions, businesses, investors, and civil society organizations for mitigation and adaptation. The campaigns are part of UNFCCC-backed institutional arrangements linking intergovernmental climate governance with actions beyond national commitments to support the implementation of the Paris Agreement, also referred to as the Global Climate Action Agenda (GCAA). Both mobilization campaigns highlight and promote the contribution of nature to climate mitigation, adaptation, and resilience. Yet, the integration of nature in climate ambition is more complex than indicated in the calls to action. We here identify key areas of concern in the alignment of climate and biodiversity goals, discussing the biophysical and socio-ecological considerations relative to (i) practices for enhancing land-based and marine sinks to limit warming; (ii) the unpredictability of biodiversity dynamics under climate change; (iii) the spatial scale at which actions can be implemented; and (iv) the types of metrics that can be used for tracking progress. We provide recommendations for the two mobilization campaigns to integrate in their criteria and metrics frameworks to support effective and equitable actions that deliver for climate, but also for nature and people. We then make a call to action for transdisciplinary knowledge production and dissemination that strengthens science-policy interactions.
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Journal articleJeong S, Ryu Y, Gentine P, et al., 2024,
Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency
, Remote Sensing of Environment, Vol: 311, ISSN: 0034-4257Advanced Very High-Resolution Radiometer (AVHRR) satellite observations have provided the longest global daily records from 1980s, but the remaining temporal inconsistency in vegetation index datasets has hindered reliable assessment of vegetation greenness trends. To tackle this, we generated novel global long-term Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation (NIRv) datasets derived from AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS). We addressed residual temporal inconsistency through three-step post processing including cross-sensor calibration among AVHRR sensors, orbital drifting correction for AVHRR sensors, and machine learning-based harmonization between AVHRR and MODIS. After applying each processing step, we confirmed the enhanced temporal consistency in terms of detrended anomaly, trend and interannual variability of NDVI and NIRv at calibration sites. Our refined NDVI and NIRv datasets showed a persistent global greening trend over the last four decades (NDVI: 0.0008 yr−1; NIRv: 0.0003 yr−1), contrasting with those without the three processing steps that showed rapid greening trends before 2000 (NDVI: 0.0017 yr−1; NIRv: 0.0008 yr−1) and weakened greening trends after 2000 (NDVI: 0.0004 yr−1; NIRv: 0.0001 yr−1). These findings highlight the importance of minimizing temporal inconsistency in long-term vegetation index datasets, which can support more reliable trend analysis in global vegetation response to climate changes.
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Journal articleDechant B, Kattge J, Pavlick R, et al., 2024,
Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches
, Remote Sensing of Environment, Vol: 311, ISSN: 0034-4257Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0.2–0.6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was relevant
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Journal articleBurtonshaw JEJ, Paluszny A, Mohammadpour A, et al., 2024,
Effects of reservoir mechanical properties on induced seismicity during subsurface hydrogen storage
, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 382, ISSN: 1364-503XThe intermittent storage of hydrogen in subsurface porous media such as depleted gas fields could be pivotal to a successful energy transition. Numerical simulations investigate the intermittent storage of hydrogen in a porous, depleted subsurface reservoir. Various parametric studies are performed to assess the effect of mechanical properties of the reservoir (i.e. Young's modulus, Poisson's ratio, Biot coefficient and permeability) on the induced fault slip of a single through-going fault that transverses the entire reservoir. Simulations are run using a three-dimensional, finite element, fully coupled hydromechanical code with explicit representations of layers and faults. The effect of the domain mesh refinement and fault mesh refinement on the fault slip versus operation time solution is investigated. The fault is observed to slip in two distinct events, one during the second injection period and one in the third injection period. The fault is not observed to slip during the storage or withdrawal periods. It is found that in order to minimize seismic risk, a reservoir rock with high Young's modulus (>40 GPa), high Poisson's ratio (>0.30) and high Biot coefficient (>0.65) would be preferable for hydrogen storage. Reservoir rocks of low Young's modulus (10-30 GPa), intermediate Poisson's ratio (0.00-0.30) and low-to-intermediate Biot coefficient (0.25-0.65), at high injection rates, were found to have higher potential of inducing large seismic events.This article is part of the theme issue 'Induced seismicity in coupled subsurface systems'.
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Journal articlePaluszny A, Schultz R, Zimmermann G, 2024,
Induced seismicity in coupled subsurface systems.
, Philos Trans A Math Phys Eng Sci, Vol: 382 -
Journal articleStrain T, Flaxman S, Guthold R, et al., 2024,
National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: a pooled analysis of 507 population-based surveys with 5·7 million participants.
, The Lancet Global Health, Vol: 12, Pages: E1232-E1243, ISSN: 2214-109XBACKGROUND: Insufficient physical activity increases the risk of non-communicable diseases, poor physical and cognitive function, weight gain, and mental ill-health. Global prevalence of adult insufficient physical activity was last published for 2016, with limited trend data. We aimed to estimate the prevalence of insufficient physical activity for 197 countries and territories, from 2000 to 2022. METHODS: We collated physical activity reported by adults (aged ≥18 years) in population-based surveys. Insufficient physical activity was defined as not doing 150 minutes of moderate-intensity activity, 75 minutes of vigorous-intensity activity, or an equivalent combination per week. We used a Bayesian hierarchical model to compute estimates of insufficient physical activity by country or territory, year, age, and sex. We assessed whether countries or territories, regions, and the world would meet the global target of a 15% relative reduction of the prevalence of insufficient physical activity by 2030 if 2010-22 trends continue. FINDINGS: We included 507 surveys across 163 countries and territories. The global age-standardised prevalence of insufficient physical activity was 31·3% (95% uncertainty interval 28·6-34·0) in 2022, an increase from 23·4% (21·1-26·0) in 2000 and 26·4% (24·8-27·9) in 2010. Prevalence was increasing in 103 (52%) of 197 countries and territories and six (67%) of nine regions, and was declining in the remainder. Prevalence was 5 percentage points higher among female (33·8% [29·9-37·7]) than male (28·7% [25·0-32·6]) individuals. Insufficient physical activity increased in people aged 60 years and older in all regions and both sexes, but age patterns differed for those younger than 60 years. If 2010-22 trends continue, the global target of a 15% relative reduction between 2010 and 2030 will not be met (posterior probability <0·01); ho
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Journal articleWang S, Ren T, Yang P, et al., 2024,
Improved temperature-dependent ice refractive index compilation in the far-infrared spectrum
, Geophysical Research Letters, Vol: 51, ISSN: 0094-8276A new ice refractive index compilation is reported for a broad spectrum ranging from 0.0443 to 106 𝜇m, focusing on the pronounced temperature-dependence of ice optical properties in the far-infrared (far-IR) segment (15-100 µm). A sensitivity study assuming spherical particles shows that selecting ice refractive indices at 12 temperatures and 215 wavelengths in the far-IR region gives sufficient accuracy in interpolated refractive indices for developing a new ice crystal optical property database. Furthermore, we demonstrate the differences between the bulk single-scattering properties computed for hexagonal ice particles with this new compilation compared to a previous iteration at three far-IR wavelengths where substantial differences are noticed between the two ice refractive index compilations. We suggest that our new ice refractive index dataset will improve downstream light-scattering applications for upcoming far-IR satellite missions and allow robust modeling of outgoing longwave radiation (OLR) under ice cloud conditions.
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ReportJennings N, Brandmayr C, 2024,
How can action to tackle climate change improve people’s health and save the NHS money?
To achieve Net Zero greenhouse gas emissions by 2050 and contribute to global efforts to avoid the worst consequences of climate change, policies are required that reduce emissions across the whole of UK society including the transport, housing and agriculture sectors. Climate action could play an important role in helping to reduce existing health inequalities, improving public health and responding to the high levels of pressure on the NHS.
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Journal articleWilson Kemsley S, Ceppi P, Andersen H, et al., 2024,
A systematic evaluation of high-cloud controlling factors
, Atmospheric Chemistry and Physics, Vol: 24, Pages: 8295-8316<jats:p>Abstract. Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback. </jats:p>
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ReportLawrance E, Newberry Le Vay J, El Omrani O, et al., 2024,
Global Agenda for Research and Action in Climate Change and Mental Health
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