Satellite Observations Research Articles

The synergy of assimilating visible and infrared radiances and radar observations

AbstractCloud‐affected satellite observations in the visible and infrared spectrum contain vast and largely complementary information on clouds and atmospheric convection and thereby constitute a promising data source for convective‐scale data assimilation. Preceding studies have demonstrated that the assimilation of either one of these observation types can lead to improved convective‐scale weather forecasts, but research on the combined assimilation of cloud‐affected visible and infrared satellite data as well as radar observations is still very scarce. In this article, we investigate the combined assimilation of multiple infrared and visible satellite channels as well as radar observations, and evaluate their analysis and forecast impact with a primary focus on clouds and precipitation. We assimilate visible (0.6‐) and thermal infrared (6.2‐ and 7.3‐ ) satellite channels in observing‐system simulation experiments (OSSEs) with a perfect‐model forecast for an idealized weather scenario. Observations are simulated synthetically and assimilated by the ensemble adjustment Kalman filter (EAKF). The forecasts used the Weather Research and Forecasting (WRF) model at 2‐km grid resolution. Results show that assimilating satellite channels in addition to radar reflectivity can improve forecasts of cloudiness and precipitation, while improvements in temperature, humidity, and wind fields are about the same as in the radar experiment. The evaluation of the analysis error for different cloud conditions revealed that the combined assimilation can mitigate the ambiguity of individual visible and infrared channels. Assimilating visible reflectance before infrared brightness temperature can remove pre‐existing erroneous water clouds and avoid the introduction of erroneous clouds by the following assimilation of infrared channels. It follows that the combined assimilation of visible and infrared radiances could be crucial to avoid shortcomings of assimilating only visible or infrared radiances in convective‐scale numerical weather prediction.

A Universal Framework for Near-Real-Time Detection of Vegetation Anomalies from Landsat Data

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods are only applicable to a particular pixel or vegetation type, resulting in a lack of universality. Also, most methods that use single characteristic parameter may ignore the multi-spectral expression of vegetation anomalies. In this study, we developed a universal framework to simultaneously detect various vegetation anomalies in NRT from Landsat observations. Firstly, Landsat surface reflectance data from the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites were selected as a reference vegetation dataset to calculate the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR), which describe vegetation conditions from the perspectives of greenness and moisture, respectively. After the elimination of cloud-contaminated pixels, the high-quality NDVI and NBR data over the BELMANIP sites were further normalized in order to remove the differences in the growth of the varying vegetation. Based on the normalized NDVI and NBR, kernel density estimation (KDE) was used to create a universal measure of undisturbed vegetation, which described the uniform spectral frequency distribution of different undisturbed vegetation with a series of accumulated probabilities on a monthly basis. Whenever a new Landsat observation is collected, the vegetation anomalies are determined according to the universal measure in NRT. To demonstrate the potential of this framework, three study areas with different anomaly types (deforestation, fire event, and insect outbreak) in distinct ecozones (rainforest, coniferous forest, and deciduous broad-leaf forest) were used. The quantitative analyses showed generally high overall accuracies (>90% with the kappa >0.82). The user accuracy for the fire event and the producer accuracy for the earlier insect infestation were relatively lower. The accuracies may be affected by the complexity of the land surface, the quality of the Landsat image, and the accumulated probability threshold.

Kolkata's green oasis: a comprehensive analysis of urban green spaces for ecosystem sustainability.

In the present study, the impact of urban growth on green spaces in Kolkata Metropolitan City (KMC) was evaluatedusing the multi-temporal satellite observations spanning the last four decades (1990-2022). The study exhibited a rapid rise in urban areas (178.38% growth; net increase 498 sq.km), leading to a significant conversion of areas intomoderate to very high built-up density zones. This urbanization has markedly altered the green-blue infrastructure, notably causing a 27% decline in urban green space (UGS) resulting anet loss of 254 sq.km. Fragmentation analysis exhibited a trend ofcompact, infill development in urban regions, contrasting with outgrowth, which hasinfluenced boththe cluster size and quality of UGS over the decades. The multi-indices and biophysical characterization of UGS concluded a deteriorating trend in terms of quantity(- 27.9%)and quality as well with reference. However, the existing UGS are primarily scattered and having less dense. Spatial estimation ofabove ground biomass(AGB) of UGS using regression analysis of field-derived AGB and L-band SAR backscatter depicted adominance of low AGB (< 30 t/ha-1) across KMC, while the certainzones with improved UGS exhibited moderate AGB levels(50-100 t/ha-1). The fuzzy AHP-based multi-criteria analysis ofurban ecological quality exhibited severe ecologicaldeterioration in the central urban areas, moderate to high in peri-urban regions, and comparatively improved ecological conditions in the peripheral ruralparts of KMC. The study also identified major native tree species for plantation strategies comprising urban afforestation, rooftop gardens, and the development of green corridors in ecologically deficient hotspot zones to improve the ecological quality within the urban landscape.

Linking interannual variability of turbidity fronts in the Eastern China Seas to local processes and ocean warming

The turbidity front is susceptible to rapid changes in ocean hydrodynamics. Understanding its variability is crucial for elucidating material transport on continental shelves in light of evolving land-ocean interactions. However, the long-term frontal variability and its controlling mechanism over the shelf sea scale still need further study. Using a decade of satellite observations, this study assesses the interannual variability of turbidity fronts in the Eastern China Seas and their responses to local processes and rapid ocean warming. A gradient-based front detection algorithm and frontal probability are used to identify the geographical locations of turbidity fronts and their variability at the interannual scale, respectively. Regional heterogeneities in interannual variations and controlling mechanisms of frontal activity are observed. Specifically, the significant (p&amp;lt;0.05) and strongest correlations show that wind wave, horizontal temperature gradient, and mixed layer depth are identified as the most important drivers of interannual variations in frontal activity in the Bohai, Yellow, and East China Seas, respectively. The El Niño-Southern Oscillation influences frontal anomalies through a delayed wind-response mechanism (&amp;gt;=4 months). Notably, the recent increase in frontal probability (+0.07%/year) in offshore areas of the Yellow and East China Seas is primarily attributed to an intensified horizontal temperature (density) gradient (+0.0005 °C/km/year) resulting from ocean warming. As ocean warming continues, the offshore transport of terrigenous materials is expected to increase, potentially enhancing ocean primary productivity and carbon sequestration, and altering ecosystem function and fisheries.

Global Atmospheric Composition Effects from Marine Isoprene Emissions.

Isoprene emissions, primarily of biogenic origin, play an important role in atmospheric chemistry and climate. However, the atmospheric implications of marine isoprene emissions remain underexplored due to sparse in situ measurements and the intricate mechanisms governing isoprene in the upper ocean. This study uses 20 years of MODIS satellite observations to upscale isoprene production and loss rates derived from laboratory experiments, enabling global modeling of aqueous isoprene concentrations and emissions. Earth system model simulations with integrated marine isoprene emissions demonstrate substantial alterations in atmospheric composition over global oceanic regions. Our investigation uncovers diurnal variations in the vertical profiles of atmospheric isoprene, indicating that surface isoprene can ascend to the mid-to-upper troposphere, where nitrogen monoxide (NO) influences isoprene epoxydiol (IEPOX) production differently over selected oceanic and terrestrial regions. These findings pave the way for future studies on the role of marine isoprene in climate models and advance our understanding of its broader implications for atmospheric chemistry under a changing climate.

Planted Forests in China Have Higher Drought Risk Than Natural Forests.

To improve the environment and mitigate climate change, China has implemented ambitious projects for natural forest protection and expanded planted forests. However, increased climate variability has led to more frequent and severe droughts, exacerbating the decline of these forests. The drought risk of planted forests is rarely assessed by considering both resistance and resilience, and comparative analyses between natural and planted forests are lacking. Here, we compared drought resistance and resilience in natural and planted forests across China using satellite observations from 2001 to 2020 to understand which forests were at higher risk of drought. The results showed that planted forests exhibited lower drought resistance and resilience compared to natural forests, particularly in subtropical broad-leaved evergreen forests and warm temperate deciduous broad-leaved forests. Moreover, drought resistance in planted forests significantly increased, while resilience decreased during 2011-2020 compared to 2001-2010, suggesting a shift in the strategies of planted forests to cope with drought stress. The higher drought risk in planted forests compared to natural forests was mainly attributed to lower forest canopy height and poorer soil nutrients, which limited resistance, and lower canopy height and severe drought characteristics (severity, duration, and frequency), which reduced resilience. These results underscore the higher potential risk of drought exposure in planted forests. To mitigate future drought impacts on planted forests under climate change, enhanced management strategies, including the preservation of natural forests and augmentation of structural diversity in planted forests, are imperative.

Estimating ocean thermocline from satellite observations with a multi-head attention-based neural network

Estimating ocean thermocline from satellite observations with a multi-head attention-based neural network

Impact of vegetation phenology on anisotropy of artificial light at night - Evidence from multi-angle satellite observations

Anisotropy of artificial light at night (ALAN) has been revealed from satellite observations, as Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) provides multi-angle measurements of ALAN. However, the knowledge behind this phenomenon is very limited. In this study, we hypothesize that vegetation phenology impacts the anisotropy of ALAN, which is defined as the change in radiant intensity with viewing zenith angle (VZA). The time series Normalized Difference Vegetation Index (NDVI) quantifying the vegetation phenology and Change Index (CI) quantifying the ALAN anisotropy were extracted from VNP13A1 and VNP46A2 products, respectively. We analyzed the effect of vegetation phenology by comparing the anisotropy of ALAN at the same location across different seasons in eleven suburban study areas in North America. The anisotropy of ALAN was found to exhibit obvious seasonal dynamic which is consistent with that of NDVI, and these two variables showed significant positive correlation at both pixel scale (0.41 < r < 0.79) and regional scale (0.56 < r < 0.92). Furthermore, we found that the seasonality of the ALAN anisotropy was significantly correlated with the seasonality of vegetation over the eleven study areas (r = 0.75). All these results suggest that vegetation growth can reduce the anisotropy of ALAN. In other words, vegetation growth leads to a more even distribution of emitted light in different directions, which supports our hypothesis. These findings are potentially useful to improve the quality of time series nighttime light data by angular normalization considering vegetation phenology and better build City Emission Function (CEF) for modeling astronomic light pollution.

Satellite Observations Reveal a Positive Relationship Between Trait-Based Diversity and Drought Response in Temperate Forests.

Climate extremes such as droughts are expected to increase in frequency and intensity with global change. Therefore, it is important to map and predict ecosystem responses to such extreme events to maintain ecosystem functions and services. Alongside abiotic factors, biotic factors such as the proportion of needle and broadleaf trees were found to affect forest drought responses, corroborating results from biodiversity-ecosystem functioning (BEF) experiments. Yet it remains unclear to what extent the behavior of non-experimental systems at large scales corresponds to the relationships discovered in BEF experiments. Using remote sensing, the trait-based functional diversity of forest ecosystems can be directly quantified. We investigated the relationship between remotely sensed functional richness and evenness and forest drought responses using data from temperate mixed forests in Switzerland, which experienced an extremely hot and dry summer in 2018. We used Sentinel-2 satellite data to assess aspects of functional diversity and quantified drought response in terms of resistance, recovery, and resilience from 2017 to 2020 in a scalable approach. We then analyzed the BEF relationship between functional diversity measures and drought response for different aggregation levels of richness and evenness of three physiological canopy traits (chlorophyll, carotenoid/chlorophyll ratio, and equivalent water thickness). Forest stands with greater trait richness were more resistant and resilient to the drought event, and the relationship of trait evenness with resistance or resilience was hump-shaped or negative, respectively. These results suggest forest functional diversity can support forests in such drought responses via a mixture of complementarity and dominance effects, the first indicated by positive richness effects and the second by negative evenness effects. Our results link ecosystem functioning and biodiversity at large scales and provide new insights into the BEF relationships in non-experimental forest ecosystems.

On the Intraseasonal Oceanic Processes Constrained by Data Assimilation: A Case Study of the Tropical Pacific

Abstract This study investigates the ability of a global ocean reanalysis at 1/12° horizontal resolution, GLORYS12, to represent oceanic processes at intraseasonal and higher-frequency scales. GLORYS12, which includes data assimilation of satellite and multi-instrument in situ observations, is compared to a twin-free simulation (with no assimilation) in the tropical Pacific Ocean. Spectral analyses show that data assimilation improves the realism of sea surface height intraseasonal variability in the entire tropical Pacific Ocean, in both amplitude and phase, with an increase in the amplitude of more than 50% for the 20–90-day band and up to 15% for the 2–20-day band. The improvement is largest along the 5°N/S latitudes, where the magnitude of tropical instability waves is maximum, but is limited along the equator where steric height variability is dominated by intraseasonal oceanic Kelvin waves, already well represented in the free simulation. Wavenumber–frequency spectra show that data assimilation constraint improves both the spatial and temporal scales of intraseasonal waves and their timing. Data assimilation impacts the realism of oceanic simulations in two ways. By modifying the background oceanic stratification, it corrects the phase speed of westward-propagating waves. It is also shown that the intraseasonal component of analysis increments (data assimilation corrections applied) is dynamically consistent and exhibits clear intraseasonal propagation. By demonstrating the benefits of data assimilation for intraseasonal processes in the tropical Pacific Ocean, this study highlights the high value of both in situ and satellite observations to constrain ocean models in a wide range of time scales.

Evaluation of biases in mid-to-high-latitude surface snowfall and cloud phase in ERA5 and CMIP6 using satellite observations

Evaluation of biases in mid-to-high-latitude surface snowfall and cloud phase in ERA5 and CMIP6 using satellite observations

Radioactive contamination transported to Western Europe with Saharan dust.

The Reggane region, where the first French atmospheric nuclear tests were conducted in the 1960s in Southern Algeria, is located in one of the most active dust source regions responsible for recurrent massive Saharan dust events reaching Western Europe and affecting air quality. After a major outbreak in March 2022, a citizen participative science campaign was launched to study the radioactivity born by the dust. One hundred ten deposit samples were collected from six countries in Western Europe with 53 demonstrated as scientifically representative. Geochemical and mineralogical sample analyses combined with satellite observations and back trajectory calculations confirmed an origin from South Algeria, including the Reggane site. Plutonium isotopic signatures, a unique nuclear bomb fingerprint, remained in the range of the global fallout signatures largely dominated by US and former USSR nuclear tests, significantly different from French fallout signatures. Radioactive contamination detected in all samples did not, however, present a risk to public health in terms of radioactivity exposure.

Russian forests show strong potential for young forest growth

Climate warming has improved conditions for boreal forest growth, yet the region’s fate as a carbon sink of aboveground biomass remains uncertain. Forest height is a powerful predictor of aboveground forest biomass, and access to spatially detailed height-age relationships could improve the understanding of carbon dynamics in this ecosystem. The capacity of land to grow trees, defined in forestry as site index, was estimated by analyzing recent measurements of canopy height against a chronosequence of forest stand age derived from the historical satellite record. Forest-height estimates were then subtracted from the predicted site index to estimate height-age growth potential across the region. Russia, which comprised 73% of the forest change domain, had strong departures from model expectation of 2.4–4.8 ± 3.8 m for the 75th and 90th percentiles. Combining satellite observations revealed a large young forest growth sink if allowed to recover from disturbance.

Estimation of Fire Counts and Fire Radiative Power Using Satellite Optical and Microwave Vegetation Indices With Random Forest Method

AbstractThe satellite microwave emissivity difference vegetation index (EDVI) has been used in previous studies to estimate FCs and FRP using traditional multivariate linear regression models. However, the nonlinear effects and contributions of numerous factors that affect forest fires cannot be disentangled by this model. Using the random forest (RF) model, this study utilized multiple EDVIs and the optical normalized difference vegetation index (NDVI) as key fuel properties to resolve the physical driving mechanisms of forest fires and to estimate the daily FCs and FRP over East Asia. The results showed that the estimated FCs and FRP were in good agreement with satellite observations, with a spatial R of 0.59 for FCs and 0.63 for FRP and a temporal R of 0.80 for FCs and 0.81 for FRP. The integration of EDVIs and NDVI into the RF model was found to improve model performance and generate overall lower systematic errors than the model without vegetation variables. Model performance was better than that in previous studies using multivariate linear regression models. In addition, EDVIs showed greater importance than NDVI. This was largely due to their daily temporal resolution that allowed EDVIs to capture forest fire dynamics in time. The combination of the RF model with satellite microwave and optical observations shows good performance and has great potential for FC and FRP estimations in global fire danger assessment.

Satellite Observations of Atmospheric Ammonia Inequalities Associated with Industrialized Swine Facilities in Eastern North Carolina.

Industrialized swine facilities adversely affect the health and well-being of Eastern North Carolina residents in the U.S. and are an issue of environmental racism. Concentrated animal feeding operations (CAFOs) emit various harmful and noxious air pollutants, including ammonia (NH3). There are limited measurements of CAFO-related air quality, contributing to disputes around its severity. We use NH3 vertical column densities from the space-based Infrared Atmospheric Sounding Interferometer (IASI) to report systematic, distributive inequalities in NH3 column enhancements (ΔNH3 columns), equal to NH3 columns less an observationally determined tropospheric background. Population-weighted block group-scale ΔNH3 columns are higher by 27 ± 3% for Black and African Americans, 35 ± 3% for Hispanics and Latinos, and 49 ± 3% for American Indians compared to non-Hispanic/Latino whites in Eastern North Carolina (April-August 2016-2021). Surface winds and air temperature influence block group-scale NH3 distributions, with higher absolute NH3 inequalities for all groups on calm days and for Black and African Americans and Hispanics and Latinos on hot days, consistent with effects from NH3 volatization downfield of facilities from, e.g., manure-covered fields, particles, and other surfaces. ΔNH3 columns correspond spatially with permitted swine facilities, with residents living multiple kilometers from swine CAFOs chronically exposed to elevated NH3. Trends in NH3 columns over 2008-2023 are driven by regional-scale atmospheric processes rather than localized NH3 changes in CAFO emissions. Results are discussed in local decision-making contexts that have broad relevance for air quality issues without protective federal regulatory standards.

Generating High Spatial and Temporal Surface Albedo with Multispectral-Wavemix and Temporal-Shift Heatmaps

Surface albedo is a key variable influencing ground-reflected solar irradiance, which is a vital factor in boosting the energy gains of bifacial solar installations. Therefore, surface albedo is crucial towards estimating photovoltaic power generation of both bifacial and tilted solar installations. Varying across daylight hours, seasons, and locations, surface albedo is assumed to be constant across time by various models. The lack of granular temporal observations is a major challenge to the modeling of intra-day albedo variability. Though satellite observations of surface reflectance, useful for estimating surface albedo, provide wide spatial coverage, they too lack temporal granularity. Therefore, this paper considers a novel approach to temporal downscaling with imaging time series of satellite-sensed surface reflectance and limited high-temporal ground observations from surface radiation (SURFRAD) monitoring stations. Aimed at increasing information density for learning temporal patterns from an image series and using visual redundancy within such imagery for temporal downscaling, we introduce temporally shifted heatmaps as an advantageous approach over Gramian Angular Field (GAF)-based image time series. Further, we propose Multispectral-WaveMix, a derivative of the mixer-based computer vision architecture, as a high-performance model to harness image time series for surface albedo forecasting applications. Multispectral-WaveMix models intra-day variations in surface albedo on a 1 min scale. The framework combines satellite-sensed multispectral surface reflectance imagery at a 30 m scale from Landsat and Sentinel-2A and 2B satellites and granular ground observations from SURFRAD surface radiation monitoring sites as image time series for image-to-image translation between remote-sensed imagery and ground observations. The proposed model, with temporally shifted heatmaps and Multispectral-WaveMix, was benchmarked against predictions from models image-to-image MLP-Mix, MLP-Mix, and Standard MLP. Model predictions were also contrasted against ground observations from the monitoring sites and predictions from the National Solar Radiation Database (NSRDB). The Multispectral-WaveMix outperformed other models with a Cauchy loss of 0.00524, a signal-to-noise ratio (SNR) of 72.569, and a structural similarity index (SSIM) of 0.999, demonstrating the high potential of such modeling approaches for generating granular time series. Additional experiments were also conducted to explore the potential of the trained model as a domain-specific pre-trained alternative for the temporal modeling of unseen locations. As bifacial solar installations gain dominance to fulfill the increasing demand for renewables, our proposed framework provides a hybrid modeling approach to build models with ground observations and satellite imagery for intra-day surface albedo monitoring and hence for intra-day energy gain modeling and bifacial deployment planning.

Using satellite-based Sun-induced chlorophyll fluorescence and spectral reflectance for improving terrestrial CO2 flux estimates of India

Abstract Significant uncertainties in terrestrial carbon fluxes exist in regions with limited ground-based observations, impacting our understanding of ecosystem carbon dynamics and emission reduction needs. This is particularly true for areas with sparse measurement networks, like India. To address this, we explore the potential of satellite measurements from various missions such as Sentinel-5 Precursor (S5P) and the Orbiting Carbon Observatory-2 (OCO-2) to improve terrestrial biosphere CO2 fluxes of India. We follow a data-driven approach, which simulates spatial and temporal distributions of Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), and ecosystem Respiration (Reco). We improve these model predictions by additionally using satellite-based Solar Induced chlorophyll Fluorescence (SIF), Soil Temperature (ST), and Soil Moisture (SM) specific to the vegetation classes of the domain. Different model refinements were performed to present the improved hourly distributions of terrestrial biospheric CO2 fluxes on a 0.1◦×0.1◦ grid from 2012 to 2020. Among them, the best-performing model simulations show reasonable agreement with eddy covariance observations for 2012 - 2018. For example, our best NEE and GPP predictions are highly correlated with observations with squared correlation coefficient (R2) values of 0.68 (NEE) and 0.74 (GPP) at the monthly scale for 2018. Based on our improved estimations, the annual NEE and GPP show values within the range from -0.38 Pg C yr-1 to -0.53 Pg C yr-1 (land C sink) and 3.39 Pg C yr-1 to 3.88 Pg C yr-1, respectively over India for 2012 - 2020. Our novel approach and findings highlight the potential of satellite-based SIF measurements to detail the ecosystem-scale vegetation responses across various biomes in India. The use of satellite observations, as demonstrated in this study, offers a scalable solution for regions lacking sufficient ground-based observations to estimate biospheric carbon fluxes reliably.

Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation

Abstract. Satellite observations of tropospheric trace gases and aerosols are evolving rapidly. Recently launched instruments provide increasingly higher spatial resolutions, with footprint diameters in the range of 2–8 km and with daily global coverage for polar orbiting satellites or hourly observations from geostationary orbits. Often the modelling system has a lower spatial resolution than the satellites used, with a model grid size in the range of 10–100 km. When the resolution mismatch is not properly bridged, the final analysis based on the satellite data may be degraded. Superobservations are averages of individual observations matching the model's resolution and are functional to reduce the data load on the assimilation system. In this paper, we discuss the construction of superobservations, their kernels, and uncertainty estimates. The methodology is applied to nitrogen dioxide tropospheric column measurements of the TROPOspheric Monitoring Instrument (TROPOMI) instrument on the Sentinel-5P satellite. In particular, the construction of realistic uncertainties for the superobservations is non-trivial and crucial to obtaining close-to-optimal data assimilation results. We present a detailed methodology to account for the representation error when satellite observations are missing due to, e.g., cloudiness. Furthermore, we account for systematic errors in the retrievals leading to error correlations between nearby individual observations contributing to one superobservation. Correlation information is typically missing from the retrieval products, where an error estimate is provided for individual observations. The various contributions to the uncertainty are analysed from the spectral fitting and the estimate of the stratospheric contribution to the column and the air mass factor for which we find a typical correlation length of 32 km. The method is applied to TROPOMI data but can be generalized to other trace gases such as HCHO, CO, and SO2 and other instruments such as the Ozone Monitoring Instrument (OMI), the Geostationary Environment Monitoring Spectrometer (GEMS), and the Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument. The superobservations and uncertainties are tested in the Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation ensemble Kalman filter system. These are shown to improve forecasts compared to thinning or compared to assuming fully correlated or uncorrelated uncertainties within the superobservation. The use of realistic superobservations within model comparisons and data assimilation in this way aids the quantification of air pollution distributions, emissions, and their impact on climate.

Climate-weather interactions modulating tropical cyclones over the Mascarene Islands 2000–2024

Tropical cyclones (TC) over the Mascarene Islands of the south-west Indian Ocean have been studied using meteorological reanalysis, satellite observations and local measurements in December to March season in the period 2000–2024. An innovative methodology was employed to create an index of TC impacts: multiplying the daily maximum wind and standardized maximum rainfall within the area 19–22 S, 54.5-58.5E, then summing to monthly interval above a threshold of 50. This time series was regressed onto a variety of fields in the Dec-Mar season to determine how climate-weather interactions modulate TCs near the islands of Mauritius and Reunion. The results highlight the importance of outflow from east Africa that passes northern Madagascar and generates cyclonic vorticity. Statistical links with Pacific El Nino Southern Oscillation and the Indian Ocean Dipole were weak but offer an advance warning of risk. Global regression maps show an inter-hemispheric dipole in SST and upper-level geopotential height (warm and high to south), associated with a zonal overturning circulation during austral summers with greater TC impacts in the Mascarene Islands. Trends over the longer period 1980–2024 were mapped to identify warming and moistening in the Mascarene area, and low level northwesterlies in the equatorial belt. The case of TC Dina 22 Jan 2002 was analyzed for meteorological structure and impacts, to place the statistical results in local context.

Quantifying the acceleration of multidecadal global sea surface warming driven by Earth’s energy imbalance

Abstract Global mean sea surface temperature (GMSST) is a fundamental diagnostic of ongoing climate change, yet there is incomplete understanding of multi-decadal changes in warming rate and year-to-year variability. Exploiting satellite observations since 1985 and a statistical model incorporating drivers of variability and change, we identify an increasing rate of rise in GMSST. This accelerating ocean surface warming is physically linked to an upward trend in Earth’s energy imbalance (EEI). We quantify that GMSST has increased by 0.54 ± 0.07 K for each GJ m–2 of accumulated energy, equivalent to 0.17 ± 0.02 K decade‒1 (W m‒2)‒1. Using the statistical model to isolate the trend from interannual variability, the underlying rate of change of GMSST rises in proportion with Earth’s energy accumulation from 0.06 K decade–1 during 1985–89 to 0.27 K decade–1 for 2019–23. While variability associated with the El Niño Southern Oscillation triggered the exceptionally high GMSSTs of 2023 and early 2024, 44% (90% confidence interval: 35%–52%) of the +0.22 K difference in GMSST between the peak of the 2023/24 event and that of the 2015/16 event is unexplained unless the acceleration of the GMSST trend is accounted for. Applying indicative future scenarios of EEI based on recent trends, GMSST increases are likely to be faster than would be expected from linear extrapolation of the past four decades. Our results provide observational evidence that the GMSST increase inferred over the past 40 years will likely be exceeded within the next 20 years. Policy makers and wider society should be aware that the rate of global warming over recent decades is a poor guide to the faster change that is likely over the decades to come, underscoring the urgency of deep reductions in fossil-fuel burning.