Explore the vast range of titles available.

Sea level and sea surface temperature inter-annual variability and trends in the Mediterranean Sea were investigated during the period 1993–2017. These were carried out using gridded absolute dynamic topography from satellite altimetry, tide gauge (TG) time series from 25 stations and gridded sea surface temperature (SST) from advanced very-high-resolution radiometer (AVHRR) data. The coastal TG data were used to verify the satellite derived sea level. Moreover, the contributions of atmospheric pressure and North Atlantic Oscillation (NAO) to sea level changes were also examined. The results revealed that the Mediterranean Sea exhibits inter-annual spatiotemporal coherent variability in both sea level and SST. The spatial variability in sea level is more significant over the Adriatic and Aegean Seas, most of the Levantine basin, and along the Tunisian shelf. Marked spatial variability in SST occurs over the central part of the Mediterranean Sea with maximum amplitude in the Tyrrhenian Sea. The highest temporal variability of sea level and SST was found in 2010 and 2003, respectively. The inter-annual variability of sea level and SST accounts for about 32% and 3% of the total variance of sea level and SST, respectively. An analysis of sea level anomaly reveled large negative values during the extended winter of 2011–2012, which may be attributed to the strong positive phase of NAO index. Satellite altimetry indicated a significant positive sea level trend of 2.7 ± 0.41 mm/year together with a significant warming of 0.036 ± 0.003 °C/year over the whole Mediterranean Sea for the period 1993–2017.

Microplastic (MP) pollution is present in all aquatic environments and is gaining critical concern. We have conducted sea surface MP monitoring with a Manta trawl at 16 sampling stations in the eastern Baltic Sea in 2016–2020. The concentrations varied from 0.01 to 2.45 counts/m 3 (0.002–0.43 counts/m 2 ), and the mean was 0.49 counts/m 3 (0.08 counts/m 2 ). The fibers and fragments had, on average, an approximately equal share in the samples. Correlation between the concentration of fibers and fragments was higher near the land and weaker further offshore. The following spatial patterns were revealed: higher mean values were detected in the Baltic Proper (0.65 counts/m 3 ) (0.11 counts/m 2 ) and the Gulf of Finland (0.46–0.65) (0.08–0.11) and lower values were detected in the Gulf of Riga (0.33) (0.06) and Väinameri Archipelago Sea (0.11) (0.02). The difference between the latter three sub-basins and the meridional gradient in the Gulf of Riga can likely be explained by the degree of human pressure in the catchment areas. The MP concentration was higher in autumn than in summer in all regions and stations, probably due to the seasonality of the biofouling and consequent sinking rate of particles. A weak negative correlation between the wind speed and the MP concentration was detected only in the central Gulf of Finland, and positive correlation in the shallow area near river mouth. We observed a 60-fold difference in MP concentrations during coastal downwelling/upwelling. Divergence/convergence driven by the (sub)mesoscale processes should be one of the subjects in future studies to enhance the knowledge on the MP pathways in the Baltic Sea.

CO2 is the primary contributor to global warming, and also the most significant anthropogenic emission gas in cities. This study investigates near-surface CO2 spatiotemporal variability patterns at the community scale to address the critical gap in urban CO2 high-resolution measurement and promote urban carbon neutrality. Combining fixed and mobile monitoring across five representative communities (1-km2 coverage) with two-hour temporal precision and 20 m spatial resolution, results revealed average CO2 concentrations of 440–480 ppm, exhibiting bimodal diurnal cycles and highlighting spatiotemporal divergent emission behaviors. Three communities peaked during 17:00–19:00 LT, while two peaked during 08:00–10:00 LT. Spatial correlation analysis identified two dominant patterns: road-adjacent “externally dominated” hotspots and “internally dominated” zones with elevated intra-community levels. Spearman correlation analysis, Random Forest, and Geographically and Temporally Weighted Regression models quantified spatial morphology and element contributions, demonstrating that building morphology exerted time-varying impacts across communities. Meanwhile, external traffic contributed 18–39% to concentration variability, while internal traffic and energy consumption drove localized peaks. The findings indicated that apart from the emission sources, the micro-scale urban spatial design elements also regulate the near-surface CO2 distribution. This high-resolution approach provides actionable insights for optimizing community layouts and infrastructure to mitigate localized emissions, advancing carbon neutrality targeted urban planning.

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate–carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy–covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO ₂ uptake period (CUP) and the seasonal maximal capacity of CO ₂ uptake (GPP ₘₐₓ). The product of CUP and GPP ₘₐₓ explained >90% of the temporal GPP variability in most areas of North America during 2000–2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 ( r ² = 0.90) and GPP recovery after a fire disturbance in South Dakota ( r ² = 0.88). Additional analysis of the eddy–covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPP ₘₐₓ than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPP ₘₐₓ and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space. Significance Terrestrial gross primary productivity (GPP), the total photosynthetic CO ₂ fixation at ecosystem level, fuels all life on land. However, its spatiotemporal variability is poorly understood, because GPP is determined by many processes related to plant phenology and physiological activities. In this study, we find that plant phenological and physiological properties can be integrated in a robust index—the product of the length of CO ₂ uptake period and the seasonal maximal photosynthesis—to explain the GPP variability over space and time in response to climate extremes and during recovery after disturbance.

We investigated mean properties and the spatiotemporal variability of eddies in the South China Sea (SCS) by analyzing more than 7000 eddies corresponding to 827 eddy tracks, identified using the winding angle method and 17 years of satellite altimetry data. Eddies are mainly generated in a northeast‐southwest direction and southwest of Luzon Strait. There is no significant difference between the numbers of two types of eddies (anticyclonic and cyclonic) in most regions. The mean radius and lifetime of eddies are 132 km and 8.8 weeks, respectively, both depending on where the eddies are formed. Anticyclonic and cyclonic eddies tend to deform during their lifetimes in different ways. Furthermore, eddy propagation and evolution characteristics are examined. In the northern SCS, eddies mainly propagate southwestward along the continental slope with velocities of 5.0–9.0 cm s−1, while in the central SCS, eddies tend to move with slight divergence but still in a quasi‐westward direction with velocities of 2.0–6.4 cm s−1. Eddy propagation in the western basin to the east of Vietnam is quite random, with no uniform propagate direction. Investigation of 38 long‐lived eddies shows that eddies have a swift growing phase during the first 12 weeks and then a slow decaying phase that affects the eddy radii and eddy energy densities. Nevertheless, vorticity has less variability. In addition, the effect of eddies on the thermocline and halocline is analyzed using 763 Argo temperature profile data. Cyclonic eddies drive the thermocline shallower and thinner and significantly strengthen the thermocline intensity, whereas anticyclonic eddies cause the thermocline to deepen and thicken and weaken the thermocline intensity to a certain degree. The halocline impacted by cyclonic eddies is also shallower and thinner than that impacted by anticyclonic eddies. Finally, eddy temporal variations are examined at seasonal and interannual scales. Eddy activity is sensitive to the wind stress curl and in the northern SCS it is also related with the strength of the background flows. Key Points Mean properties of eddies in the South China Sea Spatiotemporal variability of eddies The effect of eddies on the temperature profile

Increasing frequency and intensity of heatwaves (HWs) in a warming climate exert catastrophic impacts on human society and natural environment. However, spatiotemporal variations of HW and their driving factors still remain obscure, especially for HW changes over Eurasia, the region with the largest population of the world. Here we provide a systematic investigation of the HW changes over Eurasia and quantify the contributions of different natural and anthropogenic factors to these changes. Increasing frequency, duration and intensity of HW are observed in most parts of Eurasia, and the occurrence of the first HW event tends to be earlier as well, especially in Europe, East Asia, Central Asia, Southwest Asia, and the Mediterranean region. These intensified HW activities are particularly stronger and more widespread after 1990 s. The spatial pattern of the increasing HW trend is closely tied to the interdecadal changes of sea surface temperature in the North Pacific. More intense hot airmass convection, atmospheric circulation obstruction over the Mediterranean region and the enhanced Mongolian high hinders the southward movement of cold air and cold and wet airmass exchange. Further analyses suggest that the intensifying Eurasian HW tendency is a combined result of both climate change and human activities. Overall, the fractional contributions of climate warming, urbanization, standardized precipitation evaporation index, and Atlantic Multi-decadal Oscillation to the frequency of Eurasian HWs are 30%, 25%, 21% and 24%, respectively. It is also suggested that the relative influential rate of different driving factors for HW varies over time and differs in different areas.

Understanding the variability of spatial extents of precipitation extremes favors an accurate assessment of the severity of disasters caused by extreme precipitation events. Using a restricted neighborhood method, we identify the spatial extents of global precipitation extremes over 1983–2018 and examine their spatiotemporal variability and associated changes. Results show that the mid-latitudes shows the largest spatial extent of precipitation extremes, and the spatial extents in non-tropical regions over the Northern Hemisphere show significant seasonal differences. In non-monsoon regions, the spatial extents of precipitation extremes in autumn and winter are larger than those in spring and summer, and the annual average spatial extents of precipitation extremes all exceed 500 km, which are larger than those in monsoon regions. All the five non-monsoon regions over the Northern Hemisphere and three monsoon regions in the western Pacific show statistically significant increases in the spatial extent of precipitation extremes in most seasons.

Due to global warming, precipitation concentration is expected to change, and extreme weather events are likely to occur more frequently. This study investigates the spatiotemporal variability of precipitation concentration in Iran. For this purpose, daily precipitation data with a spatial resolution of 0.25° × 0.25° from the Asfazari database are used. Three indices, i.e., the precipitation concentration index (PCI), precipitation concentration period (PCP), and precipitation concentration degree (PCD), are used to examine the variability of precipitation concentration in Iran. The results demonstrate that the central, southeastern, and eastern parts of Iran exhibit maximum temporal precipitation concentration, while the lowest precipitation concentration is observed over the Caspian coasts and the northern half of the country. The year 1998 is selected as the change point due to the considerable difference in the values of the examined indices, and the long-term statistical period is divided into two sub-periods before and after the change. During the sub-period after the change point (1999–2019), precipitation concentration increased in the western, central, eastern, and southeastern parts of Iran, according to PCI and PCD; on the other hand, it decreased in the north, northeast, and northern coastline of the Oman Sea. Furthermore, there are great spatial differences during the occurrence of precipitation along the northern coasts, according to PCP, varying from November (along the Caspian coast) to August (along the northern foothills of the Alborz Mountains). PCP increased during the sub-period after the change point along the northern coastlines of the Persian Gulf and Oman Sea and in the north parts of the country (along the Alborz Mountains), indicating a shift in the period of precipitation from the winter to the warm seasons of spring and summer. Moreover, a decrease in PCP in the northwest and northeast suggests that the period of occurrence of precipitation shifted from the second half of the winter toward the early winter and late fall. After the year of the change point, the frequency of rainy days and precipitation decreased, and PCI and PCD increased.

Monitoring changes in freshwater availability is critical for human society and sustainable economic development. To identify regions experiencing secular change in their water resources, many studies compute linear trends in the total water storage (TWS) anomaly derived from the Gravity Recovery and Climate Experiment (GRACE) mission data. Such analyses suggest that several major water systems are under stress (Rodell et al 2009 Nature 460 999-1002; Long et al 2013 Geophys. Res. Lett. 40 3395-401; Richey et al 2015 Water Resour. Res. 51 5217-38; Voss et al 2013 Water Resour. Res. 49 904-14; Famiglietti 2014 Nat. Clim. Change. 4 945-8; Rodell et al 2018 Nature 557 651-9). TWS varies in space and time due to low frequency natural variability, anthropogenic intervention, and climate-change (Hamlington et al 2017 Sci. Rep. 7 995; Nerem et al 2018 Proc. Natl Acad. Sci.). Therefore, linear trends from a short time series can only be interpreted in a meaningful way after accounting for natural spatiotemporal variability in TWS (Paolo et al 2015 Science 348 327-31; Edward 2012 Geophys. Res. Lett. 39 L01702). In this study, we first show that GRACE TWS trends from a short time series cannot determine conclusively if an observed change is unprecedented or severe. To address this limitation, we develop a novel metric, trend to variability ratio (TVR), that assesses the severity of TWS trends observed by GRACE from 2003 to 2015 relative to the multi-decadal climate-driven variability. We demonstrate that the TVR combined with the trend provides a more informative and complete assessment of water storage change. We show that similar trends imply markedly different severity of TWS change, depending on location. Currently more than 3.2 billion people are living in regions facing severe water storage depletion w.r.t. past decades. Furthermore, nearly 36% of hydrological catchments losing water in the last decade have suffered from unprecedented loss. Inferences from this study can better inform water resource management.

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.