High Temporal Resolution Datasets Research Articles

Importance of temporal dimension and rural land cover when computing surface urban Heat Island intensity

Surface urban heat islands (SUHI) translate the difference in surface temperature between urban and surrounding rural regions. This study makes use of geostationary remote sensing data (Meteosat Second Generation) rather than polar orbiting satellites, taking advantage of its higher temporal resolution. Three cities were selected: Paris, Madrid and Milan. This allowed studying both the seasonal and diurnal cycles of SUHI. The SUHI is here computed with reference to the agricultural and forest rural land covers, and the two combined. We found that for a generic rural background, the SUHI maximum occurs at 12/20/12 UTC in spring/spring/summer with values of 3.1/1.6/4.0 °C for Paris/Madrid/Milan. Secondary peaks of SUHI are also sometimes found during the nighttime in the cities of Paris and Milan. Our results highlight the need for understanding the diurnal variability of SUHI throughout the year, considering the different city surroundings. It is found that for the same urban temperature, different SUHI intensities can occur when varying the rural cover reference. Also, for the same urban temperatures, neither SUHI intensity, nor its diurnal cycle is constant throughout the year, emphasizing the need for high temporal resolution datasets in SUHI studies.

Upsampling wave temporal resolution: Investigating wave parameters and the influence on WEC power performance

Power production of wave energy converters (WEC) predicted in the time domain utilize wave resource parameters and time-domain hydrodynamic model simulations of the WEC. While the hydrodynamic model provides high temporal resolution of power production (10’s of Hz), the wave resource parameters are often based on frequency-domain calculations with temporal resolution of 30 minutes to an hour. However, real ocean wave conditions vary on much shorter time scales. Relying on frequency-domain calculations will not be sufficient to capture the short-term variability and accurately predict WEC power production for a standardized methodology that follows power system requirements. These requirements need forecasted data with high sampling frequency for accurate energy predictions. Looking specifically at resource characterization, high temporal resolution datasets are not publicly available or do not exist for many coastal locations. Due to data availability, low temporal resolution datasets are being used in a majority of studies to generate representative wave conditions as inputs to numerical simulations. Representative wave conditions are used to generate wave spectrums. The issue with this practice is spectrums are then used to predict the efficiency of systems that will not accurately capture the variability of waves in short timeframes. Creating a standardized methodology to increase the temporal resolution of metaocean conditions to inform model development can provide better forecasting of power production. Random amplitude, Fourier coefficient methods have been suggested for WEC simulations of finite durations to improve the observed variability in wave heights and power production. Variability using this method does increase for finite durations compared to the commonly used deterministic amplitude method. In this paper we will investigate the influence of wave parameters (significant wave height, maximum wave height, and energy period) on the prediction of WEC power production. A better understanding of the influence of these parameters will provide a path towards future standardization methodology for resource inputs for time-domain modeling.

Characterizing Alterations in Cortisol Secretion During Cardiac Surgery.

Cortisol is a neuroendocrine hormone of the hypothalamus-pituitary-adrenal (HPA) axis secreted from adrenal glands in response to stimulation by adrenocorticotropic hormone (ACTH) from the anterior pituitary and corticotropin releasing hormone (CRH) from the hypothalamus. Cortisol has multiple functionalities in maintaining bodily homeostasis - including anti-inflammatory influences - through its diurnal secretion pattern (which has been studied extensively); its secretion is also increased in response to major traumatic events such as surgery. Due to the adverse health consequences of an abnormal immune response, it is crucial to understand the effect of cortisol in modulating inflammation. To address this physiological issue, we characterize the secretion of cortisol using a high temporal resolution dataset of ten patients undergoing coronary arterial bypass grafting (CABG) surgery, in comparison with a control group not undergoing surgery. We find that cortisol exhibits different pulsatile dynamics in those undergoing cardiac surgery compared to the control subjects. We also summarize the causality of cortisol's relationship with different cytokines (which are one type of inflammatory markers) by performing Granger causality analysis.Clinical relevance- This work documents time-varying patterns of the HPA axis hormone cortisol in the inflammatory response to cardiac surgery and may eventually help improve patients' prognosis post-surgery (or in other conditions) by enabling early detection of an abnormal cortisol or inflammatory response and enabling patient specific remedial interventions.

Analysis and management of herbicidal mixtures in a high-intensity agricultural landscape in Belgium.

Water bodies located in anthropogenically influenced environments, such as agricultural landscapes, may be exposed to different chemicals simultaneously or sequentially. Yet, current environmental risk assessments focus on single active substances for unintended mixtures. For 3.5 years, the present study monitored the mixture of herbicides, within an intensively managed agricultural catchment, accompanied by a stewardship program. Twelve herbicides and one metabolite were monitored on a daily to sub-daily basis, generating a unique, high temporal resolution data set, enabling an assessment of cumulative exposure in a worst-case scenario. Analyses focused on the number of events at which the herbicide mixture concentration exceeded the regulatory accepted concentration for algae and macrophytes, based on concentration addition, and the potential factors influencing the frequency of these events are considered. A low number of individual herbicides drove the toxicity and only two of these overlapped for the two organism groups, algae and macrophytes. The observed exceedance events coincided with seasonal influences, and low rainfall during the 2011 season correlated with a highly reduced number of these events. The major influence was found to be the implementation of the stewardship program, which directed farmers to use more advanced farming techniques, avoid spillages, and other point sources. The number of exceedance events was reduced by more than half for algae (9% of the daily mean samples in 2010 and 4% in 2013) and by approximately 10 times for macrophytes (36% in 2010 to 3% in 2013). This high-resolution monitoring data set illustrates how knowledge of the influencing factors can help reduce unintended exposure to chemicals and achieve real-world improvements. Overall, a single-substance assessment is protective of mixture effects. Where mixture effects do play a role, local measures to manage point sources are more effective than changes to the desk-based environmental risk assessments that focus on diffuse sources. Integr Environ Assess Manag 2023;19:1297-1306. © 2022 Cambridge Environmental Assessments RSK ADAS Ltd and Bayer AG. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<103 km2) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. However, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

Remote sensing of atmospheric HDO/H2O in southern California from CLARS-FTS

Atmospheric isotopologues of water vapor (e.g., HDO) are important tracers for understanding Earth's hydrological cycles. Most remote sensing and in-situ measurements of these isotopologues, however, are either column averaged values or sparse in space and time. Measurements targeting the planetary boundary layer (PBL), the part of the atmosphere that has high sensitivity to surface sources of water vapor isotopologues, are much rarer. In this study, we retrieved HDO and H2O columns from observations by the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer (CLARS-FTS), a mountaintop observatory on Mt. Wilson (1.67 km a.s.l.) overlooking the Los Angeles (LA) basin in southern California. CLARS-FTS observations are highly sensitive to the lower atmosphere due to the long light path along the PBL. Retrievals were conducted using spectral windows between 6000 and 7000 cm−1 from CLARS-FTS observations (2011–2019). The isotopological abundance δD, which represents the relative difference of the HDO/H2O ratio to a standard abundance ratio, is also calculated. The averaged δD retrievals are (−156.1 ± 60.0)‰ with an uncertainty of (6.1 ± 10.2)‰ for LA Basin Survey mode and (−344.7 ± 95.0)‰ with an uncertainty of (42.4 ± 31.6)‰ for Spectralon Viewing Observation mode. In LA, the δD shows a seasonal cycle that is primarily driven by the change of atmospheric humidity. A comparison analysis shows that the δD measurements by CLARS-FTS, a collocated Total Carbon Column Observing Network (TCCON), and the TROPOspheric Monitoring Instrument (TROPOMI) are in good agreement. The difference between CLARS and TCCON δD retrievals can primarily be attributed to the difference in their observation geometries. We envision that the HDO and δD measurements from CLARS-FTS provide high spatial and temporal resolution datasets for further study of hydrological processes, such as the partitioning of the water flux into soil evaporation and transpiration, standing water evaporation, or transport and mixing from the oceans, in the LA megacity.

Characterization of temporal PM2.5, nitrate, and sulfate using deep learning techniques

Water-soluble inorganic salts (WIS) contribute a large fraction of PM2.5, which are responsible for the adverse effects on human health and environment. It is important to investigate the long-term variation trend of PM2.5 and WIS for exposure assessment and effective abatement strategies development. In this study, a multi-step ahead artificial neural network (MSA-ANN) and a long short-term memory fully connected (LSTM-FC) model for PM2.5, nitrate (NO3−), and sulfate (SO42−) prediction were developed to provide high temporal resolution datasets. Results show that the kinetic formation rate of HNO3 and HSO3− used as input variables can improve the accuracy of the present model for PM2.5, NO3−, and SO42− prediction. The MSA-ANN is able to predict 80–83%, 93%, and 91% of PM2.5, NO3−, and SO42−, respectively, with t+1 prediction horizon. With increasing prediction horizon to t+4 and t+8, the present model still can track the temporal variation of PM2.5, NO3−, and SO42− very well and capture the peak values during a severe pollution event. The proposed method is applicable to predict regional temporal variation trend of PM2.5, NO3−, and SO42− and fill the data gap in area with limited WIS measurement.

TreeNet–The Biological Drought and Growth Indicator Network

The TreeNet research and monitoring network has been continuously collecting data from point dendrometers and air and soil microclimate using an automated system since 2011. The goal of TreeNet is to generate high temporal resolution datasets of tree growth and tree water dynamics for research and to provide near real-time indicators of forest growth performance and drought stress to a wide audience. This paper explains the key working steps from the installation of sensors in the field to data acquisition, data transmission, data processing, and online visualization. Moreover, we discuss the underlying premises to convert dynamic stem size changes into relevant biological information. Every 10 min, the stem radii of about 420 trees from 13 species at 61 sites in Switzerland are measured electronically with micrometer precision, in parallel with the environmental conditions above and below ground. The data are automatically transmitted, processed and stored on a central server. Automated data processing (R-based functions) includes screening of outliers, interpolation of data gaps, and extraction of radial stem growth and water deficit for each tree. These long-term data are used for scientific investigations as well as to calculate and display daily indicators of growth trends and drought levels in Switzerland based on historical and current data. The current collection of over 100 million data points forms the basis for identifying dynamics of tree-, site- and species-specific processes along environmental gradients. TreeNet is one of the few forest networks capable of tracking the diurnal and seasonal cycles of tree physiology in near real-time, covering a wide range of temperate forest species and their respective environmental conditions.

Validation of the estimation of the macrovascular contribution in multi-timepoint arterial spin labeling MRI using a 2-component kinetic model.

In this paper, the ability to quantify cerebral blood flow by arterial spin labeling (ASL) was studied by investigating the separation of the macrovascular and tissue component using a 2-component model. Underlying assumptions of this model, especially the inclusion of dispersion in the analysis, were studied, as well as the temporal resolution of the ASL datasets. Four different datasets were acquired: (1) 4D ASL angiography to characterize the macrovascular component and to study dispersion modeling within this component, (2) high temporal resolution ASL data to investigate the separation of the 2 components and the effect of dispersion modelling on this separation, (3) low temporal resolution ASL dataset to study the effect of the temporal resolution on the separation of the 2 components, and (4) low temporal resolution ASL data with vascular crushing. The model that included a gamma dispersion kernel had the best fit to the 4D ASL angiography. For the high temporal resolution ASL dataset, inclusion of the gamma dispersion kernel led to more signal included in the arterial blood volume map, which resulted in decreased cerebral blood flow values. The arterial blood volume and cerebral blood flow maps showed overall higher arterial blood volume values and lower cerebral blood flow values for the high temporal resolution dataset compared to the low temporal resolution dataset. Inclusion of a gamma dispersion kernel resulted in better fitting of the model to the data. The separation of the macrovascular and tissue component is affected by the inclusion of a gamma dispersion kernel and the temporal resolution of the ASL dataset.

Charting a perfect storm of water quality pressures

The agri-food economy can be a significant driver of water quality pressures but the role of hydro-meteorological patterns in a changing climate also requires consideration. For this purpose, an assessment was made of a ten-year synchronous high temporal resolution water quality and hydro-meteorological dataset in Irish agricultural catchments. Changes occurring to rainfall intensity and soil temperature patterns were found to be important drivers of nutrient mobility in soils. There were links between the intensity of the North Atlantic Oscillation over the decade and large shifts in baseline nutrient concentrations in catchments. The data also revealed extreme weather impacts to pollution patterns including short periods of rain induced nutrient flux, that exceeded average annual mass loads in these catchments, and drought influences on point source pollution. These influences need consideration, and may require different mitigation strategies, as links between water quality land use pressure and water quality state in regulatory reviews. In a decade of both increased land use source and hydro-meteorological transport pressures, water quality natural capital in Ireland has faced a perfect storm. Such conditions are difficult to model and only revealed in high temporal resolution datasets.

An investigation into the impact of nine catchment characteristics on the accuracy of two phosphorus load apportionment models.

Phosphorus (P) load apportionment models (LAMs), requiring only spatially and temporally paired P and flow (Q) measurements, provide outputs of variable accuracy using long-term monthly datasets. Using a novel approach to investigate the impact of catchment characteristics on accuracy variation, 91 watercourses' Q-P datasets were applied to two LAMs, BM and GM, and bootstrapped to ascertain standard errors (SEs). Random forest and regression analysis on data pertaining to catchments' land use, steepness, size, base flow and sinuosity were used to identify the individual relative importance of a variable on SE. For BM, increasing urban cover was influential on raising SEs, accounting for c.19% of observed variation, whilst analysis for GM found no individually important catchment characteristic. Assessment of model fit evidenced BM consistently outperformed GM, modelling P values to ±10% of actual P values in 85.7% of datasets, as opposed to 17.6% by GM. Further catchment characteristics are needed to account for SE variation within both models, whilst interaction between variables may also be present. Future research should focus on quantifying these possible interactions and should expand catchment characteristics included within the random forest. Both LAMs must also be tested on a wide range of high temporal resolution datasets to ascertain if they can adequately model storm events in catchments with diverse characteristics.

A Data Fusion Modeling Framework for Retrieval of Land Surface Temperature from Landsat-8 and Modis Data.

Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezing-thawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixel-based multi-spatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat-8 and MODIS data was implemented to generate the synthetic LST with Landsat-like spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsat-derived LST with structural similarity index (SSIM) of 0.83 and the index of agreement (d) of 0.84. The range of SSIM was 0.65–0.88, the root mean square error (RMSE) yielded a range of 1.6–3.4 °C, and the averaged bias was 0.6 °C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The RMSE ranged from 0.7 °C to 1.5 °C with an average value of 1.1 °C. When compared with in situ LST observations, the mean absolute error and bias were reduced after fusion with the mean absolute bias of 1.3 °C. The validation results that fused LST possesses the spatial pattern of Landsat-derived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a high-quality LST spatiotemporal dataset for various applications in environment studies.

Characterizing transport of natural and anthropogenic constituents in a long-term agricultural watershed in the northeastern United States

As recent technologies enable water samples to be collected at increasingly shorter time intervals, water quality data can more fully capture the range of conditions a stream experiences over time. Various metrics can be employed with the large, high-temporal resolution (i.e., subdaily) data sets to gain insights into the hydroclimatic and biogeochemical processes affecting chemical fate and transport. These insights can be helpful in understanding the extent to which anthropogenic activities have impacted the natural response of some constituents, such as nutrients and salts, in managed landscapes. Here, nearly four years (12,544 samples from 2015 to 2019) of water quality data for 12 constituents of interest were collected using three sampling strategies: (1) low-frequency sampling three times per week, (2) high-temporal resolution flow-paced sampling to capture stormflow, and (3) high-frequency time-paced sampling with a time interval of four hours. Seasonal trends were investigated to understand concentration variability over time, and concentration-discharge (<i>C-Q</i>) relationships were developed to categorize the transport dynamics of each constituent. Lorenz curves and Gini coefficients were employed to quantify the temporal inequality of the constituent loads discharged at the watershed outlet and understand the extents to which the transport behaviors of geogenic constituents and those affected by anthropogenic activities differed. Overall, the results suggested that nearly all of the geogenic constituents, plus nitrate-nitrogen (NO₃-N) and sulfate (SO₄-S), exhibited chemostatic dynamics with loads overwhelmingly controlled by flow variability, whereas aluminum (Al), iron (Fe), nitrite-N (NO₂-N), and orthophosphate (PO₄-P) exhibited episodic transport dynamics that were likely controlled by limited source availability. Since the transport of NO₃-N was found to be similar to the transport of common geogenic constituents for the region, this suggests that decades of agricultural activities in the watershed have led to the emergence of legacy N sources, while the episodic dynamics observed for PO₄-P suggest that best management practices appear to have prevented the emergence of P legacy sources.

A Review of Research on Warm-Sector Heavy Rainfall in China

Warm-sector heavy rainfall (WSHR) events in China have been investigated for many years. Studies have investigated the synoptic weather conditions during WSHR formation, the categories and general features, the triggering mechanism, and structural features of mesoscale convective systems during these rainfall events. The main results of WSHR studies in recent years are summarized in this paper. However, WSHR caused by micro- to mesoscale systems often occurs abruptly and locally, making both numerical model predictions and objective forecasts difficult. Further research is needed in three areas: (1) The mechanisms controlling WSHR events need to be understood to clarify the specific effects of various factors and indicate the influences of these factors under different synoptic background circulations. This would enable an understanding of the mechanisms of formation, maintenance, and organization of the convections in WSHR events. (2) In addition to South China, WSHR events also occur during the concentrated summer precipitation in the Yangtze River-Huaihe River Valley and North China. A high spatial and temporal resolution dataset should be used to analyze the distribution and environmental conditions, and to further compare the differences and similarities of the triggering and maintenance mechanisms of WSHR events in different regions. (3) More studies of the mechanisms are required, as well as improvements to the model initial conditions and physical processes based on multi-source observations, especially the description of the triggering process and the microphysical parameterization. This will improve the numerical prediction of WSHR events.

Comparison of discharge pulses in temperate and tropical rainforest headwater stream networks

We use sub-daily gage records from montane headwater channels in the Luquillo Experimental Forest of Puerto Rico (tropical rainforest) and the HJ Andrews Experimental Forest of Oregon (temperate rainforest) to characterize differences in discharge pulses (rapid, high-magnitude discharge fluctuations) and determine whether the characteristics of discharge pulses differ with respect to drainage area between these two regions. The study sites have different precipitation regimes and runoff generation mechanisms, and we quantify differences in discharge pulses between sites. The assessment of discharge pulses, which are defined as flows that are higher than one standard deviation above the historical mean flow at each site, represents a novel approach to characterizing flashy discharge events in headwater streams using high temporal resolution datasets. Our analyses indicate a clear difference between regions, with discharge in the Luquillo streams pulsing more frequently, for shorter periods of time, and at higher magnitudes than discharge in the Andrews streams. We also quantify how discharge pulses change with increasing drainage area at each of the sites. Differences in discharge pulse metrics with respect to drainage area include an increase in the number of pulses, an increase in the normalized magnitude of pulses, an increase in the standard deviation in normalized magnitude, and a decrease in kurtosis with increasing drainage area at the Luquillo site. These results indicate that there is no attenuation of discharge pulses with increasing drainage area at Luquillo. In contrast, there is a decrease in the normalized magnitude with increasing drainage area at the Andrews site, indicating some attenuation of discharge pulses with increasing drainage area. The characteristics of these pulsed events have implications for ecological processes by transporting sediment, biota, and organic matter, likely altering stream substrates, biotic communities, and organic matter retention times.

A comparative sensitivity analysis of human thermal comfort indices with generalized additive models

This paper presents a comparative sensitivity analysis of six of the widely used human thermal comfort indices. The analysis consists of the evaluation of the effect of indices ‘input parameters’ variation and change rate on the output of human energy balance and simple thermohygrometric indices. For the implementation of the sensitivity analysis, the generalized additive model’s methodology is applied on a long period and high temporal resolution dataset from Athens, Greece. The results indicate that the proposed methodology of generalized additive models is adequate for such an analysis. Moreover, this research revealed the differences in index behaviour. The thermohygrometric indices (i.e. Thermohygrometric Index and HUMIDEX) exhibit a clearly deferent sensitivity pattern in comparison to the human energy balance indices (i.e. physiologically equivalent temperature (PET), perceived temperature (PT), modified physiologically equivalent temperature (mPET) and Universal Thermal Comfort Index (UTCI)), and they are incapable to handle the complexity of the atmospheric stimuli on human thermal perception. On the other hand, human energy balance indices can follow the input parameters fluctuations but with different grades of sensitivity. PET and mPET present a moderate and gradual sensitivity both in terms of the input variation and input change rate. PT is the less sensitive index among the human energy balance investigated, but it is able to follow efficiently the input parameters variation during the measurements period. Moreover, UTCI is the most sensitive among all the selected indices for low values (and low change rate) of the input parameters but for high input parameter values (except the wind speed), UTCI exhibits a low sensitivity in comparison to the other human energy balance indices. In terms of sensitivity, the most influential input parameter is global radiation, and the less influential is vapour pressure.

Multi-proxy isotopic tracing of magmatic sources and crustal recycling in the Palaeozoic to Early Jurassic active margin of North-Western Gondwana

We trace source variations of active margin granitoids which crystallised intermittently over ~300 Ma in varying kinematic regimes, by combining zircon Lu-Hf isotopic data from Early Palaeozoic to Early Jurassic igneous and metaigneous rocks in the Mérida Andes, Venezuela and the Santander Massif, Colombia, with new whole rock Rb/Sr and Sm-Nd isotopic data, and quartz O isotopic data. These new data are unique in South America because they were obtained from discrete magmatic and metamorphic zircon populations, providing a high temporal resolution dataset, and compare several isotopic systems on the same samples. Collectively, these data provide valuable insight into the evolution of the isotopic structure of the continental crust in long-lived active margins.Phanerozoic active margin-related granitoids in the Mérida Andes and the Santander Massif yield zircon Lu-Hf model ages ranging between 0.77 Ga and 1.57 Ga which clearly define temporal trends that can be correlated with changes in tectonic regimes. The oldest Lu-Hf model ages of >1.3 Ga are restricted to granitoids which formed during Barrovian metamorphism and crustal thickening between ~499 Ma and ~473 Ma. These granitoids yield high initial 87Sr/86Sr ratios, suggesting that evolved, Rb-rich middle to upper crust was the major source of melt. Granitoids and rhyolites which crystallised during subsequent extension between ~472 Ma and ~452 Ma yield younger Lu-Hf model ages of 0.80 Ga–1.3 Ga and low initial 87Sr/86Sr ratios, suggesting that they were derived from much more juvenile, Rb-poor sources such as mafic lower crust and mantle-derived melts. The rapid change in magmatic sources at ~472 Ma can be attributed either to reduced crustal assimilation during extension, or a short pulse of crustal growth by addition of juvenile material to the continental crust. Between ~472 Ma and ~196 Ma Lu-Hf model ages remain mostly constant between ~1.0 and ~1.2 Ga. The large scatter and the absence of definite trends in initial 87Sr/86Sr ratios suggest that both mafic, Rb-poor, and evolved Rb-rich sources were important precursors of active margin magmas in Colombia and Venezuela throughout the Palaeozoic to the Early Jurassic.Previous studies have shown that the genesis of arc magmas may be stimulated by heat advection to the crust during the underplating of mantle derived melt, but the absence of permanent younging trends in Lu-Hf model ages from ~472 Ma to ~196 Ma suggests that very little new crust was generated during this period in the studied region. An overwhelming majority of the analysed igneous rocks yield zircon Lu-Hf model ages of >1 Ga which may be accounted for by documented local crustal end members of 1 Ga–1.6 Ga, and do not require contributions from the depleted mantle. Therefore, recycling of ~1 Ga and older crust was a dominant process in the north-western corner of Gondwana between ~472 Ma and ~196 Ma.This study shows that whole rock Sm-Nd and zircon Lu-Hf data can be interpreted similarly regarding the age of the source regions, whereas Rb-Sr and O isotope data from the same rocks yield valuable information regarding the geochemical nature of the source.

ENSO Drives interannual variation of forest woody growth across the tropics.

Meteorological extreme events such as El Niño events are expected to affect tropical forest net primary production (NPP) and woody growth, but there has been no large-scale empirical validation of this expectation. We collected a large high-temporal resolution dataset (for 1-13 years depending upon location) of more than 172 000 stem growth measurements using dendrometer bands from across 14 regions spanning Amazonia, Africa and Borneo in order to test how much month-to-month variation in stand-level woody growth of adult tree stems (NPPstem) can be explained by seasonal variation and interannual meteorological anomalies. A key finding is that woody growth responds differently to meteorological variation between tropical forests with a dry season (where monthly rainfall is less than 100 mm), and aseasonal wet forests lacking a consistent dry season. In seasonal tropical forests, a high degree of variation in woody growth can be predicted from seasonal variation in temperature, vapour pressure deficit, in addition to anomalies of soil water deficit and shortwave radiation. The variation of aseasonal wet forest woody growth is best predicted by the anomalies of vapour pressure deficit, water deficit and shortwave radiation. In total, we predict the total live woody production of the global tropical forest biome to be 2.16 Pg C yr-1, with an interannual range 1.96-2.26 Pg C yr-1 between 1996-2016, and with the sharpest declines during the strong El Niño events of 1997/8 and 2015/6. There is high geographical variation in hotspots of El Niño-associated impacts, with weak impacts in Africa, and strongly negative impacts in parts of Southeast Asia and extensive regions across central and eastern Amazonia. Overall, there is high correlation (r = -0.75) between the annual anomaly of tropical forest woody growth and the annual mean of the El Niño 3.4 index, driven mainly by strong correlations with anomalies of soil water deficit, vapour pressure deficit and shortwave radiation.This article is part of the discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

Observation and Numerical Simulation of Cold Clouds and Snow Particles in the Yeongdong Region

The Yeongdong region of the Korean Peninsula is vulnerable to high-impact weather events, because of its complicated geographical characteristics and East Sea effect, such that heavy snowfall episodes have frequently occurred in winter. Snow crystal play an important role in cloud and precipitation physics because it is an essential element for improvement of numerical model, and remote sensing retrieval such as radar and satellite. In this study, the high-temporal resolution (3-hourly) dataset of radiosonde soundings and snow particle photographs during the intensive observation period for 2013–2016 has been used to understand characteristics of snow particles for the different meteorological conditions in the Yeongdong region. We also attempt to simulate two episodes that occurred on 23–24 February and 13–14 December 2016; one has a single-layered cloud and the other has two-layered cloud structure. This study demonstrates that the rimed particles in the first period tend to shift to aggregates of dendrites with the decrease of 850-hPa temperature. In general, the low-level clouds in the Yeongdong region are observed along with the distinctive wind shear and strong inversion in equivalent potential temperature around 2 ~ 3 km above the sea level. The simulation successfully represents the variations of the characteristics of snow particles as well as different cloud structure for both episodes. The observation and model simulations clearly suggest that snow particles primarily depend on the 850-hPa temperature.

EOF analysis of COSMIC observations on the global zonal mean temperature structure of the Upper Troposphere and Lower Stratosphere from 2007 to 2013

This work presents the results of a Conventional Empirical Orthogonal Function Analysis on daily global zonal mean temperature profiles in the Upper Troposphere and Lower Stratosphere (15–35 km), as measured by the FORMOSAT-3/COSMIC mission from January 2007 to June 2013. For validation, results were compared with ERA-Interim reanalysis. Results show that, the leading global EOF mode (27%) from COSMIC is consistent with temperature anomalies due to the tropical cooling associated with boreal winter Sudden Stratospheric Warmings (SSW). The second global EOF mode from COSMIC (15.3%) is consistent with temperature anomalies due to the Quasi-biennial Oscillation (QBO). The third global mode from COSMIC (10.9%) is consistent with temperature anomalies due to the El Nino Southern Oscillation. This work also shows that the second northern hemisphere EOF mode from COSMIC (16.8%) is consistent with temperature anomalies due Rossby-wave breaking (RWB) which is expected to only be resolved by a high vertical and temporal resolution dataset like COSMIC. Our work concludes that the use of a high vertical and temporal resolution dataset like COSMIC yields non-seasonal EOF modes that are consistent with relatively more intricate temperature anomalies due to the SSW, QBO, ENSO and RWB.