High-resolution Time Series Research Articles

Satellite monitoring of bio-fertilizer restoration in olive groves affected by Xylella fastidiosa subsp. pauca

Xylella fastidiosa subsp. pauca (Xfp), has attacked the olive trees in Southern Italy with severe impacts on the olive agro-ecosystem. To reduce both the Xfp cell concentration and the disease symptom, a bio-fertilizer restoration technique has been used. Our study applied multi-resolution satellite data to evaluate the effectiveness of such technique at both field and tree scale. For field scale, a time series of High Resolution (HR) Sentinel-2 images, acquired in the months of July and August from 2015 to 2020, was employed. First, four spectral indices from treated and untreated fields were compared. Then, their trends were correlated to meteo-events. For tree-scale, Very High Resolution (VHR) Pléiades images were selected at the closest dates of the Sentinel-2 data to investigate the response to treatments of each different cultivar. All indices from HR and VHR images were higher in treated fields than in those untreated. The analysis of VHR indices revealed that Oliarola Salentina can respond better to treatments than Leccino and Cellina cultivars. All findings were in agreement with in-field PCR results. Hence, HR data could be used to evaluate plant conditions at field level after treatments, while VHR imagery could be used to optimize treatment doses per cultivar.

Ocean‐Forcing and Glacier‐Specific Factors Drive Differing Glacier Response Across the 69°N Boundary, East Greenland

AbstractThe Greenland ice sheet has become a significant contributor to global sea level rise over the last 40–50 years. Approximately half of Greenland's mass loss since 1992 was due to increased ice discharge from marine‐terminating outlet glaciers. Here, we present high temporal resolution (∼monthly) time series of ice frontal positions for 24 marine‐terminating outlet glaciers along Greenland's east coast between 2013 and 2020. The glaciers are located north and south of 69°N, which has previously been identified as a potential divide in glacier response to climate forcing. Frontal positions are compared to ice velocity, atmospheric and oceanic data, allowing investigation of change at both seasonal and interannual timescales. Our results reveal 19 of 24 study glaciers underwent net retreat. We find marked differences in interannual patterns of frontal position between glaciers located north and south of 69°N. South of 69°N, glaciers underwent multiyear retreat initiated in 2016, which we attribute to over‐winter calving, resulting from warmer ocean waters and repeated ice‐mélange break‐up. North of 69°N glaciers show either limited or gradual changes in frontal positions and retreat patterns are characterized by more year‐on‐year variability between glaciers. Although similar atmospheric conditions occur across both regions, glaciers north of 69°N experience minimal change in ocean conditions, and are strongly influenced by glacier‐specific factors. Our results show that 69°N continues to represent a boundary between different glacier responses and climate forcing, which is likely to persist under current conditions.

Using quantile regression and relative entropy to assess the period of anomalous behavior of marine mammals following tagging.

Tagging of animals induces a variable stress response which following release will obscure natural behavior. It is of scientific relevance to establish methods that assess recovery from such behavioral perturbation and generalize well to a broad range of animals, while maintaining model transparency. We propose two methods that allow for subdivision of animals based on covariates, and illustrate their use on narwhals (Monodon monoceros) and bowhead whales (Balaena mysticetus), captured and instrumented with Acousonde™ behavioral tags, but with a framework that easily generalizes to other marine animals and sampling units. The narwhals were divided into two groups based on handling time, short ( min) and long ( min), to measure the effect on recovery. Proxies for energy expenditure (VeDBA) and rapid movement (jerk) were derived from accelerometer data. Diving profiles were characterized using two metrics (target depth and dive duration) derived from depth data. For accelerometer data, recovery was estimated using quantile regression (QR) on the log-transformed response, whereas depth data were addressed using relative entropy (RE) between hourly distributions of dive duration (partitioned into three target depth ranges) and the long-term average distribution. Quantile regression was used to address location-based behavior to accommodate distributional shifts anticipated in aquatic locomotion. For all narwhals, we found fast recovery in the tail of the distribution (<3 h) compared with a variable recovery at the median (∼1-10 h) and with a significant difference between groups separated by handling time. Estimates of bowhead whale recovery times showed fast median recovery (<3 h) and slow recovery at the tail (>6 h), but were affected by substantial uncertainty. For the diving profiles, as characterized by the component pair (target depth, dive duration), the recovery was slower (narwhals-long: h; narwhals-short: h; bowhead whales: <9h) and with a difference between narwhals with short vs long handling times. Using simple statistical concepts, we have presented two transparent and general methods for analyzing high-resolution time series data from marine animals, addressing energy expenditure, activity, and diving behavior, and which allows for comparison between groups of animals based on well-defined covariates.

A dynamically coherent pattern of rhythms that matches between distant species across the evolutionary scale

We address the temporal organization of circadian and ultradian rhythms, crucial for understanding biological timekeeping in behavior, physiology, metabolism, and alignment with geophysical time. Using a newly developed five-steps wavelet-based approach to analyze high-resolution time series of metabolism in yeast cultures and spontaneous movement, metabolism, and feeding behavior in mice, rats, and quails, we describe a dynamically coherent pattern of rhythms spanning over a broad range of temporal scales (hours to minutes). The dynamic pattern found shares key features among the four, evolutionary distant, species analyzed. Specifically, a branching appearance given by splitting periods from 24 h into 12 h, 8 h and below in mammalian and avian species, or from 14 h down to 0.07 h in yeast. Scale-free fluctuations with long-range correlations prevail below ~ 4 h. Synthetic time series modeling support a scenario of coexisting behavioral rhythms, with circadian and ultradian rhythms at the center of the emergent pattern observed.

Revenue-Based Allocation of Electricity Network Charges for Future Distribution Networks

This paper investigates the economic implications that high penetrations of distributed energy resources (DER) have in future distribution networks, and proposes a novel scalable scheme for the assignment of use of network charges based on individual participant nodes’ revenue. For validation purposes, a techno-economic simulation is proposed to understand how power and revenue flows will change. A year-long high-resolution quasi-static time series (QSTS) simulation, two price schemes, four trading environments, and four DER allocation methods from the literature are used to study economic benefits for individual participants and the supplier. Testing is performed using the IEEE 33-bus and 123-bus networks, and an Irish urban medium voltage feeder. Revenue flow is presented as an indicator of which participant nodes are profiting more from grid usage, and therefore should be responsible for greater network charges, this is validated against traditional and alternative schemes. Important reductions in use of network charges are seen especially by participant nodes with a higher PV generation-to-load and self-consumption rates. The proposed method is only relevant when dynamic tariffs are in place and/or local trading is enabled. Ultimately, results suggest that the income from network charges received by the supplier is increased when dynamic tariffs are used.

Geoinformatics-based investigation of slope failure and landslide damming of Chenab River, Lahaul-Spiti, Himachal Pradesh, India

A huge landslide blocked the flow of the Chenab River near Nalda village on the morning of 13th August 2021 in the Lahaul-Spiti district of Himachal Pradesh, which led to the flooding of several villages (Nalda, Jasrath, Tarang, etc.) in the Udaipur subdivision. The slope (∼200 ​m height, ∼220 ​m width, and approx. 30 ​m depth) on the left bank of the Chenab River failed, which brought colossal soil and debris. This resulted in the damming of the Chenab River near Leh Baring village, which lies upstream of Nalda Village Bridge and opposite to Junde village, creating a huge water reservoir that later started overflowing, posing a major threat to downstream villages. This caused damage to the houses located downstream, which were submerged, animals were also washed away, and a large part of agricultural land was also inundated. The event was observed and studied using satellite images of high-resolution obtained from Google Earth and Sentinel 2A. The Spatio-temporal satellite images were used to observe the scars developed in the region over the past few years along the river, which evidently show the early signatures of slope failure. To analyze the stability of the hill slope, the factor of safety of the hill was evaluated using SLOPE/W GeoStudio software. Slope map and drainage density map were also generated, showing the vulnerability of the hill slope. The debris flow runout simulation of the event was performed using the RAMMS debris flow model to calculate the volume of the landslide and the water reservoir formed due to damming of the Chenab River. The volume of the landslide (debris) due to slope failure was approx. 2.22 million m3 and the water reservoir volume was approximately 0.3 million m3. The significant factors accountable for the landslide i.e. the slope of the hill, geomorphology of the area, lithology (phyllites), presence of narrow and elongated valleys, the confluences of the river in the area, and continuous rainfall in the region during that period were evaluated. The paper also emphasizes monitoring of such vulnerable areas based on high-resolution time series satellite images, which are available on a regular basis to avoid the loss of human lives in the future.

Online Prediction of Molded Part Quality in the Injection Molding Process Using High-Resolution Time Series.

Process-data-supported process monitoring in injection molding plays an important role in compensating for disturbances in the process. Until now, scalar process data from machine controls have been used to predict part quality. In this paper, we investigated the feasibility of incorporating time series of sensor measurements directly as features for machine learning models, as a suitable method of improving the online prediction of part quality. We present a comparison of several state-of-the-art algorithms, using extensive and realistic data sets. Our comparison demonstrates that time series data allow significantly better predictions of part quality than scalar data alone. In future studies, and in production-use cases, such time series should be taken into account in online quality prediction for injection molding.

Formation, maintenance and diurnal variability of subsurface chlorophyll maximum during the summer monsoon in the southern Bay of Bengal

The vertical structure of chlorophyll in stratified oceanic waters exhibits a maximum substantially below the surface, known as the subsurface chlorophyll maximum (SCM). In the southern Bay of Bengal (BoB), a 12-day high-resolution time-series observation at 89°E and 8°N, during the 2016 summer monsoon showed that the SCM was located within the barrier layer (BL) and the euphotic layer. The strong BL (N2=3×10−4 s−2) located between 30 to 70 m, associated with a low salinity (<33.8 psu) water near the surface, maintained the stratification and the SCM. This SCM oscillated diurnally with an amplitude of 0.1 to 0.2 mg m−3. The lowest values of average chlorophyll in the SCM occurred around 06:00 hours (local time) and reached a maximum around 16:00 hours, four hours after the maximum in surface shortwave radiation. A coupled physical–biogeochemical one-dimensional model was used to examine the formation, maintenance and diurnal variability of the SCM in the southern BoB. The wind speed (<10 m s−1) that prevailed in the region during the weak phase of the monsoon was insufficient to break the BL and cause a surface bloom. The model results revealed that the surface shortwave radiation drove the diurnal cycle of the growth rate of phytoplankton at the SCM, hence the diurnal cycle of phytoplankton biomass and chlorophyll. The model’s control simulation showed the dominance of small phytoplankton, and its growth rate was limited by light and nutrients at the SCM. Whereas, in the ML, the phytoplankton growth rate was limited by nutrients. In the model, the regenerated production supported around 60% of the chl-a at the location of SCM. The subsurface high-salinity core, a prominent feature in the southern BoB, did not influence the SCM. This paper offers new insights into understanding the SCM formation, maintenance and diurnal variability in stratified tropical oceans.

Time series transcriptome analysis implicates the circadian clock in the Drosophila melanogaster female’s response to sex peptide

Sex peptide (SP), a seminal fluid protein ofDrosophilamelanogaster males, has been described as driving a virgin-to-mated switch in females, through eliciting an array of responses including increased egg laying, activity, and food intake and a decreased remating rate. While it is known that SP achieves this, at least in part, by altering neuronal signaling in females, the genetic architecture and temporal dynamics of the female's response to SP remain elusive. We used a high-resolution time series RNA-sequencing dataset of female heads at 10 time points within the first 24 h after mating to learn about the genetic architecture, at the gene and exon levels, of the female's response to SP. We find that SP is not essential to trigger early aspects of a virgin-to-mated transcriptional switch, which includes changes in a metabolic gene regulatory network. However, SP is needed to maintain and diversify metabolic changes and to trigger changes in a neuronal gene regulatory network. We further find that SP alters rhythmic gene expression in females and suggests that SP's disruption of the female's circadian rhythm might be key to its widespread effects.

Short-term response to iron resupply in an iron-limited open ocean diatom reveals rapid decay of iron-responsive transcripts.

In large areas of the ocean, iron concentrations are insufficient to promote phytoplankton growth. Numerous studies have been conducted to characterize the effect of iron on algae and how algae cope with fluctuating iron concentrations. Fertilization experiments in low-iron areas resulted primarily in diatom-dominated algal blooms, leading to laboratory studies on diatoms comparing low- and high-iron conditions. Here, we focus on the short-term temporal response following iron addition to an iron-starved open ocean diatom, Thalassiosira oceanica. We employed the NanoString platform and analyzed a high-resolution time series on 54 transcripts encoding proteins involved in photosynthesis, N-linked glycosylation, iron transport, as well as transcription factors. Nine transcripts were iron-responsive, with an immediate response to the addition of iron. The fastest response observed was the decrease in transcript levels of proteins involved in iron uptake, followed by an increase in transcript levels of iron-containing enzymes and a simultaneous decrease in the transcript levels of their iron-free replacement enzymes. The transcription inhibitor actinomycin D was used to understand the underlying mechanisms of the decrease of the iron-responsive transcripts and to determine their half-lives. Here, Mn-superoxide dismutase (Mn-SOD), plastocyanin (PETE), ferredoxin (PETF) and cellular repressor of EA1-stimulated genes (CREGx2) revealed longer than average half-lives. Four iron-responsive transcripts showed statistically significant differences in their decay rates between the iron-recovery samples and the actD treatment. These differences suggest regulatory mechanisms influencing gene transcription and mRNA stability. Overall, our study contributes towards a detailed understanding of diatom cell biology in the context of iron fertilization response and provides important observations to assess oceanic diatom responses following sudden changes in iron concentrations.

Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks

Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machine learning models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests and XGBoost for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data. Our study indicated several significant results across experimental settings that suggest the utility of deep learning for capturing more precise representations of patient health for augmented surgical decision support. Multi-task learning improved efficiency by reducing computational resources without compromising predictive performance. Integrated gradients interpretability mechanisms identified potentially modifiable risk factors for each complication. Monte Carlo dropout methods provided a quantitative measure of prediction uncertainty that has the potential to enhance clinical trust. Multi-task learning, interpretability mechanisms, and uncertainty metrics demonstrated potential to facilitate effective clinical implementation.

Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory.To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology.In this paper, we introduce a posteriori time series aggregation schemes that preserve chronology and hence allow modelling of storage technologies. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. They do this by using operational variables (generation, transmission and storage patterns) in addition to time series inputs when aggregating.We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available.

Impact-based classification of extreme rainfall events using a simplified overland flow model

ABSTRACT In this study, we present a new rainfall classification method based on the potential of individual rainstorm events to generate and sustain overland flow on a simplified urban model surface. Conventional rainfall classification approaches using peak intensities or rainfall sums can lead to misjudgements of the impact of rainfall events, as they do not explicitly consider the potential of rain to generate and sustain substantial amounts of overland flow on typical sealed urban surfaces. The new method of impact-based rainfall classification proposed here comprises three steps: Identification of rainfall events from high-resolution time series, computation of overland flow depths for each event, and impact-based classification of rainfall events as a function of the magnitude of overland flow. The accuracy of this approach was evaluated by its ability to describe seasonal and annual occurrences of and variations in rainstorm impacts for high-resolution series at three different urban stations in Germany.

Comparison of Three Different Random Forest Approaches to Retrieve Daily High-Resolution Snow Cover Maps from MODIS and Sentinel-2 in a Mountain Area, Gran Paradiso National Park (NW Alps)

In the Alpine environment, snow plays a key role in many processes involving ecosystems, biogeochemical cycles, and human wellbeing. Due to the inaccessibility of mountain areas and the high spatial and temporal heterogeneity of the snowpack, satellite spatio-temporal data without gaps offer a unique opportunity to monitor snow on a fine scale. In this study, we present a random forest approach within three different workflows to combine MODIS and Sentinel-2 snow products to retrieve daily gap-free snow cover maps at 20 m resolution. The three workflows differ in terms of the type of ingested snow products and, consequently, in the type of random forest used. The required inputs are the MODIS/Terra Snow Cover Daily L3 Global dataset at 500 m and the Sentinel-2 snow dataset at 20 m, automatically retrieved through the recently developed revised-Let It Snow workflow, from which the selected inputs are, alternatively, the Snow Cover Extent (SCE) map or the Normalized Difference Snow Index (NDSI) map, and a Digital Elevation Model (DEM) of consistent resolution with Sentinel-2 imagery. The algorithm is based on two steps, the first to fill the gaps of the MODIS snow dataset and the second to downscale the data and obtain the high resolution daily snow time series. The workflow is applied to a case study in Gran Paradiso National Park. The proposed study represents a first attempt to use the revised-Let It Snow with the purpose of extracting temporal parameters of snow. The validation was achieved by comparison with both an independent dataset of Sentinel-2 to assess the spatial accuracy, including the snowline elevation prediction, and the algorithm’s performance through the different topographic conditions, and with in-situ data collected by meteorological stations, to assess temporal accuracy, with a focus on seasonal snow phenology parameters. Results show that all of the approaches provide robust time series (overall accuracies of A1 = 93.4%, and A2 and A3 = 92.6% against Sentinel-2, and A1 = 93.1%, A2 = 93.7%, and A3 = 93.6% against weather stations), but the first approach requires about one fifth of the computational resources needed for the other two. The proposed workflow is fully automatic and requires input data that are readily and globally available, and promises to be easily reproducible in other study areas to obtain high-resolution daily time series, which is crucial for understanding snow-driven processes at a fine scale, such as vegetation dynamics after snowmelt.

Origins of scaling laws in microbial dynamics

Analysis of high-resolution time series data from the human and mouse gut microbiomes revealed that the gut microbial dynamics can be characterized by several simple scaling laws. It is still unknown if those scaling laws are universal across different habitats, e.g., different body sites, host species, or even free-living microbial communities. Moreover, the underlying mechanisms responsible for those scaling laws remain poorly understood. Here, we demonstrate that those scaling laws are not unique to gut microbiome, but are universal across different habitats, from human skin and oral microbiome to marine plankton bacteria and eukarya communities. Moreover, we find that completely shuffled time series yield very similar scaling laws (up to the change of some exponent values), which prompts us to conjecture that the universal scaling laws in various microbiomes are largely driven by temporal stochasticity of the host or environmental factors. To quantify the temporal stochasticity of those microbiome time series, we perform noise type analysis, finding that the noise types for those time series are all dominated by white and pink noises, indicating their very weak temporal structure and strong temporal stochasticity. Finally, we leverage a simple population dynamics model with both deterministic interspecies interactions and stochastic noise to confirm our conjecture. In particular, we find that the emergence of those scaling laws is jointly determined by interspecies interactions and linear multiplicative noises. The presented results deepen our understanding of the nature of scaling laws in microbial dynamics.

Exploring the Potential of Gaofen-1/6 for Crop Monitoring: Generating Daily Decametric-Resolution Leaf Area Index Time Series

High spatiotemporal resolution time series of leaf area index (LAI) are essential for monitoring crop dynamics and validating coarse-resolution LAI products. The optical satellite sensors at decametric-resolution have historically suffered from a long revisit cycle and cloud contamination issues that hampered the acquisition of frequent and high-quality observations. The 16-m/4-day resolution of the new generation Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites provide an unprecedented opportunity to address these limitations. Here we developed an effective strategy to generate daily 16-m LAI maps combing GF-1/6 data and ground LAINet measurements. All high-quality GF-1/6 observations were utilized first to derive smoothed time series of vegetation indices (VIs). Second, a random forest regression (RF-r) model was trained to link the VIs with corresponding field LAI measurements. The trained RF-r was finally employed to generate the LAI maps. Results demonstrated the reliability of the reconstructed daily VIs (relative error < 1%) and the derived LAI time series, which greatly benefited from GF-1/6 high frequency observations. The direct comparison with field LAI measurements by LAI-2200/LI-3000 showed the good performance of retrieved LAI maps, with Bias, RMSE and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.05, 0.59 and 0.75, respectively. The LAI time series well captured the spatiotemporal variation of crop growth. Furthermore, the continuous GF-1/6 LAI maps outperformed Sentinel-2 LAI estimates both in terms of temporal frequency and accuracy. Our study indicates the potential of GF-1/6 to generate continuous decametric-resolution LAI maps for fine-scale agricultural monitoring.

A Robust Framework for Resolution Enhancement of Land Surface Temperature by Combining Spatial Downscaling and Spatiotemporal Fusion Methods

Land surface temperature (LST) products with high spatial resolution and short revisiting cycles are crucial for environmental studies. However, due to the tradeoff between spatial and temporal resolutions of satellite observations, such data are not directly available. Spatial downscaling and spatiotemporal fusion methods are existing solutions for this problem, but their robustness is limited under different surface conditions. Here, we propose a Robust Framework for Combining Downscaling and spatiotemporal Fusion methods (RFCDF) to generate the synthesized daily high-resolution LST with high accuracy in different landscapes. RFCDF introduces a novel weighting strategy that determines pixel-level weights using an empirical function under the constraint of the image-level weights of two predictions. We implement the framework using the thermal sharpening algorithm (TsHARP) and Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing and Baotou, as well as Landsat 8 and simulated coarse resolution imagery in nine sub-regions with different surface landscapes in Beijing. Our results demonstrate that RFCDF can generate more accurate estimations and preserve more spatial details than either individual or combination methods, improving accuracy by 0.1–0.6 K and 0.4–1.3 K in the two study areas, respectively. Moreover, the proposed framework is robust, reducing the root mean square error of estimations by 8-24% under different surface conditions. RFCDF can also generate dense high-resolution LST time series, which is crucial for studying the surface thermal environment at a finer scale.

Mechanical properties prediction of tire cord steel via multi-stage neural network with time-series data

ABSTRACT Cord steel is a kind of high-quality wire, whose mechanical properties will affect the safety and service life of tire. Therefore, the prediction model of mechanical properties during production process is very important to ensure the quality stability. In the paper, the Multi-Stage Neural Network with Time-Series data (MSNNTS) is proposed to mine the rich information of high-resolution time-series data and represent multistage process to achieve accurate mechanical properties prediction. According to the results, the best mean relative error, for tensile strength prediction, is about 1.25% and the hit rate with 3% error limit is about 98% on the testing set. It also obtains good results in predicting reduction of area. The results show that the method is of great significance to improve the quality stability and uniformity of cord steel.

Future temperature and salinity in Puget Sound, Washington State, under CMIP6 climate change scenarios

AbstractClimate change will reshape estuarine ecosystems through bottom-up and top-down processes, directly affecting species at all trophic levels. To better understand future regional climate change effects on sea surface temperature and salinity, we used empirical downscaling to derive high-resolution time series of future sea surface temperature and salinity in Puget Sound (Washington State, USA). Downscaling was based on scenario outputs of two coarse-resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) general circulation models, GFDL-CM4 and CNRM-CM6-1-HR. We calculated 30-year climatologies for historical and future simulations, calculated the anomalies between historical and future projections, interpolated to a fine-resolution, and applied these downscaled anomalies to a Regional Ocean Modeling System (ROMS) time series, yielding short-term and long-term delta-downscaled forecasts. Downscaled output for Puget Sound showed temperature and salinity variability between scenarios and models, but overall, there was a strong model agreement of future surface warming and freshening in Puget Sound. Spatially, we found regional differences for both temperature and salinity, including higher temperatures in South Puget Sound and lower salinity in Whidbey Basin. Interpreting and applying downscaled CMIP6 projections of temperature and salinity will help assess climate change vulnerability and inform future ecosystem-based management decisions in Puget Sound and other coastal and estuarine systems.

Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China

Abstract. Aerosols are complex compounds that greatly affect the global radiation balance and climate system and even human health; in addition, aerosols are currently a large source of uncertainty in the numerical simulation process. The arid and semi-arid areas have fragile ecosystems with abundant dust but lack related high-accuracy aerosol data. To solve these problems, we use the bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data and multiple environmental covariates, to produce a monthly advanced-performance, full-coverage, and high-resolution (250 m) AOD product (named FEC AOD, fusing environmental covariates AOD) covering the arid and semi-arid areas. Then, based on the FEC AOD products, we analyzed the spatiotemporal AOD pattern and further discussed the interpretation of environmental covariates to AOD. The results show that the bagging trees ensemble model has a good performance, with its verification R2 values always remaining at 0.90 and the R2 being 0.79 for FEC AOD compared with AERONET AOD product. The high-AOD areas are located in the Taklimakan Desert and on the Loess Plateau, and the low-AOD areas are concentrated in southern Qinghai province. The higher the AOD, the stronger the interannual variability. Interestingly, the AOD reflected a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert, while the southern Qinghai province AODs showed almost no significant change between 2000 and 2019. The annual variation characteristics show that the AOD was largest in spring (0.267±0.200) and smallest in autumn (0.147±0.089); the annual AOD variation pattern showed different features, with two peaks in March and August over Gansu province but only one peak in April in other provinces/autonomous regions. Farmlands and construction lands have high AOD levels compared to other land cover types. Meteorological factors demonstrate the maximum interpretation ability of the AODs on all set temporal scales, followed by the terrain factors, while surface properties have the smallest explanatory abilities; the corresponding average contributions are 77.1 %, 59.1 %, and 50.4 %, respectively. The capability of the environmental covariates to explain the AOD varies seasonally in the following sequence: winter (86.6 %) &gt; autumn (80.8 %) &gt; spring (79.9 %) &gt; summer (72.5 %). In this research, we provide a pathbreaking high spatial resolution (250 m) and long time series (2000–2019) FEC AOD dataset covering arid and semi-arid regions to support atmospheric and related studies in northwest China; the full dataset is available at https://doi.org/10.5281/zenodo.5727119 (Chen et al., 2021b).