Daily Time Scale Research Articles

Quantifying the impacts of the Three Gorges Dam on the spatial–temporal water level dynamics in the upper Yangtze River estuary

Abstract. Understanding the alterations in spatial–temporal water level dynamics caused by natural and anthropogenic changes is essential for water resources management in estuaries, as this can directly impact the estuarine morphology, sediment transport, salinity intrusion, navigation conditions, and other factors. Here, we propose a simple triple linear regression model linking the water level variation on a daily timescale to the hydrodynamics at both ends of an estuary. The model was applied to the upper Yangtze River estuary (YRE) to examine the influence of the world's largest dam, the Three Gorges Dam (TGD), on the spatial–temporal water level dynamics within the estuary. It is shown that the regression model can accurately reproduce the water level dynamics in the upper YRE, with a root mean squared error (RMSE) of 0.061–0.150 m seen at five gauging stations for both the pre- and post-TGD periods. This confirms the hypothesis that the response of water level dynamics to hydrodynamics at both ends is mostly linear in the upper YRE. The regression model calibrated during the pre-TGD period was used to reconstruct the water level dynamics that would have occurred in the absence of the TGD's freshwater regulation. Results show that the spatial–temporal alterations in water levels during the post-TGD period are mainly driven by the variation in freshwater discharge due to the regulation of the TGD, which results in increased discharge during the dry season (from December to March) and a dramatic reduction in discharge during the wet-to-dry transitional period. The presented method to quantify the separate contributions made by changes in boundary conditions and geometry to spatial–temporal water level dynamics is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled or climate-driven estuaries worldwide.

A novel multi-scale parameter estimation approach to the Hargreaves-Samani equation for estimation of Penman-Monteith reference evapotranspiration

The main focus of this study is to develop a multi-scale surrogate model for the FAO-56 Penman-Monteith (PM) evapotranspiration (ETo) using Hargreaves-Samani (HS) equation, which uses only temperature as a hydrometeorological variable to estimate ET. This feature is particularly useful for scarce data regions and climate change impact assessment studies, where the direct estimation of ETo from the PM equation can be problematic. As the parameters of the HS equation may vary across space, a Bayesian approach was adopted to estimate (or recalibrate) them rather than relying on the fixed values as suggested in the traditional model. The Bayesian approach allows a sound development of our model in a multi-scale temporal framework, where the ETo at daily, monthly and annual scales are jointly used to estimate the HS equation parameters. The proposed and reference models are applied and tested using meteorological data from 17 stations located across the Han river basin in South Korea. The results indicate that the traditional HS equation with fixed parameters and without recalibration tends to overestimate the reference ET for all stations. The locally recalibrated approach to the HS equation at a daily temporal scale can effectively reduce the systematic bias associated with the use of the traditional HS equation but fails to reproduce the underlying distribution of ETo at different temporal scales (e.g., monthly and annual). This leads to a large systematic bias in ETo at these scales. In contrast, the proposed multi-scale surrogate model offers a more precise estimation of the reference ET at a daily timescale as well as at the aggregated monthly and annual temporal scales. This is particularly useful to minimize the systematic bias often observed when using traditional surrogate models for the reference ET in hydrological studies such as rainfall-runoff modeling and assessment of climate change impact on water resources.

From an identity process theory perspective: a daily investigation of why and when ostracism triggers ingratiation

ABSTRACT Although existing studies suggest the relationship between ostracism and ingratiation, the knowledge about why and when ostracism promotes ingratiatory behaviors remains limited. Drawing from identity process theory, the current study examines the influence of ostracism on ingratiatory behaviors through the mediating role of self-identity threat on a daily timescale and the cross-level moderation of core self-evaluation. Through a diary study of 117 Chinese college students across 14 consecutive days, we found that daily ostracism had a positive indirect effect on daily ingratiatory behaviors through daily self-identity threat. Core self-evaluation of students weakened the indirect effect, such that only students with low core self-evaluation engaged in daily ingratiatory behaviors to cope with self-identity threat from ostracism. More importantly, supplemental analyses suggested that averaged daily ingratiatory behaviors were negatively related to perceived ostracism one week later. We discussed several theoretical and practical implications of these findings and proposed future research directions.

Interannual Variability of the 12‐hr Tide in the Mesosphere and Lower Thermosphere in 15 Years of Meteor‐Radar Observations Over Rothera (68°S, 68°W)

AbstractThe solar tides of the mesosphere and lower thermosphere (MLT) show great variability on time scales of days to years, with significant variability at interannual time scales. However, the nature and causes of this variability remain poorly understood. Here, we present measurements made over the interval 2005–2020 of the interannual variability of the 12‐hr tide as measured at heights of 80–100 km by a meteor radar over Rothera (68°S, 68°W). We use a linear regression analysis to investigate correlations between the 12‐hr tidal amplitudes and several climate indices, specifically the solar cycle (as measured by F10.7 solar flux), El Niño Southern Oscillation (ENSO), the Quasi‐Biennial Oscillation (QBO) at 10 and 30 hPa and the Southern Annular Mode (SAM). Our observations reveal that the 12‐hr tide has a large amplitude and a clearly defined seasonal cycle with monthly mean values as large as 35 m s−1. We observe substantial interannual variability, with monthly mean 12‐hr tidal amplitudes at 95 km exhibiting a two standard‐deviation range (2σ) in spring of 13.4 m s−1, 11.2 m s−1 in summer, 18.6 m s−1 in autumn, and 7.0 m s−1 in winter. We find that F10.7, QBO10, QBO30, and SAM all have significant correlations to the 12‐hr tidal amplitudes at the 95% level, with a linear trend also present. Whereas we detect very minimal correlation with ENSO. These results suggest that variations in F10.7, the QBO and SAM may contribute significantly to the interannual variability of 12‐hr tidal amplitudes in the Antarctic MLT.

Symmetry breaking in repeating fast radio bursts

ABSTRACT Repeating fast radio bursts (FRB) show temporal symmetry breaking on millisecond time-scales (the ‘sad trombone’). On a time-scale of days, the repetitions of FRB 180916B occur at frequency-dependent phases of its 16.3 d period. Some models predict that all such periodic repeating FRB have the same sign of temporal asymmetry, while others predict that sources with both signs are equally abundant. Future observations of other periodically modulated repeating FRB may distinguish among models on this basis.

Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020

Investigating spatiotemporal variations of atmospheric aerosols is important for climate change and environmental research. Although satellite aerosol optical depth (AOD) retrieved by the MAIAC (Multiangle Implementation of Atmospheric Correct) algorithm provides a unique opportunity to represent global aerosol loading with high spatiotemporal resolution, accurate assessment of long-term aerosol loading countrywide is still challenging due to its non-random missingness. This study aimed to develop an adaptive spatiotemporal high-resolution imputation modeling framework for AOD that incorporates random forest models and multisource data (the simulated AOD, meteorological, and surface condition data) to support full-coverage long- and short-term aerosol studies in China. Aided by the time-stratified approach, the imputation model was constructed for each day, and the MAIAC AOD was used as the target variable. The proposed approach could effectively capture the massive spatiotemporal variability in a large amount of data and deliver full-coverage AODs with high accuracies at a daily timescale (i.e., overall validation R2 against ground-level AOD measurements of 0.77). We then employed the proposed approach to impute the daily MAIAC retrieved AOD towards complete coverage for China for 2003–2020. Due to the complete coverage, the spatial pattern of monthly/seasonal/yearly mean AOD imputations has better representativeness than that of original MAIAC retrievals. Comparison analysis shows that the monthly/seasonal/yearly aerosol loading over most of China tends to be underestimated by temporal aggregates of original satellite-retrieved AODs. Such underestimation is particularly severe in summer and over the North China Plain (the amount of underestimation >0.2). Consequently, our full-coverage AOD imputations can advance scientific research and environmental management by supporting national and local complete pictures of both short-term episodes and long-term trends in atmospheric aerosols.

Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation

Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.

Comparison of High-Resolution Satellite Precipitation Products in Sub-Saharan Morocco

Precipitation is a crucial source of data in hydrological applications for water resources management. However, several regions suffer from limited data from a ground measurement network. Remotely sensed data may provide a viable alternative for these regions. This study aimed to evaluate six satellite products (GPM-F, CHIRPS, PERSIANN-CCS-CDR, GPM-L, GPM-E and PDIR-Now), with high spatio-temporal resolution, in the sub-Saharan regions of Morocco. Precipitation observation data from 33 rain-gauge stations were collected and used over the period from September 2000 to August 2020. The assessment was performed on three temporal scales (daily, monthly and annually) and two spatial scales (pixel and basin scales), using different quantitative and qualitative statistical indices. The results showed that the GPM-F product performed the best, according to the different evaluation metrics, up to events with 40 mm/day, while the GPM near real-time products (GPM-E and GPM-L) were better at detecting more intense rainfall events. At the daily time scale, GPM-E and GPM-L and, on monthly and annual scales, CHIRPS and PERSIANN-CCS-CDR, provided satisfactory precipitation estimates. Moreover, the altitude-based analysis revealed a bias increasing from low to high altitudes. The continental and mountainous basins showed the lowest performance compared to the other locations closer to the Atlantic Ocean. The evaluation based on the latitudes of rain gauges showed a decrease of bias towards the most arid zones. These results provide valuable information in a scarcely gauged and arid region, showing that GPM-F could be a valuable alternative to rain gauges.

Interactive impacts of atmospheric, soil, and vegetation conditions on land surface energy partitioning over a coastal wetland in Northern China

Elucidating environmental controls on land surface energy partitioning at ecosystem levels is critical for better understanding water and energy cycles across landscapes; however, the underlying effects of changing atmospheric, soil, and vegetation conditions on the energy partitioning in coastal wetlands are not well understood. Here, the temporal variations in surface evaporative fraction (EF; defined as latent heat flux-LE divided by the sum of LE and sensible heat flux-H) and its responses to environmental conditions were analyzed at half-hourly and daily timescales, using measurements from a Bowen ratio tower situated in an herbaceous coastal wetland in Tianjin, Northern China. Results showed that net radiation (Rn) was mainly allocated to H with an average H/Rn of 0.90 and daily EF was averaged at 0.14 during the study period. The EF patterns in the studied coastal wetland were significantly different from those in terrestrial ecosystems, which was mainly attributed to the complex interactions in local atmospheric, soil, and vegetation conditions. Specifically, significant directional relationships among environmental variables were detected through the Granger causality analysis (GCA), especially at half-hourly timescales. The GCA and boosted regression tree methods showed that energy-related factors (i.e., air temperature and Rn) were the dominant controls on EF. Poor vegetation status, which was affected by salinity and flood stress, primarily limited LE and subsequently temporal EF variability. During the wet period, atmospheric conditions changed with the prevailing onshore winds, resulting in the dominant control of relative humidity on EF variations. Overall, the EF variability in coastal wetlands was not solely controlled by energy but was more tightly constrained by atmospheric conditions and vegetation dynamics, which offers new insights into energy exchanges in coastal wetland ecosystems, especially for determining the coupling strength between atmosphere and land surface processes.

En-GARD: A Statistical Downscaling Framework to Produce and Test Large Ensembles of Climate Projections

Abstract Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

A framework for comparing two rainfields based on spatial structure: A case of radar against selected satellite precipitation products over southeast Queensland, Australia

• Rain gauge networks are not adequate to capture the complex spatial structure. • Radar and satellite precipitation products capture the spatial structure of rainfall. • Fine spatial scale rainfall data can be derived from satellite precipitation products. • SSI and correlogram are good tools for comparing two rainfields. • Some freely available satellite precipitation products are as good as radar rainfall. Rain gauge networks are not adequate to capture the complex spatial structure of rainfall for meaningful urban flood studies. While radar and satellite precipitation products (SPPs) are being developed to alleviate the limitations of rain gauge networks, they need to be evaluated for regional applications. This study compared radar against 3 SPPs of IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement), MSWEP (Multi-Source Weighted-Ensemble Precipitation) and PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System) that have sub-daily records for southeast Queensland, Australia, based on the spatial structure identified through the structural similarity index (SSI) and two-dimensional correlogram at the daily timescale. The SPPs were downscaled to 1 km × 1 km grid resolution to conform with the radar dataset using inverse distance weighting and bilinear interpolation. All three SPPs, that are freely available, reflected very well the similarity (>90 %) in the spatial pattern with that of the radar, although IMERG was marginally better. However, MSWEP appeared to be the closest to the gauge dataset, better than even the expensive radar dataset, stemming from its much better similarity in the mean similarity statistic. Nevertheless, there could be some atmospheric climatic factors that may cause one to select one SPP over the others for a given wet day, and this needs to be explored for the possibility of selecting different SPPs for different wet days.

Coupled Atmosphere–Ocean Variations on Timescales of Days Observed in the Western Tropical Pacific Warm Pool During Mid‐March 2020

AbstractFrom 11 to 12 March 2020, we observed atmosphere and ocean variations during a temporal warming of the western tropical Pacific warm pool and the associated active convection on the equator by the R/V Mirai and unmanned surface vehicles, known as Wave Gliders. The warming, which extended the warm pool eastward at a speed of approximately 2.9 m s−1, was found to be caused mainly by high sensitivity of the temperature of the thin mixed layer to shortwave and longwave radiation flux. During the warming, westward‐propagating inertia‐gravity wave disturbances in addition to intensified northerly wind intermittently enhanced turbulent latent heat release into the atmosphere (>150 W m−2), leading to heavy rainfall from midnight to morning, so‐called diurnal dancing, in the eastern region of the warm pool. The strong air–sea coupling was related to the zonal wind variation trapped near the sea level, which caused their convergences, also intensified atmospheric convection. Subsequently (13–14 March), due to the atmospheric density stratification and wind speed changes, atmospheric disturbances radiated upward and ceased to be coupled with the ocean.

On the Unusual Variability of 2MASS J06195260–2903592: A Long-lived Disk around a Young Ultracool Dwarf

We present the characterization of the low-gravity M6 dwarf 2MASS J06195260-2903592, previously identified as an unusual field object based on its strong IR excess and variable near-IR spectrum. Multiple epochs of low-resolution (R ≈ 150) near-IR spectra show large-amplitude (≈0.1–0.5 mag) continuum variations on timescales of days to 12 yr, unlike the small-amplitude variability typical for field ultracool dwarfs. The variations between epochs are well-modeled as changes in the relative extinction (ΔA V ≈ 2 mag). Similarly, Panoramic Survey Telescope and Rapid Response System 1 optical photometry varies on timescales as long as 11 yr (and possibly as short as an hour) and implies comparable A V changes. Near Earth Object Wide-field Infrared Survey Explorer mid-IR light curves also suggest changes on 6 month timescales, with amplitudes consistent with the optical/near-IR extinction variations. However, near-IR spectra, near-IR photometry, and optical photometry obtained in the past year indicate that the source can also be stable on hourly and monthly timescales. From comparison to objects of similar spectral type, the total extinction of 2MASS J0619-2903 seems to be A V ≈ 4–6 mag, with perhaps epochs of lower extinction. Gaia Early Data Release 3 (EDR3) finds that 2MASS J0619-2903 has a wide-separation (1.′2 = 10,450 au) stellar companion, with an isochronal age of Myr and a mass of M ☉. Adopting this companion’s age and EDR3 distance (145.2 ± 0.6 pc), we estimate a mass of 0.11–0.17 M ☉ for 2MASS J0619-2903. Altogether, 2MASS J0619-2903 appears to possess an unusually long-lived primordial circumstellar disk, perhaps making it a more obscured analog to the “Peter Pan” disks found around a few M dwarfs in nearby young moving groups.

Emotional Reactivity to Daily Family Conflicts: Testing the Within-Person Sensitization.

Although the sensitization hypothesis posits that heightened reactivity to interparental conflict is linked to adolescent psychopathology, limited studies tested whether sensitization would emerge in parent-adolescent conflict and across ethnicity or culture. This study revisits the sensitization hypothesis by examining adolescent emotional reactivity to interparental and parent-adolescent conflicts on a daily timescale. The sample included 163 adolescents (55% girls; Mage = 12.79) and their parents (78% females; Mage = 45.46) who completed a 10-day reports in Taiwan. Multilevel modeling results showed that, instead of interparental conflict, adolescents with greater histories of parent-adolescent conflict exhibited higher emotional reactivity when parent-adolescent conflict was higher. The findings underscore the importance of parent-adolescent conflict in evaluating adolescent developmental risk.

Multi-year mapping of flood autumn irrigation extent and timing in harvested croplands of arid irrigation district

ABSTRACT Flood irrigation after crop harvest, e.g. autumn irrigation (AI), is a common irrigation practice in arid and semi-arid regions like Hetao Irrigation District (HID) in Northwest China to increase soil moisture and leach soil salt. Detailed information about the extent, timing, and amount of AI is imperative for modeling agro-hydrological processes and irrigation management. However, little attention is given to the identification of the above AI factors. There are basically three major difficulties in estimating the annual changes in AI, including a suitable index to identify AI, temporal instability of thresholds, and an effective validation method for irrigation timing. Therefore, this study proposes a simple and effective threshold-based method to extract the extent and timing of AI in the HID using MODIS water indices at a daily timescale. The Multi-Band Water Index (MBWI) time series is first reconstructed using an adaptive weighted Savitzky-Golay filter and then used to identify the AI extent and time. The proposed model has a stronger generalization capability both in time and space due to robust thresholds selected from the Z-score normalized feature variable. The model is validated both at pixels generated by the segmentation of Sentinel-derived MBWI using a threshold-based model and at sampling points from the field survey. Results show that the model performed well with an overall accuracy of more than 90.0% for the irrigation area. The overall accuracies of irrigation timing are 76.4% and 91.7% based on the middle-to-late and whole irrigation periods, respectively. We found a decreasing trend in the AI area and a gradual delay in the starting time of AI in the HID, mainly due to changes in cropping patterns, climate, and irrigation fees. Overall, the model is promising in identifying flood irrigation extent and timing in large irrigation districts and is helpful for irrigation scheduling.

Rapid quasi-periodic oscillations in the relativistic jet of BL Lacertae.

Blazars are active galactic nuclei (AGN) with relativistic jets whose non-thermal radiation is extremely variable on various timescales1-3. This variability seems mostly random, although some quasi-periodic oscillations (QPOs), implying systematic processes, have been reported in blazars and other AGN. QPOs with timescales of days or hours are especially rare4 in AGN and their nature is highly debated, explained by emitting plasma moving helically inside the jet5, plasma instabilities6,7 or orbital motion in an accretion disc7,8. Here we report results of intense optical and γ-ray flux monitoring of BL Lacertae (BL Lac) during a dramatic outburst in 2020 (ref. 9). BL Lac, the prototype of a subclass of blazars10, is powered by a 1.7 × 108 MSun (ref. 11) black hole in an elliptical galaxy (distance = 313 megaparsecs (ref. 12)). Our observations show QPOs of optical flux and linear polarization, and γ-ray flux, with cycles as short as approximately 13 h during the highest state of the outburst. The QPO properties match the expectations of current-driven kink instabilities6 near a recollimation shock about 5 parsecs (pc) from the black hole in the wake of an apparent superluminal feature moving down the jet. Such a kink is apparent in a microwave Very Long Baseline Array (VLBA) image.

Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

AbstractThis study systematically assessed the performance of the Integrated Multi‐satellitE Retrievals (IMERG) for Global Precipitation Measurement V06, including the near‐real‐time “Late Run” (IMERG‐L) and the post‐real‐time “Final Run” (IMERG‐F), over Zhejiang Province (ZJP), China. The evaluation was conducted at daily and hourly timescales for a full year and for each season, based on dense rain gauge observations and continuous and categorical validation statistics. For the full year and for each season, IMERG‐F outperformed IMERG‐L in representing the spatial pattern of multiyear mean precipitation. For regional mean of ZJP, IMERG‐F and IMERG‐L overestimated the daily/hourly precipitation for the full year by 6.51 and 4.98%, respectively. Among seasons, the regional mean relative biases for IMERG‐F were between 5.65 and 8.63%; however, for IMERG‐L, they exhibited notable variations with a maximum of 11.09% in fall and a minimum of 0 in spring. Bias composition suggested that the regional mean overestimations were largely due to false bias for the full year and for each season, except in winter, wherein it was due to hit bias. Spatially, the biases for the full year and for each season commonly arose from false and hit biases at daily timescale, and from false and miss biases at hourly timescale. Based on the remaining continuous metrics (i.e., root‐mean‐square‐error [RMSE], correlation coefficient [CC], and Kling–Gupta Efficiency [KGE]) and all categorical metrics, the IMERG daily/hourly performance was acceptable on regional and grid scales throughout the year and in all seasons. From a region‐average perspective, IMERG‐F outperformed IMERG‐L according to CC, RMSE, and KGE, but both products showed the same performance overall based on all categorical metrics; most grids also share these characteristics. This study provides a valuable reference for IMERG developers to improve product accuracy from the perspective of the final postprocessing step and for potential users in ZJP.

Rainfall in the Greater and Lesser Antilles: Performance of five gridded datasets on a daily timescale

Study regionThe studied region is the Greater Antilles (Cuba, Hispaniola, Jamaica and Puerto Rico) and the Lesser Antilles (Southern part of the Caribbean arc). Study focusThe performance of MSWEP, CHIRPS, PERSIANN-CDR, ERA-5 and GPM IMERG were evaluated to highlight their qualities and shortcomings and to guide researchers in the choice of these rainfall datasets to use for hydro-meteorological applications in this study area. Five quantitative (RMSE, KGE and his three components) and three qualitative (POD, FAR and CSI) statistical metrics are used to evaluate the amount and detection capacity of the rainfall datasets. Heavy rainfall percentiles are calculated to assess the ability of rainfall datasets to estimate rare and extreme rainfall. New hydrological insights for the regionMSWEP performs well for most statistical metrics and is recommended for most hydro-meteorological research. CHIRPS and PERSIANN-CDR performs well in estimating the annual rainfall seasonality and are recommended for research on water resources management (irrigation, energy production, etc.). CHIRPS also performs well in estimating heavy rainfall percentiles and is also recommended for statistical research of heavy rainfall events. ERA-5 and GPM IMERG have a good ability to capture wet and dry days and is recommended for determination of climatic research or atmospheric sciences applications. However, bias reduction methods for these rainfall gridded datasets are advised before applications due to their low KGE and high RMSE.

Spatio-temporal analysis of PM2.5 and policies in Northwestern South America

This paper analyzes the spatio-temporal variations, and exceedances of the PM2.5 concentrations in Northwestern South America at different scales to assess the implemented policies and identify the involved phenomena. Through reanalysis and ground-based data, we found that high PM2.5 levels in most cities of the region are caused by wildfires and local emissions, including the capital cities of Venezuela, Ecuador, Colombia, and Panamá. In-situ measurements suggest that the majority of the cities comply with the local but not with the WHO guidelines, indicating that local annual limits should be more restrictive. Two peaks in the daily variations of PM2.5 (related to vehicle emissions) and also a steeper decrease around noon (associated with an increase in wind speed and in the boundary layer height) were identified. The trend-analysis shows that Bogotá and Medellín have a decreasing PM2.5 annual-trend (between −0.8μgm−3 and −1.7μgm−3) that corresponds to effective policies. In contrast, Cali has a positive annual-trend (0.8μgm−3) most likely because of Short-Range Transport produced by a northerly-flow from a highly polluted neighboring city, which also affects Cali's PM2.5 diurnal cycle, or by local-dynamics. The exceedances show that the policies are working on an annual but not at a daily time-scale. These results serve as a first input for additional studies, with the aim of gaining a better understanding of the contaminant before adapting current policies or implementing new policies and measures that need to include a joint international, regional, and inter-city efforts regarding pollution transport.

Intraseasonal Melting of Northern Barents Sea Ice Forced by Circumpolar Clockwise-Propagating Atmospheric Waves during Early Summer

Abstract Arctic sea ice intraseasonal variation (ISV) is crucial for understanding and predicting atmospheric subseasonal variations over the middle and high latitudes but unclear. Sea ice concentration (SIC) over the northern Barents Sea (NBS) features large ISV during the melting season (April–July). Based on the observed SIC, this study finds that the NBS SIC ISV in the melting season is dominated by 30–60-day periodicity. The composite analysis, using 34 significant 30–60-day sea ice melting events during 1989–2017, demonstrates that 30–60-day circumpolar clockwise-propagating atmospheric waves (CCPW) are concurrent with the NBS SIC ISV, which features zonal wavenumber 1 along 65°N and a typical quasi-barotropic structure. Further analysis finds that the 30–60-day surface air temperature (SAT) evidently leads the SIC variations by nearly 6 days over the NBS, which is primarily caused by low-level meridional thermal advection linked with the 30–60-day CCPW. The positive anomalies of the downward sensible heat and longwave radiative fluxes, caused by the increased SAT and atmospheric moisture, play the dominant roles in melting the sea ice on the 30–60-day time scale over the NBS. The increased atmospheric moisture is mainly ascribed to the increased horizontal moisture advection influence by the 30–60-day CCPW. This study strongly suggests that the atmospheric ISV is a crucial precursor for NBS sea ice intraseasonal changes in boreal summer, and more accurate subseasonal predictions of atmospheric circulation, temperature, and moisture are indispensable for improving sea ice subseasonal prediction over the Arctic region. Significance Statement Northern Barents Sea (NBS) sea ice intraseasonal variation (ISV) is crucial for understanding mid- to high-latitude climate variations as well as new trans-Arctic shipping predictions but lacks solid knowledge. This study found that the 30–60-day variation is the dominant ISV periodicity of NBS sea ice change during summer, which is essentially modulated by circumpolar clockwise-propagating atmospheric waves. The atmospheric wave-induced meridional thermal advection modulates the surface temperature and atmospheric moisture, causes the changes of downward sensible heat and longwave radiative fluxes, and eventually dominantly regulates the 30–60-day sea ice variations. The mechanism of sea ice ISV strongly suggests that accurately predicting the atmospheric fields is indispensable for obtaining more accurate sea ice subseasonal prediction.