Monthly Time Scales Research Articles

Downscaling WGHM-Based Groundwater Storage Using Random Forest Method: A Regional Study over Qazvin Plain, Iran

Climate change, urbanization, and a growing population have led to a rapid increase in groundwater (GW) use. As a result, monitoring groundwater changes is essential for water managers and decision-makers. Due to the lack of reliable and insufficient in situ information, remote sensing and hydrological models may be counted as alternative sources to assess GW storage changes on regional and global scales. However, often, these hydrological models have a low spatial resolution for water-related applications on a small scale. Therefore, the main purpose of this study is to downscale the GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5 degrees) to a finer spatial resolution (0.1 degrees) using fine spatial resolution auxiliary datasets (0.1 degrees), such as evaporation (E), surface (SRO), subsurface runoff (SSRO), snow depth (SD), and volumetric soil water (SWVL), from the ERA5-Land model, as well as the global precipitation (Pre) measurement (GPM-IMERG) product. The Qazvin Plain in central Iran was selected as the case study region, as it faces a severe decline in GW resources. Different statistical regression models were tested for the GWSA downscaling to find the most suitable method. Moreover, since different water budget components (such as precipitation or storage) are known to have temporal lead or lag relative to each other, the approach also incorporates a time shift factor. The most suitable regression model with the highest skill score during the training-validation was selected and applied to predict the final 0.1-degree GWSA. The downscaled results showed high agreement with the in situ groundwater levels over the Qazvin Plain on both interannual and monthly time scales, with a correlation coefficient of 0.989 and 0.62, respectively. Moreover, the downscaled product represents clear proof that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at a higher spatial resolution. The major benefit of the proposed method lies in the utilization of only the auxiliary data that are available with global coverage and are free of charge, while not requiring in situ GW records for training or prediction. Therefore, the proposed downscaling technique can potentially be applied at a global scale and to aquifers in other geographical regions.

Decadal and Intra-Annual Variability of the Indian Ocean Freshwater Budget

Abstract The global freshwater cycle is intensifying: wet regions are prone to more rainfall, while dry regions experience more drought. Indian Ocean rim countries are especially vulnerable to these changes, but its oceanic freshwater budget—which records the basinwide balance between evaporation, precipitation, and runoff—has only been quantified at three points in time (1987, 2002, 2009). Due to this paucity of observations and large model biases, we cannot yet be sure how the Indian Ocean’s freshwater cycle has responded to climate change, nor by how much it varies at seasonal and monthly time scales. To bridge this gap, we estimate the magnitude and variability of the Indian Ocean’s freshwater budget using monthly varying oceanic data from May 2016 through April 2018. Freshwater converged into the basin with a mean rate and standard error of 0.35 ± 0.07 Sv (1 Sv ≡ 106 m3 s−1), indicating that basinwide air–sea fluxes are net evaporative. This balance is maintained by salty waters leaving the basin via the Agulhas Current and fresher waters entering northward across the southern boundary and via the Indonesian Throughflow. For the first time, we quantify seasonal and monthly variability in Indian Ocean freshwater convergence to find amplitudes of 0.33 and 0.16 Sv, respectively, where monthly changes reflect variability in oceanic, rather than air–sea, fluxes. Compared with the range of previous estimates plus independent measurements from a reanalysis product, we conclude that the Indian Ocean has remained net evaporative since the 1980s, in contrast to long-term changes in its heat budget. When disentangling anthropogenic-driven changes, these observations of decadal and intra-annual natural variability should be taken into account.

Improvements to the Land Surface Air Temperature Reconstruction in NOAAGlobalTemp: An Artificial Neural Network Approach

Abstract NOAA global surface temperature (NOAAGlobalTemp) is NOAA’s operational global surface temperature product, which has been widely used in Earth’s climate assessment and monitoring. To improve the spatial interpolation of monthly land surface air temperatures (LSATs) in NOAAGlobalTemp from 1850 to 2020, a three-layer artificial neural network (ANN) system was designed. The ANN system was trained by repeatedly randomly selecting 90% of the LSATs from ERA5 (1950–2019) and validating with the remaining 10%. Validations show clear improvements of ANN over the original empirical orthogonal teleconnection (EOT) method: the global spatial correlation coefficient (SCC) increases from 65% to 80%, and the global root-mean-square difference (RMSD) decreases from 0.99° to 0.57°C during 1850–2020. The improvements of SCCs and RMSDs are larger in the Southern Hemisphere than in the Northern Hemisphere and are larger before the 1950s and where observations are sparse. The ANN system was finally fed in observed LSATs, and its output over the global land surface was compared with those from the EOT method. Comparisons demonstrate similar improvements by ANN over the EOT method: The global SCC increased from 78% to 89%, the global RMSD decreased from 0.93° to 0.68°C, and the LSAT variability quantified by the monthly standard deviation (STD) increases from 1.16° to 1.41°C during 1850–2020. While the SCC, RMSD, and STD at the monthly time scale have been improved, long-term trends remain largely unchanged because the low-frequency component of LSAT in ANN is identical to that in the EOT approach. Significance Statement The spatial interpolation method of an artificial neural network has greatly improved the accuracy of land surface air temperature reconstruction, which reduces root-mean-square error and increases spatial coherence and variabilities over the global land surface from 1850 to 2020.

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.

Description and comparison of 21st century thermosphere data

The quality and distribution in time and space of available atmospheric observations are crucial for the accuracy of semi-empirical thermosphere models. However, datasets can be inconsistent, and their qualities and resolutions are often unequal. The main thermospheric density datasets of this century are briefly described and then compared to each other when possible in order to quantify differences. Total mass densities used in the comparisons include all high-resolution CHAMP, GRACE and GOCE data, Swarm A, daily-mean Stella, global daily mean TLE densities, and the SET HASDM density database. The temperature data from TIMED-SABER are also reviewed.The recently updated daily-mean TLE densities (TLE2021) are 2–10 % smaller on average than the previous version (TLE2015). The differences are not constant offsets per altitude level, but fluctuations of up to 5 % are present. Compared to HASDM densities for 6 altitudes from 250 to 675 km, TLE2021 is 15–20 % smaller at 250 km, and then the difference diminishes with altitude to reach the same average value at 575 km. These mean differences also fluctuate by a few percent on time scales of months, to 10 % over half a solar cycle at 575 km. The TLE2021 and HASDM densities are larger than the accelerometer-inferred CHAMP, GRACE and GOCE densities and average offsets are 10–15 % and 10–20 %, respectively. The comparison to Swarm-A and Swarm-B showed mean offsets of 10 % and less, with significant positive trends seen in the comparison with HASDM. Finally, largest differences are found for Stella and HASDM at 800 km, up to 45 % with strong semiannual variations.This study clearly shows that the available density data cannot be simply assimilated or combined without first accurately calibrating the data. The HASDM database is a valuable asset due to its considerable coverage in space and time, but its uncertainty and true resolution are not well understood and are still being evaluated. Data compatibility requires employing physically accurate and harmonized aerodynamic force models in the density derivation procedure, which is presently not achieved. The accuracy of the procedure, independent of the quality of the instrument (GNSS receiver, ground-based orbit determination, or accelerometer), inevitably decreases with altitude due to weakening of the drag signal to noise ratio.The TIMED-SABER instrument provides measurements of pressure and temperature in the lower thermosphere. SABER temperature uncertainty is well-known. The SABER dataset now exceeds twenty years and has been continuously operating that entire time. It was ingested in NRLMSIS 2.0 and comparisons show the much-improved fit in comparison with NRLMSISE-00. The lower thermosphere temperatures significantly modify density at higher altitudes, and its measurement is essential for modeling and assessment.

Spatio-Temporal Assessment of Satellite Estimates and Gauge-Based Rainfall Products in Northern Part of Egypt

Egypt’s climate is generally dry all over the country except for the Northern Mediterranean Coast. The Egyptian Meteorological Authority (EMA) uses few meteorological stations to monitor weather events in the entire country within the area of one million square kilometers, which makes it scarce with respect to spatial distribution. The EMA data are relatively expensive to obtain. Open access rainfall products (RP) are commonly used to monitor rainfall as good alternatives, especially for data-scarce countries such as Egypt. This paper aims to evaluate the performance of 12 open access rainfall products for 8 locations in the northern part of Egypt, in order to map the rainfall spatial distribution over the northern part of Egypt based on the best RP. The evaluation process is conducted for the period 2000–2018 for seven locations (Marsa-Matrouh, Abu-Qeir, Rasheed, Port-Said, Tanta, Mansoura, and Cairo-Airport), while it is conducted for the period 1996–2008 for the Damanhour location. The selected open access rainfall products are compared with the ground stations data using annual and monthly timescales. The point-to-pixel approach is applied using four statistical indices (Pearson correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and bias ratio (Pbias)). Overall, the results indicate that both the African Rainfall Estimation Algorithm (RFE) product and the Climate Prediction Center (CPC) product could be the best rainfall data sources for the Marsa-Matrouh location, with relatively higher r (0.99–0.93 for RFE and 0.99–0.89 for CPC) and NSE (0.98–0.79 for RFE and 0.98–0.75 for CPC), in addition to lower RMSE (0.94–7.78 for RFE and 0.92–12.01 for CPC) and Pbias (0.01–11.95% for RFE and −2.22–−12.15% for CPC) for annual and monthly timescales. In addition, the Global Precipitation Climatology Centre (GPCC) and CPC give the best rainfall products for the Abu-Qier and Port-Said locations. GPCC is more suitable for the Rasheed location. The most appropriate rainfall product for the Tanta location is CHIRPS. The current research confirms the benefits of using rainfall products after conducting the recommended performance assessment for each location.

Precipitation stable isotope composition, moisture sources, and controlling factors in Xi'an, Northwest China

Seasonal and interannual variations in precipitation stable isotopes are crucial for clarifying complex moisture sources at the inland margins of the summer monsoon such as Northwest China. Stable hydrogen and oxygen isotope data (δD, δ18O) of totally 317 event-based precipitation samples from 2016 to 2020 in Xi'an show a consistent annual cycle in which, maximum δD and δ18O values occur in the pre-monsoon season (April–June), and minimum in the late monsoon to post-monsoon (November–March) seasons. Values of d-excess are lowest in the monsoon season. The local meteoric water line is δD = 7.5δ18O + 9.7. Precipitation δ18O in monthly time scale correlates weakly with relative humidity in the monsoon season, and with precipitation amount in the pre-monsoon season. Maps of potential water sources and wind fields, considered with monsoon intensity indices and the isotope data, are consistent with moisture sources as follows: during the monsoon, moisture advected by air flow mainly from the Bay of Bengal or recycled from land surfaces along this path; during the pre-monsoon, moisture advected in westerly circulation passing north of the Tibetan Plateau; and during the post-monsoon, recycled monsoon moisture advected from the south or southwest of Xi'an, and influenced by the intensity of the preceding South Asian Summer Monsoon (SASM). Moisture recycled from land surfaces is important in all seasons. The SASM index controlled the variation of mean seasonal δ18O, but not precipitation amount in the monsoon season. The westerly index (WI) controlled the precipitation amount in the pre-monsoon season and mean seasonal δ18O in the post-monsoon season. Seasonal change in moisture sources is the main reason of the seasonal variation of mean precipitation δ18O.

Quantifying the Ocean's Biological Pump and Its Carbon Cycle Impacts on Global Scales.

The biological pump transports organic matter, created by phytoplankton productivity in the well-lit surface ocean, to the ocean's dark interior, where it is consumed by animals and heterotrophic microbes and remineralized back to inorganic forms. This downward transport of organic matter sequesters carbon dioxide from exchange with the atmosphere on timescales of months to millennia, depending on where in the water column the respiration occurs. There are three primary export pathways that link the upper ocean to the interior: the gravitational, migrant, and mixing pumps. These pathways are regulated by vastly different mechanisms, making it challenging to quantify the impacts of the biological pump on the global carbon cycle. In this review, we assess progress toward creating a global accounting of carbon export and sequestration via the biological pump and suggest a path toward achieving this goal.

Analysis of Permeation and Diffusion Coefficients to Infer Aging Attributes in Polymers Subjected to Supercritical CO2 and H2 Gas at High Pressures.

There is a need to understand the permeation flux behavior of polymers exposed to high-pressure and -temperature fluids continuously for long time intervals. This study investigates evidence of structural alterations in polymer specimens as indicators of material aging through the monitoring of transport coefficients at pressure steps from 10 barg to 400 barg and temperatures ranging between 30 °C and 90 °C. The continuous flow permeation methodology is a well-established technique described in the literature for applications from membrane separation processes to polymeric pressure barriers used for complex fluid containment in the oil and gas industry. In this study, a novel methodology has been used that allows the permeating flux of supercritical CO2 and H2 gas through raised-temperature polyethylene and polyvinylidene fluoride films at varying elevated temperatures and pressures to be determined, over timescales of several months using gas chromatography. During these long-term measurements, changes in the test conditions, principally in temperature and stepwise increases in differential gas pressure, were made in order to determine the activation energy for permeation along with the transport coefficients of permeation, diffusion, and sorption. At no time was the polymer film allowed to outgas during the temperature or pressure alterations. The permeation experiments are complemented by differential scanning calorimetry tests to track changes in polymer crystallinity before and after exposure of the specimen to plasticizing gases, which revealed the extent of structural alterations inflicted on the specimen due to high temperature and pressure loads. It is seen that specimens that were exposed to starting high pressures aged more than those that had gradual increases in feed pressure. Furthermore, the relationship between transport coefficients and fractional free volume in the polymer upon exposure to high pressure and temperature conditions is explored. Lastly, the benefit of using fugacity in place of feed pressure for the calculation of the permeability coefficient is discussed. This study contributes to the understanding of the effect of prolonged exposure of the polymeric specimens to CO2 and H2 gas under stepwise pressure and temperature loading on their flux behaviors and crystallinity, and to candidate polyethylene-based specimens for oil field deployment.

Five Years of Observations of the Circumpolar Cyclones of Jupiter

AbstractThe regular polygons of circumpolar cyclones, discovered by Juno in 2017, are one of the most puzzling features of Jupiter. Here we show new recent global pictures of the North polar cyclones' structure. These are the first simultaneous images of the whole structure since 2017, and we find that it remained almost unperturbed, just like the South one. The observation of these long‐lasting structures poses questions regarding the formation mechanism of cyclones, and on their vertical structure. Data by Juno/JIRAM infrared camera collected over the last 5 years show that cyclones migrate around what may seem like equilibrium positions, with timescales of a few months but, aside from that, the cyclones systems are very stable. Our analysis of the observations shows that the motion of cyclones around their equilibrium position is uncorrelated with their position if a barotropic approximation (β‐drift) is assumed. Thus, a different dynamical explanation than the barotropic β‐drift is needed to explain the stability of the observed features. Each cyclone has a peculiar morphology, which differs from the others and is stable over the observed lapse of time in most cases.

Plutonium mobilization from contaminated estuarine sediments, Esk Estuary (UK)

Since 1952, liquid radioactive effluent containing238-242Pu, 241Am, 237Np, 137Cs, and 99Tc has been released with authorization from the Sellafield nuclear complex (UK) into the Irish Sea. This represents the largest source of plutonium (Pu) discharged in all western Europe, with 276 kg having been released. In the Eastern Irish Sea, the majority of the transuranic activity has settled into an area of sediments (Mudpatch) located off the Cumbrian coast. Radionuclides from the Mudpatch have been re-dispersed via particulate transport in fine-grained estuarine and intertidal sediments to the North-East Irish Sea, including the intertidal saltmarsh located at the mouth of the Esk Estuary. Saltmarshes are highly dynamic systems which are vulnerable to external agents (sea level change, erosion, sediment supply, and freshwater inputs), and their stability remains uncertain under current sea level rise projections and possible increases in storm activity. In this work, we examined factors affecting Pu mobility in contaminated sediments collected from the Esk Estuary by conducting leaching experiments under both anoxic and oxic conditions. Leaching experiments were conducted over a 9-month period and were periodically sampled to determine solution phase Pu via multicollector-inductively coupled plasma-mass spectrometry (MC-ICP-MS), and to measure redox indicators (Eh, pH and extractable Fe(II)). Microbial community composition was also characterized in the sediments, and at the beginning and end of the anoxic/oxic experiments. Results show that: 1) Pu leaching is about three times greater in solutions leached under anoxic conditions compared to oxic conditions, 2) the sediment slurry microbial communities shift as conditions change from anoxic to oxic, 3) Pu leaching is enhanced in the shallow sediments (0–10 cm depth), and 4) the magnitude of Pu leached from sediments is not correlated with total Pu, indicating that the biogeochemistry of sediment-associated Pu is spatially heterogeneous. These findings provide constraints on the stability of redox sensitive Pu in biogeochemically dynamic/transient environments on a timescale of months and suggests that anoxic conditions can enhance Pu mobility in estuarine systems.

Trends in reconstructed monthly, seasonal and annual flows for Irish catchments (1900–2016)

Trends in reconstructed monthly, seasonal and annual flows for Irish catchments (1900–2016)

Determination of Long-Term Soil Apparent Thermal Diffusivity Using Near-Surface Soil Temperature on the Tibetan Plateau

The knowledge of soil apparent thermal diffusivity (k) is important for investigating soil surface heat transfer and temperature. Long-term k determined using the near-surface soil temperature is limited on the Tibetan Plateau (TP). The main objective of this study is to determine k with a conduction–convection method using the near-surface soil temperature measured at three sites during 2014–2016 on the TP. The hourly, daily, and monthly k values of the 0.0 m to 0.20 m layer were obtained. The hourly and daily k values ranged from 0.3 × 10−6 m2 s−1 to 1.9 × 10−6 m2 s−1 at the wet site, and from 1.0 × 10−7 m2 s−1 to 4.0 × 10−7 m2 s−1 at the two dry sites. For the monthly timescale, k ranged from 0.4 (±0.0) × 10−6 m2 s−1 to 1.1 (±0.2) × 10−6 m2 s−1 at the wet site, and varied between 1.7 (±0.0) × 10−7 m2 s−1 and 3.3 (±0.2) × 10−7 m2 s−1 at the two dry sites. The k was not constant over a day, and it varied seasonally to different degrees at different sites and years. The variation of k with soil moisture (θ) appeared to be roughly similar for unfrozen soil at these sites and years, namely, k increased sharply before reaching the peak as θ increased, and then it tended to be stable or varied slightly with further increases in θ. This variation trend was consistent with previous studies. However, the relationship between k and θ changed when soil temperature was below 0 °C, because ice had higher k than water. The correlation coefficients (r) between k and θ ranged from 0.37 to 0.80, and 0.80 to 0.92 on hourly and monthly timescales, respectively. The monthly and annual k values were significantly correlated (r: 0.73~0.93) to the Normalized Difference Vegetation Index (NDVI). The results broaden our understanding of the relationship between in situ k and θ. The presented values of k at various timescales can be used as soil parameters when modeling land–atmosphere interactions at these TP regions.

Fast mechanisms linking the Labrador Sea with subtropical Atlantic overturning

We use an ocean general circulation model and its adjoint to analyze the causal chain linking sea surface buoyancy anomalies in the Labrador Sea to variability in the deep branch of the Atlantic meridional overturning circulation (AMOC) on inter-annual timescales. Our study highlights the importance of the North Atlantic Current (NAC) for the north-to-south connectivity in the AMOC and for the meridional transport of Lower North Atlantic Deep Water (LNADW). We identify two mechanisms that allow the Labrador Sea to impact velocities in the LNADW layer. The first mechanism involves a passive advection of surface buoyancy anomalies from the Labrador Sea towards the eastern subpolar gyre by the background NAC. The second mechanism plays a dominant role and involves a dynamical response of the NAC to surface density anomalies originating in the Labrador Sea; the NAC adjustment modifies the northward transport of salt and heat and exerts a strong positive feedback, amplifying the upper ocean buoyancy anomalies. The two mechanisms spin up/down the subpolar gyre on a timescale of years, while boundary trapped waves rapidly communicate this signal to the subtropics and trigger an adjustment of LNADW transport on a timescale of months. The NAC and the eastern subpolar gyre play an essential role in both mechanisms linking the Labrador Sea with LNADW transport variability and the subtropical AMOC. We thus reconcile two apparently contradictory paradigms about AMOC connectivity: (1) Labrador Sea buoyancy anomalies drive AMOC variability; (2) water mass transformation is largest in the eastern subpolar gyre.

Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is a complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) and a multi-layered perceptron artificial neural network (mlp-NN), over the Southwestern United States (SWUS), based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations on monthly and seasonal timescales from 1990–2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from the North American Regional Reanalysis (NARR) dataset. The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BIAS). The ANFIS model generally performs better than the mlp-NN model in predicting regional dustiness over the SWUS region, with r = 0.77 and 0.83 for monthly and seasonal fine dust, respectively. AI models perform better in predicting regional dustiness on a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust on both monthly and seasonal timescales. Compared to precipitation, air temperature is the more important predictor of regional dustiness on both monthly and seasonal timescales. The relative importance of air temperature is higher on the monthly timescale than the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models can predict monthly and seasonal fine and coarse dust, based on the limited climatic data, with good accuracy and with potential implications for research in data sparse regions.

Evaluating Magnitude Agreement and Occurrence Consistency of CHIRPS Product with Ground-Based Observations over Medium-Sized River Basins in Nepal

High spatio-temporal resolution and accurate long-term rainfall estimates are critical in sustainable water resource planning and management, assessment of climate variability and extremes, and hydro-meteorology-related water system decisions. The recent advent of improved higher-resolution open-access satellite-based rainfall products has emerged as a viable complementary to ground-based observations that can often not capture the rainfall variability on a spatial scale. In a developing country such as Nepal, where the rain-gauge monitoring network is sparse and unevenly distributed, satellite rainfall estimates are crucial. However, substantial errors associated with such satellite rainfall estimates pose a challenge to their application, particularly in complex orographic regions such as Nepal. Therefore, these precipitation products must be validated before practical usage to check their accuracy and occurrence consistency. This study aims to assess the reliability of the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product against ground-based observations from 1986 to 2015 in five medium-sized river basins in Nepal, namely, Babai, Bagmati, Kamala, Kankai, and the West Rapti river basin. A set of continuous evaluation metrics (correlation coefficient, root mean square error, relative bias, and Kling-Gupta efficiency) were used in analyzing the accuracy of CHIRPS and categorical metrics (probability of detection, critical success index, false alarm ratio, and frequency bias index). The Probability of Detection and Critical Success Index values were found to be considerably low (<0.4 on average), while the false alarm ratio was significant (>0.4 on average). It was found that CHIRPS showed better performance in seasonal and monthly time scales with high correlation and indicated greater consistency in non-monsoon seasons. Rainfall amount (less than 10 mm and greater than 150 mm) and rainfall frequency was underestimated by CHIRPS in all basins, while the overestimated rainfall was between 10 and 100 mm in all basins except Kamala. Additionally, CHIRPS overestimated dry days and maximum consecutive dry days in the study area. Our study suggests that CHIRPS rainfall products cannot supplant the ground-based observations but complement rain-gauge networks. However, the reliability of this product in capturing local extreme events (such as floods and droughts) seems less prominent. A high-quality rain gauge network is essential to enhance the accuracy of satellite estimations.

Reliability of Gridded Precipitation Products for Water Management Studies: The Case of the Ankavia River Basin in Madagascar

Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, gridded precipitation products (GPPs) from merged satellite-/gauge-/model-based rainfall products constitute an attractive alternative. The quality of which must, nevertheless, be verified. The objective of this study was to evaluate, at different time scales, the reliability of 6 GPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The GPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), the European Centre Medium-Range Weather Forecasts ECMWF Reanalysis on global land surface (ERA5-Land), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 Final (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), and the African Rainfall Estimation (RFEv2) products. The results suggest that IMERG (R2 = 0.63, slope of linear regression a = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other GPPs at the daily scale, followed by RFEv2 (R2 = 0.41, a = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R2 = 0.30, a = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All GPPs, with the exception of the ERA5, overestimate the ‘no rain’ class (0–0.2 mm/day). ARC2, IMERG, PERSIANN, and RFEv2 all underestimate rainfall occurrence in the 0.2–150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (>150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range of 2–100 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R2 = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at an hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R2 = 0.20). Overall, the IMERG product can be regarded as the most reliable gridded precipitation source at monthly, daily, and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall >100 mm/day may, nevertheless, restrict its use.

Ocean acidification in the Western Pacific: Boron isotopic composition recorded in a tropical massive coral core from Lanyu Islet SE Taiwan

Boron (B) and B isotopic compositions (δ11B) in biogenic carbonates are useful proxies for pH reconstruction in the ocean. However, high-resolution archives are scarce due to associated sampling and analytical difficulty. In this study, a modern long-lived massive coral skeleton (Porites lobata) from Lanyu Islet off southeast Taiwan was drilled and used for high-resolution major/trace element analyses, including trace elements B and δ11B, as well as oxygen and carbon isotopes, to investigate the associated environmental changes during 1991–1997. To avoid complicated biological influence, the top-most tissue layer was excluded in this study. The coralline records show a clear temporal trend in metal/Ca-based sea surface temperatures (SSTs) on annual and monthly timescales. In particular, the Mg/Ca-SSTs, the most sensitive temperature proxy at the site, show a significant warming trend (+0.2°C year−1) during the study period. On the other hand, subtle changes in the annual δ11B record were identified, corresponding to ~0.2 pH unit, which is comparable with other coral records in the Pacific, e.g., the South China Sea (SCS), Guam Island, Flinders, and Arlington Reef, as well as the in-situ seawater pH measurement at Hawaii station. This corresponds to an acidification rate of ~0.25 pH unit 100 year−1, similar to other coralline data, in-situ pH/pCO2 measurement, or model predictions, and emphasizes the importance of ocean acidification due to anthropogenic activities. Combined with the Mg/Ca-SST, the intra-annual data show a clear seasonal cycle with higher pH in winter, consistent with the pCO2 at the oceanic surface. These chemical and isotopic results in corals conclude that marine biogenic carbonates are informative for oceanic pH reconstruction and can provide new insights into the relationships between climate changes and environmental responses on the coast of Taiwan.

On the optical properties of resonant drag instabilities: variability of asymptotic giant branch and R Coronae Borealis stars

ABSTRACT In dusty cool-star outflow or ejection events around asymptotic giant branch (AGB) or R Coronae Borealis or RCB-like stars, dust is accelerated by radiation from the star and coupled to the gas via collisional drag forces. It has recently been shown that such dust-gas mixtures are unstable to a super-class of instabilities called the resonant drag instabilities (RDIs), which promote dust clustering. We therefore consider idealized simulations of the RDIs operating on a spectrum of dust grain sizes subject to radiative acceleration (allowing for different grain optical properties), coupled to the gas with a realistic drag law, including or excluding the effects of magnetic fields and charged grains, and calculate for the first time how the RDIs could contribute to observed variability. We show that the RDIs naturally produce significant variations (spatially and temporally) ($\sim 10\!-\!20{{\ \rm per\ cent}}$ 1 σ-level) in the extinction, corresponding to $\sim 0.1\!-\!1\,$mag level in the stellar types above, on time-scales of order months to a year. The fluctuations are surprisingly robust to the assumed size of the source as they are dominated by large-scale modes, which also means their spatial structure could be resolved in some nearby systems. We also quantify how this produces variations in the line-of-sight grain size-distribution. All of these variations are similar to those observed, suggesting that the RDIs may play a key role driving observed spatial and temporal variability in dust extinction within dusty outflow/ejection events around cool stars. We further propose that the measured variations in grain sizes could directly be used to identify the presence of the RDIs in close by systems with observations.

Clarifying the impact of climatic parameters on vegetation in Moulvibazar district

In this research, the temporal trends of vegetation from 2000 to 2019 as well as meteorological variables contribution to vegetation change were investigated using the GLAM NDVI, rainfall and temperature data. The MAKESENS revealed that the vegetation growth rate was slow, particularly on a yearly time scale. On the other hand, the rainfall and temperature had a major impact on vegetation growth on a monthly-time scale with a time lag. The lagged effect of rainfall and temperature on vegetation was shown to be a promotion (based on cross-correlation analysis). There was high value of r (0.804) between vegetation and rainfall for a certain lag period, which was significant (P ≤ 0.05) as per the cross-correlation. Rainfall had a 4-month lag effect on vegetation development, while temperature had a 5 (r = 0.74), - 2 (r = 0.84), - 3 (r = 0.68) month lag effect on vegetation growth. This study's findings revealed changes in vegetation and highlighted the importance of rainfall and temperature in regulating vegetation dynamics. Finally, this study recommended that the effect of more climatic variables on vegetation should be investigated in the context of human activities to better conserve the environment.