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Complexity evaluation of hydro-climatic datasets is a challenging but essential pre-requisite for accurate modeling and subsequent planning. Changes in climate and anthropogenic interventions amplify the complexity of hydro-climatic time-series. Understanding persistence and fractal features may help us to develop new and robust modeling frameworks which can work well under non-stationary and non-linear environments. Classical fractal hydrology, rooted in statistical physics, has been developed since the 1980s and the modern alternatives based on de-trending, complex network, and time–frequency principles have been developed since 2002. More specifically, this review presents the procedures of Multifractal Detrended Fluctuation Analysis (MFDFA) and Arbitrary Order Hilbert Spectral Analysis (AOHSA), along with their applications in the field of hydro-climatology. Moreover, this study proposes a complex network-based fractal analysis (CNFA) framework for the multifractal analysis of daily streamflows as an alternative. The case study proves the efficacy of CNMFA and shows that it has the flexibility to be applied in visibility and inverted visibility schemes, which is effective in complex datasets comprising both high- and low-amplitude fluctuations. The comprehensive review showed that more than 75% of the literature focuses on characteristic analysis of the time-series using MFDFA rather than modeling. Among the variables, about 70% of studies focused on analyzing fine-resolution streamflow and rainfall datasets. This study recommends the use of CNMF in hydro-climatology and advocates the necessity of knowledge integration from multiple fields to enhance the multifractal modeling applications. This study further asserts that transforming the characterization into operational hydrology is highly warranted. [ABSTRACT FROM AUTHOR]

Basal melting of ice shelves has become one of the main causes of mass loss from the Greenland ice sheet. However, most studies have focused on individual ice shelves, making it difficult to gain a more comprehensive understanding of basal melting across Greenland ice shelves. To address this issue, we utilized timestamped ArcticDEM strip data coregistered with ICESat‐2 data to estimate the basal melt rates of the ice shelves in North Greenland at a resolution of 150 m from 2013 to 2022, employing a mass conservation approach within the Lagrangian framework. Additionally, to investigate the influence of temperature on basal melt rates, a basic analysis correlating the basal melt rates with temperatures was conducted. Overall, the mass loss caused by basal melting of the six ice shelves has amounted to 27.86 ± 35.63 Gt yr−1, accounting for approximately 90% of the non‐calving mass loss, equivalent to a sea level rise of 0.08 ± 0.10 mm yr−1, far exceeding surface mass loss and glacier calving. The two larger ice shelves, Petermann and 79° North (79N), have contributed to 85% of the basal melt mass loss. Regarding the spatiotemporal distribution, the basal melt rates have gradually decreased from near the grounding line to the ice shelf front. Apart from the Ryder ice shelves, the basal melting of the other ice shelves is in a state of accelerated ablation. Moreover, compared to the skin temperature of the ice shelf, the sea water potential temperature has a greater impact on the basal melt rate. Plain Language Summary: The basal melting of ice shelves in north Greenland is one of the major drivers of mass loss, contributing substantially to global sea level rise. However, current studies mostly focus on individual ice shelves at relatively low spatiotemporal resolutions, making it difficult to comprehensively understand the status of basal melting across Greenland ice shelves. Therefore, we utilized freely available high‐resolution ArcticDEM strip data and ICESat‐2 data to obtain annual DEM data for six ice shelves in north Greenland. We then calculated the basal melt rates for the period 2013 to 2022 using a mass conservation approach. Additionally, we analyzed the influence of the ice shelf skin temperature and sea water potential temperature on the basal melt rate. We found that from 2013 to 2022, the ice shelves in north Greenland predominantly experienced accelerated melting. Additionally, the sea water potential temperature had a significant influence on the basal melt rates of the ice shelves. The results indicate that ArcticDEM data can be utilized to estimate high‐resolution basal melt rates for large areas of north Greenland, an important result for studying ice sheet mass loss and sea level rise in the Greenland region. Key Points: ArcticDEM strip data can be used to estimate the high‐resolution basal melt rates of ice shelves in north GreenlandAccelerating basal melt rates of ice shelves in north Greenland caused greater mass loss, approximately 90% of the non‐calving mass lossThe sea water potential temperature has a significant impact on the basal melt rates of ice shelves [ABSTRACT FROM AUTHOR]

Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization of PM2.5 and PM10 of three cities in India wase considered using the detrended fluctuation procedure from 2018 to 2021. The cross-correlation of PM concentration in a multifractal viewpoint using the multifractal cross-correlation analysis (MFCCA) framework is proposed in this study. It was observed that PM2.5 was more multifractal and complex than PM10 at all the locations. The PM–gaseous pollutant (GP) and PM–meteorological variable (MV) correlations across the scales were found to be weak to moderate in different cities. There was no definite pattern in the correlation of PM with different meteorological and gaseous pollutants variables. The nature of correlation in the pairwise associations was found to be of diverse and mixed nature across the time scales and locations. All the time series exhibited multifractality when analyzed pairwise using multifractal cross-correlation analysis. However, there was a reduction in multifractality in individual cases during PM–GP and PM–MV paired analyses. The insights gained into the scaling behavior and cross-correlation structure from this study are valuable for developing prediction models for PMs by integrating them with machine learning techniques. [ABSTRACT FROM AUTHOR]

Studying atmospheric temperature characteristics is crucial under climate change, as it helps us to understand the changing patterns in temperature that have significant implications for the environment, ecosystems, and human well-being. This study presents the comprehensive analysis of the spatiotemporal variability of scaling behavior of daily temperature series across the whole Indian mainland, using a Multifractal Detrended Fluctuation Analysis (MFDFA). The analysis considered 1° × 1° datasets of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and diurnal temperature range (DTR) (TDTR = Tmax − Tmin) from 1951 to 2016 to compare their scaling behavior for the first time. Our results indicate that the Tmin series exhibits the highest persistence (with the Hurst exponent ranging from 0.849 to unity, and a mean of 0.971), and all four-temperature series display long-term persistence and multifractal characteristics. The variability of the multifractal characteristics is less significant in North–Central India, while it is highest along the western coast of India. Moreover, the assessment of multifractal characteristics of different temperature series during the pre- and post-1976–1977 period of the Pacific climate shift reveals a notable decrease in multifractal strength and persistence in the post-1976–1977 series across all regions. Moreover, for the detection of climate change and its dominant driver, we propose a new rolling window multifractal (RWM) framework by evaluating the temporal evolution of the spectral exponents and the Hurst exponent. This study successfully captured the regime shifts during the periods of 1976–1977 and 1997–1998. Interestingly, the earlier climatic shift primarily mitigated the persistence of the Tmax series, whereas the latter shift significantly influenced the persistence of the Tmean series in the majority of temperature-homogeneous regions in India. [ABSTRACT FROM AUTHOR]

The Caribbean basin is a geographical area with a high prevalence of asthma due to mineral dust. As such, it is crucial to analyze the dynamic behavior of particulate pollutants in this region. The aim of this study was to investigate the relationships between particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM2.5 and PM10) using Hilbert–Huang transform (HHT)-based approaches, including the time-dependent intrinsic correlation (TDIC) and time-dependent intrinsic cross-correlation (TDICC) frames. The study utilized datasets from Puerto Rico from between 2007 and 2010 to demonstrate the relationships between two primary particulate matter concentration datasets of air pollution across multiple time scales. The method first decomposes both time series using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to obtain the periodic scales. The Hilbert spectral analysis identified two dominant peaks at a weekly scale for both PM types. High amplitude contributions were sustained for long and continuous time periods at seasonal to intra-seasonal scales, with similar trends in spectral amplitude observed for both types of PM except for monthly and intra-seasonal scales of six months. The TDIC method was used to analyze the resulting modes with similar periodic scales, revealing the strongest and most stable correlation pattern at quarterly and annual cycles. Subsequently, lagged correlations at each time scale were analyzed using the TDICC method. For high-frequency PM10 intrinsic mode functions (IMFs) less than a seasonal scale, the value of the IMF at a given time scale was found to be dependent on multiple antecedent values of PM2.5. However, from the quarterly scale onward, the correlation pattern of the PM2.5-PM10 relationship was stable, and IMFs of PM10 at these scales could be modeled by the lag 1 IMF of PM2.5. These results demonstrate that PM2.5 and PM10 concentrations are dynamically linked during the passage of African dust storms. [ABSTRACT FROM AUTHOR]

ABSTRACT Global surface temperature is significantly influenced by different climate forcings operating at specific time scales. This study investigates the association between global surface temperature and the Pacific Decadal Oscillation ( PDO) in a multiscaling framework in terms of both time scale of variability and non-stationarity. First, the ensemble empirical mode decomposition ( EEMD) is used for multiscale disintegration of the Global Surface Temperature Anomaly ( GSTA) and PDO datasets. A close matching of the periodicity of different modes of PDO and GSTA is noticed, and the subsequent cross-correlation analysis of the modes showed that their linear association is the most perceptible at the slowly varying trend component. The correlation between the different modes is further analyzed using a multiscale dynamic correlation method namely, time-dependent intrinsic correlation ( TDIC). This study found a strong long-range positive correlation between the time series pairs in decadal and inter-decadal modes exceeding 20-year periodicity. Further it is found that, the multiscale teleconnection between PDO and GSTA is not always of unique character but associated with localized reversals in the nature of correlation in the time domain. The study further observed a similar pattern of correlation for both cold phases of the 20th century (1901-1924 and 1947-1976); whereas the pattern of correlation is different for the warm phases of PDO (1925-1946 and 1977-1995) in different process scales. [ABSTRACT FROM AUTHOR]