Reanalysis Data Research Articles

High-resolution assessment of wind energy potential in the Hami region of Northwestern China

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W/m² in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh/year. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China's renewable energy goals.

The Parameterized Oceanic Front-Guided PIX2PIX Model: A Limited Data-Driven Approach to Oceanic Front Sound Speed Reconstruction

In response to the demand for high-precision acoustic support under the condition of limited data, this study utilized high-resolution reanalysis data and in situ observation data to extract the Kuroshio Extension Front (KEF) section through front-line identification methods. By combining the parameterized oceanic front model and the statistical features of big data, the parameterized oceanic front was reconstructed. A proxy dataset was generated using the Latin hypercube sampling method, and the sound speed reconstruction model based on the PIX2PIX model was trained and validated using single sound speed profiles at different positions of the oceanic front, combined with the parameterized oceanic front model. The experimental results show that the proposed sound speed reconstruction model can significantly improve the reconstruction accuracy by introducing the parameterized front model as an additional input, especially in the shallow-water area. The mean absolute error (MAE) of the full-depth sound speed reconstruction for this model is 0.63~0.95 m·s−1, and the structural similarity index (SSIM) is 0.76~0.78. The MAE of the sound speed section within a 1000 m depth is reduced by 6.50~37.62%, reaching 1.95~3.31 m·s−1. In addition, the acoustic support capabilities and generalization of the model were verified through ray tracing models and in situ data. This study contributes to advancing high-precision acoustic support in data-limited oceanic environments, laying a solid groundwork for future innovations in marine acoustics.

Enhanced stratosphere-troposphere and tropics-Arctic couplings in the 2023/24 winter

The stratosphere-troposphere and the tropics-Arctic couplings were intermittently enhanced in the 2023/24 winter. Here we used ERA5 reanalysis data and found that due to the amplification of planetary wavenumber 1 and 2 pulses, three displacement-type sudden stratospheric warming events occurred in one winter under the background conditions of warming equatorial middle and east Pacific, active equatorial convections, and easterly stratospheric equatorial winds. During the sudden stratospheric warming events, the stratospheric disturbances propagated downward to the surface, followed by continental cold surges. The residual meridional circulation was strengthened across the tropics and Arctic, anomalously more water vapor was transported into the stratosphere in tropics, while ozone content diminished in the lower stratosphere and grew in the upper stratosphere over the tropics. Meanwhile, water vapor and ozone over the Arctic exhibited a dipping pattern from the upper to the lower stratosphere.

Analysis of Macro- and Microphysical Characteristics of Ice Clouds over the Tibetan Plateau Using CloudSat/CALIPSO Data

Utilizing CloudSat/CALIPSO satellite data and ERA5 reanalysis data from 2007 to 2016, this study analyzed the distributions of optical and physical characteristics and change characteristics of ice clouds over the Tibetan Plateau (TP). The results show that the frequency of ice clouds in the cold season (November to March) on the plateau is over 80%, while in the warm season (May to September) it is around 60%. The average cloud base height of ice clouds in the warm season is 3–5 km, and mostly around 2 km in the cold season. The average cloud top height in the warm season is around 5–8 km, while in the cold season it is mainly around 4.5 km. The average thickness of ice clouds in both seasons is around 2 km. The statistical results of microphysical characteristics show that the ice water content is around 10−1 to 103 mg/m3, and the effective radius of ice clouds is mainly in the range of 10–90 μm. Both have their highest frequency in the west of the TP and lowest in the northeast. A comprehensive analysis of the change in temperature, water vapor, and ice cloud occurrence frequency shows that the rate of increase in water vapor in the warm season is greater than that in the cold season, while the rates of increase in both surface temperature and ice cloud occurrence are smaller than in the cold season. The rate of increase in temperature in the warm season is around 0.038 °C/yr, and that in the cold season is around 0.095 °C/yr. The growth rate of thin ice clouds in the warm season is around 0.15% per year, while that in the cold season is as high as 1% per year. The results suggest that the surface temperature change may be related to the occurrence frequency of thin ice clouds, with the notable increase in temperature during the cold season possibly being associated with a significant increase in the occurrence frequency of thin ice clouds.

Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events

Providing an accurate prediction of Significant Wave Height (SWH), and specially of extreme SWH events, is crucial for coastal engineering activities and holds major implications in several sectors as offshore renewable energy. With the aim of overcoming the challenge of skewness and imbalance associated with the prediction of these extreme SWH events, a fuzzy-based cascade ensemble of regression models is proposed. This methodology allows to remarkably improve the predictive performance on the extreme SWH values, by using different models specialised in different ranges on the target domain. The method’s explainability is enhanced by analysing the contribution of each model, aiding in identifying those predictor variables more characteristic for the detection of extreme SWH events. The methodology has been validated tackling a long-term SWH prediction problem, considering two case studies over the southwest coast of the United States of America. Both reanalysis data, providing information on various meteorological factors, and SWH measurements, obtained from the nearby stations and the station under examination, have been considered. The goodness of the proposed approach has been validated by comparing its performance against several machine learning and deep learning regression techniques, leading to the conclusion that fuzzy ensemble models perform much better in the prediction of extreme events, at the cost of a slight deterioration in the rest of the samples. The study contributes to advancing the SWH prediction field, specially, to understanding the behaviour behind extreme SWH events, critical for various sectors reliant on oceanic conditions.

Marine heatwaves suppress ocean circulation and large vortices in the Gulf of Alaska

Large-scale anticyclonic vortices forming along the Gulf of Alaska continental slope serve as fertile ecosystems for marine life, significantly shaping the distribution of primary productivity, with 40–80% of the gulf’s open ocean surface chlorophyll-a concentrated in their cores. Between 2013 and 2023, Alaska experienced some of the largest and longest marine heatwaves ever recorded in the world’s oceans, persistently altering its ecosystem and fisheries. Here, using 30 years of satellite and reanalysis data, we find that the coastal upwelling atmospheric forcing conditions associated with the heatwaves have also significantly suppressed the Gulf of Alaska’s ocean circulation and the formation of large anticyclones. Climate model simulations spanning from 1850 to 2100 suggest that future changes in the Aleutian Low pressure system will lead to a 60% increase in upwelling extremes (>2 standard deviations), further weakening the ocean anticyclones. However, large uncertainties remain in the mechanisms controlling the Aleutian Low’s response to climate forcing in the models.

Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG

Abstract. Atmospheric transport models are often used to simulate the distribution of greenhouse gases (GHGs). This can be in the context of forward modeling of tracer transport using surface–atmosphere fluxes or flux estimation through inverse modeling, whereby atmospheric tracer measurements are used in combination with simulated transport. In both of these contexts, transport errors can bias the results and should therefore be minimized. Here, we analyze transport uncertainties in the commonly used Weather Research and Forecasting (WRF) model coupled with the greenhouse gas module (WRF-GHG), enabling passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF's transport is constrained by global meteorological fields via initialization and at the lateral boundaries of the domain of interest. These global fields were generated by assimilating various meteorological data to increase the accuracy of modeled fields. However, in limited-domain models like WRF, the winds in the center of the domain can deviate considerably from these driving fields. As the accuracy of the wind speed and direction is critical to the prediction of tracer transport, maintaining a close link to the observations across the simulation domain is desired. On the other hand, a link that is too close to the global meteorological fields can degrade performance at smaller spatial scales that are better represented by the mesoscale model. In this work, we evaluated the performance of strategies for keeping WRF's meteorology compatible with meteorological observations. To avoid the complexity of assimilating meteorological observations directly, two main strategies of coupling WRF-GHG with ERA5 meteorological reanalysis data were tested over a 2-month-long simulation over the European domain: (a) restarting the model daily with fresh initial conditions (ICs) from ERA5 and (b) nudging the atmospheric winds, temperatures, and moisture to those of ERA5 continuously throughout the simulation period, using WRF's built-in four-dimensional data assimilation (FDDA) in grid-nudging mode. Meteorological variables and simulated mole fractions of CO2 and CH4 were compared against observations to assess the performance of the different strategies. We also compared planetary boundary layer height (PBLH) with radiosonde-derived estimates. Either nudging or daily restarts similarly improved the meteorology and GHG transport in our simulations, with a small advantage of using both methods in combination. However, notable differences in soil moisture were found that accumulated over the course of the simulation when not using frequent restarts. The soil moisture drift had an impact on the simulated PBLH, presumably via changing the Bowen ratio. This is partially mitigated through nudging without requiring daily restarts, although not entirely alleviated. Soil moisture drift did not have a noticeable impact on GHG performance in our case, likely because it was dominated by other errors. However, since the PBLH is critical for accurately simulating GHG transport, we recommend transport model setups that tie soil moisture to observations. Our method of frequently re-initializing simulations with meteorological reanalysis fields proved suitable for this purpose.

Seasonal Characteristics of Air–Sea Exchanges over the South Coast of Matara, Sri Lanka

Air–sea exchanges play a crucial role in intense weather events over Sri Lanka, particularly by providing the heat and moisture that fuel heavy rainfall. We present a year-round dataset of meteorological observations from the southern shoreline of Sri Lanka in the equatorial Indian Ocean for 2017, aiming to investigate its seasonal characteristics and evaluate the performance of reanalysis data in this region. The observations reveal distinct diurnal and seasonal patterns. During the winter and spring, higher shortwave (646.2 W/m2) and longwave radiation (−86.9 W/m2) are coupled with higher temperatures (30.6 °C) and lower humidity (67.4% at noon). In contrast, the Indian summer monsoon period features reduced shortwave (579.8 W/m2) and longwave radiation (−58.6 W/m2), lower temperatures (29.2 °C), higher humidity (over 79.7%), and stronger winds (6.25 m/s). The observations were compared with the ERA5 reanalysis dataset to evaluate the regional performance. The reanalysis data correlated well with the observed data for the radiation, temperature, and sensible heat flux, although notable deviations occurred in terms of the wind speed and latent heat flux. During the impact of Tropical Cyclone Ockhi, the reanalysis data tended to underestimate both the wind speed and precipitation. This dataset will provide vital support for studies on monsoons and coastal atmospheric convection, as well as for model initialization and synergistic applications.

Synoptic Scale Controls and Aerosol Effects on Fog and Low Stratus Life Cycle Processes in the Po Valley, Italy

AbstractFog and low stratus clouds (FLS) form as a result of complex interactions of multiple factors in the atmosphere and at the land surface and impact both the anthropogenic and natural environments. Here, we analyze the role of synoptic conditions and aerosol loading on FLS occurrence and persistence in the Po valley in northern Italy. By applying k‐means clustering to reanalysis data, we find that FLS formation in the Po valley is either based on radiative processes or moisture advection from the Mediterranean sea. Satellite‐based data on FLS persistence shows longer persistence of radiatively formed FLS events, likely due to air mass stagnation and a temperature inversion. Ground‐based aerosol optical depth observations further reveal that FLS event duration is significantly higher under high aerosol loading. The results underline the combined effect of topography, moisture advection and aerosol loading on the FLS life cycle in the Po valley.

Interdecadal Variations in the Seasonal Cycle of Explosive Growth of Southern Hemisphere Storms with Impacts on Southern Australian Rainfall

Interdecadal variations, since the middle of the 20th century, in the seasonal cycle of Southern Hemisphere extratropical synoptic scale weather systems, are studied and related to associated anomalies in Southern Australian rainfall over south-west Western Australia (SWWA) and southeast Australia (SEA). A data-driven method is employed in which atmospheric fluctuations, specified from 6-hourly lower-tropospheric reanalysis data, are spectrally analysed in space and time to determine the statistics of the intensity and growth rates of growing and decaying eddies. Extratropical storms, blocking and north-west cloud band weather types are investigated in two frequency bands, with periods less than 4 days and between 4 and 8 days, and in three growth rate and three decay rate bins. Southern Australian rainfall variability is found to be most related to changes in explosive storms particularly in autumn and winter. During the first 10 years of the Australian Millennium Drought (AMD), from 1997 to 2006, dramatic changes in rainfall and storminess occurred. Rainfall declines ensued over SEA in all seasons, associated with corresponding reductions in the intensity of fast-growing storms with periods less than 4 days. These changes, compared with the 20-year timespans of 1949 to 1968 and 1975 to 1994, also took place for the longer duration of 1997 to 2016, apart from summer. Over SWWA, autumn and winter rainfall totals have decreased systematically with time for each of the 10-year and 20-year timespans analysed. Southern Australian rainfall variability is also found to be closely related to the local, hemispheric or global features of the circulation of the atmosphere and oceans that we characterise by indices. Local circulation indices of sea level pressure and 700 hPa zonal winds are good predictors of SWWA and SEA annual rainfall variability particularly in autumn and winter with vertical velocity generally less so. The new Subtropical Atmospheric Jet (SAJ) and the Southern Ocean Regional Dipole (SORD) indices are found to be the most skilful non-local predictors of cool season SWWA rainfall variability on annual and decadal timescales. The Indian Ocean Dipole (IOD) and Southern Oscillation Index (SOI) are the strongest non-local predictors of SEA annual rainfall variability from autumn through to late spring, while on the decadal timescale, different indices dominate for different 3-month periods.

Aberrant Mitochondrial tRNA Genes Appear Frequently in Animal Evolution.

Mitochondrial tRNAs have acquired a diverse portfolio of aberrant structures throughout metazoan evolution. With the availability of more than 12,500 mitogenome sequences, it is essential to compile a comprehensive overview of the pattern changes with regard to mt-tRNA repertoire and structural variations. This, of course, requires reanalysis of the sequence data of more than 250,000 mt-tRNAs with a uniform workflow. Here, we report our results on the complete reannotation of all mitogenomes available in the RefSeq database by September 2022 using mitos2. Based on the individual cases of mt-tRNA variants reported throughout the literature, our data pinpoint the respective hotspots of change, i.e. Acanthocephala (Lophotrochozoa), Nematoda, Acariformes and Araneae (Arthropoda). Less dramatic deviations of mt-tRNAs from the norm are observed throughout many other clades. Loss of arms in animal mt-tRNA clearly is a phenomenon that occurred independently many times, not limited to a small number of specific clades. The summary data here provide a starting point for systematic investigations into the detailed evolutionary processes of structural reduction and and loss of mt-tRNAs as well as a resource for further improvements of annotation workflows for mt-tRNA annotation.

Long-Term Trends in Significant Wave Heights in The Mediterranean as an Indicator of Climate Change

The Mediterranean coastal zones, particularly along the Nile Delta, are increasingly vulnerable to the impacts of climate change. This study examines the historical changes in the significant wave height along the Nile Delta coast over the past 84 years (1940-2023) at a depth of 20 meters. By synthesizing a comprehensive long-term wave data series using ERA5 reanalysis data and the MIKE21-SW model, the study establishes a robust foundation for analysis. To ensure accuracy, the observed wind data were contrasted against ERA5 data, and they were found to be similar to a great extent. Furthermore, the model's accuracy was rigorously validated against the observational data, ensuring the reliability of the results. Three methods were used to obtain the trends for the maximum wave height to ensure robust and reliable trend assessments (i.e., linear regression, Theil-Sen, and ElasticNet). The results were obtained and analyzed, revealing a statistically significant increase in the maximum wave heights during the study period. Particularly, seasonal analysis revealed a positive trend in maximum wave heights during winter and spring (0.15 to 0.67 cm/year respectively). Furthermore, the monthly analysis showed a positive trend in the wave heights for all months except July, August, and September. Specifically, there were notable increases from September to December, ranging from 0.24 to 0.92 cm/year. These findings are consistent with the global trends of increasing wave heights due to climate change. It is essential for developing adaptive coastal management strategies and designing resilient structures. Consequently, the results will assist coastal authorities in mitigating the impacts of climate change on coastal zones.

Reducing uncertainties of climate projections on solar energy resources in Brazil.

The share of solar power in Brazil's electrical grid has rapidly increased, relieving GHG emissions and diversifying energy sources for greater energy security. Besides that, solar resource is susceptible to climate change, adding uncertainty to electrical grid resilience. This study uses satellite and reanalysis data to evaluate the performance of CMIP6 models in replicating and predicting surface solar irradiance (SSR) in Brazil. The results from the most reliable models indicate an increase in SSR by 2% to 8% in most regions, with a decrease of around 3% in the South. These findings highlight the potential for increased photovoltaic (PV) yield if backed by supportive public policies while underlining the importance of uncertainty assessment of climate models.

Does ERA5-Land Effectively Capture Extreme Precipitation in the Yellow River Basin?

ERA5-Land is a valuable reanalysis data resource that provides near-real-time, high-resolution, multivariable data for various applications. Using daily precipitation data from 301 meteorological stations in the Yellow River Basin from 2001 to 2013 as benchmark data, this study aims to evaluate ERA5-Land’s capability of monitoring extreme precipitation. The evaluation study is conducted from three perspectives: precipitation amount, extreme precipitation indices, and characteristics of extreme precipitation events. The results show that ERA5-Land can effectively capture the spatial distribution patterns and temporal trends in precipitation and extreme precipitation; however, it also exhibits significant overestimation and underestimation errors. ERA5-Land significantly overestimates total precipitation and indices for heavy precipitation and extreme precipitation (R95pTOT and R99pTOT), with errors reaching up to 89%, but underestimates the Simple Daily Intensity Index (SDII). ERA5-Land tends to overestimate the duration of extreme precipitation events but slightly underestimates the total and average precipitation of these events. These findings provide a scientific reference for optimizing the ERA5-Land algorithm and for users in selecting data.

Genome-Wide Identification and Expression Analysis of the Melon Aldehyde Dehydrogenase (ALDH) Gene Family in Response to Abiotic and Biotic Stresses

Through the integration of genomic information, transcriptome sequencing data, and bioinformatics methods, we conducted a comprehensive identification of the ALDH gene family in melon. We explored the impact of this gene family on melon growth, development, and their expression patterns in various tissues and under different stress conditions. Our study discovered a total of 17 ALDH genes spread across chromosomes 1, 2, 3, 4, 5, 7, 8, 11, and 12 in the melon genome. Through a phylogenetic analysis, these genes were classified into 10 distinct subfamilies. Notably, genes within the same subfamily exhibited consistent gene structures and conserved motifs. Our study discovered a pair of fragmental duplications within the melon ALDH gene. Furthermore, there was a noticeable collinearity relationship between the melon’s ALDH gene and that of Arabidopsis (12 times), and rice (3 times). Transcriptome data reanalysis revealed that some ALDH genes consistently expressed highly across all tissues and developmental stages, while others were tissue- or stage-specific. We analyzed the ALDH gene’s expression patterns under six stress types, namely salt, cold, waterlogged, powdery mildew, Fusarium wilt, and gummy stem blight. The results showed differential expression of CmALDH2C4 and CmALDH11A3 under all stress conditions, signifying their crucial roles in melon growth and stress response. RT-qPCR (quantitative reverse transcription PCR) analysis further corroborated these findings. This study paves the way for future genetic improvements in melon molecular breeding.

Differential expression of nuclear-derived mitochondrial succinate dehydrogenase genes in metabolically active buffalo tissues.

Buffaloes are crucial to agriculture, yet mitochondrial biology in these animals is less studied compared to humans and laboratory animals. This research examines tissue-specific variations in mitochondrial succinate dehydrogenase (SDH) gene expression across buffalo kidneys, hearts, brains, and ovaries. Understanding these variations sheds light on mitochondrial energy metabolism and its impact on buffalo health and productivity, revealing insights into enzyme regulation and potential improvements in livestock management. RNA-seq data from buffalo kidney, heart, brain, and ovary tissues were reanalyzed to explore mitochondrial SDH gene expression. The expression of SDH subunits (SDHA, SDHB, SDHC, SDHD) and assembly factors (SDHAF1, SDHAF2, SDHAF3, SDHAF4) was assessed using a log2 fold-change threshold of + 1 for up-regulated and - 1 for down-regulated transcripts, with significance set at p < 0.05. Hierarchical clustering and differential expression analyses were performed to identify tissue-specific expression patterns and regulatory mechanisms, while Gene Ontology and KEGG pathway analyses were conducted to uncover functional attributes and pathway enrichments across different tissues. Reanalysis of RNA-seq data from different tissues of healthy female buffaloes revealed distinct expression patterns for SDH subunits and assembly factors. While SDHA, SDHB, and SDHC showed variable expression across tissues, SDHAF2, SDHAF3, and SDHAF4 exhibited tissue-specific profiles. Significant up-regulation of SDHA, SDHB, and several assembly factors was observed in specific tissue comparisons, with fewer down-regulated transcripts. Gene ontology and KEGG pathway analyses linked the up-regulated transcripts to mitochondrial ATP synthesis and the respiratory electron transport chain. Notably, tissue-specific variations in mitochondrial function were particularly evident in the ovary. This study identifies distinct SDH gene expression patterns in buffalo tissues, highlighting significant down-regulation of SDHA, SDHB, SDHC, and assembly factors in the ovary. These findings underscore the critical role of mitochondria in tissue-specific energy production and metabolic regulation, suggest potential metabolic adaptations, and emphasize the importance of mitochondrial complex II. The insights gained offer valuable implications for improving feed efficiency and guiding future research and therapies for energy metabolism disorders.

Climate warming positively affects hydrological connectivity of typical inland river in arid Central Asia

Hydrological connectivity is crucial for understanding water-ecosystem dynamics, as it serves as a key link between different landscape units. However, the variability of hydrological connectivity in Central Asia remains unexplored, which poses challenges to a comprehensive understanding of ecohydrological processes. This study investigates the spatiotemporal patterns and driving mechanisms of hydrological connectivity in the Tarim River Basin (TRB), Central Asia, from 1990 to 2020, employing a novel approach that integrates remote sensing and reanalysis data. The results indicate an increasing trend in the hydrological connectivity index (HCI), with approximately 60% of the TRB exhibiting significant increases. Climate change exerts the greatest direct (0.59) and total (0.64) effects on HCI, with potential evapotranspiration (19.2%) and temperature (12.6%) being the dominant factors. In mountainous regions, climate change (0.65) is the primary driver, while human activities have a greater impact in the plains (−0.27). These findings offer a new framework for studying ecohydrological processes in arid regions.

Increased Frequency of Consecutive Positive IOD Events Under Global Warming

AbstractConsecutive positive Indian Ocean Dipole (pIOD) induces more severe climate impacts than a single pIOD because of multi‐year accumulation of precipitation anomalies. Using CMIP6 outputs and reanalysis data, we show that the observed increasing trend of consecutive pIOD frequency will continue in future. The simulated frequency of consecutive pIOD increases by 131.3% over 1950–2100. More than 65% pIOD will manifest as consecutive pIOD events in the second half of this century. The increase in consecutive pIOD is dominated by the rise in mixed consecutive pIOD that contains both ENSO‐pIOD and independent pIOD. Mixed consecutive pIOD that start with ENSO‐pIOD increases fastest among all types of consecutive pIOD events. The increase is contributed by three factors: higher ENSO‐pIOD frequency, weaker biennial component of ENSO forcing, and more active pIOD triggers that are independent from ENSO. Climate extremes associated with consecutive pIOD are therefore expected to occur more frequently under global warming.

Annotating full-scan MS data using tandem MS libraries.

Full-scan mass spectrometry (MS) data from both liquid chromatography (LC) and MS imaging capture multiple ion forms, including their in-source fragments. Here we leverage such fragments to structurally annotate full-scan data from LC-MS or MS imaging by matching against peak intensity scaled tandem MS spectral libraries using precursor-tolerant reverse match scoring. Applied to inflammatory bowel disease and imaging datasets, we show the approach facilitates re-analyses of data in public repositories.

Variations of Planetary Wave Activity in the Lower Stratosphere in February as a Predictor of Ozone Depletion in the Arctic in March

This study is dedicated to the investigation of the relationship between the wave activity in February and temperature variations in the Arctic lower stratosphere in March. To study this relationship, the correlation coefficients (CCs) between the minimum temperature of the Arctic lower stratosphere in March (Tmin) and the amplitude of the planetary wave with zonal number 1 (PW1) in February were calculated. Tmin determines the conditions for the formation of polar stratospheric clouds (PSCs) following the chemical destruction of the ozone layer. The NCEP and ERA5 reanalysis data and the modern and future climate simulations of the Earth system models INM CM5 and SOCOLv4 were employed. It is shown that the maximum significant CC value between Tmin at 70 hPa in the polar region in March and the amplitude of the PW1 in February in the reanalysis data in the lower stratosphere is 0.67 at the pressure level of 200 hPa. The CCs calculated using the model data are characterized by maximum values of ~0.5, also near the same pressure level. Thus, it is demonstrated that the change in the planetary wave activity in the lower extratropical stratosphere in February can be one of the predictors of the Tmin. For further analysis of the dynamic structure in the lower stratosphere, composites of 10 seasons with the lowest and highest Tmin of the Arctic lower stratosphere in March were assembled. For these composites, differences in the vertical distribution and total ozone content, surface temperature, and residual meridional circulation (RMC) were considered, and features of the spatial distribution of wave activity fluxes were investigated. The obtained results may be useful for the development of forecasting of the Arctic winter stratosphere circulation, especially for the late winter season, when substantial ozone depletion occurs in some years.