Archives of observed weather data present unique opportunities for scientists to obtain long time series of the historical climate for many regions of the world. Unfortunately, most of these observational records are to-date available only on paper, and thus require digitization and transcription to facilitate analysis of climatic trends. Here we present a new open-source software, MeteoSaver, that uses machine learning (ML) algorithms to transcribe handwritten records of historical weather data. MeteoSaver version 1.0 processes images of tabular sheets alongside user-defined configuration settings, performing transcription through five sequential steps: (i) image pre-processing, (ii) table and cell detection, (iii) transcription, (iv) quality assessment and quality control, and (v) data formatting and upload. As an illustration and evaluation of the software, we apply MeteoSaver to ten pictured sheets of handwritten temperature and precipitation observations from the Democratic Republic of the Congo. The results show that 95 %-100 % of the daily temperature values can be transcribed, of which a median of 74.4 % reached the highest internal quality flag and 74 % matches with the manually transcribed record, yielding a median mean absolute error of 0.3 degrees C. These results illustrate that MeteoSaver can be applied to a range of handwriting styles and varying tabular dimensions, paper sizes, and maintenance conditions, highlighting its potential for transcribing tabular meteorological observations from multiple regions, especially if the sheets have a consistent format. Overall, our open-source software can help address the challenges of limited available hydroclimatic data within many regions of the world, by helping to save millions of handwritten records of historical weather data presently stored in archives, and expedite research on the climate and environmental changes in data scarce regions.

Billions of historical weather observations dating back centuries remain in the original paper form, vulnerable to permanent loss due to deterioration and unusable by modern science. Africa, in particular, faces significant challenges in climate impact research due to the scarcity of consistent, high-quality historical observational data. The Climate Data Rescue-Africa (CliDaR-Africa) project engages second-year undergraduates at Maynooth University, Ireland, in participatory, classroom-based digitization of unique African meteorological records. This paper presents detailed outcomes from the CliDaR-Africa projects during 2023 and 2024 which successfully digitised over 300,000 unique sub-daily and daily meteorological observations from stations in Madagascar and the Central African Republic spanning 1949 to 1966. Initial analysis of the rescued Madagascar records reveals several notable extreme weather events, underscoring the country's high vulnerability to hazards such as hot spells, droughts, heavy rainfall, and particularly tropical cyclones. Among these is a sequence of tropical cyclones which received limited international coverage either at the time or in the intervening years. By bringing these overlooked extremes to light, the data potentially alters our understanding of extremes and their unusualness in the modern era. Complementary documentary evidence corroborates the meteorological findings and provides rare, detailed insights into the socio-economic consequences, illustrating how these extremes impacted on the communities and economies of Madagascar at the time.

We assess and illustrate the benefits of high-altitude attainment of balloon-borne radiosonde soundings, up to and beyond 10 hPa level compared to, for example, 30 hPa, at operational stations and at sites of the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN). We first discuss technical challenges and the possible solutions for balloon soundings at these higher altitudes. Then, we assess the role of high-ascent radiosonde measurements in climate monitoring and various process studies, contributions to satellite calibration and validation, and impacts on numerical weather prediction systems. The analysis herein shows that the extra costs and technical challenges involved in consistent attainment of high ascents are more than outweighed by the benefits for a broad variety of real-time and delayed-mode applications. Consistent attainment of high ascents should therefore be pursued across the GRUAN network and the broader observational network.

Accurate modelling and prediction of sediment transport in aquatic environments is essential for sustainable coastal and riverine management. Current capabilities rely on physical process-based numerical models and fine-scale sediment flux measurements. High-resolution hydroacoustic instrumentation has emerged as a promising tool for such measurements. However, challenges arise due to the inherent complexity of ultrasound scattering processes. This study introduces a numerical modelling using a point-particle approach to simulate the echoes backscattered by such instrumentation in sediment-laden flow conditions. The model considers geometric, statistical, particle cloud, and flow-induced effects on sediment velocity, concentration, and flux estimates using an acoustic concentration and velocity profiler as a reference. The model performance is assessed here under unidirectional constant flow conditions in terms of velocity, concentration, and time-resolved sediment flux estimates for a large range of the particles' advection speed and sampled volume sizes. Application to the estimation of the measurement accuracy of sediment flux in these flows is also considered, with a final error on the flux seen to be partially controlled by the residence time of particles within the sampled volumes. The proposed model provides insights into scattering processes and offers a tool for investigating robust sediment flux estimation techniques in various flow conditions.

Although much work has been conducted worldwide to discover, rescue and digitise historical weather observations, there remains a lack of access to weather observations from many regions of the world. One such region is Africa which has a scarcity of historical observations available from the pre satellite era which is preventing important research taking place on past extreme weather events and the climate of the continent. The need for this data becomes more urgent when we consider that the workforce in African countries is disproportionately employed in climate-exposed sectors with over 60% employed in the agricultural sector where crops are completely dependent on rainfall. The ACMAD (African Centre of Meteorological Applications for Development) collection offers an opportunity to improve the temporal and spatial data available across the continent of Africa with data available in some countries as far back as the late nineteenth century. An inventory has been created of the collection and shows that within it a large amount of unique data exists that has not been previously inventoried by other sources. This includes numerous weather stations that have never been inventoried before. With an estimated four million images within the collection, numerous methods are required to digitise the unique data. The team in ICARUS (Irish Climate Analysis Research UnitS), Maynooth University, launched a project called CliDAR-Africa, where we assisted second year undergraduate geography students to digitise unique data from stations in Madagascar and Guinea. Discussion on the use of AI as a tool to transcribe data have also taken place. Quality issues with the images within the collection also exist and the team have been developing a citizen science project where inferior quality images can be identified. Once identified it is hoped that the quality of these images can be improved at a later date.In my presentation I will discuss the various issues with the ACMAD collection the ICARUS team are attempting to solve, the results following the inventorying of the collection, efforts to digitise data from Madagascar, proposed projects involving Citizen Science and AI, and finally the important need to rescue African meteorological data in order to improve our knowledge of climate change and extreme weather events in Africa.

Access to climate data is essential if we are to better understand the climate of the past, present and future. Climate scientists require data to reconstruct past climate and extreme weather events, to create seasonal forecasts and to produce climate projections. Various private and public sector actors also require climate data as part of their climate-related decision-making and planning. Historical data can assist the insurance sector by providing information on past extreme weather events. Farmers require data to understand how the future climate will impact their output. The data can also help populations who live along coastlines better understand the changing nature of storm surges. Finally, those concerned about biodiversity can use the data to understand how climate change may impact flora and fauna in future. Our proposed poster will provide a visual representation of the various services offered by the Copernicus Climate Change Service for data rescue and surface meteorological data access over land and marine domains. We shall outline key tools available including the data rescue service and the data deposition service. We will also provide an overview of the data available via the C3S data store (CDS). We shall outline progress to date on improved curation of the fundamental data record of surface meteorological holdings. This includes key collections that have been recently secured via Copernicus agreements and new open data policies from various European National Meteorological and Hydrological Services. We will provide information on what data will be available in the next data release and plans for the very final release of the current contract. Finally, we will highlight how you can get involved to help improve the curation and access to the fundamental data record. The core mission of the Copernicus Climate Change Service is to “support adaptation and mitigation policies of the European Union by providing consistent and authoritative information about climate change”. Rescuing historical data and making that data freely accessible forms an important part of this core mission and is vital as Europe and the rest of the world prepare for further changes to the global climate.

We investigate the changing nature of the frequency, magnitude and spatial extent of extreme temperatures in Ireland from 1931 to 2022. We develop an extreme value model that captures spatial and temporal non-stationarity in extreme daily maximum temperature data. We model the tails of the marginal variables using the generalised Pareto distribution and the spatial dependence of extreme events by a semi-parametric Brown-Resnick r-generalised Pareto process, with parameters of each model allowed to change over time. We use weather station observations for modelling extreme events since data from climate models (not conditioned on observational data) can over-smooth these events and have trends determined by the specific climate model configuration. However, climate models do provide valuable information about the detailed physiography over Ireland and the associated climate response. We propose novel methods which exploit the climate model data to overcome issues linked to the sparse and biased sampling of the observations. Our analysis identifies a temporal change in the marginal behaviour of extreme temperature events over the study domain, which is much larger than the change in mean temperature levels over this time window. We illustrate how these characteristics result in increased spatial coverage of the events that exceed critical temperatures.

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.11388387, Smith et al., 2024a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO2e yr−1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here.

A new data rescue portal is designed to facilitate and coordinate the rescue of weather and climate data from around the world. It is hosted at https://datarescue.climate.copernicus.eu/. The practical information, data rescue (DARE) projects and metadata inventories originate from initiatives of both the World Meteorological Organization (WMO) and Copernicus Climate Change Service (C3S). All practical information of the WMO I-DARE portal is merged into this current project and will be replaced by the new portal. IEDRO and ACRE are collaborators for the content of the website.The mission of the portal is as follows:a collaborative framework on sharing information, best practices, know-how, guidance, metadata on data rescue projects and activities worldwide, provides a single entry point for accessing information on the status of climate data being digitized or in need of recovery and digitization, and enables collaboration among organizations, development agencies, donors, scientists, NGOs, citizens, to work on the data recovery and digitization of climate heritage which is at risk of loss forever. One of the main features on the website is the opportunity to highlight your data rescue project. By making your efforts known and publicly available, chances are decreased that the same data is rescued twice by other groups. Additionally, we encourage owners of rescued data to share this in a global repository, so that the valued data will not get lost again. The data portal and project collection also serves as a starting point for donors to select viable DARE projects which need support. It offers donors the perspective that DARE is done following internationally agreed procedures and that rescued data are shared with the global community rather than the data ending-up in some poorly accessible local file system, which is a multiplier of investment.The C3S Datarescue work package contributes and monitors the efforts on using Artificial Intelligence (AI) and Deep Learning for Optical Character Recognition (OCR) to aid data rescue efforts. The development of a proof of concept for a data rescue image repository is in line with future methods to retrieve valuable meteorological data from scanned paper records with OCR. During the presentation, we will present the latest progress of the C3S DARE work package on the DARE portal, which includes articles on AI efforts to rescue meteorological data.

State‐of‐the‐art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse‐input reanalysis products which are constrained by observed sea surface temperatures, sea‐ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse‐input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour‐based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long‐term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post‐2015 when the NOAA‐CIRES‐DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases.

It is commonly acknowledged that global warming will impact on our coastal environment by an increase in sea level, higher wave conditions and stronger storm surges. However, our capability to predict the impact this may have on the coastal environment is relatively limited and in particular the influence such changes may have on sediment (sand and mud) transport pathways are surprisingly difficult to forecast. This lack of predictability is due in part to our limited understanding of the basic mechanisms that drive sediment transport and it is in this area that acoustics has begun to make a significant contribution. We look back at recent advances in the application of acoustics to sediment transport processes and look forward to future developments.

This analysis highlights the potential value in reanalysing early national meteorological records from around the world. These were oftentimes measured via techniques that preceded standardisation of instrumentation and methods of observation and thus could be subject to considerable biases and uncertainties. This analysis uses the techniques pioneered by WMO record assessment teams. The highest currently recognised air temperature (33.3 ∘C) ever recorded in the Republic of Ireland was logged at Kilkenny Castle in 1887. The original observational record however no longer exists. Given that Ireland is now the only country in Europe to have a national heat record that was set in the 19th century, a reassessment of the verity of this record is both timely and valuable. The present analysis undertakes a fundamental reassessment of the plausibility of the 1887 temperature record using methods similar to those used to assess various weather extremes under WMO auspices over recent years. Specifically, we undertake an inter-station reassessment using sparse available records and make recourse to the new and improved 20CRv3 sparse-input reanalysis product. Neither surrounding available stations nor the reanalysis offer substantive support for the Kilkenny record of 33.3 ∘C being correct. Moreover, recent data rescue efforts have uncovered several earlier extreme values, one of which exceeds the Kilkenny value (33.5 ∘C on 16 July 1876 recorded at the Phoenix Park). However, the sparsity of early observational networks, a distinct lack of synoptic support from 20CRv3 for many of the extreme heat values, and the fact that these measurements were obtained using non-standard exposures lead us to conclude that there is grossly insufficient evidence to support any of these 19th century extremes as robust national heat record candidates. Data from the early 20th century onwards benefit from a denser network of stations undertaking measurements in a more standardised manner, many under the direct auspices of Met Éireann and its predecessors, adhering to WMO guidance and protocols. This enables more robust cross-checking of records. We argue that the Met Éireann-recognised 20th century heat record from Boora in 1976 is verified as the most plausible robust national temperature record based upon the synoptic situation and comparisons with nearby neighbouring stations. This measurement of 32.5 ∘C thus likely constitutes the highest reliably recorded temperature measurement in the Republic of Ireland. Ultimately, the formal decision on any reassessment and reassignment of the national record rests with the national meteorological service, Met Éireann.

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth's energy imbalance, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.8000192, Smith et al., 2023a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human-induced warming reached 1.14 [0.9 to 1.4] ∘C averaged over the 2013–2022 decade and 1.26 [1.0 to 1.6] ∘C in 2022. Over the 2013–2022 period, human-induced warming has been increasing at an unprecedented rate of over 0.2 ∘C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 54 ± 5.3 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climate.

There is considerable import in creating more complete, better understood holdings of early meteorological data. Such data permit an improved understanding of climate variability and long-term changes. Early records are particularly incomplete in the tropics, with implications for estimates of global and regional temperature. There is also a relatively low level of scientific understanding of how these early measurements were made and, as a result, of their homogeneity and comparability to more modern techniques and measurements. Herein we describe and analyse a newly rescued set of long-term, up to six-way parallel measurements undertaken over 1884–1903 in Mauritius, an island situated in the southern Indian Ocean. Data include (i) measurements from a well-ventilated room, (ii) a shaded thermograph, (iii) instruments housed in a manner broadly equivalent to a modern Stevenson screen, (iv) a set of measurements by a hygrometer mounted in a Stevenson screen, and for a much shorter period (v) two additional Stevenson screen configurations. All measurements were undertaken within an ∼ 80 m radius of each other. To our knowledge this is the first such multidecadal multi-instrument assessment of meteorological instrument transition impacts ever undertaken, providing potentially unique insights. The intercomparison also considers the impact of different ways of deriving daily and monthly averages. The long-term comparison is sufficient to robustly characterize systematic offsets between all the instruments and seasonally varying impacts. Differences between all techniques range from tenths of a degree Celsius to more than 1 ∘C and are considerably larger for maximum and minimum temperatures than for means or averages. Systematic differences of several tenths of a degree Celsius also exist for the different ways of deriving average and mean temperatures. All differences, except two average temperature series pairs, are significant at the 0.01 level using a paired t test. Given that all thermometers were regularly calibrated against a primary Kew standard thermometer maintained by the observatory, this analysis highlights significant impacts of instrument exposure, housing, siting, and measurement practices in early meteorological records. These results reaffirm the importance of thoroughly assessing the homogeneity of early meteorological records.

Anthropogenic forcing is driving energy accumulation in the Earth system, including increases in the sensible heat content of the atmosphere, as measured by dry‐bulb temperature—the metric that is almost universally used for communications about climate change. The atmosphere is also moistening, though, representing an accumulation of latent heat, which is partly concealed by dry‐bulb temperature trends. We highlight that, consistent with basic theory, latent heat gains are outpacing sensible heat gains over about half of the Earth's surface. The difference is largest in the tropics, where global “hotspots” of total heat accumulation are located, and where regional disparities in heating rates are very poorly represented by dry‐bulb temperatures. Including latent heat in climate‐change metrics captures this heat accumulation and therefore improves adaptation‐relevant understanding of the extreme humid heat and precipitation hazards that threaten these latitudes so acutely. For example, irrigation can lower peak dry‐bulb temperatures, but amplify latent heat content by a larger margin, intensifying dangerous heat stress. Based on a review of the research literature, our Perspective therefore calls for routine use of equivalent temperature, a measure that expresses the combined sensible and latent heat content of the atmosphere in the familiar units of °C or K. We recognize that dry‐bulb air temperature must remain a key indicator of the atmospheric state, not least for the many sectors that are sensitive to sensible heat transfer. However, we assert here that more widespread use of equivalent temperature could improve process understanding, public messaging, and adaptation to climate change.

Abstract Estuarine salt intrusion greatly threatens freshwater supply in surrounding lands. Physical barriers, which reduce the estuary length (L), are widely constructed to control salt intrusion. Yet, the role of L in salt intrusion remains unknown. Using a process‐based, idealized, semi‐analytical three‐dimensional model, we systematically investigate for the first time this unknown for tide‐dominated, periodically weakly stratified estuaries. Results show decreasing L significantly reduces salinities for short estuaries (L < Lw/4, with Lw the dominant tidal wavelength), but not for long estuaries. Tidal pumping remains a key salt importer in most estuaries, regardless of L. However, substantial decreases in L relative to Lw/4 can change the dominant landward salt importer from tidal pumping to horizontal diffusion. The latter, together with gravitational circulation, weakens responses of salt intrusion to changes in tidal and river forcing in short estuaries. This study highlights the importance of considering L to understanding and mitigating salt intrusion.

We provide an updated sea level dataset for Dublin for the period 1938–2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the mean low water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of sea level rise of 1.1 mm yr−1 during 1953–2016 (95 % credible interval from 0.6 to 1.6 mm yr−1), and a rate of 7 mm yr−1 during 1997–2016 (95 % credible interval from 5 to 8.8 mm yr−1). The overall sea level rise is in line with expected trends, but large multidecadal variability has led to higher rates of rise in recent years.

other ARM data, an assessment of the radiosonde data quality was performed and post-processing corrections applied producing an ARM site Best Estimate (BE) product. The SNPP targeted radiosondes were integrated into the NOAA Products Validation System (NPROVS+) system, which collocated the radiosondes with satellite products (NOAA, National Aeronautics and Space Administration [NASA], European Organisation for the Exploitation of Meteorological Satellites [EUMETSAT], Geostationary Operational Environmental Satellite [GOES], Constellation Observing System for Meteorology, Ionosphere, and Climate [COSMIC]) and Numerical Weather Prediction (NWP forecasts for use in product assessment and algorithm development. This work was a fundamental, integral, and cost-effective part of the SNPP validation effort and provided critical accuracy assessments of the SNPP temperature and water vapor soundings.

Asymmetric tidal turbulence (ATT) strongly influences estuarine health and functioning. However, its impact on the three-dimensional estuarine dynamics and the feedback of water motion and salinity distribution on ATT remain poorly understood, especially for short estuaries (estuarine length ≪ tidal wavelength). This study systematically investigates the abovementioned interactions in a short estuary for the first time, considering periodically weakly stratified conditions. This is done by developing a three-dimensional semi-analytical model (combining perturbation method with finite element method) that allows a dissection of the contributions of different processes to ATT, estuarine circulation, and salt transport. The generation of ATT is dominated by (i) strain-induced periodic stratification and (ii) asymmetric bottom-shear-generated turbulence, and their contributions to ATT are different both in amplitude and phase. The magnitude of the residual circulation related to ATT and the eddy viscosity–shear covariance (ESCO) is about half of that of the gravitational circulation (GC) and shows a “reversed” pattern as compared to GC. ATT generated by strain-induced periodic stratification contributes to an ESCO circulation with a spatial structure similar to GC. This circulation reduces the longitudinal salinity gradients and thus weakens GC. Contrastingly, the ESCO circulation due to asymmetric bottom-shear-generated turbulence shows patterns opposite to GC and acts to enhance GC. Concerning the salinity dynamics at steady state, GC and tidal pumping are equally important to salt import, whereas ESCO circulation yields a significant seaward salt transport. These findings highlight the importance of identifying the sources of ATT to understand its impact on estuarine circulation and salt distribution.