The Amazon shelf of South America is known to be highly contrasted in its surface carbon dioxide concentrations, from very high concentrations near the estuary, and very low concentrations downstream in the saltier Amazon plume, which results in a great contrast in carbon dioxide exchange with the atmosphere. During three cruises in 2020-2023 (Eurec4A-OA, Tara-Microbiomes legs 5, 6 and 7, Amaryllis), dissolved inorganic carbon (DIC) concentration, its isotopic composition (δ13C-DIC), the water isotopic composition (d18O-H2O and d2H-H2O), as well as inorganic nutrients and surface CO2 partial pressure (pCO2) were measured on the Amazon shelf of South America during three cruises in different seasons. These data are used to better understand mixing in the continuum between river water and open-ocean waters, and the biogeochemical processes taking place on the shelf close to the Amazon and Para river estuaries. The water isotopes are furthermore used to identify different freshwater origins.The accuracy of the data is discussed as well as its representativeness. The data are then combined to first identify large variations of the river freshwater sources, compatible with 2021 being a year of very large discharge, and 2023 a year of exceptional low discharge. In addition, the data mostly from August and September 2021 identify a smaller influence of sources and sinks of dissolved inorganic carbon in the mixing shelf region than what had been earlier observed during the Amasseds cruise data in November-December 1991, a much lower river discharge period. This indicates that there might be a larger seasonal and/or interannual variability of these processes than what was earlier assessed. Measured pCO2 data on the Amazon shelf in 2021 are then discussed in this context.

The accurate sensing of ocean salinity is key to enhance our understanding of the flow of ocean currents and air-sea heat flux. Passive microwave remote sensing instruments have been utilized to sense the ocean salinity such as Soil Moisture and Ocean Salinity (SMOS) satellite of ESA. The spectral sensitivity of the microwave instruments to salinity is critical for accurate retrieval of ocean salinity.

The European Space Agency's Sentinel-1 (S-1) satellite mission has captured more than 10 million images of the ocean surface using C-band synthetic aperture radar (SAR WV mode). While machine learning is a promising approach for detecting and quantifying various geophysical signatures in these images, scientists are limited by the cost of manual data annotation for any particular task. We propose to use contrastive self-supervised learning on the full archive of unannotated WV-mode images to train a semantic embedding model named WV-Net. In experiments, we show that WV-Net embeddings outperform those from models that were pretrained with natural images (ImageNet) on four downstream tasks: multilabel classification [0.96 average area under the receiver operating characteristic (AUROC) vs 0.95], wave height regression [0.50 root-mean-square error (RMSE) vs 0.60], near-surface air temperature regression (0.90 RMSE vs 0.97), and unsupervised image retrieval [0.41 class-averaged mean average precision (mAP) vs 0.37]. WV-Net embeddings also scale better in data-sparse settings, and fine-tuned WV-Net models are more robust to hyper-parameter choices. The WV-Net foundation model is publicly available and can be adapted to a variety of data analysis and exploration tasks in geophysical research.

Over the past two decades, a regional collaboration, now part of the US Marine Biodiversity Observation Network (MBON), has established the Wilkinson Basin Time Series (WBTS) and the Coastal Maine Time Series (CMTS) stations to observe change at subannual as well as multiannual scales in plankton of the western Gulf of Maine (GoM), USA. The stations are strategically located to monitor plankton in the Maine Coastal Current, a regional production driver, and in Wilkinson Basin, the primary deep basin in the western GoM. Here, we develop seasonal indices tracking change in mesozooplankton biomass and abundance of the planktonic copepod, Calanus finmarchicus, the energy-rich copepod that supports the regional ecosystem. The time series spans a shift in oceanographic conditions that occurred around 2010. In Wilkinson Basin, the abundance of C. finmarchicus varies by over an order of magnitude during its annual life cycle. At the WBTS station, the fall/winter late-stage abundance of C. finmarchicus has declined up to 80% between 2005 and 2023. The fall/winter abundance decline is likely related to a change in supply from the western Scotian Shelf after 2010 combined with increased seasonal predation mortality. However, in spring the abundance of C. finmarchicus remained steady, although initially increased after 2010. The trend in spring abundance corresponds to slight increases in chlorophyll a standing stock in late winter/early spring, favoring C. finmarchicus egg production. Similar trends in mesozooplankton biomass reflect the predominance of C. finmarchicus in the zooplankton community. We propose that the abundance cycles and trends in C. finmarchicus and biomass be reported regularly as seasonal indices, serving as a sentinel indicator of subarctic western GoM pelagic ecosystem function.

Abstract A dataset of multi‐tagged sea surface roughness synthetic aperture radar (SAR) satellite images was established near Barbados from January to June 2016 to 2019. It is an advancement of the Sentinel‐1 Wave Mode TenGeoP‐SARwv (a labelled SAR imagery dataset of 10 geophysical phenomena from Sentinel‐1 wave mode) dataset that targets SAR marine atmospheric boundary layer (MABL) coherent structures. Twelve tags define roll vortices, convective cells, mixed rolls and convective cells, fronts, rain cells, cold pools and low winds. Examples are provided for each signature. The final dataset is comprised of 2100 Sentinel‐1 wave mode SAR images acquired at 36 incidence angle over an 8° × 8°region centered at 51° W, 15° N. Each image is tagged with one or multiple phenomena by five experts. This strategy extends the TenGeoP‐SARwv by identifying coexisting phenomena within a single SAR image and by the addition of mixed roll/cell states and cold pools. The dataset includes PNG‐formatted SAR image files along with two text files containing the file name, the central latitude/longitude, expert tags for each image, and all dataset metadata. There is a high degree of consensus among expert tags. The dataset complements existing hand‐labelled ocean SAR image datasets and offers the potential for new deep‐learning SAR image classification model developments. Future use is also expected to yield new insights into the tradewind MABL processes such as structure transitions and their relation to the stratification.

The Gulf of Maine (GoM) hosts a variety of fish and sea mammals, beaches, and active commercial fishery. Understanding, monitoring, and predicting the status of and future changes in its food web and water quality are key goals of an ocean observing system that utilizes in situ buoys, gliders, shipboard surveys, satellite remote sensing, and numerical modeling. This study defines and explores the utility of a new Gulf-specific water mass exchange predictor designed to capture changes in winter inflow of fresh and cold waters from the upstream Scotian Shelf using soil moisture active passive satellite sea surface salinity (SSS) in the eastern GoM. A data assimilative ocean circulation model is used to characterize and assess results. GoM food web dynamics and water quality both depend on lower trophic productivity associated with Gulf-wide phytoplankton and zooplankton communities, and these are fundamentally controlled by water temperature and inorganic nutrients that often change due to varied exchange with the adjoining offshore North Atlantic and upstream Nova Scotian Shelf Water (SSW) that flows around southwestern Nova Scotia. Once in the Gulf, most of the SSW inflow to the eastern GoM is advected along southwestern Nova Scotia. This eastern GoM area (termed eGoM) is a useful gauge area where SSS reflects variations in SSW inflow. Results indicate that the SMAP-derived winter eGoM salinity index can help explain interannual variability in GoM conditions during the ensuing spring to summer, the seasons influenced by several advective pathways, as discussed in the study.

Abstract. The air-sea CO2 flux in the coastal ocean is a key component of the global carbon budget. However, due to the scarcity of data, the many sources and sinks of carbon and their complex interactions in these waters remain poorly understood. In 2021, the Tara schooner collected 14,000 km of CO2 fugacity (fCO2) measurements along the coast of South America, including in the Amazon River-Ocean continuum (https://doi.org/10.5281/zenodo.13790065, Olivier et al., 2024a). The interactions between the Amazon River and its oceanic plume are complex, and under a combined influence of many processes such as tides and bathymetry. Downstream of the Amazon River plume, the fCO2 is low compared with that of the atmosphere, reaching a minimum of 42 μatm. In the river, fCO2 reaches up to 3000 μatm. South of the estuary, the waters of the North Brazil Current have a fCO2 exceeding 400 μatm. Along the Brazil Current, fCO2 is around 400 μatm and decreases, as does temperature, as the schooner sails away from the equator. Nevertheless, in all the data collected in this coastal environment, salinity varies greatly, and therefore describes best the variability of fCO2. Despite the strong variability and uncertainties in the data, comparison with discrete samples of other carbonate parameters shows that the mean differences (2 µatm) are within the range of uncertainties related to the chemical formula used for the comparison. This data set helps to fill the gap in our knowledge of the behavior of fCO2 in the under-sampled region of the Brazilian coast.

Ocean waves are essential elements across the air-sea interface, regulating momentum and energy transfer. The mixture of wind sea and ocean swell coupled with surface winds results in diverse sea state conditions that modify the local air-sea interaction. Previous classifications of wind waves and swells are mostly binary that are insufficient to represent the complexity of sea states. In this study, we utilize wind and wave measurements from the China-France Oceanography Satellite (CFOSAT) to construct an observational wind-wave ensemble. Four key parameters: wind speed, significant wave height, inverse wave age, and spectral width are selected out of six variables based on their correlations. Employing the unsupervised learning of k-means clustering, global sea states are categorized into six distinct classes. These classes, characterized by unique centroids and separated in the feature space, represent specific wind regimes and degrees of wave development. Global occurrence highlights that each sea state is region-specific, bridging the spatial gap of swell and wind sea dominated areas, respectively. This new grouping scheme complements the traditional wind sea and/or swell classification by resolving the diversity of wave regimes. The six-class classification enables us to identify transitional states and hybrid conditions that may have been overlooked in the binary classification scheme, which shall help investigate the impact of ocean waves on the air-sea interaction under varying sea states.

The European Space Agency's Copernicus Sentinel-1 (S-1) mission is a constellation of C-band synthetic aperture radar (SAR) satellites that provide unprecedented monitoring of the world's oceans. S-1's wave mode (WV) captures 20x20 km image patches at 5 m pixel resolution and is unaffected by cloud cover or time-of-day. The mission's open data policy has made SAR data easily accessible for a range of applications, but the need for manual image annotations is a bottleneck that hinders the use of machine learning methods. This study uses nearly 10 million WV-mode images and contrastive self-supervised learning to train a semantic embedding model called WV-Net. In multiple downstream tasks, WV-Net outperforms a comparable model that was pre-trained on natural images (ImageNet) with supervised learning. Experiments show improvements for estimating wave height (0.50 vs 0.60 RMSE using linear probing), estimating near-surface air temperature (0.90 vs 0.97 RMSE), and performing multilabel-classification of geophysical and atmospheric phenomena (0.96 vs 0.95 micro-averaged AUROC). WV-Net embeddings are also superior in an unsupervised image-retrieval task and scale better in data-sparse settings. Together, these results demonstrate that WV-Net embeddings can support geophysical research by providing a convenient foundation model for a variety of data analysis and exploration tasks.

The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly (SLA) and SSB input parameters—comprising the significant wave height (SWH), wind speed (WS), and mean wave period (MWP)—are constructed using SWOT’s nadir altimeter data. The analyses corroborate the following key SSB modelling assumption central to empirical developments: the SLA noise due to all factors, aside from sea state change, is zero-mean. Global variance reduction tests on the SSB model’s performance using corrected SLA differences show that correction skill estimation using a specific (1D, 2D, or 3D) SSB model is unstable when using short time difference intervals ranging from 1 to 5 days, reaching a stable asymptotic limit after 5 days. It is proposed that this result is related to the temporal auto- and cross-correlations associated with the SSB model’s input parameters; the present study shows that SSB wind-wave input measurements take time (typically 1–4 days) to decorrelate in any given region. The latter finding, obtained using unprecedented high-frequency satellite data from multiple ocean basins, is shown to be consistent with estimates from an ocean wave model. The results also imply that optimal time-differencing (i.e., >4 days) should be considered when building SSB model data training sets. The SWOT altimeter data analysis of the temporal cross-correlations also permits an evaluation of the relationships between the SSB input parameters (SWH, WS, and MWP), where distinct behaviors are found in the swell- and wind-sea-dominated areas, and associated time scales are less than or on the order of 1 day. Finally, it is demonstrated that computing cross-correlations between the SLA (with and without SSB correction) and the SSB input parameters offers an additional tool for evaluating the relevance of candidate SSB input parameters, as well as for assessing the performance of SSB correction models, which, so far, mainly rely on the reduction in the variance of the differences in the SLA at crossover points.

The Amazon shelf of South America is known to be highly contrasted in its surface carbon dioxide concentrations, from very high concentrations near the estuary, and very low concentrations downstream in the saltier Amazon plume, which results in a great contrast in carbon dioxide exchange with the atmosphere. During three cruises in 2020-2023 (Eurec4A-OA, Tara-Microbiomes legs 5, 6 and 7, Amaryllis), dissolved inorganic carbon (DIC) concentration, its isotopic composition (δ13C-DIC), the water isotopic composition (d18O-H2O and d2H-H2O), as well as inorganic nutrients and surface CO2 partial pressure (pCO2) were measured on the Amazon shelf of South America during three cruises in different seasons. These data are used to better understand mixing in the continuum between river water and open-ocean waters, and the biogeochemical processes taking place on the shelf close to the Amazon and Para river estuaries. The water isotopes are furthermore used to identify different freshwater origins. The accuracy of the data is discussed as well as its representativeness. The data are then combined to first identify large variations of the river freshwater sources, compatible with 2021 being a year of very large discharge, and 2023 a year of exceptional low discharge. In addition, the data mostly from August and September 2021 identify a smaller influence of sources and sinks of dissolved inorganic carbon in the mixing shelf region than what had been earlier observed during the Amasseds cruise data in November-December 1991, a much lower river discharge period. This indicates that there might be a larger seasonal and/or interannual variability of these processes than what was earlier assessed. Measured pCO2 data on the Amazon shelf in 2021 are then discussed in this context.

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may suggest that interannual changes in the Bering Strait mass transport are the sole and dominant factor shaping the salinity distribution in the downstream Chukchi Sea. Using satellite sea surface salinity (SSS) retrievals and altimetry-based estimates of the Bering Strait transport, the relationship between the Strait transport and Chukchi Sea SSS distributions is analyzed from 2010 onward, focusing on the ice-free summer to fall period. A comparison of five different satellite SSS products shows that anomalous SSS spatially averaged over the Chukchi Sea during the ice-free period is consistent among them. Observed interannual temporal change in satellite SSS is confirmed by comparison with collocated ship-based thermosalinograph transect datasets. Bering Strait transport variability is known to be driven by the local meridional wind stress and by the Pacific-to-Arctic sea level gradient (pressure head). This pressure head, in turn, is related to an Arctic Oscillation-like atmospheric mean sea level pattern over the high-latitude Arctic, which governs anomalous zonal winds over the Chukchi Sea and affects its sea level through Ekman dynamics. Satellite SSS anomalies averaged over the Chukchi Sea show a positive correlation with preceding months’ Strait transport anomalies. This correlation is confirmed using two longer (>40-year), separate ocean data assimilation models, with either higher- (0.1°) or lower-resolution (0.25°) spatial resolution. The relationship between the Strait transport and Chukchi Sea SSS anomalies is generally stronger in the low-resolution model. The area of SSS response correlated with the Strait transport is located along the northern coast of the Chukotka Peninsula in the Siberian Coastal Current and adjacent zones. The correlation between wind patterns governing Bering Strait variability and Siberian Coastal Current variability is driven by coastal sea level adjustments to changing winds, in turn driving the Strait transport. Due to the Chukotka coastline configuration, both zonal and meridional wind components contribute.

Eddy covariance (EC) air-sea CO2 flux measurements have been developed for large research vessels, but have yet to be demonstrated for smaller platforms. Our goal was to design and build a complete EC CO2 flux package suitable for unattended operation on a buoy. Published state-of-the-art techniques that have proven effective on research vessels, such as airstream drying and liquid water rejection, were adapted for a 2-m discus buoy with limited power. Fast-response atmospheric CO2 concentration was measured using both an off-the-shelf ("stock") gas analyzer (EC155, Campbell Scientific, Inc.) and a prototype gas analyzer ("proto") with reduced motion-induced error that was designed and built in collaboration with an instrument manufacturer. The system was tested on the University of New Hampshire (UNH) air-sea interaction buoy for 18 days in the Gulf of Maine in October 2020. The data demonstrate the overall robustness of the system. Empirical postprocessing techniques previously used on ship-based measurements to address motion sensitivity of CO2 analyzers were generally not effective for the stock sensor. The proto analyzer markedly outperformed the stock unit and did not require ad hoc motion corrections, yet revealed some remaining artifacts to be addressed in future designs. Additional system refinements to further reduce power demands and increase unattended deployment duration are described.

ABSTRACTMeasuring boundary layer stratification, wind shear, and turbulence remains challenging for wind resource assessment. In particular, larger eddy scales have the greatest impact on turbine load fluctuations, and there are few in situ methods to observe them adequately. Satellite remote sensing using synthetic aperture radar (SAR) is an alternative approach. In this study, eddy‐related signatures in 704 high‐resolution images are related to stratification through a bulk Richardson number ( ) measured by a buoy near Martha's Vineyard, the US epicenter of offshore wind. Variations in SAR‐observed atmospheric boundary layer eddies, or lack of them, correspond to specific regimes. Accounting for strong vertical wind shear, typically under stable stratification, is critical for energy production and turbine loads, and SAR directly identifies these conditions by the absence of energetic eddies. SAR also provides a regional climatology of atmospheric stratification for offshore wind assessment, complementing other observations, and with potential application worldwide.

The Poseidon-4 radar altimeter on board Sentinel-6 “Michael Freilich” (S6-MF) offers unique opportunities to assess the impact of ocean surface motion on Delay-Doppler altimetry. In this paper, earlier “frozen-sea” studies of the instrument response to an isolated sea surface facet are extended to include the effect of surface motions in its Delay-Doppler Map signature. Integrating this elementary signature over the instrument field of view, an analytical stacked echo waveform model, the IASCO waveform model, is then derived. This waveform is validated against the well-established SAMOSA waveform model for the special case of a frozen sea. Model sensitivity to changes in surface significant wave height, vertical velocity standard deviation, and the “Geophysical Doppler” vector U GD projection along the satellite ground-track velocity are discussed. These developments provide theoretical and analytical means to jointly exploit the S6-MF conventional and Delay-Doppler radar waveforms to improve estimates of Essential Climate Variables (Sea Level, Significant Wave Height), and to retrieve and map two new observables, the along-track projection of the “Geophysical Doppler” vector and the ocean waves vertical velocity standard deviation. These new variables, being sensitive to higher-order spectral moments of the wave directional spectrum, may help to mitigate sea state range bias impacts on altimeter sea level measurements.

The strength of the atmospheric Aleutian Low pressure system varies interannually and has a distinct impact on sea surface temperature (SST), sea level, and other oceanic parameters along the North Pacific subarctic front. These impacts are caused by variable zonal winds through their effects on meridional Ekman transport and air-sea fluxes. While the SST response is well known on an interannual (ENSO) to decadal (PDO) scale, the response of sea surface salinity (SSS) is less known due to relatively sparse observations. The SSS response originates in the western Pacific and is concentrated along the North Pacific subarctic front, reaching a few tenths of psu in the upper 100 m, as demonstrated by satellite SSS, Argo salinity data, and model simulations. SSS anomalies, in contrast to SST anomalies, behave like passive tracers that are advected eastward in the North Pacific Current across the whole basin and, unexpectedly, sometimes intensify to the east. After reaching the eastern boundary, they continue predominantly southward along the California coast, remaining detectable by satellite SSS all the way to the southern tip of the California peninsula.

In this study, we present an extension to existing numerical retrackers of synthetic-aperture radar (SAR) altimetry signals. To our knowledge at the time of writing this manuscript, it offers the most consistent retrieval of geophysical parameters compared to low-resolution mode (LRM) retracking results. We achieve this by additionally estimating the standard deviation of vertical wave-particle velocities σv and a new parameter ux, linked to a residual Doppler in the returned radar echoes, which can be related to wind speed and direction. Including this new parameter into the SAR stack retracker mitigates sea surface height estimation errors by up to two centimeters for Sentinel-6MF SAR mode results. Additionally, we found a closed-form equation to describe ux as a function of eastward and northward wind variables, which allows mitigating the effects of this parameter on a SAR stack within level 1B processing and generating a lookup table to correct sea surface height estimates in SAR mode. This additionally opens up the door to estimating the wind speed and direction from SAR altimetry stacks. Additionally, we discuss how this new retracker performs with respect to different planned future baseline processor changes of Sentinel-6MF, namely F09 and F10, by attempting to imitate their level 2 processing. This is achieved by processing cycles 017 to 051 (nearly a full year) of Sentinel-6MF level 1A data on a global scale. We observe that the new retracking method is, on average, more accurate with respect to LRM. However, there is a slight increase in measurement noise due to the introduction of an additional parameter. To ensure that the results of the new retracker are not biased, we retrack using both the new method and the SINCS-OV ZSK retracker on Sentinel-6MF stack data produced in a Monte Carlo simulation. We analyze the simulation results with respect to accuracy, precision, and correlations between estimated parameters. We show that the accuracy of the new retracker is better than SINCS-OV ZSK but less precise, which could be related to higher correlation coefficients—especially with respect to the new parameter ux—between estimated parameters.

Accurate observations of atmospheric composition and exchange of greenhouse gases between the ecosystems and the atmosphere are critical for constraining climate models. Infrared gas analyzers (IRGA) using either broad band non-dispersive or narrow band tunable laser technologies are widely used for this purpose. Typically, such analyzers are installed on stationary meteorological towers over land; but an increasing number of systems are being deployed on mobile platforms and buoys to extend the spatial coverage and include measurements over water. One technological challenge is that the motion of the platform influences the gas concentration measurements. Empirical correction methods have been proposed, but their universality is limited because the source of these sensor-related effects and their underlining mechanisms have not been understood. In this study we identified the dominant source of the error: orientation-dependent temperature stabilization of the thermoelectrically cooled infrared detector. To further investigate this hypothesis and gain insights to a solution, a new prototype closed-path IRGA with an improved infrared detector was developed. In the study, we compared the performance of the prototype to standard models of commercially available IRGA measuring CO2 and H2O. Tilt experiments with side-by-side mounted IRGAs were first conducted on a controlled laboratory platform with independent pitch and roll axes. Over the ±30° range of angular position, the orientation-correlated errors were reduced by a factor of 4 to 10 on CO2 and a factor of 2 to 8 on H2O. Subsequent testing was performed duplicating realistic buoy motion in a deep-water tank with typical at-sea combined pitch and roll motion. In these tests, improvements in the measurement errors were similar to the laboratory experiments. Implications for the correction of past field measurements and insights for further sensor optimization and system improvements are discussed.

One long-standing technical problem affecting the accuracy of eddy correlation air-sea CO2 flux estimates has been motion contamination of the CO2 mixing-ratio measurement. This sensor-related problem is well known but its source remains unresolved. This report details an attempt to identify and reduce motion-induced error and to improve the infrared gas analyzer (IRGA) design. The key finding is that a large fraction of the motion sensitivity is associated with the detection approach common to most closed-and open-path IRGA employed today for CO2 and H2O measurements. A new prototype sensor was developed to both investigate and remedy the issue. Results in laboratory and deep-water tank tests show marked improvement. The prototype shows a factor of 4-10 reduction in CO2 error under typical at-sea buoy pitch and roll tilts in comparison with an off-the-shelf IRGA system. A similar noise reduction factor of 2-8 is observed in water vapor measurements. The range of platform tilt motion testing also helps to document motion-induced error charac-teristics of standard analyzers. Study implications are discussed including findings relevant to past field measurements and the promise for improved future flux measurements using similarly modified IRGA on moving ocean observing and air-craft platforms.

The Chukchi Sea is a marginal sea in the Arctic with a mixed layer that is a few psu units saltier than ambient open Arctic water. Such higher salinity is maintained by salty and warm Pacific water inflow through the Bering Strait, implying that changes in inflow characteristics should affect the thermohaline properties of the Chukchi Sea. Recently, two additional controlling factors have been highlighted - the strength of boundary currents along the Siberian coast, and meridional exchanges due to wind-driven transport. In this note, we illustrate that anomalous fresh Chukchi Sea surface salinity in summer-autumn 2021 may be related to the anomalous southward shift of the ice edge and its meltwater source. These anomalous ice conditions occur concurrently with anomalously low Beaufort High, anomalous westerly cyclonic winds over ice-covered and open water Chukchi Sea, and related southward Ekman transport of late season meltwater. The September 2021 ice expansion was the largest in 1981-2021 detrended ice records.