Explore the vast range of titles available.

A purely data‐driven and transformer‐based model with a novel self‐attention mechanism (3D‐Geoformer) is used to make predictions by adopting a rolling predictive manner similar to that in dynamical coupled models. The 3D‐Geoformer yields a successful prediction of the 2021 second‐year cooling conditions that followed the 2020 La Niña event, including covarying anomalies of surface wind stress and three‐dimensional (3D) upper‐ocean temperature, the reoccurrence of negative subsurface temperature anomalies in the eastern equatorial Pacific and a corresponding turning point of sea surface temperature (SST) evolution in mid‐2021. The reasons for the successful prediction with interpretability are explored comprehensively by performing sensitivity experiments with modulating effects on SST due to wind and subsurface thermal forcings being separately considered in the input predictors for prediction. A comparison is also conducted with physics‐based modeling, illustrating the suitability and effectiveness of 3D‐Geoformer as a new platform for El Niño and Southern Oscillation studies. Plain Language Summary The tropical Pacific experienced the prolonged cooling conditions during 2020–2022 (often called a triple La Niña), which exerted great impacts on the weather and climate globally. However, physics‐derived coupled models still have difficulty in accurately making long‐lead real‐time predictions for sea surface temperature (SST) evolution in the tropical Pacific. With the rapid development of deep learning‐based modeling, purely data‐driven models provide an innovative way for SST predictions. Here, a transformer‐based deep learning model is used to evaluate its performance in predicting the evolution of SST in the tropical Pacific during 2020–2022 and explore process representations that are important for SST evolution during 2021, including subsurface thermal effect and surface wind forcing on SST, the crucial factors determining the second‐year prolonged La Niña conditions and turning point of SST evolution. A comparison is made between the completely differently constructed physics‐derived dynamical coupled model and the pure‐data driven deep learning model, showing they both can be used for predictions of SST evolution in the 2021 second‐year cooling conditions. This indicates that it is necessary to adequately represent the thermocline feedback in predictive models, either in dynamical coupled models or purely data‐driven models, so that El Niño and Southern Oscillation predictions can be improved. Key Points A transformer‐based deep learning model is used for El Niño‐Southern Oscillation multivariate prediction in a rolling predictive manner The purely data‐driven model successfully predicts the 2021 second‐year La Niña and turning point of temperature evolution in mid‐2021 Applications of purely data‐driven model for process representations and understanding are demonstrated as in dynamical coupled models

The thermal expansion effects of GNSS stations are influenced by not only temperature variations, but also bedrock depths and types. Unfortunately, the current studies treat the subsurface GNSS monument and their nearby bedrock as a whole, without taking into account the inconsistencies among bedrock depths and types, while the existing full-spectrum finite element method (FEM) cannot be easily extended to consider the bedrock information. To solve this problem, we propose a refined full-spectrum temperature-induced subsurface thermal expansion model (FSH BDT ) that considers both seasonal and non-seasonal temperature variations as well as bedrock information based on the half-space harmonic model. Results show that the full-spectrum half-space harmonic model (FSH), which considers only seasonal and non-seasonal temperature variations, can obtain comparable results to the FEM and even outperform the FEM for inland stations. In addition, the depth and type of bedrock have significant effects on the annual amplitude and phase of thermal expansion-induced vertical displacement. In particular, we find that the station displacement increases by more than 1 mm and the annual phase delays by up to 10° for high-latitude and deeper bedrock stations when bedrock depths are taken into account. The FSH BDT improves the correlation coefficient between GNSS height and mass load displacements by up to 42.3% compared to the FEM and explains up to 8.2% of the nonlinear variation in the GNSS height time series. Our work confirms the advantage of rigorous subsurface thermal expansion modeling to correct the nonlinear variations of global GNSS stations, which might provide a potential opportunity to improve the terrestrial reference frame toward the goal of 1 mm accuracy.

To improve our understanding of hydrothermal activity on the Yellowstone Plateau volcanic field, we collected and analyzed a large data set of δ2H, δ18O, and the 3H concentrations of circum‐neutral and alkaline waters. We find that (a) hot springs are fed by recharge throughout the volcanic plateau, likely focused through fractured, permeable tuff units. Previous work had stressed the need for light δ2H water recharge restricted to the northern part of the plateau or recharge during past cold periods. However, new data from the Y‐7 drill hole suggests that recharge is not restricted to a certain area or a cold period. (b) δ18O values of thermal waters in the geyser basins are shifted from the global meteoric water line by temperature‐dependent water‐rock reactions with higher subsurface temperatures resulting in a greater shift. (c) Large temporal variations in the isotopic composition of meteoric water recharge and small temporal variability in the isotopic composition of hot spring discharge implies that the volume of groundwater in, and around the Yellowstone caldera is substantially larger than the volume of annual water recharge. (d) Hot springs discharged through different rhyolitic units correlate with identifiable differences in δ2H and δ18O compositions, 3H concentrations, and water chemistry that imply equilibration at different temperatures and travel along different flow paths. (e) Based on measured 3H concentrations, we calculate that hot spring waters in the central part of the geyser basins mostly contain <2% post‐1950 meteoric water, whereas waters discharged at the basin margins contain larger fractions of post‐1950s meteoric water.

The monitoring of permafrost is important for assessing the effects of global environmental changes and maintaining and managing social infrastructure, and remote sensing is increasingly being used for this wide-area monitoring. However, the accuracy of the conventional method in terms of temperature factor and soil factor needs to be improved. To address these two issues, in this study, we propose a new model to evaluate permafrost with a higher accuracy than the conventional methods. In this model, the land surface temperature (LST) is used as the upper temperature of the active layer of permafrost, and the temperature at the top of permafrost (TTOP) is used as the lower temperature. The TTOP value is then calculated by a modified equation using precipitation–evapotranspiration (PE) factors to account for the effect of soil moisture. This model, referred to as the TTOP-LST zero-curtain (TLZ) model, allows us to analyze subsurface temperatures for each layer of the active layer, and to evaluate the presence or absence of the zero-curtain effect through a time series analysis of stratified subsurface temperatures. The model was applied to the Qinghai–Tibetan Plateau and permafrost was classified into seven classes based on aspects such as stability and seasonality. As a result, it was possible to map the recent deterioration of permafrost in this region, which is thought to be caused by global warming. A comparison with the mean annual ground temperature (MAGT) model using local subsurface temperature data showed that the average root mean square error (RMSE) value of subsurface temperatures at different depths was 0.19 degrees C, indicating the validity of the TLZ model. A similar analysis based on the TLZ model is expected to enable detailed permafrost analysis in other areas.

The 2018 InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) Mission has the mission goal of providing insitu data for the first measurement of the geothermal heat flow of Mars. The Heat Flow and Physical Properties Package (HP 3 ) will take thermal conductivity and thermal gradient measurements to approximately 5 m depth. By necessity, this measurement will be made within a few meters of the lander. This means that thermal perturbations from the lander will modify local surface and subsurface temperature measurements. For HP 3 ’s sensitive thermal gradient measurements, this spacecraft influence will be important to model and parameterize. Here we present a basic 3D model of thermal effects of the lander on its surroundings. Though lander perturbations significantly alter subsurface temperatures, a successful thermal gradient measurement will be possible in all thermal conditions by proper ( > 3 m depth) placement of the heat flow probe.

Understanding mechanisms of tropical Pacific decadal variability (TPDV) is of high importance for differentiating between natural climate variability and human induced climate change as this region sustains strong global teleconnections. Here, we use an ocean general circulation model along with a Lagrangian tracer simulator to investigate the advection of density compensated temperature anomalies (“spiciness mechanism”) as a potential contributor to TPDV during the 1980–2016 period. Consistent with observations, we find the primary regions of spiciness generation in the eastern subtropics of each hemisphere. Our results indicate that 75 % of the equatorial subsurface water originates in the subtropics, of which two thirds come from the Southern hemisphere. We further show two prominent cases where remotely generated spiciness anomalies are advected to the equatorial Pacific, impacting subsurface temperature. The relative contribution of Northern versus Southern Hemisphere prominence and/or interior versus western boundary pathways depends on the specific event. The anomalously warm case largely results from advection via the Southern hemisphere interior (65 % ), while the anomalously cold case largely results from advection via the Northern hemisphere western boundary (48 % ). The relatively slow travel times from the subtropics to the equator (> 4 years) suggests that these spiciness anomalies underpin a potentially predictable contribution to TPDV. However, not all decadal peaks in equatorial spiciness can be explained by remotely generated spiciness anomalies. In those cases, we propose that spiciness anomalies are generated in the equatorial zone through changes in the proportion of Northern/Southern hemisphere source waters due to their different mean spiciness distribution.

Both lateral surface and subsurface water flow affect soil moisture dynamics, yet most land surface models only solve subsurface water movement vertically. Here, we use a 3D ecosystem model that considers both land surface and subsurface hydrologic processes to simulate soil moisture, which is then used to drive a 1-D vertical soil thermal model to simulate the soil moisture effects on soil thermal dynamics in central Alaska. Our coupled model improves soil temperature (ST) estimates by 43.5% in comparison with observational data. Soil moisture has little effect on ST during the wet season (−1.5%) and a substantial influence during the dry season (60%). Spatially, water lateral flow has significant impacts on both soil moisture and ST, causing model estimates for thawed areas in the transition season to increase by ∼10% in the study area. Our results highlight the importance of considering dynamical soil moisture, as well as lateral flow effects, on soil thermal dynamics in permafrost regions.

In inland settings, groundwater discharge thermally modulates receiving surface water bodies and provides localized thermal refuges; however, the thermal influence of intertidal springs on coastal waters and their thermal sensitivity to climate change are not well studied. We addressed this knowledge gap with a field- and model-based study of a threatened coastal lagoon ecosystem in southeastern Canada. We paired analyses of drone-based thermal imagery with in situ thermal and hydrologic monitoring to estimate discharge to the lagoon from intertidal springs and groundwater-dominated streams in summer 2020. Results, which were generally supported by independent radon-based groundwater discharge estimates, revealed that combined summertime spring inflows (0.047 m3 s−1) were comparable to combined stream inflows (0.050 m3 s−1). Net advection values for the streams and springs were also comparable to each other but were 2 orders of magnitude less than the downwelling shortwave radiation across the lagoon. Although lagoon-scale thermal effects of groundwater inflows were small compared to atmospheric forcing, spring discharge dominated heat transfer at a local scale, creating pronounced cold-water plumes along the shoreline. A numerical model was used to interpret measured groundwater temperature data and investigate seasonal and multi-decadal groundwater temperature patterns. Modelled seasonal temperatures were used to relate measured spring temperatures to their respective aquifer source depths, while multi-decadal simulations forced by historic and projected climate data were used to assess long-term groundwater warming. Based on the 2020–2100 climate scenarios (for which 5-year-averaged air temperature increased up to 4.32∘), modelled 5-year-averaged subsurface temperatures increased 0.08–2.23∘ in shallow groundwater (4.2 m depth) and 0.32–1.42∘ in the deeper portion of the aquifer (13.9 m), indicating the depth dependency of warming. This study presents the first analysis of the thermal sensitivity of groundwater-dependent coastal ecosystems to climate change and indicates that coastal ecosystem management should consider potential impacts of groundwater warming.

The thermal regime of streams is a relevant driver of their ecological functioning. As this regime is presently submitted to numerous alterations (among others, impoundments, and climate change), it seems important to study both their effects and potential recovery from the latter. Thus, we investigated the surface and hyporheic water temperature along a small headwater stream with contrasting environmental contexts: forest landscape, open grassland landscape without riparian vegetation, several artificial run-of-the-river impoundments and one discharge point of a by-pass impoundment. The main objectives were to study the influence of these contrasting contexts on surface and subsurface water temperature at a local scale. Contrasting contexts were supposed to create effects on both surface and hyporheic thermal regimes at a local scale. Differences of thermal regimes between surface and hyporheos were expected, as well as between geological contexts. Sensors located at multiple stations allowed monitoring of stream and hyporheos temperature along the stream, while comparison with adjacent reference stream allowed for surface water thermal regime benchmark. Impoundments and landscapes significantly influenced stream thermal regime at a local scale (impoundments created up to +3.7°C temperature increase in average). Their effect on hyporheos thermal regime was less marked than the ones generated by solar radiation or geological features. Hyporheos thermal regime varies from stream one by temperature dynamics delay (up to 18h) and decrease (up to -7°C between surface and hyporheos temperature in average). These coupled effects create a mosaic of thermal habitats, which could be used for river biodiversity preservation and restoration.

Sea surface temperature (SST) bias in the climate models has been a focus in the past, but subsurface temperature biases have not received much attention yet. In this study, subsurface temperature biases in the tropical North Pacific (TNP) are investigated by analyzing the CMIP6, CMIP5 and OMIP products, and by performing ocean model simulations. It is found that almost all the CMIP and OMIP simulations have a pronounced subsurface warm bias (SWB) in the northeastern tropical Pacific (NETP), and the model developments over the past decade do not indicate obvious improvements in bias pattern and magnitude from CMIP5 to the latest version CMIP6. This SWB is primarily caused by the model deficiencies in the simulated surface wind stress curl (WSC) in the NETP, which is too weak to produce a sufficient Ekman upwelling, a bias that also exists in OMIP simulations. The uncertainties in the parameterizations of the oceanic vertical mixing processes also make a great contribution, and it is demonstrated that the estimated oceanic vertical diffusivities are overestimated both in the upper boundary layer and the interior in the CMIP and OMIP simulations. The relationships between the SWB and the misrepresented oceanic vertical mixing processes are investigated by conducting several ocean-only experiments, in which the upper boundary layer mixing is modified by reducing the wind stirring effect in the Kraus-Turner type bulk mixed-layer scheme, and the interior mixing is constrained by using the Argo-derived diffusivity. By applying these modifications to oceanic vertical mixing schemes, the SWB is greatly reduced in the NETP. The consequences of this SWB are further analyzed. Because the thermal structure in the NETP can influence the simulations of oceanic circulations and equatorial upper-ocean thermal structure, the large SWB in the CMIP6 models tends to produce a weak equatorward water transport in the subsurface TNP, a weak equatorial upwelling and a warm equatorial upper ocean.