General Circulation Model Research Articles

Remote Forcing for a Circulation Pattern Favorable to Surface Melt over the Ross Ice Shelf

Abstract The Ross Ice Shelf (RIS) experiences surface melt events in summer, which could accelerate ice loss and destabilize the ice sheet in a warming world. This study links the interannual variability of RIS surface melt to the northerly wind anomaly over the Ross Sea sector, which is established in association with the quasigeostrophic barotropic Rossby wave trains from the tropical Pacific and subtropical Australia toward West Antarctica. Atmospheric general circulation model experiments suggest that these Rossby wave trains are regulated by El Niño–related sea surface temperature (SST) anomalies in the tropical central–eastern Pacific and atmospheric heating anomalies over western Australia. El Niño provides an important forcing of the atmospheric circulation anomalies over the Ross Sea via inducing a Rossby wave train, and most surface melt events over the RIS happen during El Niño years. In addition, the anomalous atmospheric heating over western Australia, which is independent of El Niño, is another important forcing that triggers a Rossby wave train extending from subtropical Australia to the Ross Sea. The northerly flow toward the Ross Sea induces strong poleward moisture and heat transport, which further contributes to surface melt over the RIS. Significance Statement During austral summer, surface melt occurs over the Ross Ice Shelf, accelerates ice loss, and poses ice-sheet destabilization risks in a warming world. The northerly wind anomaly over the Ross sector provides strong poleward heat and moisture transport and is favorable for the surface melt. This wind anomaly is influenced by two remote forcings, El Niño and heating anomaly over western Australia, through generating Rossby wave trains from the tropics and subtropical Australia. This study reveals a previously unexplored relationship that atmospheric heating over western Australia influences large-scale circulation, contributing to surface melt.

Optimizing climate model selection in regional studies using an adaptive weather type based framework: a case study for extreme heat in Belgium

Selecting climate model projections is a common practice for regional and local studies. This process often relies on local rather than synoptic variables. Even when synoptic weather types are considered, these are not related to the variable or climate impact driver of interest. Therefore, most selection procedures may not sufficiently account for atmospheric dynamics and climate change impact uncertainties. This study outlines a selection methodology that addresses both these shortcomings. Our methodology first optimizes the Lamb Weather Type classification for the variable and region of interest. In the next step, the representation of the historical synoptic dynamics in Global Climate Models (GCMs) is evaluated and accordingly, low-performing models are excluded. In the last step, indices are introduced that quantify the climate change signals related to the impact of interest. Using these indices, a scoring method results in assessing the suitability of GCMs. To illustrate the applicability of the methodology, a case study of extreme heat in Belgium was carried out. This framework offers a comprehensive method for selecting relevant climate projections, applicable in model ensemble-based research for various climate variables and impact drivers.

Predicting the spatial distribution of Turkish hornworts (Anthocerotophyta) under changing climatic conditions

Climate change is a phenomenon that can affect all living organisms. To see its effect, many methods and techniques have been improved recently. Species distribution modelling (SDM) is the most popular technique for this. Here, the distribution of hornworts (Anthoceros caucasicus, Anthoceros punctatus, Phaeoceros laevis and Phymatoceros bulbiculosus), which are a group of non-vascular land plants native to Türkiye, was studied under changing climate using SDM. Three global climate models (GCMs) (CMCC-ESM2, HadGEM3-GC31-LL and MIROC6), two scenarios (SSP1-2.6 and 5-8.5) and four time periods (2021–2040, 2041–2060, 2061–2080 and 2081–2100) were considered. A total of 89 occurrence records of the hornwort species A. caucasicus, A. punctatus and P. laevis were considered for SDM. The potential distribution of P. bulbiculosus could not be modelled due to a lack of sufficient coordinates. Although the patterns between current and future distributions did not change significantly, there were notable differences in the distribution of modelled species in the SSP 5–8.5 scenario. In particular, the MIROC6 model showed an anomalous pattern for A. punctatus in the SSP 5–8.5 scenario. The SDMs indicate that the model will be a valuable resource for the conservation of these species at local and regional levels throughout Türkiye.

Future projection of extreme precipitation using a pseudo-global warming method: A case study of the 2013 Alberta flooding event

The June 2013 extreme precipitation event in Alberta resulted in devastating flash floods that caused significant economic losses and societal disruption. In this study, two high-resolution experiments were conducted using the Weather Research and Forecasting (WRF) model to study the change of the 2013 Alberta extreme precipitation event in a warmer climate. The control experiment was forced with 6-hourly ERA-Interim reanalysis data, while the sensitivity experiment was forced with perturbed ERA-Interim reanalysis data with climate change signals derived from ten global climate models under the Representative Concentration Pathway 8.5 emission scenario. The results indicate that the 2013 Alberta extreme precipitation event is projected to exhibit two significant characteristics in a warming climate. First, precipitation is expected to increase over the Canadian Rocky Mountain region and eastern British Columbia. Second, the precipitation is expected to decrease over the Alberta and Saskatchewan Prairies. Future changes in the extreme precipitation event are associated with changes in the cyclone evolution, moisture transport, and atmospheric stability change caused by climate change. We also found that the increase in atmospheric stability due to the decrease of relative humidity in the lower atmosphere cause less precipitation to form over the plains and later enhance the orographic precipitation in the Canadian Rockies. In addition to the general increase of precipitable water under global warming, this mechanism causes the storm's precipitation to be more concentrated near the Canadian Rockies. The findings from this study could be beneficial for understanding future changes in extreme precipitation events that share similar characteristics.

Change of global land extreme temperature in the future

Understanding future temperature extremes is pivotal to preparing for and mitigating the impacts of climate change. This study proposed machine learning techniques to develop a multi-model ensemble model for high-resolution projection of global land temperature extremes under different emission scenarios, hence providing enhanced precision over previous climate model projections. By utilizing the NEX-GDDP-CMIP6 dataset with bias adjustment and the Gradient Booster algorithm, we reduced the biases that existed in Global Climate Models. The model significantly reduces the root mean square errors (RMSEs) for both the daily maximum and daily minimum temperature extremes. A future scenario analysis revealed that global temperature extremes would substantially increase under high-emission scenarios, highlighting the urgency for stringent emission reduction commitments. This study also identified regions like Greenland, the Tibetan Plateau, and the regional Arctic Archipelago as potential hotspots of temperature extremes under these scenarios. The multi-model ensemble approach, tuned with machine learning and driven by high-resolution data, contributes to climate science by providing refined insights into future temperature extremes, thereby offering direction to climate change mitigation and adaptation strategies.

Spatial heterogeneity in the potential distribution of Aedes mosquitoes in India under current and future climatic scenarios

Aedes is the most globally distributed mosquito genus in the 21st century and transmits various arboviral diseases. The rapid expansion of Ae. Aegypti and Ae. albopictus breeding habitats is a significant threat to global public health, driven by temperature and precipitation changes. In this study, bioclimatic variables were employed to predict the spatial distribution of Ae. aegypti and Ae. albopictus in India. The reference coordinate points of (n = 583) Aedes occurrences at a scale of ∼1 km and nineteen bioclimatic factors were retrieved to train SDM (Species Distribution Models) for both species. Maximum entropy modelling was used to predict the species’ fundamental climatic niche distributions. Future projections were made using global climate models for 2021–2040 and 2081–2100 separately. The models performed reasonably well (AUC > 0.77). Both species thrived in reduced diurnal temperature and higher annual mean temperatures, with suitability increasing alongside precipitation. Ae. aegypti’s projected present and future distribution was broader than that of Ae. Albopictus. The expansion of Aedes suitability varied under different future climatic scenarios. Suitability for Ae. aegypti could expand from between 17.6 and 41.1 % in 2100 under SSP (shared socioeconomic pathways) scenarios 1 and 3, respectively, whereas for Ae. albopictus suitability increased from between 10.2 and 25 % under SSP scenarios 1 and 3 respectively. Preparing for future epidemics and outbreaks requires robust vector distribution models to identify high-risk areas, allocate resources for surveillance and control, and implement prevention strategies.

Climate change impact on water scarcity in the Hub River Basin, Pakistan

The Hub River Basin (HRB), a critical transboundary water source for Sindh and Baluchistan provinces in Pakistan, may face worsening water scarcity due to climate change and population growth. This study aims to assess the current state of water scarcity in the HRB and assesses its vulnerability to these pressures in future. To evaluate the baseline water scarcity in the HRB, a calibrated and validated Soil and Water Assessment Tool (SWAT) was established. Five General Circulation Models (GCMs) were employed to project the future climate under Representative Concentration Pathways (RCP 4.5 and 8.5) for the HRB. Sector-specific indicators were also used to assess the temporal and altitudinal sensitivity of the basin to climate change. These climate projections were incorporated in the SWAT model to simulate flows for three different periods: Early Future (EF; 2010–2039), Mid Future (MF; 2040–2069), and Far Future (FF; 2070–2099). The SWAT model results indicate significant increase in mean flows simulated by SWAT, ranging from 15.27 to 52.78 m3/s under RCP 4.5 and RCP 8.5 compared to baseline flows at HRB. Additionally, the study examines the temporal variation in basin stress and scarcity levels using Falkenmark and Water scarcity indicators. The findings indicate a general decrease in the basin's stress and scarcity levels, potentially benefiting water users of the HRB, especially under RCP8.5. This study offers crucial insights for shaping policies and strategies to adapt to climate change and population growth, ultimately aiming to minimize their impacts on HRB's water resources. By informing water managers and promoting sustainable water management practices, this research can help prevent future conflicts over water allocation and infrastructure development linked with the HRB.

Diversity pattern of Symplocos tree species in China under climate change scenarios: Toward conservation planning

Symplocos, a genus rich in endemic and keystone species in China's subtropical forests, plays a vital role in maintaining biodiversity. Climate change impacts on Symplocos trees affect ecosystem composition, dynamics, and functions. This study delves into the potential ramifications of climate change on the distribution and richness of Symplocos species across China under different climatic conditions. A comprehensive analysis was conducted using occurrence records of 29 species of Symplocos, focusing on habitat suitability modeling and spatial analysis, to demonstrate the possible effects of climate change on the distribution and diversity patterns of Symplocos species in China. Environmental data, including bioclimatic variables, were collected, and the future climate scenarios were projected using the combined average of the two global climate models. Four modeling algorithms were applied in an ensemble species distribution models to assess the distribution of Symplocos species and decipher the pattern of species richness and endemism. The results indicate high accuracy and performance of the models with a mean AUC of 0.94 and TSS of 0.77, particularly the random forest. The analysis identified key bioclimatic variables influencing Symplocos species distribution, with precipitation patterns (precipitation of driest month and precipitation of warmest quarter) and temperature variability (temperature mean diurnal range) playing significant roles. Predictions suggest a reduction in Symplocos species richness over the next six decades, particularly in southern and southwestern China, with potential gains in other regions. Overall, this study highlights the vulnerability of Symplocos species to climate change and underscores the importance of proactive conservation efforts and forest plantations to mitigate future loss in Symplocos species diversity.

Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin.

Impacts of climate change on spatial wheat yield and nutritional values using hybrid machine learning

Abstract Wheat’s nutritional value is critical for human nutrition and food security. However, more attention is needed, particularly regarding the content and concentration of iron (Fe) and zinc (Zn), especially in the context of climate change (CC) impacts. To address this, various controlled field experiments were conducted, involving the cultivation of three wheat cultivars over three growing seasons at multiple locations with different soil and climate conditions under varying Fe and Zn treatments. The yield and yield attributes, including nutritional values such as nitrogen (N), Fe and Zn, from these experiments were integrated with national yield statistics from other locations to train and test different machine learning (ML) algorithms. Automated ML leveraging a large number of models, outperformed traditional ML models, enabling the training and testing of numerous models, and achieving robust predictions of grain yield (GY) (R 2 > 0.78), N (R 2 > 0.75), Fe (R 2 > 0.71) and Zn (R 2 > 0.71) through a stacked ensemble of all models. The ensemble model predicted GY, N, Fe, and Zn at spatial explicit in the mid-century (2020–2050) using three Global Circulation Models (GCMs): GFDL-ESM4, HadGEM3-GC31-MM, and MRI-ESM2-0 under two shared socioeconomic pathways (SSPs) specifically SSP2-45 and SSP5-85, from the downscaled NEX-GDDP-CMIP6. Averaged across different GCMs and SSPs, CC is projected to increase wheat yield by 4.5%, and protein concentration by 0.8% with high variability. However, it is expected to decrease Fe concentration by 5.5%, and Zn concentration by 4.5% in the mid-century (2020–2050) relative to the historical period (1980–2010). Positive impacts of CC on wheat yield encountered by negative impacts on nutritional concentrations, further exacerbating challenges related to food security and nutrition.

Spatiotemporal modeling of the potential impact of climate change on shifts in bioclimatic zones in Morocco.

This study aims to contribute to the understanding of the impact of climate change on bioclimatic zones in Morocco, providing insights into potential shifts and emphasizing the need for adaptation measures to protect vulnerable species and ecosystems. To achieve this, we utilized eight general circulation models (GCMs) to simulate climate conditions under two representative concentration scenarios (RCP4.5 and RCP8.5) for two future time points (2050 and 2070). The modeling of bioclimatic zone shifts was accomplished through the implementation of the random forest (RF) algorithm. Our findings indicate that the subhumid and humid areas are expected to experience the most significant shifts, particularly toward the semi-arid zone. Shifts from subhumid to semi-arid were the most pronounced, ranging from 17.91% (RCP8.5 in 2070) to 25.68% (RCP8.5 in 2050), while shifts from humid to semi-arid ranged from 10.16% (RCP4.5 in 2050) to 22.27% (RCP8.5 in 2070). The Saharan and arid zones are expected to be the least affected, with less than 1% and 11% of their original extent expected to change, respectively. Moreover, our results suggest that forest species such as Atlas cedar and oaks are among the most vulnerable to these shifts. Overall, this study highlights the inevitability of climate change's impact on Moroccan ecosystems and provides a basis for adaptation measures, especially considering the species adapted to the bioclimatic conditions that will dominate the respective affected regions.

Modeling freezing and BioGeoChemical processes in Antarctic sea ice

AbstractThe Antarctic sea ice, which undergoes annual freezing and melting, plays a significant role in the global climate cycle. Since satellite observations in the Antarctic region began, 2023 saw a historically unprecedented decrease in the extent of sea ice. Further ocean warming and future environmental conditions in the Southern Ocean will influence the extent and amount of ice in the Marginal Ice Zones (MIZ), the BioGeoChemical (BGC) cycles, and their interconnected relationships. The so‐called pancake floes are a composition of a porous sea ice matrix with interstitial brine, nutrients, and biological communities inside the pores. The ice formation and salinity are both dependent on the ambient temperature. To realistically model these multiphasic and multicomponent coupled processes, the extended Theory of Porous Media (eTPM) is used to develop Partial Differential Equations (PDEs) based high‐fidelity models capable of simulating the different seasonal variations in the region. All critical variables like salinity, ice volume fraction, and temperature, among others, are considered and have their equations of state. The phase transition phenomenon is approached through a micro‐macro linking scheme. In this paper, a phase‐field solidification model [4] coupled with salinity is used to model the microscale freezing processes and up‐scaled to the macroscale eTPM model. The evolution equations for the phase field model are derived following Landau‐Ginzburg order parameter gradient dynamics and mass conservation of salt allowing to model the salt trapped inside the pores. A BGC flux model for sea ice is set up to simulate the algal species present in the sea ice matrix. Ordinary differential equations (ODE) are employed to represent the diverse environmental factors involved in the growth and loss of distinct BGC components. Processes like photosynthesis are dependent on temperature and salinity, which are derived through an ODE‐PDE coupling with the eTPM model. Academic simulations and results are presented as validation for the mathematical model. These high‐fidelity models eventually lead to their incorporation into large‐scale global climate models.

Assessing the Spurious Impacts of Ice-Constraining Methods on the Climate Response to Sea-Ice Loss using an Idealised Aquaplanet GCM

Abstract Coupled climate model simulations designed to isolate the effects of Arctic sea-ice loss often apply artificial heating, either directly to the ice or through modification of the surface albedo, to constrain sea ice in the absence of other forcings. Recent work has shown that this approach may lead to an overestimation of the climate response to sea-ice loss. In this study, we assess the spurious impacts of ice-constraining methods on the climate of an idealised aquaplanet general circulation model (GCM) with thermodynamic sea ice. The true effect of sea-ice loss in this model is isolated by inducing ice loss through reduction of the freezing point of water, which does not require additional energy input. We compare results from freezing point modification experiments with experiments where sea-ice loss is induced using traditional ice-constraining methods, and confirm the result of previous work that traditional methods induce spurious additional warming. Furthermore, additional warming leads to an overestimation of the circulation response to sea-ice loss, which involves a weakening of the zonal wind and storm track activity in midlatitudes. Our results suggest that coupled model simulations with constrained sea ice should be treated with caution, especially in boreal summer, where the true effect of sea-ice loss is weakest but we find the largest spurious response. Given that our results may be sensitive to the simplicity of the model we use, we suggest that devising methods to quantify the spurious effects of ice-constraining methods in more sophisticated models should be an urgent priority for future work.

Assessment of seven different global climate models for historical temperature and precipitation in Hatay, Türkiye

Assessment of seven different global climate models for historical temperature and precipitation in Hatay, Türkiye

Global climate modelling of Saturn’s atmosphere, Part V: Large-scale vortices

This paper presents an analysis of large-scale vortices in the atmospheres of gas giants, focusing on a detailed study conducted using the Saturn-DYNAMICO global climate model (GCM). Large-scale vortices, a prominent feature of gas giant atmospheres, play a critical role in their atmospheric dynamics. By employing three distinct methods – manual detection, machine learning via artificial neural networks (ANN), and dynamical detection using the Automated Eddy-Detection Algorithm (AMEDA) – we characterise the spatial, temporal, and dynamical properties of these vortices within the Saturn-DYNAMICO GCM. Our findings reveal a consistent production of vortices due to well-resolved eddy-to-mean flow interactions, exhibiting size and intensity distributions broadly in agreement with observational data. However, notable differences in vortex location, size, and concentration highlight the model’s limitations and suggest areas for further refinement. The analysis underscores the importance of zonal wind conditions in influencing vortex characteristics and suggests that more accurate modelling of giant planet vortices may require improved representation of moist convection and jet structure. This study not only provides insights into the dynamics of Saturn’s atmosphere as simulated by the GCM but also offers a framework for comparing vortex characteristics across observations and models of planetary atmospheres.

Multivariate bias correction and downscaling of climate models with trend-preserving deep learning

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

A Predicted Pause in the Rapid Warming of the Northwest Atlantic Shelf in the Coming Decade

AbstractThe capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi‐decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse‐resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream.

Methane storm characteristics and evolution in simulations of Titan’s hydroclimate

Methane precipitation is a key component of the climate on Titan, and has been shown to impact surface features. Recent general circulation models (GCMs) have reproduced Titan’s hydroclimate, including precipitation, with increasing accuracy, yet characterization of their simulated precipitation events is lacking. We investigate the characteristics and evolution of methane storms simulated over 40 Titan years using the Titan Atmospheric Model, a validated GCM. Storms are identified and tracked using the density-based spatial clustering of applications with noise (DBSCAN) algorithm, allowing them to be followed through time and space. We find that storms follow seasonality expected from observations and prior modeling, occur preferentially in the summer hemisphere, and tend to start over high topography. The population of storms is bimodal in traits corresponding to intensity, area, and duration, with a large population of small, short-lived, and weakly precipitating storms and a smaller population of exceptionally large, long-lasting, and intense storms. These largest storms tend to evolve similarly over their lifetimes, peaking early in intensity and in the middle of their lives in area. We also find temporal clustering of storms, in alignment with observations and the proposed relaxation-oscillation model of Titan’s methane precipitation. These storm clusters emerge quasi-periodically following long dry spells during which evaporation of surface methane recharges atmospheric moisture. Approximately five clusters occur per Titan year, and their locations are strongly seasonal. Overall, our quantitative descriptions of storms and storm clusters over a long timescale provide additional insight into Titan’s methane cycle and surface features, and may assist in the planning of future missions such as Dragonfly.

A decade (2008–2017) of water stable isotope composition of precipitation at Concordia Station, East Antarctica

Abstract. A 10-year record of oxygen and hydrogen isotopic composition of precipitation is presented here: from 2008 to 2017, 1483 daily precipitation samples were collected year-round on a raised platform at Concordia Station, East Antarctica. Weather data were retrieved from the Italian Antarctic Meteo-Climatological Observatory automatic weather station (AWS), while ERA5 was used to estimate total precipitation. The δ–temperature relationships were moderately high for daily data (r2=0.63 and 0.64 for δ18O and δ2H, respectively) and stronger using monthly data (r2=0.82 for both δ18O and δ2H), with a slope of about 0.5 ‰ °C−1 for δ18O/TAWS (3.5 ‰ °C−1 for δ2H/TAWS), which remains consistent also using annual averages. The isotopic composition of precipitation is the input signal of the snow–ice system, and this dataset will be useful to improve the interpretation of paleoclimate records and promote a better understanding of the post-depositional processes affecting the isotopic signal in ice cores. This dataset represents a benchmark for the evaluation of isotope-enabled general circulation models. Here, the ECHAM6-wiso output was compared to experimental data, showing moderately good relationships for δ18O and δ2H but not for d-excess, nonetheless marking a substantial improvement from the previous release of the model.

Enhanced generation of internal tides under global warming

A primary driver of deep-ocean mixing is breaking of internal tides generated via interactions of barotropic tides with topography. It is important to understand how the energy conversion from barotropic to internal tides responds to global warming. Here we address this question by applying a linear model of internal tide generation to coupled global climate model simulations under a high carbon emission scenario. The energy conversion to high-mode internal tides is projected to rise by about 8% by the end of the 21st century, whereas the energy conversion to low-mode internal tides remains nearly unchanged. The intensified near-bottom stratification under global warming increases energy conversion into both low and high-mode internal tides. In contrast, the intensified depth-averaged stratification reduces the modal horizontal wavenumber of internal tides, leading to increased (decreased) energy conversion into high (low)- mode internal tides. Our findings imply stronger mixing over rough topography under global warming, which should be properly parameterized in climate models for more accurate projections of future climate changes.