Global Model Research Articles

Sensitivity of global hydrological models to potential evapotranspiration estimation methods in the Senegal River Basin (West Africa)

Study regionSenegal River Basin in West Africa Study focusThis paper aims to evaluate the sensitivity of global hydrological models to potential evapotranspiration (PET) methods in the Senegal River Basin. Potential evapotranspiration is estimated using 21 methods and its influence on the performance of three GR models (GR4J, GR5J and GR6J) is investigated in five catchments. The data used are mean rainfall, discharge and observed and calculated PET over the period 1984-1995. PET is calculated based on observed climate data and those from NASA POWER reanalysis data. The methodology consists in: (i) comparing the consistency of reanalysis data with respect to the observed PET, (ii) assessing the robustness of GR models and their sensitivity to different PET estimation methods. The evaluation criteria used to assess the performance of the hydrological model are KGE and PBIAS. New hydrological insights for the regionGood consistency is obtained between PET calculated with observed and reanalysis data. The GR4J and GR5J are more efficient to simulate flows in the Senegal River sub-basins. The aerodynamic PET methods perform well with the three hydrological models. However, in this context where data are scarce, temperature methods such as Droogers and Allen are a good choice for hydrological modeling. The results also show that the GR models have the ability to adapt to poorly estimated PET.

Dengue hemorrhagic fever prediction in coastal area using geographically weighted regression

Dengue hemorrhagic fever (DHF) is still a health problem globally, including in Indonesia. Geographical and climatic conditions in coastal areas are different from other areas, which may impact differences in environmental risk factors for dengue. This study aims to create a prediction model for the incidence of DHF in coastal areas. The research was conducted in Bantul Regency, Indonesia, involving data from 2015-2019. Dengue incidence data were collected from the health office. Climatic data were from climatology station. Data on altitude and shoreline distance were obtained by geographic information system (GIS) processing. Population density and wide settlement area are obtained from the Bureau of Statistics. The geographically weighted regression (GWR) analysis was carried out using GWR4. The results showed that GWR with a weighting of Fixed Bi-Square Kernel obtained an R2 value of 0.7768, better than the global model (R2 0.5254). It indicates that DHF (Y) in Bantul Regency is 77.68% determined by population density (X1), altitude (X2), settlement area (X3), shoreline distance (X4) and rainfall (X5) and the remaining 22.32% are influenced by other variables which are not investigated. Geographically, the predictor variables explain the DHF incidence with a strong category in the central region, and weak in coastal area.

Regional Sea Level Response to External Forcings from the Twentieth to the Twenty-First Century

Abstract Using a range of Detection and Attribution Model Intercomparison Project (DAMIP) simulations from phase 6 of Coupled Model Intercomparison Project (CMIP6), we study the response of dynamic sea level (DSL) to external anthropogenic climate forcing [greenhouse gases (GHGs), aerosols, and stratospheric ozone] with a focus on the differences over the twentieth and twenty-first century. In the second half of the twentieth century, the DSL nonuniformity in the Northern Hemisphere (NH) was relatively small due to a cancellation between the effects of increasing GHGs and aerosols. In contrast, the DSL signal in the Southern Hemisphere (SH) over this period was large because stratospheric ozone depletion reinforced the effects of increasing GHGs. In the twenty-first century, the DSL response has been intensified in the NH because the warming effects of diminishing aerosols have acted to reinforce the effects of increasing GHGs. Meanwhile, the distribution of SH DSL has also become uneven although stratospheric ozone recovery has partially offset the effects of rising GHGs. Using a global ocean circulation model, we decompose the changes in the twenty-first century DSL into distinct responses to surface forcings including sea surface temperature, salinity, and wind stress. Our results show that the dipole-like pattern of DSL in the North Pacific can be attributed largely to sea surface warming, while the dipole-like pattern in the North Atlantic is attributed to subpolar surface salinity freshening. The belted pattern of DSL changes in the Southern Ocean is induced by both surface warming and intensifying/poleward-shifting westerly winds.

Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling.

In this paper, we propose FGPR: a Federated Gaussian process ( GP) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local computations. Notably, the resulting global model excels in personalization as FGPR jointly learns a shared prior across all devices. The predictive posterior is then obtained by exploiting this shared prior and conditioning on local data, which encodes personalized features from a specific dataset. Theoretically, we show that FGPR converges to a critical point of the full log-marginal likelihood function, subject to statistical errors. This result offers standalone value as it brings federated learning theoretical results to correlated paradigms. Through extensive case studies on several regression tasks, we show that FGPR excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.

A new model for plasmapause locations derived from IMAGE RPI and Van Allen probe data

The outer boundary of the plasmasphere, the plasmapause, is characterized by a sharp electron density gradient that changes under varying space weather conditions. We developed a new model, called Neustrelitz/ESOC PlasmaPause Model (NEPPM), for providing plasmapause location in terms of -shell utilizing electron density measurements from the Van Allen probes from 2012 to 2018 and the IMAGE satellite data from 2001 to 2005. Both datasets were preprocessed, and algorithms were developed for automatic detection of plasmapause location. The suggested model provides a simple ellipse-based approach determined by the semi-major axis, the eccentricity, and the orientation angle of the semi-major axis. The modelled varies as a function of the index and magnetic local time . The NEPPM results are compared with the Global Core Plasma Model (GCPM). The plasmapause bulge in the evening hours follows the level of geomagnetic activity. The NEPPM will complete the NPSM (Neustrelitz PlasmaSphere Model), which was derived from dual-frequency GPS measurements on-board the CHAMP satellite mission.

FedKG: A Knowledge Distillation-Based Federated Graph Method for Social Bot Detection

Malicious social bots pose a serious threat to social network security by spreading false information and guiding bad opinions in social networks. The singularity and scarcity of single organization data and the high cost of labeling social bots have given rise to the construction of federated models that combine federated learning with social bot detection. In this paper, we first combine the federated learning framework with the Relational Graph Convolutional Neural Network (RGCN) model to achieve federated social bot detection. A class-level cross entropy loss function is applied in the local model training to mitigate the effects of the class imbalance problem in local data. To address the data heterogeneity issue from multiple participants, we optimize the classical federated learning algorithm by applying knowledge distillation methods. Specifically, we adjust the client-side and server-side models separately: training a global generator to generate pseudo-samples based on the local data distribution knowledge to correct the optimization direction of client-side classification models, and integrating client-side classification models’ knowledge on the server side to guide the training of the global classification model. We conduct extensive experiments on widely used datasets, and the results demonstrate the effectiveness of our approach in social bot detection in heterogeneous data scenarios. Compared to baseline methods, our approach achieves a nearly 3–10% improvement in detection accuracy when the data heterogeneity is larger. Additionally, our method achieves the specified accuracy with minimal communication rounds.

Global climate modeling of the Jupiter troposphere and effect of dry and moist convection on jets

The atmosphere of Jupiter is characterized by banded jets, including an equatorial super-rotating jet, by an intense moist convective activity, and by perturbations exerted by vortices, waves, and turbulence. Even after space exploration missions to Jupiter and detailed numerical modeling of Jupiter, questions remain about the mechanisms underlying the banded jets and the role played by dry and moist convection in maintaining these jets. We report three-dimensional simulations of the Jupiter weather layer using a global climate model (GCM) called Jupiter-DYNAMICO, which couples hydrodynamical integrations on an icosahedral grid with detailed radiative transfer computations. We added a thermal plume model for Jupiter that emulates the effect of mixing of heat, momentum, and tracers by dry and moist convective plumes that are left unresolved in the GCM mesh spacing with a physics-based approach. Our Jupiter-DYNAMICO global climate simulations show that the large-scale Jovian flow, in particular the jet structure, could be highly sensitive to the water abundance in the troposphere and that an abundance threshold exists at which equatorial super-rotation develops. In contrast to our dry (or weakly moist) simulations, simulations that include the observed amount of tropospheric water exhibit a clear-cut super-rotating eastward jet at the equator and a dozen eastward mid-latitude jets that do not migrate poleward. The magnitudes agree with the observations. The convective activity simulated by our thermal plume model is weaker in the equatorial regions than in mid to high latitudes, as indicated by lightning observations. Regardless of whether they are dry or moist, our simulations exhibit the observed inverse energy cascade from small (eddies) to large scales (jets) in a zonostrophic regime.

SEISMONOISY: A Quasi-Real-Time Seismic Noise Network Monitoring System

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time.

Short-Term Traffic Flow Forecasting Method Based on Secondary Decomposition and Conventional Neural Network–Transformer

Because of the random volatility of traffic data, short-term traffic flow forecasting has always been a problem that needs to be further researched. We developed a short-term traffic flow forecasting approach by applying a secondary decomposition strategy and CNN–Transformer model. Firstly, traffic flow data are decomposed by using a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Secondly, the IMF1 obtained from the CEEMDAN is further decomposed into some sub-series by using Variational Mode Decomposition (VMD) algorithm. Thirdly, the CNN–Transformer model is established for each IMF separately. The CNN model is employed to extract local spatial features, and then the Transformer model utilizes these features for global modeling and long-term relationship modeling. Finally, we obtain the final results by superimposing the forecasting results of each IMF component. The measured traffic flow dataset of urban expressways was used for experimental verification. The experimental results reveal the following: (1) The forecasting performance achieves remarkable improvement when considering secondary decomposition. Compared with the VMD-CNN–Transformer, the CEEMDAN-VMD-CNN–Transformer method declined by 25.84%, 23.15% and 22.38% in three-step-ahead forecasting in terms of MAPE. (2) It has been proven that our proposed CNN–Transformer model could achieve more outstanding forecasting performance. Compared with the CEEMDAN-VMD-CNN, the CEEMDAN-VMD-CNN–Transformer method declined by 13.58%, 11.88% and 11.10% in three-step-ahead forecasting in terms of MAPE.

Global prototype distillation for heterogeneous federated learning.

Federated learning is a distributed machine learning paradigm where the goal is to collaboratively train a high quality global model while private training data remains local over distributed clients. However, heterogenous data distribution over clients is severely challenging for federated learning system, which severely damage the quality of model. In order to address this challenge, we propose global prototype distillation (FedGPD) for heterogenous federated learning to improve performance of global model. The intuition is to use global class prototypes as knowledge to instruct local training on client side. Eventually, local objectives will be consistent with the global optima so that FedGPD learns an improved global model. Experiments show that FedGPD outperforms previous state-of-art methods by 0.22% ~1.28% in terms of average accuracy on representative benchmark datasets.

Regional evaluation of global geopotential models and three types of digital elevation models with ground-based gravity and GNSS/levelling data using several techniques over Sudan

Abstract To evaluate the performance of the global geopotential models (GGMs) in a more unbiased way, ground-based gravity and GNSS/levelling datasets are highly required. In this study, the eight latest releases of the satellite-only and combined GGMs are evaluated on the regional scale using the available terrestrial gravity and GNSS/Levelling data over Sudan, considering the spectral consistency issue by applying the spectral enhancement method (SEM). The evaluation process consists of three stages: firstly, the eight GGMs are evaluated globally with each other by using different degree variances in terms of geoid heights, gravity anomalies, and signal-to-noise ratio (SNR); secondly, the GGMs are compared against the Earth Gravitational Model 2008 (EGM2008) on a regional scale over Sudan; thirdly, apply the SEM strategy by incorporating high (SEM_WITHOUT_RTM technique) and very-high (SEM technique) frequencies of the gravity field spectrum from the EGM2008 and high-resolution residual terrain model (RTM), respectively. For reliable robustness of the latter evaluation process, three different DEMs are used, namely, SRTM30, ASTER30, and GTOPO30. Our findings on the evaluation process using SEM_WITHOUT_RTM technique show improved gravity anomalies solutions regarding differences of standard deviations (STD) from 19–20.7 mGal to about 14 mGal. When applying the SEM technique, more improvements are achieved, providing STD differences in gravity anomalies and geoid heights of about 12 mGal and 45 cm, respectively. Among the three applied DEMs, it has been found that despite the slight refinements, the ASTER30 and GTOPO30 models show better performance than the SRTM30 model.

A new approach to crystal habit retrieval from far-infrared spectral radiance measurements

Abstract. To generate reliable climate predictions, global models need accurate estimates of all the energy fluxes contributing to the Earth's radiation budget (ERB). Clouds in general, and more specifically ice clouds, play a key role in the determination of the ERB as they may exert either a feedback or a forcing action, depending on their optical and microphysical properties and physical state (solid/liquid). To date, accurate statistics and climatologies of cloud parameters are not available. Specifically, the ice cloud composition in terms of ice crystal shape (or habit) is one of the parameters with the largest uncertainty. The Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) experiment, foreseen to be the ninth Earth Explorer mission of the European Space Agency, will measure, for the first time spectrally resolved from space, the entire upwelling spectrum emitted by the Earth from 100 to 1600 cm−1. The far-infrared portion of the Earth spectrum, especially from 200 to 600 cm−1, is very sensitive to cloud ice crystal shapes; thus, FORUM measurements could also represent an opportunity to study the ice cloud composition in terms of ice crystal habit mixtures. To investigate this possibility, we developed an accurate and advanced scheme allowing us to model ice cloud optical properties – also in cases of clouds composed of mixed ice crystal habits. This feature is in fact necessary because in situ measurements acquired over the years also point out that the shape of ice cloud crystals varies depending on the crystal size range. In our model, the resulting cloud optical properties are also determined by the input habit fractions. Thus, the retrieval of these fractions from spectral radiance measurements can be attempted. Using 375 different cloudy scenarios, we assess the performance of our retrieval scheme in the determination of crystal habit mixtures starting from FORUM-simulated measurements. The most relevant error components affecting the retrieved cloud parameters are not very large and are of random nature; thus, FORUM measurements will allow us to set up an accurate climatology of cloud parameters. To provide an example of the benefit that one could get from the habit mixture retrievals, we also show the improved accuracy of the thermal outgoing fluxes calculations compared to using assumed mixtures.

Potential decoupling of CO2 and Hg uptake process by global vegetation in the 21st century

Mercury (Hg), a potent neurotoxin posing risks to human health, is cycled through vegetation uptake, which is susceptible to climate change impacts. However, the extent and pattern of these impacts are largely unknown, obstructing predictions of Hg’s fate in terrestrial ecosystems. Here, we evaluate the effects of climate change on vegetation elemental Hg [Hg(0)] uptake using a state-of-the-art global terrestrial Hg model (CLM5-Hg) that incorporates plant physiology. In a business-as-usual scenario, the terrestrial Hg(0) sink is predicted to decrease by 1870 Mg yr−1 in 2100, that is ~60% lower than the present-day condition. We find a potential decoupling between the trends of CO2 assimilation and Hg(0) uptake process by vegetation in the 21st century, caused by the decreased stomatal conductance with increasing CO2. This implies a substantial influx of Hg into aquatic ecosystems, posing an elevated threat that warrants consideration during the evaluation of the effectiveness of the Minamata Convention.

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

Illuminating Snow Droughts: The Future of Western United States Snowpack in the SPEAR Large Ensemble

AbstractSeasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow‐tourism economies. While the frequency and severity of snow droughts (SD), that is, anomalously low snowpacks, are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify with observations alone. Using a 30‐member large ensemble from a state‐of‐the‐art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations‐based data set, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5‐8.5 (SSP5‐8.5) and 5 times more frequent under SSP2‐4.5, compared to a 1921–2011 average. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track late winter and spring snowpack across WUS watersheds, finding differences in the onset time of a “no‐snow” threshold between regions and large internal variability within the ensemble that are both on the order of decades. We attribute the inter‐regional variability to differences in the regions' mean winter temperature and the intra‐regional variability to irreducible internal climate variability which is not well‐explained by temperature variations alone. Despite strong scenario forcing, internal climate variability will continue to drive variations in SD and no‐snow conditions through 2100.

Statistical downscaling of precipitation in northwestern Iran using a hybrid model of discrete wavelet transform, artificial neural networks, and quantile mapping

AbstractDownscaling of daily precipitation from Global Circulation Models (GCMs)-predictors at a station level, especially in arid and semi-arid regions, has remained a formidable challenge yet. The current study aims at proposing a coupled model of Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and Quantile Mapping (QM) for statistical downscaling of daily precipitation. Given the historic (1978–2005) and future (2006–2100) predictors of eight-selected GCMs under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, a viable DWT-ANN model was developed for each station. Subsequently, we linked QM to DWT-ANN for bias correction and drizzle effect postprocessing of the DWT-ANN-historic/future projected precipitation. The skill of DWT-ANN-QM was demonstrated using various evaluation metrics, including Taylor diagram, Quantile–Quantile plot, Empirical Cumulative Distribution Function, and wet/dry spell analysis. We appraise the efficacy of the coupled model at 12 weather stations over the Gharehsoo River Basin (GRB) in northwestern Iran. Compared to the observed wet/dry spells, the dry-spells were better simulated via DWT-ANN-QM rather than the wet-spells wherein length and exceedance probability of the spells were overestimated. Results indicated that the future precipitation across the GRB will rise, on average, from 10 to 17% depending on weather station. Seasonal spatial distribution of the middle future (2041–2070) precipitation illustrated that an increase for fall and winter, especially, is expected, whereas the amount of the future spring and summer precipitation is projected to be declined. Having been developed and tested in a semi-arid basin, the efficacy of the coupled model should be further assessed in more humid climates.

Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)

Abstract. During the last few decades, the impact of multiphase chemistry on secondary organic aerosols (SOAs) has been demonstrated to be the key to explaining laboratory experiments and field measurements. However, global atmospheric models still show large biases when simulating atmospheric observations of organic aerosols (OAs). Major reasons for the model errors are the use of simplified chemistry schemes of the gas-phase oxidation of vapours and the parameterization of heterogeneous surface reactions. The photochemical oxidation of anthropogenic and biogenic volatile organic compounds (VOCs) leads to products that either produce new SOA or are taken up by existing aqueous media like cloud droplets and deliquescent aerosols. After partitioning, aqueous-phase processing results in polyols, organosulfates, and other products with a high molar mass and oxygen content. In this work, we introduce the formation of new low-volatility organic compounds (LVOCs) to the multiphase chemistry box model CAABA/MECCA. Most notable are the additions of the SOA precursors, limonene and n-alkanes (5 to 8 C atoms), and a semi-explicit chemical mechanism for the formation of LVOCs from isoprene oxidation in the gas and aqueous phases. Moreover, Henry's law solubility constants and their temperature dependences are estimated for the partitioning of organic molecules to the aqueous phase. Box model simulations indicate that the new chemical scheme predicts the enhanced formation of LVOCs, which are known for being precursor species to SOAs. As expected, the model predicts that LVOCs are positively correlated to temperature but negatively correlated to NOx levels. However, the aqueous-phase processing of isoprene epoxydiols (IEPOX) displays a more complex dependence on these two key variables. Semi-quantitative comparison with observations from the SOAS campaign suggests that the model may overestimate methylbutane-1,2,3,4-tetrol (MeBuTETROL) from IEPOX. Further application of the mechanism in the modelling of two chamber experiments, one in which limonene is consumed by ozone and one in which isoprene is consumed by NO3 shows a sufficient agreement with experimental results within model limitations. The extensions in CAABA/MECCA are transferred to the 3D atmospheric model MESSy for a comprehensive evaluation of the impact of aqueous- and/or aerosol-phase chemistry on SOA at a global scale in a follow-up study.

Is Bias Correction in Dynamical Downscaling Defensible?

AbstractLocalized projections of 21st‐century hydroclimate variables obtained from downscaling Global Climate Model (GCM) output are central to informing regional impact assessments and infrastructure planning. Regional GCM biases can be significant and, for dynamical downscaling, can be addressed either before (a priori) or after (a posteriori) downscaling. However, a priori bias correction (APBC) has generally unexplored effects on climate change signals. Here we analyze dynamically downscaled solutions of CMIP6 GCMs over the Western U.S., with and without APBC, and quantify APBC's impact on climate change signals relative to other irreducible uncertainty sources. For temperature and precipitation, the uncertainty introduced by APBC is negligible compared to that arising from GCM choice or internal variability. Furthermore, APBC greatly reduces regional models' unrealistically high snow‐water‐equivalent (SWE) biases that result directly from GCM errors. We leverage this finding to encourage the dynamical downscaling community to adopt APBC as a standard operating procedure.

Rapid high-latitude cooling in the southeastern Pacific sector driven by North Atlantic warming during 1979–2013 in CESM1

During the 1979–2013 satellite observation period, the sea surface temperature (SST) has cooled substantially in the high-latitude Southern Ocean, with the most pronounced cooling tendency centered in the southeastern Pacific domain. Previous hypotheses have commonly ascribed the recent Southern Ocean cooling to either the tropical eastern Pacific cooling or North Atlantic and tropical Indian Ocean SST warming. However, the mechanisms underpinning significant cooling in the southeastern Pacific sector remain debatable. By diagnosing three pacemaker experiments with a state-of-the-art global climate model in which SSTs in the North Atlantic, tropical central-eastern Pacific, and tropical Indian Ocean-western Pacific are individually nudged to mimic the observed trajectory, we show that the North Atlantic dominates in the cold SST response of the southeastern Pacific sector during 1979–2013. Anomalous North Atlantic warming initiates a quasi-stationary Rossby wave response to a south-to-north cross-equatorial Hadley circulation strengthening, leading to an enhanced Amundsen Sea Low. As a result, due primarily to the increased low-level marine cloud cover, the net surface shortwave radiation reduction triggers rapid SST cooling in the southeastern Pacific domain. The southeastern Pacific cold SST anomalies are further maintained via the shortwave radiation-low-cloud-SST positive feedback on decadal timescales. Our results suggest that the shortwave radiation-low-cloud SST feedback is fundamental to the observed long-term cooling of the high-latitude Southern Ocean, with profound climate consequences worldwide.

The role and deployment timing of direct air capture in Saudi Arabia’s net-zero transition

The Kingdom of Saudi Arabia (KSA) has pledged to achieve net-zero greenhouse gas emissions by 2060. Direct air carbon capture and storage (DACCS) is critical for the country to meet its net-zero target given its reliance on fossil fuels and limited options for carbon dioxide removal (CDR). However, the role of DACCS in KSA’s national climate change mitigation has not been studied in the literature. In this study, we aim to understand the potential role of DACCS and the effect of its deployment timing in KSA’s transition toward its net-zero target using the Global Change Analysis Model (GCAM)-KSA, which is a version of GCAM with KSA split out as an individual region. We find that the annual DACCS CO2 sequestration in KSA reaches 0.28–0.33 Gt yr−1 by 2060 depending on its deployment timing. Early DACCS deployment, driven by its early and rapid cost reduction worldwide, could bring significant savings (∼420 billion USD during 2020–2060) in the cost of climate change mitigation in KSA, approximately 17% reduction relative to delayed DACCS deployment. Our study suggests a strong role for KSA to proactively invest in the R&D of DACCS, initiate early DACCS deployment, and explore a broad suite of CDR options.