Coupled Ocean Atmosphere Research Articles

Coastal CUBEnet: an integrated observation and modeling system for sustainable Northern Gulf of Mexico coastal areas

The University of Southern Mississippi has developed the coastal CUBEnet environment. Coastal CUBEnet is a high-resolution, coastal ocean sensor, modeling, and data sharing web-based network that provides the environmental intelligence needed to support the complex modeling of the interlinked processes in the northern Gulf of Mexico. With near-real time data delivery via a common infrastructure, CUBEnet uses state of the art sensors to provide a set of networked measurements systems, visualization tools, and model developments to gain an understanding of the Gulf of Mexico’s marine environments. CUBEnet is also a mechanism for improved human engagement with Gulf of Mexico resources and provides stake holders with the data needed to make informed coastal, environmental, and economic decisions. The Coastal CUBEnet’s data environment utilizes both stationary and uncrewed mobile systems and high-resolution distributed sensors to create a networked platform across the northern coastal Gulf of Mexico. CUBEnet’s modeling environment has developed an implementation of The Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) Model for the Mississippi Bight region that has been applied to investigate shore to shelf advective exchange processes, and their influence on coastal water quality conditions that support the region’s prolific marine ecosystem. CUBEnet’s modeling environment provides prototype modeling applications that are supported by real-time observations of key coastal environmental variables. CUBEnet’s Web accessible visualization tools provide parameter fields and vertical profiles from hydrodynamic models and field observations. Nowcasts and forecast results are available for the Eastern LA coastline, MS coastline, Mobile Bay, and the West coast of Florida.

Statistical Predictability of Euro-Mediterranean Subseasonal Anomalies: The TeWA Approach

Abstract It is widely known from energy balances that global oceans play a fundamental role in atmospheric seasonal anomalies via coupling mechanisms. However, numerical weather prediction models still have limitations in long-term forecasting due to their nonlinear sensitivity to initial deep oceanic conditions. As the Mediterranean climate has highly unpredictable seasonal variability, we designed a complementary method by supposing that 1) delayed teleconnection patterns provide information about ocean–atmosphere coupling on subseasonal time scales through the lens of 2) partially predictable quasi-periodic oscillations since 3) forecast signals can be extracted by smoothing noise in a continuous lead-time horizon. To validate these hypotheses, the subseasonal predictability of temperature and precipitation was analyzed at 11 reference stations in the Mediterranean area in the 1993–2021 period. The novel method, presented here, consists of combining lag-correlated teleconnections (15 indices) with self-predictability techniques of residual quasi-oscillation based on wavelet (cyclic) and autoregressive integrated moving average (ARIMA) (linear) analyses. The prediction skill of this teleconnection–wavelet–ARIMA (TeWA) combination was cross-validated and compared to that of the ECMWF’s Seasonal Forecast System 5 (SEAS5)–ECMWF model (3 months ahead). Results show that the proposed TeWA approach improves the predictability of first-month temperature and precipitation anomalies by 50%–70% compared with the forecast of SEAS5. On a moving-averaged daily scale, the optimum prediction window is 30 days for temperature and 16 days for precipitation. The predictable ranges are consistent with atmospheric bridges in teleconnection patterns [e.g., Upper-Level Mediterranean Oscillation (ULMO)] and are reflected by spatial correlation with sea surface temperature (SST). Our results suggest that combinations of the TeWA approach and numerical models could boost new research lines in subseasonal-to-seasonal forecasting. Significance Statement The Mediterranean climate presents a high natural variability that makes skillful seasonal forecasts very difficult to achieve. We propose to complement the current forecasting methods with a statistical approach that combines two conceptual models: First, climate anomalies (cold/warm or dry/wet periods) are considered as smooth waves (with slow changes); and second, atmospheric and oceanic indices perform the role of atmosphere–ocean interactions, which impact Mediterranean climate variability in a delayed way. The key findings are that combining both sides, a better predictability of climate variability is provided, which is an opportunity to improve natural resource management and planning.

Assessment of short‐range forecast error atmosphere–ocean cross‐correlations from the Met Office coupled numerical weather prediction system

AbstractOperational data assimilation systems for coupled atmosphere–ocean prediction are usually “weakly coupled”, in which there is no explicit interaction between the atmosphere and ocean within the data assimilation step. Explicitly allowing for cross‐correlations between the ocean and the atmosphere may have potential benefits in improving the consistency of atmosphere and ocean analyses, as well as allowing a better use of observations at the interface. To understand whether such correlations are significant on the time‐scales of numerical weather prediction, we investigate the atmosphere–ocean cross‐correlations of short‐term forecast errors from the Met Office coupled prediction system, considering their temporal and spatial variability. We find that significant correlations exist between atmosphere and ocean forecast errors on these time‐scales, and that these vary diurnally, from day to day, spatially and synoptically. For correlations between errors in the atmospheric wind and ocean temperature, positive correlations in the North Atlantic region are found to be synoptically dependent, with correlation structures extending into the ocean throughout the deep mixed layer, beyond a depth of 100 m. In contrast, negative correlations over the Indian Ocean are very shallow and are associated with the diurnal cycle of solar radiation. The significance and variability of cross‐correlations indicates that there should be a benefit from including them in data assimilation systems, but it will be important to allow for some flow‐dependence in the correlations. Furthermore, the differing vertical extents of the cross‐correlations in different regions implies the need for situation‐dependent localisation of ensemble correlations when including them in coupled data assimilation systems.

The Impact of Interactive Ocean Dynamics on Atlantic Sea Surface Temperature Variability

Abstract The impact of interactive ocean dynamics on internal variations of Atlantic sea surface temperature (SST) is investigated by comparing preindustrial control simulations of a fully coupled atmosphere–ocean–ice model to the same atmosphere–ice model with the ocean replaced by a motionless slab layer (henceforth slab ocean model). Differences in SST variability between the two models are diagnosed by an optimization technique that finds components whose variance differs as much as possible. This technique reveals that Atlantic SST variability differs significantly between the two models. The two components with the most extreme enhancement of SST variance in the slab ocean model resemble the tripole SST pattern associated with the North Atlantic Oscillation (NAO) and the Atlantic multidecadal variability (AMV) pattern. This result supports previous claims that ocean dynamics are not necessary for the AMV, although ocean dynamics lead to slight increases in the memory of both the AMV and the NAO tripole. The component with the most extreme enhancement of SST variance in the fully coupled model resembles the Atlantic Niño pattern, confirming the ability of our technique to isolate physical modes known to require ocean dynamics. The second component with more variance in the fully coupled model is a mode of subpolar SST variability. Both the reemergence of SST anomalies and changes in ocean heat transport lead to increased SST variance and memory in the subpolar Atlantic. Despite large differences in the mean and variability of SST, atmospheric variability is quite similar between the two models, confirming that most atmospheric variability is generated by internal atmospheric dynamics.

Diagnostic Method for Atmosphere–Ocean Coupling Over Tropical Oceans at the Sub‐Seasonal Timescale

AbstractThis study introduces a novel diagnostic method to assess tropical atmosphere‐ocean coupling, using a two‐dimensional plane defined by the 90‐day high‐pass‐filtered sea surface temperature (SST) and column water vapor (CWV). The method was applied to reanalysis data and high‐resolution coupled atmosphere‐ocean simulation data. In the Indo–Pacific warm pool region, the phase relationship between SST and CWV remained consistent across both reanalysis and simulation data sets. However, differences in the temporal evolution of these variables were observed in the central Pacific region. The heat budget analysis results indicate that the differences between the two data sets in the central Pacific are related to variations in the effects of atmospheric disturbances on SST. This study demonstrates the potential of our novel diagnostic method for evaluating atmosphere–ocean coupling in climate models.

Robust Polar Amplification in Ice-Free Climates Relies on Ocean Heat Transport and Cloud Radiative Effects

Abstract A fundamental divide exists between previous studies that conclude that polar amplification does not occur without sea ice and studies that find that polar amplification is an inherent feature of the atmosphere independent of sea ice. We hypothesize that a representation of climatological ocean heat transport is key for simulating polar amplification in ice-free climates. To investigate this, we run a suite of targeted experiments in the slab ocean aquaplanet configuration of CESM2-CAM6 with different profiles of prescribed ocean heat transport, which are invariant under CO2 quadrupling. In simulations without climatological ocean heat transport, polar amplification does not occur. In contrast, in simulations with climatological ocean heat transport, robust polar amplification occurs in all seasons. What is causing this dependence of polar amplification on ocean heat transport? Energy-balance model theory is incapable of explaining our results and in fact would predict that introducing ocean heat transport leads to less polar amplification. We instead demonstrate that shortwave cloud radiative feedbacks can explain the divergent polar climate responses simulated by CESM2-CAM6. Targeted cloud locking experiments in the zero ocean heat transport simulations are able to reproduce the polar amplification of the climatological ocean heat transport simulations, solely by prescribing high-latitude cloud radiative feedbacks. We conclude that polar amplification in ice-free climates is underpinned by ocean–atmosphere coupling, through a less negative high latitude shortwave cloud radiative feedback that facilitates enhanced polar warming. In addition to reconciling previous disparities, these results have important implications for interpreting past equable climates and climate projections under high-emissions scenarios. Significance Statement Polar amplification is a robust feature of climate change in the modern-day climate. However, previous climate modeling studies fundamentally do not agree on whether polar amplification occurs in ice-free climates. In this study, we find in a state-of-the-art climate model that, if ocean heat transport is neglected, the response to an increase in CO2 is not polar amplified, whereas robust polar amplification occurs if ocean heat transport is included. Using targeted model experiments, we diagnose cloud radiative effects as the driver of this divergent behavior. We conclude that polar amplification is a robust feature of the atmosphere–ocean system. Our results have important implications for interpreting past warm climates and future projections under high-emissions scenarios.

A case study on the impact of real-time land cover changes in the intertidal zone on coastal meteorological predictions using a coupled atmosphere–ocean model

A case study on the impact of real-time land cover changes in the intertidal zone on coastal meteorological predictions using a coupled atmosphere–ocean model

The sampling method for optimal precursors of El Niño–Southern Oscillation events

Abstract. The El Niño–Southern Oscillation (ENSO) is a significant climate phenomenon that appears periodically in the tropical Pacific. The intermediate coupled ocean–atmosphere Zebiak–Cane (ZC) model is the first and classical one designed to numerically forecast the ENSO events. Traditionally, the conditional nonlinear optimal perturbation (CNOP) approach has been used to capture optimal precursors in practice. In this paper, based on state-of-the-art statistical machine learning techniques1, we investigate the sampling algorithm proposed in Shi and Sun (2023) to obtain optimal precursors via the CNOP approach in the ZC model. For the ZC model, or more generally, the numerical models with a large number O(104−105) of degrees of freedom, the numerical performance, regardless of the statically spatial patterns and the dynamical nonlinear time evolution behaviors as well as the corresponding quantities and indices, shows the high efficiency of the sampling method compared to the traditional adjoint method. The sampling algorithm does not only reduce the gradient (first-order information) to the objective function value (zeroth-order information) but also avoids the use of the adjoint model, which is hard to develop in the coupled ocean–atmosphere models and the parameterization models. In addition, based on the key characteristic that the samples are independently and identically distributed, we can implement the sampling algorithm by parallel computation to shorten the computation time. Meanwhile, we also show in the numerical experiments that the important features of optimal precursors can still be captured even when the number of samples is reduced sharply.

Northern Hemisphere winter atmospheric teleconnections are intensified by extratropical ocean-atmosphere coupling

The role of extratropical atmosphere-ocean coupling in generating and maintaining large-scale atmospheric low-frequency variability remains an open question owing to vigorous atmospheric internal fluctuations. Here, we use coupled and uncoupled large-ensemble global model simulations to clarify how the coupling intensifies atmospheric teleconnection patterns in the Northern Hemisphere winter. We show that the extratropical coupling selectively enhances the variance of three principal modes of variability, explaining 13%, 11%, and 10% of the total variance of Pacific/North American, North Atlantic Oscillation, and Warm-Arctic Cold-Eurasian patterns, respectively. Atmosphere-ocean coupling reduces damping to lower-tropospheric available potential energy, which in turn increases kinetic energy by changing energy transfer within the mode. The extratropical ocean is overall passive (adjustable) to large-scale atmospheric variation, thus contributing to the prominence of these modes. The geographical dependence of available potential energy damping suggests the existence of mode-specific sweet spots where the influence of coupling operates efficiently, providing a clue to improving the model biases in variance and signal-to-noise ratio of these modes.

SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea

We explore to what extent data-driven prediction models have skills in forecasting daily sea-surface temperature (SST), which are comparable to or perform better than current physics-based operational systems over long-range forecast horizons. Three hybrid deep learning-based models are developed within the South China Sea (SCS) basin by integrating deep neural networks (back propagation, long short-term memory, and gated recurrent unit) with traditional empirical orthogonal function analysis and empirical mode decomposition. Utilizing a 40-year (1982–2021) satellite-based daily SST time series on a 0.25° grid, we train these models on the first 32 years (1982–2013) of detrended SST anomaly (SSTA) data. Their predictive accuracies are then validated using data from 2014 and tested over the subsequent seven years (2015–2021). The models’ forecast skills are assessed using spatial anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), with ACC proving to be a stricter metric. A forecast skill horizon, defined as the lead time before ACC drops below 0.6, is determined to be 50 days. The models are equally capable of achieving a basin-wide average ACC of ~0.62 and an RMSE of ~0.48 °C at this horizon, indicating a 36% improvement in RMSE over climatology. This implies that on average the forecast skill horizon for these models is beyond the available forecast length. Analysis of one model, the BP neural network, reveals a variable forecast skill horizon (5 to 50 days) for each individual day, showing that it can adapt to different time scales. This adaptability seems to be influenced by a number of mechanisms arising from the evident regional and global atmosphere–ocean coupling variations on time scales ranging from intraseasonal to decadal in the SSTA of the SCS basin.

Regional Cloud Forecast Verification Using Standard, Spatial, and Object-Oriented Methods

Abstract This study assesses the accuracy of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) forecasts for clouds within stable and unstable environments (thereafter refers as “stable” and “unstable” clouds). This evaluation is conducted by comparing these forecasts against satellite retrievals through a combination of traditional, spatial, and object-based methods. To facilitate this assessment, the Model Evaluation Tools (MET) community tool is employed. The findings underscore the significance of fine-tuning the MET parameters to achieve a more accurate representation of the features under scrutiny. The study’s results reveal that when employing traditional pointwise statistics (e.g., frequency bias and equitable threat score), there is consistency in the results whether calculated from Method for Object-Based Diagnostic Evaluation (MODE)-based objects or derived from the complete fields. Furthermore, the object-based statistics offer valuable insights, indicating that COAMPS generally predicts cloud object locations accurately, though the spread of these predicted locations tends to increase with time. It tends to overpredict the object area for unstable clouds while underpredicting it for stable clouds over time. These results are in alignment with the traditional pointwise bias scores for the entire grid. Overall, the spatial metrics provided by the object-based verification methods emerge as crucial and practical tools for the validation of cloud forecasts. Significance Statement As the general Navy meteorological and oceanographic (METOC) community engages in collaboration with the broader scientific community, our goal is to harness community tools like MET for the systematic evaluation of weather forecasts, with a specific focus on variables crucial to the Navy. Clouds, given their significant impact on visibility, hold particular importance in our investigations. Cloud forecasts pose unique challenges, primarily attributable to the intricate physics governing cloud development and the complexity of representing these processes within numerical models. Cloud observations are also constrained by limitations, arising from both top-down satellite measurements and bottom-up ground-based measurements. This study illustrates that, with a comprehensive understanding of community tools, cloud forecasts can be consistently verified. This verification encompasses traditional evaluation methods, measuring general qualities such as bias and root-mean-squared error, as well as newer techniques like spatial and object-based methods designed to account for displacement errors.

Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region

Abstract. In this article the development of a high-resolution Earth System Model (ESM) for the Baltic Sea region is described. In contrast to conventional coupling approaches, the presented model features an additional (technical) component, the flux calculator, which calculates fluxes between the model components on a common exchange grid. This approach naturally ensures conservation of exchanged quantities, a locally consistent treatment of the fluxes, and facilitates interchanging model components in a straightforward manner. The main purpose of this model is to downscale global reanalysis or climate model data to the Baltic Sea region as typically, global model grids are too coarse to resolve the region of interest sufficiently. The regional ESM consists of the Modular Ocean Model 5 (MOM5) for the ocean and the COSMO model in CLimate Mode (CCLM, version 5.0_clm3) for the atmosphere. The bi-directional ocean–atmosphere coupling allows for a realistic air–sea feedback that outperforms the traditional approach of using uncoupled standalone models, as typically pursued with the EURO-CORDEX protocol. In order to address marine environmental problems (e.g., eutrophication and oxygen depletion), the ocean model is internally coupled with the marine biogeochemistry model, ERGOM, set up for the Baltic Sea's hydrographic conditions. The regional ESM can be used for various scientific questions such as climate sensitivity experiments, reconstruction of ocean dynamics, study of past climates, and natural variability, as well as investigation of ocean–atmosphere interactions. Therefore, it can serve for a better understanding of natural processes via attribution experiments that relate observed changes to mechanistic causes.

Connection of Summer Surface Temperature Anomalies between the East European Plain–West Siberian Plain and North America on the Interannual Time Scale

Abstract This study identifies a significantly positive relationship between summer surface air temperature (SAT) anomalies over two remote regions in the Eurasian continent and North America during the period 1979–2021 on the interannual time scale. The former region includes the East European Plain and the West Siberian Plain, and the latter region includes central and eastern North America. The regionally averaged summer SAT anomalies show a correlation coefficient of 0.66 between these two regions, which is significant at the 99% confidence level. This intercontinental SAT relationship can be explained by a wavelike pattern of circulation anomalies, which is the leading mode of upper-tropospheric circulation anomalies over the middle and high latitudes of the Northern Hemisphere in summer. Further analysis suggests that the sea surface temperature (SST) anomalies over the Pacific and North Atlantic in the preceding spring, being coupled with the leading mode of atmospheric circulation anomalies over the Pacific–Atlantic sector, persist into summer and affect the SATs in the two remote regions, resulting in the intercontinental SAT connection. Significance Statement Summer surface air temperature (SAT) has profound effects on public health and agricultural production. Here we find a significantly positive relationship between interannual variations of summer SATs over two remote regions, one in the Eurasian continent and the other in North America. This intercontinental relationship in SATs can be explained as a result of atmosphere–ocean coupling over the Pacific and North Atlantic in the preceding spring. The result is likely to be a critical implication for the seasonal forecast of SAT variations over the two regions. In addition, the concurrence of higher or lower temperatures in these two regions may have impacts on global grain production, since these two regions include many major grain-producing areas in the world.

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases.

The Influence of Time-Varying Sea Ice Concentration on Antarctic and Southern Ocean Numerical Weather Prediction

Abstract Although operational weather forecasting centers are increasingly using global coupled atmosphere–ocean–ice models to replace atmosphere-only models for short- and medium-range (10 day) weather forecasting, the influence of sea ice on such forecasting has yet to be fully quantified, especially in the Southern Ocean. To address this gap, a polar-specific version of the Weather Research and Forecasting Model is implemented with a circumpolar Antarctic domain to investigate the impact of daily updates of sea ice concentration on short- and medium- range weather forecasting. A statistically significant improvement in near-surface atmospheric temperature and humidity is shown from +24 to +192 h when updating the daily sea ice concentration in the model. The forecast skill improvements for 2-m temperature and dewpoint temperature are enhanced from June to September, which is the period of late sea ice advance. Regionally, model improvement is shown to occur in most sea ice regions, although the improvement is strongest in the Ross Sea and Weddell Sea sectors. The surface heat budget also shows remarkable improvement in outgoing radiative heat fluxes and both sensible and latent heat fluxes. This idealized research demonstrates the nonnegligible effect of including more accurate time-varying sea ice concentration in numerical weather forecasting. Significance Statement The purpose of this study is to understand how a more realistic Antarctic sea ice field may influence the skill of short- and medium-range weather forecasts. Many operational atmospheric numerical weather prediction (NWP) models use a static forecast field through the time frame of the model’s forecast—often 3–10 days. In this study, we updated the sea ice concentration field daily and compared the forecast outcomes with those from model runs using a static sea ice concentration field. We found the forecast skill of near-surface temperature and humidity show the most significant improvements in our idealized experiments. This indicates the importance of incorporating improved dynamic sea ice representation in Antarctic short- to medium-range operational weather forecasting.

Topographically channeled ocean-atmosphere coupling in the southern Caribbean and summer climate variability

This research links global climate to regional weather by considering Caribbean trade wind strength in the context of the large-scale Walker circulation across the Pacific-Atlantic basins, and localized processes involving air-sea interactions between freshwater flux, the ocean mixed-layer depth, and topographic channeling of airflow north of Colombia. Trade wind driven coastal upwelling in the southern Caribbean is enhanced by the Andes Mountains, and creates a focal point for summer climate variability. This emerges in empirical orthogonal function (EOF) analysis of June-July surface zonal winds in the period 1979-2022. Highest EOF loading occurs at 12° N, 75° W northwest of Colombia. Point-to-field correlations with the EOF time score reflect a Pacific-Atlantic thermal dipole and Walker circulation linked with the El Niño-Southern Oscillation (ENSO). As southern Caribbean trade winds weaken, run-off increases, the upper ocean becomes buoyant, and westward currents slacken. Composite differences show that slow trade-wind conditions in June-July induce a counter-current that spreads warm fresh water northeastward from Colombia. This plume disperses toward the Antilles Islands with sufficient memory to triple the number of tropical cyclones in August-September. A slow trade-wind case study in June 2011 emphasizes key air-sea interactions. Channeling of the large-scale airflow north of the Andes Mountains creates a narrow atmospheric bridge for transmission of ENSO signals.

Signal‐to‐noise errors in free‐running atmospheric simulations and their dependence on model resolution

AbstractEnsemble forecasts have been shown to better predict observed Atlantic climate variability than that of their own ensemble members. This phenomenon—termed the signal‐to‐noise paradox—is found to be widespread across models, timescales, and climate variables, and has wide implications. The signal‐to‐noise paradox can be interpreted as forecasts underestimating the amplitude of predictable signals on seasonal‐to‐decadal timescales. The cause of this remains unknown. Here, we examine sea level pressure variability from a very large multi‐model ensemble of uninitialized atmosphere‐only simulations, focusing on boreal winter. To assess signal‐to‐noise errors, the ratio of predictable components (RPC) is examined globally, as well as for regional climate indices: the North Atlantic Oscillation, Arctic Oscillation, Southern Annular Mode, and an Arctic index. Our analyses reveal significant correlations between the multi‐model ensemble‐mean and observations over large portions of the globe, particularly the tropics, North Atlantic, and North Pacific. However, RPC values greater than one are apparent over many extratropical regions and in all four climate indices. Higher‐resolution models produce greater observation‐model correlations and greater RPC values than lower‐resolution models in all four climate indices. We find that signal‐to‐noise errors emerge more clearly at higher resolution, but the amplitudes of predictable signals do not increase with resolution, at least across the range of resolutions considered here. Our results suggest that free‐running atmospheric models underestimate predictable signals in the absence of sea surface temperature biases, implying that signal‐to‐noise errors originate in the atmosphere or in ocean–atmosphere coupling.

Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc

Abstract. The confrontation of complex Earth system model (ESM) codes with novel supercomputing architectures poses challenges to efficient modeling and job submission strategies. The modular setup of these models naturally fits a modular supercomputing architecture (MSA), which tightly integrates heterogeneous hardware resources into a larger and more flexible high-performance computing (HPC) system. While parts of the ESM codes can easily take advantage of the increased parallelism and communication capabilities of modern GPUs, others lag behind due to the long development cycles or are better suited to run on classical CPUs due to their communication and memory usage patterns. To better cope with these imbalances between the development of the model components, we performed benchmark campaigns on the Jülich Wizard for European Leadership Science (JUWELS) modular HPC system. We enabled the weather and climate model Icosahedral Nonhydrostatic (ICON) to run in a coupled atmosphere–ocean setup, where the ocean and the model I/O is running on the CPU Cluster, while the atmosphere is simulated simultaneously on the GPUs of JUWELS Booster (ICON-MSA). Both atmosphere and ocean are running globally with a resolution of 5 km. In our test case, an optimal configuration in terms of model performance (core hours per simulation day) was found for the combination of 84 GPU nodes on the JUWELS Booster module to simulate the atmosphere and 80 CPU nodes on the JUWELS Cluster module, of which 63 nodes were used for the ocean simulation and the remaining 17 nodes were reserved for I/O. With this configuration the waiting times of the coupler were minimized. Compared to a simulation performed on CPUs only, the MSA approach reduces energy consumption by 45 % with comparable runtimes. ICON-MSA is able to scale up to a significant portion of the JUWELS system, making best use of the available computing resources. A maximum throughput of 170 simulation days per day (SDPD) was achieved when running ICON on 335 JUWELS Booster nodes and 268 Cluster nodes.

Development of a Numerical Prediction Model for Marine Lower Atmospheric Ducts and Its Evaluation across the South China Sea

The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South China Sea domain was conducted to evaluate the short-term forecasting effectiveness of lower atmospheric ducts. The assessment of sounding observation data revealed that the prediction model performed well in predicting the characteristics of all types of ducts. The mean values of the forecasting errors were slightly lower than the reanalysis data but had lower levels of correlation coefficients. At an altitude of about 2000 m, the forecasted error of modified atmospheric refractivity reached peak values and then decreased gradually with increasing altitude. The accuracy of forecasted surface ducts was higher than that of elevated ducts. Noticeable land–sea differences were identified for the spatial distributions of duct characteristics, and the occurrence rates of both the surface and elevated ducts were high at sea. As for the differences among the forecasts of 24, 48, and 72 h ahead, the differences primarily occurred at altitude levels below 20 m and 500 m~1500 m, which are consistent with the differences in the duct height.

Non-ENSO Precursors for Northwestern Pacific Summer Monsoon Variability with Implications for Predictability

Abstract The influence of El Niño–Southern Oscillation (ENSO) in the Asian monsoon region can persist through the post-ENSO summer, after the sea surface temperature (SST) anomalies in the tropical Pacific have dissipated. The long persistence of coherent post-ENSO anomalies is caused by a positive feedback due to interbasin ocean–atmospheric coupling, known as the Indo-western Pacific Ocean capacitor (IPOC) effect, although the feedback mechanism itself does not necessarily rely on the antecedence of ENSO events, suggesting the potential for substantial internal variability independent of ENSO. To investigate the respective role of ENSO forcing and non-ENSO internal variability, we conduct ensemble “forecast” experiments with a full-physics, globally coupled atmosphere–ocean model initialized from a multidecadal tropical Pacific pacemaker simulation. The leading mode of internal variability as represented by the forecast-ensemble spread resembles the post-ENSO IPOC, despite the absence of antecedent ENSO forcing by design. The persistent atmospheric and oceanic anomalies in the leading mode highlight the positive feedback mechanism in the internal variability. The large sample size afforded by the ensemble spread allows us to identify robust non-ENSO precursors of summer IPOC variability, including a cool SST patch over the tropical northwestern Pacific, a warming patch in the tropical North Atlantic, and downwelling oceanic Rossby waves in the tropical Indian Ocean south of the equator. The pathways by which the precursors develop into the summer IPOC mode and the implications for improved predictability are discussed.