Coupled Global Model Research Articles

Influence of waves on the three-dimensional distribution of plastic in the ocean

The world's oceans are facing plastic pollution, 80 % of which of terrestrial origin flowing from the mismanaged waste of coastal populations and from river discharge. To study the fate of this pollution, the three-dimensional trajectories of neutral plastic particles continuously released for 24 years according to realistic source scenarios are computed using currents from a global ocean-wave coupled model at 14∘ resolution and from a reference ocean-only model. These Lagrangian simulations show that neutral particles accumulate at the surface in the subtropical convergence zones from where they penetrate to about 250 m depth and strongly disperse over 40∘ of latitude. About 5.3 % of the particles remain at the surface with the wave-coupled model currents, whereas only 2 % for the uncoupled model, with some modulation in the location of the convergence zones. Increased surface retention results from upward vertical velocities induced by widespread divergence of waves-induced Stokes transport in the surface layers.

An ADRC Parameters Self-Tuning Control Strategy of Tension System Based on RBF Neural Network

High precision control of substrate tension is the premise and guarantee for producing high-quality products in roll-to-roll precision coating machine. However, the complex relationships in tension system make the problems of decoupling control difficult to be solved, which has limited the improvement of tension control accuracy for the coating machine. Therefore, an ADRC parameters self-tuning decoupling strategy based on RBF neural network is proposed to improve the control accuracy of tension system in this paper. Firstly, a global coupling nonlinear model of the tension system is established according to the composition of the coating machine, and the global coupling model is linearized based on the first-order Taylor formula. Secondly, according to the linear model of the tension system, a parameters self-tuning decoupling algorithm of the tension system is proposed by integrating feedforward control, ADRC and RBF. Finally, the simulation results show that the proposed tension control strategy has good decoupling control performance and effectively improves the tension control accuracy for the coating machine.

Biases and teleconnections in the Met Office Global Coupled Model version 5.0 (GC5) – insights for seasonal prediction and Australia

The Australian Bureau of Meteorology (The Bureau) has been involved in the package testing and assessment process of the UK Met Office Global Coupled Model Version 5.0 (GC5) configuration. GC5 will underpin the Met Office’s next seasonal prediction system, global coupled numerical weather prediction (NWP) system and Earth System Model. It will also likely be the next version of The Bureau’s seasonal prediction system, and the version to replace the global atmosphere-only NWP system to be the first global coupled NWP system at The Bureau. The GC5 configuration includes a new sea-ice model and substantial updates to almost all areas of model physics. We have evaluated the present-day climate simulation, and compared it to observations and with previous versions GC4 and GC2. Our assessment focuses on the climate mean state and variabilities relevant to Australian seasonal prediction, including the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode and the Madden–Julian Oscillation. Notably, in comparison to its predecessor (GC4), GC5 shows significant improvements in the eastern Pacific mean state but a slight degradation in the Indian Ocean in terms of the mean state and variability. These and other results provide us with early insights of the potential performance of the next sub-seasonal or seasonal forecast system. Longstanding issues in the seasonal prediction system associated with the equatorial eastern Indian Ocean biases and an overactive ENSO and IOD will likely remain; however, improvements over the eastern equatorial Pacific in GC5 hold promise of improved prediction skill of ENSO and its teleconnections.

Realistic ocean initial condition for stimulating the successful prediction of extreme cold events in the 2020/2021 winter

In the first half of winter 2020/2021, several unprecedented cold events occurred in most parts of China and caused record-breaking low temperatures in many cities. However, seasonal predictions related to the onset and evolution of extreme cold events are still challenging. In this study, we first evaluated the short-term climate prediction skills in winter of the CAS-ESM-c global coupled model and revealed the key dynamic processes of the onset and development of 2020/2021 extreme cold events based on the ensemble forecasts starting on October 1st, 2020. Under the background provided by the synergistic effect of the warm Arctic and the cold tropical Pacific (La Niña), the model captured the abnormal meridional atmospheric pattern well, including the intensification of the Ural Blocking High and the negative phase of the Arctic Oscillation, forecasted the negative geopotential height anomalies over the Eastern Asian region, and finally, successfully predicted the outbreak of cold waves in December 2020 two months in advance. Moreover, the dominant differences in the initial ocean fields between the best and the worst forecast members were further compared to isolate the triggering factor for predicting cold events by CAS-ESM-c. The initial warm sea surface temperature over the Barents Sea can gradually form a meridional pattern between the warm Arctic and the cold Asian continent, which could lead to a reduction in the large-scale meridional temperature gradient at mid-high latitudes and weaken the atmospheric baroclinicity with more conducive to the southward outbreak of cold waves, highlighting that realistic Arctic ocean conditions in autumn can be reasonably assimilated into coupled models to stimulate the successful prediction of extreme cold events in the 2020/2021 winter.

Extratropical Transition of Tropical Cyclones in a Multiresolution Ensemble of Atmosphere-Only and Fully Coupled Global Climate Models

Abstract Tropical cyclones undergo extratropical transition (ET) in every ocean basin. Projected changes in ET frequency under climate change are uncertain and differ between basins, so multimodel studies are required to establish confidence. We used a feature-tracking algorithm to identify tropical cyclones and performed cyclone phase-space analysis to identify ET in an ensemble of atmosphere-only and fully coupled global model simulations, run at various resolutions under historical (1950–2014) and future (2015–50) forcing. Historical simulations were evaluated against five reanalyses for 1979–2018. Considering ET globally, ensemble-mean biases in track and genesis densities are reduced in the North Atlantic and western North Pacific when horizontal resolution is increased from ∼100 to ∼25 km. At high resolution, multi-reanalysis-mean climatological ET frequencies across most ocean basins as well as basins’ seasonal cycles are reproduced better than in low-resolution models. Skill in simulating historical ET interannual variability in the North Atlantic and western North Pacific is ∼0.3, which is lower than for all tropical cyclones. Models project an increase in ET frequency in the North Atlantic and a decrease in the western North Pacific. We explain these opposing responses by secular change in ET seasonality and an increase in lower-tropospheric, pre-ET warm-core strength, both of which are largely unique to the North Atlantic. Multimodel consensus about climate change responses is clearer for frequency metrics than for intensity metrics. These results help clarify the role of model resolution in simulating ET and help quantify uncertainty surrounding ET in a warming climate.

Will Increasing Climate Model Resolution Be Beneficial for ENSO Simulation?

AbstractIncreasing climate model resolution offers multifaceted benefits, such as improving modeled tropical cyclones. However, the extent to which it benefits El Niño‐Southern Oscillation (ENSO) simulation remains unknown. Here, comprehensive information on the sensitivity of ENSO performance to various resolutions is provided, based on a multi‐model and multi‐resolution ensemble of global coupled models. Overall, the reduced model biases of the equatorial sea surface temperature (SST) and precipitation mean state in higher‐resolution models may be attributed to increased oceanic resolution, and thus, better resolved eddy‐driven heat transport. ENSO spatial patterns were reproduced clearer in the eddy‐present models, likely due to the improved mean state and associated surface thermodynamic feedback. However, increasing atmospheric resolution alone deteriorates ENSO asymmetry, which may be due to the degradation of nonlinear atmospheric feedback. It remains challenging to alleviate the SST−shortwave‐flux feedback bias, which is a major source of too‐weak net heat flux feedback, irrespective of model resolution.

Intraseasonal scale ensemble forecasts of precipitation and evapotranspiration for the Madeira River basin using different physical parameterizations

Eta Regional Model of CPTEC-INPE is used to obtain intraseasonal (30-day) 8-member ensemble forecasts over the Madeira River basin for the period 2002–2012. The initial and boundary conditions are taken from Atmospheric General Circulation Global Model in six members and from Global Coupled Ocean-Atmosphere Model in two members. The intraseasonal forecasts produced by dynamic downscaling with Eta Regional model ensemble have satisfactory skill. The skill of the ensemble mean is better than the individual members up to 15-days lead time forecasts. The ensemble mean reproduces the seasonal cycle and spatial distribution of the hydrological variables. Members with the relaxation technique of Betts-Miller-Janjic produced better results. The forecasts by the members that used Kain-Fritsch scheme presented larger deviations from observations. Substantial improvements in skill are obtained through bias correction. This is the first work to attempt dynamic downscaling over the Madeira Basin in the intraseasonal time scale for a period of 10 years. The ensemble downscaled products have potential to be fed into surface hydrological models for forecasting droughts and floods and related hydrological variables over the basin.

S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

Abstract A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

Decadal Variability of Southeast US Rainfall in an Eddying Global Coupled Model

AbstractOcean variability is a dominant source of remote rainfall predictability, but in many cases the physical mechanisms driving this predictability are not fully understood. This study examines how ocean mesoscales (i.e., the Gulf Stream SST front) affect decadal Southeast US (SEUS) rainfall, arguing that the local imprint of large‐scale teleconnections is sensitive to resolved mesoscale features. Based on global coupled model experiments with eddying and eddy‐parameterizing ocean, we find that a resolved Gulf Stream improves localized rainfall and remote circulation response in the SEUS. The eddying model generally improves the air‐sea interactions in the Gulf Stream and the North Atlantic Subtropical High that modulate SEUS rainfall over decadal timescales. The eddy‐parameterizing simulation fails to capture the sharp SST gradient associated with the Gulf Stream and overestimates the role of tropical Pacific SST anomalies in the SEUS rainfall.

Projection of future sea level rise in the East Asian Seas based on Global Ocean-Sea Ice Coupled Model with SRES A1B Scenario

To project the future sea level rise in the East Asian Seas due to global warming, regional sea level variations are downscaled from three climate system models (GFDL-CM2.1, ECHAM5/MPI-OM, MIROC3.2(hires)) using a global ocean-sea ice coupled model with non-Boussinesq approximation. Based on the SRES A1B Scenario, the projected ensemble mean sea level rise (rate of rise) for the East Sea, Yellow Sea and East China Sea from 1995 to 2050 is 15.60cm (2.84mm/year), 16.49cm (3.0mm/year) and 16.43cm (2.99mm/year), respectively. With the inclusion of the future change of land ice melting and land water storage, the mean sea level rise (rate of rise) increases to 33.55cm (6.10mm/year) for the East Sea, and 34.38~34.44cm (6.25~6.26mm/year) for the Yellow and East China Seas. The present non-Boussinesq ocean model experiment shows that the future sea level rise in the East Sea is mainly due to the steric component changes by heat content increase. On the other hand, the future sea level rise in the Yellow and East China Seas appears to be mainly associated with the non-steric component change by water mass convergence.

Evaluation of the Ability of CMIP6 Global Climate Models to Simulate Precipitation in the Yellow River Basin, China

Choosing an appropriate GCM (Global Climate Model, GCM) is of great significance for the simulation of the hydrological cycle over a basin under future climate scenarios. In this study, the Rank Score Method (RS) with eight indicators were applied to comprehensively evaluate the suitability of 19 GCMs issued in the Sixth Global Atmosphere and Coupled Model Intercomparison Project (CMIP6) to the Yellow River Basin (YRB). The results indicated that: 1) The GCMs perform differently in simulating precipitation over the YRB with the top six GCMs ranking from MRI-ESM2-0, ACCESS-CM2, CNRM-CM6-1, CNRM-ESM2-1, FGOALS-f3-L, to MPI-ESM1-2-HR. 2) Most GCMs overestimated the precipitation, and poorly simulated the phase distribution of extremes mainly due to overstimulation of wet season span and precipitation amount in the season, although all GCMs could capture decadal feature of annual precipitation. Meanwhile, it is also found that most GCMs underestimated summer precipitation and overestimated spring precipitation. 3) The GCMs well simulated the spatial distribution of annual precipitation, with an overestimation in the source area, and an underestimation in the northern part of the middle reaches of YRB.

Causes and Consequences of Sea Ice Initialization Shock in Coupled NWP Hindcasts with the GC2 Climate Model

AbstractWe present a coupled retrospective forecast (hindcast) study using the Met Office Global Coupled Model, version 2 (GC2), in which we identify and mitigate causes of initialization shock that lead to rapid error growth in sea ice forecasts. Sea ice state variables and volume budget terms as a function of forecast lead time are evaluated relative to analyses from an uncoupled Met Office ocean–sea ice analysis system [Forecast Ocean Assimilation Model, version 13 (FOAMv13)]. Two sources of initialization shock are highlighted and addressed, both of which are related to effective differences in physics between the analysis system and coupled forecast model. The primary shock to sea ice state variables arises from the use of a salinity-independent freezing temperature for seawater in GC2 as opposed to a salinity-dependent formulation in FOAMv13. A secondary effect arises from differences in the sea ice roughness and hence air–ice drag in the GC2 forecast model compared to the FOAMv13 analysis system. Generalizing from the findings of this study, we suggest that using nonnative analyses as initial conditions for coupled numerical weather prediction (NWP) studies will likely make them prone to initialization shock in some model components, to the detriment of forecast skill. To reduce the undesirable impacts of initialization shock on short-range forecast skill noted in this study we would therefore recommend the use of initial conditions (analyses) physically consistent with the native model components of the coupled forecast model, a native coupled analysis likely being the optimal initialization method.

A Novel Initialization Technique for Decadal Climate Predictions

Model initialization is a matter of transferring the observed information available at the start of a forecast to the model. An optimal initialization is generally recognized to be able to improve climate predictions up to a few years ahead. However, systematic errors in models make the initialization process challenging. When the observed information is transferred to the model at the initialization time, the discrepancy between the observed and model mean climate causes the drift of the prediction toward the model-biased attractor. Although such drifts can be generally accounted for with a posteriori bias correction techniques, the bias evolving along the prediction might affect the variability that we aim at predicting, and disentangling the small magnitude of the climate signal from the initial drift to be removed represents a challenge. In this study, we present an innovative initialization technique that aims at reducing the initial drift by performing a quantile matching between the observed state at the initialization time and the model state distribution. The adjusted initial state belongs to the model attractor and the observed variability amplitude is scaled toward the model one. Multi-annual climate predictions integrated for 5 years and run with the EC-Earth3 Global Coupled Model have been initialized with this novel methodology, and their prediction skill has been compared with the non-initialized historical simulations from CMIP6 and with the same decadal prediction system but based on full-field initialization. We perform a skill assessment of the surface temperature, the heat content in the ocean upper layers, the sea level pressure, and the barotropic ocean circulation. The added value of the quantile matching initialization is shown in the North Atlantic subpolar region and over the North Pacific surface temperature as well as for the ocean heat content up to 5 years. Improvements are also found in the predictive skill of the Atlantic Meridional Overturning Circulation and the barotropic stream function in the Labrador Sea throughout the 5 forecast years when compared to the full field method.

Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

AbstractThe usefulness of dynamical downscaling of seasonal reforecasts of Indian Monsoon is explored to address the seasonal mean biases in the reforecasts. Almost all the current generation global coupled models, including the Climate Forecast System version 2 (CFSv2, T126 ∼110 km), exhibit systematic mean dry bias over the central Indian region during the summer monsoon season. Cold sea surface temperature (SST) biases in the Indian Ocean and a weak monsoon circulation due to a colder tropospheric temperature contribute to this dry bias. Such systematic biases restrict the use of skillful forecasts from these models in climate applications (such as agriculture or hydrology). Dynamical downscaling of seasonal forecasts (∼110 km resolution) using the Weather Research and Forecasting (WRF) model coupled to a simple ocean mixed layer model (OML; WRFOML) at 38 km resolution significantly reduces the majority of the systematic biases reported earlier. The seasonal mean dry bias reduces to 16% in WRFOML as compared to 44% (33%) in the CFSv2‐T126 (WRFCTL) over the Indian land region. Warmer SSTs in the Indian Ocean and a more robust monsoon circulation emanating from a realistic simulation of the tropospheric temperature reduced the systematic biases in WRFOML compared to CFSv2‐T126 and WRFCTL. Additionally, category‐wise rainfall distributions are also improved drastically in the downscaled simulations (WRFOML). Downscaled reforecasts with reduced systematic biases have better suitability for climate applications.

Impact of air\u2013sea coupling on the climate change signal over the Iberian Peninsula

In this work we use a regional atmosphere–ocean coupled model (RAOCM) and its stand-alone atmospheric component to gain insight into the impact of atmosphere–ocean coupling on the climate change signal over the Iberian Peninsula (IP). The IP climate is influenced by both the Atlantic Ocean and the Mediterranean sea. Complex interactions with the orography take place there and high-resolution models are required to realistically reproduce its current and future climate. We find that under the RCP8.5 scenario, the generalized 2-m air temperature (T2M) increase by the end of the twenty-first century (2070–2099) in the atmospheric-only simulation is tempered by the coupling. The impact of coupling is specially seen in summer, when the warming is stronger. Precipitation shows regionally-dependent changes in winter, whilst a drier climate is found in summer. The coupling generally reduces the magnitude of the changes. Differences in T2M and precipitation between the coupled and uncoupled simulations are caused by changes in the Atlantic large-scale circulation and in the Mediterranean Sea. Additionally, the differences in projected changes of T2M and precipitation with the RAOCM under the RCP8.5 and RCP4.5 scenarios are tackled. Results show that in winter and summer T2M increases less and precipitation changes are of a smaller magnitude with the RCP4.5. Whilst in summer changes present a similar regional distribution in both runs, in winter there are some differences in the NW of the IP due to differences in the North Atlantic circulation. The differences in the climate change signal from the RAOCM and the driving Global Coupled Model show that regionalization has an effect in terms of higher resolution over the land and ocean.

Can we trust CMIP5/6 future projections of European winter precipitation?

IPCC models project a likely increase in winter precipitation over northern Europe under a high-emission scenario. These projections, however, typically rely on relatively coarse ∼100 km resolution models that can misrepresent important processes driving precipitation, such as extratropical cyclone activity, and ocean eddies. Here, we show that a pioneering 50 km atmosphere–1/12° ocean global coupled model projects a substantially larger increase in winter precipitation over northwestern Europe by mid-century than lower-resolution configurations. For this increase, both the highest ocean and atmosphere model resolutions are essential: only the eddy-rich (1/12°) ocean projects a progressive northward shift of the Gulf Stream. This leads to a strong regional ocean surface warming that intensifies air–sea heat fluxes and baroclinicity. For this then to translate into a strengthening of North Atlantic extratropical cyclone activity, the 50 km atmosphere is essential, as it enables enhanced diabatic heating from water vapor condensation and an acceleration of the upper-level mean flow, which weaken vertical stability. Our results suggest that all recent IPCC climate projections using traditional ∼100 km resolution models could be underestimating the precipitation increase over Europe in winter and, consequently, the related potential risks.

Exploring Viscosity Space in an Eddy‐Permitting Global Ocean Model: Is Viscosity a Useful Control for Numerical Mixing?

AbstractA generic shortcoming of constant‐depth (or “z‐coordinate”) ocean models such as MOM5 and Nucleus for European Models of the Ocean (NEMO) is a tendency for the advection scheme to produce unphysical numerical diapycnal mixing, which may exceed the explicitly parameterized mixing based on observed physical processes. Megann (2018, https://doi.org/10.1016/j.ocemod.2017.11.001) estimated the effective diapycnal diffusivity in the Global Ocean Version 5.0 (GO5.0) 0.25° global implementation of the NEMO model and showed that this was up to 10 times the explicit diffusivity used in the model's mixing scheme and argued that this was at least partly caused by large transient vertical velocities on length scales comparable to the horizontal grid scale. The current UK global NEMO configuration GO6, as used in the Global Coupled Model version 3.1 (GC3.1) and UK Earth System Model (UKESM1), is integrated in forced mode at 0.25° resolution with a range of viscosity parameterizations. In the present study, the effective diffusivity is evaluated for each integration and compared with the explicit value from the model mixing scheme, as well as with that in the control (using the default viscosity). It is shown that there is a strong correspondence between lower viscosity and enhanced numerical mixing and that larger viscosities lead to a marked reduction in the unrealistic internal temperature drift seen in the control configuration, without incurring excessive damping of the large‐scale circulation, mixed layer depths, or sea ice cover. The results presented here will inform the choices made in global ocean configurations used in climate and Earth System models following the sixth Coupled Model Intercomparison Project (CMIP6).

Prediction of Arctic Temperature and Sea Ice Using a High-Resolution Coupled Model

AbstractUnder global warming, surface air temperature has risen rapidly and sea ice has decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-m air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°–90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System, version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the maximum decrease trend of SIC by 1 month, in association with the heat flux forcing from the ocean surface to lower atmosphere. While the predictive skill is generally much lower for the detrended variations, we find a close relationship between the tropical Pacific El Niño–Southern Oscillation and the Arctic detrended 2-m T anomalies. This indicates potential seasonal to interannual predictability of the Arctic natural variations.

Pressure‐Driven Poiseuille Flow Inherited From Mesozoic Mantle Circulation Led to the Eocene Separation of Australia and Antarctica

AbstractThe separation between Australia and Antarctica occurred during the final stages of the break‐up of Pangea. Reconstructions of the rifting of the Australian plate away from Antarctica show fast spreading rates since Mid‐Eocene (45 Ma). These reconstructions can be used to understand and quantify the forces driving the Australia/Antarctica separation, and to test hypotheses on mechanisms that may be of shallow (i.e., lithosphere) or deep (i.e., mantle) origin. Analytical calculations indicate that plate‐boundary forces are highly unlikely to be a plausible candidate to explain such a separation. Thus, we use a recently developed global coupled models of mantle and lithosphere dynamics, here we show that this event, whose kinematics are reproduced in our models within the bounds of the reconstruction uncertainties, owes to a significant degree to the pressure‐driven asthenospheric Poiseuille flow associated with the mantle buoyancy field inherited from viscous circulation history throughout the Mesozoic. On the contrary, in simulations when such a buoyancy field is replaced by another one resulting from a random distribution of mantle temperature–thus not representative of Earth’s mantle circulation history–the rapid northward motion of Australia does not occur. Similarly, suppressing contemporaneous plate‐boundary processes (i.e., subduction of the Pacific ridge at the Aleutians and healing of the India‐Australia ridge) from our models does not have a noticeable effect on the Australia‐Antarctica kinematics. Thus, a pressure‐driven Poiseuille mantle flow must be considered, at least in this example and possible elsewhere, as a main driver of plate tectonics.

Global marine heatwave events using the new CMIP6 multi-model ensemble: from shortcomings in present climate to future projections

In recent years, research related to the occurrence of marine heatwave (MHW) events worldwide has been increasing, reporting severe impacts on marine ecosystems which led to losses of marine biodiversity or changes in world fisheries. Many of these studies, based on regional and global coupled models, show relevant biases in the MHW properties when compared with observations. In this study, the MHW frequency of occurrence, the duration and mean intensity over the global oceans are characterized, taking advantage of the new global climate model (GCM) dataset, from the Coupled Model Project Intercomparison Phase 6 (CMIP6). The MHWs result for the historical period are compared with observations, and the future projected changes are characterized under three socioeconomic pathways (SSPs) (SSP1, SSP2 and SSP5), for the middle and end of century (2041–2070 and 2071–2100). The results show a reasonable agreement between the modeled and observed MHW property trends, indicating increases in the frequency, duration and intensity of MHWs along the historical period. For the period 1982–2014, both the ∼2 mean observed events per year and the mean intensity of 0.35 °C above the threshold are underestimated by the multi-model ensemble (MME) mean by 21% and 31%, respectively, while the observed duration of ∼12 d are overestimated by 100%. The future MHWs are expected to increase in duration and intensity, where a near permanent MHW occurs with reference to the historical climate conditions, mainly by the end of the 21st century. The future MHWs intensity, projected by the MME mean, increases in the range of 0.2 °C to 1.5 °C, from the least to the most severe pathways. The GCMs biases obtained with CMIP6 revealed to be in line with the CMIP5 biases, reinforcing the need to use high spatial resolution models to characterize MHW.