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The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the "pattern effect." To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation. Plain Language Summary: Many properties of the atmosphere are affected by the temperature of the ocean surface. Knowing how strong these effects are would help us to better predict global warming. The response to a given surface warming depends on where the warming occurs. To account for this, researchers sometimes simulate the response to individual patches of warming and then assume the response to an arbitrary warming pattern can be summed together from these patch responses. This is sometimes called the atmospheric Green's function method, and it works well at recreating the atmospheric response to historical temperature changes. We are organizing a Green's Function Model Intercomparison Project (GFMIP), in which participants will apply the method consistently for many climate models. This paper presents the GFMIP protocol. In the course of developing this protocol, we found that the atmospheric response to warming is not proportional in all cases: the response to surface warming is not the opposite of the response to surface cooling; warming twice as much doesn't cause twice as much of a response; and making a patch of warming twice as large doesn't cause twice as large a response. GFMIP will help us figure out how to account for this nonlinearity. Key Points: The Green's Function Model Intercomparison Project (GFMIP) explores the atmospheric response to surface temperature patch perturbationsThis paper presents the GFMIP protocol, which was generated using insights from past studies and new sensitivity testsGreen's functions reconstruct the response to historical temperatures, but nonlinearities can affect responses to other warming patterns [ABSTRACT FROM AUTHOR]

When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub‐cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid‐scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry‐to‐wet 10‐days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas. Plain Language Summary: In nature, cold pools are formed by cold air masses descending from the low to mid‐troposphere in thunderstorms. When these drafts reach the surface, they spread out horizontally. A manifestation of cold pools is the relatively high speed at the gust front, which can lift environmental air producing new convective cells. Moreover, depending on ambient conditions, the cold pools may help organize the new convective cells, increasing their aggregation and forming the so‐called mesoscale convective systems (MCSs). MCSs, which cover hundreds to thousands of km2, significantly impact the global scale circulation, energy budget, hydrological cycle, and population safety. Forecasting MCSs is challenging for global circulation models (GCM) due to the broad spectrum of scales of the involved atmospheric phenomenon. The computational limitations, at present and for some time to come, do not allow running in real‐time GCMs, which explicitly solves all relevant scales of motion. This paper describes a methodology to account for essential interplays between cold pools edges and moist convection to be applied in the GCMs of weather and climate forecasting. We show that the method improves the model simulation of the main types of MCSs over the Amazon Basin and other continental areas. Key Points: A model scheme for including effect of cold pools edges in triggering new convective cells and storm propagation is presentedThe scheme is coupled with a convection parameterization and applied in the modeling of moist convective systemsThe method improves the organization, longevity, propagation, and severity of the simulated mesoscale convective systems [ABSTRACT FROM AUTHOR]

The non‐local model of mixing based on internal wave breaking, IDEMIX, is implemented as an enhancement of a turbulent kinetic energy closure model in three non‐eddy resolving general circulation ocean models that differ in the discretization and choice of computational grids. In IDEMIX internal wave energy is generated by an energy flux resulting from near‐inertial waves induced by wind forcing at the surface, and at the bottom, by an energy flux that parameterizes the transfer of energy between baroclinic and barotropic tides. In all model simulations with IDEMIX, the mixing work is increased compared to the reference solutions without IDEMIX, reaching values in better agreement with finestructure observations. Furthermore, the horizontal structure of the mixing work is more realistic as a consequence of the heterogeneous forcing functions. All models with IDEMIX simulate deeper thermocline depths related to stronger shallow overturning cells in the Indo‐Pacific. In the North Atlantic, deeper mixed layers in simulations with IDEMIX are associated with an increased Atlantic overturning circulation and an increase of northward heat transports toward more realistic values. The response of the deep Indo‐Pacific overturning circulation and the weak bottom cell of the Atlantic to the inclusion of IDEMIX is incoherent between the models, suggesting that additional unidentified processes and numerical mixing may confound the analysis. Applying different tidal forcing functions leads to simulation differences that are small compared to differences between the different models or between simulations with IDEMIX and without IDEMIX. Plain Language Summary: Waves in the ocean interior play a fundamental role for ocean dynamics since they can carry energy over long distances and, once they break, lead to turbulent mixing. This turbulent mixing can cause dense water masses to rise from the deep ocean with a direct impact on large‐scale currents. The wave dynamics occur on spatial scales that cannot be resolved in global ocean or climate models. To account for these processes, we apply the new parameterization IDEMIX that describes internal wave generation, propagation, and mixing. Using three different ocean models with and without IDEMIX ensures that we can identify model‐specific effects of the parameterization and discriminate them from those independent of the model. We find that the simulated mixing patterns agree better with observations once IDEMIX is applied. Large‐scale currents and the vertical temperature distribution are substantially affected by the internal wave parameterization. Whether this leads to an improved agreement with observed currents and water mass properties depends on the specific model and on numerical effects. In most cases, simulations with IDEMIX are not very sensitive to details of how the internal wave model is driven by tidal energy input. Key Points: the IDEMIX closure for a consistent representation of internal wave‐induced mixing is evaluated in three state‐of‐the‐art ocean modelsonly in simulations with IDEMIX can the models reproduce the magnitude and spatial variability of the observed mixing workmost changes with IDEMIX can be attributed to stronger mixing, but some effects are confounded by other processes and numerical mixing [ABSTRACT FROM AUTHOR]

The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise‐to‐noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training data set is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere‐Ocean General Circulation Model simulations. The neural network utilized for training is a U‐Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single‐model initial‐condition large ensembles, the ensemble mean, and the U‐Net's predictions. The U‐Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U‐Net encounters challenges in capturing the changes in the North Atlantic Ocean. This methodology holds potential for extension to other physical variables, facilitating insights into the climate change triggered by external forcings over the long term. Plain Language Summary: To anticipate future climate change, it is crucial to detect and understand the impacts of human activities. However, distinguishing the effects of anthropogenic forcing from natural climate variations in observational data is challenging. Natural climate variability, known as internal variability, arises from the chaotic nature of atmospheric and oceanic circulation, and from the interactions among the ocean, atmosphere, and land. Here, a novel approach is introduced to distinguish the changes caused by human activities from internal variability. It is applied to the surface air temperature evolution from 1901 to 2020. This method uses an artificial neural network designed to separate the internal from the human‐induced variability. An unprecedented number of climate model simulations are used, enabling precise estimation of human‐forced variability in these climate models. The spatio‐temporal variations are distinguished by applying a well‐known methodology previously used to remove noise from images. The method's performance is evaluated, revealing errors regarding the internal variability that are typically one‐fourth of the actual variations. Regions with important internal variability or with low agreement among models exhibit the largest errors. Overall, the skills are comparable to other existing approaches, but improvements are anticipated. Key Points: We present a new method to separate the forced and internal variability of the surface air temperatureWe utilize a U‐Net trained with global climate models outputs and implement a noise to noise methodology to eliminate internal variabilityThe results are assessed through the utilization of very large ensemble simulations of two distinct climate models [ABSTRACT FROM AUTHOR]

A single column model with parameterized large‐scale (LS) dynamics is used to better understand the response of steady‐state tropical precipitation to relative sea surface temperature under various representations of radiation, convection, and circulation. The large‐scale dynamics are parametrized via the weak temperature gradient (WTG), damped gravity wave (DGW), and spectral weak temperature gradient (Spectral WTG) method in NCAR's Single Column Atmosphere Model (SCAM6). Radiative cooling is either specified or interactive, and the convective parameterization is run using two different values of a parameter that controls the degree of convective inhibition. Results are interpreted in the context of the Global Atmospheric System Studies ‐Weak Temperature Gradient (GASS‐WTG) Intercomparison project. Using the same parameter settings and simulation configuration as in the GASS‐WTG Intercomparison project, SCAM6 under the WTG and DGW methods produces erratic results, suggestive of numerical instability. However, when key parameters are changed to weaken the large‐scale circulation's damping of tropospheric temperature variations, SCAM6 performs comparably to single column models in the GASS‐WTG Intercomparison project. The Spectral WTG method is less sensitive to changes in convection and radiation than are the other two methods, performing qualitatively similarly across all configurations considered. Under all three methods, circulation strength, represented in 1D by grid‐scale vertical velocity, is decreased when barriers to convection are reduced. This effect is most extreme under specified radiative cooling, and is shown to come from increased static stability in the column's reference radiative‐convective equilibrium profile. This argument can be extended to interactive radiation cases as well, though perhaps less conclusively. Plain Language Summary: Single column models, as the name suggests, only model the vertical dimension of the atmosphere. They are simpler than full‐scale 3D global circulation models, but nonetheless play an important role in model development and in better understanding physical phenomena. We use NCAR's Single Column Atmosphere Model (SCAM6) to better understand tropical rainfall. In a single column model, the atmospheric wind coming from other locations (the large‐scale circulation) must be either specified or approximated using a parameterization. We implement three different parameterizations of the large‐scale circulation into SCAM6 and assess how SCAM6 responds to various changes while using these parameterizations. The Global Atmospheric System Studies‐Weak Temperature Gradient (GASS‐WTG) Intercomparison project assessed the performance of various other single column models using two parameterizations of the large‐scale circulation. We fold SCAM6 into this Intercomparison, and we find that SCAM6 performs substantially differently to its peers under the settings used in the GASS‐WTG Intercomparison project, but more comparably to its peers when key circulation parameters are relaxed. Another notable finding is that, across all three parameterizations of large‐scale dynamics, circulation strength decreases when we reduce SCAM6's barrier to convection. Key Points: We use three different methods to parameterize the large‐scale (LS) dynamics in NCAR's single column atmospheric model (SCAM6)As in the Global Atmospheric System Studies Intercomparison, we test SCAM6's response to various boundary conditions and model parametersUnder all three methods, circulation strength is decreased when barriers to convection are reduced [ABSTRACT FROM AUTHOR]

Tropical high cloud cover decreases with surface warming in most general circulation models. This reduction, according to the "stability‐iris" hypothesis, is thermodynamically controlled and linked to a decrease in the radiatively‐driven clear‐sky convergence, when the peak anvil clouds rise because of the rising isotherms. The influence of the large‐scale dynamical changes on the tropical high cloud fraction remains difficult to disentangle from the local thermodynamic influence, given that the mean meridional circulation remains inextricably tied to the local thermodynamic structure of the atmosphere. However, using idealized general circulation model simulations, we propose a novel method to segregate the dynamical impact from the thermodynamic impact on the tropical high cloud fraction. To this end, our investigation primarily focuses on the mechanisms underpinning changes in the high cloud cover in the deep tropics in response to extratropical surface warming, when the tropical sea surface temperatures remain invariant. Net convective detrainment of ice cloud condensates decreases at the peak detrainment region, without a rise in its altitude. We also find that the importance of depositional growth of ice cloud condensates in controlling the high cloud fraction response in the deep tropics varies with altitude. Plain Language Summary: The cloud feedback associated with changes in the tropical high cloud cover is one of the major uncertainties in calculating the current estimates of climate sensitivity, which is a measure of how much the Earth's average surface temperature would increase if we double the amount of atmospheric carbon dioxide. When the surface becomes warmer in the tropics, the tropical high cloud cover decreases. However, this raises an important question: how do circulation changes independent of the temperature changes within the tropics impact the tropical high cloud cover? Using idealized general circulation model simulations, we found that the tropical high cloud fraction decreases as a result of circulation changes induced by extratropical warming, even when the tropical sea surface temperatures are held constant. Both convective and cloud microphysical processes play significant roles in controlling the changes in tropical high cloud fraction, and their relative importance varies with altitude. Understanding the different factors responsible for the changes in high cloud cover is important, as the area covered by these tropical high clouds can affect how much the Earth warms under climate change. Key Points: An indirect weakening of the Hadley circulation decreases the convectively‐detrained ice cloud condensates in the deep tropicsA concurrent reduction in the net vertical transport of water vapor limits the depositional growth of ice cloud condensatesThe relative influence of depositional growth to net convective detrainment on the tropical high cloud response is altitude‐dependent [ABSTRACT FROM AUTHOR]

To improve cloud simulation in a general circulation model, we develop a new cloud macrophysics scheme that treats detrained cumulus generated by convective detrainment process separately from pure stratus; prognoses two symmetric triangular probability density functions of total specific humidity for each detrained cumulus and pure stratus; and diagnoses both cloud fraction and cloud condensate in all liquid, ice, and mixed‐phases in a consistent and unified way without any adjustment to remove empty or very dense cloud. Supersaturation is allowed within ice cloud. The new scheme ("NEW") is compared with the previous model ("OLD") using single‐column simulations for subtropical marine stratocumulus (DYCOMS2) and continental deep convection (ARM97) cases. In DYCOMS2, both NEW and OLD produce vertical profiles of grid‐mean cloud condensate similar to large‐eddy simulation. In ARM97, compared with OLD, NEW simulates less sporadic vertical profiles of in‐cloud condensates, due to consistent diagnosis of cloud fraction and cloud condensate; more continuously‐varying detrained cumulus with time, due to prognostic treatment of detrained cumulus; and ice cloud fraction and ice condensate similar to those of OLD, in spite of completely different treatment of ice cloud processes. The global performance of NEW is similar to OLD with improved relative humidity. Compared to OLD, NEW simulates more and improved cloud condensate, but less and degraded cloud fraction, particularly, in the lower troposphere. Detrained cumulus is moister and colder and has a larger moisture variance than pure stratus. Overall, NEW simulates stronger condensation‐deposition rates than OLD, due in part to the separate treatment of detrained cumulus and pure stratus. Plain Language Summary: This study suggests a new cloud parameterization scheme in a general circulation model that can represent clouds in nature in a realistic way. Our new cloud scheme separates two types of stratus, called detrained cumulus and pure stratus, by using a set of prognostic equations. Moreover, both liquid, ice, and mixed‐phase clouds are treated in a consistent way. We found that the new scheme successfully operates for two single‐column simulations for subtropical marine stratocumulus and continental deep convection cases. In addition, we performed the global simulation and found that the new scheme simulated more cloud condensate but less cloud fraction than the old scheme with some improvements. Key Points: We develop a new cloud macrophysics scheme by prognosing two triangular probability density functions for each detrained cumulus and pure stratusThe new scheme computes both cloud fraction and cloud condensate in all liquid, ice, and mixed‐phases in a consistent and unified wayThe new scheme simulates more cloud condensate but less cloud fraction than the old scheme with improved relative humidity and global performance [ABSTRACT FROM AUTHOR]

In recent years, efforts have been made to include tides in both operational ocean models as well as climate and earth system models. The accuracy of the barotropic tides is often limited by the model topography, which is in turn limited by model horizontal resolution. In this work, we explore the reduction of barotropic tidal errors in an ocean general circulation model (Modular Ocean Model version 6; MOM6) using sub‐grid scale topography representation. We follow the methodology from Adcroft (2013, https://doi.org/10.1016/j.ocemod.2013.03.002), which utilizes statistics from finer resolution topographic data sets to represent sub‐grid scale features with a light computational cost in a structured finite volume formulation. The geometric effect from sub‐grid scale topography can be introduced to the model with only a few parameters at each grid cell. The porous barriers, which are implemented at the walls of the grid cells, are used to modify transport between grid cells. Our results show that the globally averaged tidal error in lower‐resolution simulations is significantly reduced with the use of porous barriers. We argue this method is a potentially useful tool to improve simulations of tides (and other flows) in low‐resolution simulations. Plain Language Summary: In recent years, significant attention has been given to simulating tides in diverse ocean and climate models, because of the roles of tides in impacting sea levels. In order to better predict future sea level changes, it is therefore essential to improve the accuracy of surface tides simulated in numerical ocean models. One known source of errors that could impact the accuracy of modeled tides stems from the accuracy of ocean topography in the models, which relies on how many grid boxes (i.e., model resolution) are used to represent the complicated seafloor elevations. The model's resolution, however, is often constrained by the efficiency of computations. Our objective in this study is to enhance the portrayal of ocean topography at a given resolution to minimize tidal errors. To achieve this, we employ a technique known as "porous barriers," which uses approximations to mimic the effects of ocean topography that would be omitted in the models, without imposing significant computational burdens. Our findings demonstrate that this technique of porous barriers can substantially reduce tidal errors in our ocean model. We also expect a wider application of this technique to numerous other physical processes within the ocean. Key Points: The accuracy of tides in ocean models is affected by the representation of topography, which is constrained by model horizontal resolutionThe geometric effect of the sub‐grid scale topographic features can be approximated in the model with the porous barriersThe porous barriers can significantly reduce globally averaged errors for barotropic tides [ABSTRACT FROM AUTHOR]

Gravity wave parameterizations contribute to uncertainties in middle atmosphere modeling. To investigate the potential for using machine learning to represent atmospheric gravity waves and the impact of implementing such schemes in a general circulation model (GCM), we train a random forest (RF) emulator on outputs from an existing complex parameterization scheme for orographic gravity wave drag (GWD). The performance of the RF emulator is then evaluated with a focus on stratospheric climatology and variability in climate simulations from the middle atmosphere resolving Beijing Climate Center Atmospheric Circulation Model. In offline tests, the predicted orographic GWD by the RF agrees remarkably well with the target GWD throughout the troposphere and the middle atmosphere. The RF emulator can reproduce the observed climatology of zonal‐mean wind and air temperature in the GCM simulation, as well as its target scheme. Compared to the target orographic GWD parameterization scheme, the RF emulator can reproduce the breakdown of the polar vortex in the Southern Hemisphere stratosphere. This study demonstrates the feasibility of using machine learning to emulate parameterized orographic GWD for modeling the stratosphere with a GCM. Plain Language Summary: Machine learning has been utilized to learn atmospheric physical parameterizations such as moist convection, cloud microphysics, radiation, nonorographic gravity waves, etc. However, it remains unknown whether it is feasible to apply machine learning algorithms to parameterize orographic gravity waves and how such schemes perform when interactively coupled in atmospheric general circulation models. Here we employ the widely used random forest algorithm to learn from an existing complex parameterization scheme for orographic gravity wave drag. Then, we implement the random forest scheme in a high‐top atmospheric general circulation model to test its performance. We find that the random forest scheme acts as well as the target parameterization scheme. This study demonstrates the potential of using machine learning to emulate parameterized orographic gravity waves for modeling the middle atmosphere. In future work, it is recommended that machine learning may be used to develop new parameterization schemes directly from observations or high‐resolution model outputs to overcome critical deficiencies in current orographic gravity wave parameterizations, helping to reduce the common biases in simulating the stratosphere with general circulation models. Key Points: Parameterized gravity wave drag from unresolved orography can be emulated with a random forestThe random forest emulator simulates vertical distributions of the zonal wind and air temperature quite well in a general circulation model (GCM)The GCM configured with the random forest scheme reproduces a reasonable breakdown of the austral polar vortex in the stratosphere [ABSTRACT FROM AUTHOR]

To improve the simulation of nocturnal precipitation, we develop a parameterization for the elevated convection that is launched from the level of maximum grid‐mean moist static energy, when grid‐mean vertical flow is upward at the launching interface. The parameterized elevated convection is forced by both cold pool‐driven and partially resolved external mesoscale organized flows. Properties of the external mesoscale flow are estimated from grid‐mean vertical velocity and three dimensional advection tendencies of temperature and moisture. The new parameterization is implemented into a unified convection scheme (UNICON) and tested for both the single‐column case at the Southern Great Plain (SGP) site in US and in global simulations. At the SGP site, the parameterized elevated convection strengthens nocturnal convection, increases nocturnal precipitation, and better simulates the observed diurnal cycle of precipitation. It appears that the elevated and surface‐based convections interact with each other by stabilizing the atmospheric column, which affects subsequent convection. Global simulation shows that the elevated convection mostly occurs over the continents during the night, and also over the oceanic mid‐latitude warm air advection and storm track regions during summer. Without degrading global mean climate, the elevated convection improves the simulation of nocturnal precipitation in the mid‐US but simulates somewhat strong nocturnal precipitation in northeastern Asia. It seems that a key to simulate observed nocturnal precipitation is to appropriately parameterize the impacts of external organized flow on the elevated convection. Plain Language Summary: Precipitation has a strong diurnal cycle as a result of complex interactions between the atmosphere and the surface through moist convection. The diurnal cycle of precipitation has a significant impact on surface energy and water budgets, and its accumulated effect may appear even on the climate timescale. However, most general circulation models (GCM) poorly simulate the observed diurnal cycle of precipitation. Particularly, it has been difficult to simulate nocturnal precipitation over the central US during summer. To improve the simulation of nocturnal precipitation, we develop a new parameterization for the elevated convection. Both single‐column and global test simulations show that the new parameterization increases nocturnal precipitation and better simulates the observed diurnal cycle of precipitation. Our research will contribute to improving the performance of GCM and so better simulating current and future climates. Key Points: To improve the simulation of nocturnal precipitation, we develop a parameterization for the elevated convectionIn the mid‐US, the new parameterization increases nocturnal precipitation and better simulates the observed diurnal cycle of precipitationA key to simulate observed nocturnal precipitation is to parameterize the impacts of external mesoscale flow on the elevated convection [ABSTRACT FROM AUTHOR]

The accuracy of Earth system models is compromised by unknown and/or unresolved dynamics, making the quantification of systematic model errors essential. While a model parameter estimation, which allows parameters to change spatio‐temporally, shows promise in quantifying and mitigating systematic model errors, the estimation of the spatio‐temporally distributed model parameters has been practically challenging. Here we present an efficient and practical method to estimate time‐varying parameters in high‐dimensional spaces. In our proposed method, Hybrid Offline and Online Parameter Estimation with ensemble Kalman filtering (HOOPE‐EnKF), model parameters estimated by EnKF are constrained by results of offline batch optimization, in which the posterior distribution of model parameters is obtained by comparing simulated and observed climatological variables. HOOPE‐EnKF outperforms the original EnKF in synthetic experiments using a two‐scale Lorenz96 model and a simple global general circulation model. One advantage of HOOPE‐EnKF over traditional EnKFs is that its performance is not greatly affected by inflation factors for model parameters, thus eliminating the need for extensive tuning of inflation factors. We thoroughly discuss the potential of HOOPE‐EnKF as a practical method for improving parameterizations of process‐based models and prediction in real‐world applications such as numerical weather prediction. Plain Language Summary: Earth system models help us understand and predict the behavior of our planet, but their accuracy is limited due to unknown or unresolved factors. Adjusting the parameters of these models based on the changing patterns in time and space can help improve their accuracy. However, this has been a challenging task. In this study, we introduce a new method called Hybrid Offline and Online Parameter Estimation with ensemble Kalman filtering (HOOPE‐EnKF) that efficiently estimates these changing parameters. This method combines an existing EnKF with offline batch optimization, which compares long‐term simulation with observations to fine‐tune the model. Our experiments show that HOOPE‐EnKF performs better than the original EnKF and is less sensitive to certain hyperparameters, reducing the need for time‐consuming calibrations. HOOPE‐EnKF has a great potential for improving the accuracy of models and predictions in real‐world applications, like weather forecasting, and can help scientists better understand our planet's complex systems. Key Points: Ensemble Kalman filter (EnKF) is extended to estimate high‐dimensional time‐varying model parametersThe proposed method is insensitive to the choice of covariance inflation factorsThe proposed method successfully quantifies systematic model errors from observations [ABSTRACT FROM AUTHOR]