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Scafetta (2022, https://doi.org/10.1029/2022gl097716) purports to test Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models through a comparison of temperature changes over three decades. Unfortunately, the paper contains numerous conceptual and statistical errors that undermine all of the conclusions. First, no uncertainty is given for the observational temperature difference, making it impossible to assess compatibility with any model result. Second, the CMIP6 data are the ensemble means for each model, but the metric being tested is sensitive to the internal variability and so the full ensemble for each model must be used. When this is corrected, the conclusion that "all models with ECS > 3.0°C overestimate the observed global surface warming" is not sustained. Third, the statistical test in Section 2 would reject all models even in a perfect model setup given sufficient ensemble members, thus the second conclusion "that spatial t‐statistics rejects the data‐model agreement" is also not sustainable. Plain Language Summary: Comparisons of models and observations need to account from multiple sources of uncertainty in both the observations and due to the chaotic dynamics of the weather. The analyses in Scafetta (2022, https://doi.org/10.1029/2022gl097716) do not take either of these issues into account and thus the conclusions in that paper are not supportable. Key Points: Scafetta (2022) contains errors in both of the statistical tests used that make the conclusions unsupportable [ABSTRACT FROM AUTHOR]

Air‐entraining whitecaps provide an important source of bubbles over the global oceans, yet the rate at which the associated air is entrained is not well known. This lack of understanding limits the ability to accurately parameterize bubble‐mediated gas exchange and sea spray aerosol flux. In this paper I present a model to predict the total volume of air entrained by individual whitecaps and extend it to estimate the rate at which air is entrained per unit sea surface area. The model agrees well with existing models and measurements and can be forced by the rate at which energy is dissipated by the wavefield which can be routinely provided by spectral wave models. I then use the model to present the first distributions of the estimated total volume of air entrained by individual whitecaps, as well as their rate of air entrainment and air degassing. Plain Language Summary: The amount of air in the oceans in the form of bubbles at any given time is not well known because of the difficulty associated with making in‐situ measurements. This lack of knowledge inhibits how well ocean‐atmosphere exchange processes that are driven by air and bubbles can be represented in climate models. In this paper, I present a new model to estimate the volume of air entrained by individual breaking waves called whitecaps, as well as how quickly the air is entrained into the oceans and how quickly it leaves the oceans when bubbles rise to the surface and burst. Key Points: A model for the entrainment of air by oceanic whitecaps is presented which agrees well with existing models and measurementsDistributions of the estimated volume of air entrained by individual oceanic whitecaps are presented for the first timeKey uncertainties in the air fraction and entrainment velocities of individual whitecaps remain due to a lack of measurements [ABSTRACT FROM AUTHOR]

An array of atmospheric profile observations consists of three‐dimensional vectors representing pressure, temperature, and humidity, with each profile forming a continuous curve in this three‐dimensional space. In this paper, the Signature method, which can quantify a profile's curve, was adopted for the atmospheric profiles, and the accuracy of profile representations was investigated. The description of profiles by the signature was confirmed with adequate accuracy. The machine‐learning‐based model, developed using the signature, exhibited a high level of annual accuracy with minimal absolute mean differences in temperature and water vapor mixing ratio (<2.0 K or g kg−1). Notably, the model successfully captured the vertical structure and atmospheric instability, encompassing drastic variations in water vapor and temperature, even during intense rainfall. These results indicate the Signature method can comprehensively describe the vertical profile with information on how ordered values are correlated. This concept would potentially improve the representation of the atmospheric vertical structure. Plain Language Summary: The atmospheric profile can be visualized as a three‐dimensional curve representing pressure, temperature, and humidity. By utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphere. In this paper, we confirmed the accuracy of atmospheric profile representation using signatures and introduced the characteristics of signatures revealed through machine‐learning models. The description of profiles by the signature was confirmed with adequate accuracy. Moreover, the model demonstrated robust annual accuracy, with minimal temperature and water vapor discrepancies. It effectively captured the vertical structure and instability of the atmosphere, even during heavy rainfall, characterized by significant temperature and water vapor content fluctuations. Key Points: The utilization possibility of the Signature method for atmospheric profiles was confirmedBy utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphereThe machine‐learning model developed with the signature can predict the profiles with high annual accuracy [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) and North Atlantic tripole sea surface temperature (SST_tri) are important modes in the atmosphere and ocean over the North Atlantic, respectively. The link between the two is well‐known. However, this link weakened during 1980–2001, which is particularly pronounced in late winter and was not detected in early winter. This phenomenon has not been well revealed. The role of NAO in the above correlation changes was discussed. In late winter, a significant eastward shift (up to 20° longitude) of NAO south center during 1980–2001 was observed in both observation and CMIP6, accompanied by the eastward expansion of NAO north center. Spatial shift of the NAO forced the region of strong air‐sea interactions to shift and resulting in the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale. Plain Language Summary: A significant correlation between the North Atlantic Oscillation (NAO) and the North Atlantic tripole sea surface temperature (SST_tri) is widely recognized. However, this study found that the correlation between the two was significantly weakened in late winter during 1980–2001 while no such change was detected in early winter. Therefore, this paper will focus on the late winter and discuss the role of NAO spatial structure in the above correlation changes. When the NAO south center is located over the North Atlantic, both observations and the climate models indicate that the corresponding North Atlantic SST shows a significant tripole pattern. However, during 1980–2001, the NAO south center shift significantly eastward from the North Atlantic toward Western Europe (up to 20° longitude). At the same time, the NAO north center expanded eastward significantly. The eastward shift of the NAO results in the significant eastward shift of the turbulent heat flux and wind stress anomalies. Shift in the region of strong air‐sea interaction led to the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale and also provide implications for subseasonal‐seasonal predictions of Eurasian climate extremes. Key Points: The link between NAO and North Atlantic tripole SST weakened obviously during 1980–2001 late winter, which was not detected in early winterThe eastward shift of ∼20° longitude in NAO south center forced the strong air‐sea interactions region to shift in observations and CMIP6NAO's spatial shift in late winter caused the changes in the link between NAO and North Atlantic tripole SST [ABSTRACT FROM AUTHOR]

Over‐estimation of summer precipitation over the Tibetan Plateau (TP) is a well‐known and persistent problem in most climate models. This study demonstrates the impact of a Gaussian Probability Density Function cloud fraction scheme on rainfall simulations using the Weather Research and Forecasting model. It is found that this scheme in both 0.1° and 0.05° resolutions significantly reduces the wet bias through both local feedbacks and large‐scale dynamic process. Specifically, increased cloud water/ice content with this scheme reduces surface shortwave radiation, and consequently surface heat fluxes and evapotranspiration. This, in turn, dampens the large‐scale thermal effect of the TP and weakens the exaggerated monsoon circulation and low‐level moisture convergence. It is this large‐scale dynamic process that contributes the most (∼70%) to the wet bias reduction. Although this paper presents a modeling study, it highlights the cloud radiative feedback to the large‐scale dynamics and precipitation over the TP. Plain Language Summary: Despite numerous attempts to correct the overestimation of summer precipitation over the Tibetan Plateau (TP) in current global and regional climate models, the issue persists. This study applies the Gaussian Probability Density Function (GPDF) cloud fraction scheme in the Weather Research and Forecasting model at two different resolutions (0.1° and 0.05°) during a summer over the TP. The results show that the GPDF scheme significantly mitigates the precipitation overestimation, particularly in the high‐resolution modeling. We explored the physical processes, both local and remote, that contribute to this improvement. Specifically, an increase in cloudiness reduces the amount of radiation reaching the land surface. This decrease in surface radiative heating not only reduces local evaporation but also weakens the thermal effect of the TP. The latter is a major driver of the South Asian monsoon that conveys moisture to the TP, and its weakening reduces moisture convergence over the TP. Both the decreases in local evaporation and remote moisture convergence contribute to the alleviation of the precipitation overestimation, and the latter plays a dominant role. These findings provide a unique perspective for reducing the wet bias over the TP, focusing on the surface available energy and associated remote moisture processes. Key Points: The use of the Gaussian Probability Density Function cloud fraction scheme in high resolution greatly reduces wet bias over the Tibetan Plateau (TP) during summerMore cloud water/ice with the scheme lessens TP's thermal effect, causing a weaker South Asian monsoon and moisture convergenceWet bias reduction is mainly governed by the decrease in remote moisture rather than local evapotranspiration [ABSTRACT FROM AUTHOR]

The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary: The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points: The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect techniqueThe Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate modelsThe median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations. Plain Language Summary: Atmospheric rivers (ARs) are a weather pattern that brings high amounts of atmospheric water and winds in a relatively narrow region. ARs are typically considered a "west coast" phenomenon, largely because the majority of the scientific research on ARs has focused on ARs in western coastal regions: particularly the western United States (US). ARs occur in continental interiors, but there has been some debate about whether these ARs represent the same type of weather as those in western coastal regions. This paper uses two objective methods for identifying ARs and finds times when ARs are present in three locations in the eastern half of the US: Norman, OK, Bloomington, IN, and Washington, DC. Examination of weather conditions during these AR times shows remarkable similarity to conditions associated with west coast ARs. This gives strong evidence that ARs do occur in the eastern half of the US. This result is important because it suggests that ARs may be important for water resources and extreme weather in the eastern half of the US, just as they are in the western US. This result also suggests that ARs may be important for water resources and extremes in other continental interiors. Key Points: Atmospheric rivers (ARs) east of the Rockies are associated with baroclinic wavesWestern coastal ARs and eastern/midwest ARs are dynamically similarSynoptic‐scale uplift, combined with convective instability, provide efficient mechanisms for generating precipitation [ABSTRACT FROM AUTHOR]

It is shown that a recently developed hybrid modeling approach that combines machine learning (ML) with an atmospheric global circulation model (AGCM) can serve as a basis for capturing atmospheric processes not captured by the AGCM. This power of the approach is illustrated by three examples from a decades‐long climate simulation experiment. The first example demonstrates that the hybrid model can produce sudden stratospheric warming, a dynamical process of nature not resolved by the low resolution AGCM component of the hybrid model. The second and third example show that introducing 6‐hr cumulative precipitation and sea surface temperature (SST) as ML‐based prognostic variables improves the precipitation climatology and leads to a realistic ENSO signal in the SST and atmospheric surface pressure. Plain Language Summary: This paper introduces and tests schemes for efficiently enabling significant expansion of the utility and scope of a recently introduced hybrid modeling technique that combines machine learning with an atmospheric global circulation model (AGCM). Simulation experiments are carried out with an implementation of the approach on a low resolution simplified AGCM. An examination of the simulated atmospheric circulation suggests that the hybrid model can capture dynamical process not captured by the AGCM. Moreover, the addition of precipitation and sea surface temperature (SST) as machine learning predicted physical quantities to the model improves the precipitation climatology and leads to a realistic El Niño‐La Niña signal in the SST and atmospheric surface pressure. Key Points: A hybrid system combining an atmospheric global circulation model (AGCM) with a machine‐learning component can capture processes not captured by the AGCMMachine learning provides a flexible framework to introduce additional prognostic variables into the hybrid modelThe prototype hybrid model tested in the paper is stable and has a realistic climate in decades‐long simulation experiments [ABSTRACT FROM AUTHOR]

This paper expands on work showing that the winter North Atlantic Oscillation (NAO) is predictable on decadal timescales to quantify the skill in capturing the North Atlantic eddy‐driven jet's location and speed. By focusing on decadal predictions made for years 2–9 from the sixth Coupled Model Intercomparison Project over 1960–2005 we find that there is significant skill in jet latitude and, especially, jet speed associated with the skill in the NAO. However, the skill in the NAO, jet latitude and speed indices appears to be sensitive to the period over which it is assessed. In particular, skill drops considerably when evaluating hindcasts up to the present day as models fail to capture the recent observed northern shift and strengthening of the winter eddy‐driven jet, and more thus positive NAO. We suggest that the drop in atmospheric circulation skill is related to reduced skill in North Atlantic sea surface temperatures. Plain Language Summary: Climate models have the capability to predict the evolution of mean atmospheric circulation over long timescales, from annual to decadal and longer. However, models are overestimating the chaotic, unpredictable component of the climate's variability and, although model predictions follow the observed oscillations of the climate, the strength of these oscillations is critically underestimated. While climate models have demonstrated skill in predicting the North Atlantic Oscillation (NAO), this paper assesses model skill in predicting the evolution of the wintertime North Atlantic eddy‐driven jet, with the aim to highlight how much of the predictive skill of the NAO is derived from accurate predictions of the jet's state. We find levels of skill similar to that for the NAO, with slightly higher skill for the jet's strength (speed) over its location (latitude of maximum speed). We also notice a drop in skill over the last decade, as models fail to capture the latest trends in the NAO and jet's evolution, and suggest that it might be related to degradation in skill at predicting surface temperature variability. Key Points: The winter North Atlantic eddy‐driven jet is predictable on decadal time‐scales with skill in jet speed comparable to that for the North Atlantic Oscillation (NAO)Anomalies in the jet are substantially smaller than expected from skill alone and so suffer from noise overestimationSkill drops significantly in the last decade, as hindcasts do not capture the return to positive NAO conditions post‐2010 [ABSTRACT FROM AUTHOR]

The ability of global transport models to go up in resolution becomes discriminating for greenhouse gas atmospheric inversions. This paper describes the porting on Graphics Processing Units of the global transport model currently used in the European operational Copernicus Atmosphere Monitoring Service (CAMS) for CO2 and N2O inversions. It represents an important milestone to achieve sub‐degree resolution. The code includes not only the direct model but also its tangent‐linear and its adjoint versions which are needed in variational inversions. Tests were carried out for CO2 at a resolution of 2.50° in longitude, 1.27° in latitude and 79 layers in the vertical, corresponding to 1,626,768 3D cells, 4.5 times more than the current standard resolution of the model used in the CAMS reanalyzes. A month's worth of computation of the tangent‐linear and of the adjoint versions now takes 2.5 min, including 50 s for reading meteorological data. Plain Language Summary: Atmospheric transport models are intensively used to infer global greenhouse gas emissions and removals, from atmospheric measurements: a single global analysis involves repeated and long transport simulations. This intensive use has limited the spatial resolution of such analyses despite an increasing need for national greenhouse gas budgets, despite an increasing number of corresponding space‐based observations at kilometer resolution, and despite an increasing number of high‐quality surface measurements made in sites marked by strong local influences. For the global transport model currently used in the European operational atmosphere monitoring service, our objective within the next 5 years is to make it reach a resolution of about 50 km over the whole globe. This paper describes an important milestone in this direction with the porting of this transport model on hardware components initially developed for video display but now used for high performance computing. Tests were carried out at a resolution of 2.50° in longitude, 1.27° in latitude and 79 layers in the vertical, corresponding to 4.5 times more 3D cells than the current standard resolution of the model. The direct model itself now takes less time than reading the input meteorological data. Key Points: The workload in a Eulerian transport model on a longitude‐latitude grid can be embarrassingly parallel enough to run efficiently on a Graphics Processing UnitThe transport model of the Laboratoire de Météorologie Dynamique was run in a new grid of 1,626,768 3D cellsThe calculation time in the direct version of the model is now less than the time to read the input meteorological data [ABSTRACT FROM AUTHOR]

A mass‐conserving method to downscale precipitation from global climate models (GCMs) using sub‐grid‐scale topography and modeled 700‐mb wind direction is presented. Runoff is simulated using a stand‐alone hydrological model, with this and several other methods as inputs, and compared to runoff simulated using historical observations over the western contiguous United States. Results suggest the mitigation of grid‐scale biases is more critical than downscaling for some regions with large wet biases (e.g., the Great Basin and Upper Colorado). In other regions (e.g., the Pacific Northwest) the new method produces more realistic sub‐grid‐scale variability in runoff compared to unadjusted GCM output and a simpler downscaling method. The presented method also brings the runoff centroid timing closer to that simulated with observations for all subregions examined. Plain Language Summary: Due to limitations in computing power which necessitates coarse spatial resolution, climate models do not include many details on mountains and their impact on precipitation. For this reason, it is difficult to estimate the impact of climate change on the availability of water for human consumption in places like the western United States, where mountain snowpack is an important source of water. This paper presents a way to adjust precipitation estimates from climate models by using some simple statistics about nearby mountains and valleys. The adjusted precipitation is then used in a hydrologic model to calculate the runoff that is simulated with different precipitation inputs. Results show that in most cases, the precipitation adjustment improves estimates of the resultant runoff relative to simulations with observed precipitation. The main exceptions are in very dry areas where the climate model produces far too much precipitation. With further work, the proposed adjustment may be integrated into the climate model itself to make it easier for those managing water resources (e.g., controlling reservoir levels) to use the model output to plan and adapt to climate change. Key Points: A mass‐conserving method for downscaling orographic precipitation improves modeled runoff from the CESM2Considering upwind topography further improves modeled runoff compared to simpler adjustmentsNot tuning to individual model grid points makes this method more generalizable than many existing statistical downscaling methods [ABSTRACT FROM AUTHOR]

Oxygen chemistry plays a central role in the production of plasma in the ionosphere. While molecular oxygen (O2) is the third most abundant constituent in the thermosphere, molecular and atomic oxygen densities are difficult to quantify accurately. Consequently, modeled densities have significant uncertainties. New stellar occultation measurements from the Global‐scale Observations of Limb and Disk (GOLD) mission provide data that can quantify, with high precision, the O2 density and its daily and local time variations. These observations are compared to the Mass Spectrometer Incoherent Scatter radar family of empirical models and the physics based Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension. While GOLD observations show a strong diurnal structure with local time, the models display a semidiurnal structure in O2. The local time structure of O2 is critical for accurately modeling plasma densities in the ionosphere. Plain Language Summary: This paper compares new observations from the Global‐scale Observations of Limb and Disk mission of molecular oxygen (O2) in the lower thermosphere (130–200 km in altitude) to widely used models in the aeronomy community. The main finding is that the local time structure of the observed O2 has a minimum at 6 hr local time and a peak near 18 hr local time while the models all show a structure that has peaks at both 6 and 18 hr local time. This significant difference not only influences the total neutral thermospheric density results from the models, but may ultimately impact the calculated ionospheric plasma density and its temporal variability. Understanding ionospheric plasma densities is vital for the proper modeling of the propagation of communications and navigational signals. Key Points: Global‐scale Observation of Limb and Disk molecular oxygen (O2) density indicates a diurnal local time structure, while empirical and numerical models have a semidiurnal structure Seasonal variability in O2 density is important for understanding the local time structure Observed vertical O2 density structure has discrepancies with models, with larger differences at 170 km than 140 km [ABSTRACT FROM AUTHOR]

The quasi‐biennial oscillation (QBO) impacts the Madden‐Julian Oscillation (MJO) activity with a stronger MJO in QBO easterly (QBOE) than QBO westerly (QBOW) winters. However, this relationship is poorly represented in the current generation climate models. For the first time, this paper applies a stratospheric zonal‐mean nudging in a subseasonal prediction system to capture it. Two strong MJO cases in a QBO‐neutral winter are investigated. The QBO temperature and zonal wind anomalies are added separately as well as together to the stratosphere using nudging in MJO case hindcast. Only by nudging the QBO temperature anomalies while leaving the zonal wind free, can the prediction system capture the observed QBO‐MJO connection. The tropopause instability is found positively correlated to the MJO amplitude, but it cannot fully explain the captured connection. The free‐evolving zonal wind anomalies in the stratosphere due to the nudged QBO temperature are crucial for the captured connection. Plain Language Summary: The Madden‐Julian Oscillation (MJO) as a dominant subseasonal mode in the tropical troposphere is strongly modulated by the quasi‐biennial oscillation (QBO), which is the dominant interannual mode in the tropical stratosphere. However, such QBO‐MJO connection is missing in the long‐term simulations of all current climate models. Here, we show that by adding the QBO temperature signals into the MJO case simulation using the CESM2 subseasonal prediction system, the QBO‐MJO connection can be captured. Interestingly, the QBO temperature alone is not enough to explain the captured connection. The free‐evolving zonal wind generated by the nudged temperature is also crucial to capturing the QBO‐MJO relationship. These results provide a new perspective to understand the QBO‐MJO connection and improve its representation in climate models. Key Points: QBO‐MJO connection is captured in initialized MJO case studies by adding QBO temperature anomalies to the stratosphereThe free‐evolving zonal wind shear in the stratosphere is necessary to capture the QBO‐MJO connectionTropopause instability changes can not fully explain the captured QBO‐MJO connection [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of tropical Atlantic on El Niño‐Southern Oscillation (ENSO) predictability through an empirical dynamical model‐ Linear Inverse Model (LIM). By selectively including or excluding the coupling between tropical Atlantic and tropical Pacific in LIM, we find that the tropical Atlantic dynamics significantly improve the Eastern Pacific (EP)‐ENSO prediction and weaken the EP‐ENSO predictability barrier (PB), with the Equatorial Atlantic (EA) mode playing a more important role than the Tropical North Atlantic (TNA) mode. The tropical Atlantic impacts on Central Pacific (CP)‐ENSO predictability and PB are relatively smaller. Consistent with observations, the tropical Atlantic can weaken PB in most CMIP6 models. The evolution of the tropical Atlantic optimum initial structures confirms the important influence of the EA mode on the eastern tropical Pacific. Therefore, the tropical Atlantic dynamics, especially the EA mode, should be appropriately considered to improve the prediction of EP‐ENSO and weaken the EP‐ENSO PB. Plain Language Summary: El Niño–Southern Oscillation (ENSO) is one of the dominant climate modes in the tropical Pacific on interannual timescales, with significant global impacts. The tropical Atlantic sea surface temperature (SST) variability, especially in the tropical North Atlantic (TNA) and equatorial Atlantic (EA), is shown to have important impacts on ENSO variability. Using a linear dynamical model, we find that including the dynamics of the tropical Atlantic, especially the EA SST mode, can significantly improve the forecast skill of Eastern Pacific ENSO and weaken the EP‐ENSO spring predictability barrier (SPB), which is a sudden reduction of ENSO prediction skill in the boreal spring. However, Central Pacific (CP)‐ENSO's forecast skill is less influenced by the tropical Atlantic dynamic. Consistent with the observations, the topical Atlantic can weaken ENSO SPB in most CMIP6 climate models. Our results suggest that the tropical Atlantic dynamics should be appropriately considered to improve the forecast skill of EP‐ENSO. Key Points: A linear dynamical model is used to study the impact of the tropical Atlantic on El Niño‐Southern Oscillation (ENSO) predictabilityTropical Atlantic dynamics can improve Eastern Pacific (EP)‐ENSO predictability and weaken EP‐ENSO spring predictability barrier while the impact on Central Pacific‐ENSO is limitedThe Equatorial Atlantic mode plays an important role in the tropical Atlantic influence of EP‐ENSO prediction [ABSTRACT FROM AUTHOR]

This paper evaluates sea surface temperature (SST) biases of coupled models participating in Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. Overall, CMIP6 models perform better than CMIP5 ones in reproducing SST climatology, with lower multi‐model ensemble mean (MME) globally averaged absolute bias (1.17 vs. 1.31 K). MME bias in global mean annual SST shifts from cooling (−0.09 ± 0.52 K) to warming (0.23 ± 0.60 K). Regionally, in CMIP6 cooling biases over the Northwest Pacific and North Atlantic are reduced by 20% and 18%, while warming biases over the Northeast Pacific, Southeast Atlantic and Southern Ocean are increased by 25%, 16% and 107% respectively. These changes are mainly attributed to the combined effects from aggravated positive (or alleviated negative) bias in clear‐sky surface downward longwave radiation, and alleviated negative bias in cloud radiative effect, partially reduced by enhanced cooling bias in clear‐sky surface downward shortwave radiation. Plain Language Summary: As the primary approach to projecting future climate change, state‐of‐the‐art climate models still suffer pronounced biases in climatological annual mean sea surface temperature (SST), such as cold biases over the Northwest Pacific and North Atlantic, and warm biases over the Northeast Pacific, Southeast Pacific, Southeast Atlantic and Southern Ocean. We have evaluated the changes in mean‐state SST biases between the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. CMIP6 models perform better in reproducing SST climatology with lower absolute bias, which is attributed to the process‐level improvement. Overall, annual global mean SST bias shifts from cold (−0.09 ± 0.52 K) to warm (0.23 ± 0.60 K), which is mainly due to the regionally alleviated cooling biases or aggravated warming biases. This warmer shift is contributed by the increased positive (or decreased negative) bias in clear‐sky surface downward longwave radiation and decreased negative bias in cloud radiative effect. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) models perform better than CMIP5 ones, with significantly lower global‐mean absolute bias in annual sea surface temperature (SST)Global‐mean SST bias is with a warmer shift (+0.32 K) in CMIP6, with salient regional cold biases alleviated and warm biases aggravatedReduced cold bias in cloud radiative effect and positive change in bias in clear‐sky surface downward longwave together account for the shift [ABSTRACT FROM AUTHOR]

Since the early 1990s the Pacific Walker circulation shows a multi‐decadal strengthening, which contradicts future model projections. Whether this trend, evident in many climate indices especially before the 2015 El Niño, reflects the coupled ocean‐atmosphere response to global warming or the negative phase of the Pacific Decadal Oscillation (PDO) remains debated. Here we show that sea surface temperature trends during 1980–2020 are dominated by three signals: a spatially uniform warming trend, a negative PDO pattern, and a Northern Hemisphere‐Indo‐West Pacific warming pattern. The latter pattern, which closely resembles the transient ocean thermostat‐like response to global warming emerging in a subset of CMIP6 models, shows cooling in the central‐eastern equatorial Pacific but warming in the western Pacific and tropical Indian Ocean. Together with the PDO, this pattern drives the Walker circulation strengthening in the equatorial band. Historical simulations appear to underestimate this pattern, contributing to the models' inability to replicate the Walker cell strengthening. Plain Language Summary: This paper investigates the observed changes in the tropical Pacific during the satellite era, including the recent decadal strengthening of the atmospheric zonal circulation—the Walker cell. We aim to understand the extent to which these changes represent a forced response to rising CO2 concentrations versus natural variability. We apply an approach in which we decompose the observed sea surface temperature trends into three components—a pattern associated with the Pacific Decadal Oscillation, which is part of natural variability, a uniform warming pattern, and a residual pattern. This residual pattern shows a remarkable resemblance to a forced ocean thermostat‐like transient response generated in some of the climate models, characterized by equatorial Pacific (EP) cooling, and a broad warming of the Northern Hemisphere, and the Indian Ocean and West Pacific. These results challenge studies arguing that the recent strengthening of the Pacific Walker cell can be explained simply by multi‐decadal natural variability in the tropics. Furthermore, the inability of climate models at large to fully capture this forced pattern with historical forcing puts into focus the reliability of future projections of climate change in the tropical Pacific, specifically the timing of emergence of the eastern EP warming. Key Points: A multi‐decadal strengthening of the Pacific Walker cell is observed in a wide range of indices, especially after 1990A Northern Hemisphere ‐ Indo West Pacific warming sea surface temperature pattern, which differs from the Pacific Decadal Oscillation, is evident since 1980This pattern resembles a forced response to abrupt CO2 forcing, emerging in a subset of climate models, and contributes to the Walker circulation strenthening [ABSTRACT FROM AUTHOR]

Despite lower‐than‐average total precipitation in the western states of the U.S., the 2021 "precipitation roller coaster" defined as large precipitation swings has pointed to a strong hydroclimatic intensity (HYINT). Here we examine the 2021 HYINT using an index—a product of the average precipitation intensity (INT) and dry spell length (DSL). HYINT exhibited an extremely high value in the western U.S. in 2021. INT and DSL contribute differently to the 2021 HYINT, with large spatial variability. Overall, the 2021 extreme HYINT in central California and Utah is tied more to large INT, than to DSL. Meanwhile, the historical trends in INT and DSL may have contributed to the extreme 2021 HYINT event. The fraction of attributable risk framework reveals that the 2021 extreme HYINT is more likely to occur with anthropogenic forcing (e.g., 7.3 times more likely for HYINT exceeding 1.3) than natural forcing alone. Plain Language Summary: The western U.S. is a hotspot for studying climate change impacts on the hydrological cycle. Despite lower‐than‐average total precipitation in 2021, the contrasting dryness and wetness in the western U.S. has been widely reported as a "precipitation roller coaster." In this paper we quantified the "precipitation roller coaster" using an index (hydroclimatic intensity [HYINT])—a product of average precipitation intensity during wet days and dry spell length (DSL). The study found that the 2021 extreme HYINT event was largely attributable to the combined impacts of precipitation intensity and DSL in California and Utah, with precipitation intensity playing a more important role. In contrast, the 2021 precipitation event in other western states exhibited divergent contributions from precipitation intensity and DSL. The southwestern U.S. has been identified as a hotspot for increasing HYINT, which is tied more to the increasing DSL than the precipitation intensity. The trends in DSL and precipitation intensity may have played a key role in driving the 2021 extreme HYINT event. Using climate model experiments with and without anthropogenic forcing, an extreme HYINT event in the western U.S. is more likely to occur with anthropogenic forcing. Key Points: Hydroclimatic intensity (HYINT) exhibited extremely high values in parts of the western U.S. in 2021, mainly caused by average precipitation intensityHYINT shows a significant rising trend in most of the southwestern U.S. mainly tied to a rising dry spell length trendThe extreme HYINT event is more likely to occur under anthropogenic forcing than natural forcing alone [ABSTRACT FROM AUTHOR]

We propose an integrated dynamics‐physics coupling framework for weather and climate‐scale models. Each physical parameterization would be advanced on its natural time scale, revise the thermodynamics to include moist effects, and finally integrated into the relevant components of the dynamical core. We show results using a cloud microphysics scheme integrated within the dynamical core of the Geophysical Fluid Dynamics Laboratory System for High‐resolution prediction on Earth‐to‐Local Domains weather model to demonstrate the promise of this concept. We call it the in‐line microphysics as it is in‐lined within the dynamical core. Statistics gathered from 1 year of weather forecasts show significantly better prediction skills when the model is upgraded to use the in‐line microphysics. However, we do find that some biases are degraded with the in‐line microphysics. The in‐line microphysics also shows larger‐amplitude and higher‐frequency variations in cloud structures within a tropical cyclone than the traditionally‐coupled microphysics. Finally, we discuss the prospects for further development of this integrated dynamics‐physics coupling. Plain Language Summary: Resolved‐scale air flow ("dynamics") and sub‐grid parameterizations ("physics") are two essential components of a weather or climate model. They work together through dynamics‐physics coupling in weather and climate models. However, traditionally dynamics and physics are engineered in isolation and developed independently in models, and many parts of the physics run at a physically‐inappropriate time frequency, or with heat transfers that are inconsistent with the dynamics, leading to errors. This paper proposes an integrated dynamics‐physics coupling framework that can significantly improve weather prediction skills. A concrete example is the cloud and precipitation physics integrated within the dynamics in a global weather model developed at Geophysical Fluid Dynamics Laboratory. When a large number of 10‐day forecasts are run, the version with integrated cloud and precipitation physics shows significantly lower errors and higher skill, especially for large‐scale weather patterns, compared to a traditionally‐coupled physics scheme. The integrated physics also shows promise for improved simulation of high‐impact weather events such as hurricanes. The prospects for the integration of other physics processes are also discussed. Key Points: A new integrated dynamics‐physics coupling framework is designed to enhance dynamics‐physics interaction and thermodynamic consistencyIn‐line microphysics coupling shows significant improvements to weather prediction skills in Geophysical Fluid Dynamics Laboratory System for High‐resolution prediction on Earth‐to‐Local DomainsIntegrated physics shows promise for improved simulation of high‐impact weather events such as hurricane [ABSTRACT FROM AUTHOR]

The North American monsoon (NAM) is an important source of rainfall to much of Mexico and southwestern United States. Westerly winds (westerlies) can suppress monsoon circulation and impact monsoon timing, intensity, and extent. Recent Arctic warming is reducing the temperature gradient between the equator and the pole, which could weaken the westerlies; however, the implications of these changes on the NAM are uncertain. Here we present a new composite index of the Holocene NAM. We find monsoon strength reached a maximum circa 7,000 years ago and has weakened since then. Proxy observations of temperature, hydroclimate and upwelling, along with model simulations, show that the NAM was modulated by the westerlies over the Holocene. If the observed Holocene pattern holds for current warming, a weaker meridional temperature gradient and weaker westerlies could lead to a stronger future NAM. Plain Language Summary: The North American monsoon (NAM) is a main source of summertime rain in Mexico and southwestern North America. Here we explore how the NAM rainfall has changed over the past 11,000 years using information from natural archives of past climate (called "proxies") as well as climate models. We find that monsoon was strongest around 7,000 years ago and has weakened since then. This weakening of the monsoon appears to be caused by a strengthening of the mid‐latitude jet stream, which was influenced by changes in high‐latitude versus low‐latitude temperatures over that period. This paper explores the relationship between temperatures, the jet stream, and the NAM in data and models, which is relevant to future climate in the region. Key Points: A new multi‐proxy‐based estimate of the Holocene North American monsoon (NAM) is presentedProxy and modeled evidence show westerly winds were a dominant control on the Holocene NAMHigh‐latitude temperature drove changes in the Holocene westerly winds and thus the NAM [ABSTRACT FROM AUTHOR]

The paper investigates the applicability of machine learning (ML) to weather prediction by building a reservoir computing‐based, low‐resolution, global prediction model. The model is designed to take advantage of the massively parallel architecture of a modern supercomputer. The forecast performance of the model is assessed by comparing it to that of daily climatology, persistence, and a numerical (physics‐based) model of identical prognostic state variables and resolution. Hourly resolution 20‐day forecasts with the model predict realistic values of the atmospheric state variables at all forecast times for the entire globe. The ML model outperforms both climatology and persistence for the first three forecast days in the midlatitudes, but not in the tropics. Compared to the numerical model, the ML model performs best for the state variables most affected by parameterized processes in the numerical model. Key Points: A low‐resolution, global, reservoir computing‐based machine learning (ML) model can forecast the atmospheric stateThe training of the ML model is computationally efficient on a massively parallel computerCompared to a numerical (physics‐based) model, the ML model performs best for the state variables most affected by parameterized processes [ABSTRACT FROM AUTHOR]

Atmospheric deep moist convection can organize into cloud systems, which impact the Earth's climate significantly. High‐resolution simulations that correctly reproduce organized cloud systems are necessary to understand the role of deep convection in the Earth's climate system. However, there remain issues regarding convergence with respect to grid spacing. To investigate the resolution necessary for a reasonable simulation of deep convection, we conducted grid‐refinement experiments using state‐of‐the‐art atmospheric models. We found that the structure of an updraft ensemble in an organized cloud system converges at progressively smaller scales as the grid spacing is reduced. The gap between two adjacent updrafts converges to a particular distance when the grid spacing becomes as small as 1/20–1/40 of the updraft radius. We also found that the converged inter‐updraft distance value is not significantly different between Reynolds‐averaged Navier–Stokes simulations and large eddy simulations for grid spacings in the terra incognita range. Plain Language Summary: Meteorologists use computer simulations to predict atmospheric phenomena. When simulating the atmosphere, they divide it into small boxes and calculate the changes in wind speed, amounts of moisture and precipitation, and other important variables in each box. Here, our question is how finely we should divide the atmosphere to obtain the correct "answer" in the simulations; we call this the convergence problem. The more finely we divide the atmosphere, the more closely the simulation results approach the correct answer, but the more computational resources we need. The convergence problem is an important topic for us when carrying out accurate atmospheric simulations with limited computational power. This paper has addressed this problem. The target of our simulation is a group of cumulonimbus clouds. We performed several simulations with progressively smaller boxes to investigate how finely we should divide the atmosphere to reach convergence. We found that we should divide the atmosphere so that the width of each box is as small as 1/20 to 1/40 of the width of an upward current in an individual cumulonimbus cloud. We believe that this paper provides a new guideline for accurate atmospheric simulations. Key Points: We conducted grid refinement experiments with convection‐permitting atmospheric models to assess convergence of deep convective updraftsThe experiments reveal that the statistics of convective updrafts in organized cloud systems converge at progressively smaller scalesReynolds‐averaged Navier–Stokes simulations and large eddy simulations reach almost the same converged structure [ABSTRACT FROM AUTHOR]

Model evaluation is difficult and generally relies on analysis that can mask compensating errors. This paper defines new metrics, using clusters generated from a machine learning algorithm, to estimate mean and compensating errors in different model runs. As a test case, we investigate the Southern Ocean shortwave radiative bias using clusters derived by applying self‐organizing maps to satellite data. In particular, the effects of changing cloud phase parameterizations in the MetOffice Unified Model are examined. Differences in cluster properties show that the regional radiative biases are substantially different than the global bias, with two distinct regions identified within the Southern Ocean, each with a different signed bias. Changing cloud phase parameterizations can reduce errors at higher latitudes but increase errors at lower latitudes of the Southern Ocean. Ranking the parameterizations often shows a contrast in mean and compensating errors, notably in all cases large compensating errors remain. Key Points: A novel method for climate model evaluation is used to identify both mean errors and potential compensating errorsAs an example, we apply this methodology to investigate the quality of different cloud parameterizations over the Southern OceanChanges to the cloud phase parameterizations can reduce shortwave radiative bias regionally, but large compensating errors remain [ABSTRACT FROM AUTHOR]

We retrieve ionization rates in the atmosphere caused by energetic electron precipitation from balloon observations in the polar atmosphere and compare them against ionization rates recommended for the Phase 6 of the Coupled Model Intercomparison Project. In our retrieval procedure, we consider the precipitating electrons with energies from about tens of keV to 5 MeV. Our simulations with 1‐D radiative‐convective model with interactive neutral and ion chemistry show that the difference of the Phase 6 of the Coupled Model Intercomparison Project and balloon‐based ionization rate can lead to underestimation of the NOx enhancement by more than 100% and ozone loss up to 25% in the mesosphere. The atmospheric response is different below 50 km due to considering highly energetic electrons, but it is not important because the absolute values of atmospheric impact is tiny. Ionization rates obtained from the balloon observations reveal a high variability. Plain Language Summary: The main idea of our manuscript is to demonstrate that the atmospheric ionization rates (IR) can be successfully retrieved from the long‐term balloon observations of the energetic electron precipitation (EEP) events and used to evaluate the uncertainties of the other IR data sets, for example, IR recommended for the Phase 6 of the Coupled Model Intercomparison Project (CMIP6). IR obtained from the balloon observations reveal a high variability. This means that the time resolution used in CMIP6 probably is not enough to consider high frequency variability of the precipitating electron fluxes. Using 1‐D radiative‐convective model with neutral and ion chemistry, we compared the atmospheric response to the one particular EEP observed by balloons and presented in CMIP6 data. We show that the difference of the CMIP6 and balloon‐based IRs can lead to underestimation of the NOx enhancement by more than 100% and ozone loss by up to 25% in the mesosphere. Our results are new and needed for the understanding of the potential uncertainties in CMIP6 EEP forcing. Our paper will give inspiration for the continuation of the balloon measurements of EEP‐related processes using improved instruments. Key Points: Ionization rates (IR) from energetic electron precipitation (EEP) are calculated using balloon observations and compared to the CMIP6 data setThe difference in the atmospheric response calculated with these two data sets can exceed 100% for NOx and reach 25% for O3IR obtained from balloon measurements reveal a high temporal variability, which is absent in CMIP6 data [ABSTRACT FROM AUTHOR]

This proof‐of‐concept study couples machine learning and physical modeling paradigms to develop a computationally efficient simulator‐emulator framework for generating super‐resolution (<250 m) urban climate information, that is required by many sectors. To this end, a regional climate model/simulator is applied over the city of Montreal, for the summers of 2019 and 2020, at 2.5 km (LR) and 250 m (HR) resolutions, which are used to train and validate the proposed super‐resolution deep learning (DL) model/emulator. The DL model uses an efficient sub‐pixel convolution layer to generate HR information from LR data, with adversarial training applied to improve physical consistency. The DL model reduces temperature errors significantly over urbanized areas present in the LR simulation, while also demonstrating considerable skill in capturing the magnitude and location of heat stress indicators. These results portray the value of the innovative simulator‐emulator framework, that can be extended to other seasons/periods, variables and regions. Plain Language Summary: One of the major barriers in undertaking super‐resolution (<250 m) urban climate simulations to generate climate and climate change information at high spatial and temporal resolutions, as required by many sectors, is their high computational cost. New approaches are therefore required to overcome this barrier. This paper makes use of the unique opportunity to couple machine learning and physical modeling to develop a computationally efficient simulator‐emulator framework to generate super‐resolution climate information. The trained deep neural network model generates high‐resolution urban climate data from low‐resolution (LR) inputs, considering also the urban morphology fields and inter‐variable relationships to improve output realism. The developed framework, applied to urban heat‐related variables, demonstrates the high potential of this approach as it captures well the magnitude and location of heat stress indicators. The generic nature of the developed framework makes it even more promising as it can be applied to other climate variables, periods and regions. Key Points: Innovative simulator‐emulator framework proposed to generate super‐resolution climate informationNew framework applied at city‐scale demonstrates considerable skill for urban heat‐related variablesThe generic nature of the framework enables adaptability to other climate variables and regions [ABSTRACT FROM AUTHOR]

The diurnal eastward‐propagating tide with zonal wavenumber 3 (DE3) is an important tidal component due to its ability to effectively couple the ionosphere‐thermosphere with the tropical troposphere. In this work, we present the first results of a prominent zonal wavenumber‐4 (WN4) structure in the low‐latitude ionosphere observed by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat mission during May 27–June 5, 2020. Least squares analyses of concurrent in situ ion number density measurements from the SORTIE and the Ionospheric Connection Explorer satellites near 420 and 590 km show this pronounced WN4 to be driven by DE3. Thermosphere Ionosphere Mesosphere Energetics Dynamics Sounding of the Atmosphere using Broad band Emission Radiometry temperatures and Specified‐Dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension output demonstrate that the ionospheric WN4 structure is driven by DE3 propagating from the lower thermosphere. Plain Language Summary: The extent to which terrestrial weather (below ∼30 km) can influence the ionosphere and thermosphere (IT) is a fascinating discovery of the last two decades or so. The IT is known to vary significantly from day to day, and this day‐to‐day weather is largely driven by processes originating in the lower atmosphere, especially during periods of quiet solar activity. Accurate forecasting of the IT variability thus depends on the ability to forecast the component that originates in the lower atmosphere. Ionospheric variability translates to uncertainty in navigation and communications systems, while thermospheric variability translates to uncertainty in orbital and reentry predictions. In this work, we present the first results from the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat showing a large amplitude structure with four longitudinal peaks in the ionosphere associated with the well‐known diurnal eastward‐propagating tide with zonal wavenumber 3 originating in the tropical troposphere. Our analyses presented in this paper use concurrent SORTIE, Ionospheric Connection Explorer (ICON), and Thermosphere Ionosphere Mesosphere Energetics Dynamics satellite observations during May 27–June 5, 2020 and a whole atmosphere model to interpret SORTIE measurements. Our results suggest SORTIE and ICON to be excellent and complementary observational platforms for studying the influence of terrestrial weather on IT variability. Key Points: First results from the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat show a large low‐latitude ionospheric wavenumber‐4 (WN4) structure observed concurrently by Ionospheric Connection Explorer (ICON) and Sounding of the Atmosphere using Broad band Emission RadiometrySpectral analyses of SORTIE Ion Velocity Meter (IVM), ICON IVM, and model output demonstrate that the diurnal eastward‐propagating tide with zonal wavenumber 3 tide is responsible for this strong ionosphere‐thermosphere (IT) WN4 couplingSORTIE and ICON IVM provide insights into tropical tropospheric influences on the IT by measuring poorly sampled altitudes simultaneously [ABSTRACT FROM AUTHOR]

In discussions of extreme weather trends, the subject of atmospheric blocking remains an open question, in part because of the inability of climate models to accurately reproduce blocking frequency patterns for the current climate. A number of factors have been proposed to explain this failure, including specific problems with model physics and inadequate resolution. In this paper, we show that, in the case of the National Center for Atmospheric Research Community Atmosphere Model (NCAR CAM), its underestimation of blocking frequency is caused by its mean state bias and that correcting that bias increases blocking frequency estimates over Europe and decreases the root‐mean‐square error versus reanalysis from 0.015 to 0.011. Plain Language Summary: Atmospheric blocking events are long‐lasting weather formations that can cause destructive droughts, heat waves, or flooding. However, they are often underestimated in leading climate models, which means that predictions about how common blocking will be in a changing climate are unreliable. We applied an algorithm to the model that caused it to more closely resemble the observed climate and saw improved blocking estimates, suggesting that the cause of this underestimation is the failure of the model to precisely reproduce the climatological mean state of the real atmosphere, and that improvements in blocking estimates will come from eliminating these biases in the model. With better estimates, we will be better able to predict how the frequency of blocks–and the extreme weather that accompanies them–will change in the future. Key Points: Blocking frequency is often underestimated in climate models, including in the Community Atmosphere ModelImproving the moist parametrization by switching to a super‐parametrized version increases the root‐mean‐square error versus reanalysisCorrecting the bias in model mean state improves blocking frequency estimates, decreasing the error versus reanalysis from 0.015 to 0.011 [ABSTRACT FROM AUTHOR]

Monsoon onset over the South China Sea occurs in April–May, marking the start of the wet season over East Asia. Skillful prediction of onset timing remains an open challenge. Recently, theoretical studies using idealized models have revealed feedbacks at work during the seasonal transitions of the Hadley cells and have shown that these are relevant to monsoon onset over Asia. Here, I hypothesize that monsoon onset occurs earlier in years when the atmosphere over the South China Sea is already in a state where these feedbacks are more easily triggered. I find that local anomalies in lower‐level moist static energy in the preceding January–March are well correlated with South China Sea Monsoon onset timing. This relationship remains relatively consistent on decadal timescales, while correlations with other teleconnections vary, and is used to develop a simple forecast model for onset timing that shows skill competitive with that of more complex models. Plain Language Summary: Arable land in China is estimated to feed 20% of the world's population on 7% of the world's farmlands, with much of this region watered by rainfall from the East Asian Summer Monsoon. Forecasting the arrival of the monsoon, which occurs first over the South China Sea in April–May, is therefore helpful for agricultural planning. However, producing a reliable forecast remains an open challenge. Recently, simple climate models, for example, models including seasons but with no land, have been used along with observations to investigate the most basic processes controlling climatological monsoon onset over Asia. In this paper, I suggest that year‐to‐year variations in monsoon onset timing are controlled by the same processes and, based on these, I suggest climate variables that may help in predicting when the monsoon will begin. By using this approach, I find that the "Moist Static Energy" (a quantity that combines temperature and humidity) over the South China Sea in January–March is strongly correlated with monsoon onset timing. This insight can be used to predict future onset timing competitively compared with more complex forecast models. Key Points: Recent theoretical studies have revealed feedbacks at work during monsoon onsetIf the spring atmosphere is in a state where these feedbacks are easily triggered, onset is earlierThis insight is used to produce a simple forecast of South China Sea Monsoon onset timing [ABSTRACT FROM AUTHOR]

Californian hydroclimate is strongly seasonal and prone to severe water shortages. Recent changes in climate trends have induced shifts in seasonality, thus exacerbating droughts, wildfires, and adverse water shortage effects on the environment and economy. Previous studies have examined the timing of the seasonal cycle shifts mainly as temperature driven earlier onset of the spring season. In this paper, we address quantitative changes in the onset, amounts, and termination of the precipitation season over the past 6 decades, as well as the large‐scale atmospheric circulation underpinning the seasonal cycle changes. We discover that the onset of the rainy season has been progressively delayed since the 1960s, and as a result the precipitation season has become shorter and sharper in California. The progressively later onset of the rainy season is shown to be related to the summer circulation pattern extending into autumn across the North Pacific, in particular, a delay in the strengthening of the Aleutian Low and later southward displacement of the North Pacific westerlies. Plain Language Summary: The rainy season over California is projected to show a distinct sharpening of the mean seasonal cycle, with winter precipitation increasing, and both autumn and spring precipitation decreasing. Our analysis of the past 6 decades of data for California suggests autumn decrease is already underway. A delayed start of the rainy season of 27 days since 1960s can exacerbate seasonal droughts and prolong the wildfire season. This delay occurs due to a number of conditions that controls precipitation: the summer circulation pattern has been extending throughout November across the North Pacific, and the wintertime strengthening of the Aleutian Low is delayed. Accordingly, the southward migration of the North Pacific jet stream as well as extratropical storm tracks are delayed, which marks the start of the California rainy season, are delayed. More work, using climate models, will be needed to provide a better understanding of atmospheric conditions across the North America and the North Pacific. However, our findings provide observational evidence for the projected rainfall change over California and inform ongoing discussion about the drying/wetting tendencies of the rainy season in California. Key Points: Rainfall data over the last 6 decades suggests a progressively delayed onset to the rainy season over CaliforniaA corresponding delay occurs in the transition from summer to wintertime circulation over the North PacificObserved autumn trends appear consistent with projected future shortening of the California rainy season [ABSTRACT FROM AUTHOR]

Climate models often parameterize isopycnal mixing by mesoscale eddies using a diffusion operator that acts in the isopycnal direction, multiplied by an isopycnal‐mixing coefficient. The magnitude of this coefficient is uncertain, with observational estimates ranging from 10 to 10,000 m2/s. In an idealized‐geometry ocean model, the isopycnal‐mixing coefficient is varied across a similar range without allowing the circulation to change: This leads to large changes in the ventilation of the deep ocean. Passive tracers are used to assess the impact of varying the isopycnal‐mixing coefficient on water mass distributions. Increasing the isopycnal‐mixing coefficient from 50 to 5,000 m2/s leads to a 63% reduction in the amount of North Atlantic Deep Water and a doubling in the amount of Antarctic Bottom Water in the deep Pacific ocean. This change is associated with a 700‐yr reduction in the ideal age of water in the deep Pacific ocean. Plain Language Summary: The ocean is full of eddies at the 100‐km scale that stir tracers along surfaces of constant density. Climate models generally have coarse grids that are not able to resolve these ocean eddies. Instead, climate models represent this stirring using a diffusion operator along surfaces of constant density: This diffusion operator smooths out gradients in ocean tracers like temperature, salinity, oxygen, and nutrients. Based on currently available observations, it is unclear how large this diffusion operator should be in climate models. The results shown in this paper suggest that the amount of diffusion along surfaces of constant density influences the pathway along which water reaches the deep Pacific. Improving estimates of the size of this diffusion operator would probably increase the accuracy of climate models. Key Points: Passive tracers are used to quantify the amount of deep water that originated in each region of the surface oceanIncreasing the isopycnal‐mixing coefficient reduces the fraction of NADW in the deep PacificIncreasing the isopycnal‐mixing coefficient reduces the ideal age of water in the deep Pacific [ABSTRACT FROM AUTHOR]

We assess the reliability of an indirect method of inferring the atmospheric response to projected Arctic sea ice loss from CMIP5 simulations, by comparing the response inferred from the indirect method to that explicitly simulated in sea ice perturbation experiments. We find that the indirect approach works well in winter, but has limited utility in the other seasons. We then apply a modified version of the indirect method to 11 CMIP5 models to reveal the robust and non‐robust aspects of the wintertime atmospheric response to projected Arctic sea ice loss. Despite limitations of the indirect method, we identify a robust enhancement of the Siberian High, weakening of the Icelandic Low, weakening of the westerly wind on the poleward flank of the eddy‐driven jet, strengthening of the subtropical jet, and weakening of the stratospheric polar vortex. The surface air temperature response to projected Arctic sea ice loss over mid‐latitude continents is non‐robust across the models. Plain Language Summary: The continued melt of Arctic sea ice will likely affect weather and climate in places far from the Arctic. To better understand the far‐flung implications of sea ice loss, scientists can perform bespoke climate model experiments in which sea ice is reduced, but all other climate drivers are fixed. In our paper, we test the reliability of an indirect method to infer the atmospheric response to sea ice loss, which makes use of a large set of climate model experiments originally performed for other purposes. We find that the indirect approach works well in winter, but not so well in other seasons. We then apply the indirect method to 11 climate models to reveal the robust and non‐robust aspects of the wintertime atmospheric response to projected Arctic sea ice loss—the largest such comparison to date. We found that the models agreed on quite a few aspects of the response, including warming over the Arctic, regional surface pressure changes over Siberia and Iceland, and a slowing of the mid‐latitude jet stream. On the other hand, the models disagreed on whether surface air temperatures over Europe, North America, and East Asia will warm or cool in response to future Arctic sea ice loss. Key Points: Wintertime atmospheric response to projected Arctic sea‐ice loss can be well‐estimated indirectly from CMIP5 simulationsRobust changes across models including stronger Siberian High, weaker Icelandic Low, slower eddy‐driven jet and faster subtropical jetNon‐robust changes in surface air temperature over mid‐latitude continents and sea level pressure over the North Pacific [ABSTRACT FROM AUTHOR]

The tropical arm of Atlantic Multidecadal Variability (AMV) influences climate worldwide, yet the mechanisms generating it remain unclear. Here, we examine experiments with sea surface temperature (SST)‐restoring in the extratropical North Atlantic in multiple models and use mixed‐layer heat budgets to elucidate the important mechanisms. Our results demonstrate that the tropical AMV is driven by wind‐mixed‐layer‐SST feedback. The evolution has two phases with tropical AMV SST anomalies growing from April to October and decaying from November to March. The amplitude of the growth phase surpasses that of the decay phase, resulting in overall tropical Atlantic warming during positive AMV phases. During summer, positive SST anomalies in the extratropics weaken the trade winds, resulting in a shallower mixed‐layer with reduced heat capacity. Subsequent absorption of climatological shortwave radiation in this shallower mixed‐layer then causes SSTs to warm, generating the tropical AMV. Importantly, anomalous surface heat‐fluxes make modest contributions to tropical AMV in these experiments. Plain Language Summary: The North Atlantic sea surface temperature (SST) has fluctuated significantly over periods of decades to multiple decades, a phenomenon known as the Atlantic Multidecadal Variability (AMV). It influences climate worldwide; however, questions remain about how it evolves. This study highlights the vital role of upper ocean processes, particularly the depth of oceanic mixed‐layer, in generating the tropical part of the AMV. Here, we analyzed SST‐restoring climate model experiments in which a surface temperature anomaly is added over the extratropical North Atlantic Ocean. The evolution features a summer growth phase and a winter decay phase, with stronger growth leading to overall warming during positive phases of the tropical AMV. In summer, prevailing trade winds weaken and induce a shallower depth of oceanic mixed‐layer. This leads to the absorption of solar energy in a reduced volume of ocean water, resulting in the warming of SST and the development of a positive phase of tropical AMV. Interestingly, changes in heat‐fluxes over the ocean surface and related mechanisms make little contribution to the development of tropical AMV in these experiments. These results emphasize the importance of accurate upper‐ocean processes for the simulation of AMV in coupled climate models. Key Points: Tropical Atlantic Multidecadal Variability (AMV) is driven by wind‐mixed‐layer‐sea surface temperature (SST) feedback in extratropical SST‐restoring experimentsWeakening of trade winds in summer induces a shallower mixed‐layer, reducing ocean heat capacity and generating positive tropical AMVChanges in surface heat‐fluxes or associated mechanisms are not responsible for tropical AMV in these experiments [ABSTRACT FROM AUTHOR]

Sea ice formation over open water exerts critical control on polar atmosphere‐ocean‐ice interactions, but is only crudely represented in sea ice models. In this study, a collection depth parameterization of new ice for flux polynya models is modified by including the sea ice concentration and ice growth rate as additional factors. We evaluated it in a climate model BCC‐CSM2‐MR and found that it improves simulation of Antarctic sea ice concentration and thickness in most of Indian and Atlantic sectors. Disagreement between the observed Antarctic sea ice expansion during 1981–2014 and the modeled decline still exists but is mitigated when the modified scheme is implemented. Further analysis indicates that these improvements are associated with the overcoming of premature closure of open water, which enhances the response of ocean to surface wind intensification during 1981–2014, and consequently slowdowns the sea surface temperature increase and the resulting Antarctic sea ice reduction. Plain Language Summary: Open water ice formation critically modulates sea ice variations and the associated polar atmosphere‐ocean interaction, but is not well represented in sea ice models. In this study, a modified collection depth parameterization of new ice based on an existing scheme is presented after including sea ice concentration and ice growth rate as additional factors. We evaluated this modified scheme in BCC‐CSM2‐MR and found that it can improve the simulation of mean Antarctic sea ice thickness and concentration in winter as well as Antarctic sea ice expansion from 1981 to 2014. Further analysis implies that these improvements can be attributed to the overcoming of the premature closure of open water areas in model simulations. Key Points: A modified collection thickness parameterization of new ice suitable for large‐scale climate simulations is presentedIt improves the simulation of Antarctic sea ice thickness and concentration, as well as Antarctic sea ice expansion during 1981–2014The improved simulations can be attributed to the overcoming of the premature closure of open water areas where new ice forms [ABSTRACT FROM AUTHOR]

In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of "too few" low clouds and "too rapid" SCT in global climate models. Plain Language Summary: Low clouds impose a strong radiative cooling effect on Earth's climate. Predicting low‐cloud fraction (LCF) is, however, challenging in global climate models (GCMs), partly due to some deficiencies in cloud parameterization schemes. Machine learning (ML) models might fill this gap as it is recognized as an efficient, economical, and accurate method to make predictions. In this study, we find that ML models (XGBoost) exhibit superior proficiency in predicting LCF regarding root mean squared errors and correlation coefficients compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. This improvement helps address one important issue of "too few" low clouds in GCMs. Furthermore, ML models demonstrate marked improvement in representing LCF variations when stratocumulus clouds transition to cumulus clouds, as opposed to too rapid decreases in LCF simulated by two CAMs and ERA5. Such findings testify to the unique role of ML models in refining the parameterization of LCF within GCMs. Key Points: Machine learning (ML) models substantially improve "too few" low‐cloud problems in the subtropics compared to traditional cloud schemesThey also show marked improvement in representing low‐cloud fraction (LCF) variations during the stratocumulus‐to‐cumulus transitionIncluding the effect of moisture source in ML models is crucial to representing spatiotemporal variations in LCF in the midlatitudes [ABSTRACT FROM AUTHOR]

In recent decades, an increase in rainfall has been observed on the northwestern edge of the Indian summer monsoon (ISM; NWEISM). However, no studies have focused on model performances over NWEISM, which calls for an urgent evaluation of models. Here, we utilize historical simulations from 24 CMIP6 models to demonstrate that current models tend to underestimate the observed increasing rainfall over NWEISM, with only ∼30% of the observed intensity. The models broadly capture the spring Middle East land warming, which is the main driver of increased rainfall over NWEISM. Unfortunately, most models fail to reproduce the associated significant decrease in sea level pressure over the surrounding landmasses. This deficiency results in an ineffective trigger of cross‐equatorial southwesterly winds, impeding the accurate simulation of the poleward shift of the summer low‐level jet (LLJ). Consequently, it leads to a weaker link from the Middle East warming to rainfall enhancement over NWEISM. Plain Language Summary: The Indian summer monsoon (ISM) is one of the most spectacular monsoon systems in the world. In recent decades, the summer rainfall has been found to increase over the northwestern edge of the ISM (NWEISM), such as over Pakistan and northwestern India. Reliable model predictions and early warning systems for floods in such climate‐sensitive regions are critical for minimizing the loss of lives, properties and GDPs. Unfortunately, the current climate models are unable to accurately depict the trend of increased rainfall over NWEISM. Although most models can simulate the spring Middle East land warming that triggers rainfall enhancement over NWEISM, they cannot accurately reproduce the associated summer low‐level jet (LLJ). In summary, the strong link of LLJ to the spring warming over the Middle East and enhanced rainfall over NWEISM, are not accurately captured by the models. This raises serious concerns about the reliability of future climate projections for these vulnerable regions. Key Points: Current models tend to underestimate the observed increasing rainfall over the northwestern edge of the Indian summer monsoon (NWEISM)Only 30% of the observed increasing rainfall intensity over NWEISM is reproduced by the CMIP6 modelsInaccurate simulation of the poleward shift of the summer low‐level jet induces the poor simulation of rainfall enhancement over NWEISM [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) anomalies over the North Tropical Atlantic (NTA) during the early boreal spring can trigger El Niño‐Southern Oscillation (ENSO) events in the following boreal winter. However, the future changes in the impact of the NTA on ENSO remain controversial. Here, we show distinct changes in the strength of the NTA−ENSO relationship due to global warming by comparing models from the Coupled Model Intercomparison Project (CMIP) 5 and CMIP6. The impact of the NTA on ENSO under greenhouse warming is notably enhanced in CMIP6 compared to CMIP5. A wetter mean state over the subtropical eastern North Pacific and increased oceanic sensitivity over the equatorial central Pacific are key factors that enhance the impact of the NTA SST on ENSO. Therefore, differences in the mean state under greenhouse warming between the CMIP5 and CMIP6 models can modulate the strength of the NTA−ENSO relationship. Plain Language Summary: The North Tropical Atlantic (NTA) region can affect the development of El Niño events, significantly impacting global weather systems. Our study compares two generations of climate models, CMIP5 and CMIP6, to understand how the influence of the NTA sea surface temperature (SST) on El Niño might change due to global warming. We found that the influence of the NTA SST on El Niño is stronger in the CMIP6 models than in the CMIP5 models. This stronger impact is due to changes in the climatological background states, including a wetter mean state over the subtropical eastern North Pacific and increased oceanic sensitivity over the equatorial central Pacific. Understanding these changes is crucial for improving predictions of future climate variabilities and their potential impacts on weather, ecosystems, and food production worldwide. Key Points: The relationship between the North Tropical Atlantic (NTA) and El Niño‐Southern Oscillation (ENSO) is significantly enhanced by global warming in CMIP6 compared to CMIP5Mean state change under greenhouse warming is responsible for the strengthening of the NTA‐ENSO relationship [ABSTRACT FROM AUTHOR]

Efforts to detect long‐term changes in global mean evaporation minus precipitation over the ocean remain ambiguous. Here we define an ad hoc sea surface salinity index to assess the observed and simulated intensification of the freshwater flux pattern over the global ocean and, thus, of the overall water cycle. A recent salinity reconstruction shows a long‐term amplification of the climatological patterns, thereby supporting the popular "fresh gets fresher, salty gets saltier" paradigm. Unlike in a previous study, no systematic underestimation of this amplification is found in the latest generation of global climate models. Yet, the "fresh gets fresher" paradigm is much more robust than its "salty gets saltier" counterpart and the proposed salinity index does not yet provide a strong constraint on the model‐dependent projected intensification of the global water cycle intensification along the 21st century. Plain Language Summary: Recent changes in continental mean precipitation and evaporation remain poorly observed and thus poorly constrained in both atmospheric reanalyzes and global climate models. Here we propose a new index based on global sea surface salinity contrasts as a surrogate of changes in metric of the water cycle intensity. Overall, both observed and simulated indices support the "fresh gets fresher, salty gets saltier" paradigm, widely used to depict the overall intensification of the water cycle. Unlike in a previous study, no systematic underestimation of the observed amplification in salinity patterns is found in the latest generation of global climate models. The "salty gets saltier" response is however less robust than the "fresh gets fresher" and the proposed salinity index is not yet very useful to constrain the future intensity of the global water cycle. Key Points: Both observations and climate models evidence an amplification of the global ocean climatological contrasts in sea surface salinity (SSS)Yet, the "fresh gets fresher" paradigm is more robust than its salty counterpart, which is more sensitive to the ocean domain's definitionGlobal SSS observations do not yet provide a strong constraint on the projected intensification of the global water cycle [ABSTRACT FROM AUTHOR]

Precipitation can induce a surface sensible heat flux since the raindrops are generally cooler than the surface. This precipitation‐induced sensible heat flux (QP) is typically ignored in models. However, during heavy rainfall, QP can be large and may not be negligible such as over India during the summer monsoon season. We provide the first results of incorporating QP in a simulation that shows ∼2% (∼5%) reduction in precipitation over India compared to the simulation without QP during a monsoonal active phase in 2017 (2018). This reduction was primarily due to a reduction in vertical advection of moisture. Additionally, QP modified the spatial distribution of precipitation with 40% of the geographical area encountering alterations of at least 20% in precipitation. This change in precipitation distribution across the region can have important implications for regional agriculture and irrigation practices. Changes in the partitioning of surface heat flux components due to QP is also discussed. Plain Language Summary: Rainfall during the monsoon season in India has widespread influences on agriculture and water supply. Therefore, understanding and predicting monsoon rainfall is of utmost importance. Among many parameters, surface energy budget influences precipitation. One of the components of the surface energy budget is the sensible heat flux due to precipitation (QP), which arises because the temperature of raindrops is typically different (cooler) than the temperature of the surface. The QP is thought to be small and is neglected in climate models. By incorporating QP in a regional climate model, we show its influence on monsoon precipitation and surface energy budget. We found that QP reduces precipitation by ∼2% and affects the spatial distribution of precipitation, which may have implications for regional agriculture and irrigation strategies. We also show that QP leads to significant changes in the magnitude and spatial distribution of surface energy budget terms. Key Points: Precipitation‐induced surface sensible heat flux is typically neglected in numerical weather and climate modelsModel simulations show that rain‐induced sensible heat flux reduces monsoonal precipitation and changes its spatial distributionChanges in the spatial distribution of monsoonal rain can have important implications for regional agriculture and irrigation practices [ABSTRACT FROM AUTHOR]

Since the 1950s, observations and climate models show an amplification of sea surface temperature (SST) seasonal cycle in response to global warming over most of the global oceans except for the Southern Ocean (SO), however the cause remains poorly understood. In this study, we analyzed observations, ocean reanalysis, and a set of historical and abruptly quadrupled CO2 simulations from the Coupled Model Intercomparison Project Phase 6 archive and found that the weakened SST seasonal cycle over the SO could be mainly attributed to the intensification of summertime westerly winds. Under the historical warming, the intensification of summertime westerly winds over the SO effectively deepens ocean mixed layer and damps surface warming, but this effect is considerably weaker in winter, thus weakening the SST seasonal cycle. This wind‐driven mechanism is further supported by our targeted coupled model experiments with the wind intensification effects being removed. Plain Language Summary: The Southern Ocean (SO) sea surface temperature (SST) has experienced a decreased seasonal cycle since the 1950s, in contrast with the overall amplified seasonal cycle in the rest of global oceans. Here we investigated observations and climate model simulations and found that the decrease of SST seasonal cycle was associated with less warming in summer than in winter during the historical period. The suppressed warming in summer is accompanied by a deepened ocean mixed layer due to the intensification of surface westerlies. An enhanced mixed layer depth means the incoming heat flux will be trapped in a deeper mixed layer in summer, causing a cooler summer SST, which then leads to a weaker SST annual cycle. When the wind intensification effect is removed in our model simulations, the decreased SST seasonal cycle in the SO disappears. Key Points: Observations and climate models show a weakening sea surface temperature (SST) seasonal cycle induced by the delayed warming of summer SST over Southern Ocean (SO)The summer mixed layer depth and wind stress show increasing trends during the same period over the SOThe summer wind‐induced mixed layer deepening suppresses SO surface warming and thus weakening the SST seasonal cycle [ABSTRACT FROM AUTHOR]

Precipitation changes in full response to CO2 increase are widely studied but confidence in future projections remains low. Mechanistic understanding of the direct radiative effect of CO2 on precipitation changes, independent from CO2‐induced SST changes, is therefore necessary. Utilizing global atmospheric models, we identify robust summer precipitation decreases across North America in response to direct CO2 forcing. We find that spatial distribution of CO2 forcing at land surface is likely shaped by climatological distribution of water vapor and clouds. This, coupled with local feedback processes, changes in convection, and moisture supply resulting from CO2‐induced circulation changes, could determine North American hydroclimate changes. In central North America, increasing CO2 may decrease summertime precipitation by warming the surface and inducing dry advection into the region to reduce moisture supply. Meanwhile, for the southwest and the east, CO2‐induced shift of subtropical highs generates wet advection, which might mitigate the drying effect from warming. Plain Language Summary: How precipitation changes in full response to increased CO2 remains unclear. Mechanistic understanding of how increasing CO2 alone changes precipitation can help us better predict future precipitation in full response to CO2 increase. We find that the precipitation responses are much stronger during summer than winter in North America. During summer, precipitation significantly decreases in central North America in response to the direct CO2 radiative effect, while in the southwest and the east, precipitation changes are small. There might be stronger longwave radiation forcing near the surface induced by CO2 increase in arid regions due to weaker masking effect of water vapor and clouds. This, along with local feedbacks, significantly warms the central region. Meanwhile, the direct forcing of CO2 induces northward shifts in subtropical highs, consequently generating anticyclonic anomalies. These wind anomalies result in drier air advected into central North America, leading to moisture divergence and hence reducing moisture supply. The reduced water supply, coupled with the warming, may decrease precipitation in the region. For the southwest and the east, the anticyclonic anomalies cause wetter air advected into the regions and generate moisture convergence, which increases moisture supply and might mitigate the drying effect caused by CO2‐induced warming. Key Points: Robust precipitation decreases during summer in response to CO2 direct radiative effect are identified in central North AmericaClimatology and changes of hydrological environment may collectively modulate the regional CO2 radiative effect on summer precipitationCO2‐induced circulation changes could alter the moisture supply to reshape the regional hydrological cycle [ABSTRACT FROM AUTHOR]

This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses. Plain Language Summary: This research examines how well a kilometer grid scale global atmospheric model—the Simple Cloud‐Resolving Energy Exascale Earth System Model (SCREAMv0)—performs in simulating clouds and rainfall over the Amazon rainforest region. The model was assessed by comparing to high‐resolution ground‐based observations from the Green Ocean Amazon field campaign supported by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. The model struggles to produce enough middle‐level clouds. When comparing the simulated rainfall to radar observations, SCREAMv0 showed good performance on the diurnal pattern of rain rate, but tends to form too many small rain clusters while failing to create large ones. A possible contributor to these errors could be the inaccurate depiction of how the earth's surface and the atmosphere interact within the model. Overall, this study shows that using detailed DOE ARM data can help improve our understanding of clouds and rainfall in global storm resolving kilometer grid scale models. Key Points: Convective processes in a global storm resolving model (SCREAMv0) are evaluated using ground‐based observations over a tropical rainforestSCREAMv0 captures the morning development of shallow convection and the early afternoon precipitation peak but lacks mid‐level congestusSCREAMv0 struggles to form large precipitation clusters greater than 128 km and produces smaller ones more often than observed [ABSTRACT FROM AUTHOR]

State‐of‐the‐art coupled climate models struggle to accurately simulate historical variability and trends of Antarctic sea ice, impacting their reliability for future projections. Increasing horizontal resolution is expected to improve the representation of coupled atmosphere‐ice‐ocean processes at high latitudes. Here, we examine the historical changes in the Antarctic sea ice area and volume in High Resolution Model Intercomparison Project simulations against satellite data sets and ocean reanalyzes to assess the benefits of increased spatial resolution. Our results do not show considerable benefits when horizontal resolutions up to 0.25° in the ocean and 25 km in the atmosphere. Limited improvements are reported in the simulated historical sea ice trends, which are nevertheless model‐dependent, and associated with the use of model components with more complex sea‐ice parameterizations. Given the high computational cost of climate‐scale simulations at high spatial resolution, we advocate prioritizing enhancements in sea‐ice physics and the interactions among model components in coupled climate simulations. Plain Language Summary: Coupled climate models are computer programs that simulate how different parts of the Earth's climate system, such as the atmosphere, oceans, land, and ice, interact with each other. Current climate models encounter difficulties in depicting past Antarctic sea ice changes. Increasing horizontal resolution in models is expected to improve the representation of coupled atmosphere‐ice‐ocean processes at high latitudes. Our study suggests that increasing model detail does not significantly improve sea ice simulations. Despite small enhancements in simulating past sea ice trends, the benefits vary by model. Given the high computational cost of running detailed climate simulations, we underscore the importance of improving how the models show sea‐ice physics and interactions between different parts of the climate system. Key Points: No systematic beneficial effect of increased spatial ocean/atmosphere resolution on representation of Antarctic sea ice cover in High Resolution Model Intercomparison ProjectEddy‐permitting ocean models weakly reduce the biased negative sea ice trends over the historical periodGiven the limited improvements with enhanced resolution are model‐dependent, improving the model's sea ice physics is a priority [ABSTRACT FROM AUTHOR]

Near‐term projections of drought in the southwestern United States (SWUS) are uncertain. The observed decrease in SWUS precipitation since the 1980s and heightened drought conditions since the 2000s have been linked to a cooling sea surface temperature (SST) trend in the Equatorial Pacific. Notably, climate models fail to reproduce these observed SST trends, and they may continue doing so in the future. Here, we assess the sensitivity of SWUS precipitation projections to future SST trends using a Green's function approach. Our findings reveal that a slight redistribution of SST leads to a wetting or drying of the SWUS. A reversal of the observed cooling trend in the Central and East Pacific over the next few decades would lead to a period of wetting in the SWUS. It is critical to consider the impact of possible SST pattern trends on SWUS precipitation trends until we fully trust SST evolution in climate models. Plain Language Summary: Precipitation trends in the southwestern United States (SWUS) are sensitive to the pattern of sea surface temperature (SST) trends in the Tropical Pacific. Since the turn of the century, a decrease in SWUS precipitation has been linked to a cooling of the Central and Eastern Pacific (1990–2020). Notably, climate models are unable to simulate this observed cooling SST trend. In this study, we answer how SWUS precipitation projections may be impacted by potential error in the simulation of future SST trends by climate models. We first demonstrate that slight changes in the pattern of SST trends leads to either a wetting or drying of the SWUS. Second, if the current 30‐year cooling trend in the Central and East Pacific switches to a warming trend, the SWUS could experience a near‐term increase in precipitation. While climate models are the main tool to predict the global response to anthropogenic climate change, we must consider and account for their error in projections of global warming. Key Points: The observed Equatorial Pacific cooling trend, unpredicted by climate models, may have led to decreased precipitation in the southwestern USWith a sea surface temperature‐precipitation Green's function, we find that small changes in sea surface temperature can either wet or dry the southwestern USA reversal of the cooling trend in the Equatorial Pacific could lead to a wetting trend in the southwestern US [ABSTRACT FROM AUTHOR]

Historically, the precipitation trend over the past few decades in the Contiguous United States (CONUS) exhibits a "Dry‐West Wet‐East" pattern; this is manifested by recent droughts/floods in the western/eastern US. However, it remains elusive what atmospheric phenomenon has potentially driven such a remarkable, and impactful precipitation pattern. Here we found that a coupled climate mode—the Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by using Atmospheric Model Intercomparison Project simulations available from the Coupled Model Intercomparison Project Phase 6. Plain Language Summary: Historically, the Contiguous United States (CONUS) was parched in the West and soaked in the East. Despite the importance of this distinct spatial pattern of precipitation variability, the drivers of this hydroclimatic phenomenon remain elusive. Therefore, there is big knowledge gap in understanding the zonal precipitation pattern during the historical period. Here we find that the Pacific Meridional Mode (PMM) can exert strong impacts on the zonal precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and the precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by the Atmospheric Model Intercomparison Project experiments of the Coupled Model Intercomparison Project (Phase 6). Our research results explain in large part the zonal precipitation pattern ("Dry‐West Wet‐East") in the CONUS, and perhaps more importantly, the viability of the PMM being used as a potential predictor/indicator for summertime precipitation over the U.S. Key Points: The Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer seasonThe modulation on precipitation is manifested as a zonal dipole pattern, tied to teleconnections associated PMM sea‐surface temperature anomaliesThe physical mechanisms based on observations were supported by CMIP6 climate model simulations [ABSTRACT FROM AUTHOR]

Observed El Niño‐Southern Oscillation (ENSO) varies between decades with high ENSO amplitude and more extreme Eastern Pacific (EP) El Niño events and decades with low ENSO amplitude and mainly weak El Niño events. Based on experiments with the CESM1 model, ENSO may lock‐in into an extreme EP El Niño‐dominated state in a +3.7 K warmer climate, while in a −4.0 K cooler climate ENSO may lock‐in into a weak El Niño‐dominated state. The state shift of ENSO with global warming can be explained by the location and amplitude of the strongest warming over the eastern equatorial Pacific, which amplifies the Bjerknes feedback and allows a southward migration of the Intertropical Convergence Zone onto the equator, a prerequisite of extreme EP El Niños. In light of these results, we discuss to what extent the state of ENSO may be a tipping element in the climate system. Plain Language Summary: El Niño‐Southern Oscillation (ENSO) is the dominant mode of climate variability with far‐reaching impacts. El Niños, the warm events, occur in different flavors. In particular extreme Eastern Pacific (EP) El Niño events are associated with heavy precipitation events and extreme droughts, thus cause large socio‐economic impacts in the Pacific region and beyond. In the observational record, they are quite rare so far. Here we present experiments of one climate model, which suggests that in a warmer climate ENSO may lock‐in in a different state, in which nearly each El Niño is an extreme EP El Niño. On the other hand in a colder climate ENSO may lock‐in in a state with nearly no extreme EP El Niños. In both climates this would have huge consequences for the socio‐economic impacts of ENSO. Against this background we raise the discussion if ENSO may be a tipping element in the climate system. Key Points: El Niño‐Southern Oscillation (ENSO) may be a tipping element in the climate system as its characteristics can be very different under colder or warmer mean climatesIn a warmer climate, ENSO can lock‐in into a state with high variability and nearly each El Niño is an extreme Eastern Pacific (EP) El NiñoMore extreme EP El Niño events would strongly increase the socio‐economic impacts of ENSO [ABSTRACT FROM AUTHOR]

The Geophysical Fluid Dynamics Laboratory (GFDL) Finite‐Volume Cubed‐Sphere (FV3) numerical forecast model was chosen in late 2016 by the National Weather Service (NWS) to serve as the dynamic core of the Next‐Generation Global Prediction System (NGGPS). The operational Global Forecasting System (GFS) physics suite implemented in FV3, however, was not necessarily suitable for convective‐scale prediction. We implemented several advanced physics schemes from the Weather Research and Forecasting (WRF) model within FV3 and ran 10 forecasts with combinations of five planetary boundary layer and two microphysics (MP) schemes, with an ~3.5‐km convection‐allowing grid two‐way nested within am ~13‐km grid spacing global grid during the 2018 Spring Forecasting Experiment at National Oceanic and Atmospheric Administration (NOAA)'s Hazardous Weather Testbed. Objective verification results show that the Thompson MP scheme slightly outperforms the National Severe Storms Laboratory MP scheme in precipitation forecast skill, while no planetary boundary layer scheme clearly stands out. The skill of FV3 is similar to that of the more‐established WRF at a similar resolution. These results establish the viability of the FV3 dynamic core for convective‐scale forecasting as part of the single‐core unification of the NWS modeling suite. Plain Language Summary: In this paper we examine how well the Finite‐Volume Cubed‐Sphere (FV3) model predicts precipitation over the Contiguous United States (CONUS) when run at a resolution sufficient to explicitly predict convective storms. FV3 was chosen to replace the operational Global Forecast System (GFS) by the National Weather Services (NWS) in late 2016, but its performance for convective‐scale regional forecasting was previously untested. The physical parameterization schemes available in FV3 were mostly from GFS and not necessarily suitable for convective‐scale predictions. We implemented several advanced physics schemes taken from a more established and most widely used convective‐scale model, the Weather Research and Forecasting (WRF) model, into FV3, including schemes for treating turbulence exchanges in atmospheric boundary layer and those for representing cloud and precipitation processes (microphysics). We ran 10 forecasts each day using different combinations of the physics schemes to evaluate their performance in FV3 as part of the 2018 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed Spring Forecasting Experiment. With these advanced schemes, FV3 is shown to be capable of predicting precipitation with skill comparable to WRF. The precipitation forecast is somewhat sensitive to the microphysics schemes used, but not particularly sensitive to the atmospheric boundary layer scheme. Our study shows that the newly selected FV3 model, when equipped with appropriate physics parameterization schemes, can serve as the foundation for the next‐generation regional forecasting models of the NWS. Key Points: The precipitation forecasting skill of FV3 with advanced physics schemes is evaluated at a convection‐allowing resolutionFV3 is shown to have comparable performance to the WRF model, despite the lack of storm‐scale initialization, suggesting its promise for predicting precipitation at a convection‐allowing resolutionThe Thompson microphysics scheme slightly outperforms the NSSL scheme in FV3, while no PBL scheme clearly outperforms others among the five schemes examined [ABSTRACT FROM AUTHOR]

Decadal climate predictability has received considerable scientific interest in recent years, yet the limits and mechanisms for decadal predictability are currently not well known. It is widely accepted that noise due to internal atmospheric dynamics at the air‐sea interface influences predictability. The purpose of this paper is to use the interactive ensemble (IE) coupling strategy to quantify how internal atmospheric noise at the air‐sea interface impacts decadal predictability. The IE technique can significantly reduce internal atmospheric noise and has proven useful in assessing seasonal‐to‐interannual variability and predictability. Here we focus on decadal timescales and apply the nonlinear local Lyapunov exponent method to the Community Climate System Model comparing control simulations with IE simulations. This is the first time the nonlinear local Lyapunov exponent has been applied to the state‐of‐the‐art coupled models. The global patterns of decadal predictability are discussed from the perspective of internal atmospheric noise and ocean dynamics. Key Points: For the first time, the nonlinear local Lyapunov exponent method has been applied to the state‐of‐the‐art coupled climate modelsDepending on region reducing internal atmospheric noise reduces decadal variability but either increases or decreases decadal predictabilityDecadal‐scale subsurface predictability is examined for the North Atlantic region [ABSTRACT FROM AUTHOR]

The equilibrium climate sensitivity estimated from different sources is inconsistent due to its dependence on the surface warming pattern. Cloud feedbacks have been identified as the major contributor to this so‐called pattern effect. We find a large unforced pattern effect in CERES data, with cloud feedback estimated from two consecutive 125‐month periods (March 2000–July 2010 and August 2010–December 2020) changing from −0.45 ± 0.85 to +1.2 ± 0.78 W/m2/K. When comparing to models, 27% of consecutive 10‐year segments in CMIP6 control runs have differences similar to the observations. We also compare the spatial patterns in the CERES data to those in climate models and find they are similar, with the East Pacific playing a key role. This suggests that the impact of the unforced pattern effect can be significant and that models are capable of reproducing its global‐average magnitude. Plain Language Summary: Climate sensitivity, the amount of surface warming in response to increasing carbon dioxide, is often used as a metric to quantify the severity of climate change. The estimates of climate sensitivity from different sources, however, are not consistent. The main reason is that the response of clouds to surface temperature changes, called cloud feedback, depends on the spatial pattern of surface warming. We investigate the impact of this so‐called pattern effect on cloud feedback in observations and climate models. Here we find that the observed cloud feedbacks calculated from two consecutive decades have significant differences in both global mean value and spatial pattern, and the climate models produce similar results. This highlights the fact that differences of surface warming patterns can have large influences on cloud feedbacks, and climate models have the ability to reproduce what happened in the real world. Key Points: The difference in cloud feedback between two ∼10‐year periods inferred from CERES is 1.6 ± 1.1 W/m2/KThe distinct difference in cloud feedback verifies the existence of a large unforced pattern effectCMIP6 models produce unforced pattern effects of similar magnitude and spatial structure to those seen in the observations [ABSTRACT FROM AUTHOR]

The East China Sea Kuroshio (ECS‐Kuroshio) and the Ryukyu Current are the major poleward heat carriers in the North Pacific. Anomalous changes of ECS‐Kuroshio and Ryukyu Current could exert substantial influence on the climate in mid‐latitude regions. However, owing to limited observations and coarse resolution of climate models, how they might change under anthropogenic warming remains unknown. Here, we find an accelerating ECS‐Kuroshio (1.5 Sv) and a decelerating (−2.2 Sv) Ryukyu Current using in‐situ observation during 1958–2022, equivalent to 7% strengthening and 20% weakening in the 65 years. The trend is also simulated by four high‐resolution climate models, with multi‐model ensemble‐mean acceleration (deceleration) of the ECS‐Kuroshio (Ryukyu Current) of 1.2 ± 0.6 Sv (−6.2 ± 2.5 Sv) over 1950–2050. The weakening subtropical wind field reduces their summed transport o. Enhanced stratification, which induces uplift of current system and weaker topography‐flow interaction, leads to the intensifying ECS‐Kuroshio and disappearing Ryukyu Current. Plain Language Summary: The East China Sea Kuroshio (ECS‐Kuroshio) and the Ryukyu Current are the major components of western boundary current (WBC) system in the North Pacific. They transport large amount of heat poleward, maintaining the mid‐latitude region warm and habitable. In this study, we find that in both observation and high‐resolution numerical models, the ECS‐Kuroshio is intensifying and the Ryukyu Current is rapidly declining. Particularly, in climate models, the Ryukyu Current is predicted to experience a 45% weakening during 1950–2050. Weakening wind field in the North Pacific tends to reduce the total transport of WBC system. The rapid weakening of the Ryukyu Current is credit to the enhanced stratification under global warming, which induces uplift of the Kuroshio east of Taiwan. Hence, less water will be blocked by bottom topography (Ilan Ridge) and bifurcates into the Ryukyu Current. Our finding highlights the needs for comprehensive studies on the local and climate effects of the evolution of the WBC system in the North Pacific. Key Points: The East China Sea Kuroshio is intensifying while the Ryukyu Current is rapidly declining in a warming climateThe change is mainly caused by enhanced stratification which induces uplift of current system and weaker topography‐flow interaction [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning. Plain Language Summary: Global climate models (GCMs) provide information on projected changes. However, their resolution is too coarse to assess future land surface changes at the regional scale. Dynamical downscaling resolves this issue, by refining the representation of hydroclimate conditions at a higher‐resolution. However, GCMs have unique historical biases that remain despite dynamical downscaling. Typically these GCM biases are ignored, resulting in assessments at the regional scale with large uncertainty. Here, we use an approach, called emergent constraint (EC), to reduce the uncertainty in projected changes in the timing of high flow conditions across the Sierra Nevada and Cascade region of the western United States. While this approach was introduced nearly two decades ago with coarse‐resolution GCM data, this is the first time the EC approach has been used at the regional scale with dynamical downscaling. From our analysis, we find high flow events are likely to occur earlier in the future for the Sierra‐Cascade region. This conclusion contains substantial uncertainty without the use of the EC approach. While this study focuses on the timing of high flows, it opens the door to a variety of follow‐on studies, seeking to produce more robust projections of hydroclimate change across the western US. Key Points: For the first time, an emergent constraint (EC) is used to reduce uncertainty in hydroclimate projections from a dynamically downscaled ensembleWe show that snowmelt‐driven basins in the western United States are very likely to experience earlier peak flows in the futureWith EC, uncertainty in future peak flow timing is reduced by roughly 39%; however, bias corrected results can differ [ABSTRACT FROM AUTHOR]

Probabilistic projections from the UK Climate Projections 2018 are presented for four global warming levels (GWLs) at 1.5, 2, 3, and 4°C above the 1850–1900 baseline. Our results show how uncertainties associated with climate models and four representative concentration pathways (RCP) emission scenarios translate to UK regional scale changes in maximum temperature and precipitation, with data also available for minimum and mean temperatures, humidity and surface net downward shortwave radiation flux. We compare weighting the likelihood of RCPs based on (hypothetical) policy decisions, against our baseline assumption that each RCP is equally likely. Differences between weighted and unweighted GWL distributions are small, particularly in relation to the full breadth of uncertainties that are incorporated into the probabilistic projections. Finally we quantify the relative importance of scenario, model and internal variability on regional projected GWLs and show that uncertainty associated with an uncertain climate response to forcings dominates at all GWLs. Plain Language Summary: Global warming levels define the global averaged increase in temperature since the pre‐industrial period. This study looks at what the regional climate in the UK could be like in the future. We estimate how temperature and precipitation in the UK might change under four different global warming levels (GWLs) (1.5, 2, 3, and 4°C above pre‐industrial times). We account for a wide range of outcomes from both climate models and future emissions scenarios. Assuming that the range of future emissions scenarios are equally likely, the center of the ranges of years that we could reach GWLs of 1.5, 2, 3, and 4°C are 2032, 2050, 2072, and 2078 respectively. The largest influence of future climate on these range estimates comes from our understanding of how the Earth actually responds to changes in greenhouse gases. Even if we knew the exact quantity of greenhouse gas emissions we would emit in the future, we still wouldn't be able to predict the exact changes in UK climate with perfect accuracy because our understanding of the regional climate response to these emissions requires further investigation. Key Points: Probabilistic climate projections from the UK are presented for four global warming levels (GWLs) across four representative concentration pathways (RCP)We show how uncertainty associated with climate models and future emission scenarios translate to UK regional scale changesUncertainty associated with an uncertain climate response to forcings dominates at all GWLs [ABSTRACT FROM AUTHOR]