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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]

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]

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]

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]

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]

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 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]

While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [ABSTRACT FROM AUTHOR]

The Arctic is notable as a region where the greatest rate of increase in precipitation associated with global warming is anticipated. The Arctic precipitation simulated by the Coupled Model Intercomparison Project Phase 6 models showed a strong increasing trend since the 1980s. We found that the forcing factor of the trend is a combination of the continued strengthening of greenhouse gas forcing and the leveling off of aerosol forcing dominated in earlier periods. From an energetic perspective, we found that the increased atmospheric radiative cooling and reduced sensible heat transport from lower latitudes contributed equally to the recent increase in Arctic precipitation. The combination of these two energetic factors suggests a doubling of the Arctic amplification factor for precipitation relative to that for temperature. Future Arctic precipitation will change in proportion to the temperature change, and the fractional contributions of the energetic factors will remain stable across various scenarios. Plain Language Summary: The Arctic region is inherently a low‐precipitation area. However, because of global warming, precipitation is expected to increase substantially in the Arctic region compared with the global average when viewed as a percentage change from the original precipitation. This severely affects climate change in the Arctic environment. The latest climate model simulations show that there has been a rapid increase in precipitation in the Arctic region in recent decades. The driving factors behind the rapid increase are the effects of the accelerating growth of greenhouse gas concentrations, which were previously suppressed by the increasing anthropogenic aerosol emissions before the 1980s. Based on the heat budget of the atmosphere, we identified important factors contributing to these precipitation changes. These include enhanced radiative cooling (responding locally to increased air temperature) and reduced heat transport from lower latitudes due to greater temperature increases at higher latitudes. Future precipitation will change in proportion to the temperature change while maintaining consistent fractional contributions across different scenarios. Key Points: Trends in Arctic precipitation in the recent and future decades are examined from multimodel simulationsThe recent rapid increase is driven by accelerating greenhouse gas concentrations and plateauing growth in anthropogenic aerosol emissionsIncreased radiative cooling and reduced poleward sensible heat transport equally contributed to the Arctic precipitation changes [ABSTRACT FROM AUTHOR]

Ocean mass increase contributes to global sea level rise, and plays an important role in understanding climate change. Here, we develop a data assimilation approach that enables the inference of ocean mass increase from global tide gauge network. This approach incorporates outputs from climate models and sea level fingerprints caused by water mass changes over land areas. The results suggest a trend of 2.15 ± 0.72 mm/yr for ocean mass increase over the period 1993–2022, which closes the global sea level budget with estimates of thermosteric sea level rise. Furthermore, the inferred ocean mass increase offers an insight into the causes of sea level rise since 1950. These findings emphasize the significance of climate models, in addition to simulating sea level changes, they contribute to understanding causes of sea level rise over the past decades. Plain Language Summary: Tide gauges record long‐term sea level changes, which encompass ocean thermal expansion and ocean mass increase due to water mass loss over global land areas. By carefully modeling these components, we can estimate the increase in global ocean mass using data from tide gauges. To this end, we construct a data assimilation approach that is driven by outputs from climate models. This approach reveals a 2.15 ± 0.72 mm/yr ocean mass trend for the satellite altimetry era, which could explain the observed global sea level rise with estimates of ocean thermal expansion. Further analysis indicates that the inferred ocean mass increase, combined along with the outputs of climate models, closely agrees with an independently reconstructed global mean sea level rise. Our findings emphasize the crucial role of climate models in comprehending the factors contributing to sea level rise over the past decades. Key Points: A data assimilation technique is driven by outputs from climate models to infer ocean mass increase from tide gaugesThe inferred ocean mass trend closes the global sea level budget over the satellite altimetry eraResults offer insights into the causes of global sea level rise since 1950 [ABSTRACT FROM AUTHOR]

A realistic representation of top‐of‐the‐atmosphere (TOA) radiation response to surface warming is key for trusting climate model projections. We show that coupled models with freely evolving ocean‐atmosphere interactions systematically underestimate the observed global TOA radiation trend during 2001–2022 in 552 simulations. Locally, even if a simulation spontaneously reproduces observed surface temperature trends, TOA radiation trends are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale surface warming pattern and from errors in the atmospheric physics affecting short‐ and longwave radiation. Models with a better representation of the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity. Our bias metric is a novel process‐based approach which links a model's current response to climate change to its behavior in the future. Plain Language Summary: A realistic representation of the radiation balance at the top of the Earth's atmosphere (TOA) by coupled climate models is essential for trust in future climate projections. Despite that relevance, it is still not clear whether the models correctly simulate the coupling between surface warming and TOA radiation because of the short observational record. We show that climate models systematically underestimate the observed increase in global TOA radiation during 2001–2022 in 552 simulations. Locally, even if a simulation reproduces observed changes in surface temperature, changes in TOA radiation are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale patterns of surface warming and from errors in the atmospheric physics which suppress the communication of the surface information to the TOA. Models that better represent the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity, that is, a weak global‐mean surface warming in response to a doubling of the atmospheric CO2 concentration above pre‐industrial levels. Our new bias metric links a model's current response to climate change to its behavior in the future. Key Points: Climate models underestimate the observed global TOA radiation trend during 2001–2022Surface warming patterns and atmospheric physics matter for the observation‐model discrepancyModels with a small bias in the coupling between surface warming and TOA radiation trends have a relatively low climate sensitivity [ABSTRACT FROM AUTHOR]

The impact of mid‐latitude oceanic frontal zones with sharp meridional sea‐surface temperature (SST) gradients on the middle atmosphere circulation during austral winter is investigated by comparing two idealized experiments with a high‐top gravity wave (GW) permitting general circulation model. Control run is performed with realistic frontal SST gradients, which are artificially smoothed in no‐front run. The control run simulates active baroclinic waves and GW generation around the mid‐latitude SST front, with GWs propagating into the stratosphere and mesosphere. In the no‐front run, by contrast, baroclinic‐wave activity is significantly suppressed, and GWs with smaller amplitude are excited and then dissipated at higher altitudes in the mesosphere. Westward wave forcing in the winter hemisphere was more pronounced in the control run up to ∼0.03 hPa, resulting in a more realistic reproduction of the middle atmospheric polar vortex. The results demonstrate the importance of realistic mid‐latitude ocean conditions for simulating the middle atmosphere circulation. Plain Language Summary: The impact of the mid‐latitude oceanic fronts characterized by sharp sea‐surface temperature (SST) gradients is investigated using a global gravity‐wave permitting atmospheric model that represents the troposphere, stratosphere and mesosphere. Two idealized experiments were conducted with different SST profiles. Control run features a realistic SST profile characterized by frontal SST gradients in mid‐latitudes, while they are smoothed out artificially in the "no‐front" run. In winter the no‐front run simulates significantly suppressed generation of synoptic‐scale cyclones and anticyclones, which results in reduced upward propagation of higher‐frequency gravity waves into the stratosphere, exerting marked impact on the large‐scale circulation extending as high as the mesopause. Notably higher gravity wave activity in the control run leads to a weaker, and more realistic wintertime polar vortex in the stratosphere and mesosphere. This study emphasizes the potential influence of mid‐latitude oceanic conditions on the atmospheric circulation, not only in the troposphere but also throughout the stratosphere and mesosphere. Key Points: High‐top global model simulations are conducted to examine the impact of a mid‐latitude oceanic front on the atmospheric circulationThe oceanic front enhances tropospheric baroclinic‐wave activity and generation of gravity waves propagating into the middle atmosphereThe enhanced gravity waves act to reduce cold bias of the wintertime polar vortex in the Southern Hemisphere middle atmosphere [ABSTRACT FROM AUTHOR]

Precipitation projections in transient climate change scenarios have been extensively studied over multiple climate model generations. Although these simulations have also been used to make projections at specific Global Warming Levels (GWLs), dedicated simulations are more appropriate to study changes in a stabilizing climate. Here, we analyze precipitation projections in six multi‐century experiments with fixed atmospheric concentrations of greenhouse gases, conducted with the UK Earth System Model and which span a range of GWLs between 1.5 and 5°C of warming. Regions are identified where the sign of precipitation trends in high‐emission transient projections is reversed in the stabilization experiments. For example, stabilization reverses a summertime precipitation decline across Europe. This precipitation recovery occurs concurrently with changes in the pattern of Atlantic sea surface temperature trends due to a slow recovery of the Atlantic Meridional Overturning Circulation in the stabilization experiments, along with changes in humidity and atmospheric circulation. Plain Language Summary: Climate model projections consistently predict that summer precipitation over Europe is expected to decline in the future as global temperatures rise under continued global warming. In our study, we use new climate model simulations that simulate a world where atmospheric concentrations of greenhouse gases are no longer increasing and the rise in global temperatures has slowed down. We show that the summer rainfall decline across Europe can, to some extent, be reversed if global temperatures were to stabilize. This has important implications for adaptation and planning decisions, particularly in so‐called climate change "hot‐spots" such as the Mediterranean. Key Points: Climate stabilization experiments show significant differences in projected precipitation compared to high‐emission transient scenariosNorthern European and Mediterranean projected summer drying is partially reversedEuropean summer precipitation changes are consistent with the atmospheric response to Atlantic SST changes [ABSTRACT FROM AUTHOR]

Mid‐tropospheric deep depressions in summer over the North Atlantic are shown to have strongly increased in the eastern and strongly decreased in the western North Atlantic region. This evolution is linked to a change in baroclinicity in the west of the North Atlantic ocean and over the North American coast, likely due to the increased surface temperature there. Deep depressions in the Eastern North Atlantic are linked to a temperature pattern typical of extreme heat events in the region. The same analysis is applied to a sample of CMIP6 model outputs, and no such trends are found. This study suggests a link between the observed increase of summer extreme heat events in the region and the increase of the number of Atlantic depressions. The failure of CMIP6 models to reproduce these events can consequently also reside in an incorrect reproduction of this specific feature of midlatitude atmospheric dynamics. Plain Language Summary: Extreme temperatures events in Western Europe have been rising fast, and current global climate models are not able to reproduce this excess. There are different hypotheses to explain this discrepancy. One is that the large‐scale atmospheric dynamics, responsible for the local weather, is not correctly represented by the models: indeed, the frequency and amplitude of some specific weather phenomena have been shown to be insufficiently reproduced, especially in summer. Here, we study one such phenomenon, namely the transient deep depressions, or extratropical cyclones, that travel across the Atlantic basin. A significant large increase of the number of these events is found in summer in the region of the North Atlantic off the western European coast. Depressions in that region are accompanied by high temperatures in continental western Europe. An ensemble of state of the art climate models are also analyzed and none of them is able to correctly reproduce the frequency of deep depressions nor their large trend, which suggests a common origin with the insufficient prediction of western European extreme heat events. Great caution should be used when analyzing climate change predictions in the region, and even more so when studying changes in complex dynamical phenomena. Key Points: Deep depression occurrences have significantly increased over the eastern side, and decreased over the western side of the North AtlanticDeep depressions are linked to high surface temperature patterns in western continental Europe but have little impact on the mean warmingGlobal Climate Models fail to reproduce the observed trends in deep depressions correctly [ABSTRACT FROM AUTHOR]

Strong positive Indian Ocean Dipole (pIOD) events like those in 1997 and 2019 caused significant flooding in East Africa. While future projections indicate an increase in pIOD events, limited historical data hinders a comprehensive understanding of these extremes, particularly for unprecedented events. To overcome this we utilize a large ensemble of seasonal reforecast simulations, which show that regional rainfall continues to increase with pIOD magnitude, with no apparent limit. In particular we find that extreme rain days are highly sensitive to the pIOD index and their seasonal frequency increases super‐linearly with higher pIOD magnitudes. It is vital that socio‐economic systems and infrastructure are able to handle not only the increasing frequency of events like 1997 and 2019 but also unprecedented seasons of extreme rainfall driven by as‐yet‐unseen pIOD events. Future studies should prioritize understanding the hydrological implications and population exposure to these unprecedented extremes in East Africa. Plain Language Summary: The Indian Ocean Dipole is a pattern of climate variability in the Indian Ocean, characterized by opposite‐signed sea surface temperature differences from normal in the west and east. During positive events (pIOD), the western Indian Ocean is warmer than usual, which disrupts the normal circulation of the atmosphere. When pIOD is strong, extreme rainfall and flooding are common in East Africa. This happened in 1997 and 2019. According to future predictions, the frequency of extreme pIOD events will increase. However, our understanding of these events is limited as there is not a large enough sample of past events in the historical data to study. To address this, we evaluate a large collection of climate model simulations called seasonal reforecasts. The results show no evidence of an upper limit to the impact of pIOD on regional rainfall. The frequency of rain days increases during pIOD, whilst extremely heavy rain days increase even more. This means that more frequent extreme pIOD seasons will likely bring more heavy rainfall impacts. It also means that if pIOD reaches a high magnitude which has not yet been recorded, the rainfall impacts are likely to be even larger than those seen in 1997 or 2019. Key Points: Unprecedented strong positive Indian Ocean Dipole (IOD) events are likely to bring extreme rainfall to East AfricaThe extreme component of the seasonal distribution of rain days is particularly sensitive to the magnitude of the IODThe impact of larger IOD events on extreme rainfall frequency increases non‐linearly with the IOD magnitude [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]

As sea ice disappears, the emergence of open ocean deep convection in the Arctic, which would enhance ice loss, has been suggested. Here, using 36 state‐of‐the‐art climate models and up to 50 ensemble members per model, we show that Arctic deep convection is rare under the strongest warming scenario. Only five models have convection by 2100, while 11 have had convection by the middle of the run. For all, the deepest mixed layers are in the eastern Eurasian basin. When that region undergoes a salinification and increasing wind speeds, the models convect; yet most models are freshening. The models that do not convect have the strongest halocline and most stable sea ice, but those that lose their ice earliest ‐because of their strongly warming Atlantic Water‐ do not have a persistent deep convection: it shuts down mid‐century. Halocline and Atlantic Water changes urgently need to be better constrained in models. Plain Language Summary: Both observations and modeling simulations suggest that deep vertical mixing (or deep convection) in winter may become the new normal in the Arctic as sea ice disappears, which further accelerates that disappearance. These simulations are often done using only one model, so here we used all models available that participated in the Climate Model Intercomparison Project Phase 6 (CMIP6), for the strongest warming scenario. We show that after removing those that are already inaccurate in the present, and even with a restrictive threshold, most models have no deep convection in the Arctic, or extremely rarely. Only five still had deep convection by the time the run finishes in 2100. We found that deep convection regions and periods are associated with a saltier, windier surface, while the rest of the Arctic and/or run freshens. The subsurface properties were crucial too: a deep halocline, that is, strongly stratified model, leads to less ice loss and no deep convection. In contrast, models with a shallow halocline and strongly warming Atlantic Water lose their ice and convect earliest, although it does not persist. As water mass properties are poorly represented in CMIP6 models, these need improving to better constrain Arctic deep convection and sea ice projections. Key Points: Oceanic deep convection does not emerge and persist in the Arctic in the majority of Climate Model Intercomparison Project Phase 6 models, despite a drop in the Nordic SeasArctic deep convection occurs only when both surface salinity and winds are increasing, year round, yet most models are fresheningA deep halocline and stable ice cover hinder deep convection, while early ice loss due to warmer Atlantific water can also stop it rapidly [ABSTRACT FROM AUTHOR]

Super‐coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super‐coarse dust accounts for less dust extinction in the visible‐to‐near‐infrared (VIS‐NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS‐NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET‐retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super‐coarse dust in the AERONET retrievals due to the limited VIS‐NIR sensitivity. An extra super‐coarse mode included in the AERONET‐retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS‐NIR. Plain Language Summary: Dust particles suspended in the atmosphere span a wide size range (0.001–100 μm). Notably, super‐coarse dust particles (diameter >10 μm) have been observed to be more abundant than what climate models suggest. Theoretically, these super‐coarse particles present little radiative signatures in visible to near‐infrared (VIS‐NIR) but significantly affect the thermal infrared (TIR) radiation. This study addresses the question of whether remote sensing techniques operating in the VIS‐NIR can capture these dust particles. We use side‐by‐side observations associated with a dust plume in both VIS‐NIR and TIR to assess whether the dust properties, including the size distribution, inferred by VIS‐NIR observations can generate well‐matched radiative signatures with TIR observations. We found that the simulated outgoing radiation at the top of the atmosphere in TIR using the VIS‐NIR‐inferred dust properties is greater than the observations because of not enough dust extinction, potentially led by the absence of super‐coarse dust. By introducing an extra super‐coarse mode in the size distribution, we found a better match with the TIR observation, while the dust optical properties retrieved in VIS‐NIR can be mostly conserved. Our result demonstrates the importance of combining VIS‐NIR and TIR observations to retrieve the dust size distribution. Key Points: Aerosol Robotic Networks (AERONET) dust retrievals are assessed by the thermal infrared (TIR) radiative closure study compared with the collocated AIRS observationsThe warm bias in the TIR radiative closure suggests the possibility of AERONET underestimating the super‐coarse dustAdding super‐coarse dust to the AERONET size distribution improves the TIR closure without deteriorating its inherent retrieval accuracy [ABSTRACT FROM AUTHOR]

While some previous studies examined the contribution of Eastern Pacific (EP) hurricanes toward precipitation in the arid Southwest US (SWUS), their potential to influence wildfires in that region has not been explored. Here we show, using observations and simulations from the Energy Exascale Earth System Model (E3SM), that recurving EP hurricanes modulate the wildfire environment in the SWUS by increasing precipitation and soil moisture, and reducing the vapor pressure deficit. This is especially the case during late season months of September–October when the likelihood of storms to recurve and make landfall increases. Further, analysis of burnt area observations reveals that for the months of September–October, recurving EP hurricanes may significantly reduce the prevalence of wildfires in the SWUS. Finally, E3SM simulations indicate that late season EP hurricanes have been on the decline, with important implications for wildfires in the SWUS. Plain Language Summary: A majority of Eastern Pacific (EP) hurricanes form and traverse westwards away from the North American coastline. Therefore, compared to tropical cyclones in some other basins, EP hurricanes have historically received less attention. However, some previous studies have shown that recurving EP hurricanes can contribute significantly to precipitation in the arid Southwest US (SWUS) region. Here, we build on these studies and demonstrate that recurving EP hurricanes significantly modulate the SWUS wildfire environment. During years with recurving EP hurricanes, the precipitation and soil moisture are enhanced, and the vapor pressure deficit is reduced, over the SWUS region. Therefore, during late season months of September–October, when recurving EP hurricanes occur, they make the environment less favorable for wildfires in the SWUS region. With climate models showing a long‐term decline in recurving EP hurricanes and the precipitation associated with them over the SWUS region, our study has substantial implications for autumn wildfires of that region. Key Points: Eastern Pacific (EP) hurricanes that recurve tend to enhance precipitation and reduce vapor pressure deficit in the Southwest US regionDuring September–October, recurving EP hurricanes may make the environment less favorable for wildfires in the Southwest USClimate model simulations suggest that Southwest US precipitation associated with recurving EP hurricanes has decreased [ABSTRACT FROM AUTHOR]

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal‐mean precipitation‐minus‐evaporation (P − E) under greenhouse‐gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P − E and increase P − E in the deep tropics. The surface‐albedo feedback shifts the location of maximum tropical P − E and increases P − E in the polar regions. The intermodel spread in the P − E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse‐rate and surface‐albedo feedbacks playing important roles in the polar regions. The P − E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the "wet‐gets‐wetter, dry‐gets‐drier" paradigm. Plain Language Summary: Climate feedbacks, which act to amplify or dampen global warming, play an important role in shaping how the climate system responds to changes in greenhouse‐gas concentrations. Here, we use an idealized climate model, which makes a simplified assumption about how energy is transported in the atmosphere, to examine how climate feedbacks influence the patterns of precipitation and evaporation change under global warming. We find that the cloud feedback acts to narrow the band of rainfall on the equator known as the Intertropical Convergence Zone and that the surface‐albedo feedback acts to shift the location of maximum rainfall. We also find that the cloud feedback accounts for most of the uncertainty associated with feedbacks in regional hydrological change under warming. The idealized model with a locked cloud feedback also simulates a change in precipitation and evaporation that is similar to a comprehensive climate model with an inactive cloud feedback. Key Points: A moist energy balance model (MEBM) is used to investigate the influence of climate feedbacks on regional hydrological changes under warmingCloud feedbacks act to narrow and increase tropical P − E and are the dominant source of feedback uncertainty in regional hydrological changesThe MEBM with a locked cloud feedback largely replicates a climate model with an inactive cloud feedback [ABSTRACT FROM AUTHOR]

This study examines the impact of ocean advection and surface freshwater flux on the non‐seasonal, upper‐ocean salinity variability in two climate model simulations with eddy‐resolving and eddy‐parameterized ocean components (HR and LR, respectively). We assess the realism of each simulation by comparing their sea surface salinity (SSS) variance with satellite and Argo float estimates. In the extratropics, the HR variance is about five times larger than that in LR and agrees with Argo. In turn, the extratropical satellite SSS variance is smaller than that from HR and Argo by about a factor of two, potentially caused by the insufficient resolution of radiometers to capture mesoscale features and their low sensitivity to SSS in cold waters. Using a simplified salinity conservation equation for the upper‐50‐m ocean, we find that the advection‐driven variance in HR is, on average, 10 times larger than the surface flux‐driven variance, reflecting the action of mesoscale processes. Plain Language Summary: This study explores the importance of ocean currents, evaporation, and rainfall for driving changes in the salt concentration in the upper ocean (known as salinity) in two climate model simulations with differing ocean resolutions. The high‐resolution model (HR) simulates ocean currents with dimensions of tens of km, while the low‐resolution model (LR) can only simulate currents with hundreds of km in size. When comparing their simulated sea surface salinity variations with those captured by satellites and autonomous floats from the Argo array, the salinity variability in the high‐resolution model is similar to the Argo data at mid to high latitudes and about five times stronger than that in the low‐resolution model. The satellite data show a variability two times smaller than HR and Argo in the same regions, potentially due to their insufficient spatial resolution at higher latitudes and their low sensitivity to the surface salinity in cold waters. Using a simple equation describing the conservation of salinity in the upper ocean, we have shown that small‐scale ocean currents drive most of the salinity variability in HR, while in LR, ocean currents play a much smaller role. Key Points: We investigate how advection and surface flux affect upper‐50‐m salinity variance in eddy‐resolving and eddy‐parameterized climate modelsThe extratropical variance in the eddy‐resolving run matches Argo and is much larger than in the eddy‐parameterized run and satellite dataThe larger upper‐ocean salinity variance in the eddy‐resolving run is predominantly driven by mesoscale ocean processes [ABSTRACT FROM AUTHOR]

Future climate projections suggest a poleward shift of the maximum intensity of tropical cyclones (TCs) over the western North Pacific. However, the global nature of the latitudinal change in TC genesis under global warming remains poorly understood. We show, using large‐ensemble high‐resolution atmospheric model simulations (d4PDF) with four warming scenarios, that the poleward shift is a robust change over the globe, attributable to the weakening of the Hadley circulation. The weakened ascent driven by the upper‐tropospheric warming suppresses the TC genesis within 5°–20° latitudes, whereas the weakened descent enhances the TC genesis in the poleward latitudes. We further estimate the poleward shift of TC genesis to emerge at the 2 K global warming over the Arabian Sea, South Atlantic and Pacific Oceans and at the 4 K warming over the North Pacific. The present results underscore the potential for increasing social and economic risks associated with TCs at higher latitudes. Plain Language Summary: Climate models have projected a decrease in TC genesis frequency in future warming. However, the global nature of the latitudinal change in TC genesis under global warming remains uncertain partly due to insufficient resolution as well as the ensemble size of climate model simulations. We show a global feature of the robust poleward shift of the TC genesis during the active seasons of both hemispheres scaled with the global warming level, which can be attributed to the weakening of the Hadley circulation. The weakened ascending branch of the Hadley circulation, driven by the increased upper tropospheric warming, potentially hinders TC genesis within 5°–20° latitudes. Conversely, the weakened descending branch of the Hadley circulation enhances the likelihood of TC genesis within 20°–35° latitudes. We further estimate that the signal of TC genesis is expected to emerge over high latitudes of the Arabian Sea, South Atlantic and South Pacific Oceans at the 2 K warming and at the 4 K warming over the North Pacific. The present analyses have significant implications not only for assessing the reliability of future TC‐related changes in climate models but also for estimating the increased TC‐related hazards at higher latitudes under global warming. Key Points: We project a global feature of the robust poleward shift of tropical cyclone (TC) genesis during active seasons of both hemispheresMore TC genesis at high latitudes can be attributed to the weakening of the Hadley circulationPoleward shift of TC genesis emerges at 2 K warming over Arabian Sea, South Atlantic and Pacific Oceans and at 4 K warming over North Pacific [ABSTRACT FROM AUTHOR]

We use a version of the NOAA Climate Forecast System with enhanced (up to 1‐m) ocean model vertical resolution to investigate the mean diurnal cycles of upper ocean temperature and currents. The model sea surface temperature diurnal cycle agrees well with a global observational analysis. The simulated time‐depth profiles of temperature and current also match closely observations from densely instrumented moorings in the tropical Pacific. Our analyses provide new insights into subsurface ocean diurnal cycles. Significant temperature diurnal range occurs, with seasonal modulation, at depths greater than 10 m across broad areas of the subtropical and midlatitude oceans. Significant current diurnal cycles are evident below 30 m across parts of the tropics, including in areas where deep‐cycle turbulence has been observed. Plain Language Summary: We used computer model simulations of Earth's atmosphere and ocean to understand how ocean temperatures and currents vary by time of day. The model has 12 levels in the top 20 m of the ocean—greater than normal for this kind of simulation. This allows realistic simulated diurnal variations of sea surface temperature (compared to global observations), and realistic changes in temperature and current at and below the surface (compared to mooring observations). These results give us confidence in the global simulations of currents, which provide new insights into diurnal variations of surface ocean velocity and turbulence below the surface. Key Points: A 1‐m vertical resolution ocean model accurately simulates global patterns of sea surface temperature mean diurnal cycleThe model gives new insights into modes of subsurface ocean temperature and current diurnal variationGlobal maps of current diurnal cycle extend understanding past that from relatively sparse observations [ABSTRACT FROM AUTHOR]

Air‐sea exchanges across oceanic fronts are critical in powering cloud formation, precipitation, and atmospheric storms. Oceanic submesoscale fronts of scales 1–10 km are characterized by strong sea surface temperature (SST) gradients. However, it remains elusive how submesoscale fronts affect the overlying atmosphere due to a lack of high‐resolution observations or models. Based on rare high‐resolution in situ observations in the Kuroshio Extension region, we quantify the air‐sea exchanges across an oceanic submesoscale front. The cross‐front SST and turbulent heat flux gradients reaches 2.4°C/km and 47 W/m2/km, respectively, far stronger than that typically found in mesoscale‐resolving products. The stronger SST gradient drives substantially stronger air‐sea fluxes and vertical mixing than mesoscale fronts, enhancing cloud formations. The intense air‐sea exchanges across submesoscale fronts are confirmed in idealized model simulations, but not resolved in mesoscale‐resolving climate models. Our finding provides essential knowledge for improving simulations of cloud formation, precipitation, and storms in climate models. Plain Language Summary: Oceanic fronts, characterized by large sea surface temperature (SST) gradients, are ubiquitous in the global ocean. Through intense heat and moisture release, these oceanic fronts induce large horizontal gradient of sea level pressure or increasing vertical mixing intensity in the lower atmosphere, are critical in powering cloud formation, precipitation, and atmospheric storms, but are sensitive to SST gradients. Oceanic submesoscale fronts of spatial scales 1–10 km are characterized by strong SST gradients. However, our knowledge of how the submesoscale fronts affect the overlying atmosphere is by and large void, due to a lack of high‐resolution observations or models. Here, based on high‐resolution in situ observations and model simulations, we show that submesoscale fronts drive much stronger air‐sea exchanges and vertical mixing as compared to mesoscale fronts, with significant implications for marine atmosphere boundary layer changes and cloud formations. Limited by the coarse resolution, the intense air‐sea exchanges across submesoscale fronts are not resolved in mesoscale‐resolving climate models. These results highlight the importance of submesoscale air‐sea interactions and call for a proper representation of submesoscale air‐sea exchanges in the next generation of climate models. Key Points: Observations show strong gradient in sea surface temperature and turbulent heat flux across a submesoscale oceanic frontSubmesoscale fronts drive substantially stronger air‐sea fluxes and vertical mixing than mesoscale frontsThe intense air‐sea exchanges across submesoscale fronts are not resolved in mesoscale‐resolving climate models [ABSTRACT FROM AUTHOR]

Based on outputs of 28 coupled models from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6), we show that the response of the Southeast Asian summer monsoon to the El Niño‐Southern Oscillation (ENSO) during post‐ENSO summer will likely strengthen in a warmer climate, which can be attributed to concurrently weakened sea‐surface temperature anomalies (SSTAs) in the western equatorial Pacific (WEP). The weakened WEP SSTAs are primarily caused by enhanced latent heat damping due to increased surface wind speed anomalies, which are associated with the eastward shift of the El Niño‐induced anomalous Walker circulation due to El Niño‐like sea surface temperature change in the tropical Pacific under global warming. Besides, the climatological zonal ocean currents will slow down due to the weakening of climatological Walker circulation, which also acts to weaken the WEP SSTAs via reducing the advection of anomalous temperature by the mean current. Plain Language Summary: As an important component of the Asian monsoon system, the Southeast Asian summer monsoon (SEASM) is crucial for the livelihoods of billions of people in East Asia, and is closely connected to a climate phenomenon called El Niño‐Southern Oscillation (ENSO). Understanding how the relationship between SEASM and ENSO will change in the future is important for enhancing our knowledge of climate change in East Asia. Outputs of 28 climate models from the Phase 6 of the Coupled Model Intercomparison Project show that ENSO will exert enhanced impacts on the SEASM in a warmer climate. The enhanced influences of ENSO on the monsoon will exacerbate the reduction of rainfall over the western North Pacific during the post‐El Niño summer. We find that such projected changes are mainly caused by weakened warm sea surface temperature (SST) anomalies (SSTAs) in the western equatorial Pacific (WEP). Further analyses indicate that the change in WEP SSTAs can be linked to the El Niño‐like change in climatological SSTs in the tropical Pacific. This study depicts detailed physical processes responsible for the projected changes in ENSO's impacts on the SEASM. Key Points: The effects of the El Niño‐Southern Oscillation on the Southeast Asian summer monsoon will strengthen under global warmingThe enhanced El Niño's impacts result from the weakened warm sea‐surface temperature (SST) anomalies in the western equatorial Pacific (WEP)The weakened WEP SST anomalies are related to the eastward shift of anomalous Walker circulation and the slackened mean zonal ocean currents [ABSTRACT FROM AUTHOR]

High‐resolution regional climate model (RCM) simulations of global warming consistently predict larger percentage increases in precipitation in the lee of midlatitude mountain ranges than on their windward slopes, indicating a weakening of the orographic rain shadow. This redistribution of precipitation could have profound consequences for water resources and ecosystems, but its underlying mechanisms are unknown. Here we show that rain‐shadow weakening is just one manifestation of a more general decrease in the influence of orography on precipitation under global warming. We introduce a simple model of precipitation change based on this principle, and find that it agrees well with an ensemble of high‐resolution simulations performed over the western United States. We argue that diminished orographic influence can be explained by the unique vertical structure of orographically forced ascent, which tends to maximize in the lower atmosphere where condensation is thermodynamically less sensitive to warming. Plain Language Summary: Mountains supply about half of the fresh water used by humans globally and often regulate the seasonal cycle of stream flows by accumulating snowpack during the wet season and releasing it over the dry season. Regional simulations tend to predict large future changes in the spatial pattern of precipitation in mountainous regions, but the reasons for these changes are not well understood. Here we show that the predicted patterns of precipitation change in the western US can be explained by a simple mechanism that causes the influence of mountains to weaken with increasing temperatures. We introduce a simple model of precipitation change based on this effect, and show that its predictions closely match those of far more sophisticated regional simulations of future warming over the western US. Key Points: Orographic influence on precipitation is reduced in simulations of global warmingFractional precipitation change scaled by warming is reproducible with a simple model [ABSTRACT FROM AUTHOR]

A key implication of the well known wet‐get‐wetter/dry‐get‐drier (WGW) scaling is that model biases in the representation of precipitation and evaporation in the present climate lead to spurious projected changes under global warming. Here we estimate the extent of such spurious changes in projections by 60 models participating in phases 5 and 6 of the Coupled Model Intercomparison Project. Utilizing known thermodynamic constraints on evaporation, we show that the WGW scaling can be applied to precipitation and evaporation separately (specific WGW scaling), which we use to correct for spurious projected changes in precipitation and evaporation over tropical oceans. The spurious changes in precipitation can be of comparable amplitude to projected changes, but are generally small for evaporation. The spurious changes may increase the uncertainty in projections of tropical precipitation and evaporation by up to 30% and 15% respectively. Plain Language Summary: It is well established that if winds are assumed unchanged, the response of the hydrological cycle to global warming will follow the simple rule that wet regions get wetter and dry regions get drier. A key implication of this rule is that climate model errors in the representation of precipitation and evaporation in the present climate lead to artificial simulated responses in a warming climate. Here we show that this simple rule can be applied separately to precipitation and evaporation over tropical oceans. We then use this rule to estimate the extent of the artificial response over tropical oceans to global warming in modern climate models, caused by errors in the representation of precipitation and evaporation. Overall, the artificial changes may increase the uncertainty in projections by up to 30% for precipitation and 15% for evaporation. Key Points: Biases in the representation of the present hydroclimate lead to spurious but correctable projected changes in precipitation and evaporationA wet‐get‐wetter/dry‐get‐drier scaling bias adjustment can be applied separately to precipitation and evaporationThe spurious changes increase uncertainty in precipitation and evaporation projections by up to 30% and 15% respectively [ABSTRACT FROM AUTHOR]

Understanding surface temperature is important for habitability. Recent work on Mars has found that the dependence of surface temperature on elevation (surface lapse rate) converges to zero in the limit of a thin CO2 atmosphere. However, the mechanisms that control the surface lapse rate are still not fully understood. It remains unclear how the surface lapse rate depends on both greenhouse effect and surface pressure. Here, we use climate models to study when and why "mountaintops are cold." We find the tropical surface lapse rate increases with the greenhouse effect and with surface pressure. The greenhouse effect dominates the surface lapse rate transition and is robust across latitudes. The pressure effect is important at low latitudes in moderately opaque (τ ∼ 0.1) atmospheres. A simple model provides insights into the mechanisms of the transition. Our results suggest that topographic cold‐trapping may be important for the climate of arid planets. Plain Language Summary: Understanding surface temperature on a planet is important for life on Earth and beyond. On Earth, we know "mountaintops are cold," which means that surface temperature decreases with elevation. However, this idea does not apply on present‐day Mars. Here, we investigate when and why the Earth‐based understanding holds for planets with different types of atmospheres. Using a global climate model, we show that both the greenhouse effect (atmospheric infrared opacity) and the pressure effect (atmospheric turbulence) are important. The weaker the greenhouse effect, or the thinner the atmosphere, the slower the surface cools with elevation. The greenhouse effect plays the dominant role, but in moderately opaque atmospheres, the pressure effect becomes important as well. Our work reveals a novel connection between climate and geomorphology. For example, on a planet with a relative thin pure O2 or N2 atmosphere, we do not expect that "mountaintops are cold." Key Points: Surface lapse rate robustly increases with atmospheric longwave optical thickness (greenhouse effect) in a general circulation modelIncreased pressure further contributes to a tropical surface lapse rate increase in moderately opaque atmospheresA simple model, assuming weak temperature gradient and highland convective adjustment, provides insight into the mechanisms [ABSTRACT FROM AUTHOR]

Quantitative methods to pinpoint the origin of atmospheric temperature anomalies (T′) associated with heatwaves are pivotal for the construction of physically plausible synoptic storylines of heatwave formation and their evaluation in models. Here, we combine a Lagrangian T′ decomposition with concepts from moisture tracking techniques to identify where and when the principal physical processes generate T′ and to attribute these sources to synoptic weather systems. Applying this framework to near‐surface and free‐tropospheric T′ associated with the record‐shattering 2021 heatwave in the Pacific Northwest shows that ascending, diabatic air streams in North Pacific cyclones contribute more than 50% of free‐tropospheric T′, whereas near‐surface T′ is mainly produced by local subsidence and diabatic heating with only marginal upstream contributions. Since free‐tropospheric T′ facilitates near‐surface accumulation of locally produced T′ by rendering the atmosphere stable to moist convection, our findings corroborate the notion of top‐down induced heatwave formation fueled by upstream diabatic processes. Plain Language Summary: Heatwaves, characterized by strongly above normal air temperatures, are amongst the most severe weather related threats to human lives and economic activities. Understanding the processes causing heatwaves and evaluating their representation in climate models requires suitable quantitative methods to diagnose the process' contributions to temperature anomalies. While it is well established which physical processes can create positive temperature anomalies, that is, transport of air from climatologically warmer regions, warming of air as it is compressed in sinking motion, and heating, for example, by a hot surface, it remains challenging to diagnose where and when these processes create temperature anomalies. Here, we present a quantitative framework based on the tracking of air parcels that allows us to obtain this kind of information. Applying this method to a recent devastating heatwave in the Pacific Northwest, we show that even though the air at the surface acquired its temperature anomaly mainly by local heating, storms over the North Pacific contributed substantially to warming the air aloft. This anomalously warm air aloft, in turn, was a pre‐requisite for the extreme accumulation of heat near the surface by suppressing the formation of thunderstorms, which would have had a cooling effect. Key Points: Novel diagnostic framework quantifies sources of atmospheric temperature anomalies (T′) generated by principal physical processesUpstream warm conveyor belts (WCBs) contributed >50% of free‐tropospheric T′ and <15% of near‐surface T′ to the 2021 Pacific Northwest (PNW) heatwaveImportance of upstream sources of free‐tropospheric T′ corroborate top‐down induced formation of PNW heatwave fueled by WCBs [ABSTRACT FROM AUTHOR]