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The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional "high–resolution" simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real‐world climate via a set of four 40 day simulations covering all 4 seasons of the year. Plain Language Summary: This paper describes the design and development of a 3 km version of the Energy Exascale Earth System Model (E3SM) atmosphere model, which has been fully rewritten in C++ using the Kokkos library for performance portability. This newly rewritten model is able to take advantage of the state–of–the–science high performance computing systems which use graphical processor units (GPUs) to mitigate much of the computational expense which typically plagues high–resolution global modeling. Taking advantage of this high–performance we are able to run four seasons of simulations at 3 km global resolution. We discuss the biases, including the diurnal cycle, by comparing model results with satellite and Atmospheric Radiation Measurement ground‐based site data. Key Points: Describes the C++/Kokkos implementation of the Simple Cloud–Resolving E3SM Atmosphere Model (SCREAMv1)SCREAMv1 leverages GPUs to surpass one simulated year per compute day at global 3 km resolutionHigh resolution improves some meso‐scale features and the diurnal cycle but large‐scale biases require improvement across all four seasons [ABSTRACT FROM AUTHOR]

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

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

Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers. Plain Language Summary: Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a difficult task. Being able to randomly simulate TCs and their features (such as wind speed) with mathematical models is therefore critical to build scenarios (and their corresponding probability) for land use planning and financial risk management. A common approach is to simulate TCs by tracking them directly in climate model outputs but this often underestimates the frequency of intense TCs while being computationally costly overall to generate a large number of events. For these reasons, many authors have looked into alternative approaches that replicate key physical features of TCs but rather using statistical models that are much less computationally demanding. This paper therefore presents a global TC model that leverages the strengths of both statistical and climate models to simulate a large number of TCs whose features are consistent with the physics and observations. As an important global phenomenon that affects TCs globally, we also integrate in our model the effects of El Niño. The paper focuses on the methodology and validation of each model component and concludes with global hazard maps for direct and indirect hits. Key Points: We present a global tropical cyclone (TC) wind model built upon a climate model large ensemble that can be used for risk analysisWe integrate ENSO into our model since it is a strong driver of storm annual frequency, cyclogenesis, trajectories, and intensityWe present global hazard maps consistent with statistical features of TC components and coherent with a global climate model [ABSTRACT FROM AUTHOR]

As a contribution to national strategic energy planning, recent developments in meteorological modeling and wind generation technologies have improved the representation of the spatio‐temporal features of wind. This paper describes an updated Italian Wind Atlas (Atlante EOLico ItaliANo [AEOLIAN]) released in the early 2000s. The objective of AEOLIAN is to guide future wind generation to accord with ambitious European greenhouse gas emission targets set for 2030 and 2050. AEOLIAN is the result of a collaboration effort between Ricerca sul Sistema Energetico (RSE) SpA and the National Center for Atmospheric Research (NCAR), which jointly developed a novel approach combining high‐resolution numerical weather modeling with the Analog Ensemble (AnEn) statistical technique. This paper uses dynamical model runs with hourly output for 1990–2019 with 4 km horizontal grid spacing. For 2015–2019, an inner grid nest with 1.33 km horizontal grid spacing is used. The AnEn is then employed to temporally extend the 5 years of high‐resolution runs back through 1990–2014 to create a 30‐year dataset for Italy and surrounding marine areas. A thorough verification is carried out using 104 observational stations homogeneously distributed throughout the territory. Compared with other state‐of‐the‐art products, AEOLIAN provides enhanced accuracy over complex terrain thanks to higher horizontal resolution and the assimilation of observational wind data over the domain, which result in a reduction of model bias on complex terrain and a better reconstruction of the wind distributions. Finally, a new WebGIS interface (https://atlanteeolico.rse-web.it/) to explore AEOLIAN data is described. [ABSTRACT FROM AUTHOR]

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

This paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society. Plain Language Summary: To prepare society for the effects of future climate change, we need to know what the future climate will be like. In this paper we explain the method that is used to construct six different scenarios that describe possible future climates of the Netherlands. The scenarios make assumptions about future greenhouse gas emissions, and are based on the outcomes of climate models that simulate the response of the climate to these emissions. The KNMI'23 climate scenarios show that strongly reducing global emissions strongly reduces the expected changes in the climate of the Netherlands. In the scenario in which global emissions continue to rise until 2080, Dutch society will have to adapt to a much stronger increases in heat and precipitation extremes, increased risks of droughts with low river discharge in summer, and increased risk of flooding due to high river discharges in winter. In the coming years the climate scenario data will be used to evaluate what needs to be done to keep the country a safe place for people to live in and to thrive in, under changing climate conditions. Key Points: We present a methodology for the construction of regional climate scenarios using a storyline approach to partition uncertaintyResults from CMIP6 are reconstructed with a GCM‐RCM initial condition ensemble to produce high‐resolution scenario data for end‐usersSix scenario variants cover emission uncertainty (high, moderate, low) and uncertainty in the regional response (dry‐trending, wet‐trending) [ABSTRACT FROM AUTHOR]

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

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

In this paper, we find a new regularity criterion of the 3D tropical climate model in terms of one component of the barotropic velocity. Our criteria of the velocity field improve the previous result by Berti et al. Moreover, we extend the regularity criteria of the Lebesgue spaces to that of larger Lorentz spaces. The key of this paper is by using interaction between partial and usual H1$$ {H}&#x0005E;1 $$ estimate to get upper bound of velocities. [ABSTRACT FROM AUTHOR]

In this paper, we examine differences in cloud adjustments (often called rapid adjustments) that occur as a direct result of abruptly increasing the solar constant by 4% or abruptly quadrupling of atmospheric CO2. In doing so, we devise a novel method for calculating the cloud adjustments for the abrupt solar forcing simulations that uses differences between coupled model simulations with abrupt solar and CO2 forcing, in combination with uncoupled, atmosphere‐only, abrupt CO2 forced experiments that have prescribed sea‐surface temperature. Our main findings are that (a) there are substantial differences in the responses of stratocumulus and cumulus clouds to solar and CO2 forcing, which follow the differences in the direct radiative effect that solar and CO2 forcing have at cloud top, and (b) there are differences in the adjustment of the average optical depth of high clouds to solar and CO2 forcing that we speculate are driven by the differences in the vertical profile of radiative heating and differences in the pattern of sea‐surface temperature change (for a fixed global mean temperature). These cloud adjustments contribute significantly to the total net cloud radiative effect, even after 150 years of simulation. Plain Language Summary: In climate change, clouds change due to a variety of mechanisms including surface temperature, dynamical circulations, and radiative forcing. In this paper, we examine the latter: how clouds respond to radiative forcing. We study this topic using climate model simulations where the brightness of the sun is abruptly increased by 4% and compare those with simulations where CO2 concentration is abruptly quadrupled. In doing so we find that, there are differences in the cloud response to changes in solar and CO2 forcing which include the occurrence of thick and thin high cloud, as well as the amount and height of low and mid‐level clouds. Key Points: Increasing CO2 causes a reduction and lowering of mid‐level and low‐level clouds which does not occur from solar forcingThere is large reduction in optically thin high clouds from solar forcing, especially as compared with CO2Even after 150 years adjustments make a significant contribution to the total net cloud radiative effect [ABSTRACT FROM AUTHOR]

The divergence method, a lightweight approach for estimating emission fluxes from satellite images, rests on a few implicit assumptions. This paper explicitly outlines these assumptions by deriving the method from first principles. The assumptions are: the enhanced mass flux is dominated by advection, normal fluxes vanish at the top and bottom of the atmosphere, steady‐state conditions apply, sources are multiplications of temporal and spatial functions, sinks are described as first‐order reactions, and effective wind fields are concentration‐weighted wind fields. No such assumptions have to be made for the background field. A "topography correction term" does not follow from the theory, but is rather shown to be a practical correction for topography‐dependent effective wind speed errors. The cross‐sectional flux method follows naturally from the derived theory, and the methods are compared. Effects of discrete pixels and finite‐difference operations are explored, leading to recommendations, primarily the recommendation to integrate over small regions only to minimize the influence of noise. Numerical examples featuring Gaussian plumes and COSMO‐GHG simulated plumes are provided. The Gaussian plume example suggests that the divergence method might underestimate emissions when assuming only advection in the presence of cross‐wind diffusion. Conversely, the cross‐sectional flux method remains unaffected, provided fluxes are integrated across the entire plume. The COSMO‐GHG example reveals frequent violations of the steady‐state assumption, although the assumption remains valid proximal to the source (<20 km in this example). It is the hope that this paper provides a solid theoretical foundation for the divergence and cross‐sectional flux methods. Plain Language Summary: Power plants, megacities, and other regions can be places where considerable amounts of gases are emitted into the atmosphere. Satellite data is capable of recording the enhanced concentrations in the atmosphere due to these sources. How does one estimate emissions of these sources based on this data? Typically by running an atmospheric inverse model; but one computationally lightweight method gaining popularity is the "divergence method." With this method, one applies a simple mathematical operation to the satellite image multiplied by the horizontal wind speeds—the result of which is related to the emissions. The method has been used before, but here we systematically state the assumptions involved; factors like how gases are transported horizontally and over time, and what happens when we apply the method on pixelated data (such as satellite images). We find that some other analyses in the literature may be partially incorrect. Another popular method (the "cross‐sectional flux") is easily derived from the same theory. Synthetic examples are used to illustrate that various assumptions are not met in realistic data; although the examples also show that one can work around these limitations by a careful application of the methods, and by acquiring more estimates over many days. Key Points: The divergence method for estimating emissions from satellite images is derived from first principlesAssumptions made on the way are made explicit, highlighting discrepancies with existing literatureA comparison is made against the cross‐sectional flux method which follows from the same theory [ABSTRACT FROM AUTHOR]

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

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

Understanding historical soil moisture (SM) variations and their relationship with temperature in high‐latitude humid regions is essential for predicting hot droughts under widespread warming. This paper presents the first‐ever annual‐resolution summer surface SM reconstruction (1736–2006 CE) in Sweden, located in northern Europe (NE). The reconstruction utilizes the paleoclimate proxy, tree‐ring δ18O, which exhibits a strong correlation with reanalysis SM data during 1948–2007 CE (r = −0.67, p < 0.001). In contrast to an unprecedented trend suggested by previous studies, our reconstruction exhibits a wetting trend in NE since the 1960s falling within the historical spectrum of trends. Both reanalysis SM data and our reconstruction show significant negative correlations with temperature at interannual timescales. However, Coupled Model Intercomparison Project Phase 6 climate models failed to capture the significant SM‐temperature relationship during the historical (1850–1947 CE) and observation periods (1948–2006 CE). This proxy‐model comparison represents a critical step toward enhancing our understanding of SM dynamics and the reliability of SM projections in high‐latitude humid regions. Plain Language Summary: Soil moisture (SM) refers to water in any form contained in soils, and it plays an important role in sustaining plant growth and influencing climate dynamics. SM deficits can result in ecological/agricultural droughts. However, our understanding of historical SM changes is limited due to the scarcity of long‐term records. Consequently, modeling historical SM changes proves challenging and prone to inaccuracies. In this study, we utilized a tree‐ring record to reconstruct centuries‐long SM changes in Sweden, located in northern Europe. Our analysis revealed a significant negative correlation between SM and temperature in both our reconstruction and SM data sets. However, state‐of‐art models failed to capture the significant relationship observed at our study site. These findings highlight the need to improve climate models to increase the accuracy of climate forecasts and better understand SM dynamics in high‐latitude humid areas. Key Points: This study presents the first 271‐year historical soil moisture (SM) reconstruction in northern Europe (NE) using the tree‐ring δ18O proxySignificant correlations between historical SM and temperature in NE were observed at interannual timescalesOur results implied that Coupled Model Intercomparison Project Phase 6 models might underestimate the SM‐temperature relationship [ABSTRACT FROM AUTHOR]

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

The development of any atmosphere or ocean model warrants a suite of test cases (TCs) to verify its spatial and temporal discretizations, order of accuracy, stability, reproducibility, portability, scalability, etc. In this paper, we present a suite of shallow water TCs designed to verify the barotropic solver of atmosphere and ocean models. These include the non‐dispersive coastal Kelvin wave; the dispersive inertia‐gravity wave; the dispersive planetary and topographic Rossby waves; the barotropic tide; and a non‐linear manufactured solution. These TCs check the implementation of the linear pressure gradient term; the linear constant or variable‐coefficient Coriolis and bathymetry terms; and the non‐linear advection terms. Simulation results are presented for a variety of time‐stepping methods as well as two spatial discretizations: a mimetic finite volume method based on the TRiSK scheme, and a high‐order discontinuous Galerkin spectral element method. The experimental procedure for conducting these numerical experiments is detailed. It underscores several key considerations that vary depending on the chosen spatial discretization method. Finally, convergence studies of every TC are conducted with refinement in both space and time, only in space, and only in time. The convergence slopes match the expected theoretical predictions. Plain Language Summary: Before running an atmosphere, ocean, or a coupled climate simulation, every model developer should ensure the correct implementation of each term in the governing equations that drive the models forward in time. This motivates the development of idealized test cases (TCs), each of which verifies a subset of terms in the governing equations with different initial and boundary conditions. Here we present a suite of six TCs for the momentum equation and sea surface height equation for ocean models in a single‐layer configuration. The computed results from the ocean model can be compared to exact solutions. The computed solution always has a small error, but is said to converge to the exact solution with reduction in grid cell size and time step. If the model converges at the expected rate, then we know that it is solving the governing equations correctly. We show results of convergence tests from two models, and share the full specifications of these TCs so that other ocean modelers may reproduce them. Key Points: A suite of test cases is presented for the verification of barotropic dynamics of ocean models, with exact and manufactured solutionsSpecifications are provided for coastal Kelvin wave, inertia‐gravity wave, planetary and topographic Rossby waves, barotropic tide, and non‐linear casesResults are presented for a variety of time‐stepping methods and two types of spatial discretizations: TRiSK and discontinuous Galerkin spectral element method [ABSTRACT FROM AUTHOR]

The Green's function approach is well‐established and widely used for modeling the surface mass loading displacements. Global mean Green's functions (MGFs) are commonly applied without considering local variations of the crustal structure. Derived from the modified layered Earth structure, the local Green's functions (LGFs) are theoretically beneficial to generate more accurate deformation, since they consider interior information of the local crust. This paper analyzed the differences among MGFs from Gutenberg–Bullen A model and two sets of LGFs derived from modified PREM Earth models consolidating with two crust models TEA12 and CRUST1.0, hereafter called PREMTEA and PREMCRU, respectively. Utilizing MGFs and two sets of LGFs, we modeled the corresponding 3D atmospheric loading displacements for 984 ITRF2014 stations and compared them with the ITRF2014 residuals. The results show that LGFs from PREMTEA and PREMCRU perform well in further promoting scatter reduction for ∼72%, ∼56%, and ∼85% of stations for the Up, East and North components, respectively. The improvements for the North components are significant (up to 3.6%). In particular, stations in the east coastal areas of North America and the west edge of Greenland exhibit further promoting scatter reduction for the East components (up to ∼2.5%), while those located in west coastline of North America show better performance for the North components. Nevertheless, there are significant anomalies in northern Europe for PREMCRU, the mutation margin of which should be carefully considered when using a resolution higher than 1°. In the area around station MORP (358.31°W, 55.21°N, sited at coastline of Britain), we suggest using PREMTEA model. Plain Language Summary: In any location worldwide, we employ a consistent set of numbers, known as load Green's functions that characterize the elastic properties of the Earth, to model the deformation caused by mass transfer on Earth's surface, which is referred to as surface mass loading effects. Most research and data products adopt same set of global mean Green's functions for any location on Earth, which neglects the differences in elastic properties of various regions of the Earth, leading to modeling errors to some extent. This study aims to optimize the modeling using local load Green's functions, with the objective of obtaining results that are more theoretically valuable. "Local" means that the Green's functions for each site are derived from an Earth model that takes into account the local crustal structure. The modeled atmospheric loading displacements exhibit enhanced alignment with GNSS position time series. This, in turn, further elucidates that deformations can be more attributed to atmospheric loading. Moreover, the model shows improved performance in the horizontal components when compared to the vertical components. Key Points: Local load Green's Functions enable a more precise modeling of loading displacements than global mean Green's FunctionsThe modeled atmospheric loading displacements based on Local Green's Functions can improve scatter reduction of GNSS position time seriesTwo sets of local load Green's Functions, based on CRUST1.0 model and TEA12 model, are compared. We recommend TEA12 for European region [ABSTRACT FROM AUTHOR]

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

Quantitative climate mobility research has, so far, focused primarily on climate change impacts on migration outcomes. This focus has led to a separation between quantitative climate migration research and the broader field of migration studies. In this paper ways are proposed for quantitative research to better address the complexity in the relationship between climate change and mobility. First technical suggestions are presented to improve upon migration model setups and designs and highlight promising developments. Then it is argued that quantitative methodologies can broaden the scope of research inquiries by examining how climate mitigation and adaptation efforts influence mobility, as well as assessing how mobility itself impacts vulnerability. [ABSTRACT FROM AUTHOR]

The paper investigates the effects of climate change on riverine ecosystems in the Upper Arno River basin, adopting a hybrid methodology relating the changes in streamflow to its related water ecosystem services. Twenty hydrological models were run using the Soil and Water Assessment Tool (SWAT), under their respective climate model and emission scenario. The streamflow variable obtained was analysed according to the method of Indicators of Hydrologic Alteration (IHA). The development of spatial maps of IHA allowed us to represent the spatial of the results according to the three river subbasins studied, with a focus on the presence of hotspots. Using IHA allowed to capture of the streamflow quantity and dynamics, through groups of magnitude, timing and rates, where the potential effect could be then linked to the concept of water ecosystem services. Finally, the results obtained were presented as a proxy that can be integrated into the existing Driver, Pressure, State, Impact, Response (DPSIR) management framework to eventually move towards an ecosystem‐based management methodology. The main results, induced from the Mann–Kendall trend analysis of the IHA, suggested that provisioning and regulating services are to be affected by climate change where the quantity of flows shows a decreasing trend under several emission scenarios, in addition to an intensification of the extreme events. This shift is accompanied by a change in the dynamics of flow, which holds the potential of affecting habitat suitability. With the emergence of studies directed towards ecosystem‐based management, the study refers to well‐established hydrological modelling to evaluate climate change as a driver and provide screening results to ensure a proactive response planning that converges with the idea of ecosystems. [ABSTRACT FROM AUTHOR]

Most real‐time rendering models for atmospheric effects have been designed and optimized for Earth's atmosphere. Some authors have proposed approaches for rendering other atmospheres, but these methods still use approximations that are only valid on Earth. For instance, the iconic blue glow of Martian sunsets can not be represented properly as the complex interference effects of light scattered at dust particles can not be captured by these approximations. In this paper, we present an approach for generalizing an existing model to make it capable of rendering extraterrestrial atmospheres. This is done by replacing the approximations with a physical model based on Mie Theory. We use the particle‐size distribution, the particle‐density distribution as well as the wavelength‐dependent refractive index of atmospheric particles as input. To demonstrate the feasibility of this idea, we extend the model by Bruneton et al. [BN08] and implement it into CosmoScout VR, an open‐source visualization of our Solar System. In a first step, we use Mie Theory to precompute the scattering behaviour of a particle mixture. Then, multi‐scattering is simulated, and finally the precomputation results are used for real‐time rendering. We demonstrate that this not only improves the visualization of the Martian atmosphere, but also creates more realistic results for our own atmosphere. [ABSTRACT FROM AUTHOR]

An analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Plain Language Summary: China is a vulnerable country to climate change due to its dense population, unbalanced social and economic development, shortage of water resources, and fragile ecosystems. How future precipitation will change over the region is of great concern for the general public and decision makers. This paper presents a first analysis of precipitation simulations from a set of five RCM (RegCM4) 21st century climate change projections, driven by coarse resolution general circulation models (GCMs) over China. We find that the spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by a precipitation underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in north China by both the GCMs and RegCM4 in all seasons except summer, when, weak correlations of the projected change patterns are found between the GCMs and nested RegCM4, indicating the greater role of the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Key Points: The spatial patterns of bias show common features between the GCMs and RegCM4RegCM4 provides greater spatial detail of present day precipitation simulation compared to the GCMs and finer structures of future changesThe change patterns across the RegCM4 projections show a high correlation, but not always between each pair of driving GCM and RegCM4 [ABSTRACT FROM AUTHOR]

The global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere--hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), using a broad range of the latest available global observation-based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ-GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ-GUESS accurately captures both interand intra-annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ-GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML-V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ-GUESS aligns with ESA-CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use. [ABSTRACT FROM AUTHOR]

Turbulence in very stable boundary layers is typically unsteady and intermittent. The study implements a stochastic modeling approach to represent unsteady mixing possibly associated with intermittency of turbulence and with unresolved fluid motions such as dirty waves or drainage flows. The stochastic parameterization is introduced by randomizing the mixing lengthscale used in a Reynolds average Navier‐Stokes (RANS) model with turbulent kinetic energy closure, resulting in a stochastic unsteady RANS model. The randomization alters the turbulent momentum diffusion and accounts for sporadic events of possibly unknown origin that cause unsteady mixing. The paper shows how the proposed stochastic parameterization can be integrated into a RANS model used in weather‐forecasting and its impact is analyzed using neutrally and stably stratified idealized numerical case studies. The simulations show that the framework can successfully model intermittent mixing in stably stratified conditions, and does not alter the representation of neutrally stratified conditions. It could thus present a way forward for dealing with the complexities of unsteady flows in numerical weather prediction or climate models. Plain Language Summary: Limited computer resources lead to a simplified representation of unresolved small‐scale processes in weather forecasting and climate models, through parameterization schemes. Among the parameterized processes, turbulent fluxes exert a critical impact on the exchange of heat, water and carbon between the land and the atmosphere. Turbulence theory was, however, developed for homogeneous and flat terrain, with stationary conditions. At nighttime or in cold environment, turbulence is typically non‐stationary, weak and intermittent and the classical theory fails. Part of the intermittent mixing is due to turbulence enhancement by small‐scale wind variability. In the following, a random modeling approach is used to enhance turbulent mixing due to small‐scale wind variability and intermittency of mixing. The proposed approach is shown to be a viable approach to represent the effect of small‐scale variability of mixing for different atmospheric flow conditions. Key Points: A stochastic parameterization of turbulence is implemented in a Reynolds average Navier‐Stokes (RANS) model to represent unsteady mixingThe introduced stochastic perturbations of the mixing length enable the simulation of intermittent turbulence in the stable boundary layerThe stochastic unsteady RANS model does not alter the simulation of neutral conditions [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive overview of the modelling tools available for integrating climate change impacts into pest risk assessments (PRA), elucidating the existing methodologies and models employed to understand the potential distributions of pests based on historical data and under future climate change scenarios. We highlight the strengths and weaknesses of these models and provide commentary on their ability to identify emerging threats due to climate change accurately and adequately, considering pest establishment likelihood, host crop exposure and the distribution of impacts. The simplest methods are based on climate‐matching models, degree‐day development models and Köppen–Geiger climate classification. Correlative species distribution models derive species–environment relationships and have been applied to PRA with mixed success. When fitted models are applied to different continents they are usually challenged to extrapolate climate suitability patterns outside the climate space used to train them. Global climate change is creating novel climates, exacerbating this 'transferability' problem. Some tools have been developed to reveal when these models are extrapolating. Process‐oriented models, which focus on mechanisms and processes rather than distribution patterns, are inherently more reliable for extrapolation to novel climates such as new continents and future climate scenarios. These models, however, require more skill and generally more knowledge of the species to craft robust models. [ABSTRACT FROM AUTHOR]

We conduct a global assessment of the spatial heterogeneity of cloud phase within the temperature range where liquid and ice can coexist. Single‐shot Cloud‐Aerosol Lidar with Orthogonal Polarization lidar retrievals are used to examine cloud phase at scales as fine as 333 m, and horizontal heterogeneity is quantified according to the frequency of switches between liquid and ice along the satellite's path. In the global mean, heterogeneity is greatest between −15 and −4°C with a peak at −5°C, when small patches of ice are prevalent within liquid‐dominated clouds. Heterogeneity "hot spots" are typically found over the extratropical continents, whereas phase is relatively homogeneous over the Southern Ocean and the eastern subtropical ocean basins, where supercooled liquid clouds dominate. Even at a fixed temperature, heterogeneity undergoes a pronounced annual cycle that, in most places, consists of a minimum during autumn or winter and a maximum during spring or summer. Based on this spatial and temporal variability, it is hypothesized that heterogeneity is affected by the availability of ice nucleating particles. These results can be used to improve the representation of subgrid‐scale heterogeneity in general circulation models, which has the potential to reduce longstanding model biases in cloud phase partitioning and radiative fluxes. Plain Language Summary: At temperatures where ice and liquid can coexist within clouds, climate models tend to produce too much ice and too little liquid compared to satellite observations. This bias is likely caused by the assumption that liquid and ice are uniformly mixed, which results in the rapid conversion of liquid to ice for thermodynamic reasons. To reduce this bias, models need to account for the spatial heterogeneity ("patchiness") of liquid and ice that exists in the real atmosphere. The goal of this paper is to quantify this spatial heterogeneity using satellite‐based lidar observations of cloud phase. We find small pockets of ice in liquid‐dominated clouds to be more common than small pockets of liquid in ice‐dominated clouds. The greatest heterogeneity is found over the midlatitude continents, whereas phase is relatively uniform over the Southern Ocean and other maritime regions with extensive low cloud cover. In the mid and high latitudes, cloud phase tends to be more heterogeneous during spring and summer and more homogeneous during autumn and winter. These results can be used in the future to improve model representations of the thermodynamic processes responsible for biases in cloud phase. Key Points: Cloud phase heterogeneity is greatest at −5°C, when small ice patches form in majority‐liquid cloudsCloud phase is relatively homogeneous over the Southern Ocean and heterogeneous over the northern continentsFor a fixed temperature, extratropical phase heterogeneity is generally greatest during local spring and summer [ABSTRACT FROM AUTHOR]

Climate models' capability of reproducing the present climate at both global and regional scales still needs improvements. The assessment of model performance critically depends on the data sets used as comparators/references. Reanalysis and gridded observational data sets have been frequently used for this purpose. However, none of these can be considered an accurate reference data set because of their associated uncertainties and full representativity. This paper, for the first time, uses in‐situ measurements from National Oceanic and Atmospheric Administration U.S. Climate Reference Network (USCRN) spanning the period 2006–2020 to assess daily temperature and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA‐Interim) involved in NA‐CORDEX. The assessment is also extended to the most recent and widely used Earth system reanalyses (ERA5, ERA‐Interim, MERRA2 and NARR) and a few in situ‐based gridded data sets (Daymet, PRISM, Livneh and CPC). Results show that biases for the different data sets are seasonally and subregionally dependent. On average, reanalysis and in situ‐based gridded data sets are warmer (with biases exceeding 0.3°C) than USCRN year‐round, while RCMs are colder (warmer) in winter (summer) with biases ranging from −0.5 (0.9)°C for RCMs at 0.44° to −0.2 (1.4)°C for CRCM5‐UQAM‐11. In situ‐based gridded data sets provide the best performance in most of the Contiguous United States (CONUS) regions compared to reanalyses and RCMs, but still have biases in regions such as the Western mountains and the Pacific Northwest. Furthermore, in most US subregions, reanalysis data sets do not outperform reanalysis‐driven RCMs. Likewise, for both reanalysis data sets and RCMs, temperature and precipitation biases vary considerably depending on the local orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis. [ABSTRACT FROM AUTHOR]

The marine biological carbon pump (BCP) stores carbon in the ocean interior, isolating it from exchange with the atmosphere and thereby coregulating atmospheric carbon dioxide (CO2). As the BCP commonly is equated with the flux of organic material to the ocean interior, termed "export flux," a change in export flux is perceived to directly impact atmospheric CO2, and thus climate. Here, we recap how this perception contrasts with current understanding of the BCP, emphasizing the lack of a direct relationship between global export flux and atmospheric CO2. We argue for the use of the storage of carbon of biological origin in the ocean interior as a diagnostic that directly relates to atmospheric CO2, as a way forward to quantify the changes in the BCP in a changing climate. The diagnostic is conveniently applicable to both climate model data and increasingly available observational data. It can explain a seemingly paradoxical response under anthropogenic climate change: Despite a decrease in export flux, the BCP intensifies due to a longer reemergence time of biogenically stored carbon back to the ocean surface and thereby provides a negative feedback to increasing atmospheric CO2. This feedback is notably small compared with anthropogenic CO2 emissions and other carbon‐climate feedbacks. In this Opinion paper, we advocate for a comprehensive view of the BCP's impact on atmospheric CO2, providing a prerequisite for assessing the effectiveness of marine CO2 removal approaches that target marine biology. [ABSTRACT FROM AUTHOR]

This study proposes a model‐based methodology to estimate design precipitation for long durations during the winter and spring seasons (October to June) through its application to the drainage areas of two dams in the Columbia River Basin, United States. For basins with large reservoir storage or snowpack, design precipitation and floods need to be estimated based on long‐duration processes rather than focusing only on flood peaks or single storm durations. This study used the advanced research version of weather research and forecasting (WRF) model to maximize the target precipitation over the drainage areas by means of the Atmospheric Boundary Condition Shifting and Relative Humidity Perturbation with relaxed moisture flux thresholds. The greatest cumulative basin‐average precipitation depths during Oct–Jun were estimated to be 1220.5 and 1595.4 mm for the drainage areas of Bonneville and Libby Dams, respectively. The 95% confidence interval (CI) of the exceedance probabilities of the estimated design precipitation depths were found to range from 10−3 to 10−5 at Bonneville Dam's drainage area. Those orders were found to be comparable with the documented exceedance probabilities of PMP/PMF in the US. The estimated design precipitation and corresponding atmospheric/land‐surface fields together will drive a physical model to estimate the design flood. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

Identifying areas expected to remain buffered from climate change and maintain biodiversity and ecological function (i.e., climate refugia) is important for climate adaptation planning. As structurally diverse transitional zones between terrestrial and aquatic environments, riparian areas are often biological hotspots and provide critical corridors for species movement, particularly in arid and semi‐arid regions. In our study region in the western and central USA, identifying riparian areas that could serve as climate refugia is a priority for wildlife managers. We mapped areas with connected riparian habitats that, based on landscape diversity and projected changes in summer temperatures and landscape runoff, are expected to serve as climate refugia. To incorporate uncertainty and balance the need for near‐ and long‐term planning, we mapped potential refugia for 2 future time periods (2040–2069, 2070–2099) based on 2 climate models that represented divergent but plausible climate outcomes. The approach we developed is not constrained by physiology or behavior of target species and can be used to identify areas expected to fare comparatively well under a wide range of future climate scenarios. Our approach can also be used to identify areas where restoration could increase riparian connectedness and climate resilience. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

Heatwaves have significant effects on ecosystems and human health. Human habitability is impacted severely as human exposure to heatwaves is projected to increase, however, the contribution of soil moisture effects to the increased exposure is unknown. We use data from four climate models, in which two experiments are used to isolate soil moisture effects and in this way to examine projected changes of soil moisture contributions to projected increases in heatwave events. Contributions from soil moisture to future population exposure to heatwaves are also investigated. With soil moisture effects combined with global warming, the longest yearly heatwaves are found to increase by up to 20 days, intensify by up to 2°C in mean temperature, with an increasing of frequency by 15% (the percentage relative to the total number of days for a year) over most mid‐latitude land regions by 2040–2070 under the SSP585 high emissions scenario. Furthermore, soil moisture changes are found to have a significant role in projected increases of multiple heatwave characteristics regionally compared with the global land area and contribute to more global population exposed to heatwaves. Plain Language Summary: Heatwaves have great impacts on human health, agriculture, economy, and many other related societal factors. Existing research has not formed a comprehensive understanding of the influences from soil moisture and drought on population exposure to global heatwaves. This study aims to provide a better perception and quantification of soil moisture effects on changes of both heatwaves per se and population exposure to heatwaves, which will help to enhance further insights of climate change impacts on human society. Using outputs from dedicated climate model experiments, it is found detectable contributions from soil moisture changes on exacerbating heatwaves and increasing population exposure to them. This study can inform evidence‐based policies and potential land management techniques such as irrigation, land radiative management, and forestation, for mitigating the impacts of climate extremes. Key Points: We conducted the first global analysis of soil moisture effects on population exposure to heatwavesEffects of projected soil moisture changes constitute a strong component in the exacerbation of heatwaves over most land regionsPopulation exposure to heatwaves rises sharply in Asia, Australia, US, Europe, and central and southern Africa [ABSTRACT FROM AUTHOR]

Cloud microphysics is a critical aspect of the Earth's climate system, which involves processes at the nano‐ and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm‐rain bulk schemes using high‐fidelity super‐droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including Ensemble Kalman Inversion and Unscented Kalman Inversion to calibrate bulk microphysics schemes with probabilistic super‐droplet simulations. We demonstrate the framework's effectiveness by calibrating a single‐moment bulk scheme, resulting in a reduction of data‐model mismatch by more than 75% compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling. Plain Language Summary: Cloud microphysics is a complex set of processes that determine the formation and evolution of particles in clouds, which affects the Earth's climate by regulating precipitation and cloud cover. However, the vast difference in scale between the microphysics and large‐scale atmospheric flows makes it impossible to simulate these processes in climate models directly. Instead, climate models use simplified methods to represent cloud microphysics, which can result in inaccuracies. In this study, we focus on calibrating the simplified models with more detailed simulations of cloud microphysics using the super‐droplet method. We demonstrate a framework for calibrating the simplified models using high‐fidelity simulations, which improves the accuracy of these models. Key Points: A calibration framework for warm‐rain bulk microphysics parameterizations is presentedThe framework relies on a library of super‐droplet simulations of a rain shaftCalibrating a single‐moment microphysics scheme with the calibration framework substantially reduces the model‐data mismatch [ABSTRACT FROM AUTHOR]

Subgrid‐scale processes, such as atmospheric gravity waves (GWs), play a pivotal role in shaping the Earth's climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid‐scale parameterizations are used to capture their effects. Recently, machine learning (ML) has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a ML parameterization for atmospheric GWs. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing GW parameterization. We estimate both offline uncertainties in raw NN output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing NN parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of ML‐based GW parameterizations, thus advancing our understanding of their potential applications in climate modeling. Plain Language Summary: Climate models are unable to resolve processes that vary on length and time scales smaller than the model resolution and timestep. For example, atmospheric gravity waves (GWs), which are waves created when winds encounter disturbances to the flow, such as mountains, convection and fronts, can have wavelengths smaller than the spacing between grid cells. Climate models use "parameterizations" to capture the effect of these processes. Machine learning based parameterizations are becoming popular because they can learn relationships purely from data. However, we do not have a good understanding of the uncertainties introduced through machine learning parameterizations. This study estimates uncertainties associated with training a neural network (NN) GW parameterization. We explore uncertainties in the NN output, as well as the uncertainties in the climate model output, when the NN is used for the GW parameterization. Key Points: Using ensembles of neural networks, we learn parametric uncertainties associated with an emulator of a gravity wave parameterizationWhen coupled to the climate model, the ensemble of neural networks reveals increased climate variabilityParametric uncertainty dominates the Quasi‐Biennial Oscillation statistics, although polar vortex properties remain robust to parameters [ABSTRACT FROM AUTHOR]

Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO2). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs. Plain Language Summary: Scientists increasingly use machine learning methods, especially neural networks (NNs), to improve weather and climate models. However, it can be challenging for an NN to learn rare, large‐amplitude events because they are infrequent in training data. In addition, NNs need to express their confidence (certainty) about a prediction and work effectively across different climates, for example, warmer climates due to increased CO2. Traditional NNs often struggle with these challenges. Here, we share insights from emulating known physics (gravity waves) with NNs in a state‐of‐the‐art climate model. We propose specific strategies for effectively learning rare events, quantifying the uncertainty of NN predictions, and making reliable predictions across various climates. For instance, one strategy to address the learning of rare events involves inflating the impact of infrequent events in the training data. We also demonstrate that several methods could be useful in determining the uncertainty of the predictions. Furthermore, we show that NNs trained on simulations of the historical period do not perform as well in warmer climates. We then improve NN performance by employing transfer learning using limited new data from warmer climates. This study provides lessons for developing robust and generalizable approaches for using NNs to improve models in the future. Key Points: Whole Atmosphere Community Climate Model's orographic, convective, and frontal gravity wave parameterizations are emulated using neural nets to inform future modeling effortsData imbalance is addressed via resampling and weighted loss; uncertainty quantification via Bayesian, dropout, and variational methodsPerformance of the neural nets in a warmer climate is improved via transfer learning with ∼1% new data [ABSTRACT FROM AUTHOR]

Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea‐surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100‐km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large‐scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14‐km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28‐km regional refinement and uniform 111‐km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4‐standard‐deviation anomaly in the seasonal‐mean NAO for 2°C SST anomalies. The lower‐resolution simulations show a weaker response with a different spatial structure. The enhanced large‐scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient‐eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher‐resolution models and that regional refinement in key regions offers a potential pathway to improve multi‐year regional climate predictions based on midlatitude SSTs. Plain Language Summary: Variations in the ocean surface temperature (SST) influence the atmospheric circulation and thus climate over land. Canonical understanding is that tropical SSTs are more important than SSTs in midlatitudes. However, this understanding is based on climate models that don't resolve processes at scales less than 100 km. Here, we show that by increasing the atmospheric model resolution to resolve features on smaller scales, such as weather fronts, we find a larger atmospheric circulation response to midlatitude SST anomalies in the North Atlantic. North Atlantic SST anomalies can be predicted multiple years in advance, and a larger atmospheric circulation response to these predictable SST anomalies therefore implies increased predictability of climate over the surrounding land regions. Key Points: There is a large circulation response to idealized Gulf Stream sea‐surface temperature (SST) anomalies in an atmospheric model with 14‐km regional grid refinementThis response is weaker or absent in simulations with 28‐km or coarser resolution, which do not fully resolve mesoscale frontal processesTransient‐eddy fluxes of heat and momentum are modified as fronts pass over warm SSTs, leading to a large‐scale circulation response [ABSTRACT FROM AUTHOR]