Abstract Seasonal forecasting in operational centers has focused predominantly on prediction of temperature and precipitation. Here, we use the skill of model forecasts against observations (actual prediction skill) and against each model ensemble member (perfect-model skill) to assess the predictability of these two variables and five additional hydrological variables across two distinct hydrologic regions (the Missouri basin and California) of the United States. The forecasts from two operational coupled models [National Centers for Environmental Prediction Climate Forecast System version 2 (NCEP CFSv2) and European Centre for Medium-Range Weather Forecasts Seasonal Forecasting System version 5 (ECMWF SEAS5)] are used. Results show variables with high memory, such as soil moisture, total water storage, and snow water equivalent, have both high perfect-model and actual prediction skill. Runoff and evapotranspiration, which are highly dependent on the amount of water storage, generally have intermediate skill. Subbasins within these two hydrologic regions show similar results. The high memory variables also maintain high skill for longer prediction lead and exhibit less seasonal variability, particularly over the Missouri basin region which experiences a lower amplitude in the annual cycle. However, over California, skill drops off more quickly with increasing lead and has a strong seasonal cycle with the highest skill during late spring and early summer. Results also demonstrate that actual prediction skill is positively related to perfect-model skill for all variables across different regions and seasons which give some promise to using perfect-model skill in ungauged areas of the world as a proxy to their real-world skill. Significance Statement Operational seasonal forecasting models are commonly evaluated based on their predictions of temperature and precipitation. Here, we assess the forecasts for these two variables and five additional variables associated with the hydrologic cycle across two distinct hydrologic regions of the United States. We find that total water storage, soil moisture, and snowpack are more dependent on past values (i.e., with high memory) and hence have the highest predictability. Evapotranspiration and runoff, which are highly dependent on water storage, have higher predictability than temperature and precipitation. Also, the variables and regions with the highest memory maintain higher skill at longer lead times and have consistent skill throughout the year. Basins and seasons that have higher intrinsic model predictability also have higher skill predicting real-world values for all variables.

The planetary boundary layer (PBL) is the atmospheric layer closest to Earth’s surface that is directly influenced by surface processes, where exchanges of momentum, heat, mass, and radiation regulate environmental conditions with direct societal relevance. Because the thermodynamic structure and dynamics of the PBL are tightly coupled to surface–atmosphere interactions, accurate characterization of these processes is essential for advancing understanding of Earth system feedbacks. However, despite their importance, significant observational gaps remain in capturing the coupled surface–PBL system across the spatial and temporal scales required for both scientific and operational applications. This perspective paper articulates the central role of surface–atmosphere interactions in PBL science and their importance for advancing multiplatform observing systems. We assess the key surface variables and their required spatiotemporal resolutions needed for accurate PBL characterization, evaluate the capabilities and limitations of current global observing systems and the Program of Record—including space-based, airborne, and ground-based assets—and review emerging technological and scientific efforts aimed at addressing these gaps. Building on this assessment, we argue that advancing toward a comprehensive, surfaceinformed PBL observing system is both a scientific and societal imperative. Such a system would overcome current observational limitations and unlock substantial benefits across a wide range of applications that depend critically on accurate PBL representation, yet remain underrecognized. By synthesizing current knowledge and defining clear observational priorities, this work aims to guide the design of future PBL global observing systems and its integration, as well as to mobilize the scientific community toward coordinated, multi-scale observations of surface–atmosphere interactions, ultimately advancing PBL science and its applications on a global scale. SIGNIFICANCE STATEMENT: The planetary boundary layer (PBL) is the lowest part of the 86

Abstract The state of California experiences numerous weather extremes, from flooding rains to devastating wildfires. West-WRF is a customized version of the Weather Research and Forecasting Model tailored for hydrometeorological prediction in the western United States. This work presents an evaluation of West-WRF hydrometeorological prediction in December 2021 for California against in situ measurements, ERA5 reanalysis, and other observational datasets. Its performance is compared with those of the global models that drive it as initial and lateral boundary conditions [Global Forecast System (GFS) and European Centre for Medium-Range Weather Forecasts (ECMWF)]. As novel artificial intelligence (AI) weather prediction models are increasingly being used, we also evaluate two such models trained using ERA5 data. We show that West-WRF driven by the GFS improves precipitation prediction at short lead times over California and reduces mean absolute error (MAE) over the GFS. The improvement in the snow water equivalent prediction is more substantial, with a larger reduction in MAE of ∼70% over the GFS, partly due to an improved snow initialization in West-WRF. West-WRF also improves the prediction of 10-m wind speed in the coastal region of Southern California. Its performance with other quantities including 2-m temperature and specific humidity is more variable. The AI models perform well in predicting the spatial pattern of daytime 10-m wind compared with ERA5 reanalysis, but their performance against in situ measurements in Southern California is comparable to that of GFS and ECMWF physics-based models, which are much worse than that of West-WRF. These evaluations also provide insights on further improvements of West-WRF. Significance Statement The state of California experiences numerous weather extremes, from flooding rains to devastating wildfires. Additionally, the water resources of the state are scarce, and their responsible management is required. This study works off an effort by the Center for Western Weather and Water Extremes (CW3E) to better predict near-term weather for the western United States. It explains why this high-resolution regional forecast model is desirable, while also explaining its drawbacks and potential routes for improvement. For example, the new model predicts precipitation very well in the Sierra Nevada and may also be useful for wildfire prediction by better predicting the 10-m wind speed over Southern California. Last, the performance of two artificial intelligence models in weather forecasting is briefly evaluated in comparison to the regional and global physics-based models.

The Energy Exascale Earth System Model version 3 (E3SMv3) represents the latest advancement in Earth system modeling developed by the U.S. Department of Energy (DOE). Building upon previous versions, E3SMv3 introduces significant updates across its coupled components to enhance capability and improve fidelity. The atmosphere component incorporates advancements in chemistry, aerosol-cloud interactions, convection, and microphysics. The ocean features a new time-stepping scheme and a higher-resolution unstructured mesh with sub-ice-shelf cavities, while the sea ice model integrates advanced snow and ice physics for more realistic cryospheric simulations. The land model introduces prognostic vegetation dynamics and a new sub-grid topographic treatment of solar radiation. A new tri-grid configuration harmonizes the horizontal grids of the land and river components for improved process coupling. It is enabled by a new non-linear remapping between the atmosphere and land. E3SMv3 underwent extensive testing through a comprehensive simulation campaign, including pre-industrial control, idealized experiments, and historical simulations spanning 1850-2024. The model demonstrates significant improvements in simulating the evolution of the historical surface temperature, particularly addressing the "pothole cooling" bias in earlier versions. Reduced aerosol-related forcing contributes to more realistic radiative forcing and better alignment with the observational record. Ocean heat content (OHC) and sea ice trends are also improved as a result.

The NASA Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) conducted 162 joint flights with two aircraft over the northwest Atlantic to study aerosol-cloud interactions (ACIs), which represent the largest uncertainty in estimating total anthropogenic radiative forcing. The combination of a high-flying King Air and low-flying HU-25 Falcon, equipped with remote sensing and in situ instruments, characterized trace gases, aerosol particles, clouds, and meteorological variables with data collected nearly simultaneously below, within, and above marine boundary layer (MBL) clouds. Flights spanning warm and cold seasons across 3 years (2020-22) provided a broad range of conditions associated with aerosol particles, cloud properties (including particle size and phase), and meteorology, ideally suited for robust ACI calculations and assessing how well models simulate a wide range of MBL clouds from stratiform to cumulus. ACTIVATE data suggest that drivers of cloud droplet number concentration Nd, including aerosol particles and MBL dynamics, vary between winter and summer months with a stronger potential to convert aerosol particles into cloud droplets in winter. Models of varying complexity not only highlight some skills in simulating winter and summer cloud types but also identify challenges that still need to be addressed such as treatment of turbulence, wet scaveng-ing, and mesoscale organization. Remote sensing advances range from new retrieval methods for Nd, cloud phase classification, vertically resolved aerosol and cloud condensation nuclei number concentration, and ocean surface wind speed. This work describes these scientific and technologi-cal advances along with efforts in outreach and open data science. SIGNIFICANCE STATEMENT: Depending on the number and type of aerosol particles there are in the air, the properties of cloud droplets can vary in number concentration, size, and lifetime, and this leads to varying effects of clouds on climate and weather. We took an ambitious approach to investigate aerosol-cloud interactions, which represent the largest uncertainty in estimating human impacts on climate change. The NASA ACTIVATE mission conducted 162 joint airborne flights over the northwest Atlantic with two spatially coordinated planes making measurements relevant to understanding clouds spanning the continuum from stratiform to cumulus clouds. Along with newfound knowledge of how clouds evolve and interact with aerosol particles, extensive technological advancements were made assisted by the carefully designed sampling strategy.

In Earth system modeling, the land surface is coupled with the atmosphere through surface turbulent fluxes. These fluxes are computed using mean meteorological variables between the surface and a reference height in the atmosphere. However, the dependence of flux computation on the reference height, which is usually set as the lowest level in the atmosphere in Earth system models, has not received much attention. Based on high-resolution large-eddy simulation (LES) data under unstable conditions, we find the setting of reference height is not trivial within the framework of current surface layer theory. With a reasonable prescription of aerodynamic roughness length (following the setting in LESs), reference heights near the top of the surface layer tend to provide the best estimate of surface fluxes, especially for the momentum flux. Furthermore, this conclusion for the sensible heat flux is insensitive to the ratio of roughness length for momentum versus heat. These results are robust, whether using the classical or revised surface layer theory. They provide a potential guide for setting the proper reference heights for Earth system modeling and can be further tested in the near future using observational data from land–atmosphere feedback observatories.

Lateral subsurface flow plays an essential role in sustaining the terrestrial ecosystem, but it is not explicitly represented in most Earth System Models. In this study, we implemented an explicit lateral saturated flow model into the E3SM land model (ELM). The model explicitly describes lateral flow in the saturated zone by representing, for each model grid, an idealized hillslope consisting of five hydrologically connected soil columns. We conducted three model experiments driven by 0.125 degrees atmospheric forcing data during 1980-2015 over California using models of the default ELM, a modified version of ELM to enhance infiltration, and the model with the lateral saturated flow model. The simulated runoff, evapotranspiration, and terrestrial water storage anomaly (TWSA) from the three simulations were evaluated against available observations, and the model explicitly representing lateral flow performs best. The new model produces greater gridcell-averaged evapotranspiration especially over the mountainous regions with moderate relief and seasonally dry climates. Most importantly, it improves the modeled seasonal variations, interannual variabilities, and the recent decadal decline of TWSA. Many of these improvements can be attributed to the enhanced ecosystem resilience to droughts as demonstrated by transpiration increases caused by lateral flow. Model sensitivity experiments suggest that subsurface runoff is most sensitive to the ratio between horizontal and vertical saturated hydraulic conductivity, followed by hillslope planforms (convergent, divergent, and uniform), number of columns, and lower boundary conditions. Future work should effectively characterize hillslopes in global models and explore the long-term influences of lateral water movement on modeled biogeochemical cycle.

Abstract The effects of small‐scale topography‐induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). In this study, a new topography‐based subgrid structure referred to as topographic units (TGU) designed to better capture subgrid topographic effects, and methods to downscale atmospheric forcing to the land TGUs have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Effects of the subgrid scheme and downscaling methods on ELM simulated land surface processes are evaluated over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5‐degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS‐level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional‐level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small‐scale surface heterogeneity in ESMs and motivate future research to understand their effects on land‐atmosphere interactions, streamflow, and water resources management over mountainous regions.

Multimodel ensemble forecasts have gained widespread use over the past decade. A yet unresolved issue is whether forecast skill benefits from the use of prior skill from each model in providing a weighted combination. Here, we use the available seasonal ensemble forecasts of six models from the North American Multi-Model Ensemble (NMME) to study various aspects of prior skill-based weighting schemes and explore ways to merge multimodel forecasts. First, we postprocess each NMME model through quantile mapping and a simple spread error adjustment. Then, using an equal weighted combination as the baseline forecast, we test merging the models together through skill-based weights by varying the prior skill metric and varying how the metrics are aggregated across the different subbasins and time of year. Results confirm prior work that the combined forecasts do outperform individual models. When evaluating prior skill, equal weighting generally performed as well as or slightly better than all weighting schemes tried. The skill of the weighting scheme was not found to be strongly dependent on prior metric but did improve when aggregating all forecasted months and subbasins together to provide one overall weight to each model. Also, we found that including an offset to the prior metric that nudged the weights closer to equal weighting improves skill especially at longer leads where individual model skill is low. Results also show that the weighting schemes performed better than regression-based techniques including multiple linear regression and random forest. SIGNIFICANCE STATEMENT: Here, we test how effective the past performance of seasonal climate models can be used for generating weights to merge multiple models together using the North American Multi-Model Ensemble (NMME) for forecasting temperature and precipitation across the western United States. Our results showed there was little benefit in using prior performance compared to weighting all six NMME models equally. The performance of the weighted forecasts was not strongly dependent on the choice of performance metric or over how many months or basins performance was pooled. An important fi nding is that when only one or two models were used for merging, performance was reduced relative to equal weighting; however, if three or more models were used, performance was nearly the same.

Previous studies proposed convective limits on Northern Hemisphere 500-hPa temperature from a maximum of ~−3 °C in the tropics to a minimum of ~−42 °C in the Arctic. Here, we further explore this topic using three current generation reanalyses. All three reanalyses indicate that there has been statistically significant trends in the yearly maximums in the coldest temperatures in the Arctic at 500 hPa (from 0.40 to 0.66 °C decade −1 ), while two have statistically significant trends in the yearly minimums in the warmest 500-hPa temperatures in the Northern Hemisphere (0.13 and 0.19 °C decade −1 ). As upper-level tropospheric winds are related to the meridional temperature gradient in the Northern Hemisphere through the thermal wind balance, we also analyze the trends in maximum zonal wind speed. There are very small trends in the yearly maximum in the highest 200-hPa zonal wind speeds in the Northern Hemisphere and a slight poleward movement in the latitude of the highest winds in the reanalyses. This does not point to the jet stream becoming wavier as was hypothesized by others. The reanalysis climatology is then used to evaluate four current generation Earth system models. These models driven by observed sea surface temperature and sea ice generally produce larger trends than represented by the reanalyses. They are all too cold when the warmest tropical temperatures are at their lowest in the mean annual cycle. Only one model produces the poleward movement of the latitude of highest winds. The reanalysis trends presented here can be used to assess which of the CMIP models are more reliable in the historic period and hence may provide more trustworthy future projections.

Accurate and reliable seasonal forecasts are important for water and energy supply management. Recognizing the important role of snow water equivalent (SWE) for water management, here we include the seasonal forecast of SWE in addition to precipitation (P) and 2-m temperature (T2m) over hydrologically defined regions of the western United States. A two-stage process is applied to seasonal predictions from two models (NCEP CFSv2 and ECMWF SEAS5) through 1) postprocessing to remove biases in the mean, variance, and ensemble spread and 2) further reducing the residual errors by linear regression using climate indices. The adjusted forecasts from the two models are combined to form a superensemble using weights based on their prior skill. The adjusted forecasts are consistently improved over raw model forecasts probabilistically for all variables and deterministically for SWE forecasts. Overall skill of the superensemble usually improves upon the skill of forecasts from individual models; however, the percentage of seasons and regions with increased skill was approximately the same as those with decreased skill relative to the top performing postprocessed individual model. Seasonal SWE has the highest prediction skill, followed by T2m, with P showing lower prediction skill. Persistence contributes strongly to the skill of SWE and moderately to the skill of T2m. Furthermore, a distinct seasonality in the skill is seen in SWE, with a higher skill from late spring through early summer.

The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

Lower‐tropospheric stability (LTS) and estimated inversion strength (EIS) have a widely accepted relationship with low cloud amount and are key observational foundations for understanding and modeling low‐level stratiform clouds. Using the updated surface‐based and satellite cloud data, we find that low cloud amount is not as strongly correlated with LTS, and not as sensitive to LTS, as established in the past. EIS does not provide a stronger correlation with low cloud amount than LTS over all eight regions (including the midlatitudes). Further analyzing the relationships between LTS and EIS with different types of low clouds, we find that there is a strong correlation of LTS and EIS with stratocumulus only. This explains the weaker correlation of low cloud fraction (including cumulus, stratocumulus, and stratus) to both LTS and EIS. These results also suggest the need to re‐evaluate these relationships in Earth system models.

2 (E3SMv2) is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics.We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5°river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid-latitudes and 30 km at the equator and poles.The model performance is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima (DECK) simulations augmented with historical simulations as well as simulations to evaluate impact of different forcing agents.The simulated climate is generally realistic, with notable improvements in clouds and precipitation compared to E3SMv1.E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K.In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Programme (WCRP) assessment.However, E3SMv2 significantly underestimates the global mean temperature in the second half of the historical record.An analysis of single-forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol-related forcing.

Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.

This study examines boundary layer turbulence derived from high temporal resolution meteorological measurements from 40 research flights over the western North Atlantic Ocean during the 2020 deployments of ACTIVATE. Frequency distributions of various turbulent quantities reveal stronger turbulence during the winter deployment than in summer and for cloud‐topped than in cloud‐free boundary layers during the summer deployment. Maximum turbulence kinetic energy (TKE) is most often within cloud from observations in winter and summer, whereas it is mostly below cloud in both seasons by a global model turbulence parameterization. Bivariate frequency distributions are consistent with the bivariate Gaussian probability distribution functions assumed for the closure of higher‐order turbulence/shallow convection parameterizations used by some global models. Turbulence simulated by the Community Atmosphere Model version 6 and the Energy Exascale Earth System Model Atmosphere Model version 2 using such parameterizations is not as strong as observed, with more TKE going into vertical wind perturbations rather than into zonal wind perturbations as observed, suggesting that the treatment of turbulence in Earth system models still needs to be further improved.

This work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid‐latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single‐forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol‐related forcing.

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface -temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the "Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction" (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global "hotspot" regions identified here; the ensemble means in some "hotspots" produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

The Western North Atlantic Ocean (WNAO) is a complex land‐ocean‐atmosphere system that experiences a broad range of atmospheric phenomena, which in turn drive unique aerosol transport pathways, cloud morphologies, and boundary layer variability. This work, Part 2 of a 2‐part paper series, provides an overview of the atmospheric circulation, boundary layer variability, three‐dimensional cloud structure, and precipitation over the WNAO; the companion paper (Part 1) focused on chemical characterization of aerosols, gases, and wet deposition. Seasonal changes in atmospheric circulation and sea surface temperature explain a clear transition in cloud morphologies from small shallow cumulus clouds, convective clouds, and tropical storms in summer, to stratus/stratocumulus and multilayer cloud systems associated with winter storms. Synoptic variability in cloud fields is estimated using satellite‐based weather states, and the role of postfrontal conditions (cold‐air outbreaks) in the development of stratiform clouds is further analyzed. Precipitation is persistent over the ocean, with a regional peak over the Gulf Stream path, where offshore sea surface temperature gradients are large and surface fluxes reach a regional peak. Satellite data show a clear annual cycle in cloud droplet number concentration with maxima (minima) along the coast in winter (summer), suggesting a marked annual cycle in aerosol‐cloud interactions. Compared with satellite cloud retrievals, four climate models qualitatively reproduce the annual cycle in cloud cover and liquid water path, but with large discrepancies across models, especially in the extratropics. The paper concludes with a summary of outstanding issues and recommendations for future work.