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Precipitation extremes are expected to intensify under climate warming, but substantial uncertainty remains in quantifying this intensification. A widely used approach, Clausius‐Clapeyron (CC) scaling, is limited by an inexplicit definition of extremes and by its neglect of factors other than local temperature. We propose a framework using the Metastatistical Extreme Value Distribution that precisely defines the extremes being analyzed, and explores, through a large observational data set, their dependence on local thermodynamics and large‐scale atmospheric circulation at different temporal scales. Our results show that thermodynamics predominantly controls changes in hourly precipitation extremes and that their rate of increase must necessarily depend on the return period, in contrast with CC‐scaling arguments. On the contrary, daily precipitation is seen to be mostly controlled by large‐scale circulation, in ways that cannot be captured by CC‐based approaches. These findings clarify the physical mechanisms responsible for future changes in hydrologic extremes and possible methods to quantify them.

The altitude effect (AE) of stable isotopes in meteoric water (δ18O and δ2H), that is, the depletion of water isotopes with increasing altitude, is an important theoretical assumption of isotope‐based paleoaltimetry. However, this assumption has recently been challenged, as many in situ observations fail to consistently demonstrate the expected negative correlation between altitude and isotope values. Here we used 1,255 records of surface water isotopes to investigate AE and inverse altitude effect (IAE) and their mechanisms in arid Central Asia. The results show that isotope altitude gradients across Central Asia are weaker than the global average. Comparisons of the gradients for both the mountain‐basin system and mountain system reveal that the windward and leeward slopes of the westerlies consistently exhibit opposite gradients: AE on the windward side and IAE on the leeward. The observed IAE on the leeward slope across all basins is influenced by topography and local circulation. The orientation of mountain ranges perpendicular to large‐scale westerly circulation blocks eastward transport of westerly moisture, and the resulting longer moisture pathways weaken AE. Stronger local circulation and sub‐cloud evaporation processes enrich water isotopes in the leeward mountain regions, diminishing AE and even leading to the emergence of IAE. Our results highlight the impact of local circulation on water isotopes during different uplift phases when using stable hydrogen and oxygen isotopes to reconstruct paleoelevation.

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 refinement This response is weaker or absent in simulations with 28‐km or coarser resolution, which do not fully resolve mesoscale frontal processes Transient‐eddy fluxes of heat and momentum are modified as fronts pass over warm SSTs, leading to a large‐scale circulation response

Radiative‐convective equilibrium simulations with the general circulation model ECHAM6 are used to explore to what extent the dependence of large‐scale convective self‐aggregation on sea‐surface temperature (SST) is driven by the convective parameterization. Within the convective parameterization, we concentrate on the entrainment parameter and show that large‐scale convective self‐aggregation is independent of SST when the entrainment rate for deep convection is set to zero or when the convective parameterization is removed from the model. In the former case, convection always aggregates very weakly, whereas in the latter case, convection always aggregates very strongly. With a nontrivial representation of convective entrainment, large‐scale convective self‐aggregation depends nonmonotonically on SST. For SSTs below 295 K, convection is more aggregated the smaller the SST because large‐scale moisture convergence is relatively small, constraining convective activity to regions with high wind‐induced surface moisture fluxes. For SSTs above 295 K, convection is more aggregated the higher the SST because entrainment is most efficient in decreasing updraft buoyancy at high SSTs, amplifying the moisture‐convection feedback. When halving the entrainment rate, convection is less efficient in reducing updraft buoyancy, and convection is less aggregated, in particular at high SSTs. Despite most early work on self‐aggregation highlighted the role of nonconvective processes, we conclude that convective self‐aggregation and the global climate state are sensitive to the convective parameterization. Key Points The dependence of convective self‐aggregation on SST is sensitive to the convective parameterization, in particular to the entrainment rate Self‐aggregation dominates the statistics of the stationary state, partly masking the direct impact of the convective parameterization The SST dependence of convective self‐aggregation is controlled by entrainment efficiency at high SSTs, and by a WISHE feedback at low SSTs

The representation of orographic drag remains a major source of uncertainty for numerical weather prediction (NWP) and climate models. Its accuracy depends on contributions from both the model grid‐scale orography and the subgrid‐scale orography (SSO). Different models use different source orography data sets and different methodologies to derive these orography fields. This study presents the first comparison of orography fields across several operational global NWP models. It also investigates the sensitivity of an orographic drag parameterization to the intermodel spread in SSO fields and the resulting implications for representing the Northern Hemisphere winter circulation in a NWP model. The intermodel spread in both the grid‐scale orography and the SSO fields is found to be considerable. This is due to differences in the underlying source data set employed and in the manner in which this data set is processed (in particular how it is smoothed and interpolated) to generate the model fields. The sensitivity of parameterized orographic drag to the intermodel variability in SSO fields is shown to be considerable and dominated by the influence of two SSO fields: the standard deviation and the mean gradient of the SSO. NWP model sensitivity experiments demonstrate that the intermodel spread in these fields is of first‐order importance to the intermodel spread in parameterized surface stress, and to current known systematic model biases. The revealed importance of the SSO fields supports careful reconsideration of how these fields are generated, guiding future development of orographic drag parameterizations and reevaluation of the resolved impacts of orography on the flow. Plain Language Summary Mountains play a governing role in global atmospheric circulation via the aerodynamic drag they exert on the atmosphere. At smaller scales they influence winds and weather, for example, instigating damaging downslope windstorms in their lee; generating winds which power onshore wind farms; and causing clear‐air turbulence, which affects commercial aviation. Consequently, it is important that mountains (or “orography”) and their effects are represented accurately in global weather and climate models. While broad mountains are well resolved by these models, smaller mountains and steep slopes are poorly resolved or unresolved. To approximate the drag exerted on the atmosphere by this “subgrid‐scale” orography (SSO), “missing” hills or mountains are assumed in each grid box, whose height, steepness, and shape are defined by data fields derived from the SSO. In this study, it is found that both model grid‐scale orography and SSO fields vary significantly across currently operational models. These differences have a profound effect on the resultant drag, and consequently on the atmospheric circulation. The implication of these results is that changes in how orography is represented in our models have the capacity to bring significant improvements in our ability to model atmospheric circulations across a range of spatial and temporal scales. Key Point Differences in orography data fields are a principal cause of variation in atmospheric drag and circulation among weather and climate models

Jeddah, the second‐largest city in the Kingdom of Saudi Arabia, experienced an unprecedented 220 mm of rainfall on November 24, 2022. This extreme rainfall, which was four times the climatological monthly mean rainfall for November, resulted in severe flooding and significant damage to infrastructure. This study investigates the underlying physical mechanisms contributing to this extreme event and its predictability using in situ and satellite observations and numerical modeling. Our analysis reveals the event initially developed as a frontal system over the northwest regions of the Red Sea through interactions between cold air from mid‐latitudes and warm air from the southeast. It reached Jeddah at 0600 UTC, November 24, accompanied by strong surface convergence, which is typical of winter rainfall in Jeddah. The system was further fueled by persistent moisture intrusion from the Mediterranean and the southern Red Sea, driven by the southeast movement of the Arabian Anticyclone. We evaluated the predictive capability of the Weather Research and Forecasting (WRF) model to forecast this extreme event at different lead times, utilizing a cloud‐resolving 1‐km configuration. The WRF model, driven by the National Centers for Environmental Prediction operational Global Forecasts, successfully reproduced the extreme rainfall event up to 5 days in advance. Even at a 5‐day lead time, the model captured the storm's movement from northwest to southeast and the qualitative spatial distribution of rainfall, consistent with satellite observations and radar reflectivity. Additionally, the predicted distribution of total precipitable water vapor aligned closely with Meteosat brightness temperatures. This demonstrates that the high predictive skill of the WRF model is due to its high‐resolution configuration, careful selection of the domain, and physical parameterizations. By addressing both the physical mechanisms and the model's performance, this work provides valuable insights into extreme rainfall forecasting and highlights the potential for mitigating the impacts of such extreme events in the Jeddah region. The illustration describes the role of large‐scale features that contributed to the most extreme rainfall event experienced over Jeddah, the second‐largest city in the Kingdom of Saudi Arabia on November 24, 2022. Overall, a frontal system formed over the northwest Red Sea region through interactions between cold air from mid‐latitudes and warmer winds from the southeast, before it gets further fueled by persistent moisture intrusion from the Mediterranean and the southern Red Sea due to the southeastward movement of the stationary Arabian Anticyclone.

Convective momentum transport (CMT) is the process of vertical redistribution of horizontal momentum by small‐scale turbulent flows from moist convection. Traditional general circulation models (GCMs) and their multiscale modeling framework (MMF) versions poorly represent CMT due to insufficient information of subgrid‐scale flows at each GCM grid. Here the explicit scalar momentum transport (ESMT) scheme for representing CMT is implemented in the Energy Exascale Earth System Model‐Multiscale Modeling Framework (E3SM‐MMF) with embedded 2‐D cloud‐resolving models (CRMs), and verified against E3SM‐MMF simulations with 3‐D CRMs and observations. The results show that representing CMT by ESMT helps reduce climatological mean precipitation model bias over the western Pacific and the ITCZ regions, which is attributed to the weakened mean easterlies over the Pacific. Also, CMT from simulations with 2‐D and 3‐D CRMs impose a similar impact on Kelvin waves by reducing their variability and slowing down their phase speed, but opposite impacts on the Madden‐Julian Oscillation (MJO) variability. The ESMT scheme readily captures the climatological mean spatial patterns of the zonal and meridional components of CMT and their variability across multiple time scales, but shows some differences in estimating its magnitude. CMT mainly affects the MJO by decelerating its winds in the free troposphere, but accelerates its near‐surface winds. This study serves as a prototype for implementing CMT scheme in the MMF simulations, highlighting its crucial role in reducing model bias in mean state and spatiotemporal variability. Plain Language Summary Small‐scale turbulent flows from moist convection typically lead to the vertical redistribution of large‐scale winds (referred to as convective momentum transport [CMT]). Due to the coarse grids that are too large to resolve small‐scale flows, traditional earth system models poorly represent the CMT, and thus rely on parameterizations that empirically describe the magnitude and vertical profiles of the CMT. In contrast, the default Energy Exascale Earth System Model‐Multiscale Modeling Framework (E3SM‐MMF) is an earth system model with a 2‐D (one horizontal dimension and one vertical dimension) cloud‐resolving model embedded within each coarse grid so as to better resolve small‐scale flows, although it still lacks the necessary information to fully calculate CMT due to the lack of the third dimension. Here we implemented the explicit scalar momentum transport (ESMT) scheme to represent CMT in the E3SM‐MMF associated with 2‐D small‐scale flows at each coarse grid. The results show that in general CMT helps reduce model biases in predicting time‐mean precipitation and winds as well as the spatiotemporal variability of tropical convection. The ESMT scheme reproduces the spatial patterns of CMT as simulated by the E3SM‐MMF model with 3‐D small‐scale flows. Lastly, we focused on the Madden‐Julian Oscillation, the dominant intraseasonal variability in the tropics, as an example to investigate the impact of CMT. Key Points Convective momentum transport (CMT) affects large‐scale circulation and convective organization, and representing CMT reduces model biases in Energy Exascale Earth System Model‐Multiscale Modeling Framework simulations Explicit scalar momentum transport scheme captures the spatial pattern of CMT comparable to 3‐D cloud‐resolving models that explicitly model CMT CMT damps the free tropospheric circulation associated with the Madden‐Julian oscillation, but accelerates its near‐surface winds

A recent intercomparison exercise proposed by the Working Group for Numerical Experimentation (WGNE) revealed that the parameterized, or unresolved, surface stress in weather forecast models is highly model‐dependent, especially over orography. Models of comparable resolution differ over land by as much as 20% in zonal mean total subgrid surface stress (τtot). The way τtot is partitioned between the different parameterizations is also model‐dependent. In this study, we simulated in a particular model an increase in τtot comparable with the spread found in the WGNE intercomparison. This increase was simulated in two ways, namely by increasing independently the contributions to τtot of the turbulent orographic form drag scheme (TOFD) and of the orographic low‐level blocking scheme (BLOCK). Increasing the parameterized orographic drag leads to significant changes in surface pressure, zonal wind and temperature in the Northern Hemisphere during winter both in 10 day weather forecasts and in seasonal integrations. However, the magnitude of these changes in circulation strongly depends on which scheme is modified. In 10 day forecasts, stronger changes are found when the TOFD stress is increased, while on seasonal time scales the effects are of comparable magnitude, although different in detail. At these time scales, the BLOCK scheme affects the lower stratosphere winds through changes in the resolved planetary waves which are associated with surface impacts, while the TOFD effects are mostly limited to the lower troposphere. The partitioning of τtot between the two schemes appears to play an important role at all time scales. Key Points: Parameterized orographic drag affects the Northern Hemisphere winter circulation at all time scales The partition of the surface stress between various parameterizations affects circulation aspects

Projected tropical precipitation changes by the end of the century include increased net precipitation over the Pacific Ocean and drying over the Indian Ocean, prompting ongoing debate about the underlying mechanisms. Previous studies argued for the importance of the zonal circulation in the longitudinally dependent tropical precipitation response, as the meridional circulation is often defined and analyzed as the zonal mean. Here we show that the projected changes in the meridional circulation are highly longitudinally dependent, and explain the zonally dependent changes in net precipitation. Our analysis exposes a zonal shift in the ascending branch of the meridional circulation, associated with a strengthened net precipitation over the central Pacific and weakened precipitation in the Indo Pacific. The zonal circulation has minor influence on these projected tropical precipitation changes. These results point to the importance of monitoring the longitudinal changes in the meridional circulation for improving our preparedness for climate change impacts. Plain Language Summary Under global warming precipitation patterns are expected to change. Substantial changes will occur in the tropics, where an increase in precipitation over the Pacific Ocean and drying over the Indian Ocean are expected. In spite of the immense climate impacts of this phenomenon, the mechanisms underlying these changes have remained unknown. This study elucidates on the mechanism controlling this change, connecting the expected precipitation changes to the large‐scale tropical circulation. By separating the three‐dimensional tropical circulation into its components along the north‐south and east‐west directions, we show that the spatial changes in north‐south circulation explain most of the projected change in tropical precipitation, while the east‐west circulation has little to no effect. These results are further supported by analysis of the future changes of tropical air mass trajectories. Key Points Climate change models project a significant precipitation increase over the tropical Pacific and drying over the tropical Indian Ocean The projected changes in the large‐scale longitudinally dependent meridional circulation can explain these precipitation/drying changes We support these results with a coupled Eulerian‐Lagrangian analysis, stressing the importance of treating the large‐scale circulation as 3D

The large‐scale atmospheric circulation is a key driver for regional climate extremes, yet its response to anthropogenic forcing remains uncertain. The Pacific trough (PT) regime is a persistent circulation pattern modulating temperature, precipitation, and fires over North America. We show that the observed boreal winter‐spring (December to May) PT frequency and duration have increased significantly over the past 76 years, contributing to amplified extreme anomalous heat over western and central Canada. These observed changes are not well represented in the climate simulations analyzed herein. However, our results indicate that rising greenhouse gas concentrations likely contribute to increased winter‐spring PT frequency, which is further modulated by sea surface temperatures (SSTs). While the recent La Niña‐like and negative Pacific Decadal Oscillation‐like SST trends have dampened this increase, our results suggest that if an eventual emergence of the modeled El Niño‐like response to elevated CO2${\\text{CO}}_{2}$were to occur in reality, it would reinstate the increase in PT frequency, duration, and downstream amplification of regional extreme heat. However, the occurrence, timing, and magnitude of this shift remain uncertain, given the complex, interlaced role of external forcings and internal variability in modulating historical trends, as well as models' inability to reproduce them. Additionally, modeling decisions regarding future trajectories for anthropogenic emissions, including aerosols and greenhouse gases, play a critical role in projecting future changes in PT frequency. Our findings underscore the need for a better understanding and modeled representation of long‐term changes in the atmospheric circulation to inform climate adaptation and risk assessment. Plain Language Summary Large‐scale atmospheric patterns can create favorable conditions for extreme events like heat waves and droughts. As the climate continues to warm due to human emissions, some of those patterns can become more persistent and frequent. Here, we explore long‐term changes in the large‐scale atmospheric circulation over North America and the potential reasons behind these changes. We find that the Pacific trough, a persistent atmospheric pattern, is increasing in duration and frequency during boreal winter and spring. This pattern exacerbates extreme heat, drought, and fires over northern North America. How this pattern will continue to change is difficult to assess given model discrepancies with observations during most of the last century. These differences may be due to the models' misrepresentation of human emissions, the associated response, or natural modes of variability. However, climate models suggest that increases in this atmospheric pattern may continue when greenhouse gas emissions overwhelm other controlling factors like aerosols or internal variability. Key Points The Pacific trough (PT) regime became more frequent and persistent during winter and spring, likely with a contribution from greenhouse gases (GHGs) Changes in PT characteristics explain a large portion of the rise in winter‐spring extreme heat across northern North America Climate models project higher PT frequency under increased GHGs, but fail to capture observed historical trends