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ABSTRACT Changes in climate in the South Atlantic region and adjacent regions have been described in numerous works using projections from global climate models from CMIP5 and CMIP6. This paper presents an evaluation of the ability of these models to reproduce the atmospheric circulation patterns (weather types) and their seasonal and inter‐annual variability. The analyses are performed based on the probability of occurrence of weather types in the historical period and in future projections. The scatter index and the relative entropy are the statistical parameters used to evaluate the models' performance in the historical period. Future projections consist of RCP2.6, 4.5 and 8.5 scenarios for the CMIP5 models and the SSP126, 245, 370 and 585 scenarios for the CMIP6 and are assessed at different time intervals: short term (2015–2039), mid‐term (2040–2069) and long term (2070–2100). The performance of projections is measured by analysing their consistency, that is, based on the similarity between projections of the same scenario in different models. The results show that the reproduction of the probability of occurrence of historical weather types and their seasonal and interannual variability was better performed by ACCESS1‐0, HadGEM2‐ES, HadGEM2‐CC, CMCC‐CM and MPI‐ESM‐P when assessing the models from CMIP5, and by HadGEM3‐GC31‐MM, ACCESS‐ESM1‐5, ACCESS‐ CM2 and MRI‐ESM‐P when assessing the models from CMIP6. As for future projections, only the BESM‐AO2‐5, GFDL‐ESM4 and HadGEM3‐GC31‐MM models showed inconsistency in one or more scenarios. [ABSTRACT FROM AUTHOR]

Water must condense into ice clouds in the coldest brown dwarfs and exoplanets. When they form, these icy clouds change the emergent spectra, temperature structure, and albedo of the substellar atmosphere. The properties of clouds are governed by complex microphysics but these complexities are often not captured by the simpler parameterized cloud models used in climate models or retrieval models. Here, we combine microphysical cloud modeling and 1D climate modeling to incorporate insights from microphysical models into a self-consistent, parameterized cloud model. Using the 1D Community Aerosol and Radiation Model for Atmospheres (CARMA), we generate microphysical water clouds and compare their properties with those from the widely used EddySed cloud model for a grid of Y dwarfs. We find that the mass of water condensate in our CARMA water clouds is significantly limited by available condensation nuclei; in models without additional seed particles for clouds added, the atmosphere becomes supersaturated. We incorporate water latent heat release in the convective and radiative parts of the atmosphere and find no significant impact on water-ice cloud formation for typical gas giant compositions. Our analysis reveals the CARMA cloud profiles have a gradual decrease in opacity of approximately 4% per bar below the cloud base. Incorporating this gradual cloud base falloff and a variable f sed parameter allows spectra generated from the parameterized Eddysed model to better match those of the microphysical CARMA model. This work provides recommendations for efficiently generating microphysically informed water clouds for future models of cold substellar objects with H/He atmospheres. [ABSTRACT FROM AUTHOR]

The Walker circulation is projected to slow down in response to greenhouse gas warming. However, detecting the impact of human activities on changes in the Walker circulation is challenging due to the significant influence of internal variability. Here, based on ensembles of multiple climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show evidence that the emergence of the human-induced weakening of Walker circulation tends to occur earlier in the middle-upper troposphere than at the surface. This earlier emergence is attributed to a more pronounced initial weakening response of the middle-upper tropospheric Walker circulation to atmospheric CO2 radiative forcing. We further reveal that the emergence time of a weaker Walker circulation varies across models. This intermodel spread is governed by an ocean thermostat that operates by modulating the zonal sea surface temperature gradient over the tropical Indo-Pacific region. Our findings address the key question of whether and how to detect human-induced large-scale atmospheric circulation changes and provide valuable insights for assessing the associated risks. This study shows that human-induced weakening of the Walker Circulation tends to occur earlier in the middle-upper troposphere than at the surface, attributed to the stronger weakening response to CO2 radiative forcing in those heights. [ABSTRACT FROM AUTHOR]

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts. There has been a surge in global datasets of urban land recently. This paper shows large discrepancies in urban area across scales among multiple such datasets, which can influence the magnitude and direction of estimated urban climate impacts. [ABSTRACT FROM AUTHOR]

Oxygen isotopes (δ18O) are the most commonly utilized speleothem proxy and have provided many foundational records of paleoclimate. Thus, understanding processes affecting speleothem δ18O is crucial. Yet, prior calcite precipitation (PCP), a process driven by local hydrology, is a widely ignored control of speleothem δ18O. Here we investigate the effects of PCP on a stalagmite δ18O record from central Vietnam, spanning 45 – 4 ka. We employ a geochemical model that utilizes speleothem Mg/Ca and cave monitoring data to correct the δ18O record for PCP effects. The resulting record exhibits improved agreement with regional speleothem δ18O records and climate model simulations, suggesting that the corrected record more accurately reflects precipitation δ18O (δ18Op). Without considering PCP, our interpretations of the δ18O record would have been misleading. To avoid misinterpretations of speleothem δ18O, our results emphasize the necessity of considering PCP as a significant driver of speleothem δ18O. This study finds that in-cave processes affect speleothem oxygen isotope records. Correcting for these processes improves agreement with other regional records and climate models, providing a more accurate reflection of past hydroclimate change. [ABSTRACT FROM AUTHOR]

It has previously been shown that trends in sensible heat from climate models have had a substantial contribution to global precipitation changes. We illustrate that this is the case also in the most recent Coupled Model Intercomparison Project Phase 6 (CMIP6). However, we find that over the period since 1980 reanalyses do not support the reduction in sensible heat from the CMIP6 models and rather estimate a global increase in sensible heat which would contribute to a precipitation reduction. Satellite data over a period of two decades over global ocean generally show an opposite sign of the sensible heat trend to the CMIP6 models, similarly to the reanalyses. [ABSTRACT FROM AUTHOR]

This paper studies the Liouville type theorems for stationary the tropical climate model on the whole space R 3 . It shows that if (u , v , θ) satisfies certain anisotropic integrability conditions on the components of the u or (u , v) , also θ satisfies certain isotropic integrability conditions, there is only a trivial solution to the stationary tropical climate model. The results are a further extension of the recent work by Chae (Appl. Math. Lett. 142:108655, 2023). [ABSTRACT FROM AUTHOR]

ABSTRACT Impacts of El Niño−Southern Oscillation (ENSO) on the quasi‐biweekly oscillation over the western North Pacific (WNP‐QBWO) in boreal winter are investigated in the study. The WNP‐QBWO in boreal winter primarily propagates westward from the tropical western Pacific to WNP. During the La Niña winter, the QBWO over the WNP has stronger intensity and propagates westward at a faster speed, while it is weaker and propagates more slowly during the El Niño winter. The possible mechanisms may involve the ENSO‐related background moisture and zonal wind vertical shear changes that can significantly modulate the WNP‐QBWO's behaviours in boreal winter. A 2.5‐layer atmospheric model is applied in the study and confirms the results. It is further revealed that the moisture change is dominant in modulating the WNP‐QBWO's intensity, while both the moisture and vertical shear changes may together contribute to the zonally propagating speed of the WNP‐QBWO in boreal winter. These results can deepen our understanding of dynamic processes associated with the WNP‐QBWO in boreal winter and are conducive to the predictability study. [ABSTRACT FROM AUTHOR]

The impact of model vertical resolutions on simulation of the Madden–Julian Oscillation (MJO) was investigated using five AMIP simulations by the Grid‐point Atmospheric Model of IAP LASG, version 3 (GAMIL3) with different vertical layers. Results showed that higher vertical resolutions produce a stronger and superior eastward propagation, coupled circulation–convection relationship, and MJO strength, as well as other convectively coupled equatorial waves when compared to the lowest vertical resolution. The improvements may be related to a better description of the tropical circulation in the higher vertical resolutions and model top, albeit without the significant improvement of MJO convection and stratospheric quasi‐biennial oscillation in all simulations. Among the four tested high resolutions, the simulations with higher vertical resolutions from the surface to about 850 hPa produced better eastward propagation and larger total explained variance of the MJO, indicating the importance of the lower troposphere in simulating the MJO. [ABSTRACT FROM AUTHOR]

Southern Hemisphere (SH) storminess has increased in the satellite era and recent work suggests comprehensive climate models significantly underestimate the trend. Here, we revisit this reanalysis-model trend discrepancy to better understand the mechanisms underlie it. A comprehensive like-for-like analysis shows reanalysis trends exhibit large uncertainty, and coupled climate model simulations exhibit weaker trends than most but not all reanalyses. However, simulations with prescribed sea surface temperature (SST) exhibit significantly greater storminess trends, particularly in the South Pacific, implying SST trend discrepancies in coupled simulations impact storminess trends. Using pacemaker simulations that correct Southern Ocean and tropical east Pacific SST trend discrepancies, we show that storminess trends in coupled simulations are underestimated because they do not capture the enhanced storminess resulting from Southern Ocean cooling and La-Nina-like teleconnection trends. Our findings emphasize large reanalysis uncertainty in SH circulation trends and the impact of regional SST trend discrepancies on circulation trends. [ABSTRACT FROM AUTHOR]

We evaluate trends in terrestrial water storage over 1950–2100 in CMIP6 climate models against a new global reanalysis from assimilating GRACE and GRACE-FO satellite observations into a hydrological model. To account for different timescales in our analysis, we select regions in which the influence of interannual variability is relatively small and observed trends are assumed to be representative of the development over longer periods. Our results reveal distinct biases in drying and wetting trends in CMIP6 models for several world regions. Specifically, we see high model consensus for drying in the Amazon, which disagrees with the observed wetting. Other regions show a high consensus of models and observations suggesting qualitatively correctly simulated trends, e.g., for the Mediterranean and parts of Central Africa. A high model agreement might therefore falsely indicate a robust trend in water storage if it is not assessed in light of the observed developments. This underlines the potential use of maintaining an adequate observational capacity of water storage for climate change assessments. [ABSTRACT FROM AUTHOR]

Accurate and detailed retrieval of global horizontal irradiance (GHI) has many benefits, for instance, in support of the energy transition towards an energy supply with a high share of renewable energy sources and for validating high-resolution weather and climate models. In this study, we apply a downscaling algorithm that combines the high-resolution visible and standard-resolution channels on board the Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) to obtain cloud physical properties and GHI at an increased nadir spatial resolution of 1 km × 1 km instead of 3 km × 3 km. We validate the change in accuracy of the high-resolution GHI in comparison to the standard-resolution product against ground-based observations from a unique network of 99 pyranometers deployed during the HOPE field campaign in Jülich, Germany, from 18 April to 22 July 2013. Over the entire duration of the field campaign, a small but statistically significant reduction in root mean square error (RMSE) of 2.8 Wm-2 is found for the high-resolution GHI at a 5 min scale. The added value of the increased spatial resolution is largest on days when GHI fluctuates strongly: for the 10 most variable days a significant reduction in the RMSE of 7.9 Wm-2 is obtained with high- versus standard-resolution retrievals. In contrast, we do not find significant differences between both resolutions for clear-sky and fully overcast days. The sensitivity of these results to temporal- and spatial-averaging scales is studied in detail. Our findings highlight the benefits of spatially dense network observations as well as a cloud-regime-resolved approach for the validation of GHI retrievals. We also conclude that more research is needed to optimally exploit the instrumental capabilities of current advanced geostationary satellites in terms of spatial resolution for GHI retrieval. [ABSTRACT FROM AUTHOR]

Salinity is an essential parameter for evaluating water quality and plays a crucial role in maintaining the stability of lake ecosystems, particularly in arid and semi-arid climates. Salinity responds to changes in climate and human activity, with significant impacts on water quality and ecosystem services. In this study, Sentinel-2A/B Multi-Spectral Instrument (MSI) images and quasi-synchronous field data were utilized to estimate lake salinity using machine learning approaches (i.e., XGB, CNN, DNN, and RFR). Atmospheric correction for MSI images was tested using six processors (ACOLITE, C2RCC, POLYMER, MUMM, iCOR, and Sen2Cor). The most accurate model and atmospheric correction method were found to be the extreme gradient boosting tree combined with the ACOLITE correction algorithm. These were used to develop a salinity model (N = 70, mean absolute percentage error = 9.95%) and applied to eight lakes in Inner Mongolia from 2016 to 2024. Seasonal and interannual variations were explored, along with an examination of potential drivers of salinity changes over time. Average salinities in the autumn and spring were higher than in the summer. The highest salinities were observed in the lake centers and tended to be consistent and homogeneous. Interannual trends in salinity were evident in several lakes, influenced by evaporation and precipitation. Climate factors were the primary drivers of interannual salinity trends in most lakes. [ABSTRACT FROM AUTHOR]

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions. [ABSTRACT FROM AUTHOR]

Reliable projections of permafrost change are crucial for estimating permafrost carbon loss. However, potential model biases in surface air temperature may yield unrealistic projections of future permafrost area. Here, by leveraging the emergent relationship between equilibrium climate sensitivity and projected changes in mean annual air temperature over High Mountain Asia (HMA), we mitigate the overestimated local warming rates and excessive thawing of permafrost associated with the "hot model" problem in models participating in the Coupled Model Intercomparison Project phase 6. After constraint, permafrost area over HMA will reduce by 37%, 64% and 99% in 2081–2100 relative to present‐day under the SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. In contrast, the unconstrained projections tend to overestimate the loss of permafrost area by nearly 10% under the low and mid‐emission scenarios due to the overestimation of local warming rates. These findings are crucial for policymaking and provide valuable insights into global permafrost projection. Plain Language Summary: This study shows a comprehensive picture of permafrost changes over High Mountain Asia (HMA) in the coming future. A subset of the newest generation of climate models participating in Coupled Model Intercomparison Project phase 6 (CMIP6) have a "hot model" problem with high equilibrium climate sensitivity (ECS) exceeding the likely range of 2.5°C–4°C assessed by Intergovernmental Panel on Climate Change Sixth Assessment Report. This indicates that the surface temperature projections in response to changes in atmospheric carbon dioxide concentrations are higher than those expected based on other evidence. Here, taking HMA as a case study, we establish a relationship between ECSs and the future changes in mean annual air temperature over HMA to constrain the future projections of warming rates and permafrost degradation. The constrained projection is 0.2°C, 0.4°C and 0.5°C lower than the unconstrained warming simulated by the CMIP6 ensemble during 2081–2100 under low, intermediate, and very high emission scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5). Based on the more reliable projections of future warming, reductions of permafrost area by 37%, 64% and 99% in 2081–2100 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively, are expected. The constraining solves the exaggerated projection of permafrost degradation caused by CMIP6 models. Key Points: Models that overestimate warming rates tend to also overestimate permafrost degradation over High Mountain AsiaAn emergent relationship between equilibrium climate sensitivity and surface warming is used to constrain future permafrost projectionsThe permafrost area will reduce by 37%, 64% and 99% in 2081–2100 relative to 2000–2016 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios [ABSTRACT FROM AUTHOR]

The influence of entrainment, a key process characterized by the entrainment rate in cumulus parameterization, on aerosol‐cloud interactions has been widely recognized. However, despite qualitative links established between entrainment and aerosol loading, a quantitative relationship based on observational evidence remains elusive. This study utilizes aircraft observations of cumulus clouds during two field campaigns to determine the quantitative relationship between entrainment rate and aerosol loading. In both campaigns, the entrainment rate is negatively correlated with aerosol loading. It is speculated that increased aerosol loading enhances cloud edge droplet evaporation, which leads to increased buoyancy and vertical velocity within the cloud, thereby reducing the entrainment rate. Further analysis shows that the response of entrainment rate to aerosol perturbations is more significant in smaller cumulus clouds with weak buoyancy and less pronounced under opposite conditions. These findings shed new light on improving the description of aerosol‐cloud interactions in cumulus parameterizations. Plain Language Summary: Clouds play a crucial role in regulating Earth's climate, and understanding how they form and evolve is important for accurate weather and climate predictions. One key process affecting cloud development is entrainment, where drier air from outside the cloud mixes into the cloud, influencing its growth. Scientists know that aerosols can impact entrainment, but the exact relationship hasn't been clear. This study uses observations from research aircraft flown through cumulus clouds to quantify the relationship between entrainment and aerosol. The results show that aerosol concentration is negatively correlated with entrainment rate, meaning less dry air mixes into the cloud. We speculate that this happens because the increased aerosols enhance the evaporation of cloud droplets at the cloud edges, making the cloud more buoyant and less likely to mix with the surrounding air. Interestingly, this decline in entrainment rate to aerosol is stronger in smaller and weaker clouds. These findings provide valuable insights into how aerosols influence cloud development and can help improve the representation of aerosol‐cloud interactions in climate models, leading to more accurate climate projections. Key Points: The relationship between cumulus entrainment rate and aerosol loading is quantified based on observation for the first timeThe plausible physical mechanism linking entrainment rate to aerosol loading has been elucidatedA steeper decline in entrainment rate with increasing aerosol loading is observed in small and weakly buoyant cumulus clouds [ABSTRACT FROM AUTHOR]

The equatorward contraction of tropical precipitation, commonly referred to as the "deep‐tropical contraction", is witnessed in the paleoclimate simulations of the early Eocene. However, the mechanism driving this contraction is still unclear. Based on the energetics framework of the Intertropical Convergence Zone (ITCZ) and the decomposition method of the latent heat flux along with the simulations of a climate system model, CESM1.2, we proposed a novel mechanism responsible for the "deep‐tropical contraction" in the early Eocene. The greenhouse gases‐induced sea surface warming amplifies the sensitivity of evaporation to surface wind speed changes through Clausius‐Clapeyron scaling, leading to an interhemispheric asymmetric enhancement of the latent heat flux. To maintain hemispheric energy balance, the cross‐equatorial atmospheric energy transport must be reduced during the solstice seasons. As a result, the solstitial location of the ITCZ shifts equatorward, causing the "deep‐tropical contraction" in the early Eocene. Plain Language Summary: The early Eocene is the warmest epoch in the last 65 million years, with a global mean temperature 9°C–23°C higher than the preindustrial period. According to state‐of‐the‐art climate models, the tropical rainfall contracted toward the equator during this extremely warm period. However, the physical mechanism causing this phenomenon remains unclear. In this study, we examined the hemispheric energy balance in the early Eocene that causes the equatorward contraction of tropical precipitation. A novel mechanism underlying this phenomenon is revealed. Based on the climate modeling of CESM1.2, we show that the GHG‐induced warmth enhances the sensitivity of evaporation to surface wind speed changes in the early Eocene. Thus, the stronger tropical trade wind in the winter hemisphere will drive out stronger latent heat flux than in the summer hemisphere. This interhemispheric asymmetric response reduces the interhemispheric heating contrast in the solstice seasons. As a result, the ascending motion in the tropical atmosphere migrates toward the equator, finally decreases the width of tropical precipitation in the early Eocene. Key Points: The "deep tropical contraction" in the early Eocene is caused by the equatorward migration of the seasonal ITCZThe equatorward migration of the solstitial ITCZ location in the early Eocene is due to decreased cross‐equatorial energy transportThe enhanced wind‐evaporation effect in the early Eocene reduces the cross‐equatorial atmospheric energy transport in the solstitial seasons [ABSTRACT FROM AUTHOR]

Marine Isotope Stage 3 (MIS 3) is characterized by significant millennial-scale climatic oscillations between cold stadials and mild interstadials, which presents a valuable case for understanding hydrological response to abrupt climate change. Through a set of coupled model simulations, our results broadly show an antiphased interhemispheric change in land monsoonal precipitation during the present-day relative to MIS 3 interstadial and the stadial–interstadial transition, with a general decrease in the Northern Hemisphere but an increase in the Southern Hemisphere. The antiphased pattern is largely caused by the change in orbital insolation during the present-day relative to MIS 3 interstadial, whereas by the weakened Atlantic meridional overturning circulation during the interstadial–stadial transition. However, there are obvious discrepancies in precipitation response and underlying mechanisms among individual monsoon domains and across different periods. Based on the moisture budget analysis, we indicate that the dynamic factor mainly explains the decreased monsoonal rainfall in the Northern Hemisphere during the present-day relative to the MIS 3 interstadial, whereas the thermodynamic term is largely responsible for the increased precipitation in the Southern Hemisphere. In contrast, the dynamic factor plays an important role in the variation of precipitation over all the monsoon zones from the MIS 3 interstadial to stadial states, with the thermodynamic term mainly contributing to the decreased tropical monsoonal precipitation in the colder Northern Hemisphere. Our results help improve the understanding of global monsoon variations under intermediate glacial climate conditions and shed light on their behaviors under potentially rapid climate change in the future. [ABSTRACT FROM AUTHOR]

The aerosol particles serving as cloud condensation and ice nuclei contribute to key cloud processes associated with cold-air outbreak (CAO) events but are poorly constrained in climate models due to sparse observations. Here we retrieve aerosol number size distribution modes from measurements at Andenes, Norway, during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) and at Zeppelin Observatory, approximately 1000 km upwind from Andenes at Svalbard. During CAO events at Andenes, the sea-spray-mode number concentration is correlated with strong over-ocean winds with a mean of 8±4 cm−3 that is 71 % higher than during non-CAO conditions. Additionally, during CAO events at Andenes, the mean Hoppel minimum diameter is 6 nm smaller than during non-CAO conditions, though the estimated supersaturation is lower, and the mean number concentration of particles that likely activated in-cloud is 109±61 cm−3 with no statistically significant difference from the non-CAO mean of 99±66 cm−3. For CAO trajectories between Zeppelin Observatory and Andenes, the upwind-to-downwind change in number concentration is the largest for the accumulation mode with a mean decrease of 93±95 cm−3, likely attributable primarily to precipitation scavenging. These characteristic properties of aerosol number size distributions during CAO events provide guidance for evaluating CAO aerosol–cloud interaction processes in models. [ABSTRACT FROM AUTHOR]

The low-level jet (LLJ) and the urban heat island (UHI) are common nocturnal phenomena. While the UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) initiative in the Paris region, continuous profiles of horizontal wind speed and vertical velocity were recorded with two Doppler wind lidars (DWLs) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site, highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼ 10 h, the mean wind speed is 9 m s -1 , and the average core height is 400 m above the city. The temporal evolution of many events shows signatures that indicate that the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The horizontal wind shear below the LLJ core induces variance in the vertical velocity (σw2) above the urban canopy layer. It is shown that σw2 is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing σw2 and strong UHI values only occur when σw2 is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health. [ABSTRACT FROM AUTHOR]

The summer MJO exhibits different characteristics from its winter counterpart, particularly distinguished by propagation in both eastward and northward directions, which is relatively less understood. Here, we explore the primary sources of the summer MJO predictability using Machine Learning (ML) based on the long-term climate model simulation and its transfer learning with the observational data. Our ML-based summer MJO prediction model shows a correlation skill of 0.5 at about 24-day forecast lead time. By utilizing eXplainable Artificial Intelligence (XAI), we discern Precipitable Water (PW) and Surface Temperature (TS) as the most influential sources for the summer MJO predictability. We especially identify the roles of PW and TS in the eastern and northern Indian Ocean (EIO and NIO) regions on the propagation characteristics of the summer MJO through XAI-based sensitivity experiments. These results suggest that ML-based approaches are useful for identifying sources of predictability and their roles in climate phenomena. [ABSTRACT FROM AUTHOR]

Teleconnections are crucial in shaping climate variability and regional climate change. The fidelity of teleconnections in climate models is important for reliable climate projections. As the observed sample size is limited, scientific judgment is required when models disagree with observed teleconnections. We illustrate this using the example of the relationship between El Niño‐Southern Oscillation (ENSO) and the northern stratospheric polar vortex (SPV), where the MIROC6 large ensemble exhibits an ENSO‐SPV correlation opposite in sign to observations. Yet the model well captures the upward planetary‐wave propagation pathway through which ENSO is known to affect the SPV. We show that the discrepancy arises from the model showing an additional linkage related to horizontal stratospheric wave propagation. Observations do not provide strong statistical evidence for or against the existence of this linkage. Thus, depending on the research purpose, a choice has to be made in how to use the model simulations. Under the assumption that the additional linkage is spurious, a physically‐based bias adjustment is applied to the SPV, which effectively aligns the modeled ENSO‐SPV relationship with the observations, and thereby removes the model‐observations discrepancy in the surface air temperature response. However, if one believed that the additional linkage was genuine and was undersampled in the observations, a different approach could be taken. Our study emphasizes that caution is needed when concluding that a model is not suitable for studying teleconnections. We propose a forensic approach and argue that it helps to better understand model performance and utilize climate model data more effectively. Plain Language Summary: Teleconnections are usually manifested as recurring patterns which link weather and climate anomalies over large distances across the globe. Therefore, they play an important role in shaping climate variability and regional climate change. The limited observational records make it difficult to draw robust conclusions. To avoid this concern, climate model simulations are widely used as they can provide a larger sample size. However, climate models do not always agree with the observed teleconnections. When this happens, scientific judgment is needed to assess the suitability of models for studying teleconnections. Here, based on an example, we introduce a forensic approach to assessing the fidelity of teleconnections in models, where a physically‐based analysis is required to understand the origin of the model‐observations discrepancy. By "forensic" we mean a scientific approach that considers various explanations for a phenomenon and attempts to disprove them through physical evidence, thereby narrowing down the possibilities. In this way, one can better understand the reason for the difference between model and observations and make an informed use of the climate model outputs. Key Points: Although the model shows an opposite‐signed teleconnection to observations, it successfully captures the known underlying causal pathwayThe existence of an additional pathway explains the discrepancy, with no strong observational evidence for or against itA bias adjustment of this additional pathway aligns the modeled relationship with observations and adjusts the related climate impacts [ABSTRACT FROM AUTHOR]

Soil moisture deficiencies exacerbate heatwaves through soil moisture‐temperature feedback, an effect that is expected to intensify with climate change, resulting in critical impacts on society and ecosystems. This study aims to investigate the evolving soil moisture‐heatwave relationship over eastern China in the future, using a convection‐permitting (CP, ∼4 km) regional climate model (RCM). The CP‐RCM model simulates historical (1998–2007) and future (2070–2099) climates over eastern China, with three pseudo‐global warming (PGW) experiments conducted under the RCP2.6, RCP4.5, and RCP8.5 scenarios. Results indicate a substantial increase in heatwave frequency (HWF) and magnitude (HWM) over eastern China, particularly under the RCP8.5 scenario. The largest HWF (up to 23 days) is expected in South China (SC), and the largest HWM (up to 3.25°C) is expected in Loess Plateau (LP) and North China Plain (NCP), indicating a pronounced future risk of heatwave in the region. Antecedent soil moisture exhibits a negative correlation with heatwave indices (HWM and HWF) in most areas of eastern China, suggesting its role in mitigating heatwaves. Quantile regression analysis shows that antecedent soil moisture exerts a stronger effect on the upper quantile of the HWF/HWM than on the lower quantile. With global warming, the amplifying effect due to soil moisture deficiency on future heatwaves is expected to expand spatially and become more pronounced. Increased soil moisture control on heatwaves can be attributed to reduced energy limitation and intensified water limitation. A comprehensive investigation across five sub‐regions reveals the role of various soil moisture regimes in modulating heatwaves over eastern China. Plain Language Summary: Global warming exacerbates heatwave events, rendering them more frequent, prolonged, and intense, posing substantial challenges to ecosystems and societies. Eastern China, one of the world's most populous regions, is vulnerable to heatwaves. Previous studies have found that soil moisture deficiencies amplify heatwaves through soil moisture‐temperature feedback, and this effect is anticipated to intensify with global warming. However, the complex effects of soil moisture's impact on heatwaves, remain inadequately explored in eastern China, particularly under global warming. Additionally, conventional coarse‐resolution models often fail to accurately capture soil moisture effects, introducing uncertainties through convective parameterization. This study examined the current and future effects of soil moisture on heatwaves across eastern China, using a convection‐permitting (4 km) regional climate model. Our findings reveal that eastern China is expected to experience more frequent and intense heatwaves in the future. Antecedent soil moisture has a stronger impact on severe heatwaves than on milder ones. Notably, the control of soil moisture on heatwaves is projected to strengthen with global warming, primarily due to evaporation regime shifting from energy to water limitation. These findings have significant implications for understanding heatwave development in the region and can inform adaptation and mitigation strategies in the face of climate change. Key Points: Pseudo‐global warming simulations at convective‐permitting resolution were used to project effect of soil moisture on future heatwavesThe amplifying effect of soil moisture deficiency on future heatwaves is intensify, due to evaporation shift from energy to water limitationThe impact of soil moisture on severe heatwaves is stronger than on mild ones [ABSTRACT FROM AUTHOR]

The Asian Summer Monsoon (ASM) convection transports aerosols and their precursors from the boundary layer to the upper troposphere and lower stratosphere (UTLS). This process forms an annually recurring aerosol layer near the tropopause. Recent observations have revealed a distinct property of the aerosol layer over the ASM region, it is nitrate‐rich. We present a newly implemented aerosol formation algorithm that enhances the representation of nitrate aerosol in the Community Aerosol and Radiation Model for Atmospheres (CARMA) coupled with the Community Earth System Model (CESM). The simulated aerosol chemical composition, as well as vertical distributions of aerosol size and mass, are evaluated using in situ and remote sensing observations. The simulated concentrations (ammonium, nitrate, and sulfate) and size distributions are generally within the error bars of data. We find nitrate, organics, and sulfate contribute significantly to the UTLS aerosol concentration between 15°–45°N and 0°–160°E. The two key formation mechanisms of nitrate‐containing aerosols in the ATAL are ammonium neutralization to form ammonium nitrate in regions where convection is active, and condensation of nitric acid in regions of cold temperature. Furthermore, including nitrate formation in the model doubles the surface area density in the tropical tropopause region between 15°–45°N and 0°–160°E, which alters the chlorine partitioning and subsequently impacts the rate of ozone depletion. Plain Language Summary: The Asian Summer Monsoon can efficiently transport pollutants into the upper troposphere and form a layer of aerosols called the Asian Tropopause Aerosol Layer (ATAL). This research investigates the two key formation mechanisms for nitrate aerosol in the ATAL: one is due to cold temperature, and one is due to ammonium neutralization. The simulations are validated against in situ measurements. It also found that O3 chemistry inside ATAL is influenced by nitrate aerosol. Key Points: A new nitrate aerosol formation algorithm is implemented for the community model CARMA to represent the Asian Tropopause Aerosol LayerThe simulated aerosol size distributions and compositions are within error bars of observationsIncluding the new nitrate formation scheme doubles the simulated surface area, changing the chlorine partitioning and ozone depletion [ABSTRACT FROM AUTHOR]

The Iceland and Greenland Seas are characterized by strong heat fluxes from the ocean to the atmosphere during wintertime. Here we characterize the atmospheric signal of this strong evaporation in terms of water vapor isotopes and investigate if such a signal can have a cumulative imprint on the ocean mixed‐layer. Observations include continuous water vapor isotope measurements, event‐based precipitation samples, and sea‐water samples taken at various depths from the research vessel Alliance during the Iceland‐Greenland Seas Project cruise in February and March 2018. In conjunction with a simulation from a regional, isotope‐enabled atmospheric model, we find that the predominant atmospheric isotope signature during predominant marine cold‐air outbreak conditions is −129.8 ± 16.6‰ for δ2H and −18.10 ± 2.87‰ for δ18O, with a d‐excess of 15.1 ± 7.9‰, indicating enhanced non‐equilibrium fractionation compared to the global average. During events of warm‐air intrusion from mid‐latitudes, near‐surface vapor becomes saturated and the vapor d‐excess approaches equilibrium or becomes negative. Similarly, precipitation d‐excess is lower and thus closer to equilibrium conditions during warm‐air intrusions. There are indications that an evaporation signal of waters exiting the Nordic Seas through Denmark Strait could be locally enhanced over seasons to years, as supported by simple model calculations. Our findings thus suggest that evaporation signals could be transferred into the ocean isotope composition in this region, potentially enabling mass‐balance constraints in isotope‐enabled coupled ocean‐atmosphere models. Plain Language Summary: The sea area between Iceland, Greenland, and Fram Strait experiences strongly variable weather conditions during wintertime. Often, cold air sweeps over open waters, leading to intense extraction of heat and water from the ocean. Also opposite conditions can occur, where air from warmer and more humid mid‐latitudes extends northward, reaches saturation, and loses heat to the underlying surface. During the Iceland‐Greenland Seas Project cruise in February and March 2018, we measured the stable water isotope composition in water vapor, snow, rain, and the ocean water column at different depths. Using these measurements and a regional weather prediction model capable of simulating the isotopic composition, we find indications for an imprint of the atmospheric evaporation that is transferred into the mixed layer of the ocean over longer times. If our finding can be confirmed from additional measurements, such information can be used to constrain models with coupled atmosphere and ocean components, such as Earth System models. Key Points: We observe pronounced variability in the stable isotope composition of the atmosphere‐ocean system during winterVapor and precipitation isotope variations reflect local and remote factors associated with different weather systemsWe find indications that weather systems can leave a cumulative d‐excess imprint in the ocean mixed layer [ABSTRACT FROM AUTHOR]

To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection‐permitting resolution. This study spans a 2‐month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features. Plain Language Summary: We tracked large‐size rain storms called mesoscale convective systems (MCSs) in the central Amazon during the wet seasons of 2014 and 2015. Data generated from the MCS tracking helps us understand how rainfall is produced as a function of the number of storms, as well as their size, rain intensity, and motion, and how those can be better simulated by weather and climate models. We found that the model produces fewer and smaller MCSs than in reality, but the total rainfall amount is often overestimated. This is because simulated MCSs produce unrealistically intense heavy rainfall events. On the other hand, light rainfall events are mostly underrepresented by the model. Thus, the model error in total precipitation is determined by how these two counterbalance each other. Our analysis also suggests model representation of the background environment can be critical for simulating realistic MCS properties. Key Points: Simulated and observed mesoscale convective system (MCS) clouds and precipitation are tracked during the 2014/15 wet seasons in the central AmazonExcessive heavy rain intensity (≥10 mm hr−1) of simulated MCS lead to an overprediction of precipitationDry bias in stratiform rainfall can also drive MCS precipitation bias when stratiform cloud cover is substantially underpredicted [ABSTRACT FROM AUTHOR]

A large portion of cloud scenes over the globe shows multiple layers composed of different phases, in general with ice clouds on the top and liquid water clouds beneath. Such multi‐layer (ML) clouds constitute major challenges in cloud observations and weather and climate modeling. This study improved a threshold algorithm for detecting ice‐over‐water ML clouds using geostationary satellites. Optimal thresholds were established for the spectral characteristics of the Advanced Himawari Imager (AHI) and the Advanced Geostationary Radiation Imager (AGRI), accounting for differences between land and ocean surfaces. Validation with collocated space radar and lidar measurements indicated the identification accuracies of approximately 82% over the land and 76% over the ocean. Annual distributions of ML clouds inferred by AHI and AGRI exhibited strong similarity. Furthermore, 6 years of hourly observations revealed distinct monthly and daily variations in ice‐over‐water clouds over the Asia–Pacific region. The ML cloud monthly variations were similar to those of the seasonal convection cycle, with occurrence frequencies over the typical regions higher in summer (maximum ∼27%) and lower (minimum 6%–10%) in winter. Regarding daily variations, ice‐over‐water clouds occurred more frequently around local noon over most of the six time zones (from UTC + 06 to UTC + 11) throughout all seasons. The refined spatiotemporal distribution of ML clouds, particularly the daily variations, is possible to improve our understanding of cloud vertical distributions and radiative effects, and has the potential to promote subsequent validation and parameterization of cloud overlapping in global climate modeling. Plain Language Summary: ML clouds with complex vertical structures introduce significant challenges in global climate models owing to a lack of accurate observations and universally applicable theories. While active satellite instruments provide valuable information about ML clouds, they are limited by temporal resolution and spatial discontinuity. This study improves on a passive geostationary satellite spectral imager‐based method for detecting ice‐over‐water clouds. Based on the advantages of geostationary satellites, the annual and monthly distributions, as well as daily variations of ice‐over‐water clouds, are revealed. Our results provide a valuable observational foundation for investigating ML cloud properties. Key Points: New thresholds of multi‐layer cloud detection algorithms for Advanced Himawari Imager and Advanced Geostationary Radiation Imager are introduced and validatedDaytime spatial and temporal variations of multi‐layer clouds over the Asia‐Pacific region are presentedThere are substantial daily variations (up to over 15%) on the multi‐layer cloud occurrence frequencies [ABSTRACT FROM AUTHOR]

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds. [ABSTRACT FROM AUTHOR]

The risk of more frequent and intensified extreme events is expected to increase with climate change. In semi-arid regions, particularly, both increased flooding and prolonged droughts pose a threat as the water resources system must be prepared for both types of extremes. To understand how future extremes may be altered, however, past extremes must be understood. For this reason, this study employs three physically based models to simulate the atmospheric, snow, and hydrologic conditions in the American River Watershed: the Weather Research and Forecasting Model and the Watershed Environmental Hydrology Hydro-Climate Model with the addition of a SNOW Module. Each model is individually calibrated and validated against either gauge data or data provided by the Parameter-Elevation Regressions on Independent Slopes Model for two 10-year periods before a full 169-year daily reconstruction is performed for the period from 1852 through 2020. To analyze the model performance under hydrologic extremes, three flood years (1997, 2006, and 2017) and two drought periods (1987-1992 and 2012-2016) are further analyzed. The reconstruction outputs are found to successfully simulate historical conditions at both daily and monthly scales. Results highlight the importance of fully accounting for the role of temperature in snow processes and the subsequent flow conditions. By understanding how the models perform under historical extreme conditions, future extreme events can be compared, and necessary adaptation plans can be conceived to ensure that the watershed is prepared for future wet and dry conditions. Additionally, the created reconstruction dataset is comprehensive, simulating conditions across the hydroclimate, at a finer spatial and temporal scale than is typically available from observation data alone. The dataset can be further analyzed to assess changes in the trends and the potential trajectory of extreme events within the American River Watershed. [ABSTRACT FROM AUTHOR]

Over the past 3 million years, humans have expanded their ecological niche and adapted to more diverse environments. The temporal evolution and underlying drivers behind this niche expansion remain largely unknown. By combining archeological findings with landscape topographic data and model simulations of the climate and biomes, we show that human sites clustered in areas with increased terrain roughness, corresponding to higher levels of biodiversity. We find a gradual increase in human habitat preferences toward rough terrains until about 1.1 million years ago (Ma), followed by a 300 thousand-year-long contraction of the ecological niche. This period coincided with the Mid-Pleistocene Transition and previously hypothesized ancestral population bottlenecks. Our statistical analysis further reveals that from 0.8 Ma onward, the human niche expanded again, with human species (e.g., H. heidelbergensis, H. neanderthalensis, and H. sapiens) adapting to rougher terrain, colder and drier conditions, and toward regions of higher ecological diversity. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are key agents in distributing extratropical precipitation and transporting moisture poleward. Climate models forced by historical anthropogenic forcing suggest an increase in AR activity in the extratropics over the past four decades. However, reanalyses indicate a ~6° to 10° poleward shift of ARs during boreal winter in both hemispheres, featuring a rise along 50°N and 50°S and a decrease along 30°N and 30°S. Our analysis demonstrates that low-frequency sea surface temperature variability in the tropical eastern Pacific exhibits a cooling tendency since 2000 that plays a key role in driving this global AR shift, mostly over extratropical oceans, through a tropical-driven eddy-mean flow feedback. This mechanism also operates on interannual timescales, controlled by the El Niño-Southern Oscillation, and is less pronounced over the Southern Ocean due to weaker eddy activity during austral summer. These highlight the sensitivity of ARs to large-scale circulation changes driven by both internal variability and external forcing in current and upcoming decades. [ABSTRACT FROM AUTHOR]

We present a detailed model atmosphere analysis of massive white dwarfs with M > 0.9 M ⊙ and T eff ≥ 11,000 K in the Montreal White Dwarf Database 100 pc sample and the Pan-STARRS footprint. We obtained follow-up optical spectroscopy of 109 objects with no previous spectral classification in the literature. Our spectroscopic follow-up is now complete for all 204 objects in the sample. We find 118 normal DA white dwarfs, including 45 massive DAs near the ZZ Ceti instability strip. There are no normal massive DBs: the six DBs in the sample are strongly magnetic and/or rapidly rotating. There are 20 massive DQ white dwarfs in our sample, and all are found in the crystallization sequence. In addition, 66 targets are magnetic (32% of the sample). We use magnetic white dwarf atmosphere models to constrain the field strength and geometry using offset dipole models. We also use magnetism, kinematics, and rotation measurements to constrain the fraction of merger remnant candidates among this population. The merger fraction of this sample increases from 25% for 0.9–1 M ⊙ white dwarfs to 49% for 1.2–1.3 M ⊙. However, this fraction is as high as 78 − 7 + 4 % for 1.1–1.2 M ⊙ white dwarfs. Previous works have demonstrated that 5%–9% of high-mass white dwarfs stop cooling for ∼8 Gyr due to the 22Ne distillation process, which leads to an overdensity of Q-branch stars in the solar neighborhood. We demonstrate that the overabundance of the merger remnant candidates in our sample is likely due to the same process. [ABSTRACT FROM AUTHOR]

We investigate the mass changes of Antarctic glaciers from 2003 to 2020, partitioning them into the contributions of surface mass balance (SMB) and ice discharge, using high-resolution ice mass change estimates derived from the combination of two different types of satellite observations (gravimetry and altimetry) and outputs from a regional climate model. Our analysis indicates that changes in ice discharge have played a dominant role in ongoing ice mass trends and their accelerations, especially in glaciers near the Amundsen and Bellingshausen Seas in West Antarctica. In particular, mass losses of the Thwaites and Pine Island Glaciers have been mostly (>90%) controlled by ice discharge, while the contribution of SMB has been relatively minor. In East Antarctica, SMB accounts for significant portions (>50%) of ice mass imbalances of glaciers in e.g., Dronning Maud Land and Wilkes Land. Ice discharge has also played a notable role in overall mass gain in the region. While our ice discharge estimates agree well with previous estimates from satellite imagery in West Antarctica, notable differences are found in glaciers of East Antarctica and the Antarctic Peninsula. This highlights the need for more observations and improved numerical models to refine these estimates. [ABSTRACT FROM AUTHOR]

Climate models exhibit biases in the mean state and in variability across different regions of the Earth. For example, atmosphere‐only models have a poleward bias in summertime jet streams across the Northern Hemisphere (NH). This can result from many processes, including misrepresentation of Rossby waves that can propagate in different directions and thereby interact with jet streams. However, Rossby‐wave biases can result from biased background state of the climate system as well. The propagation speed of Rossby waves depends on jet stream strength, thus a poleward displacement of the jet stream can hinder westward propagation of Rossby waves at higher latitudes and displace eastward propagating Rossby waves (downstream development). These biases then impact other regions resulting in biased atmospheric circulation across the NH. Indeed, in this study we confirm this via regional nudging experiments within the Norwegian Earth System Model. Namely, nudged horizontal winds over the North Pacific can improve Rossby wave statistics and thereby atmospheric circulation over Eurasia (i.e., upstream). However, nudging over the North Atlantic has little effect on boreal summer atmospheric circulation. This implies that improving biases over the North Pacific is crucial for a better representation of modelled boreal summer circulation over Eurasia. [ABSTRACT FROM AUTHOR]

An apparent increase in observed cold extremes over recent decades in the northern midlatitudes has been reported, in contrast to robust decreases predicted by climate models. This discrepancy has led to suggestions that models fail to accurately simulate changes in weather patterns caused by Arctic warming. Here, we show that the observed frequency and intensity of midlatitude cold extremes have strongly decreased since 1990 and are consistent with modeled trends. The previously reported increase in cold extremes was overestimated due to an artifact of changing data coverage. We also show that the fraction of land with observed cold extreme increases over recent decades is consistent with model internal variability on top of a near-uniform forced reduction in cold extremes across the midlatitudes. Our results provide strong evidence of a decrease in midlatitude cold extremes over recent decades and consistency between models and observations. [ABSTRACT FROM AUTHOR]

We demonstrate a 1-year lagged extratropical response to the El Niño-Southern Oscillation (ENSO) in observational analyses and climate models. The response maps onto the Arctic Oscillation and is strongest in the North Atlantic, where it resembles the North Atlantic Oscillation (NAO). Unexpectedly, these 1-year lagged teleconnections are at least as strong as the better-known simultaneous winter connections. However, the 1-year lagged response is opposite in sign to the simultaneous response such that 1 year later, El Niño is followed by a positive NAO, whereas La Niña is followed by a negative NAO. The lagged response may also interfere with simultaneous ENSO teleconnections. We show here that these effects are unlikely to be caused by residual aliasing of ENSO cycles; rather, slowly migrating atmospheric angular momentum anomalies explain both the sign and the timing of the extratropical response. Our results have implications for understanding ENSO teleconnections, explaining observed extratropical climate variability and interpreting seasonal to inter annual climate predictions. [ABSTRACT FROM AUTHOR]

Deep convection in the Indo-Pacific warm pool is vital in driving global atmospheric overturning circulations. Year-to-year variations in the strength and location of warm pool precipitation can lead to significant local and downstream hydroclimatic impacts, including floods and droughts. While the El Niño-Southern Oscillation (ENSO) is recognized as a key factor in modulating interannual precipitation variations in this region, atmospheric internal variability is often as important. Here, through targeted atmospheric model experiments, we identify an intrinsic low-frequency atmospheric mode in the warm pool region during the austral summer, and show that its impact on seasonal rainfall is comparable to ENSO. This mode resembles the horizontal structure of the Madden-Julian Oscillation (MJO), and may play a role in initiating ENSO as stochastic forcing. We show that this mode is not merely an episodic manifestation of MJO events but primarily arises from barotropic energy conversion aided by positive feedback between convection and circulation. [ABSTRACT FROM AUTHOR]

The Pacific Decadal Oscillation (PDO) index has been calculated using the INMCM5 climate model developed by the Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences. The PDO index correlation with the reanalysis data decreases from about 1.0 in November of the starting year to 0.355 in October of the following year. The model forecast for the PDO index is reliable for the first 2 years, but correlation coefficients decline significantly after that. The inertial forecast is more accurate in the first 2 years and becomes less reliable onward. The correlation coefficient for the model ensemble forecast of the PDO index with its components in the ensemble is greater than the correlation between the ensemble average and the actual PDO index. The INMCM5 climate model is suggested to have potential for considerable improvement in forecasting the PDO. [ABSTRACT FROM AUTHOR]

Global climate change is one of the most critical challenges facing natural ecosystems. As sedentary organisms, plant communities are especially vulnerable, with climate change significantly impacting their development, productivity, and distribution. This study focuses on modelling and mapping the potential distribution of black pine (Pinus nigra) within the Antalya Regional Forest Directorate under the SSP1 2.6 climate scenario for the years 2040, 2060, 2080, and 2100. The MaxEnt modelling method, combined with the HadGEM3-GC31-LL climate model from WorldClim, was used to assess the impact of climate change on black pine. The current distribution model results showed "good" predictive performance, with AUC values of 0.855 for training data and 0.851 for testing data. Key variables influencing the model included precipitation during the wettest quarter, annual precipitation, maximum temperature of the warmest month, and elevation. Projections indicate that the black pine's distribution will shrink by 2040, become increasingly fragmented by 2060, and decline further by 2080, with near-total disappearance by 2100. In conclusion, this study highlights the urgent need for adaptive management strategies to address the effects of climate change on black pine forests. [ABSTRACT FROM AUTHOR]

Given the long response time of ice sheets, simulations of the Greenland ice sheet typically exceed the availability of input climate data to reliably simulate the fast processes underlying surface mass balance. Strong feedback processes are known to make the mass balance sensitive to inter- and intra-annual variability. Even simulations with climate models do not always cover the full period of interest, motivating bridging these gaps using relatively coarsely resolved climate reconstructions or temporal interpolation methods. However, both of these approaches usually only provide information about the climatological average but not variability. We investigate how this simplification impacts the surface mass balance using the BErgen Snow SImulator. The model was run for up to 500 years using the same atmospheric climatology but different synthetic variabilities. While changing inter-annual variations has an impact of less than 5 % on the surface mass balance of the Greenland ice sheet, neglecting intra-annual variability by using a daily climatology causes a 40 % change in mass balance. Decomposing the total effect into contributions from different input variables, the biggest contributor is precipitation followed by temperature. Using a daily climatology, a small amount of snowfall every day overestimates the albedo and thus surface mass balance (SMB). We propose a correction that re-captures the effect of intermittent precipitation, reducing the SMB overestimation to 15 %–25 %. We conclude that simulations of the Greenland surface mass and energy balance should be forced with a transient climate, in particular for models that are calibrated with transient data. [ABSTRACT FROM AUTHOR]

The main components of an atmospheric model for numerical weather prediction are the "dynamical core," which describes the resolved flow, and the "physical parametrisation," which capture the effects of non‐fluid and non‐resolved fluid processes. Additionally, models used for air quality or climate applications may include a component that represents the evolution of chemicals and aerosols within the atmosphere. Though, traditionally, all these components use the same mesh with the same grid spacing, we present a formulation for the different components to use a series of nested meshes, with different horizontal grid spacings. This gives the model greater flexibility in the allocation of computational resources, so that resolution can be targeted to those parts that provide the greatest benefits in accuracy. The formulation presented here concerns the methods for mapping fields between meshes and is designed for the compatible finite‐element discretisation used by LFRic‐Atmosphere, the Met Office's next‐generation atmosphere model. Key properties of the formulation include the consistent and conservative transport of tracers on a mesh that is coarser than the dynamical core, and the handling of moisture to ensure mass conservation without generation of unphysical negative values. Having presented the formulation, it is then demonstrated through a series of idealised test cases that show the feasibility of this approach. [ABSTRACT FROM AUTHOR]

Thermodynamics is an essential ingredient of fine‐grain and coarse‐grain models of oceanic and atmospheric flows. Thermodynamic functions and conservative variables, for a given set of observable quantities such as pressure, temperature, and composition, may be defined up to a certain degree of arbitrariness, in the sense that the predictions of the fine‐grain model are insensitive to, for example, some reference enthalpies, entropies, or pressures. Since the compressible Navier–Stokes model, regarded as a "mother" fine‐grain model, is invariant with respect to arbitrary changes in reference enthalpies and entropies, restricted only by phase change, any coarse‐grain model obtained from it, even conceptually, must be invariant at least to the same extent. A framework is devised that enables the systematic examination of this invariance. It is found that the dependence of usual conservative variables on a reference pressure propagates to their fluxes and gradients, and to downgradient closures based on them. To resolve this issue, it is shown how to construct invariant "reduced" gradients and fluxes of enthalpy and entropy. Closure relationships between such "reduced" fluxes and gradients are then guaranteed to be invariant. More work is required to address the invariance of more sophisticated closures, especially shallow and deep convective closures. [ABSTRACT FROM AUTHOR]

Seasonal‐range predictability of summer climate in northwestern Europe is generally considered to be low. This is an increasing issue given the worsening impact of summer heatwaves, droughts and intense convective rainfall in a rapidly changing climate. In wintertime, predictive skill in the region is derived from a variety of sources, not least teleconnections with the El Niño‐Southern Oscillation (ENSO). Summer ENSO teleconnections, however, are often considered to be negligible. In this paper, we revisit the topic of summer teleconnections between ENSO and the North Atlantic‐European region. We build on previous work identifying upper tropospheric responses to tropical forcing, since dynamical teleconnections are most apparent at this level. Our results confirm that significantly increased geopotential heights are found stretching over the North‐Atlantic region and into western Europe when La Niña conditions are prevalent during summer. This pattern is part of the previously identified 'circumglobal' pattern of wider northern‐hemisphere height changes. We then look for these responses in a range of climate models used in operational seasonal prediction. While parts of the circumglobal pattern are weakly present, none of them produce the response seen over the North Atlantic, even when the effect of sampling on the observed teleconnection is accounted for. We additionally estimate the contribution of the previous (wintertime) phase of ENSO on the following summer. We find a significant delayed response, particularly in heights, to the earlier phase. The combination of the delayed and current responses gives height anomalies that are larger, on average, when ENSO changes phase from winter to summer. Finally, we show that a modest level of regional prediction skill from ENSO does exist. There is a contribution to skill in heights from the previous ENSO phase, but the equivalent contribution to the skill of zonal winds is smaller. [ABSTRACT FROM AUTHOR]

Subseasonal forecast models are shown to suffer from the same inconsistency in the signal‐to‐noise ratio evident in climate models. Namely, predictable signals in these models are too weak, yet there is a relatively high level of agreement with observed variability of the atmospheric circulation. The net effect is subseasonal forecast models show higher correlation with observed variability than with their own simulations; that is, the signal‐to‐noise paradox. Also, similar to climate models, this paradox is particularly evident in the North Atlantic sector. The paradox is not evident in week 1 or week 2 forecasts, and hence is limited to subseasonal time‐scales. The paradox appears to be related to an overly fast decay of northern annular mode regimes. Three possible causes of this overly fast decay and for the paradox in the Northern Hemisphere are identified: a too‐fast decay of polar stratospheric signals, overly weak downward coupling from the stratosphere to the surface in some models, and overly weak transient synoptic eddy feedbacks. Though the paradox is clearly evident in the North Atlantic, it is relatively muted in the Southern Hemisphere: southern annular mode regimes persist realistically, the stratospheric signal is well maintained, and eddy feedback is, if anything, too strong and zonal. [ABSTRACT FROM AUTHOR]

Model ensembles may provide estimates of uncertainties arising from unknown initial conditions and model deficiencies. Often, the ensemble mean is taken as the best estimate, and quantities such as the mean‐squared error between model mean and observations decrease with the number of ensemble members. But the ensemble size is often limited by available resources, and so some idea of how many ensemble members that are needed before the error has saturated would be advantageous. The behaviour with ensemble size is often estimated by producing subsamples from a large ensemble. But this strategy requires that this large ensemble is already available. Fortunately, in many situations, the dependence on ensemble size follows simple analytical relations when the quantity under interest (such as the mean‐squared error between ensemble mean and observations) is calculated over many grid points or time points. This holds both for ensemble means and the related sampling variance. Here, we present such relations and demonstrate how they can be used to estimate the gain of increasing the ensemble. Whereas previous work has mainly focused on the size of the model ensemble, we recognize that uncertainties in observations play a role. We therefore also study the effect of using the mean of an ensemble of reanalyses. We show how the analytical relations can be used to estimate the point where the gain of increasing the size of the model ensemble is dwarfed by the gain of increasing the number of reanalyses. We demonstrate these points using two climate model ensembles: a large multimodel ensemble and a large single‐model initial‐condition ensemble. [ABSTRACT FROM AUTHOR]

Ensemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from an ensemble of samples of ocean states, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long‐range correlations resulting from the limited‐size ensembles imposed by computational burden constraints. Such ad‐hoc methods were found to be not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of one‐year‐long ensemble experiments with a fully realistic EnKF‐DA system in the Red Sea using tens ‐to thousands of ensemble members. The system assimilates satellite and in‐situ observations and accounts for model uncertainties by integrating a 4‐km‐resolution ocean model with European Center for Medium Range Weather Forecast (ECMWF) atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. OceanOur results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensembles leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by: (i) amplified spurious long‐range correlations produced by the low‐rank nature of the ECMWF atmospheric forcing ensemble; and (ii) non‐Gaussianity generated by the perturbed internal physical parameterization schemes. Large‐ensemble forcing fields and non‐Gaussian DA methods might be needed to get full benefits from large ensembles in ocean DA. [ABSTRACT FROM AUTHOR]

The information entropy budget and the rate of information transfer between variables is studied in the context of a nonlinear reduced‐order atmospheric model. The key ingredients of the dynamics are present in this model; namely, the baroclinic instability, the instability related to the presence of an orography, the dissipation related to the surface friction, and the large‐scale meridional imbalance of energy. For the parameters chosen, the solutions of this system display a chaotic dynamics reminiscent of the large‐scale atmospheric dynamics in the extratropics. The detailed information entropy budget analysis of this system reveals that the linear rotation terms play a minor role in the generation of uncertainties compared with the orography and the surface friction. Additionally, the dominant contribution comes from the nonlinear advection terms, and their decomposition in synergetic (covariability) and single (impact of each single variable on the target one) components reveals that for some variables the covariability dominates the information transfer. The estimation of the rate of information transfer based on time series is also discussed, and an extension of the Liang's approach to nonlinear observables is proposed. [ABSTRACT FROM AUTHOR]

A limited‐area model (LAM) is established based on a global model (Global–Regional Integrated Forecast System; GRIST). GRIST–LAM inherits all the technical features of its global counterpart, enabling independent regional weather and climate modeling. The key advancement involves extending the original dynamical core to integrate it under the lateral boundary conditions (LBCs). As an initial development and evaluation study, this paper focuses on the consistency issue between the LAM and the global model. Three perfect‐model tests, using global solutions as LBCs and background truths, were performed to evaluate the LAM behaviors. In the pure dynamical core test, the LBC errors do not compromise the solutions within the interior domain. However, certain configurations can lead to more discontinuous solutions at the domain boundary. The solution error for a specified region decreases as the domain size increases when all other factors are equal. A small error pulse is generated during the initial stage of integration due to the presence of artificial transient waves induced by the LBCs. The model generates fine‐scale details and smaller errors based on coarser‐resolution LBCs. The consistency between LAM and LBC also influences the errors. The climate simulations demonstrate that both hydrostatic and non‐hydrostatic LAMs can reach statistical equilibrium. Regional model climates in the interior domain have higher quality but are sensitive to domain size and LBC configuration. Using a variable LBC coefficient is helpful to alleviate the artificial precipitation at the boundary. In the kilometer‐scale test, the global variable‐resolution model and its LAM counterpart show comparable results. Their performance is competitive with that of a uniform‐resolution global storm‐resolving simulation. Global variable‐resolution and LAM generate higher magnitudes in the tail part of the kinetic energy spectra due to higher local resolution and produce a consistent time evolution of precipitation. The broad implication of this study is also discussed. [ABSTRACT FROM AUTHOR]

Winds are predicted on the discrete grid of numerical weather and climate models. Winds distribute nonlinearly on the height in the near‐surface layer, and a 10 m wind prediction within the layer is often diagnosed upon the Monin–Obukhov similarity theory flux–profile relationship determined from winds at the lowest grid level, the near‐surface atmospheric stability, and surface properties, which leads to concerns that systemic biases may be introduced to the diagnosed wind. Algorithms are proposed to derive near‐surface wind profiles from the grid‐based numerical model forecasts at multiple model levels under the framework of momentum conservations with an implicit solution, associated with simple logarithmic plus linear interpolation in exceptional exemptional conditions. The diagnosed wind profile coheres to the model prediction at the grid level and exhibits differences from the profile using the conventional scheme in the quasi‐steady thermal stratification and non‐steady transitional conditions, retreating to the same logarithmic profile in the neutral condition. [ABSTRACT FROM AUTHOR]

This paper focuses on the adaptive rectangular decomposition (ARD) scheme, a wave-based method employed for acoustic simulations. ARD holds promise for diverse applications. In architectural design, it can forecast acoustical parameters, facilitating the creation of spaces with superior sound quality. Moreover, in the domain of acoustic virtual reality, ARD can offer users a more immersive and lifelike acoustic environment. This enhancement proves advantageous for all of these applications, enabling the simulation of larger environments with heightened precision. Despite its notable efficiency, ARD faces a significant drawback: the absence of atmospheric absorption modeling. The principal aim of this study is to rectify this limitation, thereby augmenting the capabilities of the ARD algorithm. [ABSTRACT FROM AUTHOR]