JGR–Atmospheres

The Earth’s atmosphere is influenced by energetic electrons coming from the magnetosphere. This energetic electron precipitation (EEP) is energized by the solar wind and directly affects in the high-latitude mesosphere and lower thermosphere (MLT). EEP forms odd nitrogen (NOx) and hydrogen oxides (HOx) which destroy ozone. During winter EEP-NOx descends to the stratosphere, establishing the indirect EEP effect. Several studies have found that EEP is related to changes in temperature and winds in the northern winter stratosphere. One of the most prominent effects of EEP is the influence on the northern polar vortex, a westerly wind system surrounding the winter pole in the middle atmosphere. Most studies of the EEP effect on dynamical features of the middle atmosphere have relied on either model simulations or reanalysis datasets which are mainly limited to stratospheric heights. We study here EEP effects on chemical and dynamical properties of the stratosphere and mesosphere in the northern hemisphere by using EOS Aura satellite’s measurements of atmospheric properties and POES satellites' measurements of precipitating electrons. We confirm earlier results showing that EEP decreases ozone and affects the temperature in the polar middle atmosphere and strengthens the stratospheric polar vortex. We show that EEP weakens the mesospheric polar vortex in late winter. This effect on polar vortex is partly due to changes in propagation and convergence of planetary waves. Accordingly, the EEP effect on the northern polar vortex depends on planetary waves not only in the stratosphere, as found in earlier studies, but also in the mesosphere.

Tropical easterly waves (TEWs) are a recurrent mode of low-latitude weather that are often convectively coupled and impact precipitation extremes. Previous work has examined the development of TEWs and their associated precipitation for individual seasons or regional domains, but no studies exist that document the importance of TEW precipitation globally. This study quantifies the precipitation associated with TEWs across the entire tropics using satellite (Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement [IMERG]) and reanalysis (Modern-Era Retrospective analysis for Research and Applications, Version 2 [MERRA-2]) data. Traditional space-time filtering of precipitation reveals a mostly similar climatological power distribution for westward traveling, synoptic period disturbances corresponding to TEWs within all data sets. Using objective tracking, we find that areas with maximum TEW frequency such as the North Atlantic, Equatorial Pacific, and Indian Ocean have the highest accumulation of TEW-associated precipitation. TEWs account for at most 30% of total annual precipitation in regions where they commonly occur and 1%–5% over much of the tropics. Vertically collocated storms, where the 850 and 700 hPa tracks correspond with each other, have higher conditional rain rates and indicate that waves with vertical development produce stronger and more organized convection. We find similar regional patterns using MERRA-2 precipitation and latent heating, although the importance and contribution of TEWs to the background are reduced compared to IMERG. While the broad pattern of TEW associated precipitation in MERRA-2 is like observations, the underestimation of rainfall contributions from TEWs, coupled with occasional false alarms in reanalysis data, suggests that MERRA-2 does not capture organized convection within TEWs correctly.

Air quality time series exhibit significant periodic patterns, which are linked to a diverse array of emission sources and atmospheric processes. To discern and distinguish these periodic drivers, we have devised a two-step decomposition approach that integrates a machine learning-based model for weather normalization with Morlet wavelet analysis. This approach was applied to a 7-year data set encompassing six regulated air pollutants across eight Chinese cities. Our analysis revealed distinct periodicities in weather-normalized concentrations of primary air pollutants: a dominant annual cycle with periodicity around 365 days, which accounts for over 50% of the variance on average and is primarily driven by recurrent winter heating activities; daily cycles characterized by regular diurnal patterns attributable to combustion sources such as traffic; and periodicities exceeding 512 days that associated with long-term regulatory policies targeting SO2. Particularly notable was the significant drop in the strength of the annual cycle in northern cities following the implementation of China's clean heating policies in 2017/2018, affirming the success of these initiatives. Additionally, diurnal dispersion and photochemistry, large-scale atmospheric circulation, and synoptical weather patterns are likely responsible for the observed daily cycle (accounting for over 40% of the variance), annual periodicities, and intra-monthly variations in the meteorologically driven concentrations, respectively. The varying power of these periodic drivers across time and locations implies the heterogeneity of emission rates and region-specific climates. This work highlights the efficacy of this decomposition approach in air quality research, warranting attention for its potential value for enhancing our understanding of air pollution dynamics.

The rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub-selection based on process-oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend-related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%-92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments.

Tropical cyclones (TCs) lead to huge economic losses and casualties in coastal regions, but multi-decadal variation of TC remains poorly understood due to the limited length of observational records and low-resolution reconstructions. Porites corals provide continuous and monthly resolved records that allow for the determination of weather-timescale (weekly to monthly) extreme events, including TC, to overcome the above limitations. Here we synthesize five coral Δδ18O records from the South China Sea (SCS) and develop a coral TC frequency index, in good agreement with post-1980 instrumental records and sediment-based TC reconstructions over the past century. Our results indicate that TC frequency in the SCS exhibits pronounced decadal variations, with active TCs corresponding to the negative phase of the Pacific Decadal Oscillation. Our findings here provide novel evidence for past TC changes in the SCS and a constraint for predicting future TC changes.

Precipitation predictability using the non-scale-aware and scale-aware convective parameterization schemes (CPSs) was investigated to assess the necessity of the CPSs within the gray-zone. This study evaluates the performance of the Weather Research and Forecasting (WRF) model's CPS for 135 heavy rainfall events (HREs) over the Korean Peninsula for 10 years (i.e., 2011–2020). We tested the Kain–Fritsch (KF) scheme (non-scale-aware) and Multi-scale Kain–Fritsch (MSKF) scheme (scale-aware) in the WRF model. The MSKF scheme shows an overall improved performance of precipitation simulation compared to the KF scheme, but the precipitation forecast performance of CPS depends on the characteristics of HREs. When the HREs are characterized by synoptic-scale atmospheric conditions with strong winds and large-scale water vapor transport, the forecast performance of both CPSs is similar because a cloud microphysics scheme can explicitly resolve most of the precipitation. However, in the case of HREs with weak synoptic forcing conditions (e.g., moisture transport and winds) related to the localized and meso-scale HREs, the MSKF scheme can improve overall simulated precipitation by increasing grid-scale precipitation and reducing the overestimation of subgrid-scale precipitation simulated in the KF scheme. Therefore, using the scale-aware CPS in the gray-zone can provide more accurate precipitation forecasts regardless of the environmental condition of the HREs.

Long-term variabilities of monthly zonal (U) and meridional winds (V) in northern polar mesosphere and lower thermosphere (MLT, ∼80–100 km) are investigated using meteor radar observations during 1999–2022 over Esrange (67.9°N, 21.1°E). The summer (June-August) mean zonal winds are characterized by westward flow up to ∼88–90 km and eastward flow above this height. The summer mean meridional winds are equatorward with strong jet at ∼85–90 km and it weakens above this height. The U and V exhibit strong interannual variability that varies with altitude and month or season. The responses of U and V anomalies (from 1999 to 2003) to solar cycle (SC), Quasi Biennial Oscillation at 10 and 30 hPa, El Niño-Southern Oscillation, North Atlantic Oscillation, ozone (O3) and carbon dioxide (CO2) are analyzed using multiple linear regression. From analysis, significant regions of correlations between MLT winds and above potential drivers vary with altitude and month. The positive responses of U and V to SC (up to 15 m/s/100 sfu) indicates the strengthening of eastward winds in mid-late winter, and poleward winds in late autumn and early winter. The O3 likely intensifies the eastward and poleward winds (∼100 m/s/ppmv) in winter and early spring. The CO2 significantly influence the eastward flow in late winter and summer (above ∼90–95 km) and strengthen the meridional circulation. The significant positive trend in U peaks in summer, late autumn and early winter (∼0.6 m/s/year), the negative trend in V is more prominent in summer above ∼90–95 km.

Accurately predicting fog is challenging due to interplay of myriad processes in its formation and high spatiotemporal variability. This study compares the performance of the Weather Research and Forecasting model with control (CNTL-WRF) and assimilated fine-grid (HRLDAS-WRF) soil fields in the Ingo-Gangetic Plain (IGP) over a 2-years winter period (2019–2020 and 2020–2021). Results show HRLDAS-WRF enhances accuracy in representing surface fog's heterogeneity and lifecycle across the IGP, demonstrating a spatial skill improvement of approximately 18% with a Fraction Skill Score of 0.44, compared to CNTL-WRF's (0.36). Employing fog classification algorithm identifies 25 dense fog episodes (Vis < 500 m) over Delhi's urban boundary layer, including 14 radiation (RAD), 5 cloud-base lowering (CBL), 3 advection + radiation (ADV + RAD), and 3 evaporation (EVA) episodes. CNTL-WRF predicts 20 episodes but misses five due to a dry bias in the initial moisture conditions. However, HRLDAS-WRF demonstrates limited vertical fog growth in various occurrences, highlighting the crucial role of fine-gridded soil states for enhanced land-surface feedback. Detailed analysis shows a 40% reduction in mean onset error for RAD fog occurrences in HRLDAS-WRF when compared to CNTL-WRF. In CBL fog episodes, both models exhibit significant radiative cooling and inversion before fog onset, leading to inaccurate predictions as RAD fog. Similarly, forecasting the abrupt development of ADV + RAD fog episodes is challenging as models struggle to replicate moisture intrusion over radiatively cooled surfaces in windy conditions. Predicting EVA fog, forms within an hour after sunrise, remains difficult due to the current model parameterization that rapidly dissipates fog soon after sunrise.

Since the distinct thermostability difference of sulfate ammonium and nitrate ammonium aerosols, their distributions, evolutions and sources could be unpredictable on a long-range transport condition. Here, we highlighted the 3-D structures and sources of SO4 2−, NO3 − and NH4 + (SNA) during two cold front episodes in east China. Cold fronts effectively uplift and transport PM2.5 and its precursors from upstream sources to the Yangtze River Delta (YRD). Specifically, in the YRD, surface SO4 2− is mostly imported from the upstreams, accounting for ∼48%, significantly higher than the contribution from the YRD itself (∼29%). In contrast, NH4NO3 is thermally unstable and more easily lost in the warmer and lower boundary layer (BL) ahead of cold front. Consequently, only 20% of the total NO3 − originates from upstreams, while the YRD contributes 28%. In the upper BL, the contribution of SO4 2− from upstreams remain high (∼49%), with only 18% originating from the YRD. However, due to the intense thermostability of NH4NO3 in colder and wetter air, the YRD’s contribution to NO3 − is 27%, and upstreams contribute 20%. The physical processes exert relatively consistent effects on variations of PM2.5 and SNA concentrations. The aerosol chemical process (AERO) of (NH4)2SO4 consistently contributes positively throughout the entire BL. Conversely, the temperature-sensitive NH4NO3 undergoes repeated dissociation/condensation and deposition, causing positive AERO contributions in upper BL and negative contributions in lower BL. Results indicate that one difference in physicochemical property of species could induce their distinct distributions and sources in large scale, and should be considered in regional air pollution control.

Although the summers of 2021 and 2022 are both in the two successive La Niña decaying stages and under the same climate background of negative Pacific Decadal Oscillation (PDO) phase and global warming trend, they exhibit significantly different and even opposite precipitation patterns in the Yangtze River Valley (YRV) as well as in the Indian monsoon region (IMR). In contrast to the abundant precipitation and lower temperature in the YRV in summer 2021, in summer 2022 the YRV experiences severe drought and extremely high temperatures, which is also accompanied by Mega-floods in the IMR. This study identifies the joint influence of sea surface temperature anomalies (SSTAs) in Niño4 and Barents Sea (BS) regions as the underlying cause for the contrast YRV precipitation anomalies in the summers of 2021 and 2022. Specifically, the cold SSTAs in both Niño4 and BS regions in summer 2021 favor stronger and southward shifted western North Pacific subtropical high (WNPSH), leading to more precipitation in the YRV, which is however generally reversed but more intense in summer 2022 because of the synergistic effect of cold Niño4 and warm BS SSTAs. Moreover, the induced extreme precipitation in the IMR in summer 2022, which is absent in summer 2021 due to the offsetting effect of cold SSTAs in both Niño4 and BS regions, in turn further strengthens the anomalous atmospheric circulations via its released large diabatic heating and serves as a relay pathway for the dramatic drought and heat wave in the YRV.

There is a great need for an accurate short-term wind speed forecast, and statistical forecasts have gained increased popularity for their computational efficiency and satisfactory skill. However, there has been no systematic research to fully explore the capabilities of statistical approaches and evaluate the applicability of probabilistic information from statistical ensemble. This study first compares the skills of different statistical methods, based on linear regression, machine learning (ML), and deep learning (DL), using three strategies (i.e., direct, recursive, and multi-output) against the three operational numerical models and their bias-corrections, for short-term wind speed forecast over Pearl River Estuary during 2018–2021. Inter-comparison between statistical forecasts reveals the dominant superiority of direct strategy. On this basis, Random Forest (RF) and Support Vector Machines (SVM) perform best compared to other statistical forecasts and bias correction of numerical forecasts throughout 48 hr lead time, while the performance of methods with simplified (linear) or more complex (DL) model structures degrades significantly. Moreover, the top 10 forecasts are utilized to account for forecast uncertainties but present a substantial under-dispersed prediction. Two traditional methods and three modern methods are implemented to perform probabilistic post-processing. Modern methods based on ML or DL present worse skills, while traditional methods, particularly for ensemble model output statistics, show added value in discriminating binary events due to limited enhancements in calibration. Overall, RF and SVM using direct strategy are highly recommended for short-term wind speed forecasts, and efforts are ongoing to address the issues of strong wind prediction and ensemble calibration.

Upscaling flux tower measurements based on machine learning (ML) algorithms is an essential approach for large-scale net ecosystem CO2 exchange (NEE) estimation, but existing ML upscaling methods face some challenges, particularly in capturing NEE interannual variations (IAVs) that may relate to lagged effects. With the capacity to characterize temporal memory effects, the Long Short-Term Memory (LSTM) networks are expected to help solve this problem. Here we explored the potential of LSTM for predicting NEE across various ecosystems using flux tower data over 82 sites in North America. The LSTM model with differentiated plant function types (PFTs) demonstrates the capability to explain 79.19% (R 2 = 0.79) of the monthly variations in NEE within the testing set, with RMSE and Mean Absolute Error values of 0.89 and 0.57 g C m−2 d−1 respectively (r = 0.89, p < 0.001). Moreover, the LSTM model performed robustly in predicting cross-site variability, with 67.19% of the sites that can be predicted by both LSTM models with and without distinguished PFTs showing improved predictive ability. Most importantly, the IAV of predicted NEE highly correlated with that in flux observations (r = 0.81, p < 0.001), clearly outperforming that by the random forest model (r = −0.21, p = 0.011). Among all nine PFTs, solar-induced chlorophyll fluorescence, downward shortwave radiation, and leaf area index are the most important variables for explaining NEE variations, collectively accounting for approximately 54.01% in total. This study highlights the great potential of LSTM for improving carbon flux upscaling with multi-source remote sensing data.

An analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120-year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing.

Ozone (O3) pollution is a focus of the international community due to its health and environmental impacts. Organic peroxyl (RO2) radicals play a significant role in O3 initiation processes, which has implications for O3 mitigation. RO2 generated from volatile organic compounds (VOCs) sources contribute substantially to O3 formation. However, quantifying the RO2 from diverse sources is a great challenge. For the first time, we proposed a new hybrid Receptor-Kinetic model to quantify sources contributions to RO2 from the perspective of molecular level and functional groups of VOCs. We revealed that 3-Hydroxy-2-butylperoxy (BUT2OLO2), 4-Hydroxy-3-methyl-1-butene-3-ylperoxy (ISOPBO2) and 1-Hydroxypropane-2-ylperoxy (HYPROPO2) radicals were the dominant RO2, which were driven by reactions of cis/trans-2-butene (from Biogenic Emissions and Solvent Usage), isoprene (from Biogenic Emissions), and propylene (from Liquid Petroleum Gas Evaporation), respectively. The three dominant RO2 radicals contributed significantly to O3 production (28%, 10% and 14%), comparing with other 16 RO2. Sensitivity studies indicated that O3 production can be decreased effectively by reducing the dominant RO2 species for each source. Quantitative evidence suggested that targeting dominant RO2 sources can be a novel direction for O3 control.

In this paper, we applied a variety of statistical methods to study gravity waves in the troposphere and lower stratosphere in the Brazilian sector, using a large database from Instituto de Controle do Espaço Aéreo (ICEA) of radiosonde measurements carried out in 2014 at 32 locations in the Brazilian territory totaling 49,652 wind and temperature profiles. The average wind profiles were computed and classified by means of a hierarchical cluster analysis. The kinetic and potential energy densities of gravity waves were determined using a detrending technique based on the Least Squares Method and the Fast Fourier Transform. By analyzing the energy density time series it was found that tropospheric average values are consistently larger in the months of winter, late autumn and early spring. Stratospheric average values of variability and kinetic energy density are also consistently larger in this period. A systematic search for quasi monochromatic waves was carried out and their main characteristics such as horizontal/vertical wavelengths and velocities were determined both in the troposphere and lower stratosphere. A correlation analysis between the troposphere and the lower stratosphere based on the measured parameters was used to investigate the wave coupling between the two layers, and no significant correlation was found. Finally, a spatial correlation analysis between energy densities measured at different aerodromes in the same atmospheric layer was carried out, showing that energy densities are spatially correlated for distances less than 3,000–4,000 km.

Seasonal forecasts are commonly issued in the form of anomalies, which are departures from the average over a specified multiyear reference period (climatology). The model climatology is estimated as the average of the retrospective forecasts over the hindcast period. However, different operational centers that provide seasonal ensemble predictions use different hindcast periods based on their model climatology. Additionally, the hindcast periods of recently developed and upgraded newer models have shifted in the recent years. In this paper, we discuss the recent challenges faced by APCC multi-model ensemble (MME) operations, especially changes in the hindcast period for individual models. Based on the results of various experiments for MME prediction, we propose changing the hindcast period, which is the most appropriate solution for APCC operation. This makes the newly developed models join the MME and increases the total number of participating models, which facilitates the skill improvement of the MME prediction.

We investigate the spatial and temporal variability of extreme precipitation events (EPEs) in the Dronning Maud Land (DML) sector of Antarctica using high-resolution ECMWF ERA5 reanalysis data. This study examines the spatial occurrence of EPEs across DML, focusing particularly on six locations spanning the coastal and interior parts of the area. The largest snowfall amounts are usually found on eastward-facing slopes in the coastal zone. EPEs occur predominantly in north-easterly to easterly flows, leading to enhanced precipitation on the windward side of the orographic features with a steep gradient. Wind during EPEs was found to be more directionally consistent in the coastal area than in the interior. An east-west couplet of a mid-tropospheric ridge and low-pressure center is essential for steering warm moist maritime airmasses into the DML region before EPEs. Approximately 40% of EPEs result from atmospheric rivers (ARs), narrow bands of moist air originating at subtropical latitudes, which provide the greatest daily precipitation amounts. From 1979 to 2018, much of the DML experienced a statistically significant (p < 0.05) increase in the number of EPEs per year, along with increased precipitation from the EPEs. These trends were associated with significant changes in moisture availability and poleward meridional winds in the Atlantic sector of the Southern Ocean. The inter-annual variability in the number of EPEs is primarily dictated by regional atmospheric variability, while the influence of the Southern Oscillation Index and Southern Annular Mode is limited.

The Maritime Continent (MC) has experienced significant anthropogenic land use changes, mainly deforestation, which has led to local surface warming and marked convergence in the lower troposphere and divergence in the upper. The remote consequences of this deforestation remain unclear and present considerable uncertainties. In this study, we employ a fully coupled climate model and a linear baroclinic model to explore the effects of altered land-atmosphere interactions due to MC deforestation on high-latitude climates. Our series of idealized experiments demonstrates that MC deforestation can induce upper-level diabatic heating. This generates a barotropic Rossby wave that moves poleward, drawing energy from the subtropical jet across the Central to Eastern Pacific regions via eddy-mean flow interactions. Such interactions amplify the Aleutian Low, promoting the northward transport of warm air, leading to notable warming anomalies. This influx of warmth contributes to sea ice melt, initiating a positive ice-albedo feedback. A lapse-rate feedback is also observed in adjacent high-latitude land areas, amplifying terrestrial warming. These reinforcing feedbacks, combined with the direct temperature transport enabled by the strengthened Aleutian Low, cumulatively result in pronounced high-latitude warming originally due to the tropical land use changes.

Exploring the spatiotemporal differences of hydroclimate variations is crucial for managing future climate change. In the Asian drylands, West Asia (WA) and arid central Asia (ACA) are both climatically dominated by the westerlies and have shown a dipole pattern in precipitation variation during the past several decades. However, it is unclear whether such a difference exists during the Holocene, mainly because the dispute between the δ18O-based early Holocene hydroclimate optimum and the pollen-based mid-Holocene optimum in WA. Here we present a precisely dated record based on lipid biomarkers from Almalou Peatland in the western Iranian Plateau. The chain length of n-alkanoic acids was interpreted as a hydroclimate indicator based on a proxy validation study. Our record reveals a wetting trend during 9–7.5 cal ka BP, a mid-Holocene hydroclimate optimum (7.5–3 cal ka BP), and a rapidly drying trend during 3–0 cal ka BP. The hydroclimate variation was supported by a water-level reconstruction from the same core. The most negative δ18O values during the early Holocene could be partly attributed to the impacts of water vapor sources. Comparing our reconstruction from WA to those from ACA, we found a dipole pattern of hydroclimate variations on the millennial timescale during the Holocene. The simulation results revealed that, unlike the persistent wetting trend in ACA, the pivotal shift of spring insolation led to a transition from wetting to drying conditions in WA, ultimately leading to the generation of dipole pattern. This demonstrates that despite the consistent control by westerlies over the Asian drylands, there are distinct spatial differences in hydroclimate responses to natural forcings.

The response of tropical high clouds to surface warming and their radiative feedbacks are uncertain. For example, it is uncertain whether their coverage will contract or expand in response to surface warming and whether such changes entail a stabilizing radiative feedback (iris feedback) or a neutral feedback. Global satellite observations with passive and active remote sensing capabilities over the last two decades can now be used to address such effects that were previously observationally limited. Using these observations, we show that the vertically averaged coverage exhibits no significant contraction or expansion. However, we find a reduction in coverage at the altitude where high clouds peak and are particularly radiatively-relevant. This results in a negative longwave (LW) feedback and a positive shortwave (SW) feedback which cancel to yield a near-zero high-cloud amount feedback, providing observational evidence against an iris feedback. Next, we find that tropical high clouds have risen but have also warmed, leading to a positive, but small, high-cloud altitude feedback dominated by the LW feedback. Finally, we find that high clouds have been thinning, leading to a near-zero high-cloud optical depth feedback from a cancellation between negative LW and positive SW feedbacks. Overall, high clouds lead the total tropical cloud feedback to be small due to the negative LW-positive SW feedback cancellations.