Circulation patterns linked to the East Asian winter monsoon (EAWM) affect precipitation, surface temperature, and air quality extremes over East Asia. These circulation patterns can in turn be influenced by aerosol radiative and microphysical effects through diabatic heating and its impacts on atmospheric vorticity. Using global model simulations, we investigate the effects of anthropogenic aerosol emissions and concentration changes on the intensity and variability of the EAWM. Comparison with reanalysis products indicates that the model captures the mean state of the EAWM well. The experiments indicate that anthropogenic aerosol emissions strengthen the Siberian High but weaken the East Asian jet stream, making the land areas of East Asia colder, drier, and snowier. Aerosols reduce mean surface air temperatures by approximately 1.5°C, comparable to about half of the difference between strong and weak EAWM episodes in the control simulation. The mechanisms behind these changes are evaluated by analyzing differences in the potential vorticity budget. Anthropogenic aerosol effects on diabatic heating strengthen anomalous subsidence over southern East Asia, establishing an anticyclonic circulation anomaly that suppresses deep convection and precipitation. Aerosol effects on cloud cover and cloud longwave radiative heating weaken stability over the eastern flank of the Tibetan Plateau, intensifying upslope flow along the western side of the anticyclone. Both circulation anomalies contribute to reducing surface air temperatures through regional impacts on thermal advection and the atmospheric radiative balance.

Formaldehyde (HCHO), a precursor to tropospheric ozone, is an important tracer of volatile organic compounds (VOCs) in the atmosphere. Two years of HCHO simulations obtained from the global chemistry transport model CHASER at horizontal resolution of 2.8° × 2.8° have been evaluated using observations from the Tropospheric Ozone Monitoring Experiment (TROPOMI), Atmospheric Tomography Mission (ATom), and multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations. CHASER reproduced the observed global HCHO spatial distribution with spatial correlation (r) of 0.93 and negative bias of 7%. The model showed good capability for reproducing the observed magnitude of the HCHO seasonality in different regions, including the background conditions. The discrepancies between the model and satellite in the Asian regions were related mainly to the underestimated and missing anthropogenic emission inventories. TROPOMI’s finer spatial resolution than that of the Ozone monitoring Experiment (OMI) sensor reduced the global model–satellite root-mean-square-error (RMSE) by 20%. The OMI and TROPOMI observed seasonal variations in HCHO abundances were consistent. However, the simulated seasonality showed better agreement with TROPOMI in most regions. The simulated HCHO and isoprene profiles correlated (R = 0.81) strongly with the ATom observations. CHASER overestimated HCHO mixing ratios over dense vegetation areas in South America and the remote Pacific (background condition) regions, mainly within the planetary boundary layer (<2 km). The simulated temporal (daily and diurnal) variations in the HCHO mixing ratio showed good congruence with the MAX-DOAS observations and agreed within the 1-sigma standard deviation of the observed values.

A document by Michal Ostrowski. Click on the document to view its contents.

Key Points: • CMIP6 models can capture the timing of annual cycle (particularly in February) and spatial patterns of SIT resembling the observations. • Compared to sea-ice area, CMIP6 models exhibit larger negative biases in thickness/volume, with a higher degree of variation among models. • Seasonal variations in sea-ice show positive (negative) relationships between sea ice area and thickness during September (February). Abstract This study assesses less-explored Southern Ocean sea-ice parameters, namely Sea-ice Thickness and Volume, through a comprehensive comparison of 26 CMIP6 models with reanalyses and satellite observations. Findings indicate that models replicate the mean seasonal cycle and spatial patterns of sea-ice thickness, particularly during its maxima in February. However, some models simulate implausible historical mean states compared to satellite observations, leading to large inter-model spread. September sea-ice thickness is consistently biased low across the models. Our results show a positive relationship between modeled mean sea-ice area and thickness in September (i.e., models with more area tend to have thicker ice); in February this relationship becomes negative. While CMIP6 models demonstrate proficiency in simulating Area, thickness accuracy remains a challenge. This study, therefore, highlights the need for improved representation of Antarctic sea-ice processes in models for accurate projections of thickness and volume changes. Plain Language Summary In this study, we investigated sea-ice thickness and volume in the Southern Ocean using data from 26 different climate models and observation datasets. Our findings show that the models generally capture the seasonal cycle and spatial patterns of sea-ice thickness well, with the highest average thickness occurring in February. We also found that the models tend to perform better in simulating sea-ice area compared to thickness. Furthermore, simulated sea-ice area and thickness tend to behave differently during different seasons-positively (negatively) covarying in September (February). The models that performed well in simulating sea-ice area faced challenges in accurately representing thickness and volume. This raises the question regarding the overall performance of such models or, more definitively, whether it's reliable to evaluate model accuracy or performance based solely on sea-ice area. Nevertheless, sea-ice thickness simulations in CMIP6 can offer a basis for initial analyses of absolute sea-ice changes in the Southern Ocean, despite the need for more reliable observational thickness.

A document by Nathan Lenssen. Click on the document to view its contents.

High clouds produced by tropical convection are expected to shrink in area as climate warms, and the radiative feedback associated with this change has long been the subject of controversy. In a recent assessment of climate sensitivity, the World Climate Research Programme (WCRP) estimated that the feedback is significantly negative, albeit with substantial uncertainty. Here we show that such a negative feedback is not supported by an ensemble of high-resolution atmospheric models. Rather, the models suggest that changes in cloud area and opacity act as a modest positive feedback. The positive opacity component arises from the disproportionate reduction in the area of thick, climate-cooling clouds relative to thin, climate-warming clouds. This suggests that thick cloud area is tightly coupled to the rate of convective overturning-which is expected to slow with warming-whereas thin cloud area is influenced by other, less-certain processes. The cloud response is examined from a novel perspective that treats high clouds as part of an optical continuum rather than entities with fixed opacity. The positive feedback differs significantly from previous estimates and leads to a 0.3 • C increase in climate sensitivity relative to a previous community assessment.

Humid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet-bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures (SST) by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TWmax). We develop a multiple linear regression model that explains 80% of variance in tropical mean TWmax and significant regional TWmax variances. The model considers warming trends and El Niño and Southern Oscillation (ENSO) indices. Looking ahead, a moderate-to-strong El Niño with an Oceanic Niño Index (ONI) of 1.5 by the end of 2023 suggests a 42% (11%, 78%) probability of breaking the tropical mean TWmax record in 2024. For an El Niño similar to 2015/2016 (ONI of 2.64), the probability escalates to 90% (50%, 99.5%). This approach also holds promise for regional TWmax predictions.

The El Niño-Southern Oscillation (ENSO) phenomenon – the dominant source of climate variability on seasonal to multi-year timescales – is predictable a few seasons in advance. Forecast skill at longer multi-year timescales has been found in a few models and forecast systems, but the robustness of this predictability across models has not been firmly established owing to the cost of running dynamical model predictions at longer lead times. In this study, we use a massive collection of multi-model hindcasts performed using model analogs to show that multi-year ENSO predictability is robust across models and arises predominantly due to skillful prediction of multi-year La Niña events following strong El Niño events.

The third phase of the Cloud Feedback Model Intercomparison Project requested that modeling centers perform a pair of simulations where the climate system is subjected to an abrupt change of the solar constant by +/- 4%. The forcing is designed to loosely match the amount of radiative forcing incurred by quadrupling atmospheric CO2 concentrations. Using these simulations, we examine how clouds respond to changes in solar forcing and act as a feedback on global surface temperature. Specifically, in this paper, we study the temperature mediated cloud changes that occur following an abrupt increase and decrease of the solar constant and compare with temperature mediated cloud changes that occur following quadrupling and halving of CO2. We seek to answer two primary questions: 1) How do cloud feedbacks differ in response to abrupt changes in CO2 and solar forcing? And 2) Are there symmetrical (equal and opposite) cloud feedbacks to an increase and a decrease in solar forcing? We find that temperature mediated cloud changes are similar from increasing solar and CO2 forcing, with the only robust difference being that there is a larger reduction of low cloud amount following solar forcing; and we find that cloud responses to warming and cooling are not symmetric, due primarily to non-linearity introduced by phase changes in mid-to-high latitude low clouds, and sea ice loss/formation.

In this paper we examine differences in cloud adjustments (often called rapid adjustments) that occur as a direct result of abruptly increasing the solar constant by 4% or abruptly quadrupling of atmospheric CO2. In doing so, we devised a novel method for calculating the cloud adjustments for the abrupt solar forcing experiment that uses differences between coupled model simulations with abrupt solar and CO2 forcing, in combination with uncoupled, atmosphere-only, abrupt CO2 forced experiments that have prescribed sea-surface temperature. Our main findings are that 1) there are substantial differences in the response of stratocumulus and cumulus clouds to solar and CO2 forcing, which follow the differences in the direct radiative effect that solar and CO2 forcing have at cloud top, and 2) there are differences in the adjustment of the average optical depth of high clouds to solar and CO2 forcing that we speculate are driven by the differences in the vertical profile of radiative heating, and differences in the pattern of sea-surface temperature change (for a fixed global mean temperature). Such adjustments do contribute significantly to the total net cloud radiative effect, even after 150 years of simulation.

Severe Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issurance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and drosonde data suggested boundary layer inflow, warm core structure, eyewall updraft and downdraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study evaluates the forecasting performance of the AI-based Pangu-Weather model, and the results highlight its better performance than the global numerical weather prediction models in forecasting tropical cyclones in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclones.

We investigate the representation of individual supercells and intriguing tornado-like vortices in a simplified, locally-refined global atmosphere model. The model, featuring grid stretching, can locally enhance the model resolution and economically reach the cloud-resolving scale. Given an unstable sheared environment, the model can simulate supercells realistically, with a near-ground vortex and funnel cloud at the center of a rotating updraft reminiscent of a tornado. An analysis of the vorticity budget suggests that the updraft core of the supercell tilts environmental horizontal vorticity into the tornado-like vortex. The updraft also acts to amplify the vortex through vertical stretching. Results suggest that the simulated vortex is dynamically similar to observed tornadoes and modeling studies at much higher horizontal resolution.

Satellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly-known microstructural properties that vary strongly in time and space. A consequence is that surface-sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an “empirical state” method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical-empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This “empirical state” approach could be used to solve many other problems of earth system data assimilation.

The eruption of the Hunga Tonga-Hunga Ha’apai volcano on 15 January 2022 was one of the most explosive eruptions of the last decades. The unprecedented amount of water vapor injected into the stratosphere increased the stratospheric water vapor burden by about 10%. Using model runs from the ATLAS chemistry and transport model and Microwave Limb Sounder (MLS) satellite observations, we show that while 20-40% more water vapor than usual was entrained into the Antarctic polar vortex as it formed (e.g., typical values of 4.6 ppm at 21.5 km increased to 6.7 ppm), the direct effect of the increased water vapor on Antarctic ozone depletion was minor. This is caused by the very low temperatures in the vortex, which limit water vapor to the saturation pressure and tend to reset any anomalies in water vapor by dehydration before they can have an effect on ozone loss.

Energetic electron precipitation (EEP) from the radiation belts into Earth’s atmosphere leads to several profound effects (e.g., enhancement of ionospheric conductivity, possible acceleration of ozone destruction processes). An accurate quantification of the energy input and ionization due to EEP is still lacking due to instrument limitations of low-Earth-orbit satellites capable of detecting EEP. The deployment of the ELFIN CubeSats marks a new era of observations of EEP with an improved pitch-angle (0°–180°) and energy (50 keV–6 MeV) resolution. Here, we focus on the EEP recorded by ELFIN coincident with electromagnetic ion cyclotron (EMIC) waves, which play a major role in radiation belt electron losses. The EMIC-driven EEP (~200 keV–~2 MeV) exhibits a pitch-angle distribution (PAD) that flattens with increasing energy, indicating a more efficient high-energy precipitation. Leveraging the combination of unique electron measurements from ELFIN and a comprehensive ionization model known as the Boulder Electron Radiation to Ionization (BERI), we quantify the energy input of EMIC-driven precipitation (on average, ~3.3x10-2 erg/cm2/s), identify its location (any longitude, 50°–70° latitude), and provide the expected range of ion-electron production rate (on average, 100–200 pairs/cm3/s), peaking in the mesosphere – a region often overlooked. Our findings are crucial for improving our understanding of the magnetosphere-ionosphere-atmosphere system as they accurately specify the contribution of EMIC-driven EEP, which serves as crucial inputs to state-of-the-art atmospheric models (e.g., WACCM), to quantify the accurate impact of EMIC waves on both the atmospheric chemistry and dynamics.

Surface Air-pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: firstly the electromagnetic fields are absorbed mainly by two atmospheric components the oxygen and water vapour, and secondly that oxygen is well mixed in the atmosphere. In this work we discuss a space-borne concept, which aims at providing near global, consistent, and regular observations for determining surface air pressure from space by a design of a multi-tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of-the-art microphysical retrievals applied to the A-Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapour, cloud liquid water and rain water and the potential of a correction via the three-tone method is discussed. Results show that accuracies of the order of few hPa are at reach.

A unique phenomenon {\textendash} merging of Equatorial Ionization Anomaly (EIA) crests, leading to an X-pattern (EIA-X) around the magnetic equator {\textendash} has been observed in the night-time ionospheric measurements by the Global-scale Observations of the Limb and Disk (GOLD) mission. A whole atmospheric general circulation model simulation reproduces this pattern. The pattern is also produced in an assimilative ionosphere model that assimilates slant Total Electron Content (slant-TEC) from Global Navigation Satellite System (GNSS) and Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2). Due to the observed similarity between measurements and simulations, the latter is used to diagnose this heretofore unexplained phenomenon. The simulation shows that the EIA-X occurs in the afternoon to evening sector at a longitude where the vertical drift is negative, which is a necessary but not sufficient condition. The simulation was performed under constant low-solar and quiescent-geomagnetic forcing conditions, therefore we suggest that one of the drivers of this phenomenon is from lower-atmospheric processes.

Low-level jets (LLJs), wind speed maxima in the lower troposphere, impact several environmental and societal phenomena. In this study we take advantage of the spatially and temporally complete meteorological dataset from ERA5 to present a global climatology of LLJs taking into consideration their formation mechanisms, characteristics and trends during the period of 1992-2021. The global mean frequency of occurrence was of 21% with values of 32% and 15% for land and ocean. We classified the LLJs into three regions: non-polar land (LLLJ), polar land (PLLJ) and coastal (CLLJ). Over LLLJ regions, the average frequency of occurrence was of 20%, with 75% of them associated with a near-surface temperature inversion i.e. associated with inertial oscillation at night. Over PLLJ regions the LLJs were also associated with a temperature inversion, but were much more frequent (59%), suggesting other driving mechanisms than the nocturnal inversion. They were also the lowest and the strongest LLJs. CLLJs were very frequent in some hotspots, specially on the west coast of the continents, with neutral to unstable stratification close to the surfaces, that became more stably stratified with increasing height. We found distinct regional trends in both the frequency and intensity of LLJs, potentially leading to changes in the emission and transport of dust aerosols, polar ice and moisture over the world. However, it is currently unclear the evolution of the trends with global warming and what the implications are for climate and weather extremes. Future studies will investigate long-term trends for LLJs and the associated implications.

A key element of successful aerobraking operations at Mars is accurate thermospheric density predictions. Evidence suggests that much of the longitude variability in Mars’ aerobraking region is associated with atmospheric tides, and the day-to-day variability is connected with tidal modulation by longer-period global-scale waves. Specifically, ultra-fast Kelvin waves (UFKWs) and their modulation of the tidal spectrum play a key role in coupling Mars’ lower ($<$$\sim$80 km) and middle ($\sim$80-100 km) atmosphere with the aerobraking region above. In this study, over 5 years of Mars Atmosphere and Volatile Evolution (MAVEN) Neutral Gas and Ion Mass Spectrometer (NGIMS) CO$_2$ density observations are employed to reveal prominent, frequent, and persistent 2.5- to 4.5-day UFKW packets in the whole Martian middle and upper thermosphere (ca. 150-200 km), and large secondary waves arising from their nonlinear interactions with the tidal spectrum. Detailed analyses focusing on a prominent $\sim$2.5-day UFKW event in late 2015 demonstrate primary and secondary wave amplitudes growing twofold with altitude from $\sim$7-14\% near 150 km to $\sim$12-25\% near 200 km and their combined effects to account for $\sim$60-80\% of the altitude-longitudinal variability of Mars’ thermospheric density. Concurrent temperature measurements from Mars Reconnaissance Orbiter (MRO) Mars Climate Sounder (MCS) reveal consistent wave signatures near 80 km altitude suggesting propagation of both primary and secondary waves from the lower atmosphere. This study demonstrates that UFKWs and secondary waves from UFKW-tide interactions are sources of significant altitude-longitude variability in the Mars’ aerobraking region that should be accounted for when analyzing satellite observations and nonlinear models.

Southeastern South America (SESA) is a highly productive agricultural region and a hot spot for land-atmosphere interactions. To evaluate the impact of dry soil moisture anomalies (SMAs) on SESA climate and the sensitivity of the regional climate response to the location of SMAs, we perform three experimental simulations using the Community Earth System Model (CESM) with prescribed dry SMAs over (1) SESA, (2) western SESA, and (3) eastern SESA. The dry eastern SESA simulation shows widespread negative precipitation anomalies. In contrast, the dry western SESA simulation shows positive precipitation anomalies over northeastern Argentina, which are associated with the enhanced southward moisture flux co-located with the South American low-level jet exit region. A composite analysis of extremely dry cases over western SESA using reanalysis data agrees with the findings from our CESM experiment. These findings have potential implications for subseasonal forecasting in this region.

As part of the REgional Carbon Cycle Assessment and Processes Phase 2 (RECCAP2) project, we developed a comprehensive African Greenhouse gases (GHG) budget for the period 2010-2019 and compared it to the budget over the 1985-2009 (RECCAP1) period. We considered bottom-up process-based models, data-driven remotely sensed products, and national GHG inventories in comparison with top-down atmospheric inversions, accounting also for lateral fluxes. We incorporated emission estimates derived from novel methodologies for termites, herbivores, and fire, which are particularly important in Africa. We further constrained global woody biomass change products with high-quality regional observations. During the RECCAP2 period, Africa’s carbon sink capacity is decreasing, with net ecosystem exchange switching from a small sink of −0.61 ± 0.58 PgCyr−1 in RECCAP1 to a small source in RECCAP2 at 0.162 (-1.793/2.633) PgCyr-1. Net CO2 emissions estimated from bottom-up approaches were 1.588 (-6.461/11.439) PgCO2yr-1, net CH4 were 78.453 (36.665/59.677) TgCH4yr-1) and net N2O were 1.81 (1.716/2.239) TgN2Oyr-1. Top-down atmospheric inversions showed similar trends. LUC emissions increased, representing one of the largest contributions at 1.746 (0.841/2.651) PgCO2eq yr-1 to the African GHG budget and almost similar to emissions from fossil fuels at 1.743 (1.531/1.956) PgCO2eq yr-1, which also increased from RECCAP1. Additionally, wildfire emissions decreased, while fuelwood burning increased. For most component fluxes, uncertainty is large, highlighting the need for increased efforts to address Africa-specific data gaps. However, for RECCAP2, we improved our overall understanding of many of the important components of the African GHG budget that will assist to inform climate policy and action.

Clear-Air Turbulence (CAT) is associated with wind shear in the vicinity of jet streams in upper atmospheric levels. This turbulence occurs in cloudless regions and causes most weather-related aircraft accidents. Recent studies have shown that in response to climate change, CAT could significantly increase over certain regions as a consequence of strengthening of jet streams. In this study we use several atmospheric reanalyses and coupled model experiments database to evaluate CAT recent and future changes in the Northern Hemisphere. Several CAT diagnostics are computed to assess the sensitivity of results to different turbulence representations. A significant positive trend in CAT frequency is found in the reanalyses in different regions in the Northern Hemisphere over the period 1980-2021. The signal-to-noise analysis shows that over North Africa, East Asia and Middle East the increase of CAT occurrence in the last decades is likely attributed to global warming. In contrast, over the North Atlantic and North Pacific the internal climate variability is too strong to detect a response to anthropogenic forcing in the observed trends. Future climate projections show that over several regions in the Northern Hemisphere, CAT is projected to increase with a high model agreement and independently of the CAT diagnostic used. The largest increase in CAT is projected to occur over East Asia. In the North Atlantic, large uncertainty remains due to lack of model agreement and differences among the various CAT diagnostics.

Ions in the F region ionosphere at 150-400 km altitude consist mainly of molecular NO+ and O2+, and atomic O+. Incoherent scatter (IS) radars are sensitive to the molecular-to-atomic ion density ratio, but its effect to the observed incoherent scatter spectra is almost identical with that of the ion temperature. It is thus very difficult to fit both the ion temperature and the fraction of O+ ions to the observed spectra. In this paper, we introduce a novel combination of Bayesian filtering, smoothness priors, and chemistry modeling to solve for F1 region O+ ion fraction from EISCAT Svalbard IS radar (75.43° corrected geomagnetic latitude) data during the international polar year (IPY) 2007-2008. We find that the fraction of O+ ions in the F1 region ionosphere is controlled by ion temperature and electron production. The median value of the molecular-to-atomic ion transition altitude during IPY varies from 187 km at 16-17 MLT to 208 km at 04-05 MLT. The ion temperature has maxima at 05-06 MLT and 15-16 MLT, but the transition altitude does not follow the ion temperature, because photoionization lowers the transition altitude. A daytime transition altitude maximum is observed in winter, when lack of photoionization leads to very low daytime electron densities. Both ion temperature and the molecular-to-atomic ion transition altitude correlate with the Polar Cap North geomagnetic index. The annual medians of the fitted transition altitudes are 14-32 km lower than those predicted by the International Reference Ionosphere.

Mesoscale convective systems (MCSs) are clusters of thunderstorms that are important in Earth’s water and energy cycle. Additionally, they are responsible for extreme events such as large hail, strong winds, and extreme precipitation. Automated object-based analyses that track MCSs have become popular since they allow us to identify and follow MCSs over their entire life cycle in a Lagrangian framework. This rise in popularity was accompanied by an increasing number of MCS tracking algorithms, however, little is known about how sensitive analyses are concerning the MCS tracker formulation. Here, we assess differences between six MCS tracking algorithms on South American MCS characteristics and evaluating MCSs in kilometer-scale simulations with observational-based MCSs over three years. All trackers are run with a common set of MCS classification criteria to isolate tracker formulation differences. The tracker formulation substantially impacts MCS characteristics such as frequency, size, duration, and contribution to total precipitation. The evaluation of simulated MCS characteristics is less sensitive to the tracker formulation and all trackers agree that the model can capture MCS characteristics well across different South American climate zones. Dominant sources of uncertainty are the segmentation of cloud systems and the treatment of splitting and merging of storms in MCS trackers. Our results highlight that comparing MCS analyses that use different tracking algorithms is challenging. We provide general guidelines on how MCS characteristics compare between trackers to facilitate a more robust assessment of MCS statistics in future studies.