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The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

Although boundary layer processes are important in climate, weather and air quality, boundary layer climatology has received little attention, partly for lack of observational data sets. We analyze boundary layer climatology over Europe and the continental U.S. using a measure of boundary layer height based on the bulk Richardson number. Seasonal and diurnal variations during 1981–2005 are estimated from radiosonde observations, a reanalysis that assimilates observations, and two contemporary climate models that do not. Data limitations in vertical profiles introduce height uncertainties that can exceed 50% for shallow boundary layers (<1 km) but are generally <20% for deeper boundary layers. Climatological heights are typically <1 km during daytime and <0.5 km at night over both regions. Seasonal patterns for daytime and nighttime differ; daytime heights are larger in summer than winter, but nighttime heights are larger in winter. The four data sets show similar patterns of spatial and seasonal variability but with biases that vary spatially, seasonally, and diurnally. Compared with radiosonde observations, the reanalysis and the climate models produce deeper layers due to difficulty simulating stable conditions. The higher‐time‐resolution reanalysis reveals the diurnal cycle in height, with maxima in the afternoon, and with amplitudes that vary seasonally (larger in summer) and regionally (larger over western U.S. and southern Europe). The lower‐time‐resolution radiosonde data and climate model simulations capture diurnal variations better over Europe than over the U.S., due to differences in local sampling times. Key Points New 25 year PBL climatology shows diurnal, seasonal, and spatial structures Two climate models and one reanalysis show PBL climates similar to radiosondes Shallow nighttime and winter PBL heights are more uncertain, too high in models

New, definitive measures of precipitation frequency provided by CloudSat are used to assess the realism of global model precipitation. The character of liquid precipitation (defined as a combination of accumulation, frequency, and intensity) over the global oceans is significantly different from the character of liquid precipitation produced by global weather and climate models. Five different models are used in this comparison representing state‐of‐the‐art weather prediction models, state‐of‐the‐art climate models, and the emerging high‐resolution global cloud “resolving” models. The differences between observed and modeled precipitation are larger than can be explained by observational retrieval errors or by the inherent sampling differences between observations and models. We show that the time integrated accumulations of precipitation produced by models closely match observations when globally composited. However, these models produce precipitation approximately twice as often as that observed and make rainfall far too lightly. This finding reinforces similar findings from other studies based on surface accumulated rainfall measurements. The implications of this dreary state of model depiction of the real world are discussed.

Human-induced warming is estimated to have increased occurrence probability (magnitude) of the record-breaking September 2022 heat event in western North America by 6–67 times (0.6–1 K) by E3SMv2 and even higher by coupled regional refined model (RRM) simulations.

This study provides comprehensive insight into the notable differences in clouds and precipitation simulated by the Energy Exascale Earth System Model Atmosphere Model version 0 and version 1 (EAMv1). Several sensitivity experiments are conducted to isolate the impact of changes in model physics, resolution, and parameter choices on these differences. The overall improvement in EAMv1 clouds and precipitation is primarily attributed to the introduction of a simplified third‐order turbulence parameterization Cloud Layers Unified By Binormals (along with the companion changes) for a unified treatment of boundary layer turbulence, shallow convection, and cloud macrophysics, though it also leads to a reduction in subtropical coastal stratocumulus clouds. This lack of stratocumulus clouds is considerably improved by increasing vertical resolution from 30 to 72 layers, but the gain is unfortunately subsequently offset by other retuning to reach the top‐of‐atmosphere energy balance. Increasing vertical resolution also results in a considerable underestimation of high clouds over the tropical warm pool, primarily due to the selection for numerical stability of a higher air parcel launch level in the deep convection scheme. Increasing horizontal resolution from 1° to 0.25° without retuning leads to considerable degradation in cloud and precipitation fields, with much weaker tropical and subtropical short‐ and longwave cloud radiative forcing and much stronger precipitation in the intertropical convergence zone, indicating poor scale awareness of the cloud parameterizations. To avoid this degradation, significantly different parameter settings for the low‐resolution (1°) and high‐resolution (0.25°) were required to achieve optimal performance in EAMv1. Plain Language Summary The Energy Exascale Earth System Model (E3SM) is a new and ongoing U.S. Department of Energy (DOE) climate modeling effort to develop a high‐resolution Earth system model specifically targeting next‐generation DOE supercomputers to meet the science needs of the nation and the mission needs of DOE. The increase of model resolution along with improvements in representing cloud and convective processes in the E3SM atmosphere model version 1 has led to quite significant model behavior changes from its earlier version, particularly in simulated clouds and precipitation. To understand what causes the model behavior changes, this study conducts sensitivity experiments to isolate the impact of changes in model physics, resolution, and parameter choices on these changes. Results from these sensitivity tests and discussions on the underlying physical processes provide substantial insight into the model errors and guidance for future E3SM development. Key Points CLUBB along with the companion changes in EAMv1 primarily account for the overall improvements in clouds and precipitation simulation Underestimate of coastal Sc in EAMv1 is due to CLUBB and model tuning; increased vertical resolution partially offsets this degradation The poor scale awareness of EAMv1 requires retuning as resolution increases, which has a large impact on model cloud behavior

We revise the convective triggering function in Department of Energy's Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1) by introducing a dynamic constraint on the initiation of convection that emulates the collective dynamical effects to prevent convection from being triggered too frequently and allowing air parcels to launch above the boundary layer to capture nocturnal elevated convection. The former is referred to as the dynamic Convective Available Potential Energy (dCAPE) trigger and the latter as the Unrestricted Launch Level (ULL) trigger. Compared to the original trigger in EAMv1 that initiates convection whenever CAPE is larger than a threshold, the revised trigger substantially improves the simulated diurnal cycle of precipitation over both midlatitude and tropical lands. The nocturnal peak of precipitation and the eastward propagation of convection downstream of the Rockies and over the adjacent Great Plains are much better captured than those in the default model. The overall impact on mean precipitation is minor with some notable improvements over the Indo‐Western Pacific, subtropical Pacific and Atlantic, and South America. In general, the dCAPE trigger helps to better capture late afternoon rainfall peak, while ULL is key to capturing nocturnal elevated convection and the eastward propagation of convection. The dCAPE trigger also primarily contributes to the considerable reduction of convective precipitation over subtropical regions and the frequency of light‐to‐moderate precipitation occurrence. However, no clear improvement is seen in intense convection and the amplitude of diurnal precipitation. Key Points A new trigger with a dynamic constraint on convection onset and the capability to detect moist instability above BL is tested in E3SM The new trigger has minor impact on the mean state, but it leads to a substantial improvement in the diurnal cycle of precipitation The dynamic constraint suppresses daytime convection, while the unrestricted launch level is key to capturing nocturnal elevated convection

Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid‐latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE‐based triggers in terms of the F1 score metric, widely used to estimate the performance of ML methods. The site‐specific ML trigger functions can achieve, respectively, 91% and 93% F1 scores at SGP and MAO. The unified trigger also has a 91% F1 score, with virtually no degradation from the site‐specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers. Plain Language Summary Deficiencies in convection trigger function, a set of conditions used to determine whether the convection will be activated at a given time in General Circulation Models (GCMs), have critical impacts on model simulated climate. This work presents a novel convection trigger function using a machine learning (ML) model. Environmental information on convective events are obtained from long‐term data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites, which represent two distinct convective regimes. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The ML trigger substantially outperforms the four CAPE‐based triggers commonly used in GCMs. The unified trigger virtually has no degradation from the site‐specific training, suggesting some promise to develop a global trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers. Key Points A machine learning convective trigger function greatly outperforms four convective available potential energy (CAPE)‐based triggers at two distinct convective regimes Insights are derived from the machine learning trigger that could be used to improve the existing traditional CAPE‐based triggers Results suggest that a unified machine learning trigger function could be developed for use in climate models

Two Earth system models are analyzed to gain insight into the processes that govern projected changes in the South Asian monsoon. Warmer present‐day base state tropical SSTs contribute to coupled processes that produce greater future tropical Pacific warming in CESM2 with less of an increase in season‐mean monsoon precipitation compared to E3SMv2. This is attributed to changes in the large‐scale east‐west atmospheric Walker circulation, with relatively larger increases in precipitation and upper‐level divergence over the tropical Pacific and increases in upper‐level convergence over South Asia in CESM2. The stronger El Niño‐like response in CESM2, which increases Pacific precipitation and upper‐level divergence farther to the east, and larger future ENSO amplitude in E3SMv2, produce a greater relative increase in future monsoon‐ENSO connections in E3SMv2 compared to CESM2. This analysis indicates that the key processes that affect future monsoon‐ENSO connections are ENSO amplitude and size of the future tropical Pacific El Niño‐like response. Plain Language Summary Two different Earth system models are analyzed to investigate processes that contribute to possible future changes of South Asian monsoon precipitation and connections to ENSO. The stronger increase of precipitation in CESM2 over the tropical Pacific, due in part to a larger El Niño‐like response of Pacific SSTs, produces less of an increase in future monsoon precipitation in CESM2 compared to E3SMv2. The eastward shift of precipitation in CESM2, along with the larger increase of ENSO amplitude in E3SMv2, produce a stronger future monsoon‐ENSO connection in E3SMv2 compared to CESM2. Key Points Warmer future tropical Pacific SSTs in CESM2 compared to E3SMv2 produce less of an increase in South Asian monsoon precipitation through the Walker Circulation Future monsoon‐ENSO connections are stronger in E3SMv2 compared to CESM2 due to larger increases in future ENSO amplitude and shifts in the Walker Circulation Key processes that affect future monsoon‐ENSO connections are ENSO amplitude and the size of the future tropical Pacific El Niño‐like response

This study examines the warm rain formation process over the global ocean in global climate models. Methodologies developed to analyze CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations are employed to investigate the cloud-to-precipitation process of warm clouds and are applied to the model results to examine how the models represent the process for warm stratiform clouds. Despite a limitation of the present study that compares the statistics for stratiform clouds in climate models with those from satellite observations, including both stratiform and (shallow) convective clouds, the statistics constructed with the methodologies are compared between the models and satellite observations to expose their similarities and differences. A problem common to some models is that they tend to produce rain at a faster rate than is observed. These model characteristics are further examined in the context of cloud microphysics parameterizations using a simplified one-dimensional model of warm rain formation that isolates key microphysical processes from full interactions with other processes in global climate models. The one-dimensional model equivalent statistics reproduce key characteristics of the global model statistics when corresponding autoconversion schemes are assumed in the one-dimensional model. The global model characteristics depicted by the statistics are then interpreted as reflecting behaviors of the autoconversion parameterizations adopted in the models. Comparisons of the one-dimensional model with satellite observations hint at improvements to the formulation of the parameterization scheme, thus offering a novel way of constraining key parameters in autoconversion schemes of global models.

The effects of differences in climate base state are related to processes associated with the present‐day South Asian monsoon simulations in the Energy Exascale Earth System Model version 2 (E3SMv2) and the Community Earth System Model version 2 (CESM2). Though tropical Pacific and Indian Ocean base state sea surface temperatures (SSTs) are over 1°C cooler in E3SMv2 compared to CESM2, and there is an overall reduction of Indian sector precipitation, the pattern of South Asian monsoon precipitation is similar in the two models. Monsoon‐ENSO teleconnections, dynamically linked by the large‐scale east‐west atmospheric circulation, are reduced in E3SMv2 compared to CESM2. In E3SMv2, this is related to cooler tropical SSTs and ENSO amplitude that is less than half that in CESM2. Comparison to a tropical Pacific pacemaker experiment shows, to a first order, that the base state SSTs and ENSO amplitude contribute roughly equally to lower amplitude monsoon‐ENSO teleconnections in E3SMv2. Plain Language Summary Two different Earth system models are analyzed to investigate how differences in simulated base state tropical sea surface temperatures (SSTs) and El Niño/Southern Oscillation (ENSO) amplitude affect the processes associated with the South Asian monsoon. Though tropical SSTs are over 1°C cooler in the Energy Exascale Earth System Model version 2 (E3SMv2) and there is overall reduced Indian sector precipitation, the regional pattern of South Asian monsoon precipitation is similar in the two models. More significantly, monsoon‐ENSO teleconnections are reduced in E3SMv2 compared to Community Earth System Model version 2 (CESM2) due to cooler mean tropical SSTs combined with ENSO amplitude in E3SMv2 that is less than half that in CESM2. Key Points Base state differences in Energy Exascale Earth System Model version 2 (E3SMv2) compared to Community Earth System Model version 2 (CESM2) include cooler tropical Indian and Pacific sea surface temperatures (SSTs) and reduced ENSO amplitude Base state differences in the two models do not appreciably affect simulations of the regional patterns of South Asian monsoon precipitation Cooler SSTs and lower amplitude ENSO in E3SMv2 combine to contribute about equally to weaker monsoon‐ENSO teleconnections compared to CESM2