Climate Model Evaluation Research Articles

Cross-Time-Scale Analysis of Year-Round Atmospheric Circulation Patterns and Their Impacts on Rainfall and Temperatures in the Iberian Peninsula

Abstract This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950–2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations significantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, interannual, and decadal scales, including significant long-term trends on their frequency. CPs influence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures—the subtropical Azores high—leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.

Comparison of indicators to evaluate the performance of climate models

AbstractThe evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a “good model”. This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.

Surface energy balance closure over melting snow and ice from in situ measurements on the Greenland ice sheet

Abstract Accurately quantifying all the components of the surface energy balance (SEB) is a prerequisite for the reliable estimation of surface melt and the surface mass balance over ice and snow. This study quantifies the SEB closure by comparing the energy available for surface melt, determined from continuous measurements of radiative fluxes and turbulent heat fluxes, to the surface ablation measured on the Greenland ice sheet between 2003 and 2023. We find that the measured daily energy available for surface melt exceeds the observed surface melt by on average 18 ± 30 W m−2 for snow and 12 ± 54 W m−2 for ice conditions (mean ± SD), which corresponds to 46 and 10% of the average energy available for surface melt, respectively. When the surface is not melting, the daily SEB is on average closed within 5 W m−2. Based on the inter-comparison of different ablation sensors and radiometers installed on different stations, and on the evaluation of modelled turbulent heat fluxes, we conclude that measurement uncertainties prevent a better daily to sub-daily SEB closure. These results highlight the need and challenges in obtaining accurate long-term in situ SEB observations for the proper evaluation of climate models and for the validation of remote sensing products.

Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model

Abstract. Given the importance of aerosols and clouds and their interactions in the climate system, it is imperative that the global Earth system models accurately represent processes associated with them. This is an important prerequisite if we are to narrow the uncertainties in future climate projections. In practice, this means that continuous model evaluations and improvements grounded in observations are necessary. Numerous studies in the past few decades have shown both the usability and the limitations of utilizing satellite-based observations in understanding and evaluating aerosol–cloud interactions, particularly under varying meteorological and satellite sensor sensitivity paradigms. Furthermore, the vast range of spatio-temporal scales at which aerosol and cloud processes occur adds another dimension to the challenges faced when evaluating climate models. In this context, the aim of this study is two-fold. (1) We evaluate the most recent, significant changes in the representation of aerosol and cloud processes implemented in the EC-Earth3-AerChem model in the framework of the EU project FORCeS compared with its previous CMIP6 version (Coupled Model Intercomparison Project Phase 6; https://pcmdi.llnl.gov/CMIP6/, last access: 13 February 2019). We focus particularly on evaluating cloud physical properties and radiative effects, wherever possible, using a satellite simulator. We report on the overall improvements in the EC-Earth3-AerChem model. In particular, the strong warm bias chronically seen over the Southern Ocean is reduced significantly. (2) A statistical, maximum covariance analysis is carried out between aerosol optical depth (AOD) and cloud droplet (CD) effective radius based on the recent EC-Earth3-AerChem/FORCeS simulation to understand to what extent the Twomey effect can manifest itself in the larger spatio-temporal scales. We focus on the three oceanic low-level cloud regimes that are important due to their strong net cooling effect and where pollution outflow from the nearby continent is simultaneously pervasive. We report that the statistical covariability between AOD and CD effective radius is indeed dominantly visible even at the climate scale when the aerosol amount and composition are favourably preconditioned to allow for aerosol–cloud interactions. Despite this strong covariability, our analysis shows a strong cooling/warming in shortwave cloud radiative effects at the top of the atmosphere in our study regions associated with an increase/decrease in CD effective radius. This cooling/warming can be attributed to the increase/decrease in low cloud fraction, in line with previous observational studies.

Diagnosis of Atmospheric Processes from a Local Perspective for the Western North Pacific Summer Monsoon Onset

Abstract The climatological western North Pacific summer monsoon onset, a so-called convection jump, occurs around the 41st pentad, corresponding to an abrupt northeastward extension of strong convection. This study investigates the process of convection jump from a local perspective. Composite analyses are performed based on the onset dates that are identified in individual years. The results show that the convective inhibition (CIN) decreases dramatically around the onset dates, while the convective available potential energy (CAPE) reaches its maximum long before the onset, suggesting that the CIN, rather than CAPE, plays a dominant role in triggering convection. Further analysis indicates that the reduction of CIN is induced by the increased low-level relative humidity, which is the result of enhanced water vapor convergence. The moisture transportation is primarily contributed by the wind transfer from easterlies to southeasterlies or southerlies along the southern boundary of the convection jump region, in accordance with the monsoon trough establishment. The present observational results may be used to evaluate climate models in simulating the stepwise evolution of summer monsoon. Significance Statement The convection jump refers to rapid convection extension to the northeastern part of the Philippines Sea in late July and corresponds to the deepening of the monsoon trough and northward shift of the western North Pacific subtropical high. Therefore, the convection jump has important impacts on the East Asian summer monsoon. The crucial role of transient atmospheric adjustment in triggering the convection jump has been shown in previous studies. However, the reason for the temporal preference of convection jump remains unknown. This study further specifies the process of atmospheric adjustments around the convection jump from a local perspective and reveals that the transition from the easterlies to southeasterlies or southerlies decreases convective inhibition and thus favors the convection jump. The present results can be used to diagnose the ability of climate models to simulate the stepwise evolution of Asian–Pacific summer monsoon.

Estimating Uncertainty in Simulated ENSO Statistics

AbstractLarge ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

Paleoclimate data provide constraints on climate models' large-scale response to past CO2 changes

The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO2 change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO2 concentrations.

Evaluating climate models to analyze drought conditions in the western region of Bangladesh

Being susceptible to natural disasters, Bangladesh is one of the most disaster-prone countries. Among the various natural calamities, droughts are a frequent occurrence in the western region of the country. Hence, this study first compared the efficacy of five bias-corrected Regional Climate Models (RCMs) - MIROC, NOAA, MPI, IPSL, and CCCma - for the Western region of Bangladesh with observed monthly precipitation and subsequent SPI values. Various evaluation methods- RMSE, Taylor Diagram, Mann–Whitney U Test, and t-test, were applied to precipitation and SPI3 values for the historical base period. Through these analyses, the MIROC model exhibited the highest level of accuracy. Accordingly, future projections for short-term droughts (SPI-3) and their characteristics were conducted using the ensemble of top three climate model under the RCM 8.5 W/m2 scenario. Short-term droughts are anticipated to become less frequent and severe in the 2060s and 2080s compared to the 2020s. Nonetheless, North-West region is projected to be more drought-prone than South-West until 2100. This research shows the importance of evaluating the better-fitting RCMs for assessing historical droughts and making reliable projections for the future. The methodology and findings can be employed in evidence-based decision-making and applied in other drought-prone areas to understand future drought risks.

Diurnal and seasonal dynamics of the canopy‐layer urban heat island of Kuwait

AbstractThis study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHICL) of Kuwait City. Observations collected at 12 locations across the country of Kuwait and for the period 2010–2022 are analysed on an hourly and 3‐hourly basis to provide monthly and diurnal insights of the city's UHICL characteristics. Research on Kuwait's UHICL was first conducted by Nasrallah et al. (International Journal of Climatology, 10, 401–405). Results presented here have been afforded the benefit of additional stations and more extensive data compared with the earlier study. Mean positive UHICL intensities, ranging from 1.1°C to 3.8°C at night, are observed consistently across all months, owing to the prevalence of clear skies from winter to summer. Negative UHICL intensities, indicating a typical daytime urban cool island (UCICL), are most prominent on summer days, exhibiting a mean hourly magnitude range between 0.6°C and 2.6°C that extends into the early parts of the evening. Heat and cool island effects are maintained up to wind speeds approaching 10 m s−1 at the urban periphery. A coastal site near the city demonstrates strong influences of the Arabian Gulf temperatures and associated sea and land breeze effects on UHICL development. The results can be used for comparison with other desert locales, in the evaluation of urban climate models, for urban planning policies and improving local weather forecasts. This study honours in memoriam Dr. Hassan Nasrallah, who produced the first UHICL study in the Arab World.

Processes and principles for producing credible climate change attribution messages: lessons from Australia and New Zealand

Extreme event attribution (EEA) information is increasingly in demand from climate services. EEA messages can: raise awareness about the effect climate change has already imposed, inform climate change liability conversations, and be combined with climate projections to inform adaptation. However, due to limitations in observations, models and methods, there are barriers towards operationalising EEA in practice. Operational services will need EEA to be done transparently and using preset formats. Here we review recent experience and practice in EEA in Australia and New Zealand with a view to inform the design of an EEA component of climate services. We present a flow chart of the processes involved, noting particular care is needed on the trigger, event definition, and climate model evaluation, with effective stage gates. We also promote the use of tailored causal network diagrams as a standard tool to inform an EEA study and communicate results, with particular care needed for messages on events with lower confidence or complex sets of influences, including tropical cyclones and extratropical cyclones. We suggest that extending EEA to impact attribution is essential for making EEA messages salient but requires an uplift in forming interdisciplinary teams and in granular exposure and vulnerability datasets and is likely to raise new interdisciplinary methodological questions. Finally, we suggest communication of EEA messages can learn more from its origins in medical epidemiology.

Performance evaluation and ranking of CMIP6 global climate models over upper blue nile (abbay) basin of Ethiopia

The use of Global Climate Models (GCMs) data is the most practical way to conduct studies on climate science. However, performance evaluation and the selection of appropriate GCMs are vital. In this research, the effectiveness of eight selected CMIP6 GCMs in simulating the annual and seasonal rainfall observed over the Ethiopian Upper Blue Nile Basin from 1988 to 2014 was assessed. Five performance metrics (PMs) were used in the study: the correlation coefficient, root mean square error, bias percentage, Kling-Gupta efficiency and Nash-Sutcliffe efficiency. The scores of the various PMs were then combined into one, and the CMIP6 GCMs were ranked using Compromised Programming (CP). The findings from the CP were verified using a spatial, Taylor Diagram (TD), and areal average annual and seasonal evaluations. Even though the PMs produced some contradicting results, the study exhibited that CP was capable to evaluate the CMIP6 GCMs consistently. A regional evaluation of the CMIP6 GCMs relative to the observed data revealed that the best-ranked CMIP6 GCMs by using CP were capable to more accurately replicate the observed annual and seasonal rainfall. The lowest-ranking CMIP6 GCMs were found to have either spatially overvalued or undervalued the amount of rainfall over the basin. The best three CMIP6 GCMs for annual rainfall, according to the results of the CP method, are BCC-CSM2-MR, MIROC6, and NorESM2-MM; for the Kiremt season, the best CMIP6 GCMs are BCC-CSM2-MR, GISS-E2-2-G, and EC-Earth3. INM-CM5-0, MIROC6, and MRI-ESM2-0 ranked highest for Bega season, and EC-Earth3, BCC-CSM2-MR, and MRI-ESM2-0 for Belg season. It is recommended using the above-ranked CMIP6 GCMs to predict the characteristics of rainfall in the UBNB. Furthermore, results suggest that the CMIP6 GCMs be evaluated with a range of PMs across the whole temporal scales and that techniques such as CP be used to identify the best-performing CMIP6 GCMs.

Bayesian Structure Learning for Climate Model Evaluation

AbstractA Bayesian structure learning approach is employed to compare and contrast interactions between the major climate teleconnections over the recent past as revealed in reanalyses and climate model simulations from leading Meteorological Centers. In a previous study, the authors demonstrated a general framework using homogeneous Dynamic Bayesian Network models constructed from reanalyzed time series of empirical climate indices to compare probabilistic graphical models. Reversible jump Markov Chain Monte Carlo is used to provide uncertainty quantification for selecting the respective network structures. The incorporation of confidence measures in structural features provided by the Bayesian approach is key to yielding informative measures of the differences between products if network‐based approaches are to be used for model evaluation, particularly as point estimates alone may understate the relevant uncertainties. Here we compare models fitted from the NCEP/NCAR and JRA‐55 reanalyses and Coupled Model Intercomparison Project version 5 (CMIP5) historical simulations in terms of associations for which there is high posterior confidence. Examination of differences in the posterior probabilities assigned to edges of the directed acyclic graph provides a quantitative summary of departures in the CMIP5 models from reanalyses. In general terms the climate model simulations are in better agreement with reanalyses where tropical processes dominate, and autocorrelation time scales are long. Seasonal effects are shown to be important when examining tropical‐extratropical interactions with the greatest discrepancies and largest uncertainties present for the Southern Hemisphere teleconnections.

Influence of GIA Uncertainty on Climate Model Evaluation With GRACE/GRACE‐FO Satellite Gravimetry Data

AbstractGlobal coupled climate models are in continuous need for evaluation against independent observations to reveal systematic model deficits and uncertainties. Changes in terrestrial water storage (TWS) as measured by satellite gravimetry missions GRACE and GRACE‐FO provide valuable information on wetting and drying trends over the continents. Challenges arising from a comparison of observed and modelled water storage trends are related to gravity observations including non‐water related variations such as, for example, glacial isostatic adjustment (GIA). Therefore, correcting secular changes in the Earth's gravity field caused by ongoing GIA is important for the monitoring of long‐term changes in terrestrial water from GRACE in particular in former ice‐covered regions. By utilizing a new ensemble of 56 individual realizations of GIA signals based on perturbations of mantle viscosities and ice history, we find that many of those alternative GIA corrections change the direction of GRACE‐derived water storage trends, for example, from gaining mass into drying conditions, in particular in Eastern Canada. The change in the sign of the TWS trends subsequently impacts the conclusions drawn from using GRACE as observational basis for the evaluation of climate models as it influences the dis‐/agreement between observed and modelled wetting/drying trends. A modified GIA correction, a combined GRACE/GRACE‐FO data record extending over two decades, and a new generation of climate model experiments leads to substantially larger continental areas where wetting/drying trends currently observed by satellite missions coincide with long‐term predictions obtained from climate model experiments.

Atmospheric Rivers in the Eastern and Midwestern United States Associated With Baroclinic Waves

AbstractAtmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

Non-uniform changes of daily precipitation in China: Observations and simulations

Daily precipitation of different intensities is expected to change differently in response to global warming. Based on station observations and simulations from the latest climate models, we investigated the non-uniform features of changes in daily precipitation frequency, intensity and amount over China. Results show that western China experiences an overall wetting trend across the spectrum of precipitation intensity, while eastern China exhibits negative trends in light-to-moderate precipitation and positive trends in heavy-to-extreme precipitation with respect to precipitation frequency and amount. Changes in precipitation intensity do not show a spatially consistent pattern of intensification in most intensity spectra, but exhibit the most pronounced intensification in heavy-to-extreme precipitation. Interestingly, changes in precipitation frequency dominate changes in the amount of precipitation for each intensity level, particularly for the spatial patterns. Although climate models show limited skills in reproducing the magnitudes of these observed changes, they show skills in simulating the sign of the changes. Also, they reasonably reproduce the observed non-uniform patterns of daily precipitation changes, especially for changes in the contributions from different intensity levels to annual total precipitation on average over the whole country. The evaluation of current climate models in simulating daily precipitation changes as a function of precipitation intensity suggests that improvement in the detection and attribution of precipitation changes in China can be gained by dividing daily precipitation into different categories.

Evaluation of ERA5 and NCEP reanalysis climate models for precipitation and soil moisture over a semi-arid area in Kuwait

Evaluation of ERA5 and NCEP reanalysis climate models for precipitation and soil moisture over a semi-arid area in Kuwait

A new diagram for performance evaluation of complex models

In the latest years the capacity and complexity of climate and environmental modeling has increased considerably. Therefore, tools and criteria for model performance evaluation are needed to ensure that different users can benefit from model selection. Among graphical tools, Taylor’s diagram is widely used to provide evaluation and comparison of model performances, with particular emphasis on climate models. Taylor’s diagram accounts for different statistical features of model outputs and observations, including correlation, variability and centered root mean square error. Not included is model bias, which is an essential feature for climate model evaluations, and it is usually calculated separately to complement the information embedded in Taylor’s diagram. In this paper a new diagram is proposed, referred to as Aras’ diagram, which allows for visual assessments of the correspondence between model outputs and reference data in terms of total error, correlation, as well as bias and variability ratios through an easy-to-interpret two-dimensional (2D) plot, allowing for proper weighting of different model features. The strengths of the new diagram are exemplified in a case study of performance evaluation of EURO-CORDEX historical experiment over Southern Italy using E-OBS as reference dataset, for three hydrological variables (i.e. daily precipitation, daily surface minimum temperature, and daily maximum surface temperature), and four popular climate indices (i.e. total annual precipitation, annual maxima of daily precipitation, annual minima of daily minimum temperatures, and annual maxima of daily maximum temperatures). The proposed diagram shows interesting properties, in addition to those already included in Taylor’s diagram, which may help promoting climate model evaluations based on their accuracy in reproducing the climatological patterns observed in time and space.

Lessons Learned from the Updated GEWEX Cloud Assessment Database

AbstractSince the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies.

The Dynamical Footprint of Year‐Round North American Weather Regimes

AbstractWeather regimes have been defined over multiple regions and used in a range of practical applications, including subseasonal‐to‐seasonal forecasting and climate model evaluation. Despite their widespread use, the extent to which regimes reflect physical modes of the atmosphere is seldom investigated. Here, we adopt a year‐round classification of four North American weather regimes, with a fifth “no regime” class, and leverage dynamical systems theory to investigate their dynamical properties. We find that when the atmospheric flow is assigned to a regime, it displays persistent characteristics and a lifecycle‐like temporal evolution. We further find that, regardless of season, these characteristics are enhanced when the atmospheric flow displays a comparatively strong projection onto the cluster‐mean of the regime to which it is assigned (while the reverse is true for a weaker projection). We interpret these results as evidence that the four North American weather regimes are physically‐meaningful, with a clear dynamical footprint.

A novel methodology for calculating the aerosol direct radiative effect using a non-uniform five-dimensional lookup table

ABSTRACT The quantification of aerosol direct radiative effects (ADREs) under clear sky conditions is essential for understanding the influence of aerosols on climate. This paper introduces a novel approach, the non-uniform five-dimensional lookup table (NU-5D-LUT) method, for calculating the ADREs under clear sky conditions. The NU-5D-LUT method considers key parameters such as aerosol optical thickness, single scattering albedo, asymmetry factor, surface albedo, and solar zenith angle. Validation against the SBDART model shows strong agreement, with correlation coefficients of 0.97 and 0.99 for TOA and BOA ADREs, respectively. Global comparisons demonstrate consistent ADRE distributions, with relative errors below 5.8% for TOA ADRE and 4.4% for BOA ADRE. The NU-5D-LUT method significantly reduces computation time, offering a practical solution for large-scale simulations and climate model evaluations in aerosol research.