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This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses. Plain Language Summary: This research examines how well a kilometer grid scale global atmospheric model—the Simple Cloud‐Resolving Energy Exascale Earth System Model (SCREAMv0)—performs in simulating clouds and rainfall over the Amazon rainforest region. The model was assessed by comparing to high‐resolution ground‐based observations from the Green Ocean Amazon field campaign supported by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. The model struggles to produce enough middle‐level clouds. When comparing the simulated rainfall to radar observations, SCREAMv0 showed good performance on the diurnal pattern of rain rate, but tends to form too many small rain clusters while failing to create large ones. A possible contributor to these errors could be the inaccurate depiction of how the earth's surface and the atmosphere interact within the model. Overall, this study shows that using detailed DOE ARM data can help improve our understanding of clouds and rainfall in global storm resolving kilometer grid scale models. Key Points: Convective processes in a global storm resolving model (SCREAMv0) are evaluated using ground‐based observations over a tropical rainforestSCREAMv0 captures the morning development of shallow convection and the early afternoon precipitation peak but lacks mid‐level congestusSCREAMv0 struggles to form large precipitation clusters greater than 128 km and produces smaller ones more often than observed [ABSTRACT FROM AUTHOR]

Present study examined the overall performance of 12 CMIP6 GCMs for rainfall, maximum and minimum temperatures for rice crop-growing seasons i.e., Boro (January to May) and Kharif (June to October) over the new alluvial zone of West Bengal. A wide range of indices i.e., index of agreement, error indices and bias estimators were utilized to put more confidence on the results. Results indicated that CMIP6 models were able to reproduce observed mean climatology and inter-annual variability of maximum and minimum temperature adequately for both seasons while a smaller number of models (3-4 models) out of a total of 12 GCM-CMIP6 models showed satisfactory performance for rainfall. The ranks assigned to the models revealed that CNRM–ESM2–1 was the best-performing model for Kharif and MRI-ESM2-0 showed the highest skill for Boro. ACCESS-CM2 and MPI-ESM1-2-LR performed worst for Kharif and Boro seasons respectively. Further, CNRM–ESM2–1 and MRIESM2-0 were used to project the future climate for Kharif and Boro seasons respectively under both moderate (SSP2-4.5) and extreme scenarios (SSP5-8.5). Higher warming was projected during Boro season than Kharif. Projections revealed increasing rainfall during Kharif season but decreasing rainfall in Boro season in both the moderate and extreme future scenarios. [ABSTRACT FROM AUTHOR]

In order to evaluate the simulation effect of the model on the dynamic characteristics of the climate system, the approximate entropy method was used to analyze the complexity of surface air temperature (SAT) based on observational data (CN05.1) and the model-simulated data by the Beijing Climate Central Climate Model (BCC-CSM2-MR) in this study. We investigated whether the simulated climate model data exhibited complexity characteristics similar to real-world meteorological elements. In this scenario, we could assess the climate model's performance by comparing the complexity of observed and simulated data. The results indicate that the BCC-CSM2-MR model can accurately reflect the complexity of SAT in China. When considering the entire year, the model exhibited greater complexity in the simulated SAT than the observations in most regions of China, indicating an overestimation of complexity by the model. When examining different seasons, the model's complexity was similar to the observations in spring, summer, and winter, indicating a better performance during these seasons. In contrast, the simulated performance is poor during autumn, mainly due to an overestimation of complexity in Qinghai, south-central Xinjiang, and western Xizang. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: This study provides an updated and rigorously tested method for evaluating the performance of climate models for applications relevant to water managers and other stakeholders. Using traditional metrics of climate model performance, both regional and global, as well as newly developed metrics based on processes important for the simulation of precipitation, we have created a generalizable, systematic, and succinct method for reducing the number of models to be considered for climate change impact applications. By reducing the number of relevant models to around 20% of the total models and not having a significant impact on future temperature and precipitation trend projections in doing so, we strongly reduce the computational effort needed to gain a realistic simulation of the future climate for a given regional impact. This method is tested with a variety of statistical tests and found to be reliable for our application over the Pacific Northwest United States. Key Points: A method for evaluation of climate models over the PNW using a combination of global and regional metrics has been developedThis allows for a reduced envelope of ESMs for impact applications without significantly affecting the future trend projections [ABSTRACT FROM AUTHOR]

A realistic representation of top‐of‐the‐atmosphere (TOA) radiation response to surface warming is key for trusting climate model projections. We show that coupled models with freely evolving ocean‐atmosphere interactions systematically underestimate the observed global TOA radiation trend during 2001–2022 in 552 simulations. Locally, even if a simulation spontaneously reproduces observed surface temperature trends, TOA radiation trends are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale surface warming pattern and from errors in the atmospheric physics affecting short‐ and longwave radiation. Models with a better representation of the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity. Our bias metric is a novel process‐based approach which links a model's current response to climate change to its behavior in the future. Plain Language Summary: A realistic representation of the radiation balance at the top of the Earth's atmosphere (TOA) by coupled climate models is essential for trust in future climate projections. Despite that relevance, it is still not clear whether the models correctly simulate the coupling between surface warming and TOA radiation because of the short observational record. We show that climate models systematically underestimate the observed increase in global TOA radiation during 2001–2022 in 552 simulations. Locally, even if a simulation reproduces observed changes in surface temperature, changes in TOA radiation are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale patterns of surface warming and from errors in the atmospheric physics which suppress the communication of the surface information to the TOA. Models that better represent the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity, that is, a weak global‐mean surface warming in response to a doubling of the atmospheric CO2 concentration above pre‐industrial levels. Our new bias metric links a model's current response to climate change to its behavior in the future. Key Points: Climate models underestimate the observed global TOA radiation trend during 2001–2022Surface warming patterns and atmospheric physics matter for the observation‐model discrepancyModels with a small bias in the coupling between surface warming and TOA radiation trends have a relatively low climate sensitivity [ABSTRACT FROM AUTHOR]

Vertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) is a key link in coupling the earth's surface and upper atmosphere. This process is usually quantified by numerical simulations, while its reliability is not well assessed until now. Using space‐time intensified ABL observations, we evaluate the ABL‐FT air mass exchange flux derived from the Weather Research and Forecast (WRF) model. A six‐site sounding experiment is conducted in the North China Plain during the wintertime of 2019. The measured data is processed to provide enough information to derive the vertical exchange flux corresponding to the model‐based result, so that a systematic comparison is conducted. Three physical processes involved in ABL‐FT vertical exchange are quantitatively evaluated, that is, temporal variation of ABL height, advection across the inclined ABL top, and vertical motion at the ABL‐FT interface. Results show that the model‐based and observation‐based fluxes are generally agreed in temporal evolution (R = 0.67, p < 0.01), both characterized by 4–6 days periodicity and diurnal cycle. Their relative mean error was about 45% during the whole study period, mainly stemming from the vertical motion term and the advection crossing term. The model inaccuracy in representing these relevant processes at the ABL top is largely responsible for the discrepancy. Besides, the difference may also be attributed to the observational uncertainty (∼22%) that is caused by the measurement's difficulties in determining ABL spatial variation and acquiring vertical velocity. Through this study, the credibility and limitation of the WRF model in deriving ABL‐FT exchange flux are quantified. Plain Language Summary: The vertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) plays a crucial role in atmospheric matter transport and energy flow, affecting weather systems, atmospheric chemistry, air pollution and climate change. This process is usually quantified by numerical simulation. This work aims to evaluate the reliability of the air exchange flux derived from a meteorological model, using comprehensive observation data in 1 month. The result shows that the model‐based exchange flux can reproduce the temporal evolution of the observation‐based exchange flux, including 4–6 days periodicity and diurnal variation. The overall relative difference between simulated and observed fluxes is about 45% during the whole study period. A more detailed comparison of each component term is conducted to investigate the causes of the discrepancy. We found that the difference is partly attributed to the model inaccuracy in representing the ABL top spatial variation and the vertical velocity at this layer. Additionally, a 22% uncertainty in observation‐based flux introduced by the relevant data processing is also an important reason. The evaluation result provides a quantitative reference to understand the biases in the meteorological model and their impact on ABL‐FT vertical exchange simulation study. Key Points: The air exchange flux between the boundary layer and free troposphere is reproduced by the WRF model with synoptic and diurnal variationsThe model‐derived exchange flux deviates from observation‐derived flux with a relative difference of 45%The deviation is mainly attributed to simulation‐observation differences in vertical motion and spatial gradient at the boundary layer top [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) exhibits a substantial impact on sea surface height (SSH), and some climate models are significantly biased in reproducing the observed ENSO impact on SSH. This study aims to assess the performance of 26 Coupled Model Intercomparison Project Phase 6 (CMIP6) models for global SSH response to ENSO using their historical simulations from 1958 to 2014 during December‒February (DJF). The observation data for SSH and sea surface temperature (SST) are derived from Ocean Reanalysis System 5 (ORAS5). The fidelity of models is evaluated based on skill metric parameters such as root mean square error, interannual variability skill score, and M-Score. In CMIP6 models, El Niño plays a dominant role to simulate enhanced SSH across eastern Pacific Ocean (Region I) and southwest tropical Indian Ocean (Region III). In contrast, El Niño induces cooler SSTs over the region bounded between 100°E–133°E, 5°S–20°N (Region II), which is further associated with reduced SSH. The inter-model relationship of ENSO amplitude with the area mean regression coefficient for SSH and SST is investigated for these three hotspot regions. Overall, models with stronger ENSO amplitude simulate higher values of SSH and SST over Region I, III, and weaker amplitude over Region II. [ABSTRACT FROM AUTHOR]

Both the attribution of historical change and future projections of droughts rely heavily on climate modeling. However, reasonable drought simulations have remained a challenge, and the related performances of the current state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) models remain unknown. Here, both the strengths and weaknesses of CMIP6 models in simulating droughts and corresponding hydrothermal conditions in drylands are assessed. While the general patterns of simulated meteorological elements in drylands resemble the observations, the annual precipitation is overestimated by ∼33% (with a model spread of 2.3%–77.2%), along with an underestimation of potential evapotranspiration (PET) by ∼32% (17.5%–17.2%). The water deficit condition, measured by the difference between precipitation and PET, is 50% (29.1%–71.7%) weaker than observations. The CMIP6 models show weaknesses in capturing the climate mean drought characteristics in drylands, particularly with the occurrence and duration largely underestimated in the hyperarid Afro-Asian areas. Nonetheless, the drought-associated meteorological anomalies, including reduced precipitation, warmer temperatures, higher evaporative demand, and increased water deficit conditions, are reasonably reproduced. The simulated magnitude of precipitation (water deficit) associated with dryland droughts is overestimated by 28% (24%) compared to observations. The observed increasing trends in drought fractional area, occurrence, and corresponding meteorological anomalies during 1980–2014 are reasonably reproduced. Still, the increase in drought characteristics, associated precipitation and water deficit are obviously underestimated after the late 1990s, especially for mild and moderate droughts, indicative of a weaker response of dryland drought changes to global warming in CMIP6 models. Our results suggest that it is imperative to employ bias correction approaches in drought-related studies over drylands by using CMIP6 outputs. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are widely used to assess future impacts associated with climate change at regional and local scales. RCMs must represent relevant climate variables in the present‐day climate to be considered fit‐for‐purpose for impact assessment. This condition is particularly difficult to meet over complex regions such as the Andes‐Amazon transition region, where the Andean topography and abundance of tropical rainfall regimes remain a challenge for numerical climate models. In this study, we evaluate the ability of 30 regional climate simulations (6 RCMs driven by 10 global climate models) to reproduce historical (1981–2005) rainfall climatology and temporal variability over the Andes‐Amazon transition region. We assess spatio‐temporal features such as spatial distribution of rainfall, focusing on the orographic effects over the Andes‐Amazon "rainfall hotspots" region, and seasonal and interannual precipitation variability. The Eta RCM exhibits the highest spatial correlation (up to 0.6) and accurately reproduces mean annual precipitation and orographic precipitation patterns across the region, while some other RCMs have good performances at specific locations. Most RCMs simulate a wet bias over the highlands, particularly at the eastern Andean summits, as evidenced by the 100%–2,500% overestimations of precipitation in these regions. Annual cycles are well represented by most RCMs, but peak seasons are exaggerated, especially at equatorial locations. No RCM is particularly skillful in reproducing the interannual variability patterns. Results highlight skills and weaknesses of the different regional climate simulations, and can assist in the selection of regional climate simulations for impact studies in the Andes‐Amazon transition zone. Plain Language Summary: Regional climate models (RCMs) are useful numerical tools to investigate future climate change impacts (e.g., future water availability, frequency of floods and droughts, regional warming). Regarding regional scale, RCMs are expected to perform better than global climate models due to finer spatial resolution. However, in the Andes‐Amazon transition region, assessing the performance of RCMs is challenging due to complex terrain and scarcity of observations. This region is of critical importance for the water cycle of local and regional ecological systems, but has been often overlooked in RCM assessments. Here, we evaluate how 30 regional climate simulations perform in representing precipitation regional contrasts, wet‐dry seasons, and year‐to‐year changes over the Andes‐Amazon transition region. We find that models perform differently over specific regions, with prominent overestimations at high altitudes by most RCMs. However, Eta RCM has the best performance regarding regional patterns of precipitation and its wet‐dry fluctuations. Besides overestimations during austral summer and spring, wet‐dry seasonal fluctuations are well simulated by most RCMs, but none excels in representing wet‐dry yearly fluctuations. Strengths and weaknesses of different regional climate simulations are shown, and can help choose the most appropriate simulations for distinct impact studies in this region. Key Points: Precipitation output from 30 regional climate simulations is assessed over the Andes‐Amazon in terms of climatology and temporal variabilityThe spatio‐temporal behavior of seasonality is well reproduced by most simulations, with overestimations during austral summer and springWhile orographic precipitation is a major challenge for most regional climate models, Eta satisfactorily reproduces climate patterns in the Andes‐Amazon region [ABSTRACT FROM AUTHOR]

Thermal processes on the Tibetan Plateau (TP) influence atmospheric conditions on regional and global scales. Given this, previous work has shown that soil moisture–driven surface flux variations feed back onto the atmosphere. Whilst soil moisture is a source of atmospheric predictability, no study has evaluated soil moisture–atmosphere coupling on the TP in general circulation models (GCMs). In this study, we use several analysis techniques to assess soil moisture-atmosphere coupling in CMIP6 simulations including: instantaneous coupling indices; analysis of flux and atmospheric behaviour during dry spells; and a quantification of the preference for convection over drier soils. Through these metrics we partition feedbacks into their atmospheric and terrestrial components. Consistent with previous global studies, we conclude substantial inter-model differences in the representation of soil moisture–atmosphere coupling, and that most models underestimate such feedbacks. Focusing on dry spell analysis, most models underestimate increased sensible heat during periods of rainfall deficiency. For example, the model-mean bias in anomalous sensible heat flux is 10 W m−2 (≈25%) smaller compared to observations. Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response. We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection. The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture–driven predictability. To improve the representation of land–atmosphere feedbacks requires developments in not only atmospheric modelling, but also surface processes, as we find weak relationships between rainfall biases and coupling indexes. [ABSTRACT FROM AUTHOR]

For this paper, a new global atmospheric model (Global-to-Regional Integrated forecast SysTem; GRIST) with improved sub-grid scale orographic parameterization was verified and assessed, with an emphasis on the precipitation caused by typhoons. Four typical typhoon cases were selected for the verification of the model. The results indicate that, compared to the control experiments, the sensitivity experiments consistently simulated the trends in the three-hour cumulative precipitation changes and the high-value regions of total precipitation better. However, the improved experiments only had an ameliorating effect on the cumulative precipitation modelling biases for Typhoon LEKIMA and Typhoon HAGUPIT, not all of them. Precipitation bias is smaller on flat land than that on mountainous land, but the precipitation bias on windward/leeward slopes depends on the typhoon case. Precipitation modelling accuracy varies considerably between flat and mountainous terrain but very little between windward and leeward slopes. The precipitation simulation is poor for all terrains, with large precipitation thresholds in three typhoon cases, but for Typhoon HOTA, after improving the terrain, the model has the ability to forecast the heavy rainfall scenarios of the mountainous terrain. [ABSTRACT FROM AUTHOR]

Many studies have shown that the climate system has the long‐range correlation (LRC) characteristics, which provide a good test bed for evaluating the performance of climate models. In this study, the LRC characteristics of the observed surface daily temperature data in China is first analysed by detrended fluctuation analysis. It is found that the daily temperature records, including daily mean temperature (Tmean), daily maximum temperature (Tmax), and daily minimum temperature (Tmin), all exhibit LRC characteristics with scaling exponents more than 0.6 in most of China. Then, the performances of two different versions for the Beijing Climate Center Climate System Model (BCC‐CSM), namely, BCC‐CSM1.1m in CMIP5 and BCC‐CMS2‐MR in CMIP6, are quantitatively evaluated by comparing the differences of the LRC characteristics between the observations and the simulations. The results indicate that BCC‐CSM2‐MR performs better than BCC‐CSM1.1(m) in western Northwest China, most of south of the middle and lower reaches of Yangtze River, North China, central Inner Mongolia for the daily Tmean and Tmax. As far as the daily Tmin is concerned, BCC‐CSM1.1(m) performs better than BCC‐CSM2‐MR in western South China and southern Xinjiang, and the performance of BCC‐CSM2‐MR in CMIP6 has not been significant improved. Compared with BCC‐CSM1.1(m), BCC‐CSM2‐MR cannot significantly improve the simulations for the LRC characteristics of the three daily temperature variables in central and eastern Qinghai‐Tibet plateau, and the areas of between lower reaches of Yellow River and Yangtze River. [ABSTRACT FROM AUTHOR]

Local, short‐duration extreme precipitation events can cause floodings and have massive economic consequences. The climate change impact on such events is of great interest, but due to the small spatio‐temporal scales involved, these are challenging to properly represent in climate models. This study analyses a new sub‐kilometre (750 m) HARMONIE‐Climate model simulation driven by ERA5 reanalysis data. Three convection‐permitting models at 750 m, 3 and 5 km grid distance are analysed and compared with driving reanalyses, intermediate model simulations and a dense rain gauge network. The representation of convective events is analysed by a range of metrics categorised as spatial, temporal and event‐focused. Precipitation events are analysed at both hourly and sub‐hourly scales and a clear difference between model performance on these scales is found. Overall, we find a better performance for HCLIM750m for most metrics, yet the added benefits of the computationally intensive sub‐kilometre scale simulation seems limited compared to the convection‐permitting models at 3 and 5 km. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are a major natural hazard and cause significant socio‐economic damages. Precipitation events are spatially extended and, thus, can cause large water accumulations, which can lead to flooding events. In order to help design flood protection infrastructure, a detailed investigation of the temporal and spatial dependencies of extreme precipitation is essential. Here, we use a statistical spatial extremes framework to systematically study the historical and projected spatial–temporal characteristics of extreme precipitation in Germany. For this purpose, we use data from 10 high‐resolution global climate model‐regional climate model (GCM‐RCM) combinations from the EURO‐CORDEX initiative and derive a statistical spatial extremes precipitation model. Our results show that there are large spreads in reproducing the temporal–spatial characters of extreme precipitation. Few climate simulations can well present the temporal clustering of observed extreme precipitation in both summer and winter. In reproducing the spatial dependencies of the observations, most GCM‐RCM combinations behave well in summer, while in winter most RCMs produce too many spatially localized extreme precipitation events. The derived statistical model, which accounts for both the spatial and temporal variability, performs well in representing the spatial dependency and intensity characteristics in summer. Furthermore, global warming will have a significant impact on the temporal and spatial dependencies of extreme precipitation in Germany. There will be more temporal‐dependent and homogeneous extreme precipitation in summer; and more temporal‐independent and localized extreme precipitation in winter. The intensity quantified by the 25‐year return level of the 10 GCM‐RCM combinations is increasing; with relative changes ranging from 5.33% to 53.24% in summer and from −15.38% to 32.33% in winter under RCP8.5. A future projection by our statistical spatial extremes model using projected temperature from GCM‐RCM combinations as a covariate shows that the 25‐year return level will increase by 3.02% under RCP2.6 and 4.16% under RCP8.5 in winter. [ABSTRACT FROM AUTHOR]

The Southern Ocean (SO) clouds exert a significant influence on the Earth's radiation budget. We analyzed ship‐based remote sensing observations of SO clouds over a 5‐month long Department of Energy (DOE) ARM Measurements of Aerosols, Radiation and Clouds over the Southern Oceans (MARCUS) field campaign to better understand cloud phase variability. We developed a method to classify eight categories of hydrometeors (ice, liquid, mixed phase, rain, drizzle, snow, aerosols, and clear sky) based on measurements of lidar, radar and radiosondes. Cloud thermodynamic phases (liquid, ice and mixed phase) at coarser scales were further derived to compare with the DOE Energy Exascale Earth System Model version 1 (E3SMv1) simulation and the Earth Model Column Collaboratory (EMC2) instrument simulator. For a scale‐aware comparison with climate model simulations, we found that spatially averaging the raw remote sensing data (e.g., backscatter and reflectivity) results in increased cloud cover and cloud liquid, whereas, directly averaging cloud phase from higher to lower resolution maintains clear air regions and is thus recommended for in‐cloud frequency comparisons. For cloud thermodynamic phases in stratiform clouds, the E3SMv1 underestimates cloud ice and overestimates cloud liquid at temperatures between −40°C and 0°C. When latitudes increase, both observations and simulations show a transition of dominant phase from liquid to ice for cloud tops as well as for the entire cloud columns, but the model underestimation of ice phase is more severe at higher latitudes. Such model bias is unlikely caused by spatial scale differences or lack of heterogeneity in cloud vertical structure in the simulation. Plain Language Summary: The Southern Ocean region of Earth consists of vast open waters with extensive cloud cover throughout the year. This cloud cover prevents the ocean water below it from absorbing solar heat and maintains global ocean and atmospheric circulation patterns. Thus, it is important to improve our understanding and our ability to model this cloud cover to better predict future changes in the Earth's climate. Here, we analyzed atmospheric data obtained by a ship traversing the open waters between Australia and Antarctica for nearly 5 months. We examined observed cloud properties such as cloud ice and liquid proportions and compared them with global climate model results. We found that the model simulates more liquid and less ice at lower temperatures when compared with observations. Such model bias becomes larger at higher latitudes, indicating that Earth's surface may absorb more solar radiation than the model estimates, especially around Antarctica. Both the observations and the model show that ice clouds are common at higher latitudes whereas liquid clouds are common at lower latitudes. Key Points: Cloud phase with respect to temperature and latitude over the Southern Ocean was identified using 5‐month ship‐based measurementsCompared with observed cloud phase, the E3SMv1 model underestimates ice and overestimates liquid phase between −40°C and 0°CObservations and simulations show similar transition from liquid to ice‐dominated cloud columns and cloud top layers from 43°S to 70°S [ABSTRACT FROM AUTHOR]

The Iberian Peninsula is a known climate change hotspot. In the last decades, the Coupled Model Intercomparison Project (CMIP) has allowed for thousands of Global Climate Model (GCM) simulations to be conducted, an important tool to assess and understand future changes in Earth's climate. The comparison of past future projections with observations provides a robust evaluation of the models' skill according to different emission scenarios. Here, a comprehensive performance assessment of GCM‐simulated mean, maximum, and minimum temperatures, and accumulated precipitation is conducted for Iberia, by retrospectively comparing historical simulations and past future projections from CMIPs 1 to 6 with reference datasets. From means to extremes, and multi‐year intra‐annual cycles to inter‐annual trends, GCM simulations are compared with Iberia0.1 and E‐OBS observational gridded datasets, and ERA5 reanalysis. The matching between the variables' distributions is assessed through the distribution added values (DAVs), a measure of gain or loss in performance between CMIPs. Results show relevant improvements in the description of the Iberian climate throughout the CMIP effort, for historical and past future periods. While the representation of intra‐annual cycles (inter‐annual trends) is enhanced after CMIP3 (CMIP2), GCMs from all CMIPs are generally able to depict the observed warming trend. Nevertheless, until 2021, a slight detrimental effect in the performance of CMIP6 models is found, in comparison with CMIP5 ones, with positive DAVs obtained only for past future temperature projections (less than 2%). A continuous monitorization of modelling accuracy for Iberia is needed, considering the increasing relevance of climate change information for adaptation strategies. [ABSTRACT FROM AUTHOR]

This study examines the deep convection populations and mesoscale convective systems (MCSs) simulated in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) winter project. A storm tracking algorithm is applied to six DYAMOND simulations and a global high‐resolution satellite cloud and precipitation data set for comparison. The simulated frequencies of tropical deep convection and organized convective systems vary widely among models and regions, although robust MCSs are generally underestimated. The diurnal cycles of MCS initiation and mature stages are well simulated, but the amplitudes are exaggerated over land. Most models capture the observed MCS lifetime, cloud shield area, rainfall volume and movement speed. However, cloud‐top height and convective rainfall intensity are consistently overestimated, and stratiform rainfall area and amount are consistently underestimated. Possible causes for the model differences compared to observations and implications for future model developments are discussed. Plain Language Summary: A new class of high‐resolution global atmosphere models is emerging for Earth system modeling. These new models can directly simulate convective storm systems and hold promises to improve the simulation of hydrological extremes such as flood‐producing rainfall and how they may change in future climate. This study assesses the fidelity of simulated convective storms from six global models against high‐resolution satellite observations. We find that the models simulate widely different frequency of convective storms in the tropics, but many do not produce storms that grow as large as observed. Several important aspects of observed storms such as the diurnal cycle, land‐ocean contrast, and storm rainfall amount are reasonably captured by the models; however, precipitation intensity is consistently overestimated and the storm rainfall area is too small. We further discussed potential causes for the model differences with observations and future model development needs. Key Points: Deep convective clouds and mesoscale convective systems are tracked in global convection‐permitting simulations and satellite observationsModels produce a diverse range of tropical deep convective systems and MCS frequencies and their proportions in key climate regionsModels reasonably simulate tropical MCS diurnal cycle and some MCS characteristics, but overestimate MCS precipitation intensity [ABSTRACT FROM AUTHOR]

The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action. [ABSTRACT FROM AUTHOR]

Bias correction (BC) is often a necessity to improve the applicability of global and regional climate model (GCM and RCM, respectively) outputs to impact assessment studies, which usually depend on multiple potentially dependent variables. To date, various BC methods have been developed which adjust climate variables separately (univariate BC) or jointly (multivariate BC) prior to their application in impact studies (i.e., the component-wise approach). Another possible approach is to first calculate the multivariate hazard index from the original, biased simulations, and bias-correct the impact model output or index itself using univariate methods (direct approach). This has the advantage of circumventing the difficulties associated with correcting the inter-variable dependence of climate variables which is not considered by univariate BC methods. Using a multivariate drought index (i.e., SPEI) as an example, the present study compares different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) applied to climate model simulations stemming from different experiments at different spatial resolutions (namely CORDEX, CORDEX-CORE and CMIP6). The BC methods are calibrated and evaluated over the same historical period (1986-2005). The proposed framework is demonstrated as a case study over a transboundary watershed, i.e. the Upper Jhelum Basin (UJB) in the Western Himalaya. Results show that (1) there is some added value of multivariate BC methods over the univariate methods in adjusting the inter-variable relationship, however, comparable performance is found for SPEI indices. (2) The best performing BC methods exhibits a comparable performance under both approaches with a slightly better performance for the direct approach. (3) The added value of the high-resolution experiments (CORDEX-CORE) compared to their coarser resolution counterparts (CORDEX) are not apparent in this study. [ABSTRACT FROM AUTHOR]

Bias correcting and downscaling climate model simulations requires reconstructing spatial and intervariable dependences of the observations. However, the existing univariate bias correction methods often fail to account for such dependences. While the multivariate bias correction methods have been developed to address this issue, they do not consistently outperform the univariate methods due to various assumptions. In this study, using 20 state-of-the-art coupled general circulation models (GCMs) daily mean, maximum and minimum temperature (Tmean, Tmax and Tmin) from the Coupled Model Intercomparison Project phase 6 (CMIP6), we comprehensively evaluated the Super Resolution Deep Residual Network (SRDRN) deep learning model for climate downscaling and bias correction. The SRDRN model sequentially stacked 20 GCMs with single or multiple input-output channels, so that the biases can be efficiently removed based on the relative relations among different GCMs against observations, and the intervariable dependences can be retained for multivariate bias correction. It corrected biases in spatial dependences by deeply extracting spatial features and making adjustments for daily simulations according to observations. For univariate SRDRN, it considerably reduced larger biases of Tmean in space, time, as well as extremes compared to the quantile delta mapping (QDM) approach. For multivariate SRDRN, it performed better than the dynamic Optimal Transport Correction (dOTC) method and reduced greater biases of Tmax and Tmin but also reproduced intervariable dependences of the observations, where QDM and dOTC showed unrealistic artifacts (Tmax < Tmin). Additional studies on the deep learning-based approach may bring climate model bias correction and downscaling to the next level. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the performance of global climate models (GCMs) from the family of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and select the best performing GCMs for future projection of precipitation in Pakistan under multiple shared socioeconomic pathways (SSPs). The spatiotemporal performance of GCMs was evaluated against the Climate Research Unit (CRU) data in simulating annual precipitation during 1951–2014, using the Taylor diagram and interannual variability skill (IVS). Moreover, the modified Mann–Kendall (mMK) and Sen's slope estimator (SSE) tests were employed to estimate significant trends in future precipitation for the period 2015–2100. Based on the comprehensive ranking index (CRI), the HadGEM3‐GC31‐MM model has the highest skill in simulating precipitation distributions followed by EC‐Earth3‐Veg‐LR, CNRM‐ESM2‐1, MPI‐ESM1‐2‐HR, CNRM‐CM6‐1, MRI‐ESM2‐0, CNRM‐CM6‐1‐HR, EC‐Earth3‐Veg, MCM‐UA‐1‐0, INM‐CM5‐0, KACE‐1‐0‐G, CAMS‐CSM1‐0, and HadGEM3‐GC31‐LL models. Furthermore, the projections of the best models ensemble mean (BMEM) showed that the study region will experience a substantial increase in precipitation under SSP3‐7.0 and SSP5‐8.5 but an indolent rise under SSP1‐2.6 and SSP2‐4.5 scenarios. The summer and annual precipitations exhibit a statistically significant increasing trend relative to the winter season under most scenarios. Moreover, the magnitude of monotonic trends in seasonal and annual precipitation progresses from low forcing scenario (SSP1‐2.6) to high forcing scenario (SSP5‐8.5). The findings of the study could provide a benchmark in selecting appropriate GCMs for future projection over a data scare region, like Pakistan. Moreover, the projected trends of future precipitation are crucial in devising adaption and mitigation actions towards sustainable planning of water resource management, food security, and disaster risk management. [ABSTRACT FROM AUTHOR]

Three generations of global climate models (GCMs), Coupled Model Intercomparison Project version 3 (CMIP3), CMIP5, and CMIP6, are evaluated for performance simulating seasonal mean and annual‐to‐decadal variability of temperature and precipitation in the Upper Colorado River Basin. Low‐frequency precipitation variability associated with drought is a particular focus and found to be a significant model shortcoming. The evaluation includes remote teleconnected atmospheric responses to the Pacific Ocean, including the El Niño/Southern Oscillation and Pacific Decadal Oscillation. GCMs have improved their simulation of the Upper Basin over model generations, but primarily in atmospheric circulation metrics. Persistent winter precipitation biases have changed little, including in multiyear precipitation variability. Users generally bias‐corrected GCM data before use; evaluation using a simple spatially and temporally averaged bias correction shows that the CMIP6 models outperform earlier generations after the bias correction, although more complex precipitation biases remain even after the simple bias correction. These model rankings will be useful when selecting GCMs for a variety of hydrological and ecological climate studies in the Upper Basin. [ABSTRACT FROM AUTHOR]

The tropical cyclone genesis frequency (TCGF) is an essential metric for gauging the performance of climate models. Previous evaluations on CMIP family models usually employ one realization for each model and show their diversities in performance. The single model initial condition large ensemble experiments provide a unique opportunity to quantify how internal variability may affect the model evaluation skill. Here, taking the TCGF in the Western North Pacific (WNP) as an example, we use two genesis potential indices as proxies to evaluate the performance of the FGOALS-g3 large ensemble simulation with 110 members. We show that while internal variability does not have a significant influence on the TCGF mean state evaluation, the TCGF-ENSO (El Niño–Southern Oscillation) relationship is significantly modulated by the decadal scale internal variability. For mean state simulation, the FGOALS-g3 large ensembles show reasonable performance in the simulation of TCGF spatial pattern but have differences compared with ERA5 in magnitude. Physical process analysis indicates that compared with ERA5, nearly all dynamic terms are more unfavorable for tropical cyclogenesis due to the cold sea surface temperature anomalies in the midlatitude, while the thermodynamic terms are more conducive to more TCs. For interannual variability, the ENSO-TCGF connection is significantly modulated by the tropical Pacific decadal variability (TPDV) mode by influencing the vertical wind shear in the WNP. Particularly, the model simulation skill depends on the choice of genesis potential indices. Our finding highlights the importance of considering decadal-scale internal variability in the evaluation of interannual ENSO-TCGF variability. [ABSTRACT FROM AUTHOR]

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate. [ABSTRACT FROM AUTHOR]

Temperature correlation network has been commonly used in climate monitoring, diagnosing and prediction using reanalysis data, however its application in the network analysis of climate dynamical models hasn’t been deeply studied. We construct a temperature correlation network based on near-surface 2m air temperature of four climate models by comparing their capability of properly capturing the structural characteristics of the temperature networks, and further conduct a comparative analysis of the topological differences among different models. The features of temperature correlation networks varied significantly among the four models, in which the ECMWF-SYSTEM5 model network has the highest connectivity among the four models, while the NCEP_CFS2 model has the lowest one. It is also revealed that the model with higher connectivity normally has a stronger correlation between the nodes of the air temperature correlation network, which is likely attributed to the model’s stronger teleconnection, regional consistency and smaller standard deviation between predicted temperature series of most two grid points. It is also implied that the model’s prediction skills have a probable relationship with the network structure. For each model, the 1-month lead prediction has the highest prediction skill corresponding to the model having the connectivity close to the observation. With the increase of prediction lead times, connectivity bias has a quick rise, and the prediction skill has an obvious decrease. However, for different models at the same prediction lead time, it is not the case that the larger the connectivity deviation the lower the prediction skill, for example, the ECMWF_SYSTEM5 model has the highest prediction skill and the largest connectivity deviation, we find that the ECMWF_SYSTEM5 model network possesses significantly higher connectivity and more distinctive small-world characteristics, which implies a stable network structure helps to improve the prediction skills of the model. Therefore, this study can deepen our understanding of climate models and provide guidance for improving the ability of models to properly simulate climate features. [ABSTRACT FROM AUTHOR]

The El Niño-Southern Oscillation (ENSO) exerts significant influences on extreme significant wave height (SWH) but climate model capabilities in reproducing the observed ENSO impact on SWH have not been evaluated. This study assesses the performances of the Coupled Model Intercomparison Project phase 5 (CMIP5) models in term of extreme SWH responses to ENSO over the Indo-Pacific Ocean focusing on December-February (DJF). 18 CMIP5 models are evaluated using their historical simulations for 1950–2005 in view of the ERA-20C reanalysis. A non-stationary generalized extreme value (GEV) analysis is employed to fit DJF maxima of 6-hourly SWHs and obtain the extreme SWH response patterns to ENSO by incorporating Niño3.4 index as a covariate. Results show that CMIP5 models can on average capture the major observed mean and extreme SWH responses to ENSO, including the increased SWH over the northeastern North Pacific (NENP) and the decreased SWH over the Maritime Continent (MC) during El Niño. The inter-model relations between ENSO characteristics and SWH responses are further examined for the two hotspot regions (NENP and MC). It is found that ENSO intensity is a dominant factor determining simulated SWH over the NENP such that models with stronger ENSO simulate stronger SWH responses. In contrast, for the MC, the sea level pressure teleconnection pattern significantly affects the inter-model spread in SWH responses, also explaining the systematic underestimation of SWH responses over the region. Implication is that ENSO intensity and atmospheric teleconnection patterns need to be considered for better simulations and reliable predictions of extreme SWH variability. [ABSTRACT FROM AUTHOR]

This work evaluates zonal winds in both hemispheres near the polar winter mesopause in the Whole Atmosphere Community Climate Model (WACCM) with thermosphere‐ionosphere eXtension combined with data assimilation using the Data Assimilation Research Testbed (DART) (WACCMX+DART). We compare 14 years (2006–2019) of WACCMX+DART zonal mean zonal winds near 90 km to zonal mean zonal winds derived from Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) geopotential height measurements during Arctic mid‐winter. 10 years (2008–2017) of WACCMX+DART zonal mean zonal winds are compared to SABER in the Antarctic mid‐winter. It is well known that WACCM, and WACCM‐X, zonal winds at the polar winter mesopause exhibit a strong easterly (westward) bias. One explanation for this is that the models omit higher order gravity waves (GWs), and thus the eastward drag caused by these GWs. We show for the first time that the model winds near the polar winter mesopause are in closer agreement with SABER observations when the winds near the stratopause are weak or reversed. The model and observed mesosphere and lower thermosphere winds agree most during dynamically disturbed times often associated with minor or major sudden stratospheric warming events. Results show that the deceleration of the stratospheric and mesospheric polar night jet allows enough eastward GWs to propagate into the mesosphere, driving eastward zonal winds that are in agreement with the observations. Thus, in both hemispheres, the winter polar night jet speed and direction near the stratopause may be a useful proxy for model fidelity in the polar winter upper mesosphere. Key Points: Evaluation of the easterly zonal wind bias in the polar winter upper mesosphere in the Whole Atmosphere Community Climate ModelThe easterly model wind bias is reduced and even eliminated when conditions in the stratosphere are dynamically disturbedThe easterly wind bias in the model may be due to the omission of higher order gravity waves and the eastward drag caused by these waves [ABSTRACT FROM AUTHOR]

Tropical cyclones generate large waves that physically damage coral communities and are commonly cited as a worsening threat to coral reefs under climate change. However, beyond projections of ocean basin‐scale changes in cyclone intensity, the other determinants of future coral reef damage such as cyclone size and duration remain uncertain. Here, we determine the extent to which downscaled cyclones represent observed cyclone characteristics that influence wave damage to Australian coral reef regions. We then investigate mid‐century (2040–2060) and end of century (2080–2100) downscaled tracks to assess whether cyclone characteristics will change with future warming under a high‐emissions scenario. We find that spatial uncertainties in downscaled cyclogenesis and track positions limit estimates of reef damage for individual coral reefs and regions. Further, the models are unable to reproduce the most reef damaging cyclones for any of the regions. The downscaled tracks capture observed cyclone characteristics, such as size, impacting the Great Barrier Reef well, but perform poorly for the Northern Territory, with mixed performance for the Coral Sea and Western Australia. We find no clear evidence that cyclones will cause more damage to Australian coral reef regions in the future, at least based on the climate models and downscaling approach examined here. There is increasing interest in using tropical cyclone projections to assess future coral reef exposure to damaging waves. We recommend caution when interpreting such projections due to large uncertainty in the mechanisms that influence reef damaging tropical cyclone characteristics and how these will change with future warming. Plain Language Summary: Tropical cyclone intensity, size and duration together determine the extent to which their waves damage coral reefs. Increased tropical cyclone intensity with climate change is often cited as evidence that tropical cyclones will cause more damage to coral reefs in the future but changes to size and duration remain uncertain. Here, we determine whether tropical cyclones simulated from climate models can represent the observed tropical cyclone characteristics that are important for estimating wave damage to coral reefs and assess how these characteristics might change in the future. We find that the tropical cyclones simulated from climate models capture the observed cyclone characteristics well for those impacting the Great Barrier Reef (with the exception of the most damaging cyclones) with mixed to poor performance for other regions. The projections of future reef damage are uncertain with some models projecting increases and others decreases. Tropical cyclone projections are used in conservation planning to identify and protect the coral reefs least exposed to future tropical cyclones. However, we find that the simulated tropical cyclone tracks explored here are unlikely to represent future reef damaging tropical cyclone characteristics well if used in conservation decision making. Key Points: Wave damage to coral communities from tropical cyclones depends on their intensity, size and duration while tracking near coral reefsDownscaled tracks capture observed cyclone characteristics impacting the Great Barrier Reef with mixed to poor performance in other regionsProjections vary by region and cyclone characteristic with some models projecting increases and others decreases in future reef damage [ABSTRACT FROM AUTHOR]

The simulation skills of the Southern Annular Mode (SAM) pattern for the CMIP6 models are evaluated in this study. Both the Atmospheric Model Intercomparison Project (AMIP) and historical experiments are utilized to understand the effect of SST forcing and air–sea interaction. The spatial correlation coefficient, standard deviation, and root‐mean‐square error (RMSE) are used as the objective metrics. The results suggest that the multimodel mean (MME) for both AMIP and historical data could capture the basic spatial patterns of the SAM for all seasons, featuring the opposite sign between the middle and high latitudes with a zonal three‐wave structure in the middle latitudes (positive phase). However, the overall skill of the AMIP simulation is higher than that of the historical simulation, which could be attributed to realistic SST forcing being applied. The simulation skill varies for different seasons: the skill for boreal spring (March–May [MAM]) is the lowest, while it is highest in boreal winter (December–February [DJF]). Further analysis shows that the simulations of the SAM asymmetric part are the key component for the simulation of the spatial pattern of the SAM, especially sensitive in MAM. The wave–current interaction simulation at mid–high latitudes is suggested to be improved for the advancement of SAM simulations in future model development. [ABSTRACT FROM AUTHOR]

This article investigates seasonal and annual trends of near‐surface temperature (NST) during the last century (1901–2018) over Ghana. The ability of 24 global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate abrupt mean temperature changes in Ghana from 1980 to 2014 was assessed. The trends' magnitude and abrupt changes were detected using Sen Slope Estimator (SSE) and sequential Mann–Kendall (SQMK) tests, respectively. Root mean square difference, correlation coefficient, and mean bias (B) were used to determine the skilfulness of GCMs in simulating NST relative to Climatic Research Unit (CRU TS4.04) datasets. Ghana's Southern belt is characterized by low temperatures, averaging 25°C to 27°C, whilst the northern belt is characterized by high temperatures (29–31°C). The inter‐annual variability of temperature over Ghana exhibits an increasing trend. Findings depict a significant increase in both the annual and seasonal temperatures of Tmin and Tmean, with a steady increase in Tmax. Results revealed the rate of increase had been higher in the country's northern regions (0.5°C) than in the south (0.3–0.4°C) in recent decades. The model performances for the interannual variability of annual and FMAM temperatures are better than their JJAS seasonal performances. The study's overall model ranking shows that Ghana's best performing models for annual, FMAM and JJAS seasons are INM‐CM5‐0, NorCPM1, and KACE‐1‐0‐G, respectively. Observations and comparisons provide useful reference values for a comprehensive assessment of various applications. [ABSTRACT FROM AUTHOR]

One of the weaknesses of current line-source models for predicting downwind concentrations from mobile sources is accounting for the dispersion of effluents. Most of the investigators in the field have taken different approaches over the last 50 years, ranging from the use of Pasquill–Gifford (P-G) dispersion curves to the use of equations based on atmospheric turbulence for point source dispersion. Madiraju and Kumar (2021) proposed a three-phase turbulence (TPT) model using the key features of mobile source dispersion that appear in the existing literature. This paper examines the performance of line-source models using an updated TPT model. The generic dispersion equations were considered from the SLINE 1.1, CALINE 4, ADMS, and SLSM models. Multiple air quality field data sets collected by other investigators near the roadways were used during this study. These include field data collected from the Idaho Falls Tracer Experiment 2008 (used as the dataset to compare with the initial model), the CALTRANS Highway 99 Tracer experiment, and the Raleigh 2006 experiment. The predicted concentrations were grouped under unstable and stable atmospheric conditions. The evaluation of the model was performed using several statistical parameters such as FB, NMSE, R2, MG, VG, MSLE, and MAPE. The results indicate that the ADMS and SLINE 1.1 models perform better than CALINE4 and SLSM. SLINE 1.1 tends to overpredict for stable atmospheric conditions and underpredict for unstable atmospheric conditions. A trial test was performed to implement the TPT model in the basic line-source model (SLSM). The results indicate that the majority (FB, NMSE, R2, and MSLE) of the indicators have improved and are in the satisfactory range of a good model performance level. [ABSTRACT FROM AUTHOR]

This study assessed the ability of six CORDEX Regional Climate Models (RCMs) in comparison to their ensemble mean (ENSEMBLE) in reproducing rainfall distribution over West Africa from 1951 to 2005. Using annual and seasonal climatological and statistical description, all the RCMs and their ensemble were evaluated against Climate Research Unit (CRU) rainfall observational product. over three homogenous climatic zones of Guinea Coast, Savannah and Sahel. Results from annual rainfall evaluation indicated that most of the RCMs and ENSEMBLE captured the temporal and spatial distribution over West Africa as they simulates single rainfall peak over the Savannah and Sahel zones. Annual statistical evaluation performed shows that most of the RCMs and ENSEMBLE are in good agreement with the CRU. In particular, RACMO outperformed the ENSEMBLE in the Guinea Coast just as CRCM, HIRHAM, RCA and REMO did over the Savannah. Seasonally, the statistical performance of the six RCMs and the ENSEMBLE in DJF season is relatively poor while most of the individual RCMs performed better than ENSEMBLE in SON season over entire West Africa, Savannah and Sahelian zone. Spatial pattern also revealed that most of the RCMs replicated mean seasonal distribution in DJF and MAM season. The overall performance of all the RCMs and the ENSEMBLE varies in different climatic zones and from one season to the other. However, CRCM and RCA consistently performed better than others and can be used in place of the ENSEMBLE to study rainfall over West Africa. [ABSTRACT FROM AUTHOR]

Moisture evaporated from the land contributing to precipitation in a given area is known as precipitation recycling and needs to be accurately represented in climate models. The Amazon and Congo basins are reported to have the highest precipitation recycling rates globally, but model representation has not yet been assessed over these regions. We evaluated recycling over the Amazon and Congo in 45 Coupled Model Intercomparison Project Phase 6 models. Regional annual means from models and reanalyzes agreed well over both basins. Models captured seasonal variation in recycling over the Congo but there was a large‐scale underestimation of recycling during the Amazon dry‐to‐wet transition season relative to ERA5, caused by models underestimating Amazon evapotranspiration and overestimating incoming wind speed and associated water vapor imports. Both regions show robust declines in precipitation recycling over the next century under future climate‐change scenarios. Our results suggest models may underestimate impacts of deforestation on regional precipitation in the Amazon. Plain Language Summary: In the Amazon and Congo basins, much of the rain that falls to Earth is transferred back to the atmosphere through evaporation over tropical forests, providing essential moisture for further rainfall events downwind. This process of rainfall recycling, which is vital for sustaining the rainforest, is disrupted when forest is cut down. For researchers to understand how deforestation in the Amazon and Congo might impact the climate it is vital that climate models accurately represent the fraction of rain coming from recycled moisture. Our study found that models represented rainfall recycling relatively well in the Congo, but underestimate recycling in the Amazon dry‐to‐wet transition season. This is important because it could mean these models underpredict the influence of deforestation on climate in the Amazon. Key Points: This study quantifies, for the first time, precipitation recycling ratios over the Amazon and Congo Basins in Coupled Model Intercomparison Project Phase 6 modelsSeasonal recycling is well represented in the Congo but there is a large‐scale underestimation in the Amazon dry‐to‐wet transition seasonThese findings have implications for model representation of land‐use change impacts in the Amazon [ABSTRACT FROM AUTHOR]

Thailand is located in the Southeast Asian region, where the summer rainfall exhibits strong interannual variability, and the successful simulation of rainfall variation in Thailand by current climate models remains a challenge. Therefore, this paper evaluates the capability of the state-of-the-art Atmospheric GCM of the Institute of Atmospheric Physics (IAP-AGCM) in simulating summer rainfall over Thailand by comparing the model's results with ground-truth observation during 1981–2012. Generally, the model shows a certain skill in reproducing the observed spatial distribution of the summer rainfall climatology and its interannual variability over Thailand, although the model underestimated both rainfall amount and its variability. Using Empirical Orthogonal Function (EOF) analysis, it is found that the IAP climate model reproduced creditably the spatial patterns of the first three dominant modes of summer rainfall in Thailand, whereas it underestimated the explained variance of the observed EOF-1 and overestimated the explained variance of the observed EOF-2 significantly. It was further found that the correlation between the observed rainfall anomalies in Thailand and the Niño3.4 index can be reproduced by the IAP model. However, the observed negative correlation is largely underestimated by the IAP climate model, and this could be the reason for the underestimation of explained variance of the EOF-1 by the IAP model. The evaluation results would be of great importance for further model improvement and thus potential application in seasonal prediction in the region. [ABSTRACT FROM AUTHOR]

A comparison between a puff atmospheric dispersion model (hereafter: PuM) and a Lagrangian particle model (hereafter: LPM) was conducted for a real case of emissions from an industrial plant, in the context of a complex and coastal site. The PuM's approach is well-known and widely adopted worldwide, thanks to the authoritative suggestions by the US-EPA for regulatory use as, according to the definitions included in its guidelines, an "alternative" to "preferred" models; LPMs are more advanced models and have gained reliability over the last two decades. Therefore, it is of interest to provide insights into the decision to adopt or recommend, in the field of atmospheric impact assessment, a more advanced, but more knowledge- and resource-intensive, modeling tool, rather than an established albeit less accurate one. An inter-comparison of the two approaches is proposed based on the use of various statistical and comparative parameters with the goal of studying their differences in reproducing maps of ground-level ambient concentration statistics for assessment purposes (annual means, hourly peaks). The models were tested under a year-long simulation. The dispersion from both a point and a volume source, belonging to an existing industrial plant, was analyzed separately. The inter-comparison was performed through the analysis of 2D ground concentration maps, scatterplots, and three classical indices from the 2D maps of annual concentration statistics. To correlate the differences among models with site characteristics, the statistics were analyzed not only globally, but also according to distance from the source, the elevation, and the land-use classification. The analysis shows that around-its-axis plume dispersion in LPM is lower than in PuM over all the land-use types except water surfaces, in agreement with the theoretical basis provided by the models. Because of its more advanced theoretical formulation, e.g., in the interaction of the plume with the complex terrain and the three-dimensional wind field, an LPM used as a comparison term allowed us to highlight the weaknesses of a more traditional approach, such as PuM, in reproducing effects such as plume up-sloping, deflection, channeling, confinement, and wind shear diffusion. [ABSTRACT FROM AUTHOR]

Single‐column models (SCMs) simulations are sometimes used to evaluate model physics and aid parameterization development. However, few studies have systematically compared SCM behavior—where column boundary conditions are specified—with that of corresponding 3D models, where columns interact dynamically. Here we address this by comparing forced responses of an SCM in radiative‐convective equilibrium (RCE) with those of a multi‐column model (MCM) where the model domain is in RCE but individual columns are not, examining what factors affect the models' comparability. We find that convective organization in the MCM depends at least as much on the convection scheme as on other mechanisms known to organize convection (e.g., radiative feedback). Moreover, convective organization emerges as a robust factor affecting SCM–MCM comparability, with more aggregated states in 3D associated with larger behavior deviations from the 1D counterpart. This is found across five convection schemes and applies to simulated mean states, linear responses to small tendency perturbations, and adjustments to doubled‐CO2 forcing. Nevertheless, we find that even when convection is organized, behavior differences between pairs of schemes in the SCM are largely preserved in the MCM. This indicates that when model physics produces accurate behavior in a 1D setup, it will be more likely to do so in a 3D setup. However, our idealized RCE framework implies that these conclusions may not apply to situations with strong large‐scale forcing or encountered over land. Lastly, we demonstrate the practical value of linear responses by showing that they can accurately predict an SCM's tropospheric adjustment to doubled‐CO2 forcing. Plain Language Summary: To study various climate processes, scientists often use 3D climate models, but these simulations use huge amounts of computing time and resources. One way to alleviate this problem is to use a single‐column model, which is a 1D vertical column extracted from a 3D model. Although these 1D simulations are very efficient, we cannot always be sure that their results are comparable to those of their parent 3D model. In this study, we find that when clouds are randomly spread across the sky (when convection is disorganized), results of 1D and 3D simulations are very similar. However, when clouds are clustered into clumps (when convection is organized), we cannot always trust the results of 1D simulations as they tend to be different from those of 3D simulations. Nevertheless, we find that when two models show very different behavior in their 1D setup, they will tend to also behave differently in their 3D setup. This tells us that 1D simulations can still be useful. We also discover that the way a model responds when it is lightly tickled (perturbed) can be used to predict its responses to a situation where the amount of carbon dioxide in the atmosphere is suddenly doubled. Key Points: Convective organization in large‐scale simulations depends on convection scheme, sometimes more so than on radiative feedbackOne‐dimensional and 3D behavior is very similar if convection is not organized in 3DA convection scheme's linear responses can be used to predict its tropospheric adjustments to doubled‐CO2 forcing [ABSTRACT FROM AUTHOR]

General circulation models are broadly able to capture the shape and structure of extratropical cyclones. Increased atmospheric resolution has been shown to improve the representation of cyclone structure. However, the intensity of cyclones, and the strength of their winds, are commonly underestimated in models. Using a cyclone compositing technique applied to the new generation HighResMIP and CMIP6 models, the representation of cyclone wind speeds and airstreams is quantified. CMIP6 models are able to capture the structure of cyclones, however winds associated with various airstreams within the cyclones are too weak. HighResMIP models show considerable improvements, with a majority of cyclone‐scale biases present in the CMIP6 models reduced in both winter and summer seasons in the Northern and Southern hemispheres. HighResMIP and CMIP6 models have similar biases on the large‐scale, with both struggling to represent the width of the upper‐level jet. Plain Language Summary: In order for accurate predictions of the future climate to be made, models must be tested to see if they reproduce features in an accurate way. In this study, we have tested the ability of climate models to represent the intensity and structure of extratropical cyclones. Previously, climate models with a higher resolution have performed better at representing the structure of these cyclones, however most models tend to fail to capture the intensity. Our results demonstrate that models do still struggle to capture the intensity of cyclones, with wind speeds being too weak. However, cyclones simulated by the highest resolution models do a much better job at simulating the intensity of the cyclones, and the strength of the wind speeds within them. In order to study features within extratropical cyclones accurately, high resolution models must be used. Key Points: The strength of cyclone wind speeds are underestimated by CMIP6 modelsHighResMIP models represent the structure and intensity of cyclones better than CMIP6 modelsElements of the large‐scale environment surrounding cyclones are not improved in HighResMIP simulations [ABSTRACT FROM AUTHOR]

In this study, 28 climate models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and 32 models from the sixth phase (CMIP6) have been evaluated for their ability to simulate large‐scale atmospheric circulations (using rainfall, wind fields, geopotential height, temperature, and moisture flux convergence) for the summer monsoon in Southeast Asia. Using a multi‐criteria decision making technique, models have been ranked based on 25 metrics which compare their performances with observation data. Results indicate a better representation of annual rainfall cycles as well as spatial pattern by CMIP6 models compared to CMIP5. Though majority of the models from both CMIPs show late onset and early retreat of the rainy season, CMIP6 GCMs simulate the onset, retreat, and the length of the rainy season closer to the observation. Large‐scale circulation patterns evaluated using spatial correlation and root mean square error (RMSE) show improvements in CMIP6 across all metrics, especially for the moisture flux convergence. Performances for large‐scale circulation are generally reflected in rainfall simulation; however, few models showed that better simulations of rainfall do not exclusively depend on their performance for large‐scale variables. Overall, CMIP6 models are found to be superior to CMIP5 models in simulating rainfall and large‐scale circulation, which is likely attributable to CMIP6 model's higher spatial resolutions, increased number of vertical levels, improved atmospheric and land surface parameterization, etc. Finally, subsets of optimal models from CMIP5 and CMIP6 that proved to be better at representing the summer monsoons in the study area are identified. These models are recommended to develop robust future projections that can be used for climate change impact and adaptation studies. [ABSTRACT FROM AUTHOR]

Previous analytical and simulation‐based analyses suggest that deeper land surface models are needed to realistically simulate the terrestrial thermal state in climate models, with implications for land‐atmosphere interactions. Analytical approaches mainly focused on the subsurface propagation of harmonics such as the annual temperature signal, and a direct comparison with climate‐change model output has been elusive. This study addresses the propagation of a harmonic pulse fitted to represent the timescale and amplitude of anthropogenic warming. Its comparison to land model simulations with stepwise increased bottom boundary depth leads to an agreement between the simulation‐based and analytical frameworks for long‐term climate trends. Any depth increase gradually decreases the relative error in the subsurface thermodynamics, and a minimum depth of 170 m is recommended to simulate the ground climate adequately. The approach provides an accurate estimate of the required land‐model depth for climate‐change simulations and assesses the relative bias in insufficiently deep land models. Plain Language Summary: Many current‐generation climate models have land components that are too shallow. Under climate change conditions, the long‐term warming trend at the surface propagates deeper into the ground than the commonly used 3–10 m. Shallow models alter the terrestrial heat storage and distribution of temperatures in the subsurface, influencing the simulated land‐atmosphere interactions. Previous studies focusing on annual timescales suggest that deeper models are required to match subsurface‐temperature observations and the classic analytical heat conduction solution. However, for a systematic investigation of land‐model deepening in the frame of anthropogenic climate change, the classic analytical solution is inaccurate because it does not mimic the timescale and amplitude of the simulated warming trend. This study intends to bridge the gap between analytical and simulation‐based estimates of the subsurface thermodynamic state by adapting the classic analytical framework to mimic long‐term anthropogenic warming. The analysis shows that a land‐model depth of at least 170 m is recommended for a proper simulation of the post‐1850 ground climate, which differs up to 30% from the estimate of the classic approach. Compared to previous studies, this provides an accurate estimate of the required land model depth for long‐term climate‐change simulations and indicates the relative bias in insufficiently deep land models. Key Points: Heat conduction from analytical and numerical estimates suggests a required land‐model depth of 170 m for climate change simulationsAgreement between numerical and analytical estimates on climate change timescales gives more confidence for the required land‐model depthStepwise land‐model depth increase gradually reduces the relative error of shallow models in climate‐change simulations [ABSTRACT FROM AUTHOR]

Predicting how regional precipitation will respond to future warming is among the most challenging undertaking in climate change projection. Despite sustained efforts to improve modeling and understanding of precipitation, the overall uncertainty in projecting regional precipitation has not been reduced substantially. Here, the potential for more robust precipitation projections is demonstrated through the use of discriminating metrics to subsample a multimodel ensemble. Using a two‐dimensional metric of precipitation and its relationship with large‐scale circulation indices in East Asia, 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are classified into three groups. Models in the top performing group projected statistically significant increasing trends in precipitation and the regional precipitation patterns are more similar to each other than to the patterns in the bottom performing group. In contrast, models in the bottom performing group projected diverse responses, with overall small drying or no significant trends in precipitation. Plain Language Summary: Precipitation is a fundamental driver of land surface hydrology and water resources, but large biases in climate model simulations of precipitation and large uncertainties in the future projections hinder the use of climate models in climate impact studies. Metrics of precipitation used in climate model evaluation usually focus on different precipitation characteristics, while ignoring important physical mechanisms linking precipitation with its environments. In this study, a two‐dimensional metric is proposed to evaluate both the simulated precipitation and its relationship with large‐scale circulation patterns in East Asia. This metric is applied to the Coupled Model Intercomparison Project Phase 5 (CMIP5) models to classify 31 models into three groups based on model performances. The metric is able to discriminate model responses: models in the top performing group show more consistent and similar responses of precipitation to future warming, featuring larger increases of future precipitation than the CMIP5 ensemble mean over China. Key Points: A two‐dimensional metric is developed to evaluate precipitation and its relationships with the large‐scale circulation in climate modelsThe new metric identifies a subset of models skillful in simulating both precipitation and its relationship with the monsoon circulationThis subset of models projects a more robust and larger increase of future precipitation than the multimodel ensemble mean over China [ABSTRACT FROM AUTHOR]

Numerical weather and climate models continue to struggle with simulating equatorial waves and tropical rainfall variability. This study presents a potential remedy—high‐resolution global models with explicitly resolved convection. A series of global nonhydrostatic simulations was produced with horizontal cell spacings between 3.75 and 480 km; the share of resolved precipitation in these simulations ranged from 88% to 2%. The simulations in which convection was mostly resolved produced much more realistic equatorial waves than the simulations in which convection was mostly parameterized. Consequently, the simulations with resolved convection produced more realistic precipitation patterns and precipitation variances. The results demonstrate that high‐resolution global models with explicitly resolved convection are a promising tool to improve tropical weather forecasts and climate projections. Plain Language Summary: Despite frequent complaints that weather forecasts are more often wrong than right, weather prediction is actually one of science's big success stories. However, even though weather forecasts have generally become more accurate, forecasts in the tropics are indeed quite poor. The computer models that form the basis of modern weather prediction struggle with simulating tropical weather phenomena such as equatorial waves—weather disturbances that travel along the equator and bring alternating periods of rain and dryness. We demonstrate that the models' handling of equatorial waves can be much improved by increasing the model resolution, that is, by changing the models so that they can simulate the atmosphere in more detail. Specifically, the distance between grid cells must be made small enough so that the models can capture the building blocks of equatorial waves: individual showers and thunderstorms (a typical thunderstorm measures only 10 km or so in the horizontal direction). Once the models are able to capture these individual storms, they are able to produce much more realistic equatorial waves, and in turn, much better forecasts of tropical rainfall. The downside is that much larger computers with increased power consumption are necessary to accommodate the more detailed models. Key Points: global simulations with resolved convection produce more realistic equatorial waves than simulation with parameterized convectionit is not the horizontal resolution per se that matters, but the fact that convection is resolved instead of parameterizedglobal models with resolved convection can improve tropical weather forecasts and climate projections relative to current‐generation models [ABSTRACT FROM AUTHOR]

This study evaluates the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison Phase 6 (CMIP6). TaiESM1 is developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution‐based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three‐dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to the majority of model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparisons between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and are among the leading CMIP6 models in terms of many key climate features. Plain Language Summary: Earth System Models are important tools for understanding how earth system changes and providing information for mitigation and adaption facing future climate changes. Taiwan Earth System Model version 1 (TaiESM1) is developed in the Research Center for Environmental Changes, Academia Sinica, Taiwan, with the goal to join the effort of the international model intercomparison—the Coupled Model Intercomparison Phase 6 (CMIP6). Based on the Community Earth System Model version 1, key improvements of TaiESM1 are in the representation of how raining systems starts, how cloud covers shade, how aerosols form and dissipate, and how topography reflects sunlight. We compare TaiESM1s performance with observations and other CMIP6 models to understand TaiESM1's capability in simulating climate systems and future climate changes. We especially focus on evaluating the climate variability on a wide range of time scales, i.e., how climate systems change on those of within one day, day‐to‐day, seasonal, year‐to‐year, and decades‐to‐decades. Our analysis shows that TaiESM1 can realistically represent the observed climate variability and would help to expand our understandings of the earth system with the global scientific community and society as a whole. Key Points: Climate variability in the historical simulation of Taiwan Earth System Model version 1 (TaiESM1) is evaluatedWhile still subject to several common biases, TaiESM1's performance of mean climate states is among the leading CMIP6 modelsTaiESM1 is capable of realistically simulating key climate features, including the diurnal rainfall, monsoon evolution, and teleconnection [ABSTRACT FROM AUTHOR]

In this study, a subset of the downscaled simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX‐SEA) was analysed to examine the representation of tropical cyclone (TC) climatology over Southeast Asia, in terms of pattern, intensity, frequency, and lifetime. A modified vortex tracking algorithm is used to detect TCs over the SEACLID/CORDEX‐SEA domain in the historical simulations from 1986 to 2005. Sensitivity tests for the detection method criteria, including vorticity, outer core wind strength, sea level pressure anomaly, and temperature anomaly at 300, 500, 700, and 850 hPa, were conducted to determine the optimum threshold configuration for each SEACLID/CORDEX‐SEA simulation used in the study. Comparison with the best track data of the Joint Typhoon Warning Center showed that model simulations underestimated the total number of TCs east of the Philippines for the 1986–2005 period but captured the annual cycle of the total number of TCs. This underestimation of TCs is possibly due to the domain used, which does not extend further east to cover most of the TC genesis area in the Western North Pacific. The structure of a typical TC from the regional climate model simulation is comparable to observed TC structure. However, results indicate that the resolution of the simulations is still not sufficient to capture the fine details of the observed TC structure, which could explain in part the lower intensification rate of TCs in the model output. [ABSTRACT FROM AUTHOR]

This study examined the overall performance of the climate models in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating the key energy and water fluxes over land. For this purpose, this study selected multiple land flux products as reference data sets and assessed the global spatial means, patterns, trends, seasonal cycles, and regional mean estimates of the sensible heat (SH), latent heat (LH), net radiation (RN), runoff (RF), and precipitation (PR) simulated by 32 CMIP6 models in recent decades. The global (Antarctica, Greenland, and hot deserts are not included) mean SH, LH, RN, RF, and PR simulated by the CMIP6 models are 37.55 ± 4.81 W m−2, 49.88 ± 5.31 W m−2, 89.10 ± 4.45 W m−2, 351.31 ± 95.28 mm yr−1, and 948.35 ± 88.77 mm yr−1, respectively. The ensemble median of CMIP6 simulations (CMIP6‐MED) can provide robust estimates of global and regional land fluxes, which are within the ranges given by the reference data sets, and highly consistent spatiotemporal patterns of these fluxes. The comparison of CMIP6‐MED with the first preferred reference data sets shows that CMIP6‐MED generally overestimates the water and energy fluxes over land, except for the simulated RF and PR in the Amazon region. The most disagreements between CMIP6‐MED and the reference data sets occur in South America (particularly the Amazon region) and the Tibetan Plateau. Finally, the sources of model biases are discussed. It is suggested that current land flux products should be widely used to optimize the structures and parameters of climate models in future work. Plain Language Summary: Land surface models are an indispensable part of weather and climate models. Sensible heat (SH), latent heat (LH), net radiation (RN), runoff (RF), and precipitation (PR) are the key components of the energy and water cycles over global land. This study evaluated these five fluxes from 1979 to 2014 simulated by 32 climate models in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), with multiple global land products. The RN and RF estimated by the CMIP6 models exhibit the lowest and highest uncertainties, respectively. The ensemble median of CMIP6 simulations (CMIP6‐MED) provides robust estimates of global and regional land fluxes, which are within reference ranges. We select the first preferred reference data sets from multiple global land products according to the evaluations against site observations and previous studies, and find that CMIP6‐MED generally overestimates the water and energy fluxes over land. The most disagreements between CMIP6 simulations and references occur in South America and the Tibetan Plateau. This study is helpful for determining the overall performance of the current climate models in simulating land surface processes. Key Points: Performance of CMIP6 models on land surface energy and water fluxes during 1979–2014 is comprehensively evaluated using multiple land flux productsEnsemble median of CMIP6 simulations can provide robust estimates of global and regional land fluxes, which are within the ranges given by reference data setsThe most disagreements between CMIP6 simulations and reference data sets occur in South America (particularly the Amazon region) and the Tibetan Plateau [ABSTRACT FROM AUTHOR]

Climate change will impact forest productivity worldwide. Forecasting the magnitude of such impact, with multiple environmental stressors changing simultaneously, is only possible with the help of process‐based models. In order to assess their performance, such models require careful evaluation against measurements. However, direct comparison of model outputs against observational data is often not reliable, as models may provide the right answers due to the wrong reasons. This would severely hinder forecasting abilities under unprecedented climate conditions. Here, we present a methodology for model assessment, which supplements the traditional output‐to‐observation model validation. It evaluates model performance through its ability to reproduce observed seasonal changes of the most limiting environmental driver (MLED) for a given process, here daily gross primary productivity (GPP). We analyzed seasonal changes of the MLED for GPP in two contrasting pine forests, the Mediterranean Pinus halepensis Mill. Yatir (Israel) and the boreal Pinus sylvestris L. Hyytiälä (Finland) from three years of eddy‐covariance flux data. Then, we simulated the same period with a state‐of‐the‐art process‐based simulation model (LandscapeDNDC). Finally, we assessed if the model was able to reproduce both GPP observations and MLED seasonality. We found that the model reproduced the seasonality of GPP in both stands, but it was slightly overestimated without site‐specific fine‐tuning. Interestingly, although LandscapeDNDC properly captured the main MLED in Hyytiälä (temperature) and in Yatir (soil water availability), it failed to reproduce high‐temperature and high‐vapor pressure limitations of GPP in Yatir during spring and summer. We deduced that the most likely reason for this divergence is an incomplete description of stomatal behavior. In summary, this study validates the MLED approach as a model evaluation tool, and opens up new possibilities for model improvement. [ABSTRACT FROM AUTHOR]

Regional Climate Models (RCMs) are the primary climate information available to public stakeholders and city-planners to support local adaptation policies. However, with resolution in the order of ten kilometres, RCMs do not explicitly represent cities and their influence on local climate (e.g. Urban Heat Island; UHI). Downscaling methods are required to bridge the gap between RCMs and city scale. A statistical–dynamical downscaling methodology is developed to quantify the UHI of the city of Paris (France), based on a Local Weather Types (LWTs) classification combined with short-term high-resolution (1-km) urban climate simulations. The daily near-surface temperature amplitude, specific humidity, precipitation, wind speed and direction simulated by the RCMs are used for the LWTs attribution. The LWTs time series is associated to randomly selected days simulated with the mesoscale atmospheric model Meso-NH coupled to the urban canopy model Town Energy Balance to calculate the UHI corresponding to the successive LWTs. The downscaling methodology is applied to the EURO-CORDEX ensemble driven by the ERA-Interim reanalysis, and evaluated for the 2000–2008 period against station observations and a 2.5-km reanalysis. The short-term dynamical simulations slightly underestimate and overestimate near-surface minimum and maximum air temperature respectively, but capture the UHI intensity with biases in the order of a tenth of a degree. RCMs show significant differences in the variables used for the LWTs attribution, but the seasonal LWT frequencies are captured. Consequently, the reconstructed temperature fields maintain the small biases of the Meso-NH simulations and the statistical–dynamical downscaling greatly improves the UHI compared to the raw data of RCMs. [ABSTRACT FROM AUTHOR]

Forecasts with the European Centre for Medium-Range Weather Forecasts’ numerical weather prediction model are evaluated using an extensive set of observations from the Arctic Ocean 2018 expedition on the Swedish icebreaker Oden. The atmospheric model (Cy45r1) is similar to that used for the ERA5 reanalysis (Cy41r2). The evaluation covers 1 month,with the icebreaker moored to drifting sea ice near the North Pole; a total of 125 forecasts issued four times per day were used. Standard surface observations and 6-hourly soundings were assimilated to ensure that the initial model error is small. Model errors can be divided into two groups. First, variables related to dynamics feature errors that grow with forecast length; error spread also grows with time. Initial errors are small, facilitating a robust evaluation of the second group; thermodynamic variables. These feature fast error growth for 6–12 hr, after which errors saturates; error spread is roughly constant. Both surface and near-surface air temperatures are too warm in the model. During the summer both are typically above zero in spite of the ongoing melt; however, the warm bias increases as the surface freezes. The warm bias is due to a too warm atmosphere; errors in surface sensible heat flux transfer additional heat from the atmosphere to the surface. The lower troposphere temperature error has a distinct vertical structure: a substantial warm bias in the lowest few 100m and a large cold bias around 1 km; this structure features a significant diurnal cycle and is tightly coupled to errors in themodelled clouds. Clouds appear too often and in a too deep layer of the lower atmosphere; the lowest clouds essentially never break up. The largest error in cloud presence is aligned with the largest cold bias at around 1 km. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the ability of a set of large ensembles (LE) of Global Climate Model (GCM) simulations from the Multi‐Model Large Ensemble Archive, Coupled Model Intercomparison Project Phase 6 (CMIP6) and database for Policy Decision making for Future climate change in reproducing the variability and change of the austral summer precipitation observed in South America along the second part of the 20th century and beginning of the 21st. LE show similar biases in the mean austral summer rainfall and interannual variability than those detected in previous model sets, such as the Coupled Model Intercomparison Project Phase 5 (CMIP5). The positive trends in south‐eastern South America (SESA) and negative ones in the southern Andes (SAn), that are the most significant observed regional features, are identified to some extent in LE simulations. The negative trend in SAn is a feature consistently shown among different models. For all models, mean trend is negative in that particular region and larger than the inter‐member dispersion for each large ensemble. Trend magnitudes in the SESA region show a larger dispersion between models than the SAn region. While the mean trend among models is consistent with the observations, models underestimate the observed trend. The multi‐model ensemble of selected models that best reproduce both mean and interannual variability features of rainfall in South America shows an 18% larger positive mean trend in SESA rainfall than those that have the worst performance. However, internal variability uncertainty is still higher than the mean trend for both sets of multi‐model ensembles [5% (18%) for selected (non‐selected) models]. Improvements in the representation of the main features of mean rainfall and variability are needed in order to better describe and reduce the uncertainties associated with the main climate change related signals in the continent. [ABSTRACT FROM AUTHOR]

We investigate the impact of horizontal resolution, convection parameterization and lateral boundary conditions on the ability of a regional climate model ‐ RegCM4 to simulate the observed temperature and precipitation climatology over Croatia and surrounding regions. An ensemble of RegCM4 simulations over Europe has been generated including two resolutions (50 and 12.5 km), two different deep convection schemes and different boundary conditions (a reanalysis and four global climate models). Using two observational data sets (with and without the undercatch correction) shows that the magnitude of RegCM4 errors in winter precipitation also depends on observational uncertainty. While the mean precipitation amounts in high‐resolution simulations are generally overestimated in the cold part of the year, low‐resolution RegCM4 simulations are comparable to most of other EURO‐CORDEX models. When forced by ERA‐Interim, RegCM4 performs relatively poorly in reproducing temporal correlations against observations, but performs well in terms of spatial correlation. Whereas precipitation amounts from high‐resolution simulations need further improvements, the RegCM4 skill in simulating near‐surface temperature is high and well suited for impact studies. [ABSTRACT FROM AUTHOR]

In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles—parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change [ABSTRACT FROM AUTHOR]