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The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

Because contemporary climate change is driven by anthropogenic forcings, it is necessary to factor humanity's impact into climate models. Given that not all humans are responsible for current climate change, and that many people are already unequally impacted by climate change, it is necessary to develop a more nuanced understanding of the human in climate modeling. Without this nuance, future climate policy, as determined by climate modeling, will potentially replicate historic inequalities that are already present in attributions to and impacts from a rapidly changing climate. This paper begins by briefly outlining how climate models are built, and the ways they are used in climate politics. Then, borrowing methods from climate modelers, this paper turns towards Sylvia Wynter's work to model the human. The ultimate aim of this paper is to think more expansively and creatively about how genres of humanity can better be represented in climate science and policy with an eye towards more just futures. [ABSTRACT FROM AUTHOR]

We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice–Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25–125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques. [ABSTRACT FROM AUTHOR]

Clouds are a key player in the global climate system, affecting the atmospheric water and energy budgets, and they are strongly coupled to the large-scale atmospheric circulation. Here, we examine the co-variability of the atmospheric energy and water budget imbalances in three different global model configurations-radiative-convective equilibrium, aqua-planet, and global simulations with land. The gradual increase in the level of complexity of the model configuration enables an investigation of the effects of rotation, meridional temperature gradient, land-sea contrast, and seasonal cycle on the co-variability of the water and energy imbalances. We demonstrate how this co-variability is linked to both the large-scale tropical atmospheric circulation and to cloud properties. Hence, we propose a co-variability-based framework that connects cloud properties to the large-scale tropical circulation and climate system and is directly linked to the top-down constrains on the system--the water and energy budgets. In addition, we examine how the water and energy budget imbalances co-variability depends on the temporal averaging scale, and explain its dependency on how stationary the circulation is in the different model configurations. Finally, we demonstrate the effect of an idealized global warming and convective aggregation on this co-variability. Plain Language Summary Understanding the link between clouds and the atmospheric circulation is considered as a major challenge in climate science. Both atmospheric circulation and clouds are intimately linked to Earth's water and energy budgets. We show that looking at how the residuals of these budgets depend on each other and on cloud properties can provide a new framework with which to study the properties of clouds, circulations, and their respective impacts on the climate system. In this paper, we present this framework, and demonstrate its usefulness for a range of different climate model complexities. We show examples of how the framework could be used to investigate the effects of climate change on clouds and circulation. Finally, we present an application for convective aggregation (the clustering of deep convective clouds in idealized climate models). [ABSTRACT FROM AUTHOR]

A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are important warm-season precipitation systems in eastern China. However, our knowledge of their climatology and capability in their simulation is still insufficient. This paper examines their characteristics over the 2008–2017 warm seasons using convection-permitting climate simulations (CPCSs) with a 3-km grid spacing that explicitly resolves MCSs, as well as a high-resolution gauge-satellite merged precipitation product. An object-based tracking algorithm is applied to identify MCSs. Results indicate that the MCS genesis and occurrence are closely related to the progression of the East Asian monsoon and are modulated by the underlying topography. On average, about 243 MCSs are observed each season and contribute 19% and 47% to total and extreme warm-season precipitation. The climatological attributes and variabilities are reasonably reproduced in the CPCS. The major model deficiencies are excessive small MCS occurrence and overmuch MCS rainfall, consequently overestimating the precipitation contributions, whereas observational uncertainties may play a role too. Both the observed and simulated MCS precipitation feature a nocturnal or morning maximum and an eastward delayed diurnal peak east of the Tibetan Plateau, in contrast to the dominant afternoon peak of non-MCS precipitation. The favorable comparison with observations demonstrates the capability of CPCSs in simulating MCSs in the Asian monsoon climate, and its usefulness in projecting the future changes of MCSs under global warming. The finding that non-MCS precipitation is responsible for the high biased afternoon precipitation provides helpful guidance for further model improvement. [ABSTRACT FROM AUTHOR]

The East Asian winter monsoon (EAWM) is one of the most important climate systems during boreal winter. It has a large meridional extent and provides a vital bridge between the mid‐to‐high latitudes and the tropics. This paper reviews the latest research on the climate variability of the EAWM, with particular emphasis on the associated processes of extratropical–tropical interaction. Some new results have been achieved in understanding the variation of the EAWM on interdecadal, interannual, and intraseasonal time scales, such as the interdecadal strengthening in the early 21st century, the unstable impact of El Niño–South Oscillation (ENSO) on the EAWM, and the intraseasonal strong cold events in East Asia. In addition, understanding of the extratropical–tropical interactions related to the EAWM has improved in several aspects, including the impacts of the EAWM on ENSO‐related climate effects, and the variation of regional Hadley circulation over the western Pacific and its climate impacts in the Asia–Australia region. Moreover, recent work has systematically evaluated the capacity of climate models to simulate the EAWM, including its climatology, interannual to interdecadal variations, and its relationship with ENSO. Finally, some scientific issues regarding our understanding of the EAWM are proposed for future investigation. [ABSTRACT FROM AUTHOR]

The often neglected Guy Stewart Callendar (a steam engineer, for whom climate science was a hobby) was the first to bring together measurements of carbon dioxide concentration with how much the world had warmed from the late-nineteenth century in two key papers in I QJRMS i (in 1938 and 1961). David gave a brief history of climate science and how this has impacted UK Government Policy over the period since the late 1980s. Joanna also noted those who were sceptical of the importance of carbon dioxide (Knut Angstrom who erroneously stated that the carbon dioxide absorption bands were swamped by water vapour, and later ideas in the 1950s that carbon dioxide was being taken out of the atmosphere by the oceans much more than in reality). [Extracted from the article]

In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth's climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model (CESM). I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (MDE) and climate dynamic exploration (CDE). MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. [ABSTRACT FROM AUTHOR]

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed "epistemic inequality": this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can be seriously considered as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine , improve their capacity to quantify and express uncertainty in climate projections. [ABSTRACT FROM AUTHOR]

Slovenia is affected by extreme and intense rainfall that triggers numerous landslides every year, resulting in significant human impact and damage to infrastructure. Previous studies on landslides have shown how rainfall patterns can influence landslide occurrence, while in this paper, we present one of the first study in Slovenia to examine the impact of climate change on landslides in the mid-21st century. To do this, we used the Representative Concentration Pathway (RCP) 4.5 climate scenario and future climatology simulated by six climate models that differed from each other as much as possible while representing measured values of past climate variables as closely as possible. Based on baseline period (1981-2010) we showed the number of days with exceedance of rainfall thresholds and the area where landslides may occur more frequently in the projection period (2041-2070). We found that extreme rainfall events are likely to occur more frequent in the future, which may lead to a higher frequency of landslides in some areas. [ABSTRACT FROM AUTHOR]

In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth's climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model (CESM). I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (MDE) and climate dynamic exploration (CDE). MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. [ABSTRACT FROM AUTHOR]

Based on the 18 realizations of three state-of-the-art climate models participated in the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper examines the changes in climatology and trends of winter diurnal temperature range (DTR) over East Asia in response to natural and anthropogenic forcings. In the observation, the climatology of DTR during 1979–2014 displays a horizontal tile from southwest to northeast over East Asia. In addition, the observed DTR also presents an obviously declining trend in recent decades, with the magnitude of roughly −0.10 °C decade−1. The historical all-forcing simulations well reproduce the geographical distributions and magnitude of climatology and trends of winter DTR over East Asia in 1979–2014. Furthermore, the winter DTR exhibits a continuous declining trend from the historical scenario to the future SSP245 scenario over East Asia, with the most significant declining during the first half of twenty-first century (i.e., 2021–2060). Based on the individual forcing experiments, it suggests that the greenhouse gases forcing plays a primary role in driving the climatology and trends of DTR over East Asia, especially for the SSP245 scenario. Further analysis indicates that the increasing precipitation under greenhouse gases forcing largely explain the declining DTR trends over East Asia, especially over the northern East Asia both in historical and SSP245 scenarios. • The observed decreasing DTR trends are reasonably captured in the all-forcing historical simulations in CMIP6 models. • The greenhouse gas forcing plays a primary role in driving the declining DTR trends over East Asia • The declining of DTR is partly attributed to the increasing precipitation over East Asia in the greenhouse gas forcing. [ABSTRACT FROM AUTHOR]

Tornado outbreaks in the United States have caused large numbers of human and economic losses. The analysis of gridded climate data to find spatial patterns of meteorological variables that produce tornadoes is often a time consuming and manual process requiring visual inspection. Therefore, the ability to train convolutional neural networks (CNNs) to recognize patterns in climate data that produce tornado outbreaks would be a huge benefit to climatologists. In this paper, three CNNs of increasing complexity, including LeNet-5, VGG-16, and Resnet-50 were trained to predict days with tornado outbreaks of varying size based on gridded values of convective available potential energy (CAPE), convective inhibition (CIN), storm relative helicity (HLCY), and geopotential height. All three models showed promising results in predicting large tornado outbreaks with greater than 20 tornadoes with VGG-16 using the variable CAPE having a slightly better performance, highlighting the importance of CAPE in the analysis of tornado environments. • CNN models and climate grids were used to predict tornado outbreaks in the US. • A feature engineering pipeline was developed to enhance image features. • Models were trained on individual climate variables and all variables combined. • The combination of all variables with VGG-16 produced the best overall result. • VGG-16 with CAPE was best at predicting days with 20 or greater tornadoes. [ABSTRACT FROM AUTHOR]