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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]

The assessment of tropical cyclone (TC) statistics requires the direct, objective, and automatic detection and tracking of TCs in reanalyses and model simulations. Research groups have independently developed numerous algorithms during recent decades in order to answer that need. Today, there is a large number of trackers that aim to detect the positions of TCs in gridded datasets. The questions we ask here are the following: does the choice of tracker impact the climatology obtained? And, if it does, how should we deal with this issue? This paper compares four trackers with very different formulations in detail. We assess their performances by tracking TCs in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. We find typical detection rates of the trackers around 80 %. At the same time, false alarm rates (FARs) greatly vary across the four trackers and can sometimes exceed the number of genuine cyclones detected. Based on the finding that many of these false alarms (FAs) are extra-tropical cyclones (ETCs), we adapt two existing filtering methods common to all trackers. Both post-treatments dramatically impact FARs, which range from 9 % to 36 % in our final catalogs of TC tracks. We then show that different traditional metrics can be very sensitive to the particular choice of tracker, which is particularly true for the TC frequencies and their durations. By contrast, all trackers identify a robust negative bias in ERA5 TC intensities, a result already noted in previous studies. We conclude by advising against using as many trackers as possible and averaging the results. A more efficient approach would involve selecting one or a few trackers with well-known and complementary properties. [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]

In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance. [ABSTRACT FROM AUTHOR]

This paper describes a new satellite simulator for the CLARA-A2 climate data record (CDR). This simulator takes into account the variable skill in cloud detection in the CLARA-A2 CDR by using a different approach to other similar satellite simulators to emulate the ability to detect clouds. In particular, the paper describes three methods to filter out clouds from climate models undetectable by observations. The first method is comparable to the current simulators in the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP), since it relies on a single visible cloud optical depth at 550 nm (τc) threshold applied globally to delineate cloudy and cloud-free conditions. Methods two and three apply long/lat-gridded values separated by daytime and nighttime conditions. Method two uses gridded varying τc as opposed to method one, which uses just a τc threshold, and method three uses a cloud probability of detection (POD) depending on the model τc. The gridded POD values are from the CLARA-A2 validation study by. Methods two and three replicate the relative ease or difficulty for cloud retrievals depending on the region and illumination. They increase the cloud sensitivity where the cloud retrievals are relatively straightforward, such as over midlatitude oceans, and they decrease the sensitivity where cloud retrievals are notoriously tricky, such as where thick clouds may be inseparable from cold snow-covered surfaces, as well as in areas with an abundance of broken and small-scale cumulus clouds such as the atmospheric subsidence regions over the ocean. The simulator, together with the International Satellite Cloud Climatology Project (ISCCP) simulator of the COSP, is used to assess Arctic clouds in the EC-Earth climate model compared to the CLARA-A2 and ISCCP H-Series (ISCCP-H) CDRs. Compared to CLARA-A2, EC-Earth generally underestimates cloudiness in the Arctic. However, compared to ISCCP and its simulator, the opposite conclusion is reached. Based on EC-Earth, this paper shows that the simulated cloud mask of CLARA-A2, using method three, is more representative of the CDR than method one used for the ISCCP simulator. The simulator substantially improves the simulation of the CLARA-A2-detected clouds, especially in the polar regions, by accounting for the variable cloud detection skill over the year. The approach to cloud simulation based on the POD of clouds depending on their τc , location, and illumination is the preferred one as it reduces cloudiness over a range of cloud optical depths. Climate model comparisons with satellite-derived information can be significantly improved by this approach, mainly by reducing the risk of misinterpreting problems with satellite retrievals as cloudiness features. Since previous studies found that the CLARA-A2 CDR performs well in the Arctic during the summer months, and that method three is more representative than method one, the conclusion is that EC-Earth likely underestimates clouds in the Arctic summer. [ABSTRACT FROM AUTHOR]

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in 5-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region's demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use 11 scenarios, varying the socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios demonstrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model. [ABSTRACT FROM AUTHOR]

TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates - internally consistently - the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process. [ABSTRACT FROM AUTHOR]

This paper presents a new version of HCLIM, a regional climate modelling system based on the ALADIN–HIRLAM numerical weather prediction (NWP) system. HCLIM uses atmospheric physics packages from three NWP model configurations, HARMONIE–AROME, ALARO and ALADIN, which are designed for use at different horizontal resolutions. The main focus of HCLIM is convection-permitting climate modelling, i.e. developing the climate version of HARMONIE–AROME. In HCLIM, the ALADIN and ALARO configurations are used for coarser resolutions at which convection needs to be parameterized. Here we describe the structure and development of the current recommended HCLIM version, cycle 38. We also present some aspects of the model performance. HCLIM38 is a new system for regional climate modelling, and it is being used in a number of national and international projects over different domains and climates ranging from equatorial to polar regions. Our initial evaluation indicates that HCLIM38 is applicable in different conditions and provides satisfactory results without additional region-specific tuning. HCLIM is developed by a consortium of national meteorological institutes in close collaboration with the ALADIN–HIRLAM NWP model development. While the current HCLIM cycle has considerable differences in model setup compared to the NWP version (primarily in the description of the surface), it is planned for the next cycle release that the two versions will use a very similar setup. This will ensure a feasible and timely climate model development as well as updates in the future and provide an evaluation of long-term model biases to both NWP and climate model developers. [ABSTRACT FROM AUTHOR]

The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8 ∘) and ocean (nominally 1 ∘) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada. [ABSTRACT FROM AUTHOR]

Coordinated experimental design and implementation has become a cornerstone of global climate modelling. Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models, by reducing the influence of ad hoc differences in model set-up or experimental boundary conditions. As it enters its 6th phase, the Coupled Model Intercomparison Project (CMIP6) has grown significantly in scope with the design and documentation of individual simulations delegated to individual climate science communities. The Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation, and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading-order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilize climate or avoid dangerous climate change. For over a decade, C4MIP has coordinated coupled climate-carbon cycle simulations, and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate-carbon cycle community has created this experimental design, the simulations also fit within the wider CMIP activity, conform to some common standards including documentation and diagnostic requests, and are designed to complement the CMIP core experiments known as the Diagnostic, Evaluation and Characterization of Klima (DECK). C4MIP has three key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation, (2) idealized coupled and partially coupled simulations with 1% per year increases in CO CO2 to enable diagnosis of feedback strength and its components, (3) future scenario simulations to project how the Earth system will re spond to anthropogenic activity over the 21st century and beyond. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set up and run the simulations. Particular attention is paid to boundary conditions, input data, and requested output diagnostics. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design. [ABSTRACT FROM AUTHOR]

While stand alone satellite and model aerosol products see wide utilization, there is a significant need in numerous atmospheric and climate applications for a fused product on a regular grid. Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003-2013 global 1×1° and 6-hourly modal aerosol optical thickness (AOT) reanalysis product. This data set can be applied to basic and applied Earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine- and coarse-mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite-retrieved precipitation, rather than the model field. The final reanalyzed fine- and coarse-mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine- and coarse-mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed. [ABSTRACT FROM AUTHOR]

The early Eocene (56 to 48 million years ago) is inferred to have been the most recent time that Earth's atmospheric CO2 concentrations exceeded 1000 ppm. Global mean temperatures were also substantially warmer than those of the present day. As such, the study of early Eocene climate provides insight into how a super-warm Earth system behaves and offers an opportunity to evaluate climate models under conditions of high greenhouse gas forcing. The Deep Time Model Intercomparison Project (DeepMIP) is a systematic model–model and model–data intercomparison of three early Paleogene time slices: latest Paleocene, Paleocene–Eocene thermal maximum (PETM) and early Eocene climatic optimum (EECO). A previous article outlined the model experimental design for climate model simulations. In this article, we outline the methodologies to be used for the compilation and analysis of climate proxy data, primarily proxies for temperature and CO2. This paper establishes the protocols for a concerted and coordinated effort to compile the climate proxy records across a wide geographic range. The resulting climate "atlas" will be used to constrain and evaluate climate models for the three selected time intervals and provide insights into the mechanisms that control these warm climate states. We provide version 0.1 of this database, in anticipation that this will be expanded in subsequent publications. [ABSTRACT FROM AUTHOR]

The need for assessing the risk of extreme weather events is ever increasing. In addition to quantification of risk today, the role of aggravating factors such as high population growth and changing climate conditions matters, too. We present the open-source software CLIMADA (CLIMate ADAptation), which integrates hazard, exposure, and vulnerability to compute the necessary metrics to assess risk and to quantify socio-economic impact. The software design is modular and object oriented, offering a simple collaborative framework and a parallelization strategy which allows for scalable computations on clusters. CLIMADA supports multi-hazard calculations and provides an event-based probabilistic approach that is globally consistent for a wide range of resolutions, suitable for whole-country to detailed local studies. This paper uses the platform to estimate and contextualize the damage of hurricane Irma in the Caribbean in 2017. Most of the affected islands are non-sovereign countries and also rely on overseas support in case disaster strikes. The risk assessment performed for this region, based on remotely available data available shortly before or hours after landfall of Irma, proves to be close to reported damage and hence demonstrates a method to provide readily available impact estimates and associated uncertainties in real time. [ABSTRACT FROM AUTHOR]

The sixth version of the Model for Interdisciplinary Research on Climate (MIROC), called MIROC6, was cooperatively developed by a Japanese modeling community. In the present paper, simulated mean climate, internal climate variability, and climate sensitivity in MIROC6 are evaluated and briefly summarized in comparison with the previous version of our climate model (MIROC5) and observations. The results show that the overall reproducibility of mean climate and internal climate variability in MIROC6 is better than that in MIROC5. The tropical climate systems (e.g., summertime precipitation in the western Pacific and the eastward-propagating Madden–Julian oscillation) and the midlatitude atmospheric circulation (e.g., the westerlies, the polar night jet, and troposphere–stratosphere interactions) are significantly improved in MIROC6. These improvements can be attributed to the newly implemented parameterization for shallow convective processes and to the inclusion of the stratosphere. While there are significant differences in climates and variabilities between the two models, the effective climate sensitivity of 2.6 K remains the same because the differences in radiative forcing and climate feedback tend to offset each other. With an aim towards contributing to the sixth phase of the Coupled Model Intercomparison Project, designated simulations tackling a wide range of climate science issues, as well as seasonal to decadal climate predictions and future climate projections, are currently ongoing using MIROC6. [ABSTRACT FROM AUTHOR]

This paper presents a system to perform large-ensemble climate stochastic forecasts. The system is based on random analogue sampling of sea-level pressure data from the NCEP reanalysis. It is tested to forecast a North Atlantic Oscillation (NAO) index and the daily average temperature in five European stations. We simulated 100-member ensembles of averages over lead times from 5 days to 80 days in a hindcast mode, i.e., from a meteorological to a seasonal forecast. We tested the hindcast simulations with the usual forecast skill scores (CRPS or correlation) against persistence and climatology. We find significantly positive skill scores for all timescales. Although this model cannot outperform numerical weather prediction, it presents an interesting benchmark that could complement climatology or persistence forecast. [ABSTRACT FROM AUTHOR]

The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden-Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing. [ABSTRACT FROM AUTHOR]

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP). [ABSTRACT FROM AUTHOR]

The PartMC-MOSAIC particle-resolved aerosol model was previously developed to predict the aerosol mixing state as it evolves in the atmosphere. However, the modeling framework was limited to a zero-dimensional box model approach without resolving spatial gradients in aerosol concentrations. This paper presents the development of stochastic particle methods to simulate turbulent diffusion and dry deposition of aerosol particles in a vertical column within the planetary boundary layer. The new model, WRF-PartMCMOSAIC-SCM, resolves the vertical distribution of aerosol mixing state. We verified the new algorithms with analytical solutions for idealized test cases and illustrate the capabilities with results from a 2-day urban scenario that shows the evolution of black carbon mixing state in a vertical column. [ABSTRACT FROM AUTHOR]

Model calibration (or "tuning") is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets, and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target. [ABSTRACT FROM AUTHOR]

The Global Monsoons Model Inter-comparison Project (GMMIP) has been endorsed by the panel of Coupled Model Inter-comparison Project (CMIP) as one of the participating model inter-comparison projects (MIPs) in the sixth phase of CMIP (CMIP6). The focus of GMMIP is on monsoon climatology, variability, prediction and projection, which is relevant to four of the "Grand Challenges" proposed by the World Climate Research Programme. At present, 21 international modeling groups are committed to joining GMMIP. This overview paper introduces the motivation behind GMMIP and the scientific questions it intends to answer. Three tiers of experiments, of decreasing priority, are designed to examine (a) model skill in simulating the climatology and interannual-to-multidecadal variability of global monsoons forced by the sea surface temperature during historical climate period; (b) the roles of the Interdecadal Pacific Oscillation and Atlantic Multidecadal Oscillation in driving variations of the global and regional monsoons; and (c) the effects of large orographic terrain on the establishment of the monsoons. The outputs of the CMIP6 Diagnostic, Evaluation and Characterization of Klima experiments (DECK), "historical" simulation and endorsed MIPs will also be used in the diagnostic analysis of GMMIP to give a comprehensive understanding of the roles played by different external forcings, potential improvements in the simulation of monsoon rainfall at high resolution and reproducibility at decadal timescales. The implementation of GMMIP will improve our understanding of the fundamental physics of changes in the global and regional monsoons over the past 140 years and ultimately benefit monsoons prediction and projection in the current century. [ABSTRACT FROM AUTHOR]

The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts. [ABSTRACT FROM AUTHOR]

Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model-data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85% of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter. [ABSTRACT FROM AUTHOR]

By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850--near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of theWorld Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: -- How does the Earth system respond to forcing? -- What are the origins and consequences of systematic model biases? -- How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs. [ABSTRACT FROM AUTHOR]

When the same weather or climate simulation is run on different high-performance computing (HPC) platforms, model outputs may not be identical for a given initial condition. While the role of HPC platforms in delivering better climate projections is to some extent discussed in the literature, attention is mainly focused on scalability and performance rather than on the impact of machine-dependent processes on the numerical solution. Here we investigate the behaviour of the Preindustrial (PI) simulation prepared by the UK Met Office for the forthcoming CMIP6 (Coupled Model Intercomparison Project Phase 6) under different computing environments. Discrepancies between the means of key climate variables were analysed at different timescales, from decadal to centennial. We found that for the two simulations to be statistically indistinguishable, a 200-year averaging period must be used for the analysis of the results. Thus, constant-forcing climate simulations using the HadGEM3-GC3.1 model are reproducible on different HPC platforms provided that a sufficiently long duration of simulation is used. In regions where El Niño–Southern Oscillation (ENSO) teleconnection patterns were detected, we found large sea surface temperature and sea ice concentration differences on centennial timescales. This indicates that a 100-year constant-forcing climate simulation may not be long enough to adequately capture the internal variability of the HadGEM3-GC3.1 model, despite this being the minimum simulation length recommended by CMIP6 protocols for many MIP (Model Intercomparison Project) experiments. On the basis of our findings, we recommend a minimum simulation length of 200 years whenever possible. [ABSTRACT FROM AUTHOR]

Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change. [ABSTRACT FROM AUTHOR]

An earth system model has been developed at Beijing Normal University (Beijing Normal University Earth System Model, BNU-ESM); the model is based on several widely evaluated climate model components and is used to study mechanisms of ocean-atmosphere interactions, natural climate variability and carbon-climate feedbacks at interannual to interdecadal time scales. In this paper, the model structure and individual components are described briefly. Further, results for the CMIP5 (Coupled Model Intercomparison Project phase 5) pre-industrial control and historical simulations are presented to demonstrate the model's performance in terms of the mean model state and the internal variability. It is illustrated that BNU-ESM can simulate many observed features of the earth climate system, such as the climatological annual cycle of surface-air temperature and precipitation, annual cycle of tropical Pacific sea surface temperature (SST), the overall patterns and positions of cells in global ocean meridional overturning circulation. For example, the El Niño-Southern Oscillation (ENSO) simulated in BNU-ESM exhibits an irregular oscillation between 2 and 5 years with the seasonal phase locking feature of ENSO. Important biases with regard to observations are presented and discussed, including warm SST discrepancies in the major upwelling regions, an equatorward drift of midlatitude westerly wind bands, and tropical precipitation bias over the ocean that is related to the double Intertropical Convergence Zone (ITCZ). [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of an experimentation strategy for investigating sensitivities in fast components of atmospheric general circulation models. The basic idea is to replace the traditional serial-in-time long-term climate integrations by representative ensembles of shorter simulations. The key advantage of the proposed method lies in its efficiency: since fewer days of simulation are needed, the computational cost is less, and because individual realizations are independent and can be integrated simultaneously, the new dimension of parallelism can dramatically reduce the turnaround time in benchmark tests, sensitivities studies, and model tuning exercises. The strategy is not appropriate for exploring sensitivity of all model features, but it is very effective in many situations. Two examples are presented using the Community Atmosphere Model, version 5. In the first example, the method is used to characterize sensitivities of the simulated clouds to time-step length. Results show that 3-day ensembles of 20 to 50 members are sufficient to reproduce the main signals revealed by traditional 5-year simulations. A nudging technique is applied to an additional set of simulations to help understand the contribution of physics-dynamics interaction to the detected time-step sensitivity. In the second example, multiple empirical parameters related to cloud microphysics and aerosol life cycle are perturbed simultaneously in order to find out which parameters have the largest impact on the simulated global mean top-of-atmosphere radiation balance. It turns out that 12-member ensembles of 10-day simulations are able to reveal the same sensitivities as seen in 4-year simulations performed in a previous study. In both cases, the ensemble method reduces the total computational time by a factor of about 15, and the turnaround time by a factor of several hundred. The efficiency of the method makes it particularly useful for the development of high-resolution, costly, and complex climate models. [ABSTRACT FROM AUTHOR]

In the ongoing political debate on climate change, global mean temperature change (▵Tglob) has become the yardstick by which mitigation costs, impacts from unavoided climate change, and adaptation requirements are discussed. For a scientifically informed discourse along these lines, systematic assessments of climate change impacts as a function of ▵Tglob) are required. The current availability of climate change scenarios constrains this type of assessment to a narrow range of temperature change and/or a reduced ensemble of climate models. Here, a newly composed dataset of climate change scenarios is presented that addresses the specific requirements for global assessments of climate change impacts as a function of ▵Tglob). A pattern-scaling approach is applied to extract generalised patterns of spatially explicit change in temperature, precipitation and cloudiness from 19 Atmosphere-Ocean General Circulation Models (AOGCMs). The patterns are combined with scenarios of global mean temperature increase obtained from the reducedcomplexity climate model MAGICC6 to create climate scenarios covering warming levels from 1.5 to 5 degrees above pre-industrial levels around the year 2100. The patterns are shown to sufficiently maintain the original AOGCMs' climate change properties, even though they, necessarily, utilise a simplified relationships between ▵ Tglob) and changes in local climate properties. The dataset (made available online upon final publication of this paper) facilitates systematic analyses of climate change impacts as it covers a wider and finer-spaced range of climate change scenarios than the original AOGCM simulations. [ABSTRACT FROM AUTHOR]

The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) consists of a series of time slice experiments targeting the long-term changes in atmospheric composition between 1850 and 2100, with the goal of documenting composition changes and the associated radiative forcing. In this overview paper, we introduce the ACCMIP activity, the various simulations performed (with a requested set of 14) and the associated model output. The 16 ACCMIP models have a wide range of horizontal and vertical resolutions, vertical extent, chemistry schemes and interaction with radiation and clouds. While anthropogenic and biomass burning emissions were specified for all time slices in the ACCMIP protocol, it is found that the natural emissions are responsible for a significant range across models, mostly in the case of ozone precursors. The analysis of selected present-day climate diagnostics (precipitation, temperature, specific humidity and zonal wind) reveals biases consistent with state-of-the-art climate models. The modelto- model comparison of changes in temperature, specific humidity and zonal wind between 1850 and 2000 and between 2000 and 2100 indicates mostly consistent results. However, models that are clear outliers are different enough from the other models to significantly affect their simulation of atmospheric chemistry. [ABSTRACT FROM AUTHOR]

Dramatic increases in climate data underlie a gradual paradigm shift in knowledge acquisition methods from physically based models to data-based mining approaches. One of the most popular data clustering/mining techniques is k -means, and it has been used to detect hidden patterns in climate systems; k -means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with "structured" data, that is, data in time and space domains, which are dominant in climate science. Here, we propose (i) a novel structural-similarity-recognition-based k -means algorithm called structural k -means or S k -means for climate data mining and (ii) a new clustering uncertainty representation/evaluation framework based on the information entropy concept. We demonstrate that the novel S k -means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we, for the first time, evaluated the "meaningfulness" of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k -means clustering with "structural" input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science. [ABSTRACT FROM AUTHOR]

ECOSMO II is a fully coupled bio-physical model of 3D hydrodynamics with an intermediate-complexity NPZD (nutrient, phytoplankton, zooplankton, detritus) type biology including sediment-water column exchange processes originally formulated for the North Sea and Baltic Sea. Here we present an updated version of the model incorporating chlorophyll a as a prognostic state variable: ECOSMO II(CHL). The version presented here is online coupled to the HYCOM ocean model. The model is intended to be used for regional configurations for the North Atlantic and the Arctic incorporating coarse to high spatial resolutions for hind-casting and operational purposes. We provide the full descriptions of the changes in ECOSMO II(CHL) from ECOSMO II and provide the evaluation for the inorganic nutrients and chlorophyll a variables, present the modelled biogeochemistry of the Nordic Seas and the Arctic, and experiment on various parameterization sets as use cases targeting chlorophyll a dynamics. We document the performance of each parameter set objectively analysing the experiments against in situ, satellite and climatology data. The model evaluations for each experiment demonstrated that the simulations are consistent with the large-scale climatological nutrient setting and are capable of representing regional and seasonal changes. Explicitly resolving chlorophyll a allows for more dynamic seasonal and vertical variations in phytoplankton biomass to chlorophyll a ratio and improves model chlorophyll a performance near the surface. Through experimenting with the model performance, we document the general biogeochemisty of the Nordic Seas and the Arctic. The Norwegian and Barents seas primary production show distinct seasonal patterns with a pronounced spring bloom dominated by diatoms and low biomass during winter months. The Norwegian Sea annual primary production is around double that of the Barents Sea while also having an earlier spring bloom. [ABSTRACT FROM AUTHOR]

Experiment outputs are now available from the Coupled Model Intercomparison Project's sixth phase (CMIP6) and the past climate experiments defined in the Palaeoclimate Modelling Intercomparison Project's fourth phase (PMIP4). All of this output is freely available from the Earth System Grid Federation (ESGF). Yet there is overhead in analysing this resource that may prove complicated or prohibitive. Here we document the steps taken by ourselves to produce ensemble analyses covering past and future simulations. We outline the strategy used to curate, adjust the monthly calendar aggregation and process the information downloaded from the ESGF. The results of these steps were used to perform analysis for several of the initial publications arising from PMIP4. We provide post-processed fields for each simulation, such as climatologies and common measures of variability. Example scripts used to visualise and analyse these fields are provided for several important case studies. [ABSTRACT FROM AUTHOR]

The article presents a study to investigate the impact of the parallel computational uncertainty due to the round-off error on climate simulations using the Community Climate System Model Version 3 (CCSM3). It conducted a series of sensitivity experiments and its analyses are focused on the Global and Nino3.4 average sea surface temperatures (SST). It suggested that the influence of the parallel computational uncertainty on Coupled General Climate Models (CGCMs) can be a quality standard.

The article presents a study which provides guidance about mapping climate variables and uncertainties in cartography. It highlights the general mapping guidelines proposed by the authors which include the use of colour symbolism, data classification scheme and a sensible sequential colour scheme. It is inferred that the approach is suitable for mapping information for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5).

The effects of horizontal resolution on the simulation of tropical cyclones were studied using the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Finite-Volume version 3 (FGOALS-f3) climate system model from the High-Resolution Model Intercomparison Project (HighResMIP) for the Coupled Model Intercomparison Project phase 6 (CMIP6). Both the low-resolution (about 100 km resolution) FGOALS-f3 model (FGOALS-f3-L) and the high-resolution (about 25 km resolution) FGOALS-f3 (FGOALS-f3-H) models were used to achieve the standard Tier 1 experiment required by HighResMIP. FGOALS-f3-L and FGOALS-f3-H have the same model parameterizations with the exactly the same parameters. The only differences between the two models are the horizontal resolution and the time step. The performance of FGOALS-f3-H and FGOALS-f3-L in simulating tropical cyclones was evaluated using observations. FGOALS-f3-H (25 km resolution) simulated more realistic distributions of the formation, movement and intensity of the climatology of tropical cyclones than FGOALS-f3-L at 100 km resolution. Although the number of tropical cyclones increased by about 50 % at the higher resolution and better matched the observed values in the peak month, both FGOALS-f3-L and FGOALS-f3-H appear to replicate the timing of the seasonal cycle of tropical cyclones. The simulated average and interannual variabilities of the number of tropical cyclones and the accumulated cyclone energy were both significantly improved from FGOALS-f3-L to FGOALS-f3-H over most of the ocean basins. The characteristics of tropical cyclones (e.g., the average lifetime, the wind–pressure relationship and the horizontal structure) were more realistic in the simulation using the high-resolution model. The possible physical linkage between the performance of the tropical cyclone simulation and the horizontal resolution were revealed by further analyses. The improvement in the response between the El Niño–Southern Oscillation and the number of tropical cyclones and the accumulated cyclone energy in FGOALS-f3 contributed to the realistic simulation of tropical cyclones. The genesis potential index and the vorticity, relative humidity, maximum potential intensity and the wind shear terms were used to diagnose the effects of resolution. We discuss the current insufficiencies and future directions of improvement for the simulation of tropical cyclones and the potential applications of the FGOALS-f3-H model in the subseasonal to seasonal prediction of tropical cyclones. [ABSTRACT FROM AUTHOR]

A key challenge in climate science is to quantify the forced response in impact-relevant variables such as precipitation against the background of internal variability, both in models and observations. Dynamical adjustment techniques aim to remove unforced variability from a target variable by identifying patterns associated with circulation, thus effectively acting as a filter for dynamically induced variability. The forced contributions are interpreted as the variation that is unexplained by circulation. However, dynamical adjustment of precipitation at local scales remains challenging because of large natural variability and the complex, nonlinear relationship between precipitation and circulation particularly in heterogeneous terrain. Building on variational autoencoders, we introduce a novel statistical model – the Latent Linear Adjustment Autoencoder (LLAAE) – that enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner. To predict circulation-induced precipitation, the Latent Linear Adjustment Autoencoder combines a linear component, which models the relationship between circulation and the latent space of an autoencoder, with the autoencoder's nonlinear decoder. The combination is achieved by imposing an additional penalty in the cost function that encourages linearity between the circulation field and the autoencoder's latent space, hence leveraging robustness advantages of linear models as well as the flexibility of deep neural networks. We show that our model predicts realistic daily winter precipitation fields at high resolution based on a 50-member ensemble of the Canadian Regional Climate Model at 12 km resolution over Europe, capturing, for instance, key orographic features and geographical gradients. Using the Latent Linear Adjustment Autoencoder to remove the dynamic component of precipitation variability, forced thermodynamic components are expected to remain in the residual, which enables the uncovering of forced precipitation patterns of change from just a few ensemble members. We extend this to quantify the forced pattern of change conditional on specific circulation regimes. Future applications could include, for instance, weather generators emulating climate model simulations of regional precipitation, detection and attribution at subcontinental scales, or statistical downscaling and transfer learning between models and observations to exploit the typically much larger sample size in models compared to observations. [ABSTRACT FROM AUTHOR]

Toward the goal of linking wind shear with the mesoscale organization of deep convection, a procedure for producing a climatology of tropical wind shear from the output of the Met Office Unified Model climate model is presented. Statistical information from wind profiles from tropical grid columns is used to produce a tractable number (10) of profiles that efficiently span the space of all wind profiles. Physical arguments are used to filter wind profiles that are likely to be associated with organized convection: only grid columns with substantial convective available potential energy (CAPE) and those with shear in the upper quartile are considered. The profiles are rotated so that their wind vectors at 850 hPa are aligned, in order to be able to group like profiles together, and their magnitudes at each level are normalized. To emphasize the effect of lower levels, where the organization effects of shear are thought to be strongest, the profiles above 500 hPa are multiplied by 14. Principal component analysis is used to truncate the number of dimensions of the profiles to seven (which explains 90 % of the variance), and the truncated profiles are clustered using a K-means clustering algorithm. The median of each cluster defines a representative wind profile (RWP). Each cluster contains information from thousands of wind profiles with different locations, times and 850 hPa wind directions. To summarize the clusters statistically, we interpret the RWPs as pseudo-wind profiles and display the geographic frequency, seasonal frequency and histograms of wind direction at 850 hPa for each cluster. Geographic patterns are evident, and certain features of the spatio-temporal distributions are matched to observed distributions of convective organization. The form of the RWPs is also matched to specific wind profiles from case studies of organized convection. By performing the analysis on climate-model output, we lay the foundations for the development of the representation of shear-induced organization in a convection parametrization scheme (CPS). This would use the same methodology to diagnose where the organization of convection occurs and modify the CPS in an appropriate manner to represent it. The procedure could also be used as a diagnostic tool for evaluating and comparing climate models. [ABSTRACT FROM AUTHOR]

Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create "ClimateNet" – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or "the background" at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning. [ABSTRACT FROM AUTHOR]

Many climate extremes, including heatwaves and heavy precipitation events, are projected to worsen under climate change, with important impacts for society. Future projections required for adaptation are often based on climate model simulations. Given finite resources, trade-offs must be made concerning model resolution, ensemble size, and level of model complexity. Here we focus on the resolution component. A given resolution can be achieved over a region using either global climate models (GCMs) or at lower cost using regional climate models (RCMs) that dynamically downscale coarser GCMs. Both approaches to increasing resolution may better capture small-scale processes and features (downscaling effect), but increased GCM resolution may also improve the representation of the large-scale atmospheric circulation (upscaling effect). The size of this upscaling effect is therefore important for deciding modelling strategies. Here we evaluate the benefits of increased model resolution for both global and regional climate models for simulating temperature, precipitation, and wind extremes over Europe at resolutions that could currently be realistically used for coordinated sets of climate projections at the pan-European scale. First we examine the benefits of regional downscaling by comparing EURO-CORDEX simulations at 12.5 and 50 km resolution to their coarser CMIP5 driving simulations. Secondly, we compare global-scale HadGEM3-A simulations at three resolutions (130, 60, and 25 km). Finally, we separate out resolution-dependent differences for HadGEM3-A into downscaling and upscaling components using a circulation analogue technique. Results suggest limited benefits of increased resolution for heatwaves, except in reducing hot biases over mountainous regions. Precipitation extremes are sensitive to resolution, particularly over complex orography, with larger totals and heavier tails of the distribution at higher resolution, particularly in the CORDEX vs. CMIP5 analysis. CMIP5 models underestimate precipitation extremes, whilst CORDEX simulations overestimate compared to E-OBS, particularly at 12.5 km, but results are sensitive to the observational dataset used, with the MESAN reanalysis giving higher totals and heavier tails than E-OBS. Wind extremes are somewhat stronger and heavier tailed at higher resolution, except in coastal regions where large coastal grid boxes spread strong ocean winds further over land. The circulation analogue analysis suggests that differences with resolution for the HadGEM3-A GCM are primarily due to downscaling effects. [ABSTRACT FROM AUTHOR]

In this study, we evaluate a set of high-resolution (25–50 km horizontal grid spacing) global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), developed as part of the EU-funded PRIMAVERA (Process-based climate simulation: Advances in high resolution modelling and European climate risk assessment) project, and from the EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) regional climate models (RCMs) (12–50 km horizontal grid spacing) over a European domain. It is the first time that an assessment of regional climate information using ensembles of both GCMs and RCMs at similar horizontal resolutions has been possible. The focus of the evaluation is on the distribution of daily precipitation at a 50 km scale under current climate conditions. Both the GCM and RCM ensembles are evaluated against high-quality gridded observations in terms of spatial resolution and station density. We show that both ensembles outperform GCMs from the 5th Coupled Model Intercomparison Project (CMIP5), which cannot capture the regional-scale precipitation distribution properly because of their coarse resolutions. PRIMAVERA GCMs generally simulate precipitation distributions within the range of EURO-CORDEX RCMs. Both ensembles perform better in summer and autumn in most European regions but tend to overestimate precipitation in winter and spring. PRIMAVERA shows improvements in the latter by reducing moderate-precipitation rate biases over central and western Europe. The spatial distribution of mean precipitation is also improved in PRIMAVERA. Finally, heavy precipitation simulated by PRIMAVERA agrees better with observations in most regions and seasons, while CORDEX overestimates precipitation extremes. However, uncertainty exists in the observations due to a potential undercatch error, especially during heavy-precipitation events. The analyses also confirm previous findings that, although the spatial representation of precipitation is improved, the effect of increasing resolution from 50 to 12 km horizontal grid spacing in EURO-CORDEX daily precipitation distributions is, in comparison, small in most regions and seasons outside mountainous regions and coastal regions. Our results show that both high-resolution GCMs and CORDEX RCMs provide adequate information to end users at a 50 km scale. [ABSTRACT FROM AUTHOR]

The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we analyze land–atmosphere interactions within four simulations performed by the WRF model from 1980 to 2012 over North America, using three different LSMs. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Our results show that the WRF simulation of the climatology of heat extremes can be 5 ∘ C warmer and 6 d longer depending on the employed LSM component, and similarly for cold extremes and heavy precipitation events. Areas showing large uncertainty in WRF-simulated extreme events are also identified in a model ensemble from three different regional climate model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. Thus, studies based on multi-model ensembles and reanalyses should include a variety of LSM configurations to account for the uncertainty arising from this model component or to test the performance of the selected LSM component before running the whole simulation. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating extreme temperature and precipitation events, which in turn will help to reduce uncertainties in climate model projections. [ABSTRACT FROM AUTHOR]

This work presents a new bias-correction method for precipitation over complex terrain that explicitly considers orographic characteristics. This consideration offers a good alternative to the standard empirical quantile mapping (EQM) method during colder climate states in which the orography strongly deviates from the present-day state, e.g. during glacial conditions such as the Last Glacial Maximum (LGM). Such a method is needed in the event that absolute precipitation fields are used, e.g. as input for glacier modelling or to assess potential human occupation and according migration routes in past climate states. The new bias correction and its performance are presented for Switzerland using regional climate model simulations at 2 km resolution driven by global climate model outputs obtained under perpetual 1990 and LGM conditions. Comparing the present-day regional climate model simulation with observations, we find a strong seasonality and, especially during colder months, a height dependence of the bias in precipitation. Thus, we suggest a three-step correction method consisting of (i) a separation into different orographic characteristics, (ii) correction of very low intensity precipitation, and (iii) the application of an EQM, which is applied to each month separately. We find that separating the orography into 400 m height intervals provides the overall most reasonable correction of the biases in precipitation. The new method is able to fully correct the seasonal precipitation bias induced by the global climate model. At the same time, some regional biases remain, in particular positive biases over high elevated areas in winter and negative biases in deep valleys and Ticino in winter and summer. A rigorous temporal and spatial cross-validation with independent data exhibits robust results. The new bias-correction method certainly leaves some drawbacks under present-day conditions. However, the application to the LGM demonstrates that it is a more appropriate correction compared to the standard EQM under highly different climate conditions as the latter imprints present-day orographic features into the LGM climate. [ABSTRACT FROM AUTHOR]

Anthropogenic increases in atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The integrated assessment community has quantified anthropogenic emissions for the shared socio-economic pathway (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentrations for these SSP scenarios – using the reduced-complexity climate–carbon-cycle model MAGICC7.0. We extend historical, observationally based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios, respectively. We also provide the concentration extensions beyond 2100 based on assumptions regarding the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non- CO2 emissions. By 2150, CO2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel-driven scenario projects CO2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from 66 % for the present day to roughly 68 % to 85 % by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterizations that reflect the Oslo Line-By-Line model results. In comparison to the representative concentration pathways (RCPs), the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are more evenly spaced and extend to lower 2100 radiative forcing and temperatures. Performing two pairs of six-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the March–April–May (MAM) season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (∼5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies' projections over the next 100 to 500 years unequivocally depict a "hockey-stick" upwards shape. The SSP concentration time series derived in this study provide a harmonized set of input assumptions for long-term climate science analysis; they also provide an indication of the wide set of futures that societal developments and policy implementations can lead to – ranging from multiple degrees of future warming on the one side to approximately 1.5 ∘ C warming on the other. [ABSTRACT FROM AUTHOR]

Based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5)-generation previous Institut Pierre Simon Laplace (IPSL) Earth system model, we designed a new version, IPSL-CM5A2, aiming at running multi-millennial simulations typical of deep-time paleoclimate studies. Three priorities were followed during the setup of the model: (1) improving the overall model computing performance, (2) overcoming a persistent cold bias depicted in the previous model generation and (3) making the model able to handle the specific continental configurations of the geological past. These developments include the integration of hybrid parallelization Message Passing Interface – Open Multi-Processing (MPI-OpenMP) in the atmospheric model of the Laboratoire de Météorologie Dynamique (LMDZ), the use of a new library to perform parallel asynchronous input/output by using computing cores as "I/O servers" and the use of a parallel coupling library between the ocean and the atmospheric components. The model, which runs with an atmospheric resolution of 3.75∘×1.875∘ and 2 to 0.5 ∘ in the ocean, can now simulate ∼100 years per day, opening new possibilities towards the production of multi-millennial simulations with a full Earth system model. The tuning strategy employed to overcome a persistent cold bias is detailed. The confrontation of a historical simulation to climatological observations shows overall improved ocean meridional overturning circulation, marine productivity and latitudinal position of zonal wind patterns. We also present the numerous steps required to run IPSL-CM5A2 for deep-time paleoclimates through a preliminary case study for the Cretaceous. Namely, specific work on the ocean model grid was required to run the model for specific continental configurations in which continents are relocated according to past paleogeographic reconstructions. By briefly discussing the spin-up of such a simulation, we elaborate on the requirements and challenges awaiting paleoclimate modeling in the next years, namely finding the best trade-off between the level of description of the processes and the computing cost on supercomputers. [ABSTRACT FROM AUTHOR]

Climate simulations require very complex numerical models. Unfortunately, they typically present biases due to parameterizations, choices of numerical schemes, and the complexity of many physical processes. Beyond improving the models themselves, a way to improve the performance of the modeled climate is to consider multi-model combinations. In the present study, we propose a method to select the models that yield a multi-model ensemble combination that efficiently reproduces target features of the observations. We used a neural classifier (self-organizing maps), associated with a multi-correspondence analysis to identify the models that best represent some target climate property. We can thereby determine an efficient multi-model ensemble. We illustrated the methodology with results focusing on the mean sea surface temperature seasonal cycle in the Senegalo-Mauritanian region. We compared 47 CMIP5 model configurations to available observations. The method allows us to identify a subset of CMIP5 models able to form an efficient multi-model ensemble. The future decrease in the Senegalo-Mauritanian upwelling proposed in recent studies is then revisited using this multi-model selection. [ABSTRACT FROM AUTHOR]

An aerosol climatology to represent aerosols in the radiation schemes of global atmospheric models was recently developed. We derived the climatology from a reanalysis of atmospheric composition produced by the Copernicus Atmosphere Monitoring Service (CAMS). As an example of an application in a global atmospheric model, we discuss the technical aspects of the implementation in the European Centre for Medium Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS) and the impact of the new climatology on the medium-range weather forecasts and 1-year simulations. The new aerosol climatology was derived by combining a set of model simulations with constrained meteorological conditions and an atmospheric composition reanalysis for the period 2003–2013 produced by the IFS. The aerosol fields of the reanalysis are constrained by assimilating the aerosol optical thickness (AOT) retrievals product by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. In a further step, we used modelled aerosol fields to correct the aerosol speciation and the vertical profiles of the aerosol reanalysis fields. The new climatology provides the monthly-mean mass mixing ratio of five aerosol species constrained by assimilated MODIS AOT. Using the new climatology in the ECMWF-IFS leads to changes in the direct aerosol radiative effect compared to the climatology previously implemented, which have a small but non-impact on the forecast skill of large-scale weather patterns in the medium-range. However, details of the regional distribution of aerosol radiative forcing can have a large local impact. This is the case for the area of the Arabian Peninsula and the northern Indian Ocean. Here changes in the radiative forcing of the mineral dust significantly improve the summer monsoon circulation. [ABSTRACT FROM AUTHOR]

The roughness sublayer (RSL) is one compartment of the surface layer (SL) where turbulence deviates from Monin–Obukhov similarity theory. As the computing power increases, model grid sizes approach the gray zone of turbulence in the energy-containing range and the lowest model layer is located within the RSL. From this perspective, the RSL has an important implication in atmospheric modeling research. However, it has not been explicitly simulated in atmospheric mesoscale models. This study incorporates the RSL model proposed by Harman and Finnigan (2007, 2008) into the Jiménez et al. (2012) SL scheme. A high-resolution simulation performed with the Weather Research and Forecasting model (WRF) illustrates the impacts of the RSL parameterization on the wind, air temperature, and rainfall simulation in the atmospheric boundary layer. As the roughness parameters vary with the atmospheric stability and vegetative phenology in the RSL model, our RSL implementation reproduces the observed surface wind, particularly over tall canopies in the winter season by reducing the root mean square error (RMSE) from 3.1 to 1.8 m s -1. Moreover, the improvement is relevant to air temperature (from 2.74 to 2.67 K of RMSE) and precipitation (from 140 to 135 mm per month of RMSE). Our findings suggest that the RSL must be properly considered both for better weather and climate simulations and for the application of wind energy and atmospheric dispersion. [ABSTRACT FROM AUTHOR]

We describe the development of the "Paleoclimate PLASIM-GENIE (Planet Simulator–Grid-Enabled Integrated Earth system model) emulator" PALEO-PGEM and its application to derive a downscaled high-resolution spatio-temporal description of the climate of the last 5×106 years. The 5×106 -year time frame is interesting for a range of paleo-environmental questions, not least because it encompasses the evolution of humans. However, the choice of time frame was primarily pragmatic; tectonic changes can be neglected to first order, so that it is reasonable to consider climate forcing restricted to the Earth's orbital configuration, ice-sheet state, and the concentration of atmosphere CO2. The approach uses the Gaussian process emulation of the singular value decomposition of ensembles of the intermediate-complexity atmosphere–ocean GCM (general circulation model) PLASIM-GENIE. Spatial fields of bioclimatic variables of surface air temperature (warmest and coolest seasons) and precipitation (wettest and driest seasons) are emulated at 1000-year intervals, driven by time series of scalar boundary-condition forcing (CO2 , orbit, and ice volume) and assuming the climate is in quasi-equilibrium. Paleoclimate anomalies at climate model resolution are interpolated onto the observed modern climatology to produce a high-resolution spatio-temporal paleoclimate reconstruction of the Pliocene–Pleistocene. [ABSTRACT FROM AUTHOR]