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As part of the terrestrial branch of the Japan-funded Arctic Climate Change Research Project (GRENE-TEA), which aims to clarify the role and function of the Arctic terrestrial system in the climate system, and assess the influence of its changes on a global scale, this model intercomparison project (GTMIP) is planned and being conducted to (1) enhance communication and understanding between the "minds and hands" (i.e., between the modelling and field scientists) and (2) assess the uncertainty and variations stemming from variability in model implementation/design and in model outputs due to climatic and historical conditions in the Arctic terrestrial regions. This paper provides an overview and the experiment protocol of Stage 1 of the project, site simulations driven by statistically fitted data created using the GRENE-TEA site observations for the last three decades. The target metrics for the model evaluation cover key processes in both physics and biogeochemistry, including energy budgets, snow, permafrost, phenology, and carbon budgets. The preliminary results on four metrics (annual mean latent heat flux, annual maximum snow depth, gross primary production, and net ecosystem production) already demonstrate the range of variations in reproducibility among existing models and sites. Full analysis on annual as well as seasonal time scales, to be conducted upon completion of model outputs submission, will delineate inter-dependence among the key processes, and provide the clue for improving the model performance. [ABSTRACT FROM AUTHOR]

Models used to investigate impacts of climatic changes on spatio-temporal vegetation dynamics need to balance required accuracy with computational feasibility. To enhance the computational efficiency of these models, upscaling methods are required that maintain key fine-scale processes influencing vegetation dynamics. In this paper, an adjustable method – the dynamic two-layer classification concept (D2C) – for the upscaling of time- and space-discrete models is presented. D2C aims to separate potentially repetitive calculations from those specific to single grid cells. The underlying idea is to extract processes that do not require information about a grid cell's neighbourhood to a reduced-size non-spatial layer, which is dynamically coupled to the original two-dimensional layer. The size of the non-spatial layer is thereby adaptive and depends on dynamic classifications according to pre-specified similarity criteria. I present how D2C can be used in a model implementation on the example of TreeMig-2L, a new, efficient version of the intermediate-complexity forest-landscape model TreeMig. To discuss the trade-off between computational expenses and accuracy, as well as the applicability of D2C, I compare different model stages of TreeMig-2L via simulations of two different application scenarios. This comparison of different model stages demonstrates that applying D2C can strongly reduce computational expenses of processes calculated on the new non-spatial layer. D2C is thus a valuable upscaling method for models and applications in which processes requiring information about the neighbourhood constitute the minor share of the overall computational expenses. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts to better understand past, present and future climate changes in a multi-model context. A total of 21 Model Intercomparison Projects (MIPs) were endorsed in its 6th phase (CMIP6), which included 190 different experiments that were used to simulate 40000 years and produced around 40 PB of data in total. This paper shows the main results obtained from the collection of performance metrics done for CMIP6 (CPMIP). The document provides the list of partners involved, the CPMIP metrics per institution/model, and the approach used for the collection and the coordination behind this process. Furthermore, a section has been included to analyze the results and prove the usefulness of the metrics for the community. Moreover, we describe the main difficulties faced during the collection and propose recommendations for future exercises. [ABSTRACT FROM AUTHOR]

Due to its extension, geography and the presence of several under-developed or developing economies, the Central Asia domain of the Coordinated Regional climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, Regional Climate Models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for present-days, testing the effects of a set of singular physical parameterizations and their mutual interaction on the simulation of monthly and seasonal values of three variables that are important for impact studies: 2-meter temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo taking into consideration the ratio of forest fractions and the soil conductivity taking into account the ratio of liquid water and ice in the soil, allows to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The results for the mean climate appear to be independent of the observational dataset used for evaluation and of the boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area. [ABSTRACT FROM AUTHOR]

The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from General Circulation Model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. This paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided on the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments will also be facilitated by the sharing of diagnostic codes via this catalogue. Any code which implements diagnostics relevant to analysing clouds - including cloud-circulation interactions and the contribution of clouds to estimates of climate sensitivity in models - and which is documented in peer-reviewed studies can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs. [ABSTRACT FROM AUTHOR]