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Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool needs to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as "CMORization". While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing of "native" climate model output, i.e., operational output produced by running the climate model through the standard workflow of the corresponding modeling institute. This is achieved by an extension of ESMValTool's preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitude–longitude grid. Extensions to ESMValTool's regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids. Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; at the moment, only surface variables are available), EC-Earth3, EMAC, ICON, and IPSL-CM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool need to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as CMORization. While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing native climate model output, i.e., raw output directly produced by the climate model. This is achieved by an extension of ESMValTool’s preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitudelongitude grid. Extensions to ESMValTool’s regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids.Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; will be fully available in ESMValTool v2.7.0), EC-Earth3, EMAC, ICON, and IPSLCM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added. [ABSTRACT FROM AUTHOR]

Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can – at least to some extent – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy. [ABSTRACT FROM AUTHOR]

Soil moisture-atmosphere feedback effects play an important role in several regions of the globe. For some of these regions, soil moisture memory may contribute significantly to the state and temporal variation of the regional climate. Identifying those regions can help to improve predictability in seasonal to decadal climate forecasts. In order to accurately simulate soil moisture memory and associated soil moisture-atmosphere interactions, an adequate representation of soil hydrology is required. The present study investigates how different setups of a soil hydrology scheme affect soil moisture memory simulated by the global climate model of the Max Planck Institute for Meteorology, ECHAM6/JSBACH. First, the standard setup is applied in which soil water is represented by a single soil moisture reservoir corresponding to the root zone. Second, a new five layer soil hydrology scheme is introduced where not only the root zone is differentiated into several layers but also layers below are added. Here, three variants of the new scheme are utilized to analyse how different characteristics of the soil hydrology and the associated fluxes influence soil moisture memory. Soil moisture memory of the different setups is analysed from global ECHAM6/JSBACH simulations forced by observed SST. Areas are highlighted where the regional climate seems to be sensitive to the improved representation of soil hydrology in the new setup and its variants. Results indicate that soil moisture memory is generally enlarged in regions during the dry season where a soil moisture buffer is present below the root zone due to the 5-layer scheme. This effect is usually enhanced when this buffer is increased. Memory tends to be weakened (strengthened) where bare soil evaporation is increased (decreased), especially in semi-arid regions and wet seasons. For some areas, this effect is compensated by a decreased (increased) transpiration. [ABSTRACT FROM AUTHOR]

The representation of long-term wetting and drying trends in total soil moisture and snow in response to anthropogenic climate change is a challenge for coupled climate models such as those participating in the CMIP5 (Coupled Model Intercomparison Project Phase 5). As a result, the inter-model spread regarding trends in such variables is large. In order to identify hot spot regions of climate-driven wetting and drying tendencies, we investigate whether satellite observed terrestrial water storage (TWS) trends obtained from 14 years (2002/04-2016/08) of GRACE (Gravity Recovery And Climate Experiment) data can be used for comparison to long-term (1861/01-2099/12) model derived TWS trends from 21 CMIP5 models.To account for discrepancies in physical representation of TWS in the CMIP5 models and GRACE as well as for interannual climate variability masking long-term trends in observations, we perform multiple analysis steps: First, we evaluate the spread in the long-term trends among the CMIP5 models and identify regions of large model consensus. Second, we relate long-term trends to trends from a historical period (1861/01-2016/08) to investigate if they are in principle detectable in present-day observations. Third, we assess the extent to which 14-year tendencies observed by GRACE can be expected to represent long-term trends. Based on this analysis, we identify hot spots of water storage trends characterized by 1) a strong model consensus, 2) a trend direction that is in agreement with GRACE.The reasons behind the wetting and drying trends in these hot spot regions are subsequently investigated by analyzing long-term changes in atmospheric conditions in the CMIP5 models. In doing so, we find that in 71% of these regions, the trends might be related to either long-term changes in vertical fluxes of precipitation, evaporation and sublimation (48%) or decrease in snowpack (23%). [ABSTRACT FROM AUTHOR]