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Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850-2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions model uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

Rising temperatures entail important changes in the soil hydrologic processes of the northern permafrost zone. Using the ICON‐Earth System Model, we show that a large‐scale thaw of essentially impervious frozen soil layers may cause a positive feedback by which permafrost degradation amplifies the causative warming. The thawing of the ground increases its hydraulic connectivity and raises drainage rates which facilitates a drying of the landscapes. This limits evapotranspiration and the formation of low‐altitude clouds during the snow‐free season. A decrease in summertime cloudiness, in turn, increases the shortwave radiation reaching the surface, hence, temperatures and advances the permafrost degradation. Our simulations further suggest that the consequences of a permafrost cloud feedback may not be limited to the regional scale. For a near‐complete loss of the high‐latitude permafrost, they show significant temperature impacts on all continents and northern‐hemisphere ocean basins that raise the global mean temperature by 0.25 K. Plain Language Summary: Landscapes in the Arctic and subarctic zone are often very wet with highly water saturated soils and an extensive lake‐ and wetland cover. To some extent, this is due to the perennially frozen soil layers that underlay large parts of these regions and inhibit the movement of water through the ground. Thus, a thawing of the frozen soils, caused by rising temperatures, may ultimately lead to a drying of the landscapes. Here, we use simulations with the ICON‐Earth System Model to show that such a drying increases regional temperatures via an atmospheric feedback: During the warm season, dryer conditions at the surface reduce the moisture transport into the atmosphere. This decreases the relative humidity in the boundary layer and the low‐altitude cloud cover. Since clouds reflect more sunlight than the snow‐free land surface, the reduced cloudiness increases the available energy, hence, temperatures and advances the thawing of the ground. Higher temperatures in the Arctic and subarctic zone, in turn, have important consequences for the net energy exchange between equatorial and polar regions. Thus, the effects of a large‐scale drying of high‐latitude soils may not be limited to the regional scale but could notably increase global mean temperatures. Key Points: Advanced degradation of permafrost may facilitate large‐scale landscape dryingDependency of clouds on terrestrial hydrology allows for feedback between permafrost thaw, diminished cloudiness and rising temperaturesThis feedback could amplify global warming notably [ABSTRACT FROM AUTHOR]

The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone, with differences in model structure and parametrizations being one of the main sources of uncertainty. One particularly challenging aspect in modelling is the representation of terrestrial processes in permafrost-affected regions, which are often governed by spatial heterogeneity far below the resolution of the models' land surface components. Here, we use the Max Planck Institute (MPI) Earth System Model to investigate how different plausible assumptions for the representation of permafrost hydrology modulate land–atmosphere interactions and how the resulting feedbacks affect not only the regional and global climate, but also our ability to predict whether the high latitudes will become wetter or drier in a warmer future. Focusing on two idealized setups that induce comparatively "wet" or "dry" conditions in regions that are presently affected by permafrost, we find that the parameter settings determine the direction of the 21st-century trend in the simulated soil water content and result in substantial differences in the land–atmosphere exchange of energy and moisture. The latter leads to differences in the simulated cloud cover during spring and summer and thus in the planetary energy uptake. The respective effects are so pronounced that uncertainties in the representation of the Arctic hydrological cycle can help to explain a large fraction of the inter-model spread in regional surface temperatures and precipitation. Furthermore, they affect a range of components of the Earth system as far to the south as the tropics. With both setups being similarly plausible, our findings highlight the need for more observational constraints on the permafrost hydrology to reduce the inter-model spread in Arctic climate projections. [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]