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Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Motivation: Accurate estimates of species distributions are crucial for biogeography, spatial conservation, and for assessing the impacts of human activities on species. However, existing approaches to estimate species distributions have typically neglected the influence of land use intensity, potentially overlooking the negative impacts of high‐intensity land uses on biodiversity. Here, we build a dataset documenting the habitat suitability of European land systems for terrestrial vertebrate species, based on a novel land system map of Europe that factors in land use intensity. Our database offers refined and up‐to‐date information on terrestrial vertebrate distributions in Europe by explicitly considering land use intensity. Main Types of Variables Contained: We created a table defining the suitability of land use classes as habitats for each species. We then built Area of Habitat (AOH) maps for each species by filtering out unsuitable habitat from the latest available estimates of species ranges. AOH maps were then compared with occurrence records from the Global Biodiversity Information Facility (GBIF). Processed datasets and R scripts are publicly available online, facilitating the use of our approach to refine expert‐based distributions for other taxa, land system classifications and regions worldwide. Spatial Location and Grain: The AOH maps cover the spatial extent of the European Union (EU) with the United Kingdom, Norway, Switzerland, and the Western Balkans. The AOH maps are at a 1 km2 resolution. Time Period and Grain: The dataset uses information published during the last 10 years. Major Taxa and Level of Measurement: Habitat suitability was documented for 1155 terrestrial vertebrate species known to occur in Europe: 279 mammals, 520 birds, 251 reptiles and 104 amphibians. Software Format: We provide the habitat suitability table in a comma‐separated values (csv) format. The AOH maps are available as raster files. R scripts are publicly accessible on GitHub. [ABSTRACT FROM AUTHOR]

Land surface processes, crucial for exchanging carbon, nitrogen, water, and energy between the atmosphere and terrestrial Earth, significantly impact the climate system. Many of these processes vary considerably at small spatial and temporal scales, in particular in mountainous terrain and complex topography. To examine the impact of spatial resolution and representativeness of input data on modelled land surface processes, we conducted simulations using the Community Land Model 5 (CLM5) at different resolutions and based on a range of input datasets over the spatial extent of Switzerland. Using high-resolution meteorological forcing and land use data, we found that increased resolution substantially improved the representation of snow cover in CLM5 (up to 52 % enhancement), allowing CLM5 to closely match performance of a dedicated snow model. However, a simple lapse-rate-based temperature downscaling provided large positive effects on model performance, even if simulations were based on coarse-resolution forcing datasets only. Results demonstrate the need for resolutions higher than 0.25° for accurate snow simulations in topographically complex terrain. These findings have profound implications for climate impact studies. As improvements were observed across the cascade of dependencies in the land surface model, high spatial resolution and high-quality forcing data become necessary for accurately capturing the effects of a declining snow cover and consequent shifts in the vegetation period, particularly in mountainous regions. This study further highlights the utility of multi-resolution modelling experiments when aiming to improve representation of variables in land surface models. By embracing high-resolution modelling, we can enhance our understanding of the land surface and its response to climate change. [ABSTRACT FROM AUTHOR]

A species pool is the set of species that could potentially colonize and establish within a community. It has been a commonly used concept in biogeography since the early days of MacArthur and Wilson’s work on Island Biogeography. Despite their simple and appealing definition, an operational application of species pools is bundled with a multitude of problems, which have often resulted in arbitrary decisions and workarounds when defining species pools. Two recently published papers address the operational problems of species pool delineations, and show ways of delineating them in a probabilistic fashion. In both papers, species pools were delineated using a process-based, mechanistical approach, which opens the door for a multitude of new applications in biogeography. Such applications include detecting the hidden signature of biotic interactions, disentangling the geographical structure of community assembly processes, and incorporating a temporal extent into species pools. Although similar in their conclusions, both ‘probabilistic approaches’ differ in their implementation and definitions. Here I give a brief overview of the differences and similarities of both approaches, and identify the challenges and advantages in their application.

Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate change impacts on biodiversity and ecosystem functioning. Here we combine a network of microclimate temperature measurements across different habitats and vertical heights with a novel radiative transfer model to map daily temperatures during the vegetation period at 10 m spatial resolution across Switzerland. Our results reveal strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 ∘ C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 ∘ C, respectively. We also found that, in open grasslands, maximum temperatures at 5 cm above ground are, on average, 3.4 ∘ C warmer than those of the macroclimate, suggesting that, in such habitats, heat exposure close to the ground is often underestimated when using macroclimatic data. Spatial interpolation was achieved by using a hybrid approach based on linear mixed-effect models with input from detailed radiation estimates from radiative transfer models that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography, and vegetation height. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by root mean squared errors ranging from 1.18 to 3.43 ∘ C, with minimum temperatures being predicted more accurately overall than maximum temperatures. The microclimate-mapping methodology presented here enables a biologically relevant perspective when analysing climate–species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change. [ABSTRACT FROM AUTHOR]

Aim: Reconstructing megafauna diversity in the past before anthropogenic impacts is crucial for developing targeted restoration strategies. We estimated the diversity and functional decline of European megafauna in the present compared with the nearest in‐time climate period analogue to the present but prior to the worldwide diffusion of Homo sapiens. Location: Europe. Time Period: Last Interglacial (LIG; ca. 127,000 years ago) to present. Major Taxa Studied: Wild, large (≥10 kg) terrestrial mammals. Methods: We assessed the distribution of 48 European megafauna species during the LIG using hindcasting modelling and fossil records. Then, we estimated the decline in megafauna community diversity and potential trait‐based functional effects from the LIG to the present, accounting for climate differences between the two periods. Results: Species richness and community biomass dropped by 70.8% (±11.7%) and by 94.5% (±9.9%). Functional diversity dropped by 80.3% (±15.3%) for herbivores and by 64.9% (±29.1%) for carnivores, while trait‐informed potential vegetation and meat consumptions dropped by 82.3% (±13.4%) and 60.5% (±26.0%). The loss in megafauna diversity and associated ecological processes were high everywhere, but particularly in western Europe for carnivores and in the East European Plain for herbivores. Potential megafauna richness in the two periods was similar if only climate‐driven differences were considered. Main Conclusions: Severe, size‐biased defaunation has degraded megafauna assemblages and megafauna‐mediated ecological processes across Europe from the LIG to the present. These patterns cannot be explained by climate differences between the two periods, thus were likely driven by prehistoric Homo sapiens. The results suggest that the structure of wild ecosystems of the present strongly deviates from the evolutionary norm, with decreased functional heterogeneity and decreased fluxes of biogeochemical compounds across the trophic networks, highlighting the importance of ambitious policies of megafauna community restoration to support ecosystem functioning. [ABSTRACT FROM AUTHOR]

Land surface processes, crucial for exchanging carbon, nitrogen, water, and energy between the atmosphere and terrestrial Earth, significantly impact the climate system. Many of these processes vary considerably at small spatial and temporal scales, in particular in mountainous terrain and complex topography. To examine the impact of spatial resolution and quality of input data on modeled land surface processes, we conducted simulations using the Community Land Model 5 (CLM5) at different resolutions and based on a range of input datasets over the spatial extent of Switzerland. Using high-resolution meteorological forcing and land-use data, we found that increased resolution not only improved the representation of snow cover in CLM5 (up to 52 % enhancement) but also propagated through the model, directly affecting gross primary productivity and evapotranspiration. These findings highlight the significance of high spatial resolution and high-confidence input datasets in land surface models, enabling better quantification and constraint of process uncertainties. They have profound implications for climate impact studies. As improvements were observed across the cascade of dependencies in the land surface model, high spatial resolution as well as high-quality forcing data becomes necessary for accurately capturing the impacts of recent climate change. This study further highlights the utility of multi-resolution modeling experiments when aiming to improve process-based representation of variables in land surface models. By embracing high-resolution modeling, we can enhance our understanding of Earth's systems and their responses to climate change. [ABSTRACT FROM AUTHOR]