The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0

Understanding natural and anthropogenic climate change processes involves using computational models that represent the main components of the Earth system: the atmosphere, ocean, sea ice, and land surface. These models have become increasingly computationally expensive as resolution is increased and more complex process representations are included. However, to gain robust insight into how climate may respond to a given forcing, and to meaningfully quantify the associated uncertainty, it is often required to use either or both ensemble approaches and very long integrations. For this reason, more computationally efficient models can be very valuable tools. Here we provide a comprehensive overview of the suite of climate models based around the HadCM3 coupled general circulation model. This model was developed at the UK Met Office and has been heavily used during the last 15 years for a range of future (and past) climate change studies, but has now been largely superseded for many scientific studies by more recently developed models. However, it continues to be extensively used by various institutions, including the BRIDGE (Bristol Research Initiative for the Dynamic Global Environment) research group at the University of Bristol, who have made modest adaptations to the base HadCM3 model over time. These adaptations mean that the original documentation is not entirely representative, and several other relatively undocumented configurations are in use. We therefore describe the key features of a number of configurations of the HadCM3 climate model family, which together make up HadCM3@Bristol version 1.0. In order to differentiate variants that have undergone development at BRIDGE, we have introduced the letter B into the model nomenclature. We include descriptions of the atmosphere- only model (HadAM3B), the coupled model with a low-resolution ocean (HadCM3BL), the high-resolution atmosphere- only model (HadAM3BH), and the regional model (HadRM3B). These also include three versions of the land surface scheme. By comparing with observational datasets, we show that these models produce a good representation of many aspects of the climate system, including the land and sea surface temperatures, precipitation, ocean circulation, and vegetation. This evaluation, combined with the relatively fast computational speed (up to 1000 times faster than some CMIP6 models), motivates continued development and scientific use of the HadCM3B family of coupled climate models, predominantly for quantifying uncertainty and for long multi-millennial-scale simulations

Information retrieved from specimens at Natural History Collections can improve the quality of field-based ecological networks

Numerous studies analyze the interactions between plants and their pollinators in ecological communities using a network approach. However, field studies rarely record all the interactions occurring in the field. In this sense Natural History Collections (NHCs) can provide information on interactions that may have been missed by field sampling. In this study we compare a network based on field sampling with a network based on data retrieved from specimens at NHCs, and we assess the degree to which these two sources of data are complementary. For this we used data available from a bee biodiversity study conducted in Southern Argentina for the South American bee genus Corynura (Halictidae: Augochlorini). Data on the floral associations of the specimens at NHCs were retrieved from the specimens’ labels, as the name of the plant species on which a given bee was captured is often recorded for many specimens at NHCs. Although field sampling recorded an unusually high number of insect-plant interactions, it misses some unique interactions present in the NHCs networks. Some structural properties of these networks are briefly analyzed, and usefulness and limitations of using NHCs data are discussed. We conclude that the information about insect-plant interactions extracted from NHCs could complement field-based data, especially in poorly sampled communities.Fil: González Vaquero, Rocío Ana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Gravel, A. I.. York University; CanadáFil: Devoto, Mariano. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin