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Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950-2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850-2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.

Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.

The variability of the zonal-mean zonal wind in the Southern Hemisphere is studied using EOF analysis and momentum budget diagnostics of NCEP reanalysis data (1978-97). The leading EOF of the zonal-mean zonal wind is well separated from the remaining EOFs and represents the north-south movement of the midlatitude jet. Analysis of the momentum budget shows that a positive feedback between the zonal-mean wind anomalies and the eddy momentum fluxes accounts for the unusual persistence of EOF1 and plays an important role in the selection of the leading EOF of midlatitude variability. Further analysis also shows a propagating feedback, common to both EOF1 and EOF2, which is responsible for the poleward drift of wind anomalies with time. The observations support the following feedback mechanism. Anomalous baroclinic wave activity is generated at the latitude of anomalous temperature gradient that, by thermal wind, coincides with the latitude of the anomalous zonal jet. The net propagation of baroclinic wave activity away from the jet gives momentum fluxes into the jet. This positive feedback is partially offset by low-frequency, equivalent barotropic eddies that propagate into the jet and remove momentum from it. The bias toward equatorward wave propagation on a sphere contributes to the poleward drift of the wind anomalies.

Output from the Climate Model Intercomparison Project Phase 3 (CMIP3) global climate models are statistically downscaled across Wisconsin, using a method that restores the observed mean, variance, and extremes of daily temperature and precipitation. The downscaled climate data for the late 20th century, mid-21st century, and late 21st-century is used to drive the National Weather Service operational snow model, SNOW-17, to produce high-resolution (0.1° × 0.1°) projections of daily snowfall, snow depth, and snow cover for Wisconsin. These snow projections will guide wildlife scientists in climate change impact studies and the development of adaptation strategies for the state, in addition to being of value to hydrologists, agricultural scientists, and other experts. SNOW-17 simulations suggest a dramatic shortening of the Wisconsin snow season, with the greatest snowfall and snow depth reductions in spring, particularly over northern Wisconsin. Snowfall is substantially reduced in response to projected warming and only slightly offset by a projected increase in cold-season precipitation. Percent reductions in snow depth are likely to be even more impressive than in snowfall, given not only a reduced frequency that falling precipitation will be in frozen form but also an enhanced snowmelt due to rising temperatures. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa., (© 2016 The Author(s).)