[1] The change in the extratropical circulation under global warming is studied using the climate models participating in the Intergovernmental Panel on Climate Change (IPCC) fourth assessment report. The IPCC models predict a strengthening and a poleward shift of the tropospheric zonal jets in response to global warming. The change in zonal jets is also accompanied by a strengthening and a poleward and upward shift of transient kinetic energy and momentum flux. Similar changes in circulation are simulated by a simple dry general circulation model (GCM) when the height of the tropopause is raised. The similarity between the simple GCM and the IPCC models suggests that the changes in midlatitude circulation are predominantly driven by a rise in the height of the tropopause, and that other factors such as increased moisture content and the change in the low-level pole-to-equator temperature gradient, play a secondary role. In addition, the variability about the ensemble-mean of the zonal wind response is significantly correlated with the variability of the tropopause height response over the polar cap, especially in the Southern Hemisphere.

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)(1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997). The leading EOF of the zonal-mean zonal wind is well separated from the remaining EOF's and represents the north/south movement of the mid-latitude 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 mid-latitude 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 which, 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 which 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.1

The variability of the zonal-mean zonal wind in the Northern Hemisphere winter (December-March) is studied using EOF analysis and momentum budget diagnostics of NCEP-NCAR reanalysis data (1976-2001). 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 westerlies. Analysis of the momentum budget shows that a positive feedback between the zonal-mean wind anomalies and the eddy momentum fluxes selects the leading EOF of midlatitude variability. Like the Southern Hemisphere, the baroclinic eddies reinforce the zonal wind anomalies while external Rossby waves damp the wind anomalies. In the Northern Hemisphere, the quasi-stationary eddies also reinforce the zonal wind anomalies, but the baroclinic eddies are most important for the positive eddy-zonal flow feedback. The observations support the following feedback mechanisms. 1) Above-normal baroclinic wave activity is generated in the region of enhanced westerlies. This leads to wave propagation out of the westerlies that is associated with reinforcing eddy momentum fluxes. 2) The westerly jet is a waveguide for external Rossby waves that tend to propagate into the jet and remove momentum from it. 3) The quasi-stationary waves respond to a refractive index anomaly in the high latitudes below the tropopause. During the high (low) index this anomaly is negative (positive) leading to an acceleration (deceleration) of the zonal wind in the high latitudes.

Statistically downscaled climate projections from nine global climate models (GCMs) are used to force a snow accumulation and ablation model (SNOW-17) across the central-eastern North American Landscape Conservation Cooperatives (LCCs) to develop high-resolution projections of snowfall, snow depth, and winter severity index (WSI) by the middle and late twenty-first century. Here, projections of a cumulative WSI (CWSI) known to influence autumn–winter waterfowl migration are used to demonstrate the utility of SNOW-17 results. The application of statistically downscaled climate data and a snow model leads to a better representation of lake processes in the Great Lakes basin, topographic effects in the Appalachian Mountains, and spatial patterns of climatological snowfall, compared to the original GCMs. Annual mean snowfall is simulated to decline across the region, particularly in early winter (December–January), leading to a delay in the mean onset of the snow season. Because of a warming-induced acceleration of snowmelt, the percentage loss in snow depth exceeds that of snowfall. Across the Plains and Prairie Potholes LCC and the Upper Midwest and Great Lakes LCC, daily snowfall events are projected to become less common but more intense. The greatest reductions in the number of days per year with a present snowpack are expected close to the historical position of the −5°C isotherm in December–March, around 44°N. The CWSI is projected to decline substantially during December–January, leading to increased likelihood of delays in timing and intensity of autumn–winter waterfowl migrations.

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 fossilpollen 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.