Abstract

The authors demonstrate that variability in vegetation cover can potentially influence oceanic variability through the atmospheric bridge. Experiments aimed at isolating the impact of variability in forest cover along the poleward side of the Asian boreal forest on North Pacific SSTs are performed using the fully coupled model, Fast Ocean Atmosphere Model–Lund Potsdam Jena (FOAM-LPJ), with dynamic atmosphere, ocean, and vegetation. The northern edge of the simulated Asian boreal forest is characterized by substantial variability in annual forest cover, with an east–west dipole pattern marking its first EOF mode. Simulations in which vegetation cover is allowed to vary over north/central Russia exhibit statistically significant greater SST variance over the Kuroshio Extension. Anomalously high forest cover over North Asia supports a lower surface albedo with higher temperatures and lower sea level pressure, leading to a reduction in cold advection into northern China and in turn a decrease in cold air transport into the Kuroshio Extension region. Variability in the large-scale circulation pattern is indirectly impacted by the aforementioned vegetation feedback, including the enhancement in upper-level jet wind variability along the north–south flanks of the East Asian jet stream.

Abstract

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.

Abstract

Transient simulations are presented of future climate and vegetation associated with continued rising levels of CO₂. The model is a fully coupled atmosphere–ocean–land–ice model with dynamic vegetation. The impacts of the radiative and physiological forcing of CO₂ are diagnosed, along with the role of vegetation feedbacks. While the radiative effect of rising CO₂ produces most of the warming, the physiological effect contributes additional warming by weakening the hydrologic cycle through reduced evapotranspiration. Both effects cause drying over tropical rain forests, while the radiative effect enhances Arctic and Indonesian precipitation.

A global greening trend is simulated primarily due to the physiological effect, with an increase in photosynthesis and total tree cover associated with enhanced water-use efficiency. In particular, tree cover is enhanced by the physiological effect over moisture-limited regions. Over Amazonia, South Africa, and Australia, the radiative forcing produces soil drying and reduced forest cover. A poleward shift of the boreal forest is simulated as both the radiative and physiological effects enhance vegetation growth in the northern tundra and the radiative effect induces drying and summertime heat stress on the central and southern boreal forest. Vegetation feedbacks substantially impact local temperature trends through changes in albedo and evapotranspiration. The physiological effect increases net biomass across most land areas, while the radiative effect results in an increase over the tundra and decrease over tropical forests and portions of the boreal forest.

Abstract

The tropical Pacific’s response to transiently increasing atmospheric CO₂ is investigated using three ensemble members from a numerically efficient, coupled atmosphere–ocean GCM. The model is forced with a 1% yr⁻¹ increase in CO₂ for 110 yr, when the concentration reaches 3 times the modern concentration. The transient greenhouse forcing causes a regionally enhanced warming of the equatorial Pacific, particularly in the far west. This accentuated equatorial heating, which is slow to arise but emerges abruptly during the last half of the simulations, results from both atmospheric and oceanic processes. The key atmospheric mechanism is a rapid local increase in the super–greenhouse effect, whose emergence coincides with enhanced convection and greater high cloud amount once the SST exceeds an apparent threshold around 27°C. The primary oceanic feedback is greater Ekman heat convergence near the equator, due to an anomalous near-equatorial westerly wind stress created by increased rising (sinking) air to the east (west) of Indonesia. The potential dependence of these results on the specific model used is discussed.

The suddenness and far-ranging impact of the enhanced, near-equatorial warming during these simulations suggests a mechanism by which abrupt climate changes may be triggered within the Tropics. The extratropical atmospheric response in the Pacific resembles anomalies during present-day El Niño events, while the timing and rapidity of the midlatitude changes are similar to those in the Tropics. In particular, a strengthening of the Pacific jet stream and a spinup of the wintertime Aleutian low seem to be forced by the changes in the tropical Pacific, much as they are in the modern climate.

Abstract

Vegetation feedbacks on climate, on the subannual time scale, are examined across six monsoon regions with a fully coupled atmosphere–ocean–ice–land model with dynamic vegetation. Initial value ensemble experiments are run in which the total vegetation cover fraction across the six monsoon regions is reduced and the climatic response assessed. Consistent responses among the regions include reductions in leaf area index, turbulent fluxes, and atmospheric moisture; enhanced subsidence; and increases in ground and surface air temperature. The most distinct changes in vertical motion, precipitable water, and precipitation occur along the flanks of the monsoon season, with small changes in midmonsoon rainfall. Unique responses to reduced vegetation cover are noted among the monsoon regions. While the monsoon is delayed and weaker over north Australia owing to diminished leaf area, it occurs earlier over China and the southwest United States. The subtropical monsoon regions are characterized by a larger decrease in sensible heat than latent heat flux, while the opposite is true for tropical monsoon regions. North Australia experiences the most substantial decline in both moisture flux convergence and precipitation.

Abstract

Projected changes in lake-effect snowfall by the mid- and late twenty-first century are explored for the Laurentian Great Lakes basin. Simulations from two state-of-the-art global climate models within phase 5 of the Coupled Model Intercomparison Project (CMIP5) are dynamically downscaled according to the representative concentration pathway 8.5 (RCP8.5). The downscaling is performed using the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 4 (RegCM4) with 25-km grid spacing, interactively coupled to a one-dimensional lake model. Both downscaled models produce atmospheric warming and increased cold-season precipitation. The Great Lakes’ ice cover is projected to dramatically decline and, by the end of the century, become confined to the northern shallow lakeshores during mid-to-late winter. Projected reductions in ice cover and greater dynamically induced wind fetch lead to enhanced lake evaporation and resulting total lake-effect precipitation, although with increased rainfall at the expense of snowfall. A general reduction in the frequency of heavy lake-effect snowstorms is simulated during the twenty-first century, except with increases around Lake Superior by the midcentury when local air temperatures still remain low enough for wintertime precipitation to largely fall in the form of snow. Despite the significant progress made here in elucidating the potential future changes in lake-effect snowstorms across the Great Lakes basin, further research is still needed to downscale a larger ensemble of CMIP5 model simulations, ideally using a higher-resolution, nonhydrostatic regional climate model coupled to a three-dimensional lake model.

Abstract

Projections of regional climate, net basin supply (NBS), and water levels are developed for the mid- and late twenty-first century across the Laurentian Great Lakes basin. Two state-of-the-art global climate models (GCMs) are dynamically downscaled using a regional climate model (RCM) interactively coupled to a one-dimensional lake model, and then a hydrologic routing model is forced with time series of perturbed NBS. The dynamical downscaling and coupling with a lake model to represent the Great Lakes create added value beyond the parent GCM in terms of simulated seasonal cycles of temperature, precipitation, and surface fluxes. However, limitations related to this rudimentary treatment of the Great Lakes result in warm summer biases in lake temperatures, excessive ice cover, and an abnormally early peak in lake evaporation. While the downscaling of both GCMs led to consistent projections of increases in annual air temperature, precipitation, and all NBS components (overlake precipitation, basinwide runoff, and lake evaporation), the resulting projected water level trends are opposite in sign. Clearly, it is not sufficient to correctly simulate the signs of the projected change in each NBS component; one must also account for their relative magnitudes. The potential risk of more frequent episodes of lake levels below the low water datum, a critical shipping threshold, is explored.

Abstract

The seasonal impacts of the dominant sea surface temperature (SST) modes to North American climate are assessed comprehensively in observations using the multivariate generalized equilibrium feedback assessment (GEFA) method. The GEFA method is first validated before applying it to observations. Impacts of each individual SST mode are quantified and the associated mechanisms are discussed. Four critical SST modes for North American climate are found: the ENSO mode, Indian Ocean Basin (IOB) mode, North Pacific first empirical orthogonal function (EOF) mode, and tropical Atlantic second EOF mode. The impacts of the ENSO mode are consistent with previous studies qualitatively, while the impact strength is further quantified here. The IOB mode has a strong influence on surface air temperature across North America, and it is demonstrated for the first time that its impact strength might even exceed that of ENSO during both winter and summer. The IOB mode also affects the year-round precipitation. A deeper understanding of the impact of North Pacific SSTs on wintertime surface air temperature is achieved: namely, positive SST anomalies in the Kuroshio Extension region correspond to colder (warmer) air in western (eastern) North America. The tropical Atlantic has a more significant influence on North American precipitation than does the extratropical Atlantic, with colder than normal tropical North Atlantic SSTs supporting wetter conditions across much of the United States, especially during autumn. Because of the linearity of GEFA, the total impacts of multiple SST modes can be obtained by the linear combination of each individual mode's impact. The GEFA method is a potentially powerful tool for seasonal climate prediction.

Abstract

This paper presents a comprehensive assessment of the observed influence of the global ocean on U.S. precipitation variability using the method of Generalized Equilibrium Feedback Assessment (GEFA), which enables an unambiguous attribution of the influence from multiple ocean basins within a unified framework. The GEFA assessment based on observations for 1950–99 suggests that the tropical Pacific SST variability has the greatest consequence for U.S. precipitation, as both ENSO and meridional modes are associated with notable responses in seasonal mean precipitation. The anomalously cold tropical Indian Ocean is a good indicator for U.S. dry conditions during spring and late winter. The impact of North Pacific SST variability is detected in springtime precipitation, yet it is overshadowed by that of the tropical Indo-Pacific on seasonal-to-interannual time scales. Tropical Atlantic forcing of U.S. precipitation appears to be most effective in winter, whereas the northern Atlantic forcing is likely more important during spring and summer.

Global ocean influence on U.S. precipitation is found to be most significant in winter, explaining over 20% of the precipitation variability in the Southwest and southern Great Plains throughout the cold seasons and in the northern Great Plains and northeast United States during late winter. The Southwest and southern Great Plains is likely the region that is most susceptible to oceanic influence, primarily to the forcing of the tropical Indo-Pacific. The Pacific Northwest is among the regions that may experience the least oceanic influence as far as precipitation variability is concerned.

Abstract

Credible modeling, tools, and guidance, regarding the changing Laurentian Great Lakes and the climatic impacts, are needed by local decision-makers to inform their management and planning. The present study addresses this need through a model evaluation study of the representation of lake–atmosphere interactions and resulting lake-effect snowfall in the Great Lakes region. Analysis focuses on an extensive ensemble of 74 historical simulations generated by 23 high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP). The model assessment addresses the modeling treatment of the Great Lakes, the spatial distribution and seasonality of climatological snowfall, the seasonal cycle of lake-surface temperatures and overlake turbulent fluxes, and the lake-effect ratio between upwind and downwind precipitation. A deeper understanding of model performance and biases is achieved by partitioning results between HighResMIP GCMs that are 1) coupled to 1D lake models versus GCMs that exclude lake models, 2) between prescribed-ocean model configurations versus fully coupled configurations, and 3) between deep Lake Superior versus relatively shallow Lake Erie. While the HighResMIP GCMs represent the Great Lakes by a spectrum of approaches that include land grid cells, ocean grid cells (with lake surface temperature and ice cover boundary conditions provided by the Met Office Hadley Center Sea Ice and Sea Surface Temperature Dataset), and 1D lake models, the current investigation demonstrates that none of these rudimentary approaches adequately represent the complex nature of seasonal lake temperature and ice cover evolution and its impact on lake–atmosphere interactions and lake-effect precipitation in the Great Lakes region.