Search

Background: Ischemia reperfusion injury (IRI) predisposes to the formation of donor-specific antibodies, a factor contributing to chronic rejection and late allograft loss. Methods: We describe a mechanism underlying the correlative association between IRI and donor-specific antibodies by using humanized models and patient specimens. Results: IRI induces immunoglobulin M–dependent complement activation on endothelial cells that assembles an NLRP3 (NOD-like receptor pyrin domain-containing protein 3) inflammasome via a Rab5-ZFYVE21-NIK axis and upregulates ICOS-L (inducible costimulator ligand) and PD-L2 (programmed death ligand 2). Endothelial cell–derived interleukin-18 (IL-18) selectively expands a T-cell population (CD4+CD45RO+PD-1 hi ICOS+CCR2+CXCR5–) displaying features of recently described T peripheral helper cells. This population highly expressed IL-18R1 and promoted donor-specific antibodies in response to IL-18 in vivo. In patients with delayed graft function, a clinical manifestation of IRI, these cells were Ki-67+IL-18R1+ and could be expanded ex vivo in response to IL-18. Conclusions: IRI promotes elaboration of IL-18 from endothelial cells to selectively expand alloreactive IL-18R1+ T peripheral helper cells in allograft tissues to promote donor-specific antibody formation.

Continental-scale snow cover represents a broad thermal forcing on monthly-to-intraseasonal time scales, with the potential to modify local and remote atmospheric circulation. A previous GCM study reported robust transient-eddy responses to prescribed anomalous North American (NA) snow cover. The same set of experiments also indicated a robust upper-level stationary wave response during spring, but the nature of this response was not investigated until now. Here, the authors diagnose a deep, snow-induced, tropospheric cooling over NA and hypothesize that this may represent a pathway linking snow to the stationary wave response. A nonlinear stationary wave model is shown to reproduce the GCM stationary wave response to snow more accurately than a linear model, and results confirm that diabatic cooling is the primary driver of the stationary wave response. In particular, the total nonlinear effects due to cooling, and its interactions with transient eddies and orography, are shown to be essential for faithful reproduction of the GCM response. The nonlinear model results confirm that direct effects due to transients and orography are modest. However, with interactions between forcings included, the total effects due to these terms make important contributions to the total response. Analysis of observed NA snow cover and stationary waves is qualitatively similar to the patterns generated by the GCM and linear/nonlinear stationary wave models, indicating that the snow-induced signal is not simply a modeling artifact. The diagnosis and description of a snow–stationary wave relationship adds to the understanding of stationary waves and their forcing mechanisms, and this relationship suggests that large-scale changes in the land surface state may exert considerable influence on the atmosphere over hemispheric scales and thereby contribute to climate variability.

In this study, statistical linkages between autumn streamflow in the northeast United States and preceding summer sea surface temperatures are developed to establish predictive potential for climate-informed seasonal streamflow forecasts in this region. Predictor regions with physically plausible teleconnections to local streamflow are identified and evaluated in a multivariate and nonlinear framework using local regression techniques. Three such regions are identified, located in the Bering Sea, the tropical Pacific just west of Mexico, and the tropical Atlantic off the coast of Africa. Asymmetries in each region's univariate local regression result are apparent, and bivariate local regressions are used to attribute these asymmetries to interactions with physical mechanisms associated with the other two regions, and possibly other unaccounted for climatic predictors. A bivariate model including the tropical Pacific and tropical Atlantic regions yields the strongest local regression result, explaining 0.68 of the interannual streamflow variability. An analogous multivariate linear regression analysis is only able to explain 0.20 of the streamflow variability and thus the use of nonlinear methods' results in a marked improvement in streamflow simulation capability. Cross-validation considerably weakens the streamflow forecasts using this model; however, forecast skill may improve with a longer period of record or the inclusion of additional predictors. Copyright © 2010 Royal Meteorological Society

The snowpack is an important seasonal surface water storage reservoir that affects the availability of water resources during the spring and summer seasons in mid–high latitudes. Not surprisingly, interannual variations in snow cover extent and snow water equivalent have been extensively studied in arid regions such as western North America. This study broadens the focus by examining snow depth as an alternative snowpack metric, and considers its variability over different parts of North America. The authors use singular value decomposition (SVD) in conjunction with linear and partial correlation to show that regional snow-depth variations can be largely explained by the winter North Atlantic Oscillation (NAO) and the Pacific–North American (PNA) modes of atmospheric variability through distinct mechanistic pathways involving regional winter circulation patterns and hydrologic fluxes. The high index phase of the NAO generates positive winter air temperature anomalies over eastern parts of North America, causing thinning of the winter snowpack via snowmelt. Meanwhile, the high index phase of the PNA generates negative winter snowfall anomalies across midlatitudinal areas of North America, which also serve to thin the snowpack. Positive PNA anomalies have also been shown to increase temperatures and decrease snow depths over western North America. The PNA influence extends across the continent, whereas the NAO influence is limited to eastern North America. The winter snow-depth variations associated with all of these pathways exhibit seasonal persistence, which ultimately yield regional-scale spring snow-depth anomalies throughout much of North America.

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Abstract: Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility.

The radiative and thermal properties of widespread snow cover anomalies have the potential to modulate local and remote climate over monthly to seasonal time scales. In this study, physical and dynamical links between anomalous North American snow conditions and Northern Hemisphere climate are examined. A pair of 40-member ensemble AGCM experiments is run, with prescribed high- and low-snow forcings over North America during the course of an entire year (EY). The difference between the two ensemble averages reflects the climatic response to sustained EY snow forcing. Local surface responses over the snow forcing occur in all seasons, and a significant remote surface temperature response occurs over Eurasia during spring. A hemispheric-scale transient eddy response to EY forcing also occurs, which propagates downstream from the forcing region to Eurasia, and then reaches a maximum in extent and amplitude in spring. The evolution of this transient eddy response is indicative of considerable downstream development and is consistent with known storm-track dynamics. This transient response is shown to be a result of persistent steepened temperature gradients created by the anomalous snow conditions, which contribute to enhanced baroclinicity over the storm-track entrance regions. A second pair of experiments is run, with the prescribed high- and low-snow forcings over North America restricted to the fall season (FS). The dynamical response to FS forcing is muted compared to the EY scenario, suggesting that the seasonal timing and persistence of the snow forcing are essential for the remote teleconnection.

The wintertime Pacific–North American (PNA) teleconnection pattern has previously been shown to influence springtime snow conditions over portions of North America. This paper develops a more complete physical understanding of this linkage across the continent, using a recently released long-term, continental-scale gridded North American snow depth dataset and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis data. An empirical orthogonal function–based filtering process is used to identify and isolate the interannual snow depth variations associated with PNA. Then linear and partial correlations are employed to investigate the physical mechanisms that link winter PNA with spring snow depth. In the positive phase of PNA, the enhanced PNA pressure centers lead to warmer temperatures over northwestern North America and less precipitation at midlatitudes. The temperature and precipitation pathways act independently and in distinct geographical regions, and together they serve to reduce winter snow depth across much of North America. Winter anomalies in the snow depth field then tend to persist into spring. Dynamic mechanisms responsible for the PNA-influenced North American precipitation and temperature anomalies, involving moisture transport and cold air intrusions, are confirmed in this study and also extended to continental snow depth anomalies.

Snow–atmosphere relationships have been studied for nearly half a century, but the primary focus has been on snow extent variability, largely because of the relative scarcity of snow depth data. A recently released North American snow depth dataset, with extensive spatial coverage and multidecadal temporal duration, provides a new opportunity to compare snow depth–climate relationships with snow extent–climate relationships over North America. Robust concurrent lead and lag correlations are observed between snow depth and two major climate modes, the Pacific decadal oscillation (PDO) and the Pacific–North America (PNA) pattern, across North America and throughout the snow season. In contrast, snow extent exhibits a less coherent relationship with PDO and PNA except in late spring, which can be interpreted as a residual of the snow depth–climate mode relationship. A regional signature for the snow depth–PDO/PNA relationship is also identified, centered over interior central-western North America. Smaller scales mask the regional effect of PDO and PNA because of local snow depth variability, while larger continental scales exceed the regional domain of the climate mode teleconnections. Overall these results suggest that North American snow depth variability may have greater climatic causes and consequences than snow extent. Physical mechanisms that may be responsible for the observed snow depth–climate teleconnection patterns such as the surface energy balance, moisture transport, and atmospheric flow regimes are briefly discussed.

Snow extent and snow depth are two related characteristics of a snowpack, but they do not need to be mutually consistent. Differences between these two variables at local scales are readily apparent. However, at larger scales, which interact with atmospheric circulation and climate, snow extent is the primary variable considered, owing largely to the scarcity of snow depth data. In this study, three regional-/continental-scale gridded snow depth/snow water equivalent (SWE) datasets, derived from station observations or passive microwave satellite sensors, are utilized to quantitatively evaluate the relationship between snow extent and snow depth/SWE over North America. Various statistical methods are used to ensure the robustness of the results, including correlations, composites, and singular value decomposition analyses. Results indicate that continental-scale snow depth variations are substantial in their own right and that depth and extent anomalies are largely unrelated over broad high-latitude regions north of the snow line. Snow extent and snow depth vary more consistently in the vicinity of the snow line, especially in autumn and spring, during which precipitation and ablation can affect both variables. It is also found that deeper (shallower) winter snow translates into larger (smaller) snow-covered areas in the following spring/summer season, and also a longer (shorter) snow season, but only in specific regions. These results suggest a possible influence of snow depth on spring and summer climate. Overall, the observed lack of mutual consistency between these two snowpack variables at continental/regional scales suggests that snow depth variations may be of sufficiently large magnitude, spatial scope, and temporal duration to interact with regional–hemispheric climate, in a manner unrelated to the more extensively studied snow extent variations.

This paper presents a synopsis of recently published studies by the co-authors, which show that several land surface characteristics unique to Northern Eurasia are responsible for facilitating a causal relationship between autumn snow anomalies in this region and subsequent hemispheric winter climate patterns. The large size and extratropical location of the contiguous Eurasian land mass results in broad, continental-scale interannual snow cover extent and depth variations throughout autumn and winter, and corresponding diabatic heating anomalies. These surface anomalies occur in the presence of a large region of stationary wave activity, produced in part by the orographic barriers that separate northern/central Eurasia from southern/eastern Eurasia. This co-location of snow-forced anomalies and ambient wave energy is unique to Northern Eurasia, and initiates a teleconnection pathway involving stationary wave–mean flow interaction throughout the troposphere and stratosphere, ultimately resulting in a modulation of the winter Arctic Oscillation (AO). Complementary new results are also presented which show that partial snow cover extent or snow depth only anomalies in Northern Eurasia are insufficient to initiate the teleconnection pathway and produce a winter AO signal. This synopsis provides a useful interpretation of the earlier studies in the specific context of Northern Eurasia regional climate and environmental change.

Previous modeling studies have identified a teleconnection pathway linking observation-based early season Siberian snow perturbations to a modulation of the winter Arctic Oscillation (AO) mode. In this study, the key role of orography in producing this modeled teleconnection is explicitly investigated using numerical experiments analogous to the previous studies. The climatic response to the same snow perturbation is investigated under modified orographic barriers in southern and eastern Siberia. Reducing these barriers results in a weakening of the prevailing orographically forced region of stationary wave activity centered over Siberia, as well as the snow-forced upward wave flux anomaly that initiates the teleconnection. This diminished anomaly propagates upward, but does not extend into the stratosphere to weaken the polar vortex. Consequently, poleward refraction of upper-tropospheric waves and downward propagation of coupled wave‐mean flow anomalies, which ultimately produce the negative winter AO response, fail to develop. Thus, the mountains represent an orographic constraint on the snow‐AO teleconnection pathway. By reducing the orographic barrier, the snow-forced influx of wave energy remains in the troposphere and, instead, produces a hemispheric-scale equatorward wave refraction.

Wintertime Northern Hemisphere climate variability is investigated using large-ensemble (20) numerical GCM simulations. Control simulations with climatological surface (land and ocean) conditions indicate that the Arctic Oscillation (AO) is an internal mode of the Northern Hemisphere atmosphere, and that it can be triggered through a myriad of perturbations. In this study the role of autumn land surface snow conditions is investigated. Satellite observations of historical autumn‐winter snow cover are applied over Siberia as model boundary conditions for two snow-forced experiments, one using the highest observed autumn snow cover extent over Siberia (1976) and another using the lowest extent (1988). The ensemble-mean difference between the two snow-forced experiments is computed to evaluate the climatic response to Siberian snow conditions. Experiment results suggest that Siberian snow conditions exert a modulating influence on the predominant wintertime Northern Hemisphere (AO) mode. Furthermore, an atmospheric teleconnection pathway is identified, involving well-known wave‐ mean flow interaction processes throughout the troposphere and stratosphere. Anomalously high Siberian snow increases local upward stationary wave flux activity, weakens the stratospheric polar vortex, and causes uppertroposphere stationary waves to refract poleward. These related stationary wave and mean flow anomalies propagate down through the troposphere via a positive feedback, which results in a downward-propagating negative AO anomaly during the winter season from the stratosphere to the surface. This pathway provides a physical explanation for how regional land surface snow anomalies can influence winter climate on a hemispheric scale. The results of this study may potentially lead to improved predictions of the winter AO mode, based on Siberian snow conditions during the preceding autumn.

Numerous studies have hypothesized that surface boundary conditions or other external mechanisms drive the hemispheric mode of atmospheric variability known as the Arctic Oscillation (AO), or its regional counterpart, the North Atlantic Oscillation (NAO). However, no single external factor has emerged as the dominant forcing mechanism, which has led, in part, to the characterization of the AO–NAO as a fundamental internal mode of the atmospheric system. Nevertheless, surface forcings may play a considerable role in modulating, if not driving, the AO–NAO mode. In this study, a pair of large-ensemble atmospheric GCM experiments (with SST climatology), one with prescribed climatological snow mass and another with freely varying snow mass, is conducted to investigate the degree to which the AO–NAO is modulated by interannual variability of surface snow conditions. Statistical analysis of the results indicates that snow anomalies are not required to produce the AO–NAO mode of variability. Nevertheless...

Limpasuvan et al. (2004, hereafter LTH) consider thelife cycle of sudden stratospheric warmings (SSWs)from onset to maturity and eventually decay by com-positing multiple events over the course of 80 days.Their main emphasis is on the mature phases of the lifecycle when there is a clear chain of downward strato-sphere-to-troposphere propagation of anomalies. Inthis comment, we aim to focus on the onset resultsreported in LTH by placing the results in the context ofmore regional phenomena reported in earlier studiesand providing a more comprehensive framework forunderstanding wintertime Northern Hemisphere cli-mate variability and improved predictability.Among the results presented, LTH show that SSWsare associated with antecedent Eliassen–Palm (EP) fluxconvergence from the troposphere. These upward-propagating EP flux anomalies precede the large-scaleanomalies in the stratosphere, and subsequent down-ward-propagating anomalies yield an Arctic Oscillation(AO) signal in the lower troposphere. A number ofprevious studies provide evidence that the forcing forthe real or apparent downward propagation of zonallysymmetric, large-scale height and wind anomalies fromthe stratosphere to the troposphere has its origins re-gionally in the lower troposphere (Kuroda and Kodera1999; Cohen et al. 2002). Furthermore, these lower-tropospheric precursors have also been linked toanomalies in Eurasian snow cover (Saito et al. 2001;Gong et al. 2003), which antecede even the atmosphericprecursors. The EP flux anomalies shown in LTH aresimilar to the wave fluxes presented in Kuroda andKodera (1999), Saito et al. (2001), and Gong et al.(2003), which were shown to perturb the stratosphereand originate over Eurasia, but were not considered byLTH, who limit their analysis to zonal means. Kurodaand Kodera (1999) also show wave fluxes over the At-lantic sector, but these are observed only during themature phase of an AO event and their influence isconfined to the troposphere.Also, LTH show statistically significant heightanomalies in the both the stratosphere and the tropo

[1] The loss of Arctic sea ice has wide-ranging impacts, some of which are readily apparent and some of which remain obscure. For example, recent observational studies suggest that terrestrial snow cover may be affected by decreasing sea ice. Here, we examine a possible causal link between Arctic sea ice and Siberian snow cover during the past 3 decades using a suite of experiments with the National Center for Atmospheric Research Community Atmospheric Model version 3. The experiments were designed to isolate the influence of surface conditions within the Arctic Ocean from other forcing agents such as low-latitude sea surface temperatures and direct radiative effects of increasing greenhouse gases. Only those experiments that include the observed evolution of Arctic sea ice and sea surface temperatures result in increased snow depth over Siberia, while those that maintain climatological values for Arctic Ocean conditions result in no snow signal over Siberia. In the former, Siberian precipitation and air temperature both increase, but because surface air temperatures remain below freezing during most months, the snowpack thickens over this region. These results suggest that Arctic Ocean surface forcing is necessary and sufficient to induce a Siberian snow signal, and that other forcings in combination can modulate the strength and geographic extent of the response.

Simulated climates using numerical atmospheric general circulation models (GCMs) have been shown to be highly sensitive to the fraction of GCM grid area assumed to be wetted during rain events. The model hydrologic cycle and land-surface water and energy balance are influenced by the parameter bar-kappa, which is the dimensionless fractional wetted area for GCM grids. Hourly precipitation records for over 1700 precipitation stations within the contiguous United States are used to obtain observation-based estimates of fractional wetting that exhibit regional and seasonal variations. The spatial parameter bar-kappa is estimated from the temporal raingauge data using conditional probability relations. Monthly bar-kappa values are estimated for rectangular grid areas over the contiguous United States as defined by the Goddard Institute for Space Studies 4 deg x 5 deg GCM. A bias in the estimates is evident due to the unavoidably sparse raingauge network density, which causes some storms to go undetected by the network. This bias is corrected by deriving the probability of a storm escaping detection by the network. A Monte Carlo simulation study is also conducted that consists of synthetically generated storm arrivals over an artificial grid area. It is used to confirm the bar-kappa estimation procedure and to test the nature of the bias and its correction. These monthly fractional wetting estimates, based on the analysis of station precipitation data, provide an observational basis for assigning the influential parameter bar-kappa in GCM land-surface hydrology parameterizations.

[1] Previous literature has established a relationship between snow cover and climate on large and small scales, but has focused on the influence of the presence versus absence of snow cover. A small body of literature indicates that snow depth can also influence properties of the atmosphere, through its ability to insulate the land surface. In this study, we investigate the local surface air temperature response to perturbations in snow water equivalent over the course of a single season, using the regional climate model RegCM3. Snow depth is specified as a percentage of the climatological snow water equivalent averaged over a small domain within a larger 3000 × 3000 km model domain, both centered at Minneapolis–St. Paul, Minnesota, United States (44.87°N, 93.22°W). In response to a specified increase in snow water equivalent corresponding to a shift from shallow ( 31 cm) snow, we find a seasonally averaged reduction in surface air temperatures that is on the order of 1°C, with smaller and larger responses observed within a season. Coincident with this change, we find reduced upward sensible heat flux at the surface. Both results are consistent with those of previous studies when differences in methodology are accounted for. The response achieved here extends vertically and horizontally beyond the forcing domain, suggesting the potential for the influence of snow depth on large-scale features of climate.

[1] The impact of declining sea ice in amplifying surface air temperatures (SAT) over the Arctic Ocean is readily visible, and this “Arctic amplification” will become more pronounced as more sea ice is lost in the coming decades. The effect of sea ice loss on atmospheric temperatures and circulation patterns is of utmost significance as these changes will affect the terrestrial climate. Land-surface snow is vulnerable to these changes; hence, we search for any link between changes in Arctic sea ice and Northern Hemisphere snow cover. Analyses of observational data sets suggest that the increasing snow cover over Siberia during fall and early winter is correlated with the decreasing September Arctic sea ice over the Pacific sector. We also examine modeled covariance between sea ice and snow using historical and future simulations of the Community Climate System Model (CCSM3). Results indicate the emergence of a Siberian snow signal during the last half of the 21st century most strongly during late winter. Moreover, CCSM3 future simulations show diminishment of snow at a hemispheric scale outside of the Siberian region, which is correlated with the loss of Arctic sea ice. These results indicate that we may be seeing the first, albeit weak, signs of “Arctic amplification” on the terrestrial Arctic snowpack; that only a weak and therefore inconclusive signal would be expected at this time; and that the signal should strengthen over the coming decades.

[1] The existing literature on heat diffusion through a snow-covered land surface deals primarily with snow cover presence or duration; however, the insulation property of a snowpack can vary substantially owing to the changes in snow thickness. Using SNTHERM, a one-dimensional snowpack energy and mass balance model, we have isolated the heat diffusion response to varying initial snow depths, including snow-free conditions, and assessed the magnitude of associated temperature and insulative energy flux anomalies within the entire snowpack-soil column system. A clear and substantial land surface insulative response to snow depth is identified over hourly, intraseasonal, and interannual time scales. The overall snow-soil system warms with increasing snow depth, but the surface snow layer cools slightly, generating decreased ground heat flux and increased sensible heat flux responses. This land surface thermal response is attributable to the increased insulative capacity of deeper snowpacks, which makes the underlying soil less responsive to solar radiation and air temperature fluctuations, and facilitates sustained warming by the bottom soil layer boundary. Furthermore, the overall effect of increasing snow depth due to insulation is comparable in magnitude to the effect of snow presence versus snow absence due to albedo.

[1] Links between autumn-winter snow anomalies over North America and winter climate parameters throughout the Northern Hemisphere are examined. GCM integrations are performed from September through February, with prescribed snow forcings over North America (NA) reflecting realistic, observed high/low autumn snow conditions. Forty-member ensemble differences reveal robust responses in surface air temperature and sea level pressure (SLP) fields. Over NA a negative temperature/positive SLP response occurs while over Europe a positive temperature/negative SLP response emerges. Additionally, a dynamic wave response occurs in the troposphere, across NA and extending downgradient into Eurasia. The contribution of North American (NA) orography is evaluated via an additional pair of experiments in which mountains are removed. The resulting climatic response is mitigated considerably, which suggests a nonlinear coupling of thermal and mechanical forcings. Finally, possible physical pathways for the remote response are hypothesized, involving dynamical mechanisms consistent with previous studies.

[1] 20th and 21st century decadal scale trends and variability in winter North American snow cover extent (NA-SCE) are investigated using coupled atmosphere-ocean general circulation model experiments participating in the upcoming Intergovernmental Panel on Climate Change Fourth Assessment Report. Significant between-model variability is found, with most models underestimating mean NA-SCE. 20th century simulations are poorly correlated with observations, and, while individual ensemble members capture the magnitude of decadal scale variability, the variability of the signal is dampened in the ensemble mean, indicating that decadal-scale NA-SCE variability is associated predominantly with internal model variability rather than external forcing. Two 21st century emission scenarios with realistic (moderate or significant) greenhouse gas emission rates produce decreasing NA-SCE trends, while one unrealistic scenario with fixed concentrations produces little or no NA-SCE trend. These results suggest that snow cover may be a sensitive indicator of climate change, and that North American snow extent will probably decrease in response to greenhouse gas emissions, although the magnitude of the response may be nonlinear.

[1] The presence of snow over broad land surface regions has been shown to not only suppress local surface temperatures, but also influence various remote climate phenomena. However, the specific mechanisms and snow anomaly characteristics which produce this response are still not well understood. In this study, large-ensemble general circulation model (GCM) experiments are performed to investigate the sensitivity of the atmospheric response to snow cover vs. snow depth anomalies, and the relevant surface thermodynamic processes involved. Realistic, observation-based, autumn-winter snow forcings over Siberia are developed and applied as model boundary conditions, to evaluate the climate response to (1) comprehensive snow forcings including snow cover and snow depth components, (2) snow cover only forcings, and (3) snow forcings in the absence of a surface albedo response. Results indicate that snow cover extent anomalies are not the only significant contributor to the local temperature response; snow depth anomalies are shown to have a comparable effect. Furthermore, the albedo effect is not the predominant thermodynamic mechanism; processes related to the insulative properties of the snowpack (e.g., thermal conductivity and latent heat flux) are also involved. Lastly, we find that realistic snow cover and snow depth anomalies acting in conjunction are required to produce a local temperature response which is strong enough to distinctly modulate the winter Arctic Oscillation (AO) mode as shown in previous studies. Such a detailed understanding of the atmospheric sensitivity to snow anomaly characteristics is beneficial for effectively utilizing any potential climate predictability contained in snow anomaly signals. INDEX TERMS: 1863 Hydrology: Snow and ice (1827); 1833 Hydrology: Hydroclimatology; 3322 Meteorology and Atmospheric Dynamics: Land/atmosphere interactions; KEYWORDS: snow, climate

[1] Numerical model mean climatic response to realistic land surface snow forcings is evaluated for two different forcing regions, Siberia and North America. The atmospheric teleconnection pathway and negative winter AO mode response produced by the Siberia forcing, described by the authors in previous studies, is not produced by the comparable-extent North America forcing. It is shown that the combination of a large snow-forced local diabatic heating anomaly over a region of substantial stationary wave activity is required to produce strong upward wave activity flux anomalies which initiate the teleconnection pathway. These features are unique to Siberia, making it a critical region for reproducing the snow - winter AO statistical relationship evident in the observational record.