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  • View in gallery

    Monthly distributions of the average snow cover extent (SCE; left color bar; %) for (left) 1990 and (center) 1985, and (right) 1990 − 1985 difference (right color bar; difference in SCE percentage points) based on observed EASE-GRID data.

  • View in gallery

    As in Fig. 1, but for monthly distributions of the observed average snow water equivalent (SWE) (mm; including differences in SWE in right panels).

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    Seasonal distributions of the (a) MAM and (b) JJA average snow cover extent (SCE; %) difference between 1990 and 1985, and (c) MAM and (d) JJA SCE trend [% (37 yr)−1] in 1979–2015 based on observed satellite data.

  • View in gallery

    Normalized NH-averaged index of (a) MAM SCE from 1979 to 2015 and (b) SWE from 1979 to 2006. In each panel, the black line shows annual index values and the red line shows the corresponding 9-yr high-passed time series (which removes the long-term trend and ENSO effect). Regressed patterns of monthly (c) MAM SCE, (e) MAM Z500, and (g) MAM T500 from 1979 to 2015 (a total of 111 months) onto the 9-yr high-passed monthly MAM SCE index. Regressed patterns of monthly (d) MAM SWE, (f) MAM Z500, and (h) MAM T500 from 1979 to 2006 (a total of 84 months) onto the 9-yr high-passed monthly MAM SWE index. Dots in (c), (d), (g), and (h) and shaded areas in (e) and (f) denote values significant at the 95% confidence level based on a two-sided Student’s t test. In (e) and (f), darker blue dashed (negative) and red (positive) contours are 3-m Z500 anomaly intervals, lighter contours are −1 m (dashed) and +1 m (solid) to show the details of atmospheric responses in the tropics, and the zero contour is omitted.

  • View in gallery

    As in Fig. 4, but for (a) MJJ SCE index and (b) MJJ SWE index. The regressed patterns are (c) MJJ SCE, (e) MJJ Z500, and (g) MJJ T500 onto the MJJ SCE index and (d) MJJ SCE, (f) MJJ Z500, and (h) MJJ T500 onto the MJJ SWE index.

  • View in gallery

    (left) MAM and (right) JJA seasonal mean responses (all units are W m−2) of (a),(b) net shortwave radiation (positive downward); (c),(d) net longwave radiation (positive upward); and (e),(f) turbulent heat flux (sum of sensible and latent heat fluxes, positive upward). Each response is the ensemble mean difference between the two simulations in the SNOW_Total experiment. Statistical significance of the mean difference between these two 100-member ensembles is determined using the Student’s t test (black dots indicate >95% statistical confidence level).

  • View in gallery

    Ensemble-mean sea level pressure (SLP) responses (SNOW_Total experiment, SNOW90 minus SNOW85 difference) for 15-day periods (starting dates labeled). The contour interval is 0.5 hPa, with solid (dashed) contours denoting positive (negative) values, and zero lines are omitted. Shading denotes values significant at the 95% confidence level based on a two-sided Student’s t test.

  • View in gallery

    As in Fig. 7, but for 500-hPa geopotential height (Z500) responses, with a 5-m contour interval.

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    MAM seasonal responses (SNOW_Total experiment; 1990 minus 1985 ensemble average difference) (a) SAT (K), (b) T500 (K), (c) SLP (hPa), (d) Z500 (m), (e) U300 (m s−1), (f) soil moisture (SM, %), (g) Pr (mm month−1), and (h) vertically integrated water vapor flux vectors (Qu, Qv components; reference vector is stated in the box in the lower-right corner). In (c) and (d), darker blue dashed (negative) and red (positive) contours are at intervals of 3 m for Z500 and 0.3 hPa for SLP, with lighter contours blue dashed (negative) and red (positive) contours at intervals of 1 m for Z500 and 0.1 hPa for SLP to show the details of atmospheric responses in the tropics, and the zero contour is omitted. In (e), the contour interval is 0.3 m s−1, and the NH polar and subtropical jets are shown by overplotting 300-hPa climatological zonal wind speed ≥ 10 m s−1 (light green; interval 10 m s−1) from the control run.

  • View in gallery

    As in Fig. 9, but for JJA seasonal responses in the SNOW_Total experiment.

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    Zonal means by pressure (hPa) in the SNOW_Total experiment (1990 minus 1985 ensemble average difference), averaged for seasons (top) MAM and (bottom) JJA, of air temperature [T], height [Z], and zonal wind speed [U]. Dotted areas denote values significant at the 95% confidence level based on a two-sided Student’s t test.

  • View in gallery

    Ensemble-mean (left) MAM and (right) JJA seasonal responses to SNOW_NH experiment forcing (1990 minus 1985 ensemble average difference) in (a),(b) fields of wave activity fluxes [vectors; reference vector is 0.1 m2 s−2 for (a) and 0.15 m2 s−2 for (b)] and their divergences (shaded); (c),(d) eddy kinetic energy (EKE; m2 s−2; contour interval 2 m2 s−2); (e),(f) Eliassen–Palm vectors (E-P vectors; reference vector is 3 m2 s−2); and (g),(h) 300-hPa height tendencies (m s−1; contour interval 0.5 × 10−5 m s−1). Shaded areas in (e)–(h) denote values significant at the 95% confidence level based on a two-sided Student’s t test. For clarity, wave activity flux weaker than 0.015 m2 s−2 in (a) and (b), and E-P vectors weaker than 0.25 m2 s−2 in (c) and (d) are masked. In (c) and (d), the NH polar and subtropical jets are shown by overplotting 300-hPa climatological zonal wind speed (shaded) from the control run.

  • View in gallery

    As in Fig. 10, but for JJA responses in the SNOW_MAM experiment.

  • View in gallery

    As in Figs. 11d–f, but for JJA responses in the SNOW_MAM experiment.

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Modeled Climate Responses to Realistic Extremes of Northern Hemisphere Spring and Summer Snow Anomalies

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  • 1 Department of Atmospheric and Ocean Sciences, Fudan University, Shanghai, China
  • | 2 China Innovation Center of Ocean and Atmosphere System, Zhuhai Fudan Innovation Research Institute, Zhuhai, China
  • | 3 Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, and Joint Center for Global Change Studies, Beijing, China
  • | 4 Department of Atmospheric Sciences, Texas A&M University, College Station, Texas
  • | 5 School of Atmospheric Sciences, Nanjing University, Nanjing, Jiangsu, China
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Abstract

Northern Hemisphere (NH) snow cover extent (SCE) has diminished in spring and early summer since the 1960s. Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) estimated about half as much NH SCE reduction as observed, and thus underestimated the associated climate responses. This study investigates atmospheric responses to realistic decreasing snow anomalies using multiple ensemble transient integrations of climate models forced by observed light and heavy NH snow cover years, specifically satellite-based observations of NH SCE and snow water equivalent from March to August in 1990 (light snow) and 1985 (heavy snow), as a proxy for the trend. The primary atmospheric responses to March–August NH snow reduction are decreased soil moisture, increased surface air temperature, general tropospheric warming in the extratropics and the Arctic, increased geopotential heights, and weakening of the midlatitude jet stream and eddy kinetic energy. The localized response is maintained by persistent increased diabatic heating due to reduced snow anomalies and resulting soil moisture drying, and the remote atmospheric response results partly from horizontal propagation of stationary Rossby wave energy and also from a transient eddy feedback mechanism. In summer, atmospheric responses are significant in both the Arctic and the tropics and are mostly induced by contemporaneous snow forcing, but also by the summer soil moisture dry anomaly associated with early snow melting.

Corresponding author: Prof. Qigang Wu, qigangwu@fudan.edu.cn

Abstract

Northern Hemisphere (NH) snow cover extent (SCE) has diminished in spring and early summer since the 1960s. Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) estimated about half as much NH SCE reduction as observed, and thus underestimated the associated climate responses. This study investigates atmospheric responses to realistic decreasing snow anomalies using multiple ensemble transient integrations of climate models forced by observed light and heavy NH snow cover years, specifically satellite-based observations of NH SCE and snow water equivalent from March to August in 1990 (light snow) and 1985 (heavy snow), as a proxy for the trend. The primary atmospheric responses to March–August NH snow reduction are decreased soil moisture, increased surface air temperature, general tropospheric warming in the extratropics and the Arctic, increased geopotential heights, and weakening of the midlatitude jet stream and eddy kinetic energy. The localized response is maintained by persistent increased diabatic heating due to reduced snow anomalies and resulting soil moisture drying, and the remote atmospheric response results partly from horizontal propagation of stationary Rossby wave energy and also from a transient eddy feedback mechanism. In summer, atmospheric responses are significant in both the Arctic and the tropics and are mostly induced by contemporaneous snow forcing, but also by the summer soil moisture dry anomaly associated with early snow melting.

Corresponding author: Prof. Qigang Wu, qigangwu@fudan.edu.cn

1. Introduction

Snow anomalies affect moisture, radiation, and the energy budget, and cause both local and distant temperature responses through snow–atmosphere coupling (Namias 1985; Cohen and Rind 1991; Groisman et al. 1994). Many studies have indicated significant impacts of early season Eurasian (EA) snow cover anomalies on wintertime Northern Hemisphere (NH) atmospheric circulation (see the recent review by Henderson et al. 2018). Heavy Siberian snow cover extent (SCE) anomalies in autumn can induce a negative Arctic Oscillation (AO; Thompson and Wallace 1998) or North Atlantic Oscillation (NAO; Wallace and Gutzler 1981) atmospheric response in winter, via the vertical propagation of Rossby waves through tropospheric–stratospheric coupling (e.g., Cohen and Entekhabi 1999; Gong et al. 2003; Cohen and Fletcher 2007; Fletcher et al. 2009; Smith et al. 2011; Henderson et al. 2018). Henderson et al. (2013) use a global circulation model (GCM) coupled to a slab ocean to simulate a negative AO/NAO and significant ocean cooling as a response to anomalously heavy North American (NA) snow. Extensive autumn/winter Eurasian snow cover can have a downstream influence on atmospheric circulation over the Asia–Pacific–North America sector, mainly characterized by a deepened surface Aleutian low and/or a Pacific–North America (PNA)-like midtropospheric atmospheric response with a strong eastward extension toward Europe (e.g., Walsh and Ross 1988; Yasunari et al. 1991; Walland and Simmonds 1996; Clark and Serreze 2000; Orsolini and Kvamstø 2009; Wu et al. 2011). The storm tracks over the North Pacific respond strongly to Eurasian snow cover anomalies, and transient eddy forcing is important for maintaining the PNA response (Wu et al. 2011; Liu et al. 2017).

Over the past several decades, a significant reduction in Northern Hemisphere (NH) snow cover extent (SCE) has been observed in spring and summer, but not in autumn and winter (e.g., Brown et al. 2010; Derksen and Brown 2012; Bindoff et al. 2013; Mudryk et al. 2014). Detection and attribution studies have indicated a considerable anthropogenic influence on NH SCE reduction since the 1970s (Rupp et al. 2013; Bindoff et al. 2013). Snow reduction in spring and summer decreases the surface albedo and soil water content, which directly leads to local rapid surface warming (Matsumura et al. 2010; Matsumura and Yamazaki 2012; Alexander et al. 2010; Zhang et al. 2017), and can also generate significant large-scale atmospheric circulation anomalies. For example, spring and early summer Eurasian snow cover anomalies can modify the land–ocean thermal contrast and thereby lead to an anomalous Asian summer monsoon (e.g., Shukla and Mooley 1987; Barnett et al. 1989; Bamzai and Shukla 1999; Liu and Yanai 2002; Dash et al. 2005; Zhao et al. 2007; Wu et al. 2009; Si and Ding 2013; Xiao and Duan 2016; Zhang et al. 2017; Shen et al. 2020). Matsumura et al. (2010, 2014) show that land–atmosphere interactions from reduced May to August high-latitude EA snow mass lead to a negative AO response in summer. Alexander et al. (2010) simulate the atmospheric response to projected SCE changes in the late twenty-first century. They find that the projected reduction in NH snow cover induces continental-scale surface warming in spring and significant atmospheric circulation anomalies, such as a local low-level trough, remote Rossby wave trains, an annular pattern that is strongest in the stratosphere, and a hemispheric increase in geopotential height. Liu et al. (2020) demonstrate that a spring Tibetan Plateau (TP) heavy snow anomaly induces a simultaneous negative North Pacific Oscillation/western Pacific (NPO/WP)-like teleconnection response throughout the troposphere and stratosphere.

Relatively few studies have investigated the global atmospheric responses to current NH snow condition changes, including both SCE and snow water equivalent (SWE) in spring and summer. Rupp et al. (2013) found that phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical simulation runs estimated about half as much NH SCE reduction as observed in the last few decades. Because most climate models do not correctly reproduce the observed SCE reduction, the magnitudes and possibly characteristics of their simulated atmospheric responses are also questionable. This study models atmospheric responses to years with light and heavy NH snow cover (mainly implying fast or slow snowmelt) in ensemble simulations using realistic SCE and SWE forcing in all months from March to August derived from satellite-based observation data. The effect of the spring–summer snow cover and SWE reduction on atmospheric variables is the difference of the “light snow” minus “heavy snow” ensemble simulations. Prescribing SCE and SWE forcing based on actual light or heavy NH snow observations should correct for any model tendencies to underestimate SCE changes. We prescribe a constant climatological annual cycle of sea surface temperature (SST) and sea ice concentration (SIC) to avoid effects of ocean fluctuations and trends such as El Niño–Southern Oscillation (ENSO) and the recent Arctic sea ice reduction. Our experiment is designed to isolate the role of satellite-derived decreasing spring and summer snow cover and SWE in current climate change and to identify the mechanisms involved, which help understand the climate effects of long-term spring–summer snow reduction. This approach contrasts with the idealized experiment of Alexander et al. (2010) where the forcing is the projected terrestrial snow conditions representative of 1980–99 and 2080–99 from a climate model and has less NH snow cover in all seasons because of the warmer climate induced by increased greenhouse gases. It also contrasts with Matsumura et al. (2010, 2014), who simulate atmospheric responses to spring high-latitude snow anomalies by prescribing idealized initial heavy and light snow water equivalent that is thrice and one-third of that in the control run north of 60°N in the snow-covered region of the Eurasian continent, respectively. Finally, our study reports a comprehensive hemisphere-scale atmospheric circulation response to an NH snow anomaly. This builds on the abovementioned studies investigating snow–atmosphere interactions, which have focused on impacts of spring and summer Eurasian snow anomalies on the Asian summer monsoon.

The paper is organized as follows. Section 2 describes model experiments and methodology including data sources and analysis tools, section 3 presents results, including observational and sensitivity analyses, and section 4 summarizes conclusions.

2. Methods

a. Observational datasets

The observational datasets used here are described below, along with their data periods in this study. The first dataset is the NCEP–NCAR reanalysis (Kalnay et al. 1996), which covers 1979–2018 and includes monthly fields of 500-hPa geopotential heights (Z500) and air temperature (T500). The second observational dataset is Northern Hemisphere EASE-Grid 2.0 Weekly Snow Cover and Sea Ice Extent, Version 4 (Brodzik and Armstrong 2013, updated 2014), derived from several visible-band satellite instruments. Weekly SCE and sea ice extent (SIE) are binary variables of the presence or absence of snow cover in a land grid box or sea ice > 15% in an ocean grid box, where the stated gridbox resolution is 25 km. The third dataset used is Global Monthly EASE-Grid Snow Water Equivalent Climatology, Version 1 (Armstrong et al. 2005), which contains monthly average SWE (mm averaged in each grid box) derived from several microwave instruments (This dataset has not been updated since May 2007, but is the best available that we have found with global coverage throughout the year). For the figures, or for model input, this study interpolates or averages both EASE-GRID SCE and SWE data sources to a uniform gridbox size of 2.8° × 2.8°, which is the same horizontal resolution of the model simulations described in modeling experiments.

b. Modeling experiments

The experimental procedures are almost identical to those in our previous studies (Liu et al. 2017, 2020), except for the specific forcing, and use the same datasets. Those studies show that model ensemble simulations forced by satellite-derived observed SCE and SWE in the TP region in years with heavy versus light TP snow very closely reproduce observed impacts of such TP snow anomalies on the atmospheric circulation, including winter PNA-like and spring NPO-like features. Here, idealized model ensemble experiments are forced with realistic NH snow cover obtained from two observational SCE and SWE datasets with a common period from November 1978 to May 2007, as described above. Orsolini and Kvamstø (2009) use the same SCE dataset, but they use model diagnosed SWE to force an atmospheric general circulation model (AGCM) to simulate the impact of the Eurasian SCE changes on the NH winter circulation.

Two (from the 28 available) March–August spring and summer periods are chosen for model forcing, including 1990 with persistent low SCE and SWE over the NH (referred to as “light snow” or SNOW90), and 1985 with persistent heavy SCE and SWE (“heavy snow” or SNOW85). These two years are also chosen due to relatively large anomalies. Figures 1 and 2 show the March to August evolution of SCE and SWE for these two years, and indicate general reductions of SCE and SWE in most NH land areas, as shown in 1990 minus 1985 difference maps (right panels of Figs. 1 and 2) where many areas have absolute SCE (SWE) differences greater than 40% (50 mm) from March to June. To quantify the snow conditions for the chosen 1990 and 1985 years, Table 1 summarizes climatological three-month [March–April–May (MAM)] or two-month [June–July (JJ); August is excluded because the areas with climatological snow cover are small] SCE and SWE 1979–2006 climatological averages and standard deviations, and normalized anomalies over the EA, NA and NH regions in 1990 and 1985. In the EA region, seasonal climatological SCE areas (total SWE volumes) are 17.29 million km2 (1467 trillion kg) in MAM and 2.36 million km2 (32 trillion kg) in JJ, and in NA are 12.91 million km2 (619 trillion kg) in MAM and 4.57 million km2 (25 trillion kg) in JJ. Based on March to July SCE and SWE anomalies in Table 1, snow cover is much below average in EA and moderately light in NA in 1990, but is moderately high in EA and much above average in NA in 1985. In both years, SWE anomalies are moderate in EA and NA. The 1990 minus 1985 SCE and SWE differences are therefore sufficiently strong to investigate possible impacts of the NH, EA, and NA snow anomalies on the hemispheric circulation.

Fig. 1.
Fig. 1.

Monthly distributions of the average snow cover extent (SCE; left color bar; %) for (left) 1990 and (center) 1985, and (right) 1990 − 1985 difference (right color bar; difference in SCE percentage points) based on observed EASE-GRID data.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Fig. 2.
Fig. 2.

As in Fig. 1, but for monthly distributions of the observed average snow water equivalent (SWE) (mm; including differences in SWE in right panels).

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Table 1.

Summary of March–April–May (MAM) or June–July (JJ) seasonal areas of SCE (million km2) and seasonal SWE volumes (trillion kg), standard deviations, and normalized anomalies (MAM or JJ SCE and SWE anomalies divided by the corresponding standard deviations) over the Northern Hemisphere (NH), Eurasia (EA), and North America (NA) regions in 1990 and 1985, and the 1990 minus 1985 difference (DIFF). Permanent ice such as the Greenland ice sheet is excluded. Since few grid boxes have more than minimal snow cover in August, the summer average includes only JJ. Bold numbers indicate normalized absolute anomalies ≥ 1.70.

Table 1.

Figure 3 indicates that almost all NH areas have patterns and magnitudes of 1990 minus 1985 SCE differences (Figs. 3a,b) that are very similar to the linear 1979–2015 SCE trend (Figs. 3c,d). Because this is real data and not an idealized experiment, Fig. 3 shows that the TP region is the main exception to the general negative 1990 minus 1985 SCE difference and linear decreasing 1979–2015 SCE trend. However, north of the TP region, Mongolia and Siberia have generally low SCE in 1990, high SCE in 1985, and a decreasing 1979–2015 SCE trend, all of which are similar to elsewhere around the midlatitude NH. The second substantial exception is positive 1990 minus 1985 SWE differences in parts of Canada and Alaska (right panels of Fig. 2), in contrast to mostly negative SCE differences (right panels of Figs. 1 and 3a,b) and mixed SCE trends in MAM over the northern part of the North American continent (Fig. 3c).

Fig. 3.
Fig. 3.

Seasonal distributions of the (a) MAM and (b) JJA average snow cover extent (SCE; %) difference between 1990 and 1985, and (c) MAM and (d) JJA SCE trend [% (37 yr)−1] in 1979–2015 based on observed satellite data.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

To simulate the climatic effects of prescribed changes in terrestrial snow cover, we have conducted simulations using the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4), coupled to the Community Land Model, version 4.0 (CLM4.0). Both models have a horizontal resolution of 2.8° latitude and 2.8° longitude (T42). Climatological statistics are from a previous long CAM4/CLM4.0 control run based on constant greenhouse gas and other forcing appropriate to the late twentieth century. As listed in Table 2, three experiments are performed, each with two 100-member ensembles. The ensembles have prescribed NH EASE-Grid SCE and SWE forcing from 1990 (SNOW90, identifying atmospheric responses to limited snow cover) or 1985 (SNOW85, extensive snow). Prescribed snow is interpolated to each model time step from weekly SCE and monthly SWE. To avoid effects other than variable NH snow in the ensembles, in all scenarios, Greenland ice sheet SCE is set to 100%, and SWE is set to 1000 mm (Greenland topography is unchanged), and model-derived SCE and SWE are used in the Southern Hemisphere (SH). Every model run, including the control run, is forced by the same global climatological annual cycle of SST and SIC based on 1979–2010 observations. This approach suppresses ocean variations such as ENSO and prevents ocean feedbacks resulting from snow anomalies, but should not affect the main conclusions because the seasonal-scale ocean response would be limited.

Table 2.

Overview of model experiments. The first column assigns the name of each experiment. Each experiment includes two separate simulations with the prescribed Northern Hemisphere snow forcing, with the specified months when this forcing is applied in the second and third columns (SNOW90 and SNOW85). In the last column, each experiment performs 100 SNOW90 and 100 SNOW85 ensemble runs. Each of the 100 ensemble runs is initialized with the first 1 Mar time step from a different year of a long control run (1 Jun for SNOW_JJA), and simulates through the stated ending month.

Table 2.

The three experiments (each with SNOW90 and SNOW85 ensembles) are SNOW_Total (prescribed snow March–August), SNOW_MAM (prescribed snow March-May, model snow June–August), and SNOW_JJA (prescribed snow June–August only) (Table 2). The 100 SNOW90 and SNOW85 pairs in each ensemble are initialized with the same atmospheric states from different control run years, using the first CAM4 time step in the month when prescribed snow begins. SNOW_Total and SNOW_MAM runs start on 1 March using the selected control run year, but since MAM is identical in both runs, SNOW_MAM uses the archived SNOW_Total run in MAM and starts on 1 June with only model-generated snow in JJA. SNOW_JJA does not prescribe snow in MAM, so each run starts on 1 June using the selected control run and prescribed snow in JJA. Both runs in a pair start with identical atmospheric fields and diverge gradually due to SNOW90 or SNOW85 snow boundary conditions. The SNOW_MAM and SNOW_JJA experiments are designed to identify relative contributions to JJA responses from unlagged (JJA) snow anomalies, compared to lagged (MAM) snow anomalies appearing in JJA as soil moisture anomalies.

In CLM4.0, the parameterizations for snow and snow accumulation are based primarily on Anderson (1976) and Dai and Zeng (1997). At each time step, the model computes snowfall, net accumulation, and snow and ice melting and evaporation. Melting increases soil moisture and evaporation causes atmospheric latent heating. However, CLM4.0 has been modified so the computed SCE and SWE in the NH area (Figs. 1 and 2) at each time step is replaced by SNOW90 or SNOW85 values for the specified snow season (interpolated to each model time step from weekly or monthly values). After the above modification, energy and water are not explicitly conserved in the NH area during each realization. The response to snow forcing is obtained from the difference of ensemble means between “light snow” (SNOW90) minus “heavy snow” (SNOW85) experiments. The statistical significance of the seasonal mean or 15-day averaged difference between ensemble means is assessed using a standard t test where each ensemble is treated as an independent sample. Responses to snow forcing are calculated for regular atmospheric variables, including sea level pressure (SLP), surface air temperature (SAT), soil moisture (SM), precipitation (Pr), 500-hPa geopotential height (Z500) and air temperature (T500), 300-hPa zonal wind (U300), vertically integrated water vapor flux (Qu, Qv), and zonally averaged fields of air temperature [T], height [Z], and zonal wind speed [U].

c. Analysis tools

Snow-driven planetary waves over Siberia are a key factor in forcing a stratospheric response and associated tropospheric AO response (e.g., Clark and Serreze 2000; Gong et al. 2003; Cohen et al. 2007; Fletcher et al. 2009; Smith et al. 2011; Henderson et al. 2018). Such a snow–AO teleconnection is absent in many climate models mainly due to the lack of constructive planetary wave interference, as well as too-weak representation of vertically propagating waves and the subsequent downward propagation signal from the stratosphere into the troposphere (Furtado et al. 2015). CAM4 is a “low-top” model with 26 vertical levels extending from the surface to 3.5 hPa, so it does not depict the stratosphere in detail, and stratospheric ozone is prescribed since there is no interactive chemistry. The model runs here diagnose the potential role of stationary and transient eddies in remote teleconnection responses. Previous studies suggest that both stationary and transient eddy responses are important to maintain local and remote atmospheric responses to Eurasian snow forcing in spring (Matsumura and Yamazaki 2012; Liu et al. 2020).

First, horizontal components of 300-hPa stationary wave activity flux (WAF) defined in Plumb [1985, Eq. (7.1)] are calculated for MAM and JJA. WAF is a diagnostic tool for the propagation of stationary wave activity. Areas where the wave activity fluxes are enhanced and weakened indicate source and sink areas for stationary wave activity, respectively. The WAF response is calculated using the forced seasonal mean height, wind and temperature as the input in Eq. 7.1 of Plumb (1985). Second, eddy kinetic energy (EKE) and extended horizontal Eliassen–Palm (E-P) vectors are examined. EKE is defined as deviations of the 300-hPa horizontal wind speed (u2+υ2)/2¯ and extended horizontal E-P vectors are defined as [(υ2¯u2¯)/2,uυ¯] (Trenberth 1986) associated with 2–8-day filtered fluctuations, where u and υ are zonal and meridional wind components, respectively, the overbar is the time average, and primes denote deviations from the corresponding time mean quantities. EKE represents storm track changes, and extended E-P vectors are used as a diagnostic of the impact of transient eddies on the time-mean zonal flow (Trenberth 1986). The convergence and curl of extended E-P vectors indicate eddy-induced deceleration of the local mean zonal and meridional wind, respectively. Third, the geopotential height tendency method in Lau [1988, his Eqs. (1) and (2)] is applied to quantify the barotropic feedback of the transient eddy forcings on the mean flow at the 300-hPa level. The geopotential height tendency ∂z/∂t is proportional to the inverse Laplacian of the convergence of the vorticity flux of transient eddies (π):

zt=fg2π,
π1a2cosθ(θ1cosθθcos2θ1cosθ2λ2)uυ¯+1a2cos2θ2λθcosθ(u2¯υ2¯),

where a is Earth’s radius, f is the Coriolis parameter, g is gravitational acceleration, λ is latitude, and θ is longitude.

Finally, a regression analysis is conducted to examine the observational impacts of NH snow anomalies on atmospheric circulation. The observational analysis mainly focuses on the interannual variability. The tropical Pacific (ENSO) impacts on all atmospheric and snow fields are first removed by using a regression against the Niño-3.4 (5°N–5°S, 120°–170°W) SST (downloaded from https://www.cpc.ncep.noaa.gov) anomalies of preceding months, where the regression coefficient is selected as the maximum regression coefficient within the preceding 6 months. The monthly values are then detrended and also filtered by a 9-yr high-pass Gaussian filter to obtain the interannual signals for all fields. At last, a linear regression analysis is applied to investigate the statistical relationships between year-to-year variations of NH SCE, SWE, and other variables. The significance level of regressed results is estimated based on the Student’s t test.

3. Results

a. Observational results

Linear regressions are first performed to examine effects of the above MAM NH SCE and SWE loss on the MAM midlatitude circulation at interannual time scales. The NH normalized area-average SCE and SWE anomalies for MAM between 1979–2015 and 1979–2006, respectively, calculated using EASE-Grid satellite data, show persistently decreasing trends of NH SCE and SWE (Figs. 4a,b, black lines). In those panels, the red line is the corresponding detrended 9-yr high-passed annual SCE or SWE index value. To study the consistent hemispheric-scale influence of interannual NH SCE and SWE loss, Figs. 4e–h show the spatial patterns of MAM SCE and SWE atmospheric responses regressed onto the negative of the interannual detrended MAM SCE and SWE index values (the simultaneous response to NH averaged SCE or SWE that is one standard deviation below average). In Figs. 4c–h, the sign is reversed for each variable and the regression value thus represents the response of each variable to a one standard deviation SCE or SWE loss over the NH, about 1.47 million km2 or 276 trillion kg (Table 1), respectively.

Fig. 4.
Fig. 4.

Normalized NH-averaged index of (a) MAM SCE from 1979 to 2015 and (b) SWE from 1979 to 2006. In each panel, the black line shows annual index values and the red line shows the corresponding 9-yr high-passed time series (which removes the long-term trend and ENSO effect). Regressed patterns of monthly (c) MAM SCE, (e) MAM Z500, and (g) MAM T500 from 1979 to 2015 (a total of 111 months) onto the 9-yr high-passed monthly MAM SCE index. Regressed patterns of monthly (d) MAM SWE, (f) MAM Z500, and (h) MAM T500 from 1979 to 2006 (a total of 84 months) onto the 9-yr high-passed monthly MAM SWE index. Dots in (c), (d), (g), and (h) and shaded areas in (e) and (f) denote values significant at the 95% confidence level based on a two-sided Student’s t test. In (e) and (f), darker blue dashed (negative) and red (positive) contours are 3-m Z500 anomaly intervals, lighter contours are −1 m (dashed) and +1 m (solid) to show the details of atmospheric responses in the tropics, and the zero contour is omitted.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Figures 4c and 4d show large areas of reduced (blue tints) SCE and SWE, but increased (orange or red tints) SCE over some regions (including the western Tibetan Plateau, northeastern China, central Eurasia and the United States) and increased SWE over the TP and southern Canada. Most regions of high or low SCE and SWE are similar, but some exceptions occur. In Figs. 4e–h, both SCE and SWE reductions (increases) result in similar statistically significant positive (negative) Z500 and T500 anomalies over northern Eurasia and North America, which are mainly maintained by persistent increased diabatic heating due to reduced snow cover, snowmelt and resulting soil moisture drying averaged over the NH (Alexander et al. 2010). Figure 4e also indicates a low Z500 in the central North Pacific associated with the deepened surface Aleutian low and high Z500 in the southern North Pacific. These may be related to the heavy SCE anomaly in MAM over central Eurasia and East Asia seen in Figs. 4c and 4d. Using observational analyses and numerical experiments, Liu et al. (2020) demonstrate that a heavy TP snow cover anomaly in MAM can induce a negative height anomaly over the North Pacific, which is part of a negative WP-like teleconnection response. The North Pacific Z500 response to SWE does not show a similar pattern in Fig. 4f, possibly due to increased SWE over the TP with reduced SWE over other Eurasian areas in this specific data period. Figures 4e–h also show widespread but insignificant negative Z500 and T500 anomalies over most of the Arctic.

Figure 5 shows a similar observational analysis performed between MJJ SCE or SWE anomalies and MJJ Z500 and T500 responses. NH averaged MJJ SCE or SWE loss that is one standard deviation below average corresponds to about 1.52 million km2 or 55 trillion kg, respectively. T500 anomalies are positive over most high latitude and Arctic Ocean regions, reflecting general SAT and lower atmospheric warming associated with snow loss. Significant negative Z500 and T500 anomalies are found over positive snow anomaly regions and North America. The increasing Z500 over the Arctic and decreasing Z500 over the midlatitude resembles the negative phase of the MJJ Arctic Oscillation response to the Eurasian snow cover reduction (Matsumura et al. 2014).

Fig. 5.
Fig. 5.

As in Fig. 4, but for (a) MJJ SCE index and (b) MJJ SWE index. The regressed patterns are (c) MJJ SCE, (e) MJJ Z500, and (g) MJJ T500 onto the MJJ SCE index and (d) MJJ SCE, (f) MJJ Z500, and (h) MJJ T500 onto the MJJ SWE index.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Overall, these observational results indicate that there are strong contemporaneous connections between spring to summer NH snow anomalies and hemispheric atmospheric fields. Numerical experiments in section 3b support this finding that a net NH snow reduction in spring and summer can induce increasing Z500 and T500 over NH mid- and high-latitude areas.

b. Modeled climate responses to total snow forcing in spring and summer

Model ensemble experiments were performed as explained in section 2, forced by observed SCE and SWE anomalies from March to August in light (1990) and heavy (1985) NH snow years (the SNOW_Total experiment in Table 2), but model-generated SCE and SWE in the SH. Analyses below discuss average differences of the light minus heavy snow ensemble members.

The decreased SCE and SWE forcing first affects the local atmosphere by changing surface energy fluxes. Figure 6 shows SNOW_Total ensemble mean surface radiative and turbulent heat flux climate responses in MAM (left column) and JJA (right column) based on SNOW90 (light NH snow) minus SNOW85 (heavy NH snow) differences. Due to the albedo effect, the deficient NH SCE and SWE forcing reduces the surface albedo over a broad NH midlatitude land region, and increases net surface shortwave radiation by >20 W m−2 over many areas in spring (Fig. 6a) and early summer (not shown, but Fig. 6b shows the summer average). The warm surface also results from increased net longwave radiation (Figs. 6c,d). The snow–hydrological effect depends primarily on reduced snow mass and soil moisture. The hydrological effect of deficient snow reduces soil moisture and increases the sum of surface latent and sensible heating significantly over the warmed surface (Figs. 6e,f). Net heat fluxes are upward (downward) in the regions of snowmelt (increase) and downward in adjacent ocean areas. The atmosphere over adjacent oceans is warmed by thermal advection from the snowmelt region, which leads to a downward flux since SST is fixed (artificially cool) by experimental design. The magnitude of the surface energy flux response is large in spring and smaller in summer, approximately in proportion to the SCE anomalies. Note that regional negative shortwave and longwave radiation responses over and near the Himalayas are related to the heavy TP area SNOW90 minus SNOW85 snow cover.

Fig. 6.
Fig. 6.

(left) MAM and (right) JJA seasonal mean responses (all units are W m−2) of (a),(b) net shortwave radiation (positive downward); (c),(d) net longwave radiation (positive upward); and (e),(f) turbulent heat flux (sum of sensible and latent heat fluxes, positive upward). Each response is the ensemble mean difference between the two simulations in the SNOW_Total experiment. Statistical significance of the mean difference between these two 100-member ensembles is determined using the Student’s t test (black dots indicate >95% statistical confidence level).

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

First, the transient atmospheric circulation response to snow forcing is examined. Figures 7 and 8 show ensemble mean 15-day averages of SLP and Z500 responses. The SLP response shows a significant decrease in the NH midlatitude land areas where the snow is reduced, and (after 15 March, due to model spinup time) a significant remote positive response over the North Pacific and North Atlantic. The SLP responses, which are generally scaled with the snow forcing and the response of the net surface energy fluxes, are larger and statistically significant from March to May and weaken after June. Similarly, a small positive SLP anomaly over the TP is seen until early July where the right panels of Figs. 1 and 2 show consistent high SCE and SWE differences through June. The large-scale response exhibits a baroclinic vertical structure over EA and NA consisting of negative SLP values and positive Z500 values, and equivalent barotropic (e.g., amplifying with height) ridges over the North Pacific and North Atlantic. The Z500 anomalies gradually amplify from March to May, and are then maintained around 30–40 m from June to mid-August. The shallow baroclinic atmospheric circulation response over most NH midlatitude land area is mainly induced by enhanced surface heating due to the reduced SCE and SWE in Figs. 1, 2 and 3a,b (Hoskins and Karoly 1981). The North Pacific and North Atlantic equivalent barotropic atmospheric responses may be induced by the enhanced contrast between tropospheric warming and a relatively cold SST, causing a shift in the storm tracks, and maintained by transient eddy momentum flux feedbacks as synoptic systems move along the shifted storm track (Lau and Holopainen 1984; Trenberth 1986; Lau 1988), which will be discussed in section 3c.

Fig. 7.
Fig. 7.

Ensemble-mean sea level pressure (SLP) responses (SNOW_Total experiment, SNOW90 minus SNOW85 difference) for 15-day periods (starting dates labeled). The contour interval is 0.5 hPa, with solid (dashed) contours denoting positive (negative) values, and zero lines are omitted. Shading denotes values significant at the 95% confidence level based on a two-sided Student’s t test.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for 500-hPa geopotential height (Z500) responses, with a 5-m contour interval.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

The spring (MAM) and summer (JJA) seasonal mean responses to diminished snow cover are next shown in Figs. 9 and 10 for various atmospheric variables including SAT, T500, SLP, Z500, U300, SM, Pr, and Qu and Qv. During spring, significant midlatitude SAT warming is simulated over land, as expected, with more warming generally coinciding with a greater snow-melt rate. A deep NH tropospheric warming is also simulated over the mid- and high latitudes, as shown in T500 (Fig. 9b) and other layers (not shown), with the largest warming above northeastern Eurasia. Significant warming is simulated over the Arctic, suggesting that NH snow decline has contributed to Arctic amplification. Note that over the oceans, the surface and atmosphere are warmed by thermal advection from the snow loss region, while the SST is fixed by experimental design. This leads to a small artificial downward flux response and slight SAT cooling effects. This explains why ocean SAT responses are small in Fig. 9a.

Fig. 9.
Fig. 9.

MAM seasonal responses (SNOW_Total experiment; 1990 minus 1985 ensemble average difference) (a) SAT (K), (b) T500 (K), (c) SLP (hPa), (d) Z500 (m), (e) U300 (m s−1), (f) soil moisture (SM, %), (g) Pr (mm month−1), and (h) vertically integrated water vapor flux vectors (Qu, Qv components; reference vector is stated in the box in the lower-right corner). In (c) and (d), darker blue dashed (negative) and red (positive) contours are at intervals of 3 m for Z500 and 0.3 hPa for SLP, with lighter contours blue dashed (negative) and red (positive) contours at intervals of 1 m for Z500 and 0.1 hPa for SLP to show the details of atmospheric responses in the tropics, and the zero contour is omitted. In (e), the contour interval is 0.3 m s−1, and the NH polar and subtropical jets are shown by overplotting 300-hPa climatological zonal wind speed ≥ 10 m s−1 (light green; interval 10 m s−1) from the control run.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Fig. 10.
Fig. 10.

As in Fig. 9, but for JJA seasonal responses in the SNOW_Total experiment.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

In Figs. 9c and 9d, atmospheric circulation responses to reduced SCE indicate a baroclinic vertical structure (opposite SLP and Z500 responses) over most NH land regions and a remote equivalent barotropic structure (SLP and Z500 responses of the same sign) over the North Pacific and North Atlantic. The MAM Z500 shows a general increase in the extratropics, as expected from widespread simulated tropospheric warming. These T500 (Fig. 9b) and Z500 responses indicate that the tropospheric temperature and height gradient is weakened toward lower latitudes and strengthened toward higher latitudes, leading in Fig. 9e to a weakened (enhanced) midlatitude (polar) jet stream over 30°–60°N (north of 60°N), but a strengthened subtropical jet stream south of 30°N over the eastern North Pacific and eastern Atlantic to Africa. In Figs. 9f and 9g, significant surface responses also include increasing precipitation and drying SM where snow melts. Significant precipitation responses are simulated over ocean areas, including dipole responses with significant negative (positive) changes over the North (subtropical) Pacific, and over the eastern (western) North Atlantic. Figure 9h shows that the positive (negative) precipitation responses are approximately explained by corresponding enhanced moisture convergence (divergence). Significant cooling, decreasing precipitation, and wet SM responses are mostly simulated over the TP due to regional positive snow anomaly differences (Figs. 1 and 2).

In summer (Fig. 10), the overall responses are similar to those in spring. Ensemble-mean SAT warming and SM drying responses are weaker in summer than in spring mainly due to weak snow forcing (Figs. 1 and 2) and resulting net surface energy fluxes (Fig. 6, bottom row). However, stronger and more extensive circulation responses are found not only in remote positive SLP and Z500 responses over NH oceans, but also in warming T500 throughout the NH and reduced midlatitude U300. The most dramatic change is found in precipitation and the associated vertically integrated water vapor flux. Significant and coherent increasing precipitation is simulated over northern Europe, most of eastern Eurasia (including eastern Siberia, Mongolia and eastern China, and the western to northeastern TP) and from the Middle East into Africa, and northwestern North America due to enhanced moisture convergence into these regions, while significant decreasing precipitation is simulated over southern Europe, the southeastern TP and coastal western North America. The dipole-like response over the North Pacific and reduced precipitation over the North Atlantic are stronger in summer than in spring, and are accompanied by enhanced moisture convergence (divergence) anomalies over these ocean areas.

Figure 11 shows responses of zonally averaged atmospheric fields of air temperature [T], height [Z], and zonal wind speed [U] from 1000 to 100 hPa to the NH spring and summer snow decrease, based on the 1990 minus 1985 ensemble average difference in SNOW_Total runs. The top row shows MAM responses and the bottom row shows JJA responses. In MAM the model simulates deep NH tropospheric warming up to 300 hPa, with a broad lower tropospheric maximum warming over 35°–50°N, and also upper tropospheric and lower stratospheric cooling. Also, a barotropic height increase from the lower troposphere into the stratosphere is simulated, with a broad upper tropospheric maximum over 35°–50°N. This is accompanied by a significant reduction of midlatitude zonal mean zonal wind [U] over 30°–50°N and significant increases over 50°–70°N in the whole troposphere and over 10°–20°N below 700 hPa. In JJA, the model responses show widespread and deep extratropical tropospheric warming (20°–90°N from 1000 to 200 hPa), increasing extratropical [Z] from the surface to at least 100 hPa, and a significant reduction of [U] over 25°–50°N from the surface to the lower stratosphere and a significant increase over 10°–20°N below 700 hPa. These [Z] and [U] responses resemble changes in the negative phase of the Arctic Oscillation in MAM and JJA (Thompson and Wallace 1998; Ogi et al. 2004). These results indicate that snowmelt-induced warming and increasing height resulting from spring and summer snow reductions are not confined to the extratropical lowermost troposphere, but there are significant impacts through the whole troposphere and also into the lower stratosphere over 20°–90°N in both MAM and JJA, and in the lower stratosphere over the tropics in JJA. Figure 11 also clearly demonstrates that the tropospheric and stratospheric responses amplify in summer, although summer snow cover diminishes only in the NH high latitudes.

Fig. 11.
Fig. 11.

Zonal means by pressure (hPa) in the SNOW_Total experiment (1990 minus 1985 ensemble average difference), averaged for seasons (top) MAM and (bottom) JJA, of air temperature [T], height [Z], and zonal wind speed [U]. Dotted areas denote values significant at the 95% confidence level based on a two-sided Student’s t test.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Overall, the regression (observation-based) and model results agree about the increasing (decreasing) Z500 and T500 over NH mid- and high-latitude land areas (the subtropics). However, the simulated positive Z500 responses are largest between 30° and 70°N in MAM and JJA (Figs. 9d and 10d), while the regression Z500 responses are largest in high latitudes in MAM and MJJ (Figs. 4e,f and 5e,f). The forced T500 responses in MAM and JJA (Figs. 9b and 10b) are much stronger than in the corresponding regression results (Figs. 4g,h and 5g,h). These observational and model results may still differ for several reasons. First, strong positive height anomalies in the forced responses are caused by NH snow loss, but negative height anomalies in observational responses over the North Pacific and Atlantic are related to the heavy SCE anomaly in MAM over central Eurasia and East Asia seen in Figs. 4c and 4d. Second, Figs. 4c and 4d show SCE and SWE anomalies over most NH areas associated with detrended NH SCE and SWE that is one standard deviation below average, but the corresponding snow forcing in simulations (right panels of Figs. 1 and 2) is much stronger. Table 1 indicates that the 1990 minus 1985 differences in NH averaged SCE and SWE are about −3.97 and −3.68 times the corresponding standard deviations, respectively. Third, the regression patterns in Figs. 4 and 5 are performed using quite short SCE and SWE satellite data records, so they may not be indicative of “pure” atmospheric responses to NH snow anomalies. Fourth, the ensemble averaging process filters out most natural internal variability simulated by individual runs. This is shown in Figs. 4, 5, 9 and 10 by larger observed Z500 fluctuations than in the ensemble mean Z500 responses, although the observed SCE and SWE anomalies are much weaker than the corresponding snow forcing (Figs. 1, 2, 4 and 5). Fifth, AGCM simulations do not allow the atmosphere and ocean to act as an interactive, coupled system, which may affect some aspects of the model atmosphere’s response to NH snow forcing.

c. Mechanisms of the atmospheric response in spring and summer

The localized response to snow forcing is initialized and maintained by changes in heat and radiative fluxes, but the large-scale remote response depends on dynamical processes. The local warming SAT (Figs. 9a and 10a) and negative SLP (Figs. 9c and 10c) responses to snow forcing over most of the NH midlatitudes (except for a small area of opposite responses over the TP due to opposite TP snow anomalies) are initiated and maintained by changes in heat and radiative fluxes (Fig. 6). Enhanced surface heating due to the large NH snow reduction (SNOW90 minus SNOW85) induces a shallow baroclinic atmospheric circulation response with positive tropospheric height anomalies (Figs. 9 and 10b) almost everywhere from 35° to 90°N, consistent with increased thickness over NH land by snow reduction simulated in Alexander et al. (2010). A warm NH extratropical troposphere tends to weaken (enhance) the local meridional temperature gradient toward lower (higher) latitudes (Figs. 9b and 10b), and then weakens (strengthens) the subtropical jet (the polar front jet) (Figs. 9e and 10e). The tropospheric warm temperature and height anomalies over land are carried over the North Pacific and North Atlantic by prevailing westerlies.

In addition to the above thermodynamic processes, dynamical processes are also important for the atmospheric responses to snow forcing. Figure 12 shows seasonal mean responses of the three eddy components and geopotential height tendencies ∂z/∂t [defined in Eqs. (1), (2)]. Figures 12a and 12b shows seasonal mean WAF and its divergence. A positive divergence approximately indicates a source of WAF and corresponds to enhanced WAF. In MAM, Rossby wave propagation is clearly eastward from northern European and North American land snow-melt areas, and southeastward from Mongolian and East Asian snow-melt areas into the Pacific (Fig. 12a). Enhanced precipitation anomalies and the associated lower-level convergence and strong upper-level divergence anomalies over these land snow-melt areas produce anomalous vorticity sources. The upper-level components of these vorticity sources provide Rossby wave sources (Sardeshmukh and Hoskins 1988) and enable a stationary Rossby wave propagating into the waveguide of the subtropical jet (Branstator 2002) and the subpolar jet. This suggests that surface heating through snow-albedo and snow-hydrological effects contributes to the development of thermally generated stationary Rossby waves. This is consistent with previous studies indicating a horizontal stationary Rossby wave response to snow forcing (Gong et al. 2003; Matsumura and Yamazaki 2012; Liu et al. 2017, 2020).

Fig. 12.
Fig. 12.

Ensemble-mean (left) MAM and (right) JJA seasonal responses to SNOW_NH experiment forcing (1990 minus 1985 ensemble average difference) in (a),(b) fields of wave activity fluxes [vectors; reference vector is 0.1 m2 s−2 for (a) and 0.15 m2 s−2 for (b)] and their divergences (shaded); (c),(d) eddy kinetic energy (EKE; m2 s−2; contour interval 2 m2 s−2); (e),(f) Eliassen–Palm vectors (E-P vectors; reference vector is 3 m2 s−2); and (g),(h) 300-hPa height tendencies (m s−1; contour interval 0.5 × 10−5 m s−1). Shaded areas in (e)–(h) denote values significant at the 95% confidence level based on a two-sided Student’s t test. For clarity, wave activity flux weaker than 0.015 m2 s−2 in (a) and (b), and E-P vectors weaker than 0.25 m2 s−2 in (c) and (d) are masked. In (c) and (d), the NH polar and subtropical jets are shown by overplotting 300-hPa climatological zonal wind speed (shaded) from the control run.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Although summer snow losses occur only in high latitudes (Fig. 12b), the Rossby wave response is most intense and extensive because there are several sources of anomalous stationary wave other than snow-melt areas. The first two sources of strong WAF are located near the exit regions of the upper-tropospheric jets over the eastern North Pacific and North Atlantic, which appear to result from suppressed precipitation and upper-level convergence (Fig. 12b), and perhaps also from interaction with transient eddies as discussed later (Fig. 12h). The subtropical jet stream allows upstream extratropical wave activity to reach the jet exit regions over the North Pacific and North Atlantic, where the zonally varying flow produces strong barotropic instability that further amplifies the wave train (Simmons et al. 1983). Stationary wave activity originating in the eastern North Pacific propagates southward or southeastward to the eastern subtropical Pacific and North America along the stronger subtropical jet stream (Fig. 12d). Meanwhile, stationary wave activity originating in the eastern North Atlantic propagates through the eastern North Atlantic to central Asia and the TP via the Mediterranean region. The third and fourth source regions of stationary wave activity are from northern East Asia and northeastern North America, respectively, which appear to indicate diabatic heating associated with increasing summer precipitation (Fig. 10g) and interactions with transient eddies (Fig. 12h) over these regions. These four sources coincide approximately with the large centers in the JJA Z500 wave train response (Fig. 10d). Previous observational studies have identified these four regions as climatological Rossby wave sources in JJA (Lu and Kim 2004; Shimizu and de Albuquerque Cavalcanti 2011).

Significant ensemble averaged changes in EKE (Figs. 12c,d) and E-P vectors (Figs. 12e,f) due to snow forcing are comparable in MAM and JJA. In Fig. 12c, EKE is significantly reduced over the eastern North Pacific to North America and over the eastern North Atlantic to Europe, and increased over the western subtropical Pacific and the Arctic and adjacent NH land. This indicates that snowmelt in spring shifts the midlatitude storm track south and strengthens storm activity in the Arctic. In Fig. 12d, the most prominent eddy response is that EKE is significantly reduced uniformly in the NH midlatitudes.

Figures 12e and 12f indicate a continuous stream of westward E-P vectors and strong convergence along the weakened midlatitude storm track in MAM and JJA. Such transient eddies in the extended E-P vectors imply weakened eastward group propagation of the transient eddies relative to the mean flow, and mainly act to decrease the spring and summer mean flow in the NH midlatitudes in Figs. 9e and 10e, leading to a weakened midlatitude storm track and jet stream (Trenberth 1986). Figure 12e also shows eastward E-P vectors and significant divergence along the enhanced high-latitude storm track in MAM. This pattern implies that the eddy momentum transport leads to acceleration of the spring polar jet stream along the enhanced storm track. Eastward E-P vectors and significant divergence are found over the subtropical North Pacific and Atlantic, resulting in the acceleration of the zonal wind along a narrow band around 25°N (Fig. 9e). Also in Fig. 12e, the anomalous E-P vectors are directed both poleward (equatorward) with cyclonic (anticyclonic) curvature over the subtropical Pacific (Atlantic), which is indicative of northward (southward) wind acceleration in MAM (e.g., Trenberth 1986).

Figure 12g shows that transient eddies in MAM induce a strong decreasing height tendency at 300 hPa over northern Eurasia, the midlatitude North Pacific, North America and Africa, and a large-scale increasing height tendency only over northern Europe and the northern Pacific. In contrast, in Fig. 12h, JJA transient eddies induce an increasing 300-hPa height tendency at about 55°N from northeastern Eurasia across the North Pacific into northern North America and North Atlantic, and decreasing height tendencies in a midlatitude band about 40°N. Therefore, due to a strong dynamical connection between the Pacific or Atlantic zonal flow and synoptic eddies (Lau 1988), the forced transient eddies in Figs. 12c, 12d, 12e and 12f induce significant feedback to the mean flow. The positive (negative) centers in the geopotential height tendency patterns (∂z/∂t) in both MAM and JJA are in accord with the abovementioned convergence (divergence) and curl of E-P vectors. Comparison between the spatial pattern of Z300 and that of ∂z/∂t reveals that the transient eddies act to maintain the upper-tropospheric positive height anomalies over North Pacific and Atlantic Ocean. Positive or negative geopotential height tendency centers in Fig. 12h coincide approximately with the four sources of strong stationary Rossby wave flux in Fig. 12b. In accordance with the WAF definition, the source of strong WAF in the regions of a notably weakened storm track appears to be interaction with transient eddies. Overall, the feedback by synoptic transient eddies on the mean flow at the 300-hPa level tends to cancel the thermodynamic increasing Z300 response in spring, but tends to enhance the thermodynamic increasing Z300 response in summer. This explains the simulated strong and significant Z500, Z300, and U300 reduction responses in summer although the snow forcing is much weaker in summer, as shown in right columns in Figs. 1 and 2.

d. Sensitivity of temporal changes in snow forcing

Figure 10f shows that persistent negative soil moisture anomalies are simulated in JJA in the springtime snow reduction areas. JJA atmospheric responses in the SNOW_Total experiment in Fig. 10 should be induced by both JJA snow reduction and soil moisture anomalies (a lagging indicator) over areas with springtime snow reduction. To examine the contribution from the anomalous soil moisture forcing due to springtime snowmelt, we perform the second experiment listed in section 2 to investigate the relative roles of anomalous soil moisture forcing due to early snowmelt (SNOW_MAM). Note that snow forcing is prescribed only in MAM and model-derived snow SCE and SWE are used in JJA in each simulation.

Through May, in SNOW_MAM the surface energy flux response and atmospheric SLP and Z500 responses are identical to SNOW_Total responses shown in Figs. 6a–c and the first six panels of Figs. 7 and 8. In Figs. 13 and 14, SNOW_MAM responses in JJA are similar to those in SNOW_Total but with smaller magnitudes. Significant warming SAT areas in both NA and EA are simulated almost exclusively over dry SM areas, and significant warming T500 regions extend mainly from the Arctic and northern Russia across China to western North America (Figs. 13a,b), but those warming responses are weaker than those in SNOW_Total. Significant SLP and Z500 responses in SNOW_Total are reproduced in SNOW_MAM. Weaker but significant negative SLP responses are simulated from China to the eastern Sahara, and in the western United States, while increased SLP and Z500 are still found over the North Pacific, the North Atlantic and western Siberia and adjacent Arctic (Figs. 13c,d). Both SNOW_Total and SNOW_MAM simulate significant midlatitude U300 decreases in a band from the Sahara across the North Pacific into the United States, but the U300 response over North America in SNOW_MAM is much weaker compared to that in SNOW_Total. Furthermore, a significant precipitation decrease is mainly found in Europe, and increasing precipitation responses are found over eastern Siberia, the North China Plain and southeastern China, but those responses are much weaker than that in Fig. 10g. This suggests that snow reduction in summer is important in forcing the simulated JJA precipitation responses in the snow_Total experiment.

Fig. 13.
Fig. 13.

As in Fig. 10, but for JJA responses in the SNOW_MAM experiment.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

Fig. 14.
Fig. 14.

As in Figs. 11d–f, but for JJA responses in the SNOW_MAM experiment.

Citation: Journal of Climate 33, 22; 10.1175/JCLI-D-19-0504.1

In Fig. 14 (cf. with Figs. 11d–f), significant JJA atmospheric responses are simulated in smaller regions than in SNOW_Total, including tropospheric warming [T] north of 20°N, positive [Z] differences north of 30°N in the troposphere and stratosphere, and significantly reduced [U] between 25° and 50°N. These results suggest that the MAM snow reduction alone can still induce significant zonal mean atmospheric responses in JJA through the memory of soil moisture anomalies (Shukla and Mooley 1987), but the responses of all zonal mean fields are much weaker than in the SNOW_Total experiment. This suggests that the JJA snow decline in Fig. 3b may be more important than the decreasing summer soil moisture due to the lagged hydrological effect of spring snow loss in forcing zonal mean responses in SNOW_Total.

We also perform the third experiment, SNOW_JJA, to investigate the relative roles of JJA snow forcing only. The SNOW_JJA experiment reproduces the major JJA patterns of SNOW_Total (Figs. 10 and 11d–f) with amplitudes generally somewhat more than half as large (not shown), indicating that snow forcing in JJA only in Figs. 1 and 2 can induce significant contemporaneous atmospheric responses. Zonal mean responses show a broad maximum extratropical tropospheric warming of [T] and increase of [Z] around 60°N especially above the 300-hPa level (not shown), implying a decreasing height gradient toward lower latitudes leading to weakened [U] equatorward from 60°N, and an increasing height gradient toward higher latitudes with strengthened [U] in the high latitudes, or a shift of the midlatitude jet into the subArctic.

4. Conclusions

The amount of NH snow has diminished significantly since 1979 in the spring and summer (Bindoff et al. 2013; Mudryk et al. 2014). Based on observational and modeling evidence, Bindoff et al. (2013) concludes with high confidence that the decrease in NH snow extent since the 1970s is likely to be caused by external forcings with a major anthropogenic contribution. Multimodel averages from the CMIP5 archive project a NH SCE decrease of about 5.4% in RCP4.5, or about one standard deviation, for 2016–35 compared to 1980–99 in March and April (Brutel-Vuilmet et al. 2013). Two March–August periods in 1990 (light NH snow) and 1985 (heavy NH snow) are chosen for model forcing because the SCE and SWE differences of 1990 minus 1985 are very similar to the observed 1979–2015 linear snow reduction trend. Through large-ensemble GCM simulations of spring–summer responses to this realistic, observation-based snow forcing over the NH, our study demonstrates that NH spring and summer snow cover reduction, which occurs mostly in higher latitudes, leads to a significant decrease in soil moisture and SAT warming over almost all NH extratropical land areas. This contributes to increasing 500-hPa geopotential heights, leading to a warming of the entire NH midand high-latitude troposphere and a weakened but northward shifted extratropical westerly jet. Reduced NH snow also induces significant eddy responses in spring and summer, including the eastward propagation of stationary Rossby waves and reduced storm tracks in the NH midlatitudes. Interactions of high-frequency transient eddies with the mean flow also contribute to the overall atmospheric responses to snow forcing. NH spring and summer snow reduction in the past few decades might have caused hemispheric circulation responses that amplify the direct warming effects of the anthropogenic greenhouse gas buildup. Impacts of NH spring and summer snow reductions on NH long-term climate changes should be further investigated because NH snow cover is projected to continue to decrease in the coming decades.

Eurasian snow cover (particularly over the Tibetan Plateau) during winter and spring has long been thought to significantly influence the Asian summer monsoon at interannual and decadal time scales (e.g., Blanford 1884; Walker 1910, and many other studies). More than three decades of scientific literature document how Eurasian and North American snow-cover anomalies might modulate the hemispheric atmospheric circulation during the late autumn and into the boreal winter, mainly associated with the AO (see review by Henderson et al. 2018) and PNA teleconnections (Wu et al. 2011). Together with limited previous research on significant impacts of NH decreasing spring and summer snow (Matsumura et al. 2010; Matsumura and Yamazaki 2012; Alexander et al. 2010; Zhang et al. 2017), our observational and modeling studies qualitatively and quantitatively demonstrate that realistic Eurasian and North American snow anomalies in spring and summer can substantially modulate the entire hemispheric atmospheric circulation. Therefore, NH snow anomalies have widespread NH atmospheric circulation effects throughout the year at seasonal to interannual time scales, and possibly at decadal time scales.

Acknowledgments

This work is funded by the National Key Scientific Research Plan of China (Grant 91837206) and the National Natural Science Foundation of China (Grant 41375076), and is also supported by the Jiangsu Collaborative Innovation Center for Climate Change and the CMA-NJU Joint Laboratory for Climate Prediction Studies. All simulations were carried out at National Supercomputer Center in Tianjin, and the calculations were performed on TianHe-1 (A). We are grateful for the insightful and constructive comments from three anonymous reviewers.

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