Potential predictability of Eurasian snow cover

Transcription

1 Atmospheric Science Letters (2001) Volume 00 doi: /asle Potential predictability of Eurasian snow cover C. Adam Schlosser and Paul A. Dirmeyer Center for Ocean Land Atmosphere Studies, Calverton, MD, U.S.A. Abstract: Potential predictability and skill of simulated Eurasian snow cover are explored using a suite of seasonal ensemble hindcasts (i.e. retrospective forecasts), an ensemble climate simulation (spanning the years 1982±1998) and observations. Using remotely sensed observations of snow cover, we nd signi cant point-wise correlation over the North Atlantic and North Paci c between winter and spring averaged sea-surface temperatures and Eurasian snow cover area. The observed correlation shows no discernible pattern related to the El NinÄo-Southern Oscillation (ENSO). The hindcasts show correlation patterns similar to the observations. However, the climate simulation shows an exaggerated ENSO pattern. The results underscore the importance of initialization in seasonal climate forecasts, and that the observed potential predictability of Eurasian snowcover cannot be solely attributed to ENSO. *c 2001 Royal Meteorological Society Keywords: Prediction, snow, Eurasia. 1. INTRODUCTION Snow is an important boundary forcing in the global climate system. It is one of the most temporally and spatially varying quantities on the continental surface (Gutzler and Rosen, 1995). In addition, as a frozen form of water storage, its subsequent meltwater can serve as a signi cant source of liquid water storage in coupled hydroclimatologic variability. With its high albedo, it also has a direct impact on the local surface radiation budget which can modulate near-surface temperature variability (Dewey, 1977). In particular, both modeling and observational studies of the inverse relationship between Eurasian snow cover and subsequent precipitation variability of the India summer monsoon have received much scienti c attention over the past 100 years (Bamzai and Shukla, 1999). As such, identifying sources of predictability for Eurasian snow cover can not only lead to improvements in predicting local hydroclimatologic variability, but also has the potential to improve forecasts of remote-response climate variations. To that end, this paper focuses on the potential predictability and skill of Eurasian snow cover using the Center for Ocean Land Atmosphere Studies (COLA) atmospheric general circulation model (GCM). A suite of ensemble hindcast experiments and an ensemble climate simulation are compared to observed snow cover data. The analysis evaluates the ensemble simulations' skill in reproducing seasonal snow cover variability over Eurasia for the period 1982±1998. The differences in skill are contrasted in light of the X *c 2001 Royal Meteorological Society

2 disparities in the two ensemble simulations' response to sea-surface temperatures and initialization. Summary and closing remarks are then provided. 2. MODEL EXPERIMENTS AND OBSERVED DATA Simulations with the COLA GCM For all the simulations analysed in this study, version 1.11 (V1.11) of the COLA GCM at a spectral resolution of R40 (2.8 longitude by 1.8 latitude on the corresponding Gaussian grid) and 18 discrete vertical (sigma) levels is used. It is a research version of the global spectral model described by Sela (1980), that is very similar to that described by Kinter et al. (1997). The GCM is coupled to the simpli ed version of the simple biosphere (SSiB; Xue et al., 1991; 1996) land model. The seasonal ensemble hindcasts analysed for this study were generated by the COLA GCM as participation in the Dynamical Seasonal Prediction (DSP) Project (Shukla et al., 2000a). Ensembles of nine integrations were performed for 17 consecutive winter seasons initialized in mid-december of 1981±1997 and integrated forward for 3 months. The results for January±March (JFM) of this set are described by Shukla et al. (2000b). Since then, a complimentary set of spring integrations has been completed for the March±May (MAM) period. For both the JFM and MAM ensemble hindcasts, initial soil wetness is derived from a climatology based on operational analyses from the European Centre for Medium Range Weather Forecasts (ECMWF) using a conversion procedure described by Fennessy and Shukla (1998). Similarly, initial snow cover is set from a climatology derived from a land albedo data set (Kinter et al., 1997). Thus, there is no interannual variability in the land initial conditions. Over ocean, boundary conditions of sea-surface temperatures (SSTs) are speci ed from the weekly analysis of Reynolds and Smith (1994). The atmospheric state variables of the GCM are initialized from the National Center for Environmental Prediction (NCEP) global analyses (Kalnay et al., 1996) in mid-december for the winter simulations, and late February for spring. Integrations for winter and spring are through 0000 UTC 1 April and 0000 UTC 1 June respectively. Ensembles of nine integrations are generated from initial conditions chosen at 12-hour intervals. For the ensemble climate simulations, a framework similar to that of the Atmospheric Model Intercomparison Project (AMIP; Gates, 1992) was used (hereafter, this simulation will be referred to as the ``AMIP ensemble''). The AMIP ensemble consists of seven integrations spanning the years 1979±1998. To construct the ensemble, a single spin-up run was performed, starting at 0000UTC 1 January 1977 using climatological land-surface conditions (as mentioned above) and NCEP atmospheric analyses for initialization. Upon integration to 0000UTC 1 January 1979 (a period of 2 years), the land-surface conditions are then taken for initial conditions in the seven AMIP period integrations spanning 1979±1998. Each of the seven members receives a unique initial atmospheric state which is taken from global analyses for days surrounding 0000UTC, 1 January The boundary conditions of SSTs are speci ed from the weekly analysis of Reynolds and Smith (1994) for the years 1981±1998 of the simulation (identical to that of the DSP runs). For the period 1979±1981 (and for the control spin-up period of 1977±1979) SSTs were taken from GISST2.2 data (Rayner et al., 1996). For the period spanning the DSP hindcasts (1982±1998), the only difference between the AMIP and DSP ensembles is their initialization. The DSP hindcasts are given ``observed'' atmospheric states (via NCEP analyses) at the start of each seasonal forecast, along with climatological land conditions. The AMIP ensemble constitutes a continuous integration period that spans 1982±1998. Both of the ensemble simulations are forced with identical SST boundary conditions. Therefore, the differences between the two ensembles re ect the impact of the atmospheric and land initialization prescribed to the DSP hindcasts.

3 Snow cover observations and model output The observations of snow cover are monthly probability estimates of snow cover (i.e. the fraction of time in a month that a grid box was covered with snow) taken from weekly satellite observations of snow cover by the National Environmental Satellite, Data, and Information Service (NESDIS) at a 2 2 resolution. A complete description of this data is provided by Bamzai and Shukla (1999). Fractional coverage of snow within a grid box is not explicitly accounted for in the V1.11 COLA GCM. Rather, the effects of fractional snow cover are represented implicitly through modulation of the aggregate terrestrial albedo of a grid box. A linear transition between the albedo of the dominant vegetation and snow albedo is used, with the terrestrial albedo of the grid box equaling snow albedo (i.e. 100% snow coverage) at a snow water-equivalent depth (SWE) of 4 mm. As such, to obtain a consistent, implicit value of fractional snow coverage as simulated by the model, fractional snow cover grows linearly with respect to SWE, with 100% coverage reached at 4 mm SWE. 3. RESULTS The skill of the COLA DSP and AMIP ensembles in reproducing the observed snow cover anomalies over Eurasia is summarized in Figs 1 and 2. For both JFM and MAM, the COLA DSP hindcasts shows signi cant skill (at the 90% level for JFM and 99% level for MAM) at reproducing the observed snow cover anomalies. On the other hand, the AMIP ensemble simulation exhibits no discernable skill. In fact, the AMIP and DSP ensembles are able to concurrently reproduce only two instances when a considerable (i.e. both observed and simulated anomalies 10 6 km 2 in magnitude) snow-cover anomaly was observed: MAM 1990 and JFM The relatively poor skill of simulated Eurasian snow cover (ESC) in the AMIP ensemble has the following implications. First, the AMIP ensemble's response to SST variations, which is the only potential source of skillful signal in this experiment, is exaggerated (at least for the variability of Eurasian snow cover that results). In addition, the atmospheric (and climatological land) initialization bene cially impact the (signi cant) skill of the DSP hindcasts. By taking a point-wise temporal correlation of MAM SST to simulated MAM Eurasian snow-cover, a prominent ENSO-pattern of correlation is seen for the AMIP ensemble simulation (Fig. 3, middle panel). This pattern indicates that for an El NinÄo year above normal snow cover for Eurasia is simulated by the AMIP ensemble (and vice versa for a La NinÄa). This ENSO correspondence, however, is not seen in the observations (Fig. 3, top panel). Moreover, the correlation pattern from the DSP hindcasts is quite consistent to the observed pattern (Fig.3, bottom panel). Very similar results are seen for JFM averages (but not shown). The results show that the ENSO response in the AMIP ensemble for Eurasian snow cover most likely contributes to the lack of skill in simulating Eurasian snow cover anomalies (Figs 1 and 2). It is plausible that the SST-ESC correspondence found in the observations could be solely attributed to atmospheric circulation (i.e. the atmospheric anomalies force both the SST anomalies and ESC anomalies). However, this would imply that the atmospheric initialization of the DSP simulations must have a persistence timescale that spans the GCM integration (i.e. 3months). If the prescribed SST in the DSP run were unimportant (i.e. forced by the atmosphere), the initialized atmospheric states must persist and control the skillfully simulated ESC patterns. This is unlikely based on the fundamental limits of atmospheric predictability (Lorenz, 1965). It is more likely that the performance of the DSP simulations is a result of a synergistic combination of the realistic initialization and SST. In light of the evidence above, an exacerbated teleconnection with ENSO variability in the AMIP ensemble is implied. If poor skill in simulated Eurasian snow cover is linked to the exaggerated ENSO response, we should also see poor performance of simulated atmospheric anomalies in the AMIP ensemble, presumably during ENSO events and over Eurasia. For all but one of the ENSO years during the

4 Figure 1. Time-series of January±March (JFM) Eurasian snow-cover anomalies for the period 1982±1998. Shown are the time-series for the observations (NESDIS; black line, lled square), the DSP winter (JFM) ensemble hindcasts (blue line, open circle), and the AMIP ensemble (red line, lled circle). Shown in the top right panel within the plotting frame are the correlations of the DSP and AMIP ensemble time-series against the observations. simulations (1982±1998), the AMIP ensemble shows considerably poorer skill ( judged against NCEP reanalyses) in simulating the anomaly patterns of 1000±500 mb thickness (among other atmospheric quantities not shown) over a domain that spans Eurasia (i.e. 20N-90N, 15W-180E) as compared to the DSP hindcasts (Fig. 4, results for JFM shown, but similar results seen for MAM). In addition, most of the largest disparities in skill between the AMIP and DSP ensembles, with the DSP ensemble showing superior skill, are found during ENSO years. Moreover, the only cases in which the AMIP ensemble shows negative correlation against observations, while the DSP shows considerable positive correlation, are during ENSO years. Since the land initialization of the DSP hindcasts is climatological, the atmospheric initialization of the DSP seasonal hindcasts must provide the skillful in uence to the simulated atmospheric anomalies, which then aids in the simulation of the snow cover anomalies over Eurasia. 4. CLOSING REMARKS Our results indicate that the potential predictability of Eurasian snow cover cannot be solely attributed to ENSO variability. This nding would appear to be in contrast with previous studies which show a strong impact of ENSO on Eurasian snow cover and snow depth. These

5 Figure 2. As in Fig. 1, but for March±May (MAM) Eurasian snow-cover. studies include: Groisman et al., 1994; Yang, 1996; Ferranti and Molteni, 1999; Martineau et al. (1999); and Corti et al., 2000 (hereafter referred to as G94, Y96, FM99, M99, and C00 respectively). However, a closer inspection of the G94 and Y96 studies shows that the strong ENSO relation is largely a result of the El NinÄo events of 1972±73 and 1977±78. Upon removal of these two events (as they are not considered in this study), the ENSO correspondence is less apparent (see Figure 14b of G94 and gure 1 of Y96). In the model-based studies of FM99 and C00, Empirical Orthogonal Functions (EOFs) are used to extract spatio-temporal modes of snow depth (i.e. not snow cover) variability over Eurasia. However, the simulations of FM99 span only 2 years (1983 and 1984), and should not necessarily be deemed conclusive. In addition, the EOF of Eurasian snow depth found to be associated to ENSO in C00 explains only 16% of the total Eurasian snow-depth variance. Therefore, the potential predictability that results from this ENSO-related EOF may be overwhelmed by the remaining (i.e. 84%) unexplained variance. These results are qualitatively consistent with our ndings for Eurasian snow cover. Our AMIP simulation, whose only forced response results from SST variations, shows an exaggerated ENSO response (Fig. 3). However, the DSP run, in uenced by both initialized atmospheric anomalies and SSTs, shows a more realistic snow cover response that cannot be signi cantly associated with ENSO (Figs 1±3). The modeling results of M99 indicate a correspondence between eastern tropical Paci c variability, NAO, and winter European atmospheric variability. However, the numerical experiments closely follow those of the COLA AMIP simulation. Although the M99 simulations were seasonal (i.e. span September to March), the initial conditions were taken from a climate (AMIP-type) simulation of their model (and not observationally based). Therefore, it is reasonable to assume that the ENSO response they obtain with their model is similarly exaggerated as in the COLA AMIP simulations. Unfortunately, this cannot be con rmed as a DSP-type suite of simulations was not performed with their model.

6 Figure 3. Maps of point-wise temporal correlation of March±May (MAM) averaged sea-surface temperature against Eurasian snow-cover extent. The top panel shows the correlation using observed (NESDIS) snow cover; the middle panel are the results using simulated snow cover from the AMIP ensemble; the bottom panel gives the results using simulated snow cover from the DSP ensemble. Shading levels refer to correlation signi cance.

7 Figure 4. Skill of AMIP and DSP ensembles at simulating January±March (JFM) averaged 1000±500 mb thickness for the period 1982±1998. Skill is quanti ed as the (spatial) anomaly correlation coef cient (ACC) of simulated 1000±500 mb heights against NCEP Reanalysis over the region 20N-90N, 15W-180E (which spans Eurasia). Years in which a moderate to strong ENSO event had occurred during JFM are shaded in the plotting frame (red ˆ El NinÄo; blue ˆ La NinÄa). The lighter shade of red for 1993 denotes a weak El NinÄo occurred during the JFM period. Nevertheless, the initialization of the DSP hindcasts plays an important role toward skillful predictions of Eurasian snow cover ( for the COLA GCM). The initialization could improve the hindcast's skill by minimizing the impact of systematic atmospheric and/or continental biases that have become well established in the longer AMIP ensemble (that could degrade the skill). In addition, the processes leading to skillful predictions of Eurasian snow cover could be unpredictable beyond a certain lead-time (i.e. greater than a season) and therefore could never hoped to be skillfully predictable in an extended (i.e. multi-year) atmospheric simulation. The exact mechanisms by which the superior skill in the DSP hindcasts is achieved lies beyond the scope of this analysis, but will be investigated. Certainly, large-scale atmospheric phenomenon such as the North Atlantic Oscillation (NAO) have been shown observationally to have a consistent relationship with Eurasian snow cover (e.g. Serreze et al., However, preliminary analysis of the DSP and AMIP ensembles shows no discernable differences in simulating NAO variability for the JFM and MAM periods. Acknowledgements The authors would like to thank Dan Paolino and Larry Marx for executing the COLA DSP and AMIP ensemble simulations. The authors also thank three anonymous reviewers (and the editor) for their comments, which lead to a substantially improved paper.

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