Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 40, 1 6, doi: /grl.50129, 2013 Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system M. Sigmond, 1 J. C. Fyfe, 2 G. M. Flato, 2 V. V. Kharin, 2 and W. J. Merryfield 2 Received 24 October 2012; revised 7 December 2012; accepted 27 December [1] We assess the seasonal forecast skill of pan-arctic sea ice area in a dynamical forecast system that includes interactive atmosphere, ocean, and sea ice components. Forecast skill is quantified by the correlation skill score computed from 12 month ensemble forecasts initialized in each month between January 1979 to December We find that forecast skill is substantial for all lead times and predicted seasons except spring but is mainly due to the strong downward trend in observations for lead times of about 4 months and longer. Skill is higher when evaluated against an observation-based dataset with larger trends. The forecast skill when linear trends are removed from the forecasts and verifying observations is small and generally not statistically significant at lead times greater than 2 to 3 months, except for January/February when forecast skill is moderately high up to an 11 month lead time. For short lead times, high trend-independent forecast skill is found for October, while low skill is found for November/December. This is consistent with the seasonal variation of observed lag correlations. For most predicted months and lead times, trend-independent forecast skill exceeds that of an anomaly persistence forecast, highlighting the potential for dynamical forecast systems to provide valuable seasonal predictions of Arctic sea ice. Citation: Sigmond, M., J. C. Fyfe, G. M. Flato, V. V. Kharin, and W. J. Merryfield (2013), Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system, Geophys. Res. Lett., 40, doi: /grl Introduction [2] The rapid decline in Arctic summer sea ice has increased marine accessibility to Arctic waters. This has raised interest in predictions of Arctic sea ice on seasonal (1 12 month) time scales, which may be of potential benefit to communities, industries, and governments involved in Arctic fishing, transport, and resource extraction. The relatively long time scales in the sea ice ocean system suggest that skillful sea ice predictions might be possible. The inherent predictability of Arctic sea ice cover has been assessed in a number of perfect model studies employing ensembles of dynamical model simulations. A prognostic measure of potential predictability is obtained by comparing the ensemble spread to a measure of natural variability. In such a framework, anomalous seasonal variations in Arctic All Supporting Information may be found in the online version of this article. 1 Department of Physics, University of Toronto, Toronto, Ontario, Canada. 2 Canadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, British Columbia, Canada. Corresponding author: M. Sigmond, Department of Physics, University of Toronto, Toronto, Ontario, Canada. (sigmond@atmosp.physics.utoronto.ca) American Geophysical Union. All Rights Reserved /13/ /grl sea ice have been shown to be predictable for 1 2 years [Blanchard-Wrigglesworth et al., 2011a]. The level of predictability varies strongly with season and is relatively high in winter and summer and relatively low in spring [Holland et al., 2010]. [3] Until recently, predictions of Arctic sea ice were primarily derived from statistical models. Such models are based on empirical relationships between sea ice, ocean and atmospheric variables, and sea ice concentrations in the subsequent months [e.g., Lindsay et al., 2008]. However, it has become clear that such relationships depend on the mean state of the Arctic climate [Holland et al., 2010; Holland and Stroeve, 2011], which is rapidly changing. As a consequence, the statistical model predictions may be subject to large errors [Lindsay et al., 2008] and the realization of skillful seasonal forecasts may increasingly depend on the use of dynamical (physically based numerical) forecast models. The pioneering study of Zhang et al. [2008] employed a dynamical model to provide 1 year ensemble forecasts for September 2008 sea ice, but as their model employed historical atmospheric forcings, it could not account for coupled interactions between the ocean/sea ice system and the atmosphere. A number of seasonal forecast systems that include interactions of the ocean sea ice system with the atmosphere have recently become operational. However, the field of coupled atmosphere-ocean sea ice seasonal forecasting is still in its infancy. The initialization of sea ice and ocean fields in dynamical forecast systems is the subject of intensive research, and it is not yet clear to what extent such systems are capable of producing skillful forecasts of sea ice. [4] Here we report on the forecast skill of pan-arctic sea ice area in a seasonal forecasting system that includes interactive atmosphere, ocean, and sea ice components. We show that (1) much of the seasonal cycle of forecast skill at longer lead times can be understood by considering the seasonal cycle of sea ice trends, (2) the component of forecast skill that is independent of the trend is substantially smaller and generally not statistically significant at lead times greater than 2 to 3 months, and (3) trend-independent forecast skill is generally enhanced relative to that of an anomaly persistence forecast. 2. Model and Simulations [5] The forecasting system employed in this study is the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is extensively described in Merryfield et al. [2013], to which we refer the reader for details. CanSIPS is a multi-model system based on two coupled atmosphere-ocean-sea ice models developed at the Canadian Centre for Climate Modelling and Analysis: CanCM3, whose atmospheric component is CanAM3 (also known as AGCM3) [Scinocca et al., 2008] with 31 vertical levels, and CanCM4 [Arora et al., 2011], whose atmospheric component is

2 CanAM4 with 35 vertical levels. Both models are run at T63 horizontal resolution in the atmosphere and share the same ocean, land, and sea ice components. The CanOM4 ocean model has approximately 100 km horizontal resolution and 40 vertical levels. Sea ice thermodynamics is governed by energy balance and sea ice dynamics by cavitating fluid rheology on the atmospheric model grid. [6] Initial model states for the seasonal forecasts are obtained from a set of assimilation runs, one for each ensemble member, with atmospheric fields, sea surface temperature (SST), subsurface ocean temperatures, and sea ice concentration constrained near observation-based values in the form of gridded analyses. It is important to note that whereas sea ice concentration is relaxed toward the HadISST dataset [Rayner, 2003], no attempt is made to assimilate sea ice volume data. Initialization methods of sea ice volume are currently being developed [e.g., Tietsche et al., 2012], and the lack of reliable long-term observations makes sea ice volume initialization particularly challenging. [7] The forecast skill of Arctic sea ice cover is assessed by analyzing 12 month hindcasts (or historical forecasts) initialized at the beginning of each month from January 1979 to December Each forecast consists of 20 ensemble members, 10 for each model. We quantify deterministic forecast skill through the correlation skill score of the ensemble mean forecast of pan-arctic sea ice area. Results for sea ice extent (the area covered with larger than 15% sea ice concentration) are not shown but are qualitatively similar to those for sea ice area. 3. Results 3.1. Forecast Skill and the Role of the Trend [8] We first examine the model forecasts of sea ice area in September, the month with the smallest sea ice area. The thick black line in Figure 1 represents values obtained from HadISST, the observational dataset used to initialize the model, and the gray line represents values obtained from an alternative observation-based sea ice area dataset produced by the National Snow and Ice Data Center or NSIDC 10 6 km September sea ice area Observations(HadISST) Observations(NSIDC) lead=0 (r=0.99/0.98) lead=1 (r=0.94/0.93) lead=4 (r=0.71/0.72) lead=11 (r=0.75/0.78) Year Figure 1. Time series of pan-arctic September sea ice area for the observations (HadISST and NSIDC) and as predicted by the model for forecast leads of 0, 1, 4, and 11 months. Correlations r between the observations and forecasts (also referred to as CSS) are shown in parentheses (first number for HadISST, second for NSIDC). [Fetterer et al., 2002, updated 2009], and the colored lines indicate CanSIPS hindcasts at different lead times. A lead time of 0 months (lead = 0) corresponds to the forecasts initialized on 1 September, lead = 1 to forecasts initialized on 1 August, etc. For short lead times, the model captures both the strong observed downward trend and most of the inter-annual variability about this trend, which translates into a very high correlation with the observations (given by r-values, hereafter referred to as Correlation Skill Score (CSS), or simply forecast skill ). Even though the forecast skill remains high (r > 0.7) for longer lead times, the model forecasts at 4 and 11 months lead appear to capture little of the observed year-to-year variability. This suggests that most of the forecast skill at these longer lead times is attributable to the strong downward trend. This is consistent with the fact that both the trend and forecast skill based on the NSIDC data are slightly higher than those based on HadISST data. At longer leads, our forecast system drifts toward the behavior of the uninitialized models, which on average underestimate the long-term trend in September ice extent [Merryfield et al., 2013]. [9] Figure 2a shows the skill of the forecasts evaluated against HadISST observations for all predicted months at lead times of 0 to 11 months. Black dots represent statistical significance at the 95% confidence level as determined by bootstrapping. To account for presence of long-term trends in the observed and model data, the bootstrapping procedure is based on re-sampling residuals from the fitted linear trend instead of re-sampling actual values [Davison and Hinkley, 1997]. Highest skills occur at 0 and 1 month lead, with relatively high skill remaining for September and October at a 2 month lead time. For longer lead times, the skill exhibits a strong seasonal dependency, with the smallest skill (r 0.2) for spring (March May) and largest skill (r 0.7) for summer and early fall (June October). The high skill for summer and early fall appears to be caused by the strong trend in observed sea ice area as will be shown next. [10] Figure 2c shows the difference between the overall forecast skill (Figure 2a) and the skill computed from detrended model and observational time series. This quantity is largest for predictions of June to November sea ice area at lead times larger than about 4 months. This is consistent with the seasonality of the observed trend (Figure 2e), which also peaks in summer and early fall. Averaged over all lead times and predicted months, removing linear trends from the forecasts and observations results in a forecast skill decrease from 0.56 to We note that de-trending the observations and model forecasts with a second- or thirdorder polynomial rather than a linear function yields qualitatively similar results. [11] Due to inconsistent data sources employed in the construction of the HadISST time series, trends derived from these time series are considered unreliable [Meier et al., 2012]. Given the important role of the trend to the forecast skill, it appears relevant to also compute the forecast skill based on NSIDC observations, which do not suffer from such inconsistencies. This forecast skill, shown in Figure 2b, is substantially larger (on average by about 23%) and shows smaller amplitude of seasonal variation than forecast skill based on HadISST data. This can be explained by considering the linear trends in the NSIDC time series (Figure 2f), which are significantly larger with less seasonal dependency than in HadISST time series. When the trends are removed, 2

3 Figure 2. Top: Forecast skill of sea ice area as function of predicted month and lead time. Middle: Difference between the forecast skill and trend-independent forecast skill. Bottom: Observed trend in sea ice area as a function of month. Left row is based on HadISST and right row on the NSIDC observational dataset. Black dots represent statistical significance at the 95% confidence level, and the error bars the 95% confidence interval as determined by bootstrapping. NSIDC and HadISST linear trends differ at the 95% confidence level for all months except February and September to November. the NSIDC-based forecast skill more closely resembles the HadISST-based forecast skill (Figures 3a and 3b), both in terms of magnitude (on average, r = 0.27 for HadISST and 0.29 for NSIDC) and seasonal dependency. In short, we find that forecast skill is highly sensitive to the magnitude of the trend, especially for lead times longer than 4 months. These considerations suggest that it may be preferable to use the NSIDC rather than the HadISST dataset for the initialization of the seasonal forecasts Forecast Skill Independent of Trend [12] We next examine the forecast skill that remains after de-trending the forecast and observed time series (Figures 3a and 3b). Assuming that the trend is caused by external forcings such as increasing greenhouse gases, this trendindependent skill quantifies how well the model forecasts internally generated fluctuations. We note that this is the type of skill that has been investigated in previous studies on potential predictability [e.g., Holland et al., 2010]. Comparing trend-independent skill based on the two different observational datasets, a few robust features can be identified. Trend-independent skill is generally small and not statistically significant for lead times greater than 2 3 months and depends strongly on the season, with generally high skill for January/February and October and low skill for spring and November/December. How can this seasonal dependency of trend-independent skill be understood? [13] Some features of the achieved trend-independent forecast skill in our system are consistent with potential predictability measures reported in previous studies. The most striking feature in Figures 3a and 3b is the high trendindependent skill in January/February, which is statistically significant up to an 11 month lead time. These high skill scores are consistent with the potential predictability study of Holland et al. [2010]. They attributed this high winter predictability to the fact that the winter sea ice edge is closely related to convergence of ocean heat fluxes [Bitz et al., 2005] which are, due to the long timescales involved, relatively predictable. 3

4 Figure 3. Top: Model forecast skill of sea ice area that is independent of the trend as function of the predicted month and lead time. Middle: Trend-independent skill of a persistence forecast. The black line denotes the predicted months and lead times where high lagged correlation corresponds to melt season to growth season re-emergence of memory. For example, enhanced correlation associated with memory re-emergence between August and September would be reflected by enhanced skill of persistence for September at a 0 month lead time, whereas enhanced correlation between July and October would be reflected by enhanced skill of persistence for October at a 2 month lead time. Bottom: Difference between trend-independent model forecast skill and trend-independent forecast skill of persistence. Black dots represent statistical significance at the 95% confidence level. Low skill is found for spring, in particular for lead times of 3 7 months, which is consistent with Holland et al. [2010] and Blanchard-Wrigglesworth et al. [2011b]. [14] It is instructive to compare the seasonal dependency of trend-independent forecast skill to the seasonal dependency of memory (or persistence) in the observed system. Figures 3c and 3d show the skill of an anomaly persistence forecast for HadISST and NSIDC, which is obtained by persisting the observed anomaly preceding the forecast throughout the forecast range. For example, a September forecast with a lead time of 0 months corresponds to a forecast in which the anomaly in the preceding month (August) is persisted. This results in a forecast skill that is equivalent to the lag correlation between the September and August mean sea ice area. Comparison between Figures 3a/3b and 3c/3d reveals many similarities between dynamical forecast skill and persistence. In particular: High trend-independent forecast skill in January/February is consistent with high persistence for these months, as de-trended January/February sea ice area is well correlated to that in the preceding 11 months (except perhaps for months 3 7 for NSIDC). 4

5 Low trend-independent forecast skill in spring at lead times of 3 7 months is consistent with the low forecast skill of persistence in spring for those lead times. High trend-independent skill for October at lead times of 3 4 months is consistent with high persistence. Figures 3c and 3d show enhanced persistence for de-trended October sea ice area, which has been associated with a feature often referred to as re-emergence of memory. Blanchard- Wrigglesworth et al. [2011b] found enhanced correlation of sea ice cover anomalies in the melt season (May August) with anomalies in the following growth season (September December) and attributed this to interactions of the sea ice edge with SSTs. Consistent with Blanchard-Wrigglesworth et al. [2011b] and Chevallier and Salas-Mélia [2012], our dynamical forecast system exhibits a strong signal of memory re-emergence (Figure S1 in the Supporting Information). Observations show much weaker memory re-emergence (which would be reflected by the enhanced lag correlation along the black line in Figure 3c/3d). Comparison of Figure S1, showing lag correlations for the model, to Figures 3a/3b reveals that the strong memory reemergence in the model does not translate into significantly higher forecast skill in November/December but may explain the enhanced forecast skill for October. Trend-independent forecast skill for November and December decreases relatively quickly with increasing lead time with no statistically significant skill at lead times longer than 1 month. This minimum in skill is consistent with low persistence (Figures 3c and 3d), which Blanchard-Wrigglesworth et al. [2011b] argue is due to the rapid seasonal sea ice growth that occurs in those months Persistence as Benchmark for Model Skill [15] Not only does the skill of persistence help one to understand the seasonal dependency of the model forecast skill, it also constitutes a benchmark for the dynamical model performance. Forecast skill associated with a persistence forecast can be obtained with little computational resources. For predictions of a dynamical forecast system to be of additional value relative to such trivial statistical forecasts, model forecast skill must be larger than the skill of persistence. Figures 3e and 3f show the difference between trend-independent forecast skill and the skill of trend-independent persistence forecasts based on HadISST and NSIDC data. This quantity is positive for most months and lead times, which means that the forecast skill in CanSIPS is generally enhanced relative to that of persistence. Although the enhancement is statistically significant only for a few individual months and lead times, it is substantial (0.27 versus 0.21 for HadISST and 0.29 versus 0.19 for NSIDC) and highly significant (at the 99% confidence level for both HadISST and NSIDC) when averaged over all months and lead times. 4. Summary and Discussion [16] As Arctic seas are becoming more accessible, the socio-economic benefits associated with skillful seasonal forecasts of Arctic sea ice cover are becoming increasingly evident. Statistical models, traditionally employed to perform such forecasts, may suffer from large errors due to the rapid changes in the Arctic environment. In this study, we have presented the forecast skill of pan-arctic sea ice area in a dynamical forecast system that includes interactive atmosphere, ocean, and sea ice components. Our results highlight the importance of a careful consideration of the trend. Indeed, a large fraction of the forecast skill achieved for lead times beyond the first 2 months is directly attributable to the downward trend in sea ice cover. Forecast skill at lead times longer than 2 3 months is generally small when based on de-trended model forecasts and verifying observations, and forecast skill was found to be appreciably larger when evaluated against an observation-based dataset with larger trends. This finding emphasizes that for a clean comparison of different forecast systems, the (overall) forecast skill has to be evaluated against the same observational dataset. The seasonal variation of trend-independent skill was found to be consistent with previous potential predictability studies and with the seasonal cycle of persistence. While persistence is an important source of skill, we also find that our dynamical forecast system is able to provide additional skill. [17] The new study by Wang et al. [2013], who report on forecast skill of sea ice extent in seasonal forecasts of the NCEP Climate Forecast System version 2, presents results that show similarities to those presented here. For example, consistent with our analysis, Wang et al. [2013] show enhanced trend-independent skill for September October at a lead time of 2 months and moderately high forecast skill for winter at lead times up to 10 months. This suggests that these findings may be applicable to other seasonal forecast systems as well. [18] Sea ice predictions based on dynamical models are still in an experimental phase and are hampered by the availability of ice and ocean data needed to initialize prediction systems. For example, accurate thickness initialization may enhance skill for September sea ice cover, as winter and spring sea ice thickness have been shown to be a good predictors of perennial sea ice extent [Kauker et al., 2009; Holland et al., 2010; Holland and Stroeve, 2011; Chevallier and Salas-Mélia, 2012]. Despite these limitations, our results highlight the potential of dynamical forecasting systems for providing valuable predictions of Arctic sea ice cover. [19] Acknowledgments. MS gratefully acknowledges funding by Environment Canada through a Grants and Contributions Agreement with the University of Toronto. We thank Nathan Gillett and John Scinocca and two anonymous reviewers for helpful comments. References Arora, V. K., J. F. Scinocca, G. J. Boer, J. R. Christian, K. L. Denman, G. M. Flato, V. V. Kharin, W. G. Lee, and W. J. Merryfield (2011), Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases, Geophys. Res. Lett., 38(5), 3 8, doi: /2010gl Bitz, C. C., M. M. Holland, E. C. Hunke, and R. E. Moritz (2005), Maintenance of the sea-ice edge. J. Clim., 18, Blanchard-Wrigglesworth, E., C. M. Bitz, and M. M. Holland (2011a), Influence of initial conditions and climate forcing on predicting Arctic sea ice, Geophys. Res. Lett., 38(18), 1 5, doi: / 2011GL Blanchard-Wrigglesworth, E., K. C. Armour, C. M. Bitz, and E. DeWeaver (2011b), Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations, J. Clim., 24 (1), , doi: /2010jcli Chevallier, M., and D. Salas-Mélia (2012), The role of sea ice thickness distribution in the Arctic sea ice potential predictability: A diagnostic approach with a coupled GCM, J. Clim., 25(8), , doi: / JCLI-D

6 Davison, A. C., and D. V. Hinkley (1997), Bootstrap Methods and Their Application, 594 pp, Cambridge University Press, New York, NY, USA. Fetterer, F., K. Knowles, W. Meier, and M. Savoie (2002, updated 2009), Sea Ice Index, Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. Holland, M. M., and J. Stroeve (2011), Changing seasonal sea ice predictor relationships in a changing Arctic climate, Geophys. Res. Lett., 38(18), 1 6, doi: /2011gl Holland, M. M., D. A. Bailey, and S. Vavrus (2010), Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3, Clim. Dyn., 36(7 8), , doi: /s Kauker, F., T. Kaminski, M. Karcher, R. Giering, R. Gerdes, and M. Voß beck (2009), Adjoint analysis of the 2007 all time Arctic sea-ice minimum, Geophys. Res. Lett., 36(3), 1 5, doi: /2008gl Lindsay, R. W., J. Zhang, A. J. Schweiger, and M. A. Steele (2008), Seasonal predictions of ice extent in the Arctic Ocean, J. Geophys. Res., 113(C2), 1 11, doi: /2007jc Meier, W. N., J. Stroeve, A. Barrett, and F. Fetterer (2012), A simple approach to providing a more consistent Arctic sea ice extent timeseries from the 1950s to present, The Cryosphere Discuss., 6(4), , doi: /tcd Merryfield, W. J., W.-S. Lee, G. J. Boer, V. V. Kharin, J. F. Scinocca, G. M. Flato, R. S. Ajayamohan, J. C. Fyfe, Y. Peng and S. Palavarapu (2013), The Canadian seasonal to interannual prediction system. Part I: Models and initialization, Mon. Weather Rev., accepted. Rayner, N. A. (2003), Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res., 108(D14), doi: /2002jd Scinocca, J. F., N. A. McFarlane, M. Lazare, J. Li, and D. Plummer (2008), Technical Note: The CCCma third generation AGCM and its extension into the middle atmosphere, Atmos. Chem. Phys., 8(23), , doi: /acp Tietsche, S., D. Notz, J. H. Jungclaus, and J. Marotzke (2012), Assimilation of sea-ice concentration in a global climate model Physical and statistical aspects, Ocean Sci. Discuss., 9(4), , doi: / osd Wang, W., M. Chen, and A. Kumar (2013), Seasonal prediction of Arctic sea ice extent from a coupled dynamical forecast system, Mon. Weather Rev., doi: /mwr-d , in press. Zhang, J., M. Steele, R. Lindsay, A. Schweiger, and J. Morison (2008), Ensemble 1-year predictions of Arctic sea ice for the spring and summer of 2008, Geophys. Res. Lett., 35(8), 1 5, doi: / 2008GL