The latter is attributed to a thinning of sea ice cover over the whole Arctic, making it more sensitive to atmospheric fluctuations. In contrast to the PPP assessment, the prediction skill as measured by the anomaly correlation coefficient is found to be mostly due to external forcing. Yet, in agreement with the PPP assessment, a weak added value of the initialization is found in the Atlantic sector.
These contrasted findings regarding potential predictability and prediction skill arising from the initialization suggest that substantial improvements can be made in order to enhance the prediction skill. Decadal climate prediction is a major issue in the development of strategies for societal adaptation to the changing climate. This new domain of research, initiated by societal demand and enabled by recent achievements of ocean reanalysis, goes beyond the simple production of confident climate predictions. Still in its infancy, the achievement of decadal prediction objectives requires, as a primary step, a sufficient understanding of the mechanisms of interannual to decadal variability of the climate system, as well as improved simulations of those mechanisms in climate models Latif and Keenlyside Predictability arising from initial conditions knowledge, i.
These long-term evolving variables, as well as the physical mechanisms leading to effects on shorter time scale, can be seen as predictability sources. For near-term climate predictions, these long-term evolving modes need to be correctly phased with observations, implying a good initialization. The ocean, characterized by strong inertia and, more particularly, oceanic variability modes such as the Atlantic Meridional Overturning Circulation AMOC , have been shown to be the major source of predictability in the climate system Griffies and Bryan ; Boer ; Pohlmann et al.
Accordingly, initialization of the ocean state improves some aspects of climate forecast Smith et al. Sea ice, which is strongly influenced by the ocean Bitz et al.
Its link with the ocean suggests that it is potentially predictable up to several years, and because of its impact on the atmosphere, especially at the ice edge, sea ice predictability might, in return, be a source of predictability for the polar climate. The Arctic sea ice predictability has, until now, been investigated mostly at the seasonal time scale, dominated by the initial-value predictability.
Most of analyses dealing with the Arctic sea ice cover seasonal predictability are based on statistical relationships Walsh et al. Further, considering the rapid changes in the Arctic environment, statistical relationships may not remain valid in the future Holland and Stroeve , justifying the increasing use of physical models. Models can be used together with observations to reconstruct the past sea ice cover and further investigate statistical precursors of the sea ice Lindsay et al. Note that previously mentioned reconstructions constitute very good tools for initializing these hindcasts-forecasts.
Despite its relatively short prediction time scale, the seasonal predictability is assessed with statistical methods as well as coupled model experiments over several decade-long periods, therefore exhibiting some long-evolving trends. This raises the question of the impact of this trend, due to changes in boundary conditions, on the total predictability assessment. For decadal prediction, this is even more crucial. Indeed, initial values account for this trend and complicate the assessment of the predicted trend in response to the change in forcing during the hindcasts, which constitutes a large part of the predictability.
Longer-term Arctic sea ice predictability has been investigated using perfect model assumption, in which Global Climate Models GCMs ensemble integrations are initialized from a reference model integration Koenigk and Mikolajewicz ; Holland et al. These studies examined the upper limit of initial-value predictability and its sensitivity to the Arctic sea ice mean state Holland et al.
In examining the monthly means of spatially integrated indices, i.
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Blanchard-Wriggleworth et al. The higher predictability of SIT, i. Therefore, the understanding of their variability and predictability requires consideration at a smaller spatial scale in order to highlight regional discrepancies. A few studies have investigated the spatial distribution of the sea ice cover predictability, concluding that the sea ice cover is more predictable in the Atlantic sector than in the rest of the Arctic Koenigk and Mikolajewicz ; Koenigk et al.
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This higher predictability is attributed to sea ice anomalies advection processes in the transpolar drift stream impact area, i. A detailed description of the simulations used in this analysis is given in Sect. Responses to previously mentioned questions are detailed in Sect. Finally, results are summarized and discussed in Sect.
A full description and a basic evaluation of the system can be found in Voldoire et al. The ocean component is based on the ocean part of the NEMO model. The GELATO5 dynamic-thermodynamic sea ice model is directly embedded into the ocean component and uses the same horizontal grid. It includes elastic-viscous-plastic rheology, semi-lagrangian advection of ice slabs, ridging and rafting, parametrization of lead processes, snow-ice formation, prognostic salinity, and an advanced snow cover scheme which represents the effect of snow ageing on snow density. In the present study, 4 thickness categories are used: 0—0.
For the treatment of the vertical heat diffusion of ice, every slab of ice is divided into 9 vertical layers, and may be covered with one snow layer. To investigate the added value of the ocean initialization on the decadal predictability and prediction, we use, for comparison, CMIP5 historical experiments Taylor et al.
Ten members have been computed for the period — They differ in their initial conditions, taken from different dates of the year control simulation under preindustrial conditions performed with the model. The internal variability of those historical simulations is not synchronized with observations. Both reference time series are available over the period — All coefficients are computed over the overlapping time period; that is, For HIST, the first row corresponds to the ensemble mean, followed by minimum and maximum values obtained for individual member computation.
Analyses of the sea ice predictability in this paper are based on a set of ten-year-long, ten-member ensemble decadal hindcasts performed within the framework of CMIP5 Taylor et al. Hindcasts were initialized on January 1st of every 0, 1, 5 and 6 calendar years of a given decade spanning the — period, which corresponds to 16 start dates.http://autodiscover.manualcoursemarket.com/88.php
Effects of Arctic Sea Ice Decline on Weather and Climate: A Review
The choice to limit our analysis to this period is based on the — coverage period of the target used as a reference for prediction skill assessment. Hindcasts starting after cannot be compared to the reference for the whole integration period and are therefore treated as forecasts. The latter are randomly selected during January. This set of decadal hindcasts will henceforth be referred to as DEC. No constraint is applied directly to sea ice, atmosphere and land.
This preliminary experiment aims to produce ocean initial states compatible with the observations and the model mean state. This evaluation is performed on datasets overlapping period, namely — Clearly, the sea surface temperature and salinity restoring impose a strong constraint on sea ice edge position during winter, leading to a SIE very close to observations.
This indirect constraint is no more efficient during summer, when a negative bias in SIE is observed, in accordance with the free coupled model mean state.
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Accordingly, a very strong negative bias is found in SIV during both winter and summer. The winter and summer downward trends of both SIE and SIV are correctly represented, as shown by the correlation coefficients of interannual time series. The NUD simulation also performs well in reproducing the interannual variability around the trend for the SIE, with significant correlation coefficients of the detrended time series in both winter and summer. On the other hand, it clearly fails to reproduce the Arctic SIV interannual variability around the linear trend.
In conclusion, the sea ice initial states provided by NUD are close to observations in terms of SIE, but strongly underestimate the SIV, which is much closer to the free coupled model attractor. All external radiative forcings natural and anthropogenic are the same in all simulations, following the CMIP5 historical protocol until and the RCP8. The use of the same coupled model for all 3 sets of experiments NUD, DEC, and HIST is clearly one of the strengths of this analysis, as no uncertainties arise from the model version when comparing datasets.
The standard deviation of each lead time is averaged over all start dates. The generation ensemble methodology and its impact on predictability and prediction skill is a main issue of near-term climate prediction but is not the focus of this paper. Nevertheless, a very short analysis testing our atmospheric-only perturbation against an atmosphere—ocean perturbation technique performed on a few start dates not shown leads us to conclude that atmospheric-only perturbation was sufficient to generate the Arctic SIE and SIV ensemble spread, though it gives an upper limit of potential predictability.
Note that this PPP measure addresses initial-value predictability only. The lead time is from 0 for the year of the initialization to 9 for the tenth year of simulation. The summer sea ice cover seems to be much less predictable Fig.
Historical interannual standard deviation map of March a sea ice fraction and b sea ice thickness in m , over the period — This standard deviation is computed for each member, and then averaged over the ensemble. The winter SIC of the marginal seas exhibits a covariability known as the double dipole pattern Walsh and Johnson ; Fang and Wallace ; Deser et al. Superimposed on the large scale covariability of the North Hemisphere sea ice, regional contrasts between peripheral seas have been also identified over the full historical period Mysak and Manak and the more recent period of satellite observations Parkinson et al.
Bitz et al. The latter process differs significantly from one marginal sea to the other, as it is influenced by the fluctuations of different water masses. For example, Schlichtholz identified a strong control of the Nordic Seas winter SIA by the summer temperature in the Atlantic water core in the Barents Sea opening area.
Those regionally contrasted oceanic influences are expected to play a crucial role in sea ice cover predictability. In this section, we investigate regional PPP in six regions, shown in Fig. Finally, we consider the Central Arctic domain in order to investigate the significant SIT variability there. Regional domain locations are shown on Fig. As mentioned at the beginning of this section, DEC start dates span several decades where rapid climate condition changes occur, implying the need to take into account changes of internal variability in the PPP estimation.
In this section, the period dependence of PPP is evaluated. We separate the 16 start dates into two disjoint sets: the first containing the 8 start dates prior to , and the second the most recent 8 start dates after This split is constrained by the limited number of available start dates and the need for sufficient sampling in order to obtain statistically significant conclusions.
Ensemble standard deviation of the central Arctic March SIV considering only a the 8 start date prior to , and b the 8 start dates after The ensemble spread is taken as one standard deviation. The trend is expressed in m per decade. Solid and dashed lines correspond to anomalies from the mean and long evolving trend, respectively. The trend is assessed by a linear regression onto the CO2 concentration external forcing time series.
Considering the persistence predictions of summer sea ice indices, the rather constant scores with lead time before detrending, in addition to the insignificant scores after detrending, suggest that this persistence prediction skill comes purely from the trend.
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Get the report. All rights reserved. Powered by WordPress and the Graphene Theme. Return to top. Arctic sea ice extent in September white area compared with the median ice edge purple line in the period — The names of sea areas added by the author.