Study’s Model Simulations Show Arctic Sea Ice Reached Lowest Point On Modern Record… In The 1940s, Not Today

Regular NoTricksZone author Kenneth Richards notes on Twitter

Apparently, Arctic sea ice volume was as low in the 1940s as it has been in the 2000s.

And the highest sea ice volume of the last 100 years was about 1979 – the year the Arctic sea ice record begins.🤔

Originally tweeted by Kenneth Richard (@Kenneth72712993) on January 24, 2021.

Here is the complete study published under a CC 4.0 License.

Brief communication: Arctic sea ice thickness internal variability and its changes under historical and anthropogenic forcing

The Cryosphere, 14, 3479–3486, 2020
https://doi.org/10.5194/tc-14-3479-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Guillian Van Achter¹, Leandro Ponsoni¹, François Massonnet¹, Thierry Fichefet¹, and Vincent Legat²

• ¹Georges Lemaitre Center for Earth and Climate Research, Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
• ²Institute of Mechanics, Materials and Civil Engineering, Applied Mechanics and Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Correspondence: Guillian Van Achter ([email protected]) Received: 04 Dec 2019 – Discussion started: 10 Dec 2019 – Revised: 17 Jul 2020 – Accepted: 09 Sep 2020 – Published: 21 Oct 2020

Abstract

We use model simulations from the CESM1-CAM5-BGC-LE dataset to characterise the Arctic sea ice thickness internal variability both spatially and temporally. These properties, and their stationarity, are investigated in three different contexts: (1) constant pre-industrial, (2) historical and (3) projected conditions. Spatial modes of variability show highly stationary patterns regardless of the forcing and mean state. A temporal analysis reveals two peaks of significant variability, and despite a non-stationarity on short timescales, they remain more or less stable until the first half of the 21st century, where they start to change once summer ice-free events occur, after 2050.

How to cite. Van Achter, G., Ponsoni, L., Massonnet, F., Fichefet, T., and Legat, V.: Brief communication: Arctic sea ice thickness internal variability and its changes under historical and anthropogenic forcing, The Cryosphere, 14, 3479–3486, https://doi.org/10.5194/tc-14-3479-2020, 2020.1

Introduction

In the recent decades, Arctic sea ice has retreated and thinned significantly (Notz and Stroeve, 2016). The annual mean Arctic sea ice extent decreased by ∼2×106 km² between 1979 and 2016 (Onarheim et al., 2018). An analysis combining US Navy submarine ice draft measurements and satellite altimeter data showed that the annual mean sea ice thickness (SIT) over the Arctic Ocean at the end of the melt period decreased by 2 m between the pre-1990 submarine period (1958–1976) and the CryoSat-2 period (2011–2018) (Kwok, 2018). On long timescales (a few decades or more), retreating and thinning are projected to continue as greenhouse gas emissions are expected to rise. However, on shorter timescales (1–20 years), internal climate variability, defined as the variability of the climate system that occurs in the absence of external forcing and caused by the system’s chaotic nature, limits the predictability of climate (Deser et al., 2014) and represents a major source of uncertainty for climate predictions (Deser et al., 2012). In this context, greater knowledge of Arctic SIT internal variability and of its drivers is essential to document the true evolution of the Arctic atmosphere–ice–ocean system and to predict its future changes.

The mean spatial distribution of the Arctic SIT is relatively well documented (Stroeve et al., 2014). But there are some uncertainties around its interannual variability and its spatial modes of variability. Some studies (Lindsay and Zhang, 2006; Fuckar et al., 2016; Labe et al., 2018) already analysed the spatial distribution of Arctic sea ice variability by applying empirical orthogonal functions (EOFs) (K-means cluster analysis for Fuckar et al., 2016) to model-based historical SIT time series. Lindsay and Zhang (2006) reported a first mode nearly basinwide, while the second and third ones are orthogonal lateral modes accounting for 30 %, 18 % and 15 % of the variability, respectively. Fuckar et al. (2016) also found a nearly basinwide first mode, with an Atlantic–Pacific dipole as the second mode. Labe et al. (2018) depicted an Atlantic–Pacific dipole but as the first mode. The spatial structure and amount of explained variance of those modes are sensitive to whether and how the SIT time series is detrended. It is also model-dependent and influenced by the season and analysed period. The temporal sea ice volume (SIV) variability has been studied by Olonscheck and Notz (2017). These authors enlightened a remarkable similarity between the pre-industrial and historical internal variabilities of the annual Arctic SIV. They also noticed a decreased internal variability of winter and summer Arctic SIV for a future climate forced by the RCP8.5 scenario.

Apart from Olonscheck and Notz (2017), the studies cited above used data covering a few decades under historical forcing. In this work we use a long climate model control run under pre-industrial conditions from the CESM1-CAM5-BGC-LE dataset, which enables us to study only the internal variability of the Arctic SIT. We study the internal variability both temporally and spatially by applying a wavelet analysis and an EOF decomposition to the pan-Arctic SIV and gridded SIT anomaly time series, respectively. We also determine whether or not the SIV and SIT variability is stationary by analysing the model outputs under historical and future climate conditions with 30 ensemble members.

This paper is organised as follows. The model and its outputs are briefly described in Sect. 2. In Sect. 3, the spatial and temporal internal variability of Arctic sea ice is analysed, as well as its persistence through historical and future climate conditions. Then we explore the drivers of the main modes of internal variability. Conclusions are finally given in Sect. 4.

2 Data and methods

2.1 Sea ice thickness and volume datasets

We use the CESM1-CAM5-BGC-LE dataset (Kay et al., 2015). The Community Earth System Model Large Ensemble (CESM-LE) was designed to both disentangle model errors from internal climate variability and enable the assessment of recent past and future climate changes in the presence of internal climate variability. The CESM1(CAM5) is a CMIP5 participating model. It consists of coupled atmosphere, ocean, land and sea ice component models. It also includes a representation of the land carbon cycle, diagnostic biogeochemistry calculations for the ocean ecosystem and a model of the atmospheric carbon dioxide cycle (Moore et al., 2013; Lindsay et al., 2014). While it is not possible to validate the data in terms of SIT and SIV variabilities due to the lack of continuous observational data, the model was well validated in terms of mean state of the ice thickness and extent, as well as regarding the recent trends in the latter. Jahn et al. (2016) showed good agreement between observations and CESM1(CAM5) simulations for mean Arctic sea ice thickness and extent in the early 21st century. Barnhart et al. (2016) demonstrated that CESM1(CAM5) captures the trend of declining Arctic sea ice extent over the period of satellite observations. Based on these validation studies, we consider that the CESM1-CAM5-BGC-LE time series is a fair proxy to study the variabilities of the Arctic SIT and SIV under different forcing conditions.

In this paper, we use the monthly averaged Arctic SIT and SIV provided over the three periods (pre-industrial, historical and future). The pre-industrial period is represented by a single 1700-year control simulation with constant pre-industrial forcing. The ocean model was initialised from a state of rest (Danabasoglu et al., 2012), while the atmosphere, land and sea ice models were initialised using previous CESM1(CAM5) simulations. This experimental design allows the assessment of internal climate variability in the absence of climate change. In practical terms, we will use the last 200 years of this simulation. The historical period has one ensemble member covering the 1850–2005 period and 30 ensemble members over 1920–2005. Also with 30 ensemble members, the future climate period (2006–2100) follows the Representative Concentration Pathway (RCP) 8.5 scenario, corresponding to a total radiative forcing of 8.5 W m⁻² in 2100 relative to pre-industrial conditions (Meinshausen et al., 2011). The Canadian Archipelago region was removed from the dataset since SIT reaches unrealistic values in this area.

For the variability analysis, the trend and seasonal cycle are removed from the time series (pan-Arctic SIV and gridded SIT) so that we focus on the interannual variability. Since the spatial variability analysis uses 30 ensemble members, the SIT anomaly fields are computed by removing the ensemble mean to each member. When only one ensemble member is used, as for the temporal analysis, the anomaly is calculated by excluding the individual trend (provided by a second-order polynomial fit) of each month.

2.2 Variability analysis

To characterise the internal variability of the Arctic sea ice, we aim at inspecting how the SIV variability evolves in time and how SIT variability is characterised in space. For addressing the temporal variability, we make use of wavelet analysis, with Morlet as wavelet mother, following the methodology proposed by Torrence and Compo (1998). The wavelet analysis has the advantage of taking into account possible non-stationarity of the time series. In this paper, we show the results for one of the historical (1850–2005) members and one of the future (2006–2100) members, although we tested the robustness of the results over the 30 ensemble members as discussed later (Sect. 3.1 and 3.2).

The spatial variability is analysed by computing the EOFs on the SIT anomaly time series. This decomposition reduces the large number of variables of the original data to a few variables, but without compromising much of the explained variance. Each EOF represents a mode of SIT variability that provides a simplified representation of the state of the SIT at that time along that EOF. In other words, the EOFs themselves are fixed in time but their weighting coefficients are time-varying; the associated time series (one for each mode) indicate in which state the SIT is at any time (Hannachi, 2004). The analysis is made on the gridded SIT anomaly time series for the three periods. For the historical and future periods, the EOFs are computed over 30 ensemble members, all appended together over time (as done by Labe et al., 2018).

By applying those analyses separately over the three periods, we aim to document the internal variability in the absence of any external forcing during the pre-industrial period. By comparing the pre-industrial results with those for the historical and future periods, we estimate the evolution of the SIT and SIV internal variability under anthropogenic forcing.

3 Results

3.1 Temporal variability

The results from the wavelet analysis are presented in Fig. 1a–c, in which the wavelet power spectrum is shown as a function of time (bottom left of each subfigure). On the wavelet power spectrum, the crosshatched area denotes the “cone of influence”, in which edge effects become important, and the red lines denote the 95 % significance levels above a red noise background spectrum. The global wavelet spectrum is also shown (bottom right), which is a time-integrated power of the wavelet power spectrum. The significance level of the time-integrated wavelet spectrum is indicated by the dashed curve. It refers to the power of the red noise level at the 95 % confidence level that increases with decreasing frequency.

The temporal variability of the Arctic SIV anomaly over the pre-industrial period is depicted in Fig. 1a. The time-integrated power spectrum (bottom right) shows two peaks of significant variability. The first peak corresponds to a period centred on 8 years but spanning from 5 to 10 years. The second one corresponds to a period of 16 years spanning from 10 to 20 years. In the wavelet power spectrum, the red lines enclose regions in which the variability is significant. The two main peaks are present throughout the time span, but not always concomitantly. Depending on the time, both the 8- and 16-year periods are significant, with one of them appearing stronger in the power spectrum (Fig. 1a, bottom left panel). For instance, the 8-year peak is dominant during the 1780–1810 period, the 16-year peak during the 1750–1780 period and both peaks during the 1830–1850 period.

Over the historical period, the Arctic SIV temporal variability shows a first peak centred on 5 years and two others centred on 10 and 16 years, all with 95 % confidence (Fig. 1b). The wavelet power spectrum shows that the 16-year period is significant throughout the entire time span, while the 8-year period loses significance around certain periods of time (e.g. around 1925). The future climate SIV wavelet analysis in Fig. 1c presents a clear loss of variability after the year 2050. This loss of variability is visible in the SIV time series and is confirmed by both the wavelet power spectrum and the time-integrated power spectrum. The 2050 sudden loss of variability coincides with the ice-free summer events occurring at that time. Apart from that loss of variability, the wavelet power spectrum exhibits one band of 5-year variability during the 2015–2025 period and another band of 10-year variability during the 2025–2050 period, both bands with 95 % confidence. In Fig. 1c, the peaks are not significant on the time-integrated power spectrum because the respective variability is significant only over the first 50 years as is shown in the wavelet power spectrum (areas in red).

The main characteristics of the temporal variability of the Arctic SIV under pre-industrial conditions seem to persist under anthropogenic forcing. The two major temporal peaks of variability centred on 8 and 16 years, found in the pre-industrial run, are also present during the historical period. For the first half of the 21st century, the future projections are also dominated by the two main peaks but centred at 5 and 10 years in the integrated spectrum, and with relatively weaker power compared to the pre-industrial and historical runs. Furthermore, the SIV variability seems to be non-stationary since the power is not always above the 95 % significance level.

The wavelet analyses applied to the other 30 ensemble members of the historical and future simulations bring robustness to our results since, overall, each member shows a similar pattern of temporal variability. To promote such a multi-member comparison among the different spectra, we have first normalised all spectra (and the significance curve) by their respective maximum value so that the power ranges from 0 to 1. This step is required to make the spectrum from each member have the same weight in the averaging. As shown in Fig. 1d (blue line), the averaged spectrum is smoothed out across the time domain because the peaks from different spectra are not co-located exactly at the same periods. Nevertheless, it still shows that the variability is significant over the background red noise (see dashed red line). To complement this analysis, we have counted the number of local peaks for each period and from all 30 spectra. As shown by the black line in Fig. 1d, there is a concentration of peaks around the 8-year and 22-year periods. This spread compared to the reference historical run is somehow expected since the internal variability between the different members is not expected to be identical and even tends to increase with time (Blanchard-Wrigglesworth et al., 2011). For members covering the 21st century, the results are close to the one-member analysis discussed above.

3.2 Spatial variability

The spatial variability of the Arctic SIT anomaly is depicted by the major modes of variability in Fig. 2. Since the SIV exhibits a strong loss of variability around the year 2050, the future period for this spatial variability analysis spans from 2006 to 2050. For each period, the modes are sorted by percentage of variability explained. The first mode, which explains most of the variability, represents 22 %, 20 % and 20 % of the variability for pre-industrial, historical and future climate conditions, respectively. All periods show the same pattern of SIT spatial variability for the first mode. It corresponds to a dipole between the Fram Strait area and the East Siberian Sea (Fig. 2a, b, c). For both the pre-industrial and historical periods, the second mode of variability is a pole centred in the East Siberian Sea, but also spreading into the Arctic Basin (Fig. 2d, e). It accounts for 14 % and 11 % of the variability, respectively. The third mode of variability for the pre-industrial period corresponds to a dipole between the Laptev and Kara seas, on the one hand, and the east coast of Greenland, the Chukchi Sea and Beaufort Sea, on the other hand.

Figure 2 Modes of Arctic SIT spatial variability. From the left to the right, each row shows the first three EOFs of Arctic SIT over the pre-industrial (200 years preceding the historical integration) (a, d, g), historical (1920–2005) (b, e, h) and future (2006–2050) (c, f, i) periods, respectively. EOFs for the historical and future periods are performed over 30 ensemble members.