Reconstructing Arctic Sea Ice Over the Common Era Using Data Assimilation

Arctic sea-ice decline in recent decades has been dramatic, however few long-term records of Arctic sea ice exist to put such a decline in context. Here we employ an ensemble Kalman filter data assimilation approach to reconstruct Arctic sea-ice concentration over the last two millennia by assimilating temperature-sensitive proxy records with ensembles drawn from last millennium climate model simulations. We first test the efficacy of this method using pseudo-proxy experiments. Results showgood agreement between the target and reconstructed total Arctic sea-ice extent (R2 value and coefficient of efficiency values of 0.51 and 0.47 for perfect model experiments, and 0.43 and 0.43 for imperfect-model experiments). Imperfect-model experiments indicate that the reconstructions inherit some bias from the model prior. We assimilate 487 temperature-sensitive proxy records with two climate model simulations to produce two gridded reconstructions of Arctic sea ice over the last two millennia. These reconstructions show good agreement with satellite observations between 1979–1999 CE for total Arctic sea-ice extent with an R2 and coefficient of efficiency of about 0.60 and 0.50, respectively, for both models. Regional quantities derived from these reconstructions show encouraging similarities with independent reconstructions and sea-ice sensitive proxy records from the Barents, Baffin Bay and East Greenland seas. The reconstructions show a positive trend in Arctic sea-ice extent between around 750–1820 CE, and increases during years with large volcanic eruptions that persist about 5 years. Trend analysis of total Arctic sea-ice extent reveals that for time periods longer than 30 years, the satellite era decline in total Arctic sea-ice extent is unprecedented over the last millennium.

Probabilistic Gridded Seasonal Sea Ice Presence Forecasting using Sequence to Sequence Learning

Accurate and timely forecasts of sea ice conditions are crucial for safe shipping operations in the Canadian Arctic and other ice-infested waters. Given the recent observations on the declining trend of Arctic sea ice extent over the past decades due to global warming, machine learning (ML) approaches are deployed to provide accurate short-term to long-term forecasting. This study unlike previous ML approaches in the sea-ice forecasting domain provides a daily spatial map of the probability of ice in the study domain up to 90 days of lead time. The predictions are further used to predict freeze-up/breakup dates and show their capability to capture these events within a valid time period (7 days) at specific locations of interest to communities.

An Overview of Ocean Climate Change Indicators: Sea Surface Temperature, Ocean Heat Content, Ocean pH, Dissolved Oxygen Concentration, Arctic Sea Ice Extent, Thickness and Volume, Sea Level and Strength of the AMOC (Atlantic Meridional Overturning Circulation)

Global ocean physical and chemical trends are reviewed and updated using seven key ocean climate change indicators: (i) Sea Surface Temperature, (ii) Ocean Heat Content, (iii) Ocean pH, (iv) Dissolved Oxygen concentration (v) Arctic Sea Ice extent, thickness, and volume (vi) Sea Level and (vii) the strength of the Atlantic Meridional Overturning Circulation (AMOC). The globally averaged ocean surface temperature shows a mean warming trend of 0.062 ± 0.013°C per decade over the last 120 years (1900–2019). During the last decade (2010–2019) the rate of ocean surface warming has accelerated to 0.280 ± 0.068°C per decade, 4.5 times higher than the long term mean. Ocean Heat Content in the upper 2,000 m shows a linear warming rate of 0.35 ± 0.08 Wm–2 in the period 1955–2019 (65 years). The warming rate during the last decade (2010–2019) is twice (0.70 ± 0.07 Wm–2) the warming rate of the long term record. Each of the last six decades have been warmer than the previous one. Global surface ocean pH has declined on average by approximately 0.1 pH units (from 8.2 to 8.1) since the industrial revolution (1770). By the end of this century (2100) ocean pH is projected to decline additionally by 0.1–0.4 pH units depending on the RCP (Representative Concentration Pathway) and SSP (Shared Socioeconomic Pathways) future scenario. The time of emergence of the pH climate change signal varies from 8 to 15 years for open ocean sites, and 16–41 years for coastal sites. Global dissolved oxygen levels have decreased by 4.8 petamoles or 2% in the last 5 decades, with profound impacts on local and basin scale habitats. Regional trends are varying due to multiple processes impacting dissolved oxygen: solubility change, respiration changes, ocean circulation changes and multidecadal variability. Arctic sea ice extent has been declining by −13.1% per decade in summer (September) and by −2.6% per decade in winter (March) during the last 4 decades (1979–2020). The combined trends of sea ice extent and sea ice thickness indicate that the volume of non-seasonal Arctic Sea Ice has decreased by 75% since 1979. Global mean sea level has increased in the period 1993–2019 (the altimetry era) at a mean rate of 3.15 ± 0.3 mm year–1 and is experiencing an acceleration of ∼ 0.084 (0.06–0.10) mm year–2. During the last century (1900–2015; 115y) global mean sea level (GMSL) has rised 19 cm, and near 40% of that GMSL rise has taken place since 1993 (22y). Independent proxies of the evolution of the Atlantic Meridional Overturning Circulation (AMOC) indicate that AMOC is at its weakest for several hundreds of years and has been slowing down during the last century. A final visual summary of key ocean climate change indicators during the recent decades is provided.

Effect of model initialization and sea ice data assimilation on the seasonal forecast of September Arctic sea ice extent in a coupled ocean-sea ice model

<p>The METROMS is a coupled ocean and sea ice model based on the Regional Ocean Modeling System (ROMS) and the Los Alamos sea ice model CICE.  It was employed for seasonal forecast of the September Arctic sea ice extent (SIE) in 2019 in the Sea Ice Prediction Network (SIPN), using a regional configuration of grid resolution 20km for the Arctic, the so-called Arctic-20km configuration. In the present study, we investigate the impact of model initialization and sea ice data assimilation on the seasonal forecast of the September Arctic SIE. The ERA5 atmospheric forcing is used to driver the model. The preliminary results indicate that model initialization plays a very important role in the seasonal prediction of September Arctic SIE. Experiments using different model initializations from climate monthly mean (CMM) and actual monthly mean (AMM) indicate that the AMM generally has a much higher prediction skill. The prediction skill also increases with decreasing prediction time. With a reasonable model initialization, SIC assimilation can significantly improve the prediction skill, particularly within two months. On the contrary, SIT assimilation tends to provide relatively small contribution to the September SIE prediction when model is reasonably initialized, due mostly to the fact that no data is available in the summer period. </p>

Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections

Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and examine the sensitivity of sea ice extents and their decadal trends to sea ice concentration threshold values. Threshold choice can affect the timing of annual SIE minimums: a threshold choice as low as 30% can change the timing to August instead of September. Threshold choice impacts the value of annual SIE minimums: in particular, changing the threshold from 15% to 35% can change the annual SIE by more than 10% in magnitude. Monthly SIE data distributions are seasonally dependent. Although little impact was seen for threshold choice on data distributions during annual minimum times (August and September), there is a strong impact in May. Threshold choices were not found to impact the choice of optimal statistical models characterizing annual minimum SIE time series. However, the first ice-free Arctic summer year (FIASY) estimates are impacted; higher threshold values produce earlier FIASY estimates and, more notably, FIASY estimates amongst all considered models are more consistent. This analysis suggests that some of the threshold choice impacts to SIE trends may actually be the result of biased data due to surface melt. Given that the rapid Arctic sea ice depletion appears to have statistically changed SIE characteristics, particularly in the summer months, a more extensive investigation to verify surface melt impacts on this data set is warranted.

Constraining Uncertainties in CMIP5 Projections of September Arctic Sea Ice Extent with Observations

In agreement with observations, Earth system models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulate a decline in September Arctic sea ice extent (SIE) over the past decades. However, the spread in their twenty-first-century SIE projections is large and the timing of the first ice-free Arctic summer ranges from 2020 to beyond 2100. The uncertainties arise from three sources (internal variability, model uncertainty, and scenario uncertainty), which are quantified in this study for projections of SIE. The goal is to narrow uncertainties by applying multiple diagnostic ensemble regression (MDER). MDER links future projections of sea ice extent to processes relevant to its simulation under present-day conditions using data covering the past 40 years. With this method, we can reduce model uncertainty in projections of SIE for the period 2020–44 by 30%–50% (0.8–1.3 million km2). Compared to the unweighted multimodel mean, the MDER-weighted mean projects an about 20% smaller SIE and an earlier near-disappearance of Arctic sea ice by more than a decade for a high–greenhouse gas scenario. We also show that two different methods estimating internal variability in SIE differ by 1 million km2. Regardless, the total uncertainties in the SIE projections remain large (up to 3.5 million km2, with irreducible internal variability contributing 30%) so that a precise time estimate of an ice-free Arctic proves impossible. We conclude that unweighted CMIP5 multimodel-mean projections of Arctic SIE are too optimistic and mitigation strategies to reduce Arctic warming need to be intensified.