September Arctic sea-ice minimum predicted by spring melt-pond fraction
TL;DR: In this article, the authors used spring melt-pond area to forecast the Arctic sea-ice minimum in September and found a strong correlation between the spring pond fraction and September sea ice extent.
Abstract: Prediction of seasonal Arctic sea-ice extent is of increased interest as the region opens up due to climate change. This work uses spring melt-pond area to forecast the Arctic sea-ice minimum in September. This proves accurate, as increasing melt-ponds reduce surface albedo, allowing more melt to occur, creating a positive feedback mechanism. The area of Arctic September sea ice has diminished from about 7 million km2 in the 1990s to less than 5 million km2 in five of the past seven years, with a record minimum of 3.6 million km2 in 2012 (ref. 1). The strength of this decrease is greater than expected by the scientific community, the reasons for this are not fully understood, and its simulation is an on-going challenge for existing climate models2,3. With growing Arctic marine activity there is an urgent demand for forecasting Arctic summer sea ice4. Previous attempts at seasonal forecasts of ice extent were of limited skill5,6,7,8,9. However, here we show that the Arctic sea-ice minimum can be accurately forecasted from melt-pond area in spring. We find a strong correlation between the spring pond fraction and September sea-ice extent. This is explained by a positive feedback mechanism: more ponds reduce the albedo; a lower albedo causes more melting; more melting increases pond fraction. Our results help explain the acceleration of Arctic sea-ice decrease during the past decade. The inclusion of our new melt-pond model10 promises to improve the skill of future forecast and climate models in Arctic regions and beyond.
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TL;DR: In this paper, the authors examined the ongoing loss of Arctic sea ice across all seasons and found that recent anomalies in spring and winter sea ice coverage have been more significant than any observed drop in summer sea ice extent.
Abstract: The decline in the floating sea ice cover in the Arctic is one of the most striking manifestations of climate change In this review, we examine this ongoing loss of Arctic sea ice across all seasons Our analysis is based on satellite retrievals, atmospheric reanalysis, climate-model simulations and a literature review We find that relative to the 1981–2010 reference period, recent anomalies in spring and winter sea ice coverage have been more significant than any observed drop in summer sea ice extent (SIE) throughout the satellite period For example, the SIE in May and November 2016 was almost four standard deviations below the reference SIE in these months Decadal ice loss during winter months has accelerated from −24 %/decade from 1979 to 1999 to −34%/decade from 2000 onwards We also examine regional ice loss and find that for any given region, the seasonal ice loss is larger the closer that region is to the seasonal outer edge of the ice cover Finally, across all months, we identify a robust linear relationship between pan-Arctic SIE and total anthropogenic CO₂ emissions The annual cycle of Arctic sea ice loss per ton of CO₂ emissions ranges from slightly above 1 m² throughout winter to more than 3 m² throughout summer Based on a linear extrapolation of these trends, we find the Arctic Ocean will become sea-ice free throughout August and September for an additional 800 ± 300 Gt of CO₂ emissions, while it becomes ice free from July to October for an additional 1400 ± 300 Gt of CO₂ emissions
518 citations
Goddard Space Flight Center1, Nordland Research Institute2, University of Oslo3, National Oceanic and Atmospheric Administration4, Norwegian Polar Institute5, Fisheries and Oceans Canada6, University of Alberta7, Cold Regions Research and Engineering Laboratory8, Arctic and Antarctic Research Institute9
TL;DR: In this paper, the changes in sea ice are happening faster than models have projected and substantial uncertainties in the exact timing and high interannual variability will remain as sea ice decreases.
Abstract: Sea ice in the Arctic is one of the most rapidly changing components of the global climate system. Over the past few decades, summer areal extent has declined over 30, and all months show statistically significant declining trends. New satellite missions and techniques have greatly expanded information on sea ice thickness, but many uncertainties remain in the satellite data and long-term records are sparse. However, thickness observations and other satellite-derived data indicate a 40 decline in thickness, due in large part to the loss of thicker, older ice cover. The changes in sea ice are happening faster than models have projected. With continued increasing temperatures, summer ice-free conditions are likely sometime in the coming decades, though there are substantial uncertainties in the exact timing and high interannual variability will remain as sea ice decreases. The changes in Arctic sea ice are already having an impact on flora and fauna in the Arctic. Some species will face increasing challenges in the future, while new habitat will open up for other species. The changes are also affecting peoples living and working in the Arctic. Native communities are facing challenges to their traditional ways of life, while new opportunities open for shipping, fishing, and natural resource extraction.
422 citations
TL;DR: It is argued that through a combination of thinning and associated processes related to a warming climate the downward trend itself is steepening, and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend.
Abstract: September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability.
313 citations
QIMR Berghofer Medical Research Institute1, Loyola University New Orleans2, Smithsonian Environmental Research Center3, United States Environmental Protection Agency4, University of Wollongong5, North Carolina State University6, Aristotle University of Thessaloniki7, National Institute of Water and Atmospheric Research8, Lancaster University9, Australian National University10, Garvan Institute of Medical Research11, University of Manchester12, University of Sydney13, Erasmus University Rotterdam14, King's College London15, University of Helsinki16, Murdoch University17, University College Cork18, University of Buenos Aires19, Miami University20, Rensselaer Polytechnic Institute21, National Autonomous University of Mexico22, Linnaeus University23, University of Gothenburg24, National Cheng Kung University25, National Center for Atmospheric Research26, Swiss Federal Institute of Aquatic Science and Technology27, University of Guelph28, Leibniz Association29, Finnish Meteorological Institute30
TL;DR: The present 2017 Update Report assesses some of the highlights and new insights about the interactive nature of the direct and indirect effects of UV radiation, atmospheric processes, and climate change.
Abstract: This assessment, by the United Nations Environment Programme (UNEP) Environmental Effects Assessment Panel (EEAP), one of three Panels informing the Parties to the Montreal Protocol, provides an update, since our previous extensive assessment (Photochem. Photobiol. Sci., 2019, 18, 595-828), of recent findings of current and projected interactive environmental effects of ultraviolet (UV) radiation, stratospheric ozone, and climate change. These effects include those on human health, air quality, terrestrial and aquatic ecosystems, biogeochemical cycles, and materials used in construction and other services. The present update evaluates further evidence of the consequences of human activity on climate change that are altering the exposure of organisms and ecosystems to UV radiation. This in turn reveals the interactive effects of many climate change factors with UV radiation that have implications for the atmosphere, feedbacks, contaminant fate and transport, organismal responses, and many outdoor materials including plastics, wood, and fabrics. The universal ratification of the Montreal Protocol, signed by 197 countries, has led to the regulation and phase-out of chemicals that deplete the stratospheric ozone layer. Although this treaty has had unprecedented success in protecting the ozone layer, and hence all life on Earth from damaging UV radiation, it is also making a substantial contribution to reducing climate warming because many of the chemicals under this treaty are greenhouse gases.
193 citations
TL;DR: In this article, a long-term data record of Fram Strait sea ice area export from 1935 to 2014 is developed using a combination of satellite radar images and station observations of surface pressure across Fram Strait.
Abstract: . A new long-term data record of Fram Strait sea ice area export from 1935 to 2014 is developed using a combination of satellite radar images and station observations of surface pressure across Fram Strait. This data record shows that the long-term annual mean export is about 880 000 km2, representing 10 % of the sea-ice-covered area inside the basin. The time series has large interannual and multi-decadal variability but no long-term trend. However, during the last decades, the amount of ice exported has increased, with several years having annual ice exports that exceeded 1 million km2. This increase is a result of faster southward ice drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Greenland. Evaluating the trend onwards from 1979 reveals an increase in annual ice export of about +6 % per decade, with spring and summer showing larger changes in ice export (+11 % per decade) compared to autumn and winter (+2.6 % per decade). Increased ice export during winter will generally result in new ice growth and contributes to thinning inside the Arctic Basin. Increased ice export during summer or spring will, in contrast, contribute directly to open water further north and a reduced summer sea ice extent through the ice–albedo feedback. Relatively low spring and summer export from 1950 to 1970 is thus consistent with a higher mid-September sea ice extent for these years. Our results are not sensitive to long-term change in Fram Strait sea ice concentration. We find a general moderate influence between export anomalies and the following September sea ice extent, explaining 18 % of the variance between 1935 and 2014, but with higher values since 2004.
150 citations
References
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TL;DR: The NCEP-DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the "50-year" (1948-present) N CEP-NCAR Reanalysis Project.
Abstract: The NCEP–DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the “50-year” (1948–present) NCEP–NCAR Reanalysis Project. NCEP–DOE AMIP-II re-analysis covers the “20-year” satellite period of 1979 to the present and uses an updated forecast model, updated data assimilation system, improved diagnostic outputs, and fixes for the known processing problems of the NCEP–NCAR reanalysis. Only minor differences are found in the primary analysis variables such as free atmospheric geopotential height and winds in the Northern Hemisphere extratropics, while significant improvements upon NCEP–NCAR reanalysis are made in land surface parameters and land–ocean fluxes. This analysis can be used as a supplement to the NCEP–NCAR reanalysis especially where the original analysis has problems. The differences between the two analyses also provide a measure of uncertainty in current analyses.
5,177 citations
TL;DR: In this article, the authors developed and tested two separate methods for sea ice dynamics, and showed that the viscous-plastic rheology can be represented by a symmetric, negative definite matrix operator, leading to a faster and better behaved preconditioned conjugate gradient method.
Abstract: The standard model for sea ice dynamics treats the ice pack as a visco‐plastic material that flows plastically under typical stress conditions but behaves as a linear viscous fluid where strain rates are small and the ice becomes nearly rigid. Because of large viscosities in these regions, implicit numerical methods are necessary for time steps larger than a few seconds. Current solution methods for these equations use iterative relaxation methods, which are time consuming, scale poorly with mesh resolution, and are not well adapted to parallel computation. To remedy this, the authors developed and tested two separate methods. First, by demonstrating that the viscous‐plastic rheology can be represented by a symmetric, negative definite matrix operator, the much faster and better behaved preconditioned conjugate gradient method was implemented. Second, realizing that only the response of the ice on timescales associated with wind forcing need be accurately resolved, the model was modified so that it reduces to the viscous‐plastic model at these timescales, whereas at shorter timescales the adjustment process takes place by a numerically more efficient elastic wave mechanism. This modification leads to a fully explicit numerical scheme that further improves the model’s computational efficiency and is a great advantage for implementations on parallel machines. Furthermore, it is observed that the standard viscous‐plastic model has poor dynamic response to forcing on a daily timescale, given the standard time step (1 day) used by the ice modeling community. In contrast, the explicit discretization of the elastic wave mechanism allows the elastic‐viscous‐plastic model to capture the ice response to variations in the imposed stress more accurately. Thus, the elastic‐viscous‐plastic model provides more accurate results for shorter timescales associated with physical forcing, reproduces viscous‐plastic model behavior on longer timescales, and is computationally more efficient overall.
1,011 citations
01 Oct 1996
TL;DR: In this paper, the authors proposed an explicit discretization of the elastic wave mechanism, which allows the elastic-viscous-plastic model to capture the ice response to variations in the imposed stress more accurately.
Abstract: The standard model for sea ice dynamics treats the ice pack as a viscous-plastic material that flows plastically under typical stress conditions but behaves as a linear viscous fluid where strain rates are small and the ice becomes nearly rigid. Because of large viscosities in these regions, implicit numerical methods are necessary for timesteps larger than a few seconds. Current solution methods for these equations use iterative relaxation methods, which are time consuming, scale poorly with mesh resolution, and are not well adapted to parallel computation. To remedy this, we have developed and tested two separate methods. First, by demonstrating that the viscous-plastic rheology can be represented by a symmetric, negative definite matrix operator, we have implemented the faster and better behaved preconditioned conjugate gradient method. Second, realizing that only the response of the ice on time scales associated with wind forcing need be accurately resolved, we have modified the model to reduce to the viscous-plastic model at these time scales; at shorter time scales the adjustment process takes place by a numerically efficient elastic wave mechanism. This modification leads to a fully explicit numerical scheme which further improves the computational efficiency and is an advantage for implementations on parallel machines. Furthermore, we observe that the standard viscous-plastic model has poor dynamic response to forcing on a daily time scale, given the standard time step (1 day) used by the ice modeling community. In contrast, the explicit discretization of the elastic wave mechanism allows the elastic-viscous-plastic model to capture the ice response to variations in the imposed stress more accurately. Thus, the elastic-viscous-plastic model provides more accurate results for shorter time scales associated with physical forcing, reproduces viscous-plastic model behavior on longer time scales, and is computationally more efficient. 49 refs., 13 figs., 6 tabs.
912 citations
TL;DR: The authors showed that the observed downward trend in September ice extent exceeded simulated trends from most models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 3 (CMIP3).
Abstract: [1] The rapid retreat and thinning of the Arctic sea ice cover over the past several decades is one of the most striking manifestations of global climate change. Previous research revealed that the observed downward trend in September ice extent exceeded simulated trends from most models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 3 (CMIP3). We show here that as a group, simulated trends from the models contributing to CMIP5 are more consistent with observations over the satellite era (1979–2011). Trends from most ensemble members and models nevertheless remain smaller than the observed value. Pointing to strong impacts of internal climate variability, 16% of the ensemble member trends over the satellite era are statistically indistinguishable from zero. Results from the CMIP5 models do not appear to have appreciably reduced uncertainty as to when a seasonally ice-free Arctic Ocean will be realized.
808 citations
TL;DR: A new record low Arctic sea ice extent for the satellite era, 3.4 × 106 km2, was reached on 13 September 2012; and a new record-low sea ice area,3.0 à 0 Ã × Ã Ã 3.0 Á× Ã 6 à 1.
Abstract: [1] A new record low Arctic sea ice extent for the satellite era, 3.4 × 106 km2, was reached on 13 September 2012; and a new record low sea ice area, 3.0 × 106 km2, was reached on the same date. Preconditioning through decades of overall ice reductions made the ice pack more vulnerable to a strong storm that entered the central Arctic in early August 2012. The storm caused the separation of an expanse of 0.4 × 106 km2 of ice that melted in total, while its removal left the main pack more exposed to wind and waves, facilitating the main pack's further decay. Future summer storms could lead to a further acceleration of the decline in the Arctic sea ice cover and should be carefully monitored.
437 citations