The sensitivity of Northern Hemisphere sea ice cover to global temperature change is examined in a group of climate models and in the satellite-era observations. The models are found to have ...well-defined, distinguishable sensitivities in climate change experiments. The satellite-era observations show a larger sensitivity—a larger decline per degree of warming—than any of the models. To evaluate the role of natural variability in this discrepancy, the sensitivity probability density function is constructed based upon the observed trends and natural variability of multidecadal ice cover and global temperature trends in a long control run of the GFDL Climate Model, version 2.1 (CM2.1). This comparison shows that the model sensitivities range from about 1 to more than 2 pseudostandard deviations of the variability smaller than observations indicate. The impact of natural Atlantic multidecadal temperature trends (as simulated by the GFDL model) on the sensitivity distribution is examined and found to be minimal.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A group of twelve IPCC fourth assessment report (AR4) climate models have Arctic (60N–90N) warmings that are, on average, 1.9 times greater than their global warmings at the time of CO2 doubling in ...1%/year CO2 increase experiments. Forcings and feedbacks that impact the warming response are estimated for both Arctic and global regions based on standard model diagnostics. Fitting a zero‐dimensional energy balance model to each region, an expression is derived that gives the Arctic amplification as a function of these forcings and feedbacks. Contributing to Arctic amplification are the Arctic‐global differences in surface albedo feedback (SAF), longwave feedback and the net top‐of‐atmosphere flux forcing (the sum of the surface flux and the atmospheric heat transport convergence). The doubled CO2 forcing and non‐SAF shortwave feedback oppose Arctic amplification. SAF is shown to be a contributing, but not a dominating, factor in the simulated Arctic amplification and its intermodel variation.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Two IPCC fourth assessment report climate models have Arctic Ocean simulations that become sea‐ice‐free year around in 1%/year CO2 increase to quadrupling experiments. These runs are examined for ...evidence of accelerated climate change associated with the removal of sea ice, particularly due to increasing surface albedo feedback. Both models become seasonally ice‐free at an annual mean polar temperature of −9°C without registering much impact on the surface albedo feedback or disturbing the linear relationship between Arctic Ocean climate change and that of the surrounding region. When the polar temperature rises above −5°C, however, there is a sharp increase in the surface albedo feedback of one of the models, driving an abrupt elimination of Arctic ice and an increase in temperature above that expected from warming of the surrounding region. The transition to ice‐free conditions is more linear in the other model, with ocean heat flux playing the primary driving role.
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Meltwater from the Antarctic Ice Sheet is projected to cause up to one metre of sea-level rise by 2100 under the highest greenhouse gas concentration trajectory (RCP8.5) considered by the ...Intergovernmental Panel on Climate Change (IPCC). However, the effects of meltwater from the ice sheets and ice shelves of Antarctica are not included in the widely used CMIP5 climate models, which introduces bias into IPCC climate projections. Here we assess a large ensemble simulation of the CMIP5 model 'GFDL ESM2M' that accounts for RCP8.5-projected Antarctic Ice Sheet meltwater. We find that, relative to the standard RCP8.5 scenario, accounting for meltwater delays the exceedance of the maximum global-mean atmospheric warming targets of 1.5 and 2 degrees Celsius by more than a decade, enhances drying of the Southern Hemisphere and reduces drying of the Northern Hemisphere, increases the formation of Antarctic sea ice (consistent with recent observations of increasing Antarctic sea-ice area) and warms the subsurface ocean around the Antarctic coast. Moreover, the meltwater-induced subsurface ocean warming could lead to further ice-sheet and ice-shelf melting through a positive feedback mechanism, highlighting the importance of including meltwater effects in simulations of future climate.
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KISLJ, NUK, SBMB, UL, UM, UPUK
The authors assess the uptake, transport, and storage of oceanic anthropogenic carbon and heat over the period 1861–2005 in a new set of coupled carbon–climate Earth system models conducted for the ...fifth phase of the Coupled Model Intercomparison Project (CMIP5), with a particular focus on the Southern Ocean. Simulations show that the Southern Ocean south of 30°S, occupying 30% of global surface ocean area, accounts for 43% ± 3% (42 ± 5 Pg C) of anthropogenic CO₂ and 75% ± 22% (23 ± 9 × 1022J) of heat uptake by the ocean over the historical period. Northward transport out of the Southern Ocean is vigorous, reducing the storage to 33 ± 6 Pg anthropogenic carbon and 12 ± 7 × 1022J heat in the region. The CMIP5 models, as a class, tend to underestimate the observation-based global anthropogenic carbon storage but simulate trends in global ocean heat storage over the last 50 years within uncertainties of observation-based estimates. CMIP5 models suggest global and Southern Ocean CO₂ uptake have been largely unaffected by recent climate variability and change. Anthropogenic carbon and heat storage show a common broad-scale pattern of change, but ocean heat storage is more structured than ocean carbon storage. The results highlight the significance of the Southern Ocean for the global climate and as the region where models differ the most in representation of anthropogenic CO₂ and, in particular, heat uptake.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This article proposes a modification to the standard forcing–feedback diagnostic energy balance model to account for 1) differences between effective and equilibrium climate sensitivities and 2) the ...variation of effective sensitivity over time in climate change experiments with coupled atmosphere–ocean climate models. In the spirit of Hansen et al. an efficacy factor is applied to the ocean heat uptake. Comparing the time evolution of the surface warming in high and low efficacy models demonstrates the role of this efficacy in the transient response to CO₂ forcing. Abrupt CO₂ increase experiments show that the large efficacy of the Geophysical Fluid Dynamics Laboratory’s Climate Model version 2.1 (CM2.1) sets up in the first two decades following the increase in forcing. The use of an efficacy is necessary to fit this model’s global mean temperature evolution in periods with both increasing and stable forcing. The intermodel correlation of transient climate response with ocean heat uptake efficacy is greater than its correlation with equilibrium climate sensitivity in an ensemble of climate models used for the third and fourth Intergovernmental Panel on Climate Change (IPCC) assessments. When computed at the time of doubling in the standard experiment with 1% yr−1increase in CO₂, the efficacy is variable amongst the models but is generally greater than 1, averages between 1.3 and 1.4, and is as large as 1.75 in several models.
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Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan‐Arctic sea ice extent (SIE). In this work, we move toward stakeholder‐relevant spatial ...scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981–2015 made with a coupled atmosphere‐ocean‐sea ice‐land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that sea ice thickness initial conditions provide a crucial source of skill for regional summer SIE.
Key Points
Coupled dynamical prediction system skillfully predicts regional sea ice extent on seasonal timescales
Ocean subsurface temperature initialization yields North Atlantic regional winter skill at lead times of 5‐11 months
Sea ice thickness initialization provides a key source of summer regional skill at lead times of 1‐4 months
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A technique for estimating surface albedo feedback (SAF) from standard monthly mean climate model diagnostics is applied to the 1% yr−1carbon dioxide (CO₂)-increase transient climate change ...integrations of 12 Intergovernmental Panel on Climate Change (IPCC) fourth assessment report (AR4) climate models. Over the 80-yr runs, the models produce a mean SAF at the surface of 0.3 W m−2K−1with a standard deviation of 0.09 W m−2K−1. Relative to 2 × CO₂ equilibrium run estimates from an earlier group of models, both the mean SAF and the standard deviation are reduced. Three-quarters of the model mean SAF comes from the Northern Hemisphere in roughly equal parts from the land and ocean areas. The remainder is due to Southern Hemisphere ocean areas. The SAF differences between the models are shown to stem mainly from the sensitivity of the surface albedo to surface temperature rather from the impact of a given surface albedo change on the shortwave budget.
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The fast and slow components of global warming in a comprehensive climate model are isolated by examining the response to an instantaneous return to preindustrial forcing. The response is ...characterized by an initial fast exponential decay with ane-folding time smaller than 5 yr, leaving behind a remnant that evolves more slowly. The slow component is estimated to be small at present, as measured by the global mean near-surface air temperature, and, in the model examined, grows to 0.4°C by 2100 in the A1B scenario from the Special Report on Emissions Scenarios (SRES), and then to 1.4°C by 2300 if one holds radiative forcing fixed after 2100. The dominance of the fast component at present is supported by examining the response to an instantaneous doubling of CO₂ and by the excellent fit to the model’s ensemble mean twentieth-century evolution with a simple one-box model with no long times scales.
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The decline of Arctic sea ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring ...predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically driven SIM export and negative feedbacks from sea ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice thickness observations collected after melt onset are particularly critical for summer Arctic sea ice predictions.
Plain Language Summary
Observations over the past 40 years have documented a significant decline in Arctic sea ice extent and thickness. These rapid changes and their implications for Northern communities, shipping industries, wildlife, fisheries, and natural resource industries have created an emerging operational need for regional summer sea ice predictions. This study is motivated by the following question: How far in advance can accurate predictions of regional summer sea ice be made? Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may fundamentally limit the accuracy of predictions made before May. However, the physical mechanism for this barrier has remained elusive. In this study, we investigate this mechanism using a sea ice mass (SIM) budget analysis, which allows for a process‐based attribution of summer sea ice predictability. We considerthe relative roles of ice growth and melt (thermodynamics) and ice motion (dynamics) in determining the spring predictability barrier. We find that predictability is limited by ice motion and growth in winter and increases rapidly in spring due to melt processes. These results imply that ice thickness observations collected after spring melt onset are particularly critical for summer Arctic sea ice predictions.
Key Points
The Arctic sea ice spring predictability barrier is investigated using a daily mass budget analysis
A mechanism for the spring predictability barrier is proposed, involving three distinct predictability regimes
The spring barrier is expected to shift earlier under Arctic warming due to shifts in melt onset timing
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