Dirk Notz

The future of ice sheets and sea ice: Between reversible retreat and unstoppable loss

We discuss the existence of cryospheric “tipping points” in the Earths climate system. Such critical thresholds have been suggested to exist for the disappearance of Arctic sea ice and the retreat of ice sheets: Once these ice masses have shrunk below an anticipated critical extent, the ice–albedo feedback might lead to the irreversible and unstoppable loss of the remaining ice. We here give an overview of our current understanding of such threshold behavior. By using conceptual arguments, we review the recent findings that such a tipping point probably does not exist for the loss of Arctic summer sea ice. Hence, in a cooler climate, sea ice could recover rapidly from the loss it has experienced in recent years. In addition, we discuss why this recent rapid retreat of Arctic summer sea ice might largely be a consequence of a slow shift in ice-thickness distribution, which will lead to strongly increased year-to-year variability of the Arctic summer sea-ice extent. This variability will render seasonal forecasts of the Arctic summer sea-ice extent increasingly difficult. We also discuss why, in contrast to Arctic summer sea ice, a tipping point is more likely to exist for the loss of the Greenland ice sheet and the West Antarctic ice sheet.

Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission

Why we are losing sea ice Arctic sea ice is disappearing rapidly, leading to predictions of an ice-free summer in the near future. Simulations of the timing of summer sea-ice loss differ substantially, making it difficult to evaluate the pace of the loss. Notz and Stroeve observed a linear relationship between the monthly-mean September sea-ice area and cumulative CO2 emissions. This allowed them to predict Arctic summer sea ice directly from the observational record. Interestingly, most models underestimate this loss. Science, this issue p. 747 Arctic sea-ice loss during the summer scales directly with cumulative carbon dioxide emissions. Arctic sea ice is retreating rapidly, raising prospects of a future ice-free Arctic Ocean during summer. Because climate-model simulations of the sea-ice loss differ substantially, we used a robust linear relationship between monthly-mean September sea-ice area and cumulative carbon dioxide (CO2) emissions to infer the future evolution of Arctic summer sea ice directly from the observational record. The observed linear relationship implies a sustained loss of 3 ± 0.3 square meters of September sea-ice area per metric ton of CO2 emission. On the basis of this sensitivity, Arctic sea ice will be lost throughout September for an additional 1000 gigatons of CO2 emissions. Most models show a lower sensitivity, which is possibly linked to an underestimation of the modeled increase in incoming longwave radiation and of the modeled transient climate response.

A non-destructive method for measuring the salinity and solid fraction of growing sea ice in situ

We describe an instrument developed to make in situ measurements of salinity and solid- fraction profiles in growing sea ice. The vertical resolution of the measurements is up to a few millimeters, with a temporal resolution of up to fractions of a second. The technique is based on impedance measurements between platinum wires around which sea ice grows. Data obtained using this instrument in laboratory experiments are in good agreement with theoretical predictions. In a field test in the Arctic, the bulk salinity of growing sea ice has been measured in situ throughout the whole depth of the ice layer. The data are compared with bulk salinities obtained from ice cores, and confirm the general understanding that the bulk salinity in ice-core studies is significantly underestimated in the lower parts of the cores. The approach can also be used in other glaciological applications and for general studies of two-phase, two-component porous media.

Potential climatic transitions with profound impact on Europe

We discuss potential transitions of six climatic subsystems with large-scale impact on Europe, sometimes denoted as tipping elements. These are the ice sheets on Greenland and West Antarctica, the Atlantic thermohaline circulation, Arctic sea ice, Alpine glaciers and northern hemisphere stratospheric ozone. Each system is represented by co-authors actively publishing in the corresponding field. For each subsystem we summarize the mechanism of a potential transition in a warmer climate along with its impact on Europe and assess the likelihood for such a transition based on published scientific literature. As a summary, the ‘tipping’ potential for each system is provided as a function of global mean temperature increase which required some subjective interpretation of scientific facts by the authors and should be considered as a snapshot of our current understanding.

How well must climate models agree with observations

The usefulness of a climate-model simulation cannot be inferred solely from its degree of agreement with observations. Instead, one has to consider additional factors such as internal variability, the tuning of the model, observational uncertainty, the temporal change in dominant processes or the uncertainty in the forcing. In any model-evaluation study, the impact of these limiting factors on the suitability of specific metrics must hence be examined. This can only meaningfully be done relative to a given purpose for using a model. I here generally discuss these points and substantiate their impact on model evaluation using the example of sea ice. For this example, I find that many standard metrics such as sea-ice area or volume only permit limited inferences about the shortcomings of individual models.

Anthropogenic influence on recent circulation-driven Antarctic sea ice changes

Observations reveal an increase of Antarctic sea ice over the past three decades, yet global climate models tend to simulate a sea ice decrease for that period. Here we combine observations with model experiments (MPI-ESM) to investigate causes for this discrepancy and for the observed sea ice increase. Based on observations and atmospheric reanalysis, we show that on multidecadal time scales Antarctic sea ice changes are linked to intensified meridional winds that are caused by a zonally asymmetric lowering of the high-latitude surface pressure. In our simulations, this surface pressure lowering is a response to a combination of anthropogenic stratospheric ozone depletion and greenhouse gas increase. Combining these two lines of argument, we infer a possible anthropogenic influence on the observed sea ice changes. However, similar to other models, MPI-ESM simulates a surface-pressure response that is rather zonally symmetric, which explains why the simulated sea ice response differs from observations.

The Transient versus the Equilibrium Response of Sea Ice to Global Warming

AbstractTo examine the long-term stability of Arctic and Antarctic sea ice, idealized simulations are carried out with the climate model ECHAM5/Max Planck Institute Ocean Model (MPI-OM). Atmospheric CO2 concentration is increased over 2000 years from preindustrial levels to quadrupling, is then kept constant for 5940 years, is afterward decreased over 2000 years to preindustrial levels, and is finally kept constant for 3940 years.Despite these very slow changes, the sea ice response significantly lags behind the CO2 concentration change. This lag, which is caused by the oceans thermal inertia, implies that the sea ice equilibrium response to increasing CO2 concentration is substantially underestimated by transient simulations. The sea ice response to CO2 concentration change is not truly hysteretic and is in principle reversible.The authors find no lag in the evolution of Arctic sea ice relative to changes in annual-mean Northern Hemisphere surface temperature. The summer sea ice cover changes linearly w...

On the Potential for Abrupt Arctic Winter Sea Ice Loss

AbstractThe authors examine the transition from a seasonally ice-covered Arctic to an Arctic Ocean that is sea ice free all year round under increasing atmospheric CO2 levels. It is shown that in comprehensive climate models, such loss of Arctic winter sea ice area is faster than the preceding loss of summer sea ice area for the same rate of warming. In two of the models, several million square kilometers of winter sea ice are lost within only one decade. It is shown that neither surface albedo nor cloud feedbacks can explain the rapid winter ice loss in the climate model MPI-ESM by suppressing both feedbacks in the model. The authors argue that the large sensitivity of winter sea ice area in the models is caused by the asymmetry between melting and freezing: an ice-free summer requires the complete melt of even the thickest sea ice, which is why the perennial ice coverage decreases only gradually as more and more of the thinner ice melts away. In winter, however, sea ice areal coverage remains high as lo...

Drivers of variability in Arctic sea-ice drift speed

We explore the main drivers of seasonal and long-term variations in basin-scale Arctic sea-ice drift speed. To do so, we examine the relationship between the observed time-varying area-mean ice drift speed in the central Arctic and observed thickness and concentration as well as surface wind stress. Drift speeds are calculated from the positions of drifting buoys, thickness is based on submarine observations, concentration on satellite observations, and the wind stress comes from a global reanalysis. We find that seasonal changes in drift speed are correlated primarily with changes in concentration when concentration is low and with changes in thickness otherwise. The correlation between drift speed and concentration occurs because changing concentration changes how readily the ice responds to the synoptic-scale forcing of the atmosphere. Drift speed is correlated with neither concentration nor thickness in April and May. We show this behavior to be correlated with a decrease in the localization of deformation. This indicates that the increase in drift speed is caused by newly formed fractures not refreezing, leading to an overall reduced ice-cover strength without a detectable change in ice concentration. We show that a strong long-term trend exists in months of relatively low ice concentration. Using our analysis of the seasonal cycle, we show that the trend in concentration drives a significant portion of the drift-speed trend, possibly reinforced by a trend in cyclone activity. Hence, the trend in drift speed in this period is primarily caused by increased synoptic-scale movement of the ice pack.

The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

AbstractA seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2–4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.