The importance of spring atmospheric conditions for predictions of the Arctic summer sea ice extent

Recent studies have shown that atmospheric processes in spring play an important role for the initiation of the summer ice melt and therefore may strongly influence the September sea ice concentration (SSIC). Here a simple statistical regression model based on only atmospheric spring parameters is applied in order to predict the SSIC over the major part of the Arctic Ocean. By using spring anomalies of downwelling longwave radiation or atmospheric water vapor as predictor variables, correlation coefficients between observed and predicted SSIC of up to 0.5 are found. These skills of seasonal SSIC predictions are similar to those obtained using more complex dynamical forecast systems, despite the fact that the simple model applied here takes neither information of the sea ice state, oceanic conditions nor feedback mechanisms during summer into account. The results indicate that a realistic representation of spring atmospheric conditions in the prediction system plays an important role for the predictive skills of a model system.Swedish Research Council FORMA

Analysis of the surface mass balance for deglacial climate simulations

A realistic simulation of the surface mass balance (SMB) is essential for simulating past and future ice-sheet changes. As most state-of-the-art Earth system models (ESMs) are not capable of realistically representing processes determining the SMB, most studies of the SMB are limited to observations and regional climate models and cover the last century and near future only. Using transient simulations with the Max Planck Institute ESM in combination with an energy balance model (EBM), we extend previous research and study changes in the SMB and equilibrium line altitude (ELA) for the Northern Hemisphere ice sheets throughout the last deglaciation. The EBM is used to calculate and downscale the SMB onto a higher spatial resolution than the native ESM grid and allows for the resolution of SMB variations due to topographic gradients not resolved by the ESM. An evaluation for historical climate conditions (1980–2010) shows that derived SMBs compare well with SMBs from regional modeling. Throughout the deglaciation, changes in insolation dominate the Greenland SMB. The increase in insolation and associated warming early in the deglaciation result in an ELA and SMB increase. The SMB increase is caused by compensating effects of melt and accumulation: the warming of the atmosphere leads to an increase in melt at low elevations along the ice-sheet margins, while it results in an increase in accumulation at higher levels as a warmer atmosphere precipitates more. After 13 ka, the increase in melt begins to dominate, and the SMB decreases. The decline in Northern Hemisphere summer insolation after 9 ka leads to an increasing SMB and decreasing ELA. Superimposed on these long-term changes are centennial-scale episodes of abrupt SMB and ELA decreases related to slowdowns of the Atlantic meridional overturning circulation (AMOC) that lead to a cooling over most of the Northern Hemisphere

Ocean Response in Transient Simulations of the Last Deglaciation Dominated by Underlying Ice‐Sheet Reconstruction and Method of Meltwater Distribution

The last deglaciation was characterized by drastic climate changes, most prominently melting ice sheets. Melting ice sheets have a significant impact on the atmospheric and oceanic circulation, due to changes in the topography and meltwater release into the ocean. In a set of transient simulations of the last deglaciation with the Max Planck Institute for Meteorology Earth System Model we explore differences in the climate response that arise from different boundary conditions and implementations suggested within the Paleoclimate Modeling Intercomparison Project - Phase 4 (PMIP4) deglaciation protocol. The underlying ice-sheet reconstruction dominates the simulated deglacial millennial-scale climate variability in terms of timing and occurrence of observed climate events. Sensitivity experiments indicate that the location and timing of meltwater release from the ice sheets into the ocean are crucial for the ocean response. The results will allow a better interpretation of inter-model differences that arise from different implementations proposed within the PMIP4 protocol

Heinrich events show two-stage climate response in transient glacial simulations

Heinrich events are among the dominant modes of glacial climate variability. During these events, massive iceberg armadas were released by the Laurentide Ice Sheet, sailed across the Atlantic, and caused large-scale climate changes. We study these events in a fully coupled complex ice sheet–climate model with synchronous coupling between ice sheets and oceans. The ice discharges occur as internal variability of the model with a recurrence period of 5kyr, an event duration of 1–1.5kyr, and a peak discharge rate of about 50mSv, roughly consistent with reconstructions. The climate response shows a two-stage behavior, with freshwater release effects dominating the surge phase and ice-sheet elevation effects dominating in the post-surge phase. As a direct response to the freshwater discharge during the surge phase, the deepwater formation in the North Atlantic decreases and the North Atlantic deepwater cell weakens by 3.5Sv. With the reduced oceanic heat transport, the surface temperatures across the North Atlantic decrease, and the associated reduction in evaporation causes a drying in Europe. The ice discharge lowers the surface elevation in the Hudson Bay area and thus leads to increased precipitation and accelerated ice sheet regrowth in the post-surge phase. Furthermore, the jet stream widens to the north and becomes more zonal. This contributes to a weakening of the subpolar gyre, and a continued cooling over Europe even after the ice discharge. This two-stage behavior can explain previously contradicting model results and understandings of Heinrich Events

A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP

In this paper we introduce a Bayesian framework, which is explicit about prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on ordinary least squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (0.6–5.2, 5th–95th percentiles) using the PMIP2, PMIP3, and PMIP4 datasets for the LGM and 2.3 K (0.5–4.4) with the PlioMIP1 and PlioMIP2 datasets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (0.7–5.2) using the LGM and 2.3 K (0.4–4.5) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a tighter constraint of 2.5 K (0.8–4.0) using the restricted ensemble. We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95 % probability of climate sensitivity mostly below 5 K and only exceeding 6 K in a single and most uncertain case assuming a large structural uncertainty. The approach is compared with other approaches based on OLS, a Kalman filter method, and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, an artefact due to a flatter regression line in the case of lack of correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation for their potential use in future probabilistic estimations of climate sensitivity

The atmospheric contribution to Arctic sea-ice variability

The Arctic sea-ice cover plays an important role for the global climate system. Sea ice and the overlying snow cover reflect up to eight times more of the solar radiation than the underlying ocean. Hence, they are important for the global energy budget, and changes in the sea-ice cover can have a large impact on the Arctic climate and beyond. In the past 36 years the ice cover reduced significantly. The largest decline is observed in September, with a rate of more than 12% per decade. The negative trend is accompanied by large inter-annual sea-ice variability: in September the sea-ice extent varies by up to 27% between years. The processes controlling the large variability are not well understood. In this thesis the atmospheric contribution to the inter-annual sea-ice variability is explored. The focus is specifically on the thermodynamical effects: processes that are associated with a temperature change of the ice cover and sea-ice melt. Atmospheric reanalysis data are used to identify key processes, while experiments with a state-of-the-art climate model are conducted to understand their relevance throughout different seasons. It is found that in years with a very low September sea-ice extent more heat and moisture is transported in spring into the area that shows the largest ice variability. The increased transport is often associated with similar atmospheric circulation patterns. Increased heat and moisture over the Arctic result in positive anomalies of water vapor and clouds. These alter the amount of downward radiation at the surface: positive cloud anomalies allow for more longwave radiation and less shortwave radiation. In spring, when the solar inclination is small, positive cloud anomalies result in an increased surface warming and an earlier seasonal melt onset. This reduces the ice cover early in the season and allows for an increased absorption of solar radiation by the surface during summer, which further accelerates the ice melt. The modeling experiments indicate that cloud anomalies of similar magnitude during other seasons than spring would likely not result in below-average September sea ice. Based on these results a simple statistical sea-ice prediction model is designed, that only takes into account the downward longwave radiation anomalies or variables associated with it. Predictive skills are similar to those of more complex models, emphasizing the importance of the spring atmosphere for the annual sea-ice evolution.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.</p

A mechanism for reconciling the synchronisation of Heinrich events and Dansgaard-Oeschger cycles

The evolution of the northern hemispheric climate during the last glacial period was beset by quasi-episodic iceberg discharge events from the Laurentide ice sheet, known as Heinrich events (HEs). The paleo record places most HEs into the cold stadial of the Dansgaard-Oeschger cycle. However, not every Dansgaard-Oeschger cycle is associated with a HE, revealing a complex interplay between the two modes of glacial variability. Here, using a coupled ice sheet-solid earth model, we introduce a mechanism that explains the synchronicity of HEs and Dansgaard-Oeschger cycles. Unlike earlier studies, our mechanism does not require a trigger during the stadial. Instead, the atmospheric warming signal during the interstadial of the Dansgaard-Oeschger cycle causes enhanced ice stream thickening that leads to the HE during the late interstadial. We demonstrate that this mechanism reproduces the key HE characteristics and provides an explanation for synchronous HEs from different regions of the Laurentide ice sheet

Summers with low Arctic sea ice linked to persistence of spring atmospheric circulation patterns

The declining trend of Arctic September sea ice constitutes a significant change in the Arctic climate system. Large yearto-year variations are superimposed on this sea–ice trend, with the largest variability observed in the eastern Arctic Ocean. Knowledge of the processes important for this variability may lead to an improved understanding of seasonal and long-term changes. Previous studies suggest that transport of heat and moisture into the Arctic during spring enhances downward surface longwave radiation, thereby controlling the annual melt onset, setting the stage for the September ice minimum. In agreement with these studies, we find that years with a low September sea–ice concentration (SIC) are characterized by more persistent periods in spring with enhanced energy flux to the surface in forms of net longwave radiation plus turbulent fluxes, compared to years with a high SIC. Two main atmospheric circulation patterns related to these episodes are identified: one resembles the so-called Arctic dipole anomaly that promotes transport of heat and moisture from the North Pacific, whereas the other is characterized by negative geopotential height anomalies over the Arctic, favoring cyclonic flow from Siberia and the Kara Sea into the eastern Arctic Ocean. However, differences between years with low and high September SIC appear not to be due to different spring circulation patterns; instead it is the persistence and intensity of processes associated with these patterns that distinguish the two groups of anomalous years: Years with low September SIC feature episodes that are consistently stronger and more persistent than years with high SIC