Attribution of the spatial heterogeneity of Arctic surface albedo feedback to the dynamics of vegetation, snow and soil properties and their interactions

The Arctic warming rate is triple the global average, which is partially caused by surface albedo feedback (SAF). Understanding the varying pattern of SAF and the mechanisms is therefore critical for predicting future Arctic climate under anthropogenic warming. To date, however, how the spatial pattern of seasonal SAF is influenced by various land surface factors remains unclear. Here, we aim to quantify the strengths of seasonal SAF across the Arctic and to attribute its spatial heterogeneity to the dynamics of vegetation, snow and soil as well as their interactions. The results show a large positive SAF above -5%·K-1 across Baffin Island in January and eastern Yakutia in June, while a large negative SAF beyond 5%·K-1 is observed in Canada, Chukotka and low latitudes of Greenland in January and Nunavut, Baffin Island and Krasnoyarsk Krai in July. Overall, a great spatial heterogeneity of Arctic land warming induced by positive SAF is found with a coefficient of variation (CV) larger than 61.5%, and the largest spatial difference is detected in wintertime with a CV > 643.9%. Based on the optimal parameter-based geographic detector model, the impacts of snow cover fraction (SCF), land cover type (LC), normalized difference vegetation index (NDVI), soil water content (SW), soil substrate chemistry (SC) and soil type (ST) on the spatial pattern of positive SAF are quantified. The rank of determinant power is SCF > LC > NDVI > SW > SC > ST, which indicates that the spatial patterns of snow cover, land cover and vegetation coverage dominate the spatial heterogeneity of positive SAF in the Arctic. The interactions between SCF, LC and SW exert further influences on the spatial pattern of positive SAF in March, June and July. This work could provide a deeper understanding of how various land factors contribute to the spatial heterogeneity of Arctic land warming at the annual cycle.

Impact of the melt–albedo feedback on the future evolution of the Greenland Ice Sheet with PISM-dEBM-simple

Surface melting of the Greenland Ice Sheet contributes a large amount to current and future sea level rise. Increased surface melt may lower the reflectivity of the ice sheet surface and thereby increase melt rates: the so-called melt–albedo feedback describes this self-sustaining increase in surface melting. In order to test the effect of the melt–albedo feedback in a prognostic ice sheet model, we implement dEBM-simple, a simplified version of the diurnal Energy Balance Model dEBM, in the Parallel Ice Sheet Model (PISM). The implementation includes a simple representation of the melt–albedo feedback and can thereby replace the positive-degree-day melt scheme. Using PISM-dEBM-simple, we find that this feedback increases ice loss through surface warming by 60 % until 2300 for the high-emission scenario RCP8.5 when compared to a scenario in which the albedo remains constant at its present-day values. With an increase of 90 % compared to a fixed-albedo scenario, the effect is more pronounced for lower surface warming under RCP2.6. Furthermore, assuming an immediate darkening of the ice surface over all summer months, we estimate an upper bound for this effect to be 70 % in the RCP8.5 scenario and a more than 4-fold increase under RCP2.6. With dEBM-simple implemented in PISM, we find that the melt–albedo feedback is an essential contributor to mass loss in dynamic simulations of the Greenland Ice Sheet under future warming.

A Cloudier Picture of Ice-Albedo Feedback in CMIP6 Models

Increased solar absorption is an important driver of Arctic Amplification, the interconnected set of processes and feedbacks by which Arctic temperatures respond more rapidly than global temperatures to climate forcing. The amount of sunlight absorbed in the Arctic is strongly modulated by seasonal ice and snow cover. Sea ice declines and shorter periods of seasonal snow cover in recent decades have increased solar absorption, amplifying local warming relative to the planet as a whole. However, this Arctic albedo feedback would be substantially larger in the absence of the ubiquitous cloud cover that exists throughout the region. Clouds have been observed to mask the effects of reduced surface albedo and slow the emergence of secular trends in net solar absorption. Applying analogous metrics to several models from the 6th Climate Model Intercomparison Project (CMIP6), we find that ambiguity in the influence of clouds on predicted Arctic solar absorption trends has increased relative to the previous generation of climate models despite better agreement with the observed albedo sensitivity to sea ice variations. Arctic albedo responses to sea ice loss are stronger in CMIP6 than in CMIP5 in all summer months. This agrees better with observations, but models still slightly underestimate albedo sensitivity to sea ice changes relative to observations. Never-the-less, nearly all CMIP6 models predict that the Arctic is now absorbing more solar radiation than at the start of the century, consistent with recent observations. In fact, many CMIP6 models simulate trends that are too strong relative to internal variability, and spread in predicted Arctic albedo changes has increased since CMIP5. This increased uncertainty can be traced to increased ambiguity in how clouds influence natural and forced variations in Arctic solar absorption. While nearly all CMIP5 models agreed with observations that clouds delay the emergence of forced trends, about half of CMIP6 models suggest that clouds accelerate their emergence from natural variability. Isolating atmospheric contributions to total Arctic reflection suggests that this diverging behavior may be linked to stronger Arctic cloud feedbacks in the latest generation of climate models.

Contributions to Polar Amplification in CMIP5 and CMIP6 Models

As a step towards understanding the fundamental drivers of polar climate change, we evaluate contributions to polar warming and its seasonal and hemispheric asymmetries in Coupled Model Intercomparison Project phase 6 (CMIP6) as compared with CMIP5. CMIP6 models broadly capture the observed pattern of surface- and winter-dominated Arctic warming that has outpaced both tropical and Antarctic warming in recent decades. For both CMIP5 and CMIP6, CO2 quadrupling experiments reveal that the lapse-rate and surface albedo feedbacks contribute most to stronger warming in the Arctic than the tropics or Antarctic. The relative strength of the polar surface albedo feedback in comparison to the lapse-rate feedback is sensitive to the choice of radiative kernel, and the albedo feedback contributes most to intermodel spread in polar warming at both poles. By separately calculating moist and dry atmospheric heat transport, we show that increased poleward moisture transport is another important driver of Arctic amplification and the largest contributor to projected Antarctic warming. Seasonal ocean heat storage and winter-amplified temperature feedbacks contribute most to the winter peak in warming in the Arctic and a weaker winter peak in the Antarctic. In comparison with CMIP5, stronger polar warming in CMIP6 results from a larger surface albedo feedback at both poles, combined with less-negative cloud feedbacks in the Arctic and increased poleward moisture transport in the Antarctic. However, normalizing by the global-mean surface warming yields a similar degree of Arctic amplification and only slightly increased Antarctic amplification in CMIP6 compared to CMIP5.

The Nonlinear Radiative Feedback Effects in the Arctic Warming

The analysis of radiative feedbacks requires the separation and quantification of the radiative contributions of different feedback variables, such as atmospheric temperature, water vapor, surface albedo, cloud, etc. It has been a challenge to include the nonlinear radiative effects of these variables in the feedback analysis. For instance, the kernel method that is widely used in the literature assumes linearity and completely neglects the nonlinear effects. Nonlinear effects may arise from the nonlinear dependency of radiation on each of the feedback variables, especially when the change in them is of large magnitude such as in the case of the Arctic climate change. Nonlinear effects may also arise from the coupling between different feedback variables, which often occurs as feedback variables including temperature, humidity and cloud tend to vary in a coherent manner. In this paper, we use brute-force radiation model calculations to quantify both univariate and multivariate nonlinear feedback effects and provide a qualitative explanation of their causes based on simple analytical models. We identify these prominent nonlinear effects in the CO2-driven Arctic climate change: 1) the univariate nonlinear effect in the surface albedo feedback, which results from a nonlinear dependency of planetary albedo on the surface albedo, which causes the linear kernel method to overestimate the univariate surface albedo feedback; 2) the coupling effect between surface albedo and cloud, which offsets the univariate surface albedo feedback; 3) the coupling effect between atmospheric temperature and cloud, which offsets the very strong univariate temperature feedback. These results illustrate the hidden biases in the linear feedback analysis methods and highlight the need for nonlinear methods in feedback quantification.

The climate impact of the Drake Passage opening from a fluid dynamics point of view: the role of Antarctic glaciation

<p>Pronounced global cooling around the Eocene-Oligocene transition (EOT) was a pivotal event in Earth’s climate history, controversially associated with the opening of the Drake Passage.  Using a physical laboratory model we revisit the fluid dynamics of this marked reorganization of ocean circulation. Our differentially heated rotating annulus is a widely studied experimental set-up designed to model mid-latitude circulation in the atmosphere and the ocean, as well. Here we show, seemingly contradicting paleoclimate records that in our experiments opening the pathway yields higher values of mean water surface temperature than the “closed” configuration. This mismatch points to the importance of the crucial role ice albedo feedback plays in the investigated EOT-like transition, a component that is not captured in the laboratory model. Our conclusion is supported by numerical simulations performed in a global climate model (GCM) of intermediate complexity, where both “closed” and “open” configurations were explored, with and without active ice-albedo feedback. The GCM results indicate that sea surface temperatures would change in the opposite direction following an opening event in the two ice dynamics settings, and the results are therefore consistent both with the laboratory experiment (slight warming after opening) and the paleoclimatic data (pronounced cooling after opening). It follows that in the hypothetical case of an initially ice-free Antarctica the continent could have become even warmer after the opening, a scenario not indicated by paleotemperature reconstruction. These results provide circumstantial evidence supporting a particular EOT scenario in which Antarctica had already been – at least partially – covered with ice when the Drake Passage fully opened.</p>