A document by Lorenzo Zampieri. Click on the document to view its contents.

Improved knowledge of the contributing sources of uncertainty in projections of Arctic sea ice over the 21st century is essential for evaluating impacts of a changing Arctic environment. Here, we consider the role of internal variability, model structure and emissions scenario in projections of Arctic sea-ice area (SIA) by using six single model initial-condition large ensembles and a suite of models participating in Phase 5 of the Coupled Model Intercomparison Project. For projections of September Arctic SIA change, internal variability accounts for as much as 40-60% of the total uncertainty in the next decade, while emissions scenario dominates uncertainty toward the end of the century. Model structure accounts for approximately 60-70% of the total uncertainty by mid-century and declines to 30% at the end of the 21st century during the summer months. For projections of wintertime Arctic SIA change, internal variability contributes as much as 50-60% of the total uncertainty in the next decade and impacts total uncertainty at longer lead times when compared to the summertime. Model structure contributes most of the remaining uncertainty with emissions scenario contributing little to the total uncertainty during the winter months. At regional scales, the contribution of internal variability can vary widely and strongly depends on the month and region. For wintertime SIA change in the GIN and Barents Seas, internal variability contributes approximately 60-70% to the total uncertainty over the coming decades and remains important much longer than in other regions. We further find that the relative contribution of internal variability to total uncertainty is state-dependent and increases as sea ice volume declines. These results demonstrate the need to improve the representation of internal variability of Arctic SIA in models, which is a significant source of uncertainty in future projections.

This study isolates the influence of sea ice mean state on pre-industrial climate and transient 1850-2100 climate change within a fully coupled global model: The Community Earth System Model version 2 (CESM2). The CESM2 sea ice model physics is modified to increase surface albedo, reduce surface sea ice melt, and increase Arctic sea ice thickness and late summer cover. Importantly, increased Arctic sea ice in the modified model reduces a present-day late-summer ice cover bias. Of interest to coupled model development, this bias reduction is realized without degrading the global simulation including top-of-atmosphere energy imbalance, surface temperature, surface precipitation, and major modes of climate variability. The influence of these sea ice physics changes on transient 1850-2100 climate change is compared within a large initial condition ensemble framework. Despite similar global warming, the modified model with thicker Arctic sea ice than CESM2 has a delayed and more realistic transition to a seasonally ice free Arctic Ocean. Differences in transient climate change between the modified model and CESM2 are challenging to detect due to large internally generated climate variability. In particular, two common sea ice benchmarks - sea ice sensitivity and sea ice trends - are of limited value for comparing models with similar global warming. More broadly, these results show the importance of a reasonable Arctic sea ice mean state when simulating the transition to an ice-free Arctic Ocean in a warming world. Additionally, this work highlights the importance of large initial condition ensembles for credible model-to-model and observation-model comparisons.

The mechanisms underlying decadal variability in Arctic sea ice remain an active area of research. Here we show that variability in boreal biomass burning (BB) emissions strongly influence simulated Arctic sea ice on multi-decadal timescales. In particular, we find that a strong acceleration in Arctic sea ice decline in the early 21st century in the Community Earth System Model version 2 (CESM2) is related to increased variability in prescribed CMIP6 BB emissions through summertime aerosol-cloud interactions. Furthermore, we find that the previously reported improvement in sea ice sensitivity to CO2 emissions and global warming from CMIP5 to CMIP6 can be attributed in large part to the imposed increased BB emission variability, at least in the CESM model. These results highlight the complexities of incorporating new observational data into model forcing, while also raising the question of a BB-forced contribution to the observed accelerated early 21st century Arctic sea ice loss.

The ‘surface scattering layer’ (SSL) is the highly-scattering, coarse-grained ice layer that forms on the surface of melting, drained sea ice during spring and summer. Ice of sufficient thickness with an SSL has an observed persistent broadband albedo of ~0.65, resulting in a strong influence on the regional solar partitioning. Experiments during the MOSAiC expedition showed that the SSL re-forms in approximately one day following manual removal. Coincident spectral albedo measurements provide insight into the SSL evolution, where albedo increased on sunny days with higher solar insolation. Comparison with experiments in radiative transfer and global climate models show that the sea ice albedo is greatly impacted by the SSL thickness. The presence of SSL is a significant component of the ice-albedo feedback, with an albedo impact of the same order as melt ponds. Changes in SSL and implications for Arctic sea ice within a warming climate are uncertain.

Many modern sea ice models used in global climate models represent the subgrid-scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea-ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard five ice thickness categories and fifteen ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year-round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice. The spatial resolution of the ICESat-2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat-2; 2018-2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.

We examine the response of the Community Earth System Model versions 1 and 2 (CESM1 and CESM2) to abrupt quadrupling of atmospheric CO$_2$ concentrations (4xCO2) and to 1% annually increasing CO2 concentrations (1%CO2). Different estimates of equilibrium climate sensitivity (ECS) for CESM1 and CESM2 are presented. All estimates show that the sensitivity of CESM2 has increased by 1.5K or more over that of CESM1. At the same time the transient climate response (TCR) of CESM1 and CESM2 derived from 1%CO2 experiments has not changed significantly - 2.1K in CESM1 and 2.0K in CESM2. Increased initial forcing as well as stronger shortwave radiation feedbacks are responsible for the increase in ECS seen in CESM2. A decomposition of regional radiation feedbacks and their contribution to global feedbacks shows that the Southern Ocean plays a key role in the overall behavior of 4xCO2 experiments, accounting for about 50% of the total shortwave feedback in both CESM1 and CESM2. The Southern Ocean is also responsible for around half of the increase in shortwave feedback between CESM1 and CESM2, with a comparable contribution arising over tropical ocean. Experiments using a thermodynamic slab-ocean model (SOM) yield estimates of ECS that are in remarkable agreement with those from fully-coupled earth system model (ESM) experiments for the same level of CO2 increase. Finally, we show that the similarity of TCR in CESM1 and CESM2 masks significant regional differences in warming that occur in the 1%CO2 experiments for each model.

Understanding the variability of Antarctic sea ice is an ongoing challenge given the limitations of observed data. Coupled climate model simulations present the opportunity to examine this variability in Antarctic sea ice. Here, the daily sea ice extent simulated by the newly-released National Center for Atmospheric Research Community Earth System Model Version 2 (CESM2) for the historical period (1979-2014), is compared to the satellite-observed daily sea ice extent for the same period. The comparisons are made using a newly-developed suite of statistical metrics that estimates the variability of the sea ice extent on timescales ranging from the long-term decadal to the short term, intra-day scales. Assessed are the annual cycle, trend, day-to-day change, and the volatility, a new statistic that estimates the variability at the daily scale. Results show that the trend in observed daily sea ice is dominated by sub-decadal variability with a weak positive linear trend superimposed. The CESM2 simulates this sub-decadal variability with a strong negative linear trend superimposed. The CESM2’s annual cycle is similar in amplitude to the observed, a key difference being the timing of ice advance and retreat. The sea ice begins it advance later, reaches its maximum later and begins retreat later in the CESM2. This is confirmed by the day-to-day change. Apparent in all of the sea ice regions, this behavior suggests the influence of the semi-annual oscillation of the circumpolar trough. The volatility, which is associated with smaller scale dynamics such as storms, is smaller in the CESM2 than observed.

We analyze two preindustrial experiments from the Community Earth System Model version 2 (CESM2) to characterize the impact of sea ice physics on regional differences in coastal sea ice production around Antarctica and the resulting impact on the ocean and atmosphere. The experiment in which sea ice is a “mushy” mixture of solid ice and brine has a substantial increase in coastal sea ice frazil and snow ice production that is accompanied by decreasing congelation growth and increasing bottom melt. With mushy ice physics, the subsurface ocean is denser and saltier, there is a statistically significant increase in Antarctic Bottom Water Formation by ~0.5 Sv, but differences in ocean biogeochemistry are minimal and only in regions where the summer ice state differs. While there are no significant changes in the atmospheric circulation, using “mushy” ice physics results in decreased turbulent heat flux, atmospheric convection, and low level cloud cover.

The thermodynamic growth of sea ice is a critical factor in the mass balance of Arctic sea ice, which has important implications for Arctic communities and the global climate. However, the magnitude by which snow atop Arctic sea ice limits thermodynamic ice growth is still not fully understood. Prior work has shown that the wind-driven snow redistribution could significantly modify the heat conduction through the snow cover and hence the rate of thermodynamic ice growth. However, the effects of snow redistribution on sea ice growth have not been quantified and are not well represented in climate models. We use observations from the MOSAiC expedition to show how different facets of snow redistribution can enhance or reduce heat conduction through the snow cover for the same mean snow thickness. The net effect depends on ice topography and environmental conditions. For example, snow redistribution onto young ice in April at MOSAiC reduced heat conduction by approximately 5-15%. We quantify the impact winter and springtime snow redistribution events on the heat conduction on deformed, level, and young ice. We explore the implications of these snow redistribution processes in the Community Earth System Model and discuss priorities for improving climate models.

Climate impacts are not always easily discerned in wild populations as climate change occurs in the context of natural variability. Furthermore, species responses to climate change and variability differ among life histories. The time of emergence (ToE) identifies when the signal of anthropogenic climate change can be quantitatively distinguished from noise associated with natural variability. This concept has been applied extensively in the climate sciences, but has not yet formally been explored in the context of population dynamics. Here, we present a theoretical assessment of the ToE of climate-driven signals in population dynamics (ToEpop) to detect climate signals in populations. We identify the dependence of ToEpop on the magnitude of climate trends and variability and explore the demographic controls on ToEpop. We demonstrate that different life histories (fast species vs. slow species), demographic processes (survival, reproduction) and functional relationships between climate and demographic rates, yield population dynamics that filter trends and variability in climate differently. We illustrate empirically how to detect the point in time when anthropogenic signals in populations emerge from the envelope of natural variability for a species threatened by climate change: the emperor penguin. Finally, we propose six testable hypotheses and a road map for future research.