During the Arctic night, clouds regulate surface energy budgets through longwave warming alone. During fall, any increase in low-level opaque clouds will increase surface cloud warming and could potentially delay sea ice formation. While an increase in clouds due to fall sea ice loss has been observed, quantifying the surface warming is observationally challenging. Here, we quantify surface cloud warming using spaceborne lidar observations. By instantaneously co-locating surface cloud warming and sea ice observations in regions where sea ice varies, we find October large surface cloud warming values (> 80 W m-2) are much more frequent (~+50%) over open water than over sea ice. Notably, in November large surface cloud warming values (> 80 W m-2) occur more frequently (~+200%) over open water than over sea ice. These results suggest more surface warming caused by low-level opaque clouds in the future as open water persists later into the fall.

A document by David Bonan. Click on the document to view its contents.

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal-mean precipitation-minus-evaporation (P-E) under greenhouse-gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P-E and increase P-E in the deep tropics. The surface-albedo feedback shifts the location of maximum tropical P-E and increases P-E in the polar regions. The intermodel spread in the P-E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse-rate and surface-albedo feedbacks playing important roles in the polar regions. The P-E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the “wet-gets-wetter, dry-gets-drier” paradigm.

Anthropogenic aerosols (AER) and greenhouse gases (GHG) – the leading drivers of the forced historical change – produce different large-scale climate response patterns, with varying trend pattern correlations from negative to positive over the past century. To understand what caused the time-evolving comparison between GHG and AER responses, we apply a joint low-frequency component analysis on global sea-surface temperature and sea-surface salinity response over 1921-2020 from CESM1 single-forcing large ensemble simulations. While GHG response is well-described by its first leading mode, AER response consists of two distinct modes. The first one features global AER increase and global cooling, opposite to GHG-induced warming. The second mode features multidecadal variations in AER distributions, where the recent shift from North America/western Europe to southeast Asia emissions drives regional changes enhancing the GHG effect. We argue that AER can have both competing and synergistic effects with GHG, as their emissions change temporally and spatially.

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.

A striking feature of the Earth system is that the Northern and Southern Hemispheres reflect identical amounts of sunlight. This hemispheric albedo symmetry comprises two asymmetries: The Northern Hemisphere is more reflective in clear skies, whereas the Southern Hemisphere is cloudier. The most-cited explanation is that the clear-sky asymmetry is primarily due to the relatively-bright continents being disproportionately located in the Northern Hemisphere. However, it is the atmosphere, not the surface, that contributes most to the clear-sky asymmetry. Here we show that the continent-based component of the clear-sky surface asymmetry is largely offset by greater reflection from the Southern Hemisphere poles, allowing the clear-sky asymmetry to be dominated by aerosol. Climate model simulations suggest that aerosol emissions since the pre-industrial era have driven a large increase in the clear-sky asymmetry that would reverse in future low-emission scenarios. High-emission scenarios also show a decrease in asymmetry, but instead driven by declines in Northern Hemisphere ice and snow cover. Strong clear-sky hemispheric albedo asymmetry is therefore a transient, rather than fixed, feature of Earth’s climate. If all-sky symmetry is maintained despite changes in the clear-sky asymmetry, compensating cloud changes would have uncertain but important implications for Earth’s energy balance and hydrological cycle.

The mechanisms underlying decadal variability in Arctic sea ice remain actively debated. Here we show that variability in boreal biomass burning (BB) emissions strongly influences simulated Arctic sea ice on multi-decadal timescales. In particular, we find that a strong acceleration in 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 more than half of the reported improvement in sea ice sensitivity to CO2 emissions and global warming from CMIP5 to CMIP6 can be attributed to the increased BB variability, at least in the CESM. These results highlight a new kind of uncertainty that needs to be considered when incorporating new observational data into model forcing, while also raising questions about the role of BB emissions on the observed Arctic sea ice loss.

The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5 degC) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we use the LGM global temperature from geological proxies as a benchmark to examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time-step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed-phase clouds, which leads to more cloud ice content and a weaker shortwave cloud feedback over mid-to-high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate-calibrated CESM2 (PaleoCalibr), which simulates well the observed 20th century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (~4 degC) and a 20% weaker aerosol-cloud interaction than CESM2. PaleoCalibr represents a physically and numerically better treatment of cloud microphysics and, we believe, is a more appropriate tool than CESM2 in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection.

Why would hundreds of scientists from around the world freeze a ship in Arctic sea ice for an entire year, braving subzero temperatures and months of polar darkness? This may sound like a fictional adventure movie plot, but from September 2019 through October 2020, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) Arctic research expedition did just this. Currently, the Arctic is warming twice as fast as the global average (a phenomenon known as Arctic amplification) and due to a lack of observations, there is considerable uncertainty in climate models projecting the Arctic climate of the future. The MOSAiC expedition aims to better understand the changing Arctic climate system by gathering data from ground zero over a full seasonal cycle to augment satellite observation data. Using the expedition as an engagement hook, scientists and curriculum developers developed a high school earth science curriculum anchored by the phenomenon that climate scientists are actively trying to explain: Arctic amplification. The curriculum follows the model-based inquiry instructional framework where each lesson provides students with learning experiences (e.g., virtual reality tours of MOSAiC field sites, analyzing authentic Arctic satellite datasets) that relate back to the phenomena. Focusing on explaining natural phenomena provides an authentic context for students to learn and apply scientific understanding, which research shows can help engage students in NGSS scientific practices. Here we present an overview of the learning sequence using refinement of mental models throughout the unit and present preliminary results from pre-post assessments from two educator workshops (~100 teachers) that show that participants’ understanding of Earth’s climate system improved significantly after engaging with the curriculum. Based on these results, we expect this curriculum to be an important tool in engaging students in Earth’s systems thinking.