We assess the sea ice response to Southern Annular Mode (SAM) anomalies for pre-industrial control simulations from the Coupled Model Intercomparison Project (CMIP5). Consistent with work by Ferreira ...et al. (J Clim 28:1206–1226,
2015
. doi:
10.1175/JCLI-D-14-00313.1
), the models generally simulate a two-timescale response to positive SAM anomalies, with an initial increase in ice followed by an eventual sea ice decline. However, the models differ in the cross-over time at which the change in ice response occurs, in the overall magnitude of the response, and in the spatial distribution of the response. Late twentieth century Antarctic sea ice trends in CMIP5 simulations are related in part to different modeled responses to SAM variability acting on different time-varying transient SAM conditions. This explains a significant fraction of the spread in simulated late twentieth century southern hemisphere sea ice extent trends across the model simulations. Applying the modeled sea ice response to SAM variability but driven by the observed record of SAM suggests that variations in the austral summer SAM, which has exhibited a significant positive trend, have driven a modest sea ice decrease. However, additional work is needed to narrow the considerable model uncertainty in the climate response to SAM variability and its implications for 20th–21st century trends.
Observations show that increased Arctic cloud cover in the spring is linked with sea ice decline. As the atmosphere and sea ice can influence each other, which one plays the leading role in spring ...remains unclear. Here we demonstrate, through observational data diagnosis and numerical modeling, that there is active coupling between the atmosphere and sea ice in early spring. Sea ice melting and thus the presence of more open water lead to stronger evaporation and promote cloud formation that increases downward longwave flux, leading to even more ice melt. Spring clouds are a driving force in the disappearance of sea ice and displacing the mechanism of atmosphere‐sea ice coupling from April to June. These results suggest the need to accurately model interactions of Arctic clouds and radiation in Earth System Models in order to improve projections of the future of the Arctic.
Plain Language Summary
Arctic summer sea ice has declined by nearly 50%, leading to a larger exposed area of open water that persists longer than before. Clouds have large influences on Arctic sea ice long‐term trends and variability. Atmosphere and sea ice are believed to actively interact with each other in spring. But attributing cause and effect is difficult. Therefore, this study seeks to answer the following question: does the atmosphere primarily drive the sea ice changes or does the sea ice dominate changes in the atmosphere in spring? In this study, we isolated the atmospheric response to Arctic sea ice changes from coupled system through both observations and model simulations. It suggests that this relationship is initiated with active coupling in March. Spring clouds then become a driving force in the disappearance of sea ice from April to June. Overall, identifying the two‐way interactions between Arctic sea ice and atmosphere is a critical step to improve seasonal sea ice forecasts and future sea ice prediction. The sea ice coverage and length of the open water season is important for human activities and wildlife. The long‐term time series will inform future planning of military, civilian, and commercial infrastructure.
Key Points
Active coupling is found between the atmosphere and sea ice in early spring
Clouds are one of important drivers for sea ice melting from April to June
In the high-latitude Arctic, wintertime sea ice and snow
insulate the relatively warmer ocean from the colder atmosphere. While the
climate warms, wintertime Arctic surface heat fluxes remain ...dominated by the
insulating effects of snow and sea ice covering the ocean until the sea ice
thins enough or sea ice concentrations decrease enough to allow for direct
ocean–atmosphere heat fluxes. The Community Earth System Model version 1 Large
Ensemble (CESM1-LE) simulates increases in wintertime conductive heat fluxes
in the ice-covered Arctic Ocean by ∼ 7–11 W m−2 by
the mid-21st century, thereby driving an increased warming of the
atmosphere. These increased fluxes are due to both thinning sea ice and
decreasing snow on sea ice. The simulations analyzed here use a sub-grid-scale
ice thickness distribution. Surface heat flux estimates calculated using
grid-cell mean values of sea ice thicknesses underestimate mean heat fluxes
by ∼16 %–35 % and overestimate changes in conductive heat
fluxes by up to ∼36 % in the wintertime Arctic basin even
when sea ice concentrations remain above 95 %. These results highlight how
wintertime conductive heat fluxes will increase in a warming world even
during times when sea ice concentrations remain high and that snow and the
distribution of snow significantly impact large-scale calculations of
wintertime surface heat budgets in the Arctic.
It has been suggested that recent regional trends in Antarctic sea ice might have been caused by the formation of the ozone hole in the late twentieth century. Here we explore this by examining two ...ensembles of a climate model over the ozone hole formation period (1955–2005). One ensemble includes all known historical forcings; the other is identical except for ozone levels, which are fixed at 1955 levels. We demonstrate that the model is able to capture, on interannual and decadal timescales, the observed statistical relationship between summer Amundsen Sea Low strength (when ozone loss causes a robust deepening) and fall sea ice concentrations (when observed trends are largest). In spite of this, the modeled regional trends caused by ozone depletion are found to be almost exactly opposite to the observed ones. We deduce that the regional character of observed sea ice trends is likely not caused by ozone depletion.
Key Points
We explore the relationship between ozone, summer ASL, and fall sea ice
Our model shows ozone‐induced ASL trends and captures observed ASL/sea ice relationships
Our model, however, does not reproduce the observed austral fall regional sea ice trends
Autumn sea ice trends in the western Ross Sea dominate increases in Antarctic sea ice and are outside the range simulated by climate models. Here we use a number of independent data sets to show that ...variability in western Ross Sea autumn ice conditions is largely driven by springtime zonal winds in the high latitude South Pacific, with a lead-time of 5 months. Enhanced zonal winds dynamically thin the ice, allowing an earlier melt out, enhanced solar absorption, and reduced ice cover the next autumn. This seasonal lag relationship has implications for sea ice prediction. Given a weakening trend in springtime zonal winds, this lagged relationship can also explain an important fraction of the observed sea ice increase. An analysis of climate models indicates that they simulate weaker relationships and wind trends than observed. This contributes to weak western Ross Sea ice trends in climate model simulations.Antarctic sea ice extent continues to increase, with autumn sea ice advances in the western Ross Sea particularly anomalous. Here, based on analysis of independent datasets, the authors show that springtime zonal winds in the high latitude South Pacific drive western Ross Sea autumn sea ice conditions.
Under rising atmospheric greenhouse gas concentrations, the Arctic exhibits amplified warming relative to the globe. This Arctic amplification is a defining feature of global warming. However, the ...Arctic is also home to large internal variability, which can make the detection of a forced climate response difficult. Here we use results from seven model large ensembles, which have different rates of Arctic warming and sea ice loss, to assess the time of emergence of anthropogenically-forced Arctic amplification. We find that this time of emergence occurs at the turn of the century in all models, ranging across the models by a decade from 1994–2005. We also assess transient changes in this amplified signal across the 21st century and beyond. Over the 21st century, the projections indicate that the maximum Arctic warming will transition from fall to winter due to sea ice reductions that extend further into the fall. Additionally, the magnitude of the annual amplification signal declines over the 21st century associated in part with a weakening albedo feedback strength. In a simulation that extends to the 23rd century, we find that as sea ice cover is completely lost, there is little further reduction in the surface albedo and Arctic amplification saturates at a level that is reduced from its 21st century value.
An overview of a simulation referred to as the ‘‘Last Millennium’’ (LM) simulation of the Community Climate System Model, version 4 (CCSM4), is presented. The CCSM4 LM simulation reproduces many ...large-scale climate patterns suggested by historical and proxy-data records, with Northern Hemisphere (NH) and Southern Hemisphere (SH) surface temperatures cooling to the early 1800s Common Era by ∼0.5°C (NH) and ∼0.3°C (SH), followed by warming to the present. High latitudes of both hemispheres show polar amplification of the cooling from the Medieval Climate Anomaly (MCA) to the Little Ice Age (LIA) associated with sea ice increases. The LM simulation does not reproduce La Niña–like cooling in the eastern Pacific Ocean during the MCA relative to the LIA, as has been suggested by proxy reconstructions. Still, dry medieval conditions over the southwestern and central United States are simulated in agreement with proxy indicators for these regions. Strong global cooling is associated with large volcanic eruptions, with indications of multidecadal colder climate in response to larger eruptions. The CCSM4’s response to large volcanic eruptions captures some reconstructed patterns of temperature changes over Europe and North America, but not those of precipitation in the Asian monsoon region. The Atlantic multidecadal oscillation (AMO) has higher variance at centennial periods in the LM simulation compared to the 1850 nontransient run, suggesting a long-term Atlantic Ocean response to natural forcings. The North Atlantic Oscillation (NAO), Pacific decadal oscillation (PDO), and El Niño–Southern Oscillation (ENSO) variability modes show little or no change. CCSM4 does not simulate a persistent positive NAO or a prolonged period of negative PDO during the MCA, as suggested by some proxy reconstructions.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We use a large ensemble set of simulations and initialized model forecasts to assess changes in the initial-value seasonal predictability of summer Arctic sea ice area from the late-twentieth to the ...mid-twenty-first century. Ice thickness is an important seasonal predictor of September ice area because early summer thickness anomalies affect how much melt out occurs. We find that the role of this predictor changes in a warming climate, leading to decadal changes in September ice area predictability. In January-initialized prediction experiments, initialization errors grow over time leading to forecast errors in ice thickness at the beginning of the melt season. The magnitude of this ice thickness forecast error growth for regions important to summer melt out decreases in a warming climate, contributing to enhanced predictability. On the other hand, the influence of early summer thickness anomalies on summer melt out and resulting September ice area increases as the climate warms. Given this, for the same magnitude ice thickness forecast error in early summer, a larger September ice area anomaly results in the warming climate, contributing to reduced predictability. The net result of these competing factors is that a sweet spot for predictability exists when the ice thickness forecast error growth is modest and the influence of these errors on melt out is modest. This occurs at about 2010 in our simulations. The predictability of summer ice area is lower for earlier decades, because of higher ice thickness forecast error growth, and for later decades because of a stronger influence of ice thickness forecast errors on summer melt out.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Paris Agreement is a multinational initiative to combat climate change by keeping a global temperature increase in this century to 2°C above preindustrial levels while pursuing efforts to limit ...the increase to 1.5°C. Until recently, ensembles of coupled climate simulations producing temporal dynamics of climate en route to stable global mean temperature at 1.5 and 2°C above preindustrial levels were not available. Hence, the few studies that have assessed the ecological impact of the Paris Agreement used ad‐hoc approaches. The development of new specific mitigation climate simulations now provides an unprecedented opportunity to inform ecological impact assessments. Here we project the dynamics of all known emperor penguin (Aptenodytes forsteri) colonies under new climate change scenarios meeting the Paris Agreement objectives using a climate‐dependent‐metapopulation model. Our model includes various dispersal behaviors so that penguins could modulate climate effects through movement and habitat selection. Under business‐as‐usual greenhouse gas emissions, we show that 80% of the colonies are projected to be quasiextinct by 2100, thus the total abundance of emperor penguins is projected to decline by at least 81% relative to its initial size, regardless of dispersal abilities. In contrast, if the Paris Agreement objectives are met, viable emperor penguin refuges will exist in Antarctica, and only 19% and 31% colonies are projected to be quasiextinct by 2100 under the Paris 1.5 and 2 climate scenarios respectively. As a result, the global population is projected to decline by at least by 31% under Paris 1.5 and 44% under Paris 2. However, population growth rates stabilize in 2060 such that the global population will be only declining at 0.07% under Paris 1.5 and 0.34% under Paris 2, thereby halting the global population decline. Hence, global climate policy has a larger capacity to safeguard the future of emperor penguins than their intrinsic dispersal abilities.
Using newly developed mitigation ensembles of fully coupled climate simulations consistent with meeting the Paris Agreement objectives, we derive robust projections of future population dynamics and species persistence for the emperor penguin. We show that global climate policy has the capacity to halt the future projected declines of emperor penguins in ways that their intrinsic biological properties (i.e., dispersal abilities) do not.
Colonially-breeding seabirds have long served as indicator species for the health of the oceans on which they depend. Abundance and breeding data are repeatedly collected at fixed study sites in the ...hopes that changes in abundance and productivity may be useful for adaptive management of marine resources, but their suitability for this purpose is often unknown. To address this, we fit a Bayesian population dynamics model that includes process and observation error to all known Adélie penguin abundance data (1982-2015) in the Antarctic, covering >95% of their population globally. We find that process error exceeds observation error in this system, and that continent-wide "year effects" strongly influence population growth rates. Our findings have important implications for the use of Adélie penguins in Southern Ocean feedback management, and suggest that aggregating abundance across space provides the fastest reliable signal of true population change for species whose dynamics are driven by stochastic processes.Adélie penguins are a key Antarctic indicator species, but data patchiness has challenged efforts to link population dynamics to key drivers. Che-Castaldo et al. resolve this issue using a pan-Antarctic Bayesian model to infer missing data, and show that spatial aggregation leads to more robust inference regarding dynamics.
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