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This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of CESM-LE climate model simulations': the stationary case with pre-industrial radiative forcing and the climate change case with measured and RCP8.5 radiative forcing. Stationary control simulation 1800-year long time-series were used for stationary teleconnection and 40-member ensemble from the period 1920-2100 for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2-meter level, sea-level pressure, sea ice concentration, precipitations, geopotential height and 10-meter level wind speed. The Arctic was divided into seven areas. the Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local factors are stronger. The majority of teleconnections in winter and spring can be explained by climate indexes NAO and AO. During ongoing climate change, the teleconnection patterns did not show remarkable developments by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration. To estimate different Arctic regions' collective statistical connections with the Baltic Sea region, we calculated the correlation between the parameter and its Ridge regression estimation. Seasonal coefficient of determination, R2, were highest for winter: for temperature R2 = 0.64, for surface pressure R2 = 0.44 and for precipitation R2 = 0.35. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker with the highest R2 = 0.09 for temperature in the spring. Hence, the forecasting capacity of Arctic climate data for the Baltic Sea region is weak. Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnections pattern between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century. [ABSTRACT FROM AUTHOR]

Reanalysis data show an increasing trend in Arctic precipitation over the 20th century, but changes are not homogenous across seasons or space. The observed hydroclimate changes are expected to continue and possibly accelerate in the coming century, not only affecting pan-Arctic natural ecosystems and human activities, but also lower latitudes through the atmospheric and ocean circulations. However, a lack of spatiotemporal observational data makes reliable quantification of Arctic hydroclimate change difficult, especially in a long-term context. To understand Arctic hydroclimate and its variability prior to the instrumental record, climate proxy records are needed. The purpose of this review is to summarise the current understanding of Arctic hydroclimate during the past 2000 years. First, the paper reviews the main natural archives and proxies used to infer past hydroclimate variations in this remote region and outlines the difficulty of disentangling the moisture from the temperature signal in these records. Second, a comparison of two sets of hydroclimate records covering the Common Era from two data-rich regions, North America and Fennoscan-dia, reveals inter- and intra-regional differences. Third, building on earlier work, this paper shows the potential for providing a high-resolution hydroclimate reconstruction for the Arctic and a comparison with last-millennium simulations from fully coupled climate models. In general, hydroclimate proxies and simulations indicate that the Medieval Climate Anomaly tends to have been wetter than the Little Ice Age (LIA), but there are large regional differences. However, the regional coverage of the proxy data is inadequate, with distinct data gaps in most of Eurasia and parts of North America, making robust assessments for the whole Arctic impossible at present. To fully assess pan-Arctic hydroclimate variability for the last 2 millennia, additional proxy records are required. [ABSTRACT FROM AUTHOR]

Due to global warming, the Arctic sea ice extent (SIE) is rapidly decreasing each year. According to the Intergovernmental Panel on Climate Change (IPCC) climate model projections, the summer Arctic will be nearly sea-ice-free in the 2050s of the 21st century, which will have a great impact on global climate change. As a result, accurate predictions of Arctic sea ice are of significant interest. In most current studies, the majority of deep-learning-based SIE prediction models focus on one-step prediction, and they not only have short lead times but also limited prediction skill. Moreover, these models often lack interpretability. In this study, we construct the Ice temporal fusion transformer (IceTFT) model, which mainly consists of the variable selection network (VSN), the long short-term memory (LSTM) encoder, and a multi-headed attention mechanism. We select 11 predictors for the IceTFT model, including SIE, atmospheric variables, and oceanic variables, according to the physical mechanisms affecting sea ice development. The IceTFT model can provide 12-month SIE directly, according to the inputs of the last 12 months. We evaluate the IceTFT model from the hindcasting experiments for 2019–2021 and prediction for 2022. For the hindcasting of 2019–2021, the average monthly prediction errors are less than 0.21 ×106 km 2 , and the September prediction errors are less than 0.1 ×106 km 2 , which is superior to the models from Sea Ice Outlook (SIO). For the prediction of September 2022, we submitted the prediction to the SIO in June 2022, and IceTFT still has higher prediction skill. Furthermore, the VSN in IceTFT can automatically adjust the weights of predictors and filter spuriously correlated variables. Based on this, we analyze the sensitivity of the selected predictors for the prediction of SIE. This confirms that the IceTFT model has a physical interpretability. [ABSTRACT FROM AUTHOR]

The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency (ESA) Cloud Climate Change Initiative (Cloud_CCI) project uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84 ∘ N, 24.29 ∘ E), North Slope of Alaska (NSA; 71.32 ∘ N, 156.61 ∘ W), Ny-Ålesund (Ny-Å; 78.92 ∘ N, 11.93 ∘ E), and Summit (72.59 ∘ N, 38.42 ∘ W). The liquid water path (LWP) ground-based data are retrieved from microwave radiometers, while the cloud top height (CTH) has been determined from the integrated lidar–radar measurements. The quality of the satellite products, cloud mask and cloud optical depth (COD), has been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 3 optical depth (OD) units. When ice clouds are included, the underestimation increases to about 5 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by ≈7 g m -2 , over NSA by ≈16 g m -2 , and over Ny-Å by ≈24 g m -2. Over Summit, CCI LWP is overestimated for values ≤20 g m -2 and underestimated for values >20 g m -2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. To understand the effects of multi-layer clouds on the CTH retrievals, the statistics are compared between the single-layer clouds and all types (single-layer + multi-layer). For CTH retrievals, the Cloud_CCI product overestimates the CTH for single-layer clouds. When the multi-layer clouds are included (i.e., all types), the observed CTH overestimation becomes an underestimation of about 360–420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland–Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements made at the four high-latitude sites. [ABSTRACT FROM AUTHOR]

It is now well established that the Arctic is warming at a faster rate than the global average. This warming, which has been accompanied by a dramatic decline in sea ice, has been linked to cooling over the Eurasian subcontinent over recent decades, most dramatically during the period 1998–2012. This is a counter-intuitive impact under global warming given that land regions should warm more than ocean (and the global average). Some studies have proposed a causal teleconnection from Arctic sea-ice retreat to Eurasian wintertime cooling; other studies argue that Eurasian cooling is mainly driven by internal variability. Overall, there is an impression of strong disagreement between those holding the "ice-driven" versus "internal variability" viewpoints. Here, we offer an alternative framing showing that the sea ice and internal variability views can be compatible. Key to this is viewing Eurasian cooling through the lens of dynamics (linked primarily to internal variability with some potential contribution from sea ice; cools Eurasia) and thermodynamics (linked to sea-ice retreat; warms Eurasia). This approach, combined with recognition that there is uncertainty in the hypothesized mechanisms themselves, allows both viewpoints (and others) to co-exist and contribute to our understanding of Eurasian cooling. A simple autoregressive model shows that Eurasian cooling of this magnitude is consistent with internal variability, with some periods exhibiting stronger cooling than others, either by chance or by forced changes. Rather than posit a "yes-or-no" causal relationship between sea ice and Eurasian cooling, a more constructive way forward is to consider whether the cooling trend was more likely given the observed sea-ice loss, as well as other sources of low-frequency variability. Taken in this way both sea ice and internal variability are factors that affect the likelihood of strong regional cooling in the presence of ongoing global warming. [ABSTRACT FROM AUTHOR]

The Arctic is very susceptible to climate change and thus is warming much faster than the rest of the world. Clouds influence terrestrial and solar radiative fluxes and thereby impact the amplified Arctic warming. The partitioning of thermodynamic phases (i.e., ice crystals and water droplets) within mixed-phase clouds (MPCs) especially influences their radiative properties. However, the processes responsible for ice crystal formation remain only partially characterized. In particular, so-called secondary ice production (SIP) processes, which create supplementary ice crystals from primary ice crystals and the environmental conditions that they occur in, are poorly understood. The microphysical properties of Arctic MPCs were measured during the Ny-Ålesund AeroSol Cloud ExperimENT (NASCENT) campaign to obtain a better understanding of the atmospheric conditions favorable for the occurrence of SIP processes. To this aim, the in situ cloud microphysical properties retrieved by a holographic cloud imager mounted on a tethered balloon system were complemented by ground-based remote sensing and ice-nucleating particle measurements. During the 6 d investigated in this study, SIP occurred during about 40 % of the in-cloud measurements, and high SIP events with number concentrations larger than 10 L -1 of small pristine ice crystals occurred in 4 % of the in-cloud measurements. This demonstrates the role of SIP for Arctic MPCs. The highest concentrations of small pristine ice crystals were produced at temperatures between -5 and -3 ∘ C and were related to the occurrence of supercooled large droplets freezing upon collision with ice crystals. This suggests that a large fraction of ice crystals in Arctic MPCs are produced via the droplet-shattering mechanism. From evaluating the ice crystal images, we could identify ice–ice collision as a second SIP mechanism that dominated when fragile ice crystals were observed. Moreover, SIP occurred over a large temperature range and was observed in up to 80 % of the measurements down to -24 ∘ C due to the occurrence of ice–ice collisions. This emphasizes the importance of SIP at temperatures below -8 ∘ C, which are currently not accounted for in most numerical weather models. Although ice-nucleating particles may be necessary for the initial freezing of water droplets, the ice crystal number concentration is frequently determined by secondary production mechanisms. [ABSTRACT FROM AUTHOR]

Although snow depth on sea ice is a key parameter for sea ice thickness (SIT) retrieval, there currently does not exist reliable estimations. In the Arctic, nearly all SIT products use a snow depth climatology (the modified Warren-99 climatology, W99m) constructed from in situ data obtained prior to the first significant impacts of climate change. In the Antarctic, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the altimetric snow depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the Ka-band frequency SARAL/Altika and the Ku-band frequency CryoSat-2. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modelling and Assimilation System (PIOMAS) or its equivalent in the Antarctic the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model, and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in the Arctic)), the Advanced Microwave Scanning Radiometer-2 (AMSR2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka–Ku product (only in the Arctic). The ASD product is further validated in the Arctic against the ice mass balance (IMB) buoys, the CryoSat Validation Experiment (CryoVEx) and Operation Ice Bridge's (OIB) airborne measurements. These comparisons demonstrate that ASD is a relevant snow depth solution, with spatiotemporal patterns consistent with those of the alternative Ka–Ku DuST product but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data: comparisons with OIB's airborne snow radar in the Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in the Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction in the average SIT of about 30 cm. [ABSTRACT FROM AUTHOR]

Arctic snow and ice cover are vital indicators of climate variability and change, yet while the Arctic shows overall warming and dramatic changes in snow and ice cover, the response of these high-latitude regions to recent climatic change varies regionally. Although previous studies have examined changing snow and ice separately, examining phenology changes across multiple components of the cryosphere together is important for understanding how these components and their response to climate forcing are interconnected. In this work, we examine recent changes in sea ice, lake ice, and snow together at the pan-Arctic scale using the Interactive Multisensor Snow and Ice Mapping System 24 km product from 1997–2019, with a more detailed regional examination from 2004–2019 using the 4 km product. We show overall that for sea ice, trends toward earlier open water (-7.7 d per decade, p<0.05) and later final freeze (10.6 d per decade, p<0.05) are evident. Trends toward earlier first snow-off (-4.9 d per decade, p<0.05), combined with trends toward earlier first snow-on (-2.8 d per decade, p<0.05), lead to almost no change in the length of the snow-free season, despite shifting earlier in the year. Sea ice-off, lake ice-off, and snow-off parameters were significantly correlated, with stronger correlations during the snow-off and ice-off season compared to the snow-on and ice-on season. Regionally, the Bering and Chukchi seas show the most pronounced response to warming, with the strongest trends identified toward earlier ice-off and later ice-on. This is consistent with earlier snow-off and lake ice-off and later snow-on and lake ice-on in west and southwest Alaska. In contrast to this, significant clustering between sea ice, lake ice, and snow-on trends in the eastern portion of the North American Arctic shows an earlier return of snow and ice. The marked regional variability in snow and ice phenology across the pan-Arctic highlights the complex relationships between snow and ice, as well as their response to climatic change, and warrants detailed monitoring to understand how different regions of the Arctic are responding to ongoing changes. [ABSTRACT FROM AUTHOR]

The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60 ∘ N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO42- burdens decrease significantly in all simulations by 10 %–60 % following the reductions of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of -0.39±0.01 Wm-2 , which is -0.08 Wm-2 larger than the 1990–2010 mean forcing (-0.32 Wm-2), of which -0.24±0.01 Wm-2 was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of -0.35 to -0.40 Wm-2 for the same period, which is -0.01 to -0.06 Wm-2 larger than the 1990–2010 mean forcing of -0.35 Wm-2. The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI , while scenarios with medium to large emission mitigations led to increases in the negative RFARI , mainly due to the decrease in the positive BC forcing and the decrease in the negative SO42- forcing. The anthropogenic aerosols accounted for -0.24 to -0.26 Wm-2 of the net RFARI in 2030–2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6 ∘C in the Eclipse ensemble and 1.9 to 2.6 ∘C in the CMIP6 ensemble, compared to the 1990–2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change. [ABSTRACT FROM AUTHOR]

Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying an ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Parameter estimation (PE) is applied to the highly influential dry snow grain radius and combined with state estimation in a series of perfect model observing system simulation experiments (OSSEs). Allowing the parameter to vary in space improves performance along the sea ice edge but degrades in the central Arctic compared to requiring the parameter to be uniform everywhere, suggesting that spatially varying parameters will likely improve PE performance at local scales and should be considered with caution. We compare experiments with both PE and state estimation to experiments with only the latter and have found that the benefits of PE mostly occur after the data assimilation period, when no observations are available to assimilate (i.e., the forecast period), which suggests PE's relevance for improving seasonal predictions of Arctic sea ice. [ABSTRACT FROM AUTHOR]

The modeling of paleoclimate, using physically based tools, is increasingly seen as a strong out-of-sample test of the models that are used for the projection of future climate changes. New to the Coupled Model Intercomparison Project (CMIP6) is the Tier 1 Last Interglacial experiment for 127 000 years ago (lig127k), designed to address the climate responses to stronger orbital forcing than the midHolocene experiment, using the same state-of-the-art models as for the future and following a common experimental protocol. Here we present a first analysis of a multi-model ensemble of 17 climate models, all of which have completed the CMIP6 DECK (Diagnostic, Evaluation and Characterization of Klima) experiments. The equilibrium climate sensitivity (ECS) of these models varies from 1.8 to 5.6 ∘ C. The seasonal character of the insolation anomalies results in strong summer warming over the Northern Hemisphere continents in the lig127k ensemble as compared to the CMIP6 piControl and much-reduced minimum sea ice in the Arctic. The multi-model results indicate enhanced summer monsoonal precipitation in the Northern Hemisphere and reductions in the Southern Hemisphere. These responses are greater in the lig127k than the CMIP6 midHolocene simulations as expected from the larger insolation anomalies at 127 than 6 ka. New synthesis for surface temperature and precipitation, targeted for 127 ka, have been developed for comparison to the multi-model ensemble. The lig127k model ensemble and data reconstructions are in good agreement for summer temperature anomalies over Canada, Scandinavia, and the North Atlantic and for precipitation over the Northern Hemisphere continents. The model–data comparisons and mismatches point to further study of the sensitivity of the simulations to uncertainties in the boundary conditions and of the uncertainties and sparse coverage in current proxy reconstructions. The CMIP6–Paleoclimate Modeling Intercomparison Project (PMIP4) lig127k simulations, in combination with the proxy record, improve our confidence in future projections of monsoons, surface temperature, and Arctic sea ice, thus providing a key target for model evaluation and optimization. [ABSTRACT FROM AUTHOR]

We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice surface fraction (ISF) – SIC minus the per-grid-cell melt pond fraction (MPF) on sea ice – as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20 %–25 % for groups I and III and up to 30 %–35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from group I products agrees with MODIS within 2 %–5 % accuracy during the entire melt period from May through September. Group II and IV products overestimate MODIS Arctic average SIC by 5 %–10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates MODIS Arctic average SIC by 5 %–10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for (i) the observed differences between PMW SIC and MODIS ISF and for (ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role – particularly for groups I and II – and recommend conducting further research in this field. [ABSTRACT FROM AUTHOR]

Rapid sea ice loss events (RILEs) in a miniensemble of regional Arctic coupled climate model scenario experiments are analyzed. Mechanisms of sudden ice loss are strongly related to atmospheric circulation conditions and preconditioning by sea ice thinning during the seasons and years before the event. Clustering of events in time suggests a strong control by large-scale atmospheric circulation. Anomalous atmospheric circulation is providing warm air anomalies of up to 5K and is forcing ice flow, affecting winter ice growth. Even without a seasonal preconditioning during winter, ice drop events can be initiated by anomalous inflow of warm air during summer. It is shown that RILEs can be generated based on atmospheric circulation changes as a major driving force without major competing mechanisms, other than occasional longwave effects during spring and summer. Other anomalous seasonal radiative forcing or short-lived forcers (e.g., soot) play minor roles or no role at all in our model. RILEs initiated by ocean forcing do not occur in the model, although cannot be ruled out due to model limitations. Mechanisms found are qualitatively in line with observations of the 2007 RILE. [ABSTRACT FROM AUTHOR]

Understanding Arctic climate change requires knowledge of both the external and the local drivers of Arctic climate as well as local feedbacks within the system. An Arctic feedback mechanism relating changes in sea ice extent to an alteration of the emission of sea salt aerosol and the consequent change in radiative balance is examined. A set of idealized climate model simulations were performed to quantify the radiative effects of changes in sea salt aerosol emissions induced by prescribed changes in sea ice extent. The model was forced using sea ice concentrations consistent with present day conditions and projections of sea ice extent for 2100. Sea salt aerosol emissions increase in response to a decrease in sea ice, the model results showing an annual average increase in number emission over the polar cap (70-90° N) of 86 x 106 m-2 s-1 (mass emission increase of 23 μg m-2 s-1 . This in turn leads to an increase in the natural aerosol optical depth of approximately 23% In response to changes in aerosol optical depth, the natural component of the aerosol direct forcing over the Arctic polar cap is estimated to be between -0.2 and -0.4 W m-2 for the summer months, which results in a negative feedback on the system. The model predicts that the change in first indirect aerosol effect (cloud albedo effect) is approximately a factor of ten greater than the change in direct aerosol forcing although this result is highly uncertain due to the crude representation of Arctic clouds and aerosol-cloud interactions in the model. This study shows that both the natural aerosol direct and first indirect effects are strongly dependent on the surface albedo, highlighting the strong coupling between sea ice, aerosols, Arctic clouds and their radiative effects. [ABSTRACT FROM AUTHOR]