Coordinated numerical ensemble experiments with six different state-of-the-art atmosphere models have been used in order to evaluate the respective impact of the observed Arctic sea ice and sea ...surface temperature (SST) variations on air temperature variations in mid and high latitude land areas. Two sets of experiments have been designed; in the first set (EXP1), observed daily sea ice concentration and SST variations are used as lower boundary forcing over 1982–2014 while in the second set (EXP2) the SST variations are replaced by the daily SST climatology. The observed winter 2 m air temperature (T2m) variations are relatively well reproduced in a number of mid and high latitude land areas in EXP1, with best agreement in southwestern North America and northern Europe. Sea ice variations are important for the interannual T2m variations in northern Europe but have limited impact on all other mid and high latitude land regions. In particular, sea ice variations do not contribute to the observed opposite variations in the Arctic and mid latitude in our model experiments. The spread across ensemble members is large and many ensemble members are required to reproduce the observed T2m variations over northern Europe in our models. The amplitude of T2m anomalies in the coldest observed winters over northern Europe is not reproduced by our multi-model ensemble means. However, the sea ice conditions in these respective winters and mainly the thermodynamic response to the ice anomalies lead to an enhanced likelihood for occurrence of colder than normal winters and extremely cold winters. Still, the main reason for the observed extreme cold winters is internal atmospheric dynamics. The coldest simulated northern European winters in EXP1 and EXP2 between 1982 and 2014 show the same large scale T2m and atmospheric circulation anomaly patterns as the observed coldest winters, indicating that the models are well able to reproduce the processes, which cause these cold anomalies. The results are robust across all six models used in this study.
We examine the weakening of the Atlantic Meridional Overturning Circulation (AMOC) in response to increasing CO
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at different horizontal resolutions in a state-of-the-art climate model and in a ...small ensemble of models with differing resolutions. There is a strong influence of the ocean mean state on the AMOC weakening: models with a more saline western subpolar gyre have a greater formation of deep water there. This makes the AMOC more susceptible to weakening from an increase in CO
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since weakening ocean heat transports weaken the contrast between ocean and atmospheric temperatures and hence weaken the buoyancy loss. In models with a greater proportion of deep water formation further north (in the Greenland-Iceland-Norwegian basin), deep-water formation can be maintained by shifting further north to where there is a greater ocean-atmosphere temperature contrast. We show that ocean horizontal resolution can have an impact on the mean state, and hence AMOC weakening. In the models examined, those with higher resolutions tend to have a more westerly location of the North Atlantic Current and stronger subpolar gyre. This likely leads to a greater impact of the warm, saline subtropical Atlantic waters on the western subpolar gyre resulting in greater dense water formation there. Although there is some improvement of the higher resolution models over the lower resolution models in terms of the mean state, both still have biases and it is not clear which biases are the most important for influencing the AMOC strength and response to increasing CO
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Spatial and temporal variations of summer sea ice albedo over the Arctic are analyzed using an ensemble of historical CMIP5 model simulations. The results are compared to the CLARA-SAL product that ...is based on long-term satellite observations. The summer sea ice albedo varies substantially among CMIP5 models, and many models show large biases compared to the CLARA-SAL product. Single summer months show an extreme spread of ice albedo among models; July values vary between 0.3 and 0.7 for individual models. The CMIP5 ensemble mean, however, agrees relatively well in the central Arctic but shows too high ice albedo near the ice edges and coasts. In most models, the ice albedo is spatially too uniformly distributed. The summer-to-summer variations seem to be underestimated in many global models, and almost no model is able to reproduce the temporal evolution of ice albedo throughout the summer fully. While the satellite observations indicate the lowest ice albedos during August, the models show minimum values in July and substantially higher values in August. Instead, the June values are often lower in the models than in the satellite observations. This is probably due to too high surface temperatures in June, leading to an early start of the melt season and too cold temperatures in August causing an earlier refreezing in the models. The summer sea ice albedo in the CMIP5 models is strongly governed by surface temperature and snow conditions, particularly during the period of melt onset in early summer and refreezing in late summer. The summer surface net solar radiation of the ice-covered Arctic areas is highly related to the ice albedo in the CMIP5 models. However, the impact of the ice albedo on the sea ice conditions in the CMIP5 models is not clearly visible. This indicates the importance of other Arctic and large-scale processes for the sea ice conditions.
The record sea ice minimum (SIM) extents observed during the summers of 2007 and 2012 in the Arctic are stark evidence of accelerated sea ice loss during the last decade. Improving our understanding ...of the Arctic atmosphere and accurate quantification of its characteristics becomes ever more crucial, not least to improve predictions of such extreme events in the future. In this context, the Atmospheric Infrared Sounder (AIRS) instrument onboard NASA's Aqua satellite provides crucial insights due to its ability to provide 3-D information on atmospheric thermodynamics. Here, we facilitate comparisons in the evolution of the thermodynamic state of the Arctic atmosphere during these two SIM events using a decade-long AIRS observational record (2003–2012). It is shown that the meteorological conditions during 2012 were not extreme, but three factors of preconditioning from winter through early summer played an important role in accelerating sea ice melt. First, the marginal sea ice zones along the central Eurasian and North Atlantic sectors remained warm throughout winter and early spring in 2012 preventing thicker ice build-up. Second, the circulation pattern favoured efficient sea ice transport out of the Arctic in the Atlantic sector during late spring and early summer in 2012 compared to 2007. Third, additional warming over the Canadian archipelago and southeast Beaufort Sea from May onward further contributed to accelerated sea ice melt. All these factors may have lead the already thin and declining sea ice cover to pass below the previous sea ice extent minimum of 2007. In sharp contrast to 2007, negative surface temperature anomalies and increased cloudiness were observed over the East Siberian and Chukchi seas in the summer of 2012. The results suggest that satellite-based monitoring of atmospheric preconditioning could be a critical source of information in predicting extreme sea ice melting events in the Arctic.
Large historical and future ensemble simulations from the Max-Planck Institute and the Canadian Earth System Models and from CMIP5 have been analysed to investigate the uncertainty due to internal ...variability in multi-decadal temperature and precipitation trends over Europe. Internal variability dominates the uncertainties in temperature and precipitation trends in all seasons at 30-year time scales. Locally, seasonal 30-year temperature trends deviate up to ±3 °C from the ensemble mean trend. Thus, in the entire of Europe, local seasonal temperature changes until year 2050 from below −1 °C up to more than 4 °C are possible according to the model results. Up to 30% of all ensemble members show negative temperature trends until year 2050 in winter, up to 10% of the members in summer. Uncertainties of 30-year precipitation trends due to internal variability exceed the trends almost everywhere in Europe. Only in few European regions more than 75% of the members agree on the sign of the change until year 2050. In southern Sweden, minimum and maximum winter (summer) temperature trends in the next 30 years differ with up to 7 °C (5 °C) between individual members of the large model ensembles. Large positive temperature trends are linked to positive (negative) precipitation trends in winter (summer) in southern Sweden. This variability is attributed to the variability in large scale atmospheric circulation trends, mainly due to internal atmospheric variability. We find only weak linkages between the variability of temperature trends and the dominant decadal to multi-decadal climate modes. This indicates that there is limited potential to predict the multi-decadal variability in climate trends. The main findings from our study are robust across the large ensembles from the different models used in this study but at the local scale, the results depend also on the choice of the model.
Recent accelerated warming over the Arctic coincides with sea ice reduction and shifting patterns of land cover. We use a state‐of‐the‐art regional Earth system model, RCAO‐GUESS, which comprises a ...dynamic vegetation model (LPJ‐GUESS), a regional atmosphere model (RCA), and an ocean sea ice model (RCO), to explore the dynamic coupling between vegetation and sea ice during 1989–2011. Our results show that RCAO‐GUESS captures recent trends in observed sea ice concentration and extent, with the inclusion of vegetation dynamics resulting in larger, more realistic variations in summer and autumn than the model that does not account for vegetation dynamics. Vegetation feedbacks induce concomitant changes in downwelling longwave radiation, near‐surface temperature, mean sea level pressure, and sea ice reductions, suggesting a feedback chain linking vegetation change to sea ice dynamics. This study highlights the importance of including interactive vegetation dynamics in modeling the Arctic climate system, particularly when predicting sea ice dynamics.
Plain Language Summary
Recent accelerated warming over the Arctic is associated with dramatic changes in the physical environment, among which unprecedented sea ice decline has received particular attention. In this study, we use a regional Earth system model accounting for interactive coupling between the atmosphere, land vegetation, and sea ice dynamics to explore their potential links. Our model simulates observed spatiotemporal patterns of sea ice thickness and extent reasonably well. Furthermore, the results show that feedbacks of warming‐driven vegetation changes on the near‐surface radiation balance can cause greater variations in sea ice between seasons, which can contribute to an accelerated trend of sea ice reduction. The changes in mean sea level pressure caused by vegetation changes can alter the transport of energy and warm the land, sea, and sea ice surfaces. Downwelling longwave radiation is the dominant factor contributing to the near‐surface warming and increased sea ice melting. Our study highlights the importance of adopting fully coupled Earth system models that account for interactive effects of vegetation dynamics on the physical climate system, in particular when analyzing the reduction of sea ice in the Arctic.
Key Points
Sea ice concentration and extent are simulated by a fully coupled regional Earth system model, including interactive vegetation dynamics
Interactive vegetation dynamics increase the interannual variations of seasonal sea ice cover
Increased downwelling longwave radiation induced by vegetation feedbacks enhances sea ice melting
We review recent progress in understanding the role of sea ice, land surface, stratosphere, and aerosols in decadal‐scale predictability and discuss the perspectives for improving the predictive ...capabilities of current Earth system models (ESMs). These constituents have received relatively little attention because their contribution to the slow climatic manifold is controversial in comparison to that of the large heat capacity of the oceans. Furthermore, their initialization as well as their representation in state‐of‐the‐art climate models remains a challenge. Numerous extraoceanic processes that could be active over the decadal range are proposed. Potential predictability associated with the aforementioned, poorly represented, and scarcely observed constituents of the climate system has been primarily inspected through numerical simulations performed under idealized experimental settings. The impact, however, on practical decadal predictions, conducted with realistically initialized full‐fledged climate models, is still largely unexploited. Enhancing initial‐value predictability through an improved model initialization appears to be a viable option for land surface, sea ice, and, marginally, the stratosphere. Similarly, capturing future aerosol emission storylines might lead to an improved representation of both global and regional short‐term climatic changes. In addition to these factors, a key role on the overall predictive ability of ESMs is expected to be played by an accurate representation of processes associated with specific components of the climate system. These act as “signal carriers,” transferring across the climatic phase space the information associated with the initial state and boundary forcings, and dynamically bridging different (otherwise unconnected) subsystems. Through this mechanism, Earth system components trigger low‐frequency variability modes, thus extending the predictability beyond the seasonal scale.
Key Points
Numerous extraoceanic processes active over the decadal scale are established
Current ESMs underrepresent processes relevant for decadal predictability
Predictive skill assessment is hampered by lack of observations
The performance of the Rossby Centre regional climate model RCA4 is investigated for the Arctic CORDEX (COordinated Regional climate Downscaling EXperiment) region, with an emphasis on its ...suitability to be coupled to a regional ocean and sea ice model. Large biases in mean sea level pressure (MSLP) are identified, with pronounced too-high pressure centred over the North Pole in summer of over 5 hPa, and too-low pressure in winter of a similar magnitude. These lead to biases in the surface winds, which will potentially lead to strong sea ice biases in a future coupled system. The large-scale circulation is believed to be the major reason for the biases, and an implementation of spectral nudging is applied to remedy the problems by constraining the large-scale components of the driving fields within the interior domain. It is found that the spectral nudging generally corrects for the MSLP and wind biases, while not significantly affecting other variables, such as surface radiative components, two-metre temperature and precipitation.
Quantitative estimates of changes in wind energy resources in the Arctic were obtained using the RCA4 regional climate model under the RCP4.5 and RCP8.5 climate change scenarios for 2006–2099. The ...wind power density proportional to cubic wind speed was analyzed. The procedure for the model near-surface wind speed bias correction using ERA5 data as a reference with subsequent extrapolation of wind speed to the turbine height was applied to estimate the wind power density (WPD). According to the RCA4 simulations for the 21st century under both anthropogenic forcing scenarios, a noticeable increase in the WPD was noted, in particular, over the Barents, Kara, and Chukchi seas in winter. In summer, a general increase in the WPD is manifested over the Arctic Ocean. The changes are more significant under the RCP8.5 scenario with high anthropogenic forcing for the 21st century. According to model projections, an increase in the interdaily WPD variations does not generally lead to the deviations of wind speed to the values at which the operation of wind generators is unfeasible.
The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5 °C above preindustrial levels. However, natural internal variability may exacerbate anthropogenic warming ...to produce temporary excursions above 1.5 °C. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events.
Plain Language Summary
The Paris Agreement calls for efforts to limit human‐induced global warming to less than 1.5 °C above preindustrial levels. Observations of global mean temperature contain both human‐induced temperature change and superimposed natural variability. Natural variability may temporarily add to the underlying human‐induced warming, leading to observed temperatures that are higher than 1.5 °C for short‐term periods. This would not necessarily exceed the Paris agreement, which is usually interpreted to refer to long‐term averages, but would give an important indication that the threshold is being approached. If exceedance occurs, policy makers will require guidance regarding how long temperatures will remain above the threshold. Here we develop a new capability to predict the likelihood that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. We use decadal climate predictions that are regularly produced by several international climate prediction centers. Importantly, these predictions take into account the observed present day conditions since this is essential to predict the evolution of natural variability. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. We will update our forecasts every year to provide policy makers with advanced warning of the evolving probability and duration of future warming events.
Key Points
Early temporary excursions above 1.5 °C would provide a warning that one of the Paris Agreement thresholds is being approached
Initialized climate predictions indicate a 38% (10%) chance of at least 1 month (year) exceeding 1.5 °C in the 5 year period 2017‐2021
Five‐year mean temperatures above 1.5 °C are extremely unlikely in this period
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