scholarly journals Arctic sea-ice evolution as modeled by Max Planck Institute for Meteorology's Earth system model

2013 ◽  
Vol 5 (2) ◽  
pp. 173-194 ◽  
Author(s):  
Dirk Notz ◽  
F. Alexander Haumann ◽  
Helmuth Haak ◽  
Johann H. Jungclaus ◽  
Jochem Marotzke
2015 ◽  
Vol 42 (23) ◽  
Author(s):  
E. Blanchard‐Wrigglesworth ◽  
S. L. Farrell ◽  
T. Newman ◽  
C. M. Bitz

2015 ◽  
Vol 56 (69) ◽  
pp. 211-228 ◽  
Author(s):  
Andrew Roberts ◽  
Anthony Craig ◽  
Wieslaw Maslowski ◽  
Robert Osinski ◽  
Alice Duvivier ◽  
...  

AbstractThis work evaluates the fidelity of the polar marine Ekman layer in the Regional Arctic System Model (RASM) and Community Earth System Model (CESM) using sea-ice inertial oscillations as a proxy for ice-ocean Ekman transport. A case study is presented that demonstrates that RASM replicates inertial oscillations in close agreement with motion derived using the GPS. This result is obtained from a year-long case study pre-dating the recent decline in perennial Arctic sea ice, using RASM with sub-hourly coupling between the atmosphere, sea-ice and ocean components. To place this work in context, the RASM coupling method is applied to CESM, increasing the frequency of oceanic flux exchange from once per day in the standard CESM configuration, to every 30 min. For a single year simulation, this change causes a considerable increase in the median inertial ice speed across large areas of the Southern Ocean and parts of the Arctic sea-ice zone. The result suggests that processes associated with the passage of storms over sea ice (e.g. oceanic mixing, sea-ice deformation and surface energy exchange) are underestimated in Earth System Models that do not resolve inertial frequencies in their marine coupling cycle.


Eos ◽  
2020 ◽  
Vol 101 ◽  
Author(s):  
Sarah Stanley

Incorporating random variation of temperature, humidity, and wind offers a computationally cheap alternative to improving resolution in an Earth system model when predicting when Arctic sea ice will disappear.


2020 ◽  
Author(s):  
Patricia DeRepentigny ◽  
Alexandra Jahn ◽  
Marika Holland ◽  
Abigail Smith

<p>Over the past decades, Arctic sea ice has declined in thickness and extent and is shifting toward a seasonal ice regime. These rapid changes have widespread implications for ecological and human activities as well as the global climate, and accurate predictions could benefit a wide range of stakeholders, from local residents to governmental policy makers. However, many aspects of the polar transient climate response remain poorly understood, particularly in regard to the response of Arctic sea ice to increasing atmospheric CO<sub>2</sub> concentration and warming temperatures. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides a useful framework for understanding this response, and the participating climate model simulations are a powerful tool for advancing our understanding of present and future changes in the Arctic climate system.</p><p>Here we explore the current and future states of Arctic sea ice in the Community Earth System Model version 2 (CESM2), the latest generation of the CESM and NCAR’s contribution to CMIP6. We analyze changes in Arctic sea ice cover in two CESM2 configurations with differing atmospheric components: the “low-top” configuration with limited chemistry (CESM2-CAM) and the “high-top” configuration with interactive chemistry (CESM2-WACCM). We find that the two experiments show large differences in their simulation of Arctic sea ice over the historical period. The CESM2-CAM winter ice thickness distribution is skewed thin, with an insufficient amount of ice thicker than 3 m. This leads to a lower summer ice extent compared to the CESM2-WACCM and observations. In both experiments, the timing of first ice-free conditions is insensitive to the choice of future emissions scenario (known as the shared socioeconomic pathways, or SSPs, in CMIP6), an alarming result that points to the current vulnerable state of Arctic sea ice. However, if global warming stays below 1.5°C, the probability of an ice-free summer remains low, consistent with other recent studies. By the end of the 21<sup>st</sup> century, both experiments exhibit an accelerated decline in winter ice extent under the high emissions scenario (SSP5-8.5), leading to ice-free conditions for up to 8 months and an open-water period of 220 days or more depending on the region. Initial results show that the CESM2 simulates less ocean heat loss during the fall months compared to its previous version, delaying the formation of sea ice and leading to lower winter ice extent. Given that the CESM2 reaches a higher atmospheric CO<sub>2</sub> concentration and thus warmer global and Arctic temperatures by 2100, these results suggest the presence of emerging processes associated with a state of the Arctic climate that has never been sampled before.</p>


2020 ◽  
Author(s):  
Yevgeny Aksenov ◽  
Andrew Yool ◽  
Julien Palmieri ◽  
Katya Popova ◽  
Stephen Kelly ◽  
...  

<p>We present analysis of Arctic sea ice and ocean dynamics in the ensemble of the UK Earth System Model (UK ESM1) simulations completed under the Coupled Model Intercomparison Project Phase 6 (CMIP6) protocol. The focus of the investigation is on the future changes in the Arctic sea ice and oceanic connections and on the impact of the nutrient advection on the Arctic marine biogeochemistry and ecosystems. Changes in the balance of the oceanic inflows from the North Atlantic and North Pacific Oceans are found to have a first order effect on the watermasses and nutrients balances in the central Arctic Ocean. The simulations show that the total primary production in the Arctic Ocean is increased by 100% in the 2090s as compared to the present climate. This is caused by higher nutrients availability in the Atlantic inflowing waters and prolonged ice- free season. The faster connections through the Arctic and milder oceanic environment allows species to survive through the winter and from the second half of the century the Arctic Ocean could become a key oceanic gateway connecting the global oceans. The study is supported from the project APEAR (NE/R012865/1) NERC-BMBF and from the NERC ACSIS Programme (NE/N018044/1).</p>


2020 ◽  
Author(s):  
Oliver Gutjahr ◽  
Nils Brüggemann ◽  
Helmuth Haak ◽  
Johann H. Jungclaus ◽  
Dian A. Putrasahan ◽  
...  

Abstract. We compare the effects of four different ocean vertical mixing schemes on the ocean mean state simulated by the Max Planck Institute Earth System Model (MPI-ESM1.2) in the framework of the Community Vertical Mixing (CVMix) library. Besides the PP and KPP scheme, we implemented the TKE scheme and a recently developed prognostic scheme for internal wave energy and its dissipation (IDEMIX) to replace the often assumed constant background diffusivity in the ocean interior. We analyse in particular the effects of IDEMIX on the ocean mean state, when combined with TKE (TKE+IDEMIX). In general, we find little sensitivity of the ocean surface, but considerable effects for the interior ocean. Overall, we cannot classify any scheme as superior, because they modify biases that vary by region or variable, but produce a similar pattern on the global scale. However, using a more realistic and energetically consistent scheme (TKE+IDEMIX) produces a more heterogeneous pattern of vertical diffusion, with lower diffusivity in deep and flat-bottom basins and elevated turbulence over rough topography. In addition, TKE+IDEMIX improves the circulation in the Nordic Seas and Fram Strait, thus reducing the warm bias of the Atlantic water (AW) layer in the Arctic Ocean to a similar extent as has been demonstrated with eddy-resolving ocean models. We conclude that although shortcomings due to model resolution determine the global-scale bias pattern, the choice of the vertical mixing scheme may play an important role for regional biases.


2018 ◽  
Vol 12 (4) ◽  
pp. 1137-1156 ◽  
Author(s):  
Paul J. Kushner ◽  
Lawrence R. Mudryk ◽  
William Merryfield ◽  
Jaison T. Ambadan ◽  
Aaron Berg ◽  
...  

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.


2018 ◽  
Author(s):  
Chuncheng Guo ◽  
Mats Bentsen ◽  
Ingo Bethke ◽  
Mehmet Ilicak ◽  
Jerry Tjiputra ◽  
...  

Abstract. A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g. 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multi-millennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speedup is primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean-sea ice horizontal grid configuration that allows an increase of the ocean-sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasises model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F in general agree well with observational estimates, and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain.


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