Simulations for CMIP6 with the AWI climate model AWI-CM-1-1

Arctic precipitation increases can freshen the Arctic Ocean surface and reduce sea ice loss, and affect the mass balance of high-latitude glaciers and ice sheets (Bintanja et al., 2018;Bintanja & Selten, 2014;Min et al., 2008). Understanding what drives increased precipitation sensitivity to climate change in the Arctic is fundamental to build confidence in future projections of Arctic precipitation change and narrow their spread.Arctic amplification of both temperature and humidity changes is strongest during the cold season, when incoming solar radiation is low or zero and the Arctic energy budget is dominated by advection of heat from lower latitudes, heat release from the ocean surface and radiative cooling to space (Collins et al., 2013;Serreze & Barry, 2011;Serreze et al., 2007). Enhanced Arctic warming compared to lower latitudes has been attributed to local radiative feedbacks (Stuecker et al., 2018) and increased advection of moist air masses from lower latitudes (Woods & Caballero, 2016). The increased absorption of solar radiation by darker surfaces caused by snow and ice melt (surface albedo feedback) and the weak Arctic infrared cooling to space caused by thermal stratification (lapse-rate feedback) are the most important local feedback mechanisms contributing to Arctic amplification (Hahn et al., 2021;Pithan & Mauritsen, 2014).Prior research on Arctic amplification of precipitation changes has often been based on what we call the moisture hypothesis, arguing that additional moisture supply causes increased precipitation. This hypothesis assumes that Arctic precipitation and its increase under global warming are limited by moisture

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“…The histogram in Figure 3 uses 3‐hourly November to April data north of 60

°

N from the historical run of the AWICM climate model (Semmler et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

Arctic Amplification of Precipitation Changes—The Energy Hypothesis

Pithan

Jung

2021

Geophysical Research Letters

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“…The AWI‐CM model data are available through the Earth System Grid Federation (ESGF) and include not only the DECK and ScenarioMIP experiments (Eyring et al, 2016; O'Neill et al, 2016) with AWI‐CM‐1‐1‐MR (Semmler et al, 2018) described in the present study. At the time of writing, AWI‐CM results from the Polar Amplification Model Intercomparison Project (PAMIP; Smith et al, 2019), and results with AWI‐CM‐1‐1‐HR from the High Resolution Model Intercomparison Project (HighResMIP; Haarsma et al, 2016) are available as well (Semmler et al, 2017, 2019). Furthermore, data publications of AWI‐CM simulations are planned for OMIP (Griffies et al, 2016) and PMIP (Kageyama et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In our AWI‐CM‐1‐1‐MR CMIP6 contribution (Semmler et al, 2018), the FESOM model is run on a medium‐resolution “MR” mesh that follows the mesh design strategy proposed by Sein et al (2016, 2017) (Figure 1): The main approach is to locally increase the resolution over areas of high sea surface height (SSH) variability as obtained from satellite data. The horizontal resolution of the mesh varies from 8 km over energetically active areas such as the North Atlantic Current region to 80 km over areas with low SSH variability.…”

Section: Model and Simulation Descriptionmentioning

confidence: 99%

“…We thank the Max‐Planck‐Institute for Meteorology in Hamburg (MPI) for providing us with the ECHAM6 model code and for the fruitful discussions with various colleagues from this institute. All data are available on the Earth System Grid Federation (ESGF) (Semmler et al, 2018). H. F. Goessling and L. Mu acknowledge the financial support of the Federal Ministry of Education and Research of Germany in the framework of the research group Seamless Sea Ice Prediction (SSIP; Grant 01LN1701A).…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…The tables have evolved to a great extent over the past 3 years. All CMIP6 data are being published through the Earth System Grid Federation (ESGF) (Juckes et al, 2020)—including the AWI‐CM CMIP6 data (Semmler et al, 2018).…”

Section: Model and Simulation Descriptionmentioning

confidence: 99%

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Simulations for CMIP6 With the AWI Climate Model AWI‐CM‐1‐1

Semmler

Danilov

Gierz

et al. 2020

J Adv Model Earth Syst

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The Alfred Wegener Institute Climate Model (AWI‐CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice‐ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max Planck Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI‐CM performs better than the average of CMIP5 models. AWI‐CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea‐ice extent in September over the past 30 years is 20–30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present‐day climate under the strong emission scenario SSP585 are similar to the multi‐model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2°C to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical, and Southern Ocean. Furthermore, in the northern middle latitudes in boreal summer and autumn as well as in the southern middle latitudes, a more zonal atmospheric flow is projected throughout the year.

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Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

Tang

Sidorenko

et al. 2020

Quart J Royal Meteoro Soc

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An ensemble-based data assimilation framework for a coupled oceanatmosphere model is applied to investigate the influence of assimilating different types of ocean observations on the ocean and atmosphere simulation. The data assimilation is performed with the parallel data assimilation framework (PDAF) for the climate model AWI-CM. Observations of the ocean, namely satellite sea-surface temperature (SST) and temperature and salinity profiles, are assimilated into the ocean component. The atmospheric state is only influenced by the model dynamics. Different assimilation scenarios were carried out with different combinations of observations to investigate to what extent the assimilation into the coupled model leads to a better estimation of the state of the ocean as well as the atmosphere. The influence of the data assimilation is assessed by comparing the ocean prediction with dependent and independent ocean observations. For the atmosphere, the assimilation result is compared with the ERA-Interim atmospheric reanalysis data. The ocean temperature and salinity are improved by all the assimilation scenarios in the coupled system. The assimilation leads to a response of the atmosphere throughout the troposphere and impacts the global atmospheric circulation. Globally the temperature and wind speed are improved in the atmosphere on average. K E Y W O R D S coupled model, data assimilation, sea-surface temperature, temperature and salinity profiles 1 INTRODUCTION Traditionally, different components of the Earth system such as the ocean and the atmosphere are simulated by separate models with influences of other components being modelled as boundary conditions or forcings. However, the oceans and the atmosphere are connected and interact with each other. A consistent initial condition for these different components is required and is expected to provide a better forecast for both the ocean and the atmosphere. Earth system models simulate different components like the ocean, atmosphere, sea ice and land This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Simulations for CMIP6 with the AWI climate model AWI-CM-1-1

Cited by 14 publications

References 5 publications

Arctic Amplification of Precipitation Changes—The Energy Hypothesis

Arctic Amplification of Precipitation Changes—The Energy Hypothesis

Simulations for CMIP6 With the AWI Climate Model AWI‐CM‐1‐1

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

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