Constraints on simulated past Arctic amplification and lapse-rate feedback from observations
Abstract. The Arctic has warmed much more than the global mean during past decades. The lapse-rate feedback (LRF) has been identified as large contributor to the Arctic amplification (AA) of climate change. This particular feedback arises from the vertically non-uniform warming of the troposphere, which in the Arctic emerges as strong near-surface, and muted free-tropospheric warming. Stable stratification and meridional energy transport are two characteristic processes that are evoked as causes for this vertical warming structure. Our aim is to constrain these governing processes by making use of detailed observations in combination with the large climate model ensemble of the 6th Coupled Model Intercomparison Project (CMIP6). We build on the result that CMIP6 models show a large scatter in Arctic LRF and AA, which are positively correlated for the historical period 1951–2014. Thereby, we present process-oriented constraints by linking characteristics of the current climate to historical climate simulations. In particular, we compare a large consortium of present-day observations to co-located model data from subsets with weak and strong simulated AA and Arctic LRF in the past. Our results firstly suggest that local Arctic processes mediating the lower thermodynamic structure of the atmosphere are more realistically depicted in climate models with weak Arctic LRF and AA (CMIP6/w) in the past. In particular, CMIP6/w models show stronger inversions at the end of the simulation period (2014) for boreal fall and winter, which is more consistent with the observations. This result is based on radiosonde observations from the year-long MOSAiC expedition in the central Arctic, together with long-term radio soundings at the Utqiaǵvik site in Alaska, USA, and dropsonde measurements from aircraft campaigns in the Fram Strait. Secondly, remote influences that can further mediate the warming structure in the free troposphere are more realistically represented by models with strong simulated Arctic LRF and AA (CMIP6/s) in the past. In particular, CMIP6/s models systemically simulate a stronger Arctic energy transport convergence in the present climate for boreal fall and winter, which is more consistent with reanalysis results. Locally, we find links between changes in transport pathways and vertical warming structures that favor a positive LRF in the CMIP6/s simulations. This hints to the mediating influence of advection on the Arctic LRF. We emphasise that one major attempt of this work is to give insights in different perspectives on the Arctic LRF. We present a variety of contributions from a large collaborative research consortium to ultimately find synergy among them in support of advancing our understanding of the Arctic LRF.
Olivia Linke et al.
Status: final response (author comments only)
RC1: 'Comment on acp-2022-836', Anonymous Referee #1, 26 Jan 2023
- AC1: 'Reply on RC1', Olivia Linke, 12 Apr 2023
RC2: 'Comment on acp-2022-836', Anonymous Referee #2, 30 Jan 2023
- AC2: 'Reply on RC2', Olivia Linke, 12 Apr 2023
Olivia Linke et al.
Olivia Linke et al.
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The authors combine CMIP6 model output with reanalysis data, observations and LES model results to investigate the inter-model spread in Arctic amplification (AA) and the Arctic lapse-rate feedback (ALRF). When sorting models into models with stronger and weaker AA and ALRF, strong AA/LRF models better match reanalysis trends in heat advection, whereas weak AA/ALRF better match observed present-day inversion strength.
The presented data and work is interesting and relevant to important research questions, but I have a few major concerns on how the model-observation analysis is carried out:
Investigating only one ensemble member per model without regard for the ensemble spread might not do justice to models – even a clear mismatch with observations does not rule out that the model in question is consistent with the observed trend or phenomenon (see eg Notz 2015).
ll 22 ff and elsewhere in the manuscript: Now that the work is done, I feel that the manuscript would be stronger by focusing on what has been achieved rather than what the authors want to achieve.
l. 65 ff: The impact of clouds on the vertical temperature profile has not been introduced at this point in the manuscript.
l 205: showing that 2019/2020 is equivalent to 2009-2014 using scenario output would be stronger than just assuming it – strong changes have happened in the Arctic in the early 21st century.
For the comparison with radiosondes, I would recommend coarsegraining the radiosonde profiles to the vertical resolution of the models at least as a sensitivity test (same for NSA).
Section 2.4: Comparing March/April measurements with DJFM model data – did you check that model data looks similar for March as for the entire winter season? Do we expect the 1993 campaign to show the same climate state as the 2019 campaign?
l 385: do all models have similar inversion strengths in the reference period?
l 407: what is the timeframe covered by the Kahl (1990) study? Do we expect it to be representative of 2020 conditions?
l 487: what significance level? How did you do the bootstrap analysis?
Fig. 10 and related analysis: This shows data year-round, is there a relevant seasonal cycle?
l. 564: Cronin and Jansen (2016) would be a good reference here
l. 585-590: I think this is an important result deserving a stronger emphasis in the paper, since entrainment has not received a lot of attention in this context so far.
l. 592 “we compile a sizeable amount of observations” Here and elsewhere in the paper: There is nothing to be said against impressing the reader with the large array of observations you bring to the task in addition to CMIP and LES data, but in my view this works better if you leave being impressed to the reader.
l. 687: I think a crucial point here is that CMIP6/s models generate less warming for a given amount of sea-ice retreat. If this is correct, it should be stated more explicitly.
Cronin, T. W., & Jansen, M. F. (2016). Analytic radiative‐advective equilibrium as a model for high‐latitude climate. Geophysical Research Letters, 43(1), 449-457.
Notz, D. (2015). How well must climate models agree with observations?. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2052), 20140164.