the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Too cold, too saturated? Evaluating climate models at the gateway to the Arctic
Abstract. The Arctic wintertime energy and moisture budget are largely controlled by the advection of warm, moist air masses from lower latitudes, cooling and drying of these air masses inside the Arctic and the export of cold, dry air masses. Climate models have substantial difficulties in representing key processes in these air-mass transformations, including turbulence under stable stratification and mixed-phase cloud processes. Here, we use radiosonde profiles of temperature and moisture and surface radiation observations from Ny Ålesund, Svalbard (1993–2014), to assess the properties of air masses being imported into and exported from the central Arctic in CMIP6 climate models. In the free troposphere, models tend to be cold-biased especially for the coldest temperatures. Most models underestimate the frequency of occurence of supersaturation with respect to ice, and a sensitivity experiment suggests that this can be improved by using 2-moment microphysics, i.e. prognostic rather than prescribed ice number concentrations. Cold and dry biases are stronger in air masses being exported from the Arctic than those entering the Arctic. This suggests that previously reported cold biases and excess energy convergence in the Arctic in CMIP6 models are probably due to errors in local thermodynamic processes.
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Status: open (until 21 Nov 2024)
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RC1: 'Comment on egusphere-2024-2961', Anonymous Referee #1, 12 Nov 2024
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Peer review for manuscript: Too cold, too saturated? Evaluating climate models at the gateway to the Arctic
Climate models are widely used tool in studies about atmospheric physics and dynamics and practically the only method to predict how changes on e.g. greenhouse gases affect the future climate. Therefore, it is important evaluate their behavior against the real observed atmospheric conditions. However, these comparisons are not often straightforward to do as many issues associated e.g. representiveness of observations may cause the problems for direct comparisons. In addition, availability of observation may complicate direct comparison especially in remote areas, where observational network is sparse as in the Arctic. Climate models have often difficulties to present polar climate, probably because parametrizations better optimized for lower latitudes. Thus, the manuscript provides interesting and important information about capabilities of climate models to describe polar climate.
The manuscript present detailed comparison between climate models and radiosonde observation from Ny-Ålesund. Even though models are compared against observation from only a single measurement site in the Arctic the method to divide the comparison to two flow regimes, northerly flow and south-westerly flow, gives additional information about the sources of biases in the models. This division allows to separate the contributions of Arctic processes and mid-latitude advections in the model biases.
The study shows that there are biases in climate models in low-tropospheric temperatures and humidity and discuss possible physical causes behind these biases. The discussion about the causes behind insufficient presentation of atmospheric processes in the models focus on cloud processes and resolution of models. The experiments which are made with high-resolution model with two different cloud schemes take the discussion deeper level than only speculations. The experiments suggested that too simple cloud parametrization probably contribute biases in humidity in the climate models. However, causes behind the main result that the models have cold bias especially in the airmasses which come from the central Arctic has only speculated in the manuscript. The reasons for cold bias in the climate models are probably quite complicated, and it could be a topic for a separate study as proposed in manuscript.
Overall, the manuscript is well written conclusion based on evidence of results. Methods are appropriately described allowing readers to understand how study is done. However, there are some small issues in the manuscript which should be considered before publishing. Therefore, I recommend publishing the manuscript in Atmospheric Chemistry and Physics after minor revision.
My main concern is associated with discussion about longwave radiation in the manuscript. The focus of manuscript is temperatures in troposphere, but mostly only longwave radiation at the surface is discussed. This is understandable because observation is available only at the surface, but the effects of cloud on tropospheric cooling could be discussed more as the focus of manuscript is on tropospheric temperatures. Below there is a couple spots in text where you could discuss more about effects of cloud on tropospheric long wave cooling.
Lines 30 – 32 In addition to that clouds reduce long wave radiative cooling at surface, the increase longwave emissions from atmosphere upward and thus affect long wave atmospheric cooling.
Lines 277 – 281 The effects of high emissivity of clouds in CMIP models on tropospheric temperature could be discussed.
Specific Comments:
Line 44. “In real-world mixed-phase clouds, supercooled liquid water can be concentrated in thin (O 10m) layers near cloud top” Could you give a reference for this?
Line 109 – 112. Does this mean that formation of ice particles from water vapor without any nuclei is not included in the model but freezing of ice particle from water droplet without any ice nuclei is included?
Lines 141 -144 and lines 180 -183. Maybe mention explicitly that these results based on radiosonde observation.
Line 184. Maybe we could say that, overall there is not similar dry bias on average in the modes as there is cold bias in temperatures, or how about in terms of dew point temperature how the biases in humidity are compared with the biases in temperature?
Line 205. Figure 7 -> Figure 6
Figure 8. There are no values on y-axis in Figure 8.
Line 223. The CMIP models … or The CMIP model … ?
Figure 9. Do colors mean occurrence? Maybe because of logarithmic scale, but it seems that there are quite many ice particles in 1-moment scheme when relative humidity is low? Do I interpret the figure correctly?
Line 268. Could you explicitly mention what means upper bound?
Line 270. There is not shown land-sea fraction in Figure 2
Lines 270 - 271. Could you explain how surface type affects the downward longwave radiation?
Figures 7, 9, 12, and 13 The meaning of color scale could be mentioned in the captions of figures. I assume that the different tones of gray means frequency of occurrence.
Figure 8, 10 and 11. ”northward flow” and ”southward flow” could be also mentioned in these figures in the title of the left and right panel.
Citation: https://doi.org/10.5194/egusphere-2024-2961-RC1 -
RC2: 'Reply on RC1', Rune Grand Graversen, 13 Nov 2024
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Peer review of “Too cold, too saturated? Evaluating climate models at the gateway to the Arctic” by Pithan et al.
This study evaluates climate model against observations in Ny Ålesund on Svalbard, hereby it contributes to a suite of studies evaluating models in the Arctic where observations are sparse. The results show, for example, that models are generally having cold bias at the site in question. However the ensemble of models investigated is small and in many cases these models are variants of each other. Hence it is still an open question whether the conclusions drawn here also are valid for the full CMIP6 ensemble.
At places, the manuscript is not so clear. I here provide some comments and suggestions that the authors can take into account before consideration of publication.
Major comment:
Only a small subset of the CMIP6 models are used in this study. The authors are taking an interesting perspective by studying northerly and southerly flow in Ny Ålesund, to some extent representing Arctic and midlatitude air masses, respectively. Therefore high frequency (6-hour) data are needed, which, as the authors argue, limits selected models to a rather small subset of CMIP6. In order to give an idea of the representativeness of this subset relative to the full CMIP6, I suggest to start the study with evaluating the full CMIP6 based on monthly mean data (which or available for the full CMIP6) and against monthly means of the station data. This can be done at least for the temperature and humidity profiles. We can hereby learn of the overall biases of CMIP6 and the representativeness of the subset investigated further.Minor comments:
L20: “These processes ..” → “It is challenging to represent ..”
L21: “had”->”have”
L22-25: A reference is needed for the influence of large-scale circulation on the Arctic, e.g. Baggett and Lee, J. Atmosph. Sci., 2015 and Graversen and Burtu, Q. J. Royal Met. Sco., 2016.
L26-28: It says stable stratification “shaped by air-mass transformation”, I would rather say “mainly due to atmospheric heat advection from the south and diabatic cooling of the surface”.
L39: Remove space before full stop.
L41-43: Is this regime a curiosity or is it common in the Arctic? Please provide a reference on this aspect.
L60-61: Are the radiosondes automatically launched? Is the system in operation from 1993 to present? Please indicate.
L70: An evaluation of ERA5 in the Arctic is given here: Graham et al., J. Clim., 2019.
Fig. 1: Why not show topography for ICON as well?
L99: remove “s” on “thermodynamics”.
L122: “those”, do you mean “whom”?
L145-149:”Danmarkshavn” (without “e”). The sentence needs a reference.
L149-151: This sentence comes out of the context here and can be moved to the discussion of Fig. 3.
L153-155: This sentence regarding polar amplification of bias seems quite speculative and does not give much meaning to me.
L155-156: When discussing the results in a pan-Arctic context, one should keep in mind that the results are based on a single station only.
L158-161: How can inversions be seen in Fig. 3? The figure seems to show biases and not absolute values. In addition, this sentence seems inconsistent with the appearance of bias inversion in the described conditions for some models, for instance the GISS model. Also remove “the formation of”.
L201: Add “humidity” after “saturation”, since you later refer to a “value”. Add “variability” after “this” to avoid ambiguity.
L206-208: This seems to be inconsistent with at least three models: GISS, MRI-GCM2.0 and Had, which show considerable subsaturation.
Fig. 7: It is not clear what is shown, is it probability-density functions? Units on colour scale are not explained. The line for 700 hPa is almost invisible. Text and numbers (also in other figures) are too small in font size.
Fig. 8: Difficult to distinguish the different lines. ICON in the left frame can't be seen. Perhaps rather present a design with model mean, ICON, and OBS.
L2018-219: I suppose this sentence concerns OBS, but this is not so clear. The mode below 30 % is extremely small, and perhaps not worth mentioning.
L245: Comma after “scheme”.
L267-268: How is it seen that the AWI model exceeds the “upper bound”.
L285-286: MIROC seems not to be shown in Fig. 14.
Fig. 12, same as for Fig. 7.
Section 3.5: Mention that this section is based on only OBS. Humidity could also be investigated here.
L322-323: This statement doesn't seem to be valid for all models.
L340-342: A reference is lacking for supporting these two sentences.Citation: https://doi.org/10.5194/egusphere-2024-2961-RC2
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RC2: 'Reply on RC1', Rune Grand Graversen, 13 Nov 2024
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Data sets
Ny Alesund Radiosonde record Marion Maturilli https://doi.pangaea.de/10.1594/PANGAEA.845373
Model code and software
Code for microphysics sensitivity experiment Ann Kristin Naumann https://doi.org/10.17617/3.OD9NTK
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