the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Mar 2024
Connection of Surface Snowfall Bias to Cloud Phase Bias – Satellite Observations, ERA5, and CMIP6
Abstract. Supercooled Liquid-Containing Clouds (sLCCs) play a significant role in Earth's radiative budget and the hydrological cycle, especially through surface snowfall production. Evaluating state-of-the-art climate models with respect to their ability to simulate the frequency of occurrence of sLCCs and the frequency with which they produce snow is, therefore, critically important. Here, we compare these quantities as derived from satellite observations, reanalysis datasets, and Earth System Models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and find significant discrepancies between the data sets for mid and high latitudes in both hemispheres. Specifically, we find that the ERA5 reanalysis and ten CMIP6 models consistently overestimate the frequency of sLCCs and snowfall frequencies from sLCCs compared to CloudSat-CALIPSO satellite observations, especially over open ocean regions. The biases are very similar for ERA5 and the CMIP6 models, which indicates that the discrepancies in cloud phase and snowfall stem from differences in the representation of cloud microphysics rather than the representation of meteorological conditions. This, in turn, highlights the need for refinements in the models’ parameterizations of cloud microphysics in order for them to represent cloud phase and snowfall accurately. The thermodynamic phase of clouds and precipitation has a strong influence on simulated climate feedbacks and, thus, projections of future climate. Understanding the origin(s) of the biases identified here is, therefore, crucial for improving the overall reliability of climate models.
- Preprint
(21364 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 02 May 2024)
-
RC1: 'Comment on egusphere-2024-754', Anonymous Referee #1, 10 Apr 2024
reply
This manuscript provides a well-organized and thorough analysis of snowfall biases associated with supercooled liquid-containing clouds (sLCCs). This topic is highly relevant for improving climate model prediction, particularly regarding climate feedbacks. The study takes a comprehensive approach, comparing satellite observations (CloudSat-CALIPSO), reanalysis data (ERA5), and global climate model simulations (CMIP6) to evaluate the physical characteristics of sLCCs and their snowfall production.
Key findings from the study reveal significant discrepancies: (1) ERA5 reanalysis and ten CMIP6 models consistently overestimate sLCC frequency and snowfall compared to CloudSat-CALIPSO observations; (2) Biases appear similar between ERA5 and CMIP6 models, suggesting cloud microphysics parameterization issues rather than meteorological discrepancies; (3) Accurately representing cloud phase and snowfall is crucial for reliable climate simulations and future climate projections. The biases, primarily found over polar regions, are not inconsequential and resolving them is essential for improving the fidelity of global climate models.
This work highlights the importance of improving cloud microphysics parameterizations in climate models for accurate representation of cloud phase and snowfall. While previous research has often focused on the underestimation of supercooled liquid in mixed-phase clouds, this paper demonstrates that sLCC frequency may be a more informative metric to explain model discrepancies in radiation and precipitation fields. This is a scientifically significant result that is important because observations reveal a separation of ice and liquid in time/space, whereas current models often simulate both phases coexisting in mixed-phase clouds.
Overall, the paper makes key contributions and provides valuable insights toward our understanding of clouds and their impacts on radiation and precipitation, well within the scope of this journal. The methodology and results are presented clearly and concisely. References are balanced and appropriately cited. Figures and tables are well-structured to effectively illustrate key research findings. Notably, all code used to produce figures using CloudSat, ERA5, CMIP6 analysis has been made publicly available as well. Based on the quality of research, strength of potential impact, and relevance to ACP, I recommend acceptance for publication.
Citation: https://doi.org/10.5194/egusphere-2024-754-RC1 -
RC2: 'Comment on egusphere-2024-754', Anonymous Referee #2, 18 Apr 2024
reply
General Comments
The authors of this manuscript leverage satellite observations of clouds and precipitation from CloudSat and CALIPSO instruments in order to evaluate 10 CMIP6 models and one reanalysis product (ERA5). The analysis region includes the mid to high latitudes and the variables of interest are (1) the frequency of supercooled-liquid containing clouds (fsLCC) and (2) how often the detected sLCCs produce snowfall.
Climate models have historically struggled to accurately simulate sLCCs, particularly in the high latitudes. The observational benchmarks provided by this manuscript are important for the ongoing process of improving both historical simulations and future predictions. The authors use these benchmarks to show that both ERA5 and the mean from the 10 CMIP6 models overestimate the frequency of sLCCs and the frequency with which sLCCs produce snowfall.
Overall, the manuscript is well organized, the figures and tables are well made, and the content will likely be of interest to a wide variety of scientists working on clouds/precipitation/climate modeling/etc.
My concerns mainly arise from some of the generalizations/conclusions presented. For example, the multi-model mean from the 10 CMIP6 models sometimes appears to be generalized to all CMIP6 models or even all ESMs. Also, the issue of model versions/generations is generally left out of the discussion of model biases. I recommend the authors revisit the discussion and conclusions to make clear the scope of their results and be more precise when putting them in the context of previous studies. I have included a list of specific suggestions below this summary that I hope will be of use to the authors in revising their manuscript.
Main Suggestions
(1) Lines 291-293: “We can safely assume the temperature to be similar between the ECMWF-AUX product used in CloudSat-CALIPSO and ERA5 daily mean, while atmospheric circulation and overall cloud cover should be well constrained by the observations used in the ERA5 reanalysis.” I disagree that this is safe to assume. It is my understanding that the ECMWF-AUX product is derived from a separate ECMWF dataset, AN-ECMWF, rather than ERA5. Given the discussion in the introduction regarding meaningful differences between ECMWF versions (lines 110-125), I believe additional analysis is necessary to support the authors’ statement here, both regarding overall cloud cover and temperature. A figure comparing 2m temperature between the CloudSat-CALIPSO values and the ERA5 values as well as one that compares overall cloud cover would be necessary to rule out those differences as important factors in the sLCC and snowfall discrepancies.
(2) Figure 5. It would be interesting/helpful to understand how this figure relates back to Figure 1. Is there a relationship between the location of sLCCs vs how likely they are to be snowing? For example, DJF observations (Fig.1a) show northern Europe with some of the most frequent sLCCs but producing snow the least often (Fig. 5a). Could this be an issue relating to the higher CloudSat bins used over land that the authors noted in the methods section (lines 174-176) or a characteristic sLCCs in that location?
(3) Lines 438 – 441: “While previous studies have focused on the underestimation of supercooled liquid fraction (SLF) in mixed-phase clouds (Komurcu et al., 2014; Cesana et al., 2015; Tan and Storelvmo, 2016; Kay et al., 2016; Bruno et al., 2021; Shaw et al., 2022), this research focuses on the frequency of occurrence of sLCCs. This difference in the cloud phase metric can lead to seemingly contradicting conclusions.” I encourage the authors to address the issue that the previous studies listed not only have different metrics, but also are in some cases evaluating a different generation of climate models with markedly different cloud characteristics. Many models included in CMIP5 had too few sLCCs (e.g. Cesana, G. et al. (2012). Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP. Geophysical Research Letters, 39, L20804. https://doi.org/10.1029/2012GL053385). A problem that has been specifically addressed in some CMIP6 ESMs, resulting in a sizable increase in high-latitude cloud liquid (e.g. Lenaerts, J. T. M. et al. (2020). Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model. Journal Geophysical Research: Atmospheres, 125, e2019JD031470. https://doi.org/10.1029/2019JD031470).
(4) Lines 441-442: “We illustrate why our results do not necessarily contradict previous findings with the following example: …” Following on my previous suggestion, I recommend trying to find a specific example from the CMIP6 models included in this study and seeing if the same generation of that model is also evaluated in one of the listed previous studies. The statements in this section of the discussion seem to be attributing discrepancies only to differences in the metric (sLCC frequency vs SLF) and not addressing the important issue of model version.
(5) Lines 451-453: “In contrast, McIlhattan et al. (2017) showed that the CESM-LE underestimates the fLCC by ∼ 17% and overestimates the fsnow by ∼ 57% in the Arctic. However, since we utilize a different metric (sLCC instead of LCC), there is no reason to expect the model biases to be identical.” From my understanding of the two metrics, fLCC and fsLCC wouldn’t be likely to produce biases with the opposite sign in the high-latitudes (outside of perhaps summer) if the models contained similar cloud systems. It seems more likely that the difference between this study’s biases and those found by and McIlhattan et al. (2017) arise from differences in the models’ cloud systems. The same metrics from McIlhattan et al. (2017) were used to evaluate LCCs in CESM2 (a CMIP6 generation model; McIlhattan, E. A., et al. (2020). Arctic clouds and precipitation in the Community Earth System Model version 2. Journal of Geophysical Research: Atmospheres, 125, e2020JD032521. https://doi.org/10.1029/2020JD032521), and those findings appear to be more similar to those presented here. I strongly encourage the authors include the issue of model generation when comparing their results to earlier studies.
(6) Lines 471-475: “However, the fact that very similar biases are found in the reanalysis as in the CMIP6 models indicates that this is not the explanation for the identified biases, as weather patterns and surface conditions in ERA5 should be very close to the observed. This is an important conclusion that is possible because both ERA5 and CMIP6 models were included in the present analysis. However, we cannot rule out a modest contribution to the biases from circulation differences, even for the reanalysis.” Reanalysis products are known to be less reliable at high latitudes due in part to sparse observations (e.g. Liu, Y., and J. R. Key, 2016: Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data. J. Climate, 29, 6065–6083, https://doi.org/10.1175/JCLI-D-15-0861.1). Also, it is clear from the authors’ Figure 4 that the individual models within this CMIP6 subset do not all have biases matching ERA5. So, I do not see a clear reason to conclude that differences in weather patterns and/or surface conditions are not important contributors to the model or reanalysis biases. I recommend revising or removing this language.
(7) Section 5.2 “Implications for modeling and future projections.” The second and third paragraphs of this section include primarily very broad generalizations that do not tie directly to the results of the study. Consider revising to focus more clearly on the impact and implications of this paper’s results.
Minor Suggestions
(8) Title: It might be helpful to include the region in the title (e.g. mid-to-high latitudes) to help the paper reach the appropriate audience.
(9) Line 16-21: I suggest re-writing the opening paragraph of the introduction to improve flow and to be simpler and more declarative.
(10) Line 17-18: “…strongly influence ecosystems and human societies in these regions…” I suggest the authors be more specific here and include citations.
(11) Line 22: “Snowfall is directly linked to the cloud phase,” What aspect of snowfall is directly linked to the cloud phase? Rate? Frequency? It seems from the sentences that follow that snowfall at high latitudes is produced by both sLCCs and pure ice clouds, however, the specific link between the cloud phase and the snowfall is unclear. Consider revising.
(12) Lines 38-41: “Several studies, including Murray et al. (2021) showed that increased temperatures, especially in polar regions, have caused a shift of mixed-phase clouds towards higher latitudes and altitudes due to the ice reduction in the atmosphere in these regions. The shift in cloud phase towards more liquid and less ice leads to a reduction in the fraction of precipitation falling as snow, resulting in an expected decrease in snowfall events and duration of the snowfall season for most regions in the Northern Hemisphere (NH, Danco et al., 2016; Chen et al., 2020).” It is my understanding that mixed-phase clouds in the high latitudes (particularly the sLCCs that were introduced earlier) primarily produce snowfall, so I would not necessarily expect a snowfall decrease with an increase in mixed-phase clouds. Please clarify and include specific supporting results from the papers cited here.
(13) Lines 84-85: “Previous studies have contributed to a better understanding of the uncertainties associated with satellite measurements of clouds and precipitation” I suggest the authors be more specific on what those uncertainties are. What is the general understanding of the limitations of satellite measurements of clouds and precipitation and the magnitudes/signs of the expected biases in those measurements?
(14) Lines 195-197: “We incorporate tcrw with the 2t threshold below 0◦C, as this threshold is used to exclude any rainwater below the melting layer. By using tclw and tcrw and applying the temperature threshold, we can analyze the role of supercooled liquid water within clouds and the contribution of liquid water to the snowfall precipitation process in ERA5.” Are the authors excluding all hourly LWP values with instantaneous hourly 2 m temperature greater than 0C or excluding all daily mean LWP values where the daily mean 2 m temperature is greater than 0C? It is unclear to me if the threshold is applied before or after making the daily average.
(15) Lines 223-225: “The slight mismatch in the time range is of limited relevance, as CMIP6 model simulations are not designed to reproduce the exact temporal evolution of past weather ” It is my understanding that the CMIP6 models incorporate historical forcings so are designed to produce fairly representative simulations for the historical record. I agree that it is likely the mismatch will not change the authors’ conclusions, but perhaps it would be worthwhile to compare results from 2006-2009 to results from 2007-2010 for a model that has all years available to determine the magnitude of the difference.
(16) Figure 4: “The heatmap colors correspond to the absolute differences of area-weighted averages…“ I’m curious the reason behind using absolute difference instead of something like mean difference that would highlight the direction of the bias rather than just the magnitude. I think the figure may be more useful to readers if the direction of the bias was more clear.
(17) Lines 339-340: “While the signature of CAOs is also visible in the fsnow patterns, besides in boreal summer, land areas have lower values as these areas warm in response to increased insolation.” I am having difficulty understanding this statement, consider revising for clarity and perhaps include references to specific figure panels.
(18) Lines 375-376: “ESMs have too many sLCCs” I suggest changing the language here, since the results show only that the CMIP6 multimodel mean has too many sLCCs. There are certainly individual ESMs that produce too few, indicated by the green dots in this manuscript’s Figure 3. The same concern/suggestion goes for line 425: “This would most likely also hold for the CMIP6 models, although this cannot be confirmed.”
(19) Lines 385-388: “Milani et al. (2018) found that applying adjustments and a temperature threshold to the CloudSat snowfall retrieval led to a decrease in the estimated occurrence of snowfall events, primarily in the ocean regions surrounding Antarctica. Although these adjustments did not have the same effects everywhere, this highlights the sensitivity of the CloudSat retrievals to the assumptions made within them.” Consider including the magnitude of the decrease in events Milani et al. (2018) found in order to give readers an idea of the magnitude of uncertainty others have found coming from the observational data.
(20) Lines 486-488: “As discussed above, many models have a simple temperature-dependent cloud phase that would almost certainly cause them to overestimate the fsLCC and fsnow.” It would be useful to indicate which of the 10 models included in the study have this simple temperature dependency. Also, it seems that the specific temperature threshold used in a given model would influence the bias as well, are all the cloud phase temperature thresholds the same?
Typos/Grammatical Issues/Word Choice/Needs Citation:
(21) Line 27: “among others,” It is unclear what “others” the authors mean here, consider revising.
(22) Line 69: “ESMs have previously been shown to not accurately represent cloud phase” consider adding a regional descriptor to this statement (e.g. global, Arctic, high-latitude), I believe the majority of the citations for this sentence deal specifically with high latitude clouds.
(23) Line 76: “must lead to biases” consider softer language, since other model biases could compensate and lead to correct precipitation simulation (two wrongs making a right).
(24) Line 84: “allowing for continuous monitoring” since the overpasses are every 16 days, this seems periodic rather than continuous, please clarify.
(25) Lines 88-89: “Previous studies have shown that ESMs produce double the amount of snowfall relative to satellite observations (Heymsfield et al., 2020).“ Consider softer language. As written, this implies multiple studies have shown that all ESMs produce 2x measured snowfall.
(26) Lines 116-118: “In the Arctic, the ERA-Interim data qualitatively represented the interannual snowfall rates and seasonal cycle well but underestimated high snowfall rates significantly during summer and overestimated weak snowfall rates over open water compared to CloudSat.” Please cite.
(27) Line 150: “Our primary emphasis is on snow-producing sLCCs” From the end of the introduction, it seems the authors are interested in snow-frequency from sLCCs, so wouldn’t the emphasis be on all sLCCs and determining how often/where they are producing snow? Please clarify.
(28) Line 192 vs Line 203: “sLCC” vs “LCC” there are some consistency issues between using “super-cooled liquid containing clouds” and ”liquid containing clouds” I would guess since the temperature threshold has been applied, it should be sLCC on line 203.
(29) Figure 9: Consider including column headings. While the information is included in the caption, it would be helpful to have it on the columns as well.
(30) Lines 423-424: “reduces from 12% overestimation to 7% in November depending on the region.” Since the 12 and 7% values are means for the whole region, I suggest removing “depending on the region”.Citation: https://doi.org/10.5194/egusphere-2024-754-RC2
Interactive computing environment
CloudSat, ERA5, CMIP6 analysis Franziska Hellmuth https://github.com/franzihe/CloudSat_ERA5_CMIP6_analysis
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 190 | 41 | 12 | 243 | 8 | 9 |
- HTML: 190
- PDF: 41
- XML: 12
- Total: 243
- BibTeX: 8
- EndNote: 9
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1