the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Feb 2023
Influence of atmospheric rivers and associated weather systems on precipitation in the Arctic
Abstract. In this study, we analyse the contribution of Atmospheric Rivers (ARs), cyclones, and fronts to the total precipitation in the Arctic. We focus on two distinct periods of different weather conditions from two airborne campaigns: ACLOUD (May/June 2017) and AFLUX (March/April 2019). Both campaigns covered the northern North Atlantic sector, the area in the Arctic that is affected by the highest precipitation rates. Using ERA5 reanalysis, we identify pronounced regional anomalies with enhanced precipitation rates compared to the climatology during ACLOUD due to these weather systems, whereas during AFLUX enhanced precipitation rates occur over most of the area.
We have established a new methodology, that allows us to analyse the contribution of ARs, cyclones, and fronts to precipitation rates based on ERA5 reanalysis and different detection algorithms. Here, we distinguish whether these systems occur co-located or separately. The contributions differ seasonally. During ACLOUD (early summer), the precipitation rates are mainly associated with AR- (40 %) and front-related (55 %) components, especially if they are connected, while cyclone-related components (22 %) play a minor role. However, during AFLUX (early spring) the precipitation is mainly associated with cyclone-related components (62 %). For both seasons, snow is the dominant form of precipitation, and the small rain occurrence is almost all associated with ARs. About one-third of the precipitation can not be attributed to one of the weather systems, the so-called residual. While the residual can be found more frequently as convective than as large-scale precipitation, the rare occasion of convective precipitation (roughly 20 %) can not completely explain the residual. The fraction of precipitation classified as residual is reduced significantly when a precipitation threshold is applied that is often used to eliminate "artificial" precipitation. However, a threshold of 0.1 mm h−1 reduces the total accumulated precipitation by a factor of two (ACLOUD) and three (AFLUX) especially affecting light precipitation over the Arctic Ocean. We also show the dependence of the results on the choice of the detection algorithm serving as a first estimate of the uncertainty.
In the future, we aim to apply the methodology to the full ERA5 record to investigate whether the differences found between the campaign periods are typical for the different seasons in which they were performed and whether any trends in precipitation associated with these weather systems can be identified.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-261', Anonymous Referee #1, 13 Mar 2023
In this study, the authors develop a method to quantify the contribution of ARs, cyclones, and fronts to precipitation rate over the Atlantic sector of Arctic based on ERA5. They focus on two distinct periods from two airborne campaigns: ACLOUD and AFLUX. They found that the contribution of these weather systems to the precipitation rate differ substantially between these two periods. In particular, the precipitation rate during ACLOUD is mostly associated with AR- and front-related components while cyclone-related component dominates the precipitation rate during AFLUX. The contributions of different weather systems to precipitation phase (snow versus rain) and formation mechanism (large-scale versus convective) are also explored. Lastly, the authors also test the sensitivity of the results presented here to the AR and cyclone detection algorithms and whether a precipitation threshold is applied.
Overall, the manuscript is well written. The method developed in this study provides a new perspective on how different weather systems contribute to precipitation over Arctic. My main concern is that the authors seem to make the assumption that the different contribution by weather systems between the two studied periods arise mainly from seasonality. In my opinion, this assumption cannot be justified by the current study. I will elaborate more on my concerns in the specific comments listed below. This manuscript has the potential to be published in ACP if the concerns listed below can be properly addressed.
Specific Comments:
- Throughout the manuscript, the authors seem to implicitly assume that the difference between the two study periods are due to seasonality. For example, in lines 276, 302 and 366, the authors use the term “seasonal differences”. However, the results presented here based only on two short periods: one is 14 days and another one is 19 days. Until further study based on the full ERA5 record is conducted, I would not recommend the authors attributing the differences between two study period to seasonal differences.
- Line 96: To me, the cyclone seems to locate in the Northeastern or Eastern Greenland.
- Line 126-127: The authors should double-check the Guan & Waliser (2015) paper. I don’t think they set the lower limit of the IVT threshold to 50 kg/m/s in the polar regions.
- Line 184-186: “The effect is roughly 20 % of the residual and is slightly larger in absolute terms during 185 ACLOUD (about 8 % of the total precipitation) compared to AFLUX (about 5 %) as residual precipitation is more frequent during ACLOUD.” This sentence needs to be rewritten for better clarity. I am not sure what the authors are trying to convey here.
- Line 269-274. More explanations for the hypothesis are needed. The two points followed the first sentence in this paragraph are simply describing the difference between the two periods. They are not explanations for the hypothesis.
- Line 289: How do you weigh precipitation rates by the area? To me, you just simply sum up the precipitation rates across all the grid points over the study region.
- Table 1 caption: For the daily averaged precipitation rate, did you calculate it simply by summing up the precipitation rates across all grid points over the study region?
- Line 304: In my opinion, meridionally oriented ARs over the Arctic should be more effective in inducing precipitation. Meridionally oriented ARs over the Arctic travel across a strong temperature gradient from warmer regions to colder regions. Cold air holds less moisture. The moisture inside the ARs thus have to precipitate out. That is to say, for a given AR and a given season, the AR would induce stronger precipitation when it orients meridionally compared to when it orients zonally.
- Line 347: “For rain, the fraction of total precipitation is highest for ACLOUD with 33% and lower for AFLUX with 10%.” This sentence needs to be rewritten for better clarity.
- Line 367: Based on Figure 8b, rain occurs during AFLUX.
- Line 383: I would not use “underestimate” here. We don’t know which AR detection algorithms represent the truth.
- Line 385-386: I couldn’t find these 8% and 6% in Table 1.
- Line 390: “Consequently, …” I don’t see how previous sentences can lead to this conclusion. More explanations are needed.
- Line 445-454: Any potential explanations for why AR_GU detects larger ARs for ACLOUD, but smaller ARs for AFLUX, and why CYC_A detects larger cyclones during ACLOU, but smaller cyclones during AFLUX?
- Line 50: regions’ hydroclimate?
- Line 384: Do you mean GuS produced the strong precipitation contribution by cyclones?
- Line 435: Do you mean ACLOUD?
Citation: https://doi.org/10.5194/egusphere-2023-261-RC1 - AC1: 'Reply on RC1', Melanie Lauer, 19 May 2023
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RC2: 'Comment on egusphere-2023-261', Anonymous Referee #2, 30 Mar 2023
- AC2: 'Reply on RC2', Melanie Lauer, 19 May 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-261', Anonymous Referee #1, 13 Mar 2023
In this study, the authors develop a method to quantify the contribution of ARs, cyclones, and fronts to precipitation rate over the Atlantic sector of Arctic based on ERA5. They focus on two distinct periods from two airborne campaigns: ACLOUD and AFLUX. They found that the contribution of these weather systems to the precipitation rate differ substantially between these two periods. In particular, the precipitation rate during ACLOUD is mostly associated with AR- and front-related components while cyclone-related component dominates the precipitation rate during AFLUX. The contributions of different weather systems to precipitation phase (snow versus rain) and formation mechanism (large-scale versus convective) are also explored. Lastly, the authors also test the sensitivity of the results presented here to the AR and cyclone detection algorithms and whether a precipitation threshold is applied.
Overall, the manuscript is well written. The method developed in this study provides a new perspective on how different weather systems contribute to precipitation over Arctic. My main concern is that the authors seem to make the assumption that the different contribution by weather systems between the two studied periods arise mainly from seasonality. In my opinion, this assumption cannot be justified by the current study. I will elaborate more on my concerns in the specific comments listed below. This manuscript has the potential to be published in ACP if the concerns listed below can be properly addressed.
Specific Comments:
- Throughout the manuscript, the authors seem to implicitly assume that the difference between the two study periods are due to seasonality. For example, in lines 276, 302 and 366, the authors use the term “seasonal differences”. However, the results presented here based only on two short periods: one is 14 days and another one is 19 days. Until further study based on the full ERA5 record is conducted, I would not recommend the authors attributing the differences between two study period to seasonal differences.
- Line 96: To me, the cyclone seems to locate in the Northeastern or Eastern Greenland.
- Line 126-127: The authors should double-check the Guan & Waliser (2015) paper. I don’t think they set the lower limit of the IVT threshold to 50 kg/m/s in the polar regions.
- Line 184-186: “The effect is roughly 20 % of the residual and is slightly larger in absolute terms during 185 ACLOUD (about 8 % of the total precipitation) compared to AFLUX (about 5 %) as residual precipitation is more frequent during ACLOUD.” This sentence needs to be rewritten for better clarity. I am not sure what the authors are trying to convey here.
- Line 269-274. More explanations for the hypothesis are needed. The two points followed the first sentence in this paragraph are simply describing the difference between the two periods. They are not explanations for the hypothesis.
- Line 289: How do you weigh precipitation rates by the area? To me, you just simply sum up the precipitation rates across all the grid points over the study region.
- Table 1 caption: For the daily averaged precipitation rate, did you calculate it simply by summing up the precipitation rates across all grid points over the study region?
- Line 304: In my opinion, meridionally oriented ARs over the Arctic should be more effective in inducing precipitation. Meridionally oriented ARs over the Arctic travel across a strong temperature gradient from warmer regions to colder regions. Cold air holds less moisture. The moisture inside the ARs thus have to precipitate out. That is to say, for a given AR and a given season, the AR would induce stronger precipitation when it orients meridionally compared to when it orients zonally.
- Line 347: “For rain, the fraction of total precipitation is highest for ACLOUD with 33% and lower for AFLUX with 10%.” This sentence needs to be rewritten for better clarity.
- Line 367: Based on Figure 8b, rain occurs during AFLUX.
- Line 383: I would not use “underestimate” here. We don’t know which AR detection algorithms represent the truth.
- Line 385-386: I couldn’t find these 8% and 6% in Table 1.
- Line 390: “Consequently, …” I don’t see how previous sentences can lead to this conclusion. More explanations are needed.
- Line 445-454: Any potential explanations for why AR_GU detects larger ARs for ACLOUD, but smaller ARs for AFLUX, and why CYC_A detects larger cyclones during ACLOU, but smaller cyclones during AFLUX?
- Line 50: regions’ hydroclimate?
- Line 384: Do you mean GuS produced the strong precipitation contribution by cyclones?
- Line 435: Do you mean ACLOUD?
Citation: https://doi.org/10.5194/egusphere-2023-261-RC1 - AC1: 'Reply on RC1', Melanie Lauer, 19 May 2023
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RC2: 'Comment on egusphere-2023-261', Anonymous Referee #2, 30 Mar 2023
- AC2: 'Reply on RC2', Melanie Lauer, 19 May 2023
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Cited
Annette Rinke
Irina Gorodetskaya
Michael Sprenger
Mario Mech
Susanne Crewell
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(8319 KB) - Metadata XML
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Supplement
(31148 KB) - BibTeX
- EndNote
- Final revised paper