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1. The Iceland Greenland Seas Project

   https://doi.org/10.1175/BAMS-D-18-0217.1  

   Renfrew, I. A. ; Pickart, R. S. ; Våge, K. ; Moore, G. W. ; Bracegirdle, T. J. ; Elvidge, A. D. ; Jeansson, E. ; Lachlan-Cope, T. ; McRaven, L. T. ; Papritz, L. ; et al ( September 2019 , Bulletin of the American Meteorological Society)

   null (Ed.)

   Abstract The Iceland Greenland Seas Project (IGP) is a coordinated atmosphere–ocean research program investigating climate processes in the source region of the densest waters of the Atlantic meridional overturning circulation. During February and March 2018, a field campaign was executed over the Iceland and southern Greenland Seas that utilized a range of observing platforms to investigate critical processes in the region, including a research vessel, a research aircraft, moorings, sea gliders, floats, and a meteorological buoy. A remarkable feature of the field campaign was the highly coordinated deployment of the observing platforms, whereby the research vessel and aircraft tracks were planned in concert to allow simultaneous sampling of the atmosphere, the ocean, and their interactions. This joint planning was supported by tailor-made convection-permitting weather forecasts and novel diagnostics from an ensemble prediction system. The scientific aims of the IGP are to characterize the atmospheric forcing and the ocean response of coupled processes; in particular, cold-air outbreaks in the vicinity of the marginal ice zone and their triggering of oceanic heat loss, and the role of freshwater in the generation of dense water masses. The campaign observed the life cycle of a long-lasting cold-air outbreak over the Iceland Sea and the development of a cold-air outbreak over the Greenland Sea. Repeated profiling revealed the immediate impact on the ocean, while a comprehensive hydrographic survey provided a rare picture of these subpolar seas in winter. A joint atmosphere–ocean approach is also being used in the analysis phase, with coupled observational analysis and coordinated numerical modeling activities underway. 

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2. A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses

   https://doi.org/10.1175/MWR-D-22-0130.1  

   Zampieri, Lorenzo ; Arduini, Gabriele ; Holland, Marika ; Keeley, Sarah P. E. ; Mogensen, Kristian ; Shupe, Matthew D. ; Tietsche, Steffen ( June 2023 , Monthly Weather Review)

   Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

   This study illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea ice–covered regions of the Arctic under clear-sky conditions. The correction applied to the temperature field is consistent with the local weather and the sea ice and snow conditions, meaning that it responds to seasonal changes in sea ice cover as well as to its long-term decline due to global warming. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.

    

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3. Hydroclimatic Controls on the Isotopic (δ18 O, δ2 H, d-excess) Traits of Pan-Arctic Summer Rainfall Events

   https://doi.org/10.3389/feart.2021.651731  

   Mellat, Moein ; Bailey, Hannah ; Mustonen, Kaisa-Riikka ; Marttila, Hannu ; Klein, Eric S. ; Gribanov, Konstantin ; Bret-Harte, M. Syndonia ; Chupakov, Artem V. ; Divine, Dmitry V. ; Else, Brent ; et al ( May 2021 , Frontiers in Earth Science)

   Arctic sea-ice loss is emblematic of an amplified Arctic water cycle and has critical feedback implications for global climate. Stable isotopes (δ 18 O, δ 2 H, d-excess ) are valuable tracers for constraining water cycle and climate processes through space and time. Yet, the paucity of well-resolved Arctic isotope data preclude an empirically derived understanding of the hydrologic changes occurring today, in the deep (geologic) past, and in the future. To address this knowledge gap, the Pan-Arctic Precipitation Isotope Network (PAPIN) was established in 2018 to coordinate precipitation sampling at 19 stations across key tundra, subarctic, maritime, and continental climate zones. Here, we present a first assessment of rainfall samples collected in summer 2018 ( n = 281) and combine new isotope and meteorological data with sea ice observations, reanalysis data, and model simulations. Data collectively establish a summer Arctic Meteoric Water Line where δ 2 H = 7.6⋅δ 18 O–1.8 ( r 2 = 0.96, p < 0.01). Mean amount-weighted δ 18 O, δ 2 H, and d-excess values were −12.3, −93.5, and 4.9‰, respectively, with the lowest summer mean δ 18 O value observed in northwest Greenland (−19.9‰) and the highest in Iceland (−7.3‰). Southern Alaska recorded the lowest mean d-excess (−8.2%) and northern Russia the highest (9.9‰). We identify a range of δ 18 O-temperature coefficients from 0.31‰/°C (Alaska) to 0.93‰/°C (Russia). The steepest regression slopes (>0.75‰/°C) were observed at continental sites, while statistically significant temperature relations were generally absent at coastal stations. Model outputs indicate that 68% of the summer precipitating air masses were transported into the Arctic from mid-latitudes and were characterized by relatively high δ 18 O values. Yet 32% of precipitation events, characterized by lower δ 18 O and high d-excess values, derived from northerly air masses transported from the Arctic Ocean and/or its marginal seas, highlighting key emergent oceanic moisture sources as sea ice cover declines. Resolving these processes across broader spatial-temporal scales is an ongoing research priority, and will be key to quantifying the past, present, and future feedbacks of an amplified Arctic water cycle on the global climate system. 

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4. Stratosphere‐Troposphere Exchange of Air Masses and Ozone Concentrations Based on Reanalyses and Observations

   https://doi.org/10.1029/2021JD035159  

   Wang, Mingcheng ; Fu, Qiang ( September 2021 , Journal of Geophysical Research: Atmospheres)

   This study estimates the stratosphere–troposphere exchange (STE) of air masses and ozone concentrations averaged over 2007 to 2010 using the Modern Era Retrospective‐Analyses for Research and Applications 2 (MERRA2) and ERA5 reanalyses, and observations. The latter includes Microwave Limb Sounder (MLS) for ozone, MLS and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) for temperatures, and A‐Train measurements for diabatic heating. The extratropical downward ozone fluxes are 538 Tg year⁻¹from the ERA5 reanalysis, 543 Tg year⁻¹from the MERRA2 reanalysis, and 528–539 Tg year⁻¹from the observations, consistent with previous studies. Previous studies, however, did not consider tropical upward ozone flux. Here we show that the tropical upward ozone flux is 183–193 Tg year⁻¹, which compensates about 35% of the extratropical downward ozone fluxes and should not be neglected. After considering the tropical upward ozone flux, the global ozone STE is 346 Tg year⁻¹from the ERA5 reanalysis, 360 Tg year⁻¹from the MERRA2 reanalysis, and 336–346 Tg year⁻¹from the observations. Those estimates (347 ± 12 Tg year⁻¹) can be used as the contribution of ozone STE to the tropospheric ozone budget. We also investigate cloud radiative effects on the STE of air mass and ozone. At 380 K, cloud radiative effects enhance downward fluxes in the extratropics from both reanalyses and observation, but reduce and enhance upward fluxes in the tropics from reanalyses and observation, respectively. The discrepancy in the tropics is related to the tropical tropopause layer thin cirrus that is missing in the reanalyses. We find that cloud radiative effects enhance the global ozone STE by about 21%–29%.

    

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5. Antarctic data impact experiments with Polar WRF during the YOPP‐SH summer special observing period

   https://doi.org/10.1002/qj.4298  

   Bromwich, David H. ; Powers, Jordan G. ; Manning, Kevin W. ; Zou, Xun ( June 2022 , Quarterly Journal of the Royal Meteorological Society)

   Data impact experiments are conducted employing the Polar Weather Research and Forecasting (WRF) model during the YOPP‐SH summer special observing period (SOP) using the Antarctic Mesoscale Prediction System (AMPS) framework to determine the forecast impact of numerous additional radiosondes collected during the SOP. Hybrid variational‐ensemble three‐dimensional data assimilation is performed on model forecast domains over Antarctica and the Southern Ocean using all regular observations normally available (Experiment “NoSOP”) and using the same set plus the extra soundings launched for the SOP (Experiment “SOP”). The SOP results show better near‐surface temperature and wind‐speed forecasts than the NoSOP results, primarily over West Antarctica. Radiosonde profiles confirm that temperature and wind‐speed forecasts are improved throughout the troposphere with the addition of the SOP radiosonde data, but the results for relative humidity are variable. Temperatures are improved at lower levels early in the forecasts, whereas wind speeds are better at higher levels later in the forecasts. An evaluation against the ERA5 global reanalysis that provides a much broader perspective reveals that the improved forecast skill for the SOP experiment persists up to 72 hours for temperature, wind speed, and relative humidity. The gains, however, are primarily confined to the Antarctic continent, consistent with the additional radiosonde spatial coverage being mainly poleward of 60°S. With extra radiosondes concentrated over the Antarctic Peninsula, SOP forecasts of the region downstream of the Peninsula were significantly improved compared to NoSOP forecasts. In addition, it is found that the assimilation of the additional radiosonde data can have a greater impact on the forecasts of strong cyclones, as shown for a major coastal cyclone affecting West Antarctica, with improvements in its magnitude and track. The results also suggest that increasing radiosonde launches at lower southern latitudes would improve forecasts over the Southern Ocean, especially during austral winter.

    

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