scholarly journals Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution

2019 ◽  
Vol 13 (6) ◽  
pp. 1661-1679 ◽  
Author(s):  
Caixin Wang ◽  
Robert M. Graham ◽  
Keguang Wang ◽  
Sebastian Gerland ◽  
Mats A. Granskog

Abstract. Rapid changes are occurring in the Arctic, including a reduction in sea ice thickness and coverage and a shift towards younger and thinner sea ice. Snow and sea ice models are often used to study these ongoing changes in the Arctic, and are typically forced by atmospheric reanalyses in absence of observations. ERA5 is a new global reanalysis that will replace the widely used ERA-Interim (ERA-I). In this study, we compare the 2 m air temperature (T2M), snowfall (SF) and total precipitation (TP) from ERA-I and ERA5, and evaluate these products using buoy observations from Arctic sea ice for the years 2010 to 2016. We further assess how biases in reanalyses can influence the snow and sea ice evolution in the Arctic, when used to force a thermodynamic sea ice model. We find that ERA5 is generally warmer than ERA-I in winter and spring (0–1.2 ∘C), but colder than ERA-I in summer and autumn (0–0.6 ∘C) over Arctic sea ice. Both reanalyses have a warm bias over Arctic sea ice relative to buoy observations. The warm bias is smaller in the warm season, and larger in the cold season, especially when the T2M is below −25 ∘C in the Atlantic and Pacific sectors. Interestingly, the warm bias for ERA-I and new ERA5 is on average 3.4 and 5.4 ∘C (daily mean), respectively, when T2M is lower than −25 ∘C. The TP and SF along the buoy trajectories and over Arctic sea ice are consistently higher in ERA5 than in ERA-I. Over Arctic sea ice, the TP in ERA5 is typically less than 10 mm snow water equivalent (SWE) greater than in ERA-I in any of the seasons, while the SF in ERA5 can be 50 mm SWE higher than in ERA-I in a season. The largest increase in annual TP (40–100 mm) and SF (100–200 mm) in ERA5 occurs in the Atlantic sector. The SF to TP ratio is larger in ERA5 than in ERA-I, on average 0.6 for ERA-I and 0.8 for ERA5 along the buoy trajectories. Thus, the substantial anomalous Arctic rainfall in ERA-I is reduced in ERA5, especially in summer and autumn. Simulations with a 1-D thermodynamic sea ice model demonstrate that the warm bias in ERA5 acts to reduce thermodynamic ice growth. The higher precipitation and snowfall in ERA5 results in a thicker snowpack that allows less heat loss to the atmosphere. Thus, the larger winter warm bias and higher precipitation in ERA5, compared with ERA-I, result in thinner ice thickness at the end of the growth season when using ERA5; however the effect is small during the freezing period.

2018 ◽  
Author(s):  
Caixin Wang ◽  
Robert M. Graham ◽  
Keguang Wang ◽  
Sebastian Gerland ◽  
Mats A. Granskog

Abstract. Rapid changes are occurring in the Arctic, including a reduction in sea ice thickness and coverage and a shift towards younger and thinner sea ice. Snow and sea ice models are often used to study these ongoing changes in the Arctic, and are typically forced by atmospheric reanalyses in absence of observations. ERA5 is a new global reanalysis that will replace the widely used ERA-Interim (ERA-I). In this study, we compare the 2 m air temperature (T2M) and precipitation between ERA I and ERA5, and evaluate these products using buoy observations from Arctic sea ice. We further assess how biases in reanalyses influence the snow and sea ice evolution in the Arctic, when used to force a thermodynamic sea ice model. We find that both reanalyses have a warm bias over Arctic sea ice in relation to the buoy observations. The warm bias is smaller in the warm season, and larger in the cold season, especially when the T2M is lower than −25 °C. Interestingly, the warm bias in the new ERA5 is on average 2.1 °C (daily mean) larger than ERA-I during the cold season. While ERA-I is drier than most modern reanalyses in the Arctic, the total precipitation along the buoy trajectories is often lower in ERA5 than in ERA-I. Nonetheless, the snowfall products are broadly similar for both ERA I and ERA5. ERA-I had substantial anomalous Arctic rainfall, which is greatly reduced in ERA5. Simulations with a freezing degree days (FDD) model and a 1D thermodynamic sea ice model demonstrate that the warm bias in ERA5 acts to reduce thermodynamic ice growth. However, the lower precipitation in ERA5 results in a thinner snow pack that allows more heat loss to the atmosphere. Thus, the larger warm bias and lower precipitation in ERA5, compared with ERA I, compensate in terms of the effect on winter ice growth. Ultimately, we find slightly thicker ice at the end of growth season when using ERA5 forcing, compared with ERA-I. Thus differences in the precipitation fields of the two reanalyses have a larger influence on the sea ice evolution than the T2M.


2021 ◽  
Author(s):  
Alex Cabaj ◽  
Paul Kushner ◽  
Alek Petty

<p><span>Snow on Arctic sea ice plays many, sometimes contrasting roles in Arctic climate feedbacks. During the sea ice growth season, the presence of snow on sea ice can enhance ice growth by increasing the sea ice albedo, or conversely, inhibit sea ice growth by insulating the ice from the cold atmosphere. Furthermore, estimates of snow depth on Arctic sea ice are also a key input for deriving sea ice thickness from altimetry measurements, such as satellite lidar altimetry measurements from ICESat-2. Due to the logistical challenges of making measurements in as remote a region as the Arctic, snow depth on Arctic sea ice is difficult to observationally constrain.<br><br>The NASA Eulerian Snow On Sea Ice Model (NESOSIM) can be used to provide snow depth and density estimates over Arctic sea ice with pan-Arctic coverage within a relatively simple framework. The latest version of NESOSIM, version 1.1, is a 2-layer model with simple representations of the processes of accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Relative to version 1.0, NESOSIM 1.1 features an extended model domain, and reanalysis snowfall input scaled to observed snowfall retrieved from CloudSat satellite radar reflectivity measurements.<br><br>In this work, we present a systematic calibration, and an accompanying estimate in the uncertainty of the free parameters in NESOSIM, targeting airborne snow radar measurements from Operation IceBridge. We further investigate uncertainties in snow depth and the resulting uncertainties in derived sea ice thickness from ICESat-2 altimetry measurements using NESOSIM snow depths.</span></p>


2021 ◽  
Author(s):  
Chao-Yuan Yang ◽  
Jiping Liu ◽  
Dake Chen

Abstract. The updated Coupled Arctic Prediction System (CAPS) is evaluated, which is built on new versions of Weather Research and Forecasting model (WRF), the Regional Ocean Modeling System (ROMS), the Community Ice CodE (CICE), and a data assimilation based on the Local Error Subspace Transform Kalman Filter. A set of Pan-Arctic prediction experiments with improved/changed physical parameterizations in WRF, ROMS and CICE as well as different configurations are performed for the year 2018 to assess their impacts on the predictive skill of Arctic sea ice at seasonal timescale. The key improvements of WRF, including cumulus, boundary layer, and land surface schemes, result in improved simulation in near surface air temperature and downward radiation. The major changes of ROMS, including tracer advection and vertical mixing schemes, lead to improved evolution of the predicted total ice extent (particularly correcting the late ice recovery issue in the previous CAPS), and reduced biases in sea surface temperature. The changes of CICE, that include improved ice thermodynamics and assimilation of new sea ice thickness product, have noticeable influences on the predicted ice thickness and the timing of ice recovery. Results from the prediction experiments suggest that the updated CAPS can better predict the evolution of total ice extent compared with its predecessor, though the predictions still have certain biases at the regional scale. We further show that the CAPS can remain skillful beyond the melting season, which may have potential values for stakeholders making decisions for socioeconomical activities in the Arctic.


2009 ◽  
Vol 22 (1) ◽  
pp. 165-176 ◽  
Author(s):  
R. W. Lindsay ◽  
J. Zhang ◽  
A. Schweiger ◽  
M. Steele ◽  
H. Stern

Abstract The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of −0.57 m decade−1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.


2016 ◽  
Author(s):  
R. L. Tilling ◽  
A. Ridout ◽  
A. Shepherd

Abstract. Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release dataset is typically one month, due to the time required to determine precise satellite orbits. We use a new fast delivery CryoSat-2 dataset based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea ice thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with an average thickness difference of 5 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast delivery product also provides measurements of Arctic sea ice thickness within three days of acquisition by the satellite, and a measurement is delivered, on average, within 10, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.


2014 ◽  
Vol 14 (7) ◽  
pp. 10929-10999 ◽  
Author(s):  
R. Döscher ◽  
T. Vihma ◽  
E. Maksimovich

Abstract. The Arctic sea ice is the central and essential component of the Arctic climate system. The depletion and areal decline of the Arctic sea ice cover, observed since the 1970's, have accelerated after the millennium shift. While a relationship to global warming is evident and is underpinned statistically, the mechanisms connected to the sea ice reduction are to be explored in detail. Sea ice erodes both from the top and from the bottom. Atmosphere, sea ice and ocean processes interact in non-linear ways on various scales. Feedback mechanisms lead to an Arctic amplification of the global warming system. The amplification is both supported by the ice depletion and is at the same time accelerating the ice reduction. Knowledge of the mechanisms connected to the sea ice decline has grown during the 1990's and has deepened when the acceleration became clear in the early 2000's. Record summer sea ice extents in 2002, 2005, 2007 and 2012 provided additional information on the mechanisms. This article reviews recent progress in understanding of the sea ice decline. Processes are revisited from an atmospheric, ocean and sea ice perspective. There is strong evidence for decisive atmospheric changes being the major driver of sea ice change. Feedbacks due to reduced ice concentration, surface albedo and thickness allow for additional local atmosphere and ocean influences and self-supporting feedbacks. Large scale ocean influences on the Arctic Ocean hydrology and circulation are highly evident. Northward heat fluxes in the ocean are clearly impacting the ice margins, especially in the Atlantic sector of the Arctic. Only little indication exists for a direct decisive influence of the warming ocean on the overall sea ice cover, due to an isolating layer of cold and fresh water underneath the sea ice.


Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7011
Author(s):  
Feng Xiao ◽  
Fei Li ◽  
Shengkai Zhang ◽  
Jiaxing Li ◽  
Tong Geng ◽  
...  

Satellite altimeters can be used to derive long-term and large-scale sea ice thickness changes. Sea ice thickness retrieval is based on measurements of freeboard, and the conversion of freeboard to thickness requires knowledge of the snow depth and snow, sea ice, and sea water densities. However, these parameters are difficult to be observed concurrently with altimeter measurements. The uncertainties in these parameters inevitably cause uncertainties in sea ice thickness estimations. This paper introduces a new method based on least squares adjustment (LSA) to estimate Arctic sea ice thickness with CryoSat-2 measurements. A model between the sea ice freeboard and thickness is established within a 5 km × 5 km grid, and the model coefficients and sea ice thickness are calculated using the LSA method. Based on the newly developed method, we are able to derive estimates of the Arctic sea ice thickness for 2010 through 2019 using CryoSat-2 altimetry data. Spatial and temporal variations of the Arctic sea ice thickness are analyzed, and comparisons between sea ice thickness estimates using the LSA method and three CryoSat-2 sea ice thickness products (Alfred Wegener Institute (AWI), Centre for Polar Observation and Modelling (CPOM), and NASA Goddard Space Flight Centre (GSFC)) are performed for the 2018–2019 Arctic sea ice growth season. The overall differences of sea ice thickness estimated in this study between AWI, CPOM, and GSFC are 0.025 ± 0.640 m, 0.143 ± 0.640 m, and −0.274 ± 0.628 m, respectively. Large differences between the LSA and three products tend to appear in areas covered with thin ice due to the limited accuracy of CryoSat-2 over thin ice. Spatiotemporally coincident Operation IceBridge (OIB) thickness values are also used for validation. Good agreement with a difference of 0.065 ± 0.187 m is found between our estimates and the OIB results.


2020 ◽  
Vol 14 (4) ◽  
pp. 1325-1345 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Xuanji Wang ◽  
Mark Tschudi

Abstract. Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.


2016 ◽  
Vol 29 (2) ◽  
pp. 889-902 ◽  
Author(s):  
Rasmus A. Pedersen ◽  
Ivana Cvijanovic ◽  
Peter L. Langen ◽  
Bo M. Vinther

Abstract Reduction of the Arctic sea ice cover can affect the atmospheric circulation and thus impact the climate beyond the Arctic. The atmospheric response may, however, vary with the geographical location of sea ice loss. The atmospheric sensitivity to the location of sea ice loss is studied using a general circulation model in a configuration that allows combination of a prescribed sea ice cover and an active mixed layer ocean. This hybrid setup makes it possible to simulate the isolated impact of sea ice loss and provides a more complete response compared to experiments with fixed sea surface temperatures. Three investigated sea ice scenarios with ice loss in different regions all exhibit substantial near-surface warming, which peaks over the area of ice loss. The maximum warming is found during winter, delayed compared to the maximum sea ice reduction. The wintertime response of the midlatitude atmospheric circulation shows a nonuniform sensitivity to the location of sea ice reduction. While all three scenarios exhibit decreased zonal winds related to high-latitude geopotential height increases, the magnitudes and locations of the anomalies vary between the simulations. Investigation of the North Atlantic Oscillation reveals a high sensitivity to the location of the ice loss. The northern center of action exhibits clear shifts in response to the different sea ice reductions. Sea ice loss in the Atlantic and Pacific sectors of the Arctic cause westward and eastward shifts, respectively.


Sign in / Sign up

Export Citation Format

Share Document