Great Lakes and Arctic Ice and Climate Studies: Research, Modeling, and Prediction

Great Lakes Ice Database Update (1973-present) and Ice-Climate Studies



Figure 1: Time series of AMIC during 1973-2015 with several extreme events highlighted.

Jia Wang, Lead GLERL PI, and Anne Clites

This project will continue to update ice cover database parameters and statistics for the period 1973-present. After dropping to the ice minimum in 2011/2012 ice season (Bai et al., 2015, Dyn. Clim.), the following two winters reached the second largest ice cover (2013/14: 92% vs record high of 94% in 1979; 2014/15: 88%) of the record from 1973-2015 in the Great Lakes, caused by continuous polar vortex intrusions - possible impacts of diminishing Arctic summer sea ice on mid-latitude Great Lakes region. Therefore, stakeholders requested us to update the ice cover database up to the present and conduct in-depth research linking to Arctic climate teleconnection patterns to the Great Lakes climate and ice cover.

In this project, temporal variability of ice cover in the Great Lakes is investigated using historical satellite measurements updated to include 1973 to 2015. With high ice cover in the winters of 2013/14 and 2014/15, the downward trend documented in recent years was significantly reduced, compared to the period 1973-2013. The decadal variability in lake ice contributed to the decreasing trend. It was found that 1) Great Lakes ice cover has a linear relationship with Atlantic Multidecadal Oscillation (AMO), similar to the relationship of lake ice cover with the North Atlantic Oscillation (NAO), and 2) a quadratic relation with the Pacific Decadal Oscillation (PDO), similar to the relationship of lake ice cover with the Niño 3.4. Based on these dynamic relationships, the original multiple variable regression model established using the indices of NAO and Niño3.4 is updated by adding both AMO and PDO, as well their interference (interacting or competing) mechanism. With the AMO and PDO added, the correlation between the model and observation increases to 0.68, compared to 0.44 using NAO and Niño3.4 only.



Figure 2: Original hindcast (Bai et al. (2012)) (in blue), AMIC (black), and model with all the effects from NAO, AMO, Nino3.4, and PDO, and their combined competing mechanisms (red). The corresponding correlation between the model results and the observed AMIC are given.

References:

Ice Data

Great Lakes Ice Products

Great Lakes Coastal Forecasting System (including ice)

Bai, X., J. Wang, Q. Liu, D. Wang, and Y. Liu, 2011. Severe ice conditions in the Bohai Sea, China vs. mild ice conditions in the Great Lakes during the 2009/2010 winter with strong –AO and El Nino. J. Applied Meteorol. Climatol., 50, 1922-1935.

Bai, X., J. Wang, C. Sellinger, A. Clites, and R. Assel, 2012. Interannual variability of Great Lakes ice cover and its relationship to NAO and ENSO. J. Geophys. Res., doi:10.1029/2010JC006932

Bai, X. and J. Wang, 2012. Atmospheric teleconnection patterns associated with severe and mild ice cover on the Great Lakes, 1963-2011. Water Quality Research Journal of Canada, 43, 421-435.

Bai, X., J. Wang, R. Assel, D.J. Schwab, A. Clites, J. Bratton, M. Colton, J. Lenters, B. Lofgren, T. Wohlleben, S. Helfrich, J. Austin, 2015. A record breaking low ice cover over the Great Lakes during winter 2011/2012, Clim. Dyn. 44, 5: 1187-1213.

Clites, A., G. Leshkevich, J. Wang, K.B. Campbell, A.D. Gronewold, R.A. Assel, and X. Bai, 2014. Cold Water and High Ice Cover on Great Lakes in spring 2014. AGU EOS, 95(34): 305-306.

Wang, J., X. Bai, G. Leshkevich, M. Colton, A. Clites, and B. Lofgren, 2010: Severe ice cover on Great Lakes during winter 2008-2009, AGU EOS, 91 (5), 41-42.

Wang, J., X. Bai, H. Hu, A. Clites, M. Colton, and B. Lofgren, 2012a. Temporal and spatial variability of Great Lakes ice cover, 1973-2010. J. Climate, DOI: 10.1175/2011JCL14066.1

Wang, J., R.A. Assel, S. Walterscheid, A. Clites, and X. Bai, 2012b. Great Lakes ice climatology update: Winter 2006–2011 description of the digital ice cover data set, NOAA Technical Memorandum GLERL-155, 37 pp. 2012.

Wang, J., X. Bai, Z. Yang, A. Clites, A. Manome, B. Lofgren, J. Bratton, H. Hu, P. Chu, and G. Leshkevich, 2016. Interannual and decadal variability of Great Lakes ice cover, 1973-2015: Hindcast of lake ice using multi-variable regression models (submitted to JGLR)

Implementing improved FVCOMice model for the entire Great Lakes



Figure 3: Unstructured grids for FVCOMice covering all five Great Lakes.

Jia Wang, Lead GLERL PI, Ayumi Manome, Haoguo (CILER)

We have modified the unstable Euler forward schemes to a neutrally-stable centered differencing scheme in ocean and turbulence equations in FVCOMice that was applied to Lake Erie. It is found that diffusive temperature structure and thermocline have been significantly improved and the model is stable with reasonable CFL criterion and proper ratio (10) of internal mode to external mode time steps (Manome and Wang, submitted). We also applied the modified FVCOMice to the 5 lakes model for year 1998, but no verification was conducted. We propose to continue to 1) improve ice dynamic and thermodynamic parameters in the Great Lakes; and 2) We also propose to continue simulation using the modified FVCOMice in five Great Lakes for 2000-2015. Only after the modification with the guidance of numerical theory, can this model be widely used in the NOAA NOS and GLERL community. So, we need to improve, calibrate and validate FVCOMice model in all five lakes in the years to come.



Figure 4: Simulated ice concentration using improved FVCOMice model.

References:

Bai, X. J. Wang, DJ Schwab, Y. Yang, L. Luo, G.A. Leshkevich, and S. Liu, 2013. Modeling 1993-2008 climatology of seasonal general circulation and thermal structure in the Great Lakes using FVCOM. Ocean Modelling, DOI: 10.1016/j.ocemod.2013.02.003

Fujisaki, A, J. Wang, H. Hu, D. Schwab, N. Hawley, and R. Yerubandi, 2012, A modeling study of ice-water processes for Lake Erie using coupled ice-circulation models, J. Great Lakes Res. http://dx.doi.org/10.1016/j.jglr.2012.09.021

Fujisaki, A., J. Wang, X. Bai, G. Leshkevich, and B. Lofgren (2013), Model-simulated interannual variability of Lake Erie ice cover, circulation, and thermal structure in response to atmospheric forcing, 2003–2012, J. Geophys. Res. Oceans, 118, doi:10.1002/jgrc.20312.

Luo, L., J. Wang, D.J. Schwab, H. Vanderploeg, G. Leshkevich, X. Bai, H. Hu, D. Wang, 2012.Simulating the 1998 spring bloom in Lake Michigan using a coupled physical- biological Model, J. Geophys. Res.,117, doi:10.1029/2012JC008216

Wang, J., H. Hu, D. Schwab, G. Leshkevich, D. Beletsky, N. Hawley and A. Clites, 2010. Development of the Great Lakes Ice-circulation Model (GLIM): Application to Lake Erie in 2003-2004. Journal of Great Lakes Research, 36, 425-436, DOI: 10.1016/j.jglr.2010.04.002

Great Lakes Coastal Forecasting System (including ice)

Modeling sea ice-ocean-ecosystem responses to climate changes in the Arctic Ocean using CIOM (coupled ice-ocean model) and RUSALCA (RUSsian-American Long-term Census of the Arctic) measurements



Figures 5 and 6: 3-D CIOM-simulated sea ice area (black line) and satellite-measured sea ice area (blue) (left, in meters) in March 2008, and chl-a (right, in μg/L) over the entire Bering and Chukchi Seas in August 2008.

Jia Wang, Lead GLERL PI (funded by NOAA CPO and NASA), Haoguo Hu (CILER); Ayumi Manome (CILER)

We have used the combination of a high-resolution Coupled Ice-Ocean Model (CIOM) to improve our understanding of ocean circulation, sea ice, and phytoplankton in the Bering and Chukchi seas. The next step is to 1) extend the 3-D CIOM to cover the Arctic Ocean (Figures 5 and 6), and 2) implement the modified version of FVCOMice to the Arctic Ocean to simulate the Arctic seas ice-ocean-ecosystem for the period 2015-2020 when the RUSALCA’s moorings will be deployed in high latitude. A series of sensitivity simulations with CIOM will be conducted to the response to Arctic Dipole Anomaly (DA) to investigate the DA’s impact on SST, sea ice concentration (retreat) in the Alaska Arctic water due to the enhanced Bering Inflow. The modeling results will be discussed with PIs of RUSALCA field observation projects to design optimal sampling strategy.

We have been successfully applying CIOM in the Arctic Ocean and will continue to conduct simulations using refined model (Fig. 2) to examine the responses of ice-ocean-ecosystem responses to the diminishing summer sea ice associated with Arctic climate changes such as Dipole anomaly (DA) and Arctic Oscillation (AO). In addition, to achieve the NOAA overall Arctic goals stated in the 2014 Arctic Workshop White Paper "Predicting Arctic Weather and Climate and Related Impacts." GLERL Arctic team proposes to develop variable unstructured-grid Arctic ice-ocean-ecosystem models based on finite volume coastal ocean model (FVCOM) that can resolve the Canadian Arctic Archipelagos (ACC, Fig. 7), with the following imbedded sub-models to be developed:
  1. landfast ice model with anchoring process (Wang et al. 2014b, JGR)
  2. Ice-wave-tide model for Arctic storms (to be coupled to Arctic WRF) (Bai et al. 2015, Deep-Sea Res)
  3. lower trophic level NPZD model for ecosystem in response to diminishing Arctic summer sea ice (Wang et al. 2013, JGR)
There is a gap for landfast ice with anchoring process to simulate nearshore ice conditions for oil and gas exploration activities. There is also a gap to parameterize ice-wave-tide interactions process into the ice and ocean models and the mixing. The successful development of the three sub-models will substantially improve our capabilities to predict the Arctic weather and climate. The variable unstructured-grid (down to 1km) model can resolve complex Arctic geometry such as CAA (Fig. 7).

GLERL Arctic team has been working on the Arctic models development and application since 2007. We developed our own Coupled Ice-Ocean Model (CIOM) based on POM, and Coupled Physical-Ecosystem Model (PhEcoM) in the ice-covered seas with support from NOAA CPO. We have transferred our CIOM into Great Lakes ice forecasting system (Wang et al. 2010, JGLR).



Figure 6: CIOM-simulated surface ocean circulation in the Arctic.



Figure 7: Visualization of enlarged, unstructured grids for Arctic-FVCOMice model.

References:

Bai, X., H. Hu, J. Wang, Y. Yu, E. Cassano, J. Maslanik, 2015, Responses of surface heat flux, sea ice and ocean dynamics in the Chukchi–Beaufort Seas to storm passages during winter 2006/2007: A numerical study. Deep-Sea Res. I, 102: 101–117 http://dx.doi.org/10.1016/j.dsr.2015.04.008

Hu, H. and J. Wang, 2008. Modeling the ocean circulation in the Bering Sea, Chinese Journal of Polar Science, 19(2), 192.

Hu, H. and J. Wang, 2010. Modeling effects of tidal and wave mixing on circulation and thermohaline structures in the Bering Sea: Process studies, J. Geophys. Res., 115, C01006, doi:10.1029/2008JC005175.

Hu, H., J. Wang, and D.-R. Wang (2011), A model-data study of the 1999 St. Lawrence Island polynya in the Bering Sea, J. Geophys. Res., 116, C12018, doi:10.1029/2011JC007309.

Long, Z., W. Perrie, C.L. Tang, E. Dunlap, and J. Wang, 2012, Simulated interannual variations of freshwater content and sea surface height in the Beaufort Sea. J. Clim, DOI: 10.1175/2011JCI14121.1

Pisareva, M.N., R.S. Pickart, K. Iken, E.A. Ershova, J.M. Grebmeier, L.W. Cooper,B.A. Bluhm, C. Nobre, R.R. Hopcroft, H. Hu, J. Wang, C.J. Ashjian, K.N. Kosobokova, and T.E. Whitledge. 2015. The relationship between patterns of benthic fauna andzooplankton in the Chukchi Sea and physical forcing. Oceanography 28(3):68–83, http://dx.doi.org/10.5670/oceanog.2015.58.

Wang, J. K. Mizobata, H. Hu, M. Jin, S. Zhang, W. Johnson, and K. Shimada, 2008, Modeling Seasonal Variations of Ocean and Sea Ice Circulation in the Beaufort and Chukchi Seas: A model-data fusion study. Chinese Journal of Polar Science, 19(2), 168-184.

Wang, J., H. Hu, K. Mizobata, and S. Saitoh, 2009. Seasonal variations of sea ice and ocean circulation in the Bering Sea: A model-data fusion study. J. Geophys. Res. 114, C02011, doi:10.1029/2008JC004727.

Wang, J., H. Hu, J. Goes, J. Miksis-Olds, C. Mouw, E. D’Sa, H. Gomes, D.R. Wang, K. Mizobata, S. Saitoh , and L. Luo, 2013, Modeling seasonal variations of sea ice and plankton in the Bering and Chukchi Seas during 2007-2008, J. Geophys. Res., 118, doi:10.1029/2012JC008322

Wang, J., K. Mizobata, X. Bai, H. Hu, M. Jin, Y. Yu, M. Ikeda, W. Johnson, W. Perie, and A. Fujisaki (2014), A modeling study of coastal circulation and landfast ice in the nearshore Beaufort and Chukchi seas using CIOM, J. Geophys. Res. Oceans, 119, doi:10.1002/2013JC009258.