Improving the modeling of Arctic sea-ice dynamics through high-resolution satellite data retrievals Principal Investigator: Ronald Kwok (334) Patrick Heimbach, Pierre Rampal, Massachusetts Institute of Technology Dimitris Menemenlis (324) Poster No. RP-13 Publications: [1]P. Rampal, J. Weiss, C., Dubois, J. M., Campin, and G., Forget, “IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline”, J. Geophys. Res., doi: /2011JC007110, in press. [2]G. Spreen, R. Kwok, D. Menemenlis, and A. Nguyen, “Sea ice deformation in a coupled ocean-sea ice model and in satellite remote sensing data” (in preparation). Project Objectives: Sea ice models that are used for climate projections in support of the Intergovernmental Panel of Climate Change (IPCC) have horizontal grid spacing on the order of 100 km and therefore cannot resolve many important kinematic features, e.g., ice deformation, stress and strain rates, and lead distribution. These sub-grid-scale processes are represented by parameterizations, which express unresolved processes in terms of prognostic model variables. Recent studies have shown that some currently used assumptions, formulated in the 1970’s following the Arctic Ice Dynamics Joint Experiment (AIDJEX), are inadequate to model Arctic sea ice. The objectives of this study was to: (i)evaluate kinematic properties of sea ice in IPCC-class models vs satellite observations (ICESat, RADARSAT, and Envisat) (i)lay the groundwork for improving the functional form and predictive skill of sub-grid-scale parameterizations. These improvements are expected to directly impact simulations of present-day sea ice behavior and the eventual projection of future trends of ice coverage in a warming climate. FY10/11 Results: 1.Evaluation of sea ice in IPCC climate models [Rampal et al., 2011]: figure 1 We evaluated the representation of sea ice in IPCC climate simulations, and in particular to what extent the observed recent thinning and shrinking of the Arctic sea ice cover is reproduced. -IPCC-type climate models underestimate the observed thinning trend by a factor of 4 -This mismatch is likely due in part to a bad representation of the recent change in the sea ice kinematics, especially the sea ice drift acceleration. We suggest a number of dedicated model sensitivity studies, which would help establish if these deficiencies can be reduced using different sea ice kinematic parameterizations and better numerical integration schemes or if a totally different rheological framework is required. 2.Evaluation of model resolution on sea ice kinematics [Spreen et al., in prep]: figure 2 We evaluated linear kinematic features (LKFs) in model simulations of the Arctic Ocean carried out with three different horizontal grid spacing (18 km, 9 km, and 4.5 km). -We changed the model sea ice strength formulation to a cubic dependence on ice thickness, which reduces the difference between observations and model :The mean deformation rate difference relative to RGPS data has been reduced by 51%. 3.Arctic sea ice age in MITgcm versus satellite observations [Rampal et al., in prep]: figure 3 As a first step toward improved representation of subgrid-scale kinematics in the MITgcm sea ice model, -We added a tracer package that can be used to carry additional prognostic variables, e.g., sea ice age and categories (multi-year (MY) and first-year (FY) ice) -We compared the modeled MY ice fraction estimates with observations from QuikSCAT In a next step, we plan to quantitatively compare the simulated sea ice age with age estimates coming from different satellite data sets (ERS-1/2, QuikSCAT, SSM/I, and ICESat) over the period Significance of results: The acquisition of high quality data and the improvement of modeling capabilities for interpreting science observations are technical imperatives of any integrated Earth observation program. A programmatic objective is the most high-end use of NASA's satellite data over the Arctic, which, arguably, is through their combination with state-of-the-art models. For this purpose, advanced data assimilation capabilities available in the MITgcm, such as the Green’s function method and the adjoint method are presently being extended, in close collaboration between MIT and JPL, to enable fully-coupled sea ice-ocean state and parameter estimation. Determining the dominant processes governing Arctic sea-ice variability is a key contribution to the strategic topic areas of oceanography and climate science. This project has contributed to a two-way knowledge transfer, with modeling capabilities being transferred to JPL and expertise in satellite retrievals transferred to MIT. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California National Aeronautics and Space Administration Copyright All rights reserved. Figure 1. Annual sea ice mean thickness: models versus observations. Ensemble mean of the simulated sea ice mean thickness evolution is shown as the solid black line along with its respective linear fit for the period Observations are plotted as red circles (submarines data) and squares (ICESat data) along with their respective linear fit for the period Blue circles show the obtained ensemble mean ice thickness if we imposed a positive trend of 9% per decade on the ice speeds at gates over the period Such a positive trend on ice speeds allows reducing the gap between modeled and observed trend by 80%. Figure taken from [1]. Figure 2. Sea ice deformation rates: Left and middle panels: sea ice deformation rate during the two-month period November – December Satellite observed RGPS data (left) show much more deformation than the 4.5 km MITgcm model solution (middle). This difference is most pronounced in the seasonal ice zone (outside black contour line). Right panel: by changing the model sea ice strength parametrization (cubic instead of linear ice thickness dependence) this difference can be reduced. The two time series show the differences between the RGPS satellite deformation rate and the modeled ice deformation. The differences of the red curve using the improved ice strength parametrization are smaller than the original one shown in black. Figure taken from [2]. Figure 3. Observed (left) and modeled (right) Multi-Year (MY) sea ice fraction over the Arctic Ocean on the 1 st of January The black line on both graphics represent the contour separating the MY from the FY ice cover accordingly to QuikSCAT observations. The white contours on the right-side panel show the simulated ice thickness, in meters. The general pattern is correctly reproduced by the model, with the oldest and thickest ice cover located north of Greenland. Also, finer features like the tongue of ice crossing the central Arctic from the North of Greenland to the Laptev Sea are also strikingly reproduced.