The HIGHTSI ice model and plans in SURFEX

Transcription

1 Air Snow T in Ice with snow cover T sfc x T snow Q si F si h s h s The HIGHTSI ice model and plans in SURFEX Bin Cheng and Laura Rontu Water Ice T ice h i Finnish Meteorological Institute, FI-11 Helsinki, FINLAND HIGHTSI: One dimensional snow/ice model Numlab211

2 Surface energy and mass balance Radiative fluxes (Qs, Ql, absorbed solar radiation) Turbulent fluxes (sensible and latent heat fluxes, momentum flux) Melt ponds Subsurface melting/refreezing Snow Snow to ice transformation Thermal and Optical properties of snow Liquid water content Ice mass balance Thermal regimes (temperature profiles) Thermal properties of ice (density, salinity, impurity) Surface Albedo (snow/ice) Feedback mechanism Spatial and temporal variations Ice-ocean interactions Bottom melting Effect of oceanic heat flux Solar heating through open leads Numlab211

3 External weather forcing data: - Wind speed (m/s) - Air temperature ( C) - Moisture, in format of relative humidity % - Cloudiness (-1) - Precipitation, in format of snow liquid water content (mm/t) - Downward shortwave radiative flux (W/m2) - Downward longwave radiative flux (W/m2) - Oceanic heat flux (W/m2) - Surface albedo (-1) Numlab211

4 Structure of the coupled ABL sea ice model. The first 2 grid cells (g1-g2) from the inflow boundary represent the open sea with a fixed surface temperature and a fixed inflow temperature profile ( T/ z = -6.5 K km-1). The rest of the grid cells (g21-g92) represent sea ice with a snow cover (Cheng and Vihma, 22). ABL models Surface temperature Snow/ice model

5 For 2-D case: - Prepare weather forcing data at each grid for a selected region - Prepare weather forcing data along a trajectory

6

7 53 52 Model domain and grids grids of 1, 1, 1, 2 and Y-Latitude X-Lonitude The land/sea mask of the Gulf of St. Lawrence. This is the SAR domain and the model runs will be made for the sea region (blue area) which is located in the map above.

8 Average ice thickness (cm) Winter 22/23 red: CIS ice thicness (max/min) blue: HIGHTSI (without snow) green: HIGHTSI (with snow) Average ice thickness (cm) Winter 28/29 red: CIS ice thicness (max/min) blue: HIGHTSI (without snow) green: HIGHTSI (with snow) Julian day The blue and green lines are the HIGHTSI modeled ice thickness (averaged in basin scale). It shows that the if no snow effect, the modeled ice thickness is close to the observed maximum ice thickness, while if snow is taken into account, the modeled ice thickness is close to the CIS observed minimized ice thickness. In early winter (day 34-38), the overestimation of mean ice thickness may be linked with the initial ice condition for model run Julian day The blue and green lines are the HIGHTSI modeled ice thickness (averaged in basin scale). For this winter, the modeled average ice thickness are overestimated comparing with the CIS ice chart data for both cases, i,e, without snow and with snow.

9 Aim: step 1: HIGHTSI to SURFEX prognostic stand-alone code step 2: think about relations between HIGHTSI and FLake over lakes and HIGHTSI and ocean schemes over sea step 3: from stand-alone to HARMONIE environment in prognostic mode step 4: think about HIGHTSI-FLAKE-SEA connections to data assimilation of SST and ICE in HARMONIE Preparations - HIGHTSI to one subroutine - analyses the input-output variables and prognostic variables of HIGHTSI prognostic: temperature profile in ice/snow, ice and snow thickness diagnostic: components of surface energy balance at the surface over sea ice input: grid-scale lowest model level variables, SWDN, LWDN = the normal SURFEX ones output: prognostic and diagnostic fields

10 - what physical constants etc are already available - what name-list definitions for users' choices would be needed - study the implementation of FLake in SURFEX subroutines - define the interfaces - think about 1D and 2D application - write the needed interfaces, modifications, name-list etc. for HIGHTSI - test in 1D/2D stand-alone - think about the special features in operational HARMONIE environment - continue towards data assimilation questions