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Exercise 1

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  1. Exercise 1 Bart van den Hurk (KNMI/IMAU) Exercise 1

  2. Exercise with GSWP2 simulation • GSWP2 = Global Soil Wetness Project • 10 year (1986-1995) after 2.5yrs spin-up • worldwide (~15000 gridpoints) • Forcings: rain, snow, shortwave, longwave, T, q, u, pressure • Model version: H-TESSEL • 6 land tiles (low + high veg, bare, exposed + forest snow, interception) • explicit surface runoff • Van Genuchten soil hydraulics • Jarvis-Stewart canopy resistance • Output • daily prognostic fields (at 0:00 UTC) • daily accumulated fluxes • grouped in ~10 files per month Exercise 1

  3. All variables o_eva "SubSnow“ # Snow sublimation [kg/m2/s] "ECanop" # Interception evaporation [kg/m2/s] "TVeg" # Vegetation transpiration [kg/m2/s] "ESoil" # Bare soil evaporation [kg/m2/s] "RootMoist" # Root zone soil moisture [kg/m2] "CanopInt" # Canopy interception depth [kg/m2] o_fix "SoilDepth" # Soil depth [m] "M_fielscap" # Field capacity [m3/m3] "M_wilt" # Wilting point [m3/m3] "M_sat" # Saturated soil moisture content [m3/m3] o_gg “SoilMoist" # Soil moisture content [kg/m2] "SoilTemp" # Soil temperature [K] "AvgSurfT" # Avergae skin temperature [K] "Icetemp" # Sea ice temperature [K] "SWE" # Snow mass water eq [kg/m2] "SnowT“ # Snow temperature [K] "Snowdens" # Snow density [kg/m3] o_sub "LSoilMoist" # Diagnostic liquid soil water content [kg/m2] "SoilWet“ # Total soil wetness [-] o_sus "VegT“ # Skin temperature vegetation [K] "BaresoilT" # Skin temperature bare soil [K] "RadT" # Surface radiative temperature [K] "Albedo" # Average albedo [-] o_wat "Rainf" # rainfall rate [kg/m2/s] "Snowf" # snowfall rate [kg/m2/s] "Qs" # total runoff [kq/m2/s] "Qsm“ # snow melt [kg/m2/s] "Qsb“ # base flow [kg/m2/s] "Evap" # evaporation [kg/m2/s] "DelSoilMoist“ # Soil moisture change [kg/m2] "DelIntercept“ # Interception storage change [kg/m2] "DelSWE" # Snow Water Equivalent change [kg/m2] o_cld "SnowDepth" # Snow depth [m] "SnowFrac" # Snow fraction [-] "IceFrac" # Ice covered gridbox fraction [-] "Fdepth“ # Frozen soil depth [m] "Tdepth" # Depth to soil thaw [m] "SAlbedo“ # Snow albedo [-] o_efl "Qle“ # Average latent heat flux [W/m2] "Qh" # Average sensible heat flux [W/m2] "Qg" # Average soil heat flux [W/m2] "Qf" # Average soil fusion flux [W/m2] "SWnet" # Net shortwave radiation [W/m2] "LWnet“ # Net longwave radiation [W/m2] "DelSoilHeat" # Average soil heat content change [W/m2] "DelColdCont“ # Average snow heat content change [W/m2] Exercise 1

  4. surface characteristics surfclim.nc “MAlbedo“ # Monthly background albedo [-] “landsea" # land-sea mask [-] “geopot" # Surface geopotential [m2/s2] “cvh" # Cover fraction high vegetation [-] “cvl" # Cover fraction low vegetation [-] “tvh" # type of high vegetation [lut] “tvl" # type of low vegetation [lut] “z0m" # gridbox mean roughness [m] “lz0h" # log of thermal roughness [-] “sotype" # soil type[lut] “sdor" # standard deviation of orography [m] “clay" # fraction of clay [-] “sand" # fraction of sand[-] Exercise 1

  5. Examples of research questions Energy use Make a map of annual mean evaporative fraction Explain the patterns, discerning deserts and semi-desert tropical forests temperate grassland boreal forest Water use Make a map of annual mean runoff fraction Explain the patterns Vertical soil profiles Make seasonally varying mean vertical profiles of soil moisture and temperature for a number of regions, and explain differences Europe Sahara Amazone Siberia Temporal variability Make maps of ratio of interannual variance and mean of annual cycle of soil moisture Explain the patterns Snow Make time series of snow budget terms Explain differences in annual cycles between various regions Alps Scandinavia Himalaya Andes Land use Describe mean annual cycle of energy partitioning water partitioning For a range of land use types Parameterization For various land use types, express the dependence of evaporation on soil moisture Exercise 1

  6. Tools • Model output and scripts are on venus (linux operating system) • Generic tools • averaging fields • plotting a map • making a time series of a variable • All output and intermediate files are in netCDF • Some example linux and ferret scripts • monthly mean output • making a map of a 10-yr mean variable in a given season • making a time series of 10-yr mean variable Exercise 1

  7. Some useful linux commands • Copy: cp <file1> <file2> • Delete: rm <file(s)> • Change directory: cd <dir> • One directory up: cd ../ • Display directory contents: ls –al <*> • View contents of netCDF file ncdump –h <file> • Define a variable set var = <value> • Print a variable echo $var • Edit a file (start Exceed from your WINDOWS first) nedit <file> Exercise 1

  8. Location of files and start-up • to get zip-file with example scripts and unpack: cd ~ (goto home directory) cp /home/mfo/hurk/opdracht.tar . (get zip-file; don’t forget “.”) tar xvf opdracht.tar (unpack zip-file) • to initialize some path-settings cd opdracht/scripts source environment.txt • location of files • my directory: $BART (/home/mfo/hurk) • your directory: $HOME • your preferred work directory: $WRK ($HOME/opdracht/output) • model output: $BART/gswp/runs/global/HTESSEL • example scripts: $HOME/opdracht/scripts • defined variables: $BART/opdracht/script/vardef.inc • to call a script from your workdirectory ($WRK): ../script.sc or $WRK/../script.sc • general information $HOME/opdracht/scripts/readme Exercise 1

  9. More information • Bart van den Hurk • hurkvd@knmi.nl Exercise 1