Excitation of Earth rotation from meteorological analyses and models

Transcription

1 Excitation of Earth rotation from meteorological analyses and models David A. Salstein Atmospheric and Environmental Research, Inc., (AER) Lexington, MA, USA Centrum Badan Kosmicznych, Warsaw, Poland February 20, 2009

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3 Research & Development Division Key Technical Areas Atmospheric and Oceanic Diagnostics Data Assimilation and Prediction Planetary Atmospheres Radiation and Climate Weather Satellite Currents Ocean Mass Ice Ice Sheets Sheets Torques Torques N Sea Level Earth Rotation Polar Motion Mantle Properties Weather Balloon Winds Atmospheric Mass Gravity Geodetic Satellite Radio Source Research Applications Global ocean data assimilation experiment (GODAE) studies of sea level and bottom pressure Regional climate and hydrology studies Atmospheric and ocean angular momentum and relationship with Earth motions Expertise in application of forecasting models (MM5, WRF) Development of advanced radiation codes for NWP and climate models (RRTM, LBLRTM) Modeling and analysis of atmospheres and ice on Mars and the moons of Jupiter and Saturn Science Team Memberships Dept. of Energy Atmospheric Radiation Measurement (ARM) Program EOS Tropospheric Emission Spectrometer (TES) NASA-JPL NSCATT Science Team NASA Altimeter Teams NASA Ocean Vector Wind Science Team (OVWST) AER Company Proprietary Information Atmospheric and Environmental Research, Inc. AER, 2008

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6 Presentation areas Earth rotation variability and methods to measure it --vector: Polar motion (about two equatorial axes) and Length of day (axial) -- (LOD) Atmospheric angular momentum (AAM)--how it is measured and its climate signals: Production of AAM by analysis forecast systems. High temporal resolution fields; torques Observations assimilated by meteorological centers -- quality of analysis/forecast comparing to original observations Organizational needs, International Earth Rotation and Reference Frames Service (IERS)

7 International Earth rotation and reference frames service (IERS) Note ==>

8 ATMOSPHERE CORE TIDES GRAVITY/ GEOCENTER HYDROLOGY OCEAN MANTLE LOADING

9 METEOROLOGICAL DATA ASSIMILATION SYSTEM Raw meteorological observations: rawinsondes, aircraft, satellites, etc. Forecast fields from 6-hr forecast Make forecast with model-- may require initialization procedure Analyzed or Reanalyzed Fields from assimilation Compute angular momentum from winds and surface pressures

10 SAMPLE DATA AVAILABLE TO ATM. ANALYSIS

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13 Model: Advances the state of the atmosphere through six hours, obtaining forecasted fields of atmospheric parameters. 1. Physical laws 2. Atmospheric physics 3. Numerics 4. Lower boundary physics Data assimilation: Combines the various observations within a time window Optimal interpolation; statistical spectral interpolation; adjoint models

14 Model: Advances the state of the atmosphere through six hours, obtaining forecasted fields of atmospheric parameters. 1. Physical laws Equation of motion Thermodynamic equation Continuity equation (conservation of mass) Conservation of water substance 2. Atmospheric physics clouds, i.e., sub-grid scale cumulus parameterization radiation: shortwave (solar) incoming and longwave emitted by ground and atmosphere (primarily outgoing) 3. Numerics: models are cast in either grid-point or spectral space advancing the equations in time can be done using a number of techniques (explicit, implicit, semi-implicit.) 4. Lower boundary physics Exchange of momentum, heat, and moisture Land and ocean temperature and (soil) moisture are accounted for

15 Data assimilation: Combines the various observations within a time window 1. Optimal methods at a time step Optimal interpolation Uses error characteristics of the observation source, combined with that of the forecasted state to determine the best fit to the data, considering the data distribution and closeness to the grid point Statistical spectral interpolation (SSI) Projects all data onto modes of the atmosphere and optimally determines the mode amplitudes with the least errors 2. Methods for optimizing 4-D state of atmosphere Adjoint method: combines the data for the entire time period by running the model and its adjoint to minimize at once the optimal solution for the entire time period (not currently operational)

16 3. Initialization: Makes analysis useful as consistent initial state for forecast model Analyses comprise the initial state for forecast runs 1. Often, combined analyses have to be initialized, If used as an initial state, they may be out of balance with the modes (usually spherical harmonics) comprising the forecast model, and generate spurious waves that are nonlinearly unstable. 2. Initialization: The modes of the forecast model will be be determined, and the analyzed state projected onto each mode. The sum of such projections is the new initial state (~ low-pass filtering.)

17 Bias (mean) error Temperature differences from radiosondes, 850 hpa (K) ANAL RMS error FCST - 24 H FCST - 48 H NCEP GFS

18 Bias (mean) error Specific humidity differences from radiosondes, 850 hpa g kg -1 ANAL RMS error FCST - 24 H FCST - 48 H NCEP GFS

19 Surface Pressure Difference From Stations (Ocean Only, March 2002-Feb. 2003) hpa

20 Atmospheric angular momentumaxial Angular momentum = mass velocity radius arm to axis (mass+δmass) (velocity+δvelocity) R cos φ 1013hPa H or L(10hPa) mean rot. speed zonal wind Δmass= proportional to surface pressure anomaly; Δvelocity =wind speed, relative to rotation Cross products very small, so can separate into mass and velocity terms For axial component, depends mostly on the winds (eastwest, or zonal ) as relative change in mass is small

21 Excitation of axial rotation Unfiltered Filtered

22 Atmospheric data and atmospheric analyses Operational analyses at the world s major weather centers: (Done on a daily basis as good as the technology of the time) 1.US: National Centers for Environmental Prediction (NCEP) 2. European Center for Medium-Range Weather Forecasts (ECMWF) 3. United Kingdom Meteorological Agency 4. Japan Meteorological Agency Reanalyses (retrospective analysis) with collected data and fixed analysis system as good as technology when produced: 1. NCEP (1 and 2) 2. ECMWF (ERA40) 3. NASA

23 Excitations from Reanalysis and Operational sets NCEP

24 Zonal winds (u) -- zonally height, expressed in pressure averaged m s S 0 90 N

25 Physics of Earth rotation a. Angular momentum approach dh solid.earth dt b. Torque approach = dh fluid.layer dt dh solid.earth dt = Γ fluid.layer dh fluid.layer dt = Γ fluid.layer

26 Atmospheric excitations of Earth rotation/polar motion--ang. momentum Equatorial: Polar motion Axial:length of day

27 motion mass mass-- IB Atmosphere Ocean 1 cm High (H) Inverted Barometer Low (L) +1 hpa -1 hpa 1 cm Atmospheric mass over the ocean depresses it very nearly 1 hpa (1 millibar) = 1 cm ocean On scales > several days

28 a.) latitude latitude latitude latitude latitude c.) e.) g.) i.) Covariance annual; semiannual; terannual; subseasonal; interannual; longitude mas b.) latitude d.) latitude latitude f.) h.) latitude j.) latitude Correlation Magnitude annual; semiannual; terannual; subseasonal; interannul; longitude Regional contributions to polar motion at different time scales: from the NCEP/NCAR Reanalyses Nastula et al. (2009) Reg. Atm / Geod.

29 Combination excitations

30 Atmospheric Torques on Earth

31 Torques (NOAA/ESRL) Mountain Friction Different continental areas

32 1998

33 Atmospheric (and ocean)models alone Without data assimilation, only using boundary conditions, meteorological models describe the whole state of the atmosphere Effort: AMIP--Atmospheric model intercomparison project to check how effective models are in other scenarios AMIP-1 models ca 1993 AMIP-2 models ca 1998 CMIP Coupled (atm.-ocean intercomparison project); historical, 2000s Using angular momentum as a diagnostic to check models

34 AMIP models--no data assimilation, compare techniques Model Modeling center Resolution Land surface scheme Model Modeling center Resolution Land surface scheme CCC Canadian Centre for Climate Research T47 L32 "bucket" MPI Max Planck-Institut für Meteorologie T42 L19 "bucket" CCSR CNRM COLA Center for Climate System Research T42 L18 "bucket" MRI Centre Nationale de Recherches Meteorologiques, France T63 L45 ISBA NCAR Center for Ocean-Land- R40 L18 SiB NCEP Atmosphere Studies Meteorological Research Institute, Japan National Center for Atmospheric Research National Center for Environmental Prediction T42 L30 T42 L18 T42 L18 SiB LSM "bucket" DNM Department of Numerical Mathematics of the Russian Academy of Sciences 4x5 L21 Volodin and Lykossov (1998) PNNL Pacific Northwest National Laboratory T42 L18 BATS ECMWF GISS GLA JMA MGO European Centre for Medium-Range Weather Forecasts T63 L50 Blondin and Böttgerm (1987) Goddard Institute for 4x5 L12 Abramopoulos Space Studies et al. (1988) SUNYA UGAMP Goddard Laboratory for Atmospheres 4x5 L20 SiB UIUC Japan Meteorological Agency T63 L30 SiB UKMO Main Geophysical Observatory T30 L14 other YONU State University of New York at Albany Universities Global Atmospheric Modelling Programme, U.K. University of Illinois at Urbana- Champaign U.K. Meteorological Office T42 L x2.5 L58 4x5 L x2.5 L19 LSM MOSES other MOSES Yonsei University, Korea 4x5 L15 "bucket"

35 Seasonal (AMIP-2) Interannual Throughout most of atmosphere, models can describe seasonal and interannual variability.

36 Stratospheric AAM signals ( hpa) Seasonal Interannual In stratosphere, semiannual well described but not interannual (quasi-biennial oscillation)

37 Quasibiennial and semi-annual signals important in stratosphere

38 χ1,2,3 troposphere and stratosphere Zhou et al Explores definition of tropopause

39 AMIP Models and Polar Motion MEAN RESIDUALS, P RES CHI1, RES CHI2, SQRT(RES CHI1 2 + RES CHI2 ) 1,8 1,6 1,4 1,2 1,0 0,8 0,6 0,4 0,2 1 - CCC 2 - CCS 3 - CNN 4 - COL 5 - DNM 6 - ECM 7 - GLA 8 - JMA 9 - MGO 10 - MPI 11 - MRI 12 - NCA 13 - NCE 14 - PNM 15 - SUN 16 - UGA 17 - UIU UKM 19 - YON rad*10-7 b) rad*10-7 a) 1,8 1,6 1,4 1,2 1,0 0,8 0,6 0,4 model number MEAN RESIDUALS, P+IB RES CHI1, RES CHI2, SQRT(RES CHI1 2 + RES CHI2 2 ) 0, model number 1 - CCC 2 - CCS 3 - CNN 4 - COL 5 - DNM 6 - ECM 7 - GLA 8 - JMA 9 - MGO 10 - MPI 11 - MRI 12 - NCA 13 - NCE 14 - PNM 15 - SUN 16 - UGA 17 - UIU 18 - UKM 19 - YON 1,0 0,8 0,6 0,4 0,2 a) P / Geodetic P + IB / Geodetic 0,0 1,0 0 b) ,8 0,6 0,4 0,2 0,0 Correlation Coefficient, Chi1+iChi2, days 1 - CCC 2 - CCS 3 - CNR 4 - COL 5 - DNM 6 - ECM model number P / NCEP-NCAR P+IB / NCEP-NCAR 7 - GLA 8 - JMA 9 - MGO 10 - MPI 11 - MRI 12 - NCA 13 - NCE 14 - PNM 15 - SUN 16 - UGA 17 - UIU 18 - UKM 19 - YON 20 - NCEP-NCAR

40 MODEL OF CENTURY+ ATMOSPHERIC MODEL Atmospheric angular momentum Blue line is mean of 6 ensemble members

41 High frequency (subdiurnal information) Excitation of polar motion--wind term at each of 4 times during the day 0, 6 12, 18 UT χ 1 χ 2 Strong subdaily variation due to atm. tides NCEP/NCAR

42 Excitation of polar motion--wind term by time of day, by month Jan χ 1 χ 2 Jul Jan Jul Jun Dec Zhou et al., 2006 Jun Dec NCEP/NCAR

43 High temporal resolution angular momentum analysis with NASA system NASA s GEOS-4 Data Assimilation System: on a 1.25º longitude, 1º latitude resolution. GMAO has saved parameters from the model portion on hourly time resolution to investigate angular momentum and torques: surface pressure, winds, surface fields. At data assimilation time, there may be a discontinuity (offset), which other versions of the model need to address Run for October 2002, period including CONT02--continuous VLBI campaign. Later CONT campaigns: 2005, 2007, New efforts at NASA under the MERRA system, and from the ECMWF system

44 Excitations from GEOS-4 model/data assimilation system: Oct 2002 wind terms CHI-1 and CHI-2 WIND NASA/GEOS4 MODEL OCTOBER 2002 CHI-3 WIND NASA/GEOS4 MODEL, OCT Excitations x 10**7 1 0 Hours -1 Chi-1 wind Chi-2 wind Excitations x 10** Chi-3 wind Hours χ1 and χ2 CHI-1 and CHI-2 WIND EXCITATIONS, NASA/GEOS4 MODEL 1-2 OCT Hours χ Excitations x 10** Chi-1 wind Chi2-wind Hours

45 4 Excitations from GEOS-4 model/data assimilation system: Oct 2002 pressure terms CHI-1 PRESSURE NASA/GEOS4 MODEL OCTOBER CHI-1 PRESSURE NASA/GEOS4 MODEL OCTOBER2002 Excitaitons x 10** Chi1-pressure Excitaitons x 10**7 3 Polar motion Chi1-pressure χ Hours χ2 Hours CHI-3 PRESSURE NASA GEOS4 MODEL, OCTOBER Excitation x 10** Chi-3 pressure Length of day χ Hours

46 Diurnal signal in polar motion excitation Time series and phase of diurnal signature for chi-w excitation terms (raw and detrended). The diurnal signature vacillates in time during the month.

47 Comparisons of NASA (hourly) and NCEP analyses

48 Example of jump removal in time series: Spectra may have peaks at the high harmonics of the diurnal period due to the offsets in the data. Two techniques to remove the offsets: (1) (LDLin) extrapolate the mean jump one hour, take the difference between it and the new value, linearly interpolate these and subtract from the series for the previous six hours. (2) SSA: Smooth with singular spectrum analysis technique.

49 Summary 1. The atmosphere is an excitation source for geodetic information. Earth Rotation and torque variation depends on winds and surface pressures; -- both from angular momentum and torque considerations 2. We monitor the atmosphere for the IERS, as the Special Bureau for the Atmosphere at AER and NOAA: archive several centers; also efforts for combined signals, estimates of differences. 3. Analyses capture the data, consistent to atmospheric models. Models alone may be used as initial conditions for forecasts. 4. Diurnal variability of polar motion signals, part due to tides, determined from model hourly forecasts and data assimilation; heavily dependent on models.