COLA 20-years18 June 2004
Reanalysis -Achievements and Challenges-
Professor Lennart Bengtsson
ESSC, University of ReadingMPI for Meteorology, Hamburg
Reanalysis
-Achievements and Challenges-
• Introduction and Background
• Impact of humidity observations
• Observations and forecast skill
• Climate trends
• Challenges for the future
Results of Reanalyses
ERA40
• Covering the 45-year period 1957-2002
• Resolution T159/L60
• Using 3DVar
• Includes all available observations
• Main emphasis at ECMWF: predictability
• Most serious problem: tropical ocean precipitation
COLA 20-years18 June 2004
Vorticity generation and storm tracks
Kevin Hodges, ESSC
NH, DJF
ERA40, 850 Cyclonic Genesis (# density per month)
SH, JJA
MJJASON
ERA40, 850 Cyclonic Track Density (# density per month)
NH, DJF SH, JJA
MJJASON
ERA40, 850 Cyclonic Tracks
NH, 1999/2000 DJF SH, 2000 JJA
2000, MJJASON
COLA 20-years18 June 2004
Impact of humidity observations
Bengtsson et al., 2004a
Tellus
Global water balanceDJF 90/91 unit:1000qkm
-4.2-4.1-5.1-13.4Ocean
P-E ERA40
(no moisture)
-0.11.6-0.60.9Ocean
P-E ERA40
3.75.14.012.8Land
P-E ERA40
(no moisture)
3.64.94.312.8Land
P-E ERA40
Feb.Jan.Dec.Total
Daily assimilated water vapor, Feb. 1991
Full line ERA40, dashed ERA40, nohum
Stormtrack validation
Observed and assimilated tropical storm tracks
Global forecasts DJF 90/91• 7- day forecasts, every 6hr.• Latest ECMWF model T159/L60
• Extra-tropics 20-90N and 20-90S• 500hPa Z, normalized SD for the period
• Tropics 20N-20S• Wind vector field 850 and 250hPa
Z500
NH SH
Z500, MeanERA40 DJF 90/91
Z500, 5 day Verification
ERA40 noHum
Tropics, Winds
850hPa 250hPa
Tropics, Wind (850hPa)Mean
ERA40, 5 day
noHum, 5 day
COLA 20-years18 June 2004
Reanalysis with reduced observing systems
Bengtsson et al., 2004b
Tellus
Methodology
• We have mimicked earlier observing systems by redoing the ERA40 assimilation for limited periods.
• This has been done at the ECMWF computer system from ESSC at Reading University
Experimental periods
• DJF 1990/1991
• JJA 2000
• DJF 2000/2001
We have done four main experiments
• 1. ERA 40 - all humidity observations• 2. Exp 1 - all space observations • 3. Exp 2 - all upper air observations• 4. Exp 1 - all upper air observations
Normalized RMS for DJF 90/91
• Control -Terrestrial system
• Control -Surface system
• Control - Satellite system
• Control-No observation
Observed and assimilated QBOVertical wind profiles and zonal wind at 50 hPa
full line:obs, dashed line: sat. system, dotted line: control
NH, MSLP, Cyclones
Tracks Intensities
SH, MSLP, Cyclones
Tracks Intensities
Z500
NH SH
COLA 20-years18 June 2004
Climate trend calculations
Bengtsson et al., 2004
JGR
Annual mean global values of relative humidity f (in %) vertically averaged for 850-300 hPa and vertically integrated absolute humidity q (in kg/m2).
Integrated Water Vapor1979-1999
ECHAM5: T106/L31 using AMIP2 boundary conditions
Preliminary results:
Globally averaged results vary between 25.10 mm (1985) and 26.42 mm (1998)
Mean value for the 1990s is 1% higher than in the 1980s
Interannual variations are similar to ERA-40
Variations follow broadly temperature observations from MSU (tropospheric channel) under unchanged relative humidity (1°C is
equivalent to some 6%).
Potential problems in calculating climate trends
• Assimilating model may have systematic biases
• Observing systems have undergone major changes both in instrumentation and observational coverage
• Instrumental changes and observational representation to be considered
Can Climate Trends be Calculated from Re-Analysis Data?
• We have investigated using ERA40:
• Tropospheric temperature (MSU (TLT))
• Atmospheric water content (IWV)
• Total kinetic energy
Coupled data-assimilation
• An MPI experiment from 1997
• ( Oberhuber et al., 1998, JGR)
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Concluding remarks Reanalysis data sets have provided important understanding of climate variability
of the last 50 years
• Changes in the global observing system especially in 1979 make it difficult to assess climate trends
• The effect of such changes can be quantitatively estimated but requires dedicated re-reanalyses with selected observations
• It is required to better identify the key observations in 4D data-assimilation
• Reanalyses of the full ocean-atmosphere-land system should be done with coupled models and not by separate models.
• Reanalysis experiments are needed to guide a better design of the the global observing systems for weather and climate prediction