Reanalysis -Achievements and Challenges -

transcript

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

• 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

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

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, MeanERA40 DJF 90/91

Z500, 5 day Verification

ERA40 noHum

Tropics, Winds

850hPa 250hPa

Tropics, Wind (850hPa)Mean

ERA40, 5 day

noHum, 5 day

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

Climate trend calculations

Bengtsson et al., 2004

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

Reanalysis -Achievements and Challenges -

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