Stratospheric Temperature Variations and Trends: Recent Radiosonde Results Dian Seidel, Melissa Free NOAA Air Resources Laboratory Silver Spring, MD SPARC

Stratospheric Temperature Variations and Trends:

Recent Radiosonde Results

Dian Seidel, Melissa FreeNOAA Air Resources Laboratory

Silver Spring, MD

SPARC Temperature Trends Workshop

University of Reading, 3-4 March 2005

Topics

• New Radiosonde Datasets

• Comparison of Stratospheric Trends

• Evaluation of Sources of Differences

• Linear Trends and Other Models

• Upper-Air Measurement Requirements for Climate (time permitting)

Topics





• Upper-Air Measurement Requirements for Climate

NOAA Datasets

• CARDS became IGRA (Imke Durre, Russ Vose, NCDC)– Integrated Global Radiosonde Archive of quality controlled

(not homogeneity-adjusted) soundings– Metadata update is ongoing

• Angell (2003) reduced network from 63 to 54 stations

• Lanzante-Klein-Seidel (2003a,b) adjusted for inhomogeneities– Adjustments based on station history metadata, statistical

change-point identification, and evaluation of real abrupt changes

– 87 stations, 1948-1997

NOAA Datasets (cont.)

• LKS updated for RATPAC– Radiosonde Atmospheric Temperature Products for

Assessing Climate– Joint ARL/GFDL/NCDC effort– 16 levels, surface -10 hPa– Climate monitoring data product– Two basic datasets:

• Large-scale anomaly time series based on LKS adjustments through 1979 and first-difference method and metadata

• Station data with no adjustments post-1979

– General distribution after peer-review

Met Office Datasets

• HadRT (Parker et al. 1997) – Based on monthly-mean CLIMAT reports– 444 stations used to create gridded product– Referenced to MSU in stratosphere

• HadAT (Peter Thorne et al.)– >600 stations using to create several gridded

products– Homogenized using LKS results and neighborhood

checks, with focus on troposphere– Includes analysis of uncertainty– 9 levels, 850-30 hPa

• GCOS Upper-Air Network Monitoring Center (Mark McCarthy)

Rest of the World

• All-Russian Research Institute of Hydrometeorological Information (Alex Sterin)– Ongoing analysis of global and regional data

• Other efforts ???

Topics






Signals of large-scale, short-lived stratospheric variations in different datasets

are in good agreementQ

BO

0.00

0.01

0.02

0.03100-50 hPa MSU 4

Ang

ell-6

3

Ang

ell-5

4

Had

RT

RIH

MI

LKS

UA

H

RS

S

Had

RT

LKS

Pin

atu

bo

0.00.51.01.52.0

Ang

ell-6

3

Ang

ell-5

4

Had

RT

RIH

MI

LKS

UA

H

RS

S

Had

RT

LKS

EN

SO

0.000.040.080.120.16

19

76

-77

0.00.40.81.21.6

100-50 hPa

850-300 hPa

850-300 hPa

MSU 4

MSU 2

Trend sensitivity to LKS adjustments (solid)

HadRT and LKS agreement deteriorates with adjustments

Trends from Sondes, MSU & Reanalyses

• Large confidence intervals, but these do not address all sources of trend uncertainty

• More disparity among datasets than for shorter-term signals

• Reanalyses are outliers and are not reliable for trends

• Stratospheric cooling is stronger than tropospheric warming, but not more consistently estimated

• Sondes show more cooling than MSU

• Conventional wisdom is that sonde trends are too strong, but this is not firmly established.

Global Temperature Trends

1979-2003 Trend (K/decade)

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0

Surface

MSU LT

Fu-LT

850-300 hPa

500 hPa

300-100 hPa

MSU MT (2)

200 hPa

0100-50 hPa

MSU LS (4)

30 hPa

NCEP reanalysisERA40 reanalysisHadCRU surfaceNASA GISS surfaceNOAA GHCN surfaceVG MSURSS MSUUAH MSUHadAT raobLKS-RATPAC raob

RATPAC 1979-2003 Trends

RATPAC 1960-2003 Trends

Zonal Stratospheric Trends

RATPAC Zonal T Trends (K/decade)

Latitude

-80 -60 -40 -20 0 20 40 60 80-2.0

-1.5

-1.0

-0.5

0.0

0.5

1960-150 1960-100 1960-70 1960-50 1960-30 1979-150 1979-100 1979-70 1979-50 1979-30



Latitude

-80 -60 -40 -20 0 20 40 60 80-2.0

-1.5

-1.0

-0.5

0.0

0.5

1960-150 1960-100 1960-70 1960-50 1960-30



Latitude

-80 -60 -40 -20 0 20 40 60 80-2.0

-1.5

-1.0

-0.5

0.0

0.5

1979-150 1979-100 1979-70 1979-50 1979-30

Topics






Comparing Effects of 3 Factors to Evaluate HadRT / LKS Trend Differences

• Data Adjustments – compare trends from adjusted and unadjusted data (see above)

• Spatial and temporal sampling – compare trends from subsampled datasets and complete datasets, using MSU and reanalysis as complete datasets

• Source radiosonde data – compare trends from 71 common stations

Spatial sampling differences71 stations in common

HadRT and LKS trends agree better at 71 common stations than for full networks

(time sampling at month-to-month level only)

(ADJ)

Subsampling MSU makes little difference to MSU/sonde discrepancy

3 factors have comparable effects on global trend differences, with adjustments dominating in the stratosphere

Roles of Factors Vary Regionally

Topics






Alternative models may provide better fits (Seidel and Lanzante, 2004)

• Models evaluated using Bayesian Information Criterion

• Net stratospheric temperature change depends on model selected– Linear -1.13 K– Sloped steps -0.88 K– Censored, flat steps -

0.83– Censored, linear -0.99

• Different models suggest different physical interpretations

MSU4

1980 1985 1990 1995 2000

-1.0

1.0

1.0

1.0

1.0

-1.0

-1.0

1.0

-1.0

1.0

3.0

0.0

0.0

0.0

0.0

0.0

0.0

2.0

0.0

2.0

Observations

Censored: Linear+AR(1)

Censored: Flat Steps+AR(1)

model

model

residuals

residuals

Sloped Steps+AR(2)

model

residuals

Topics






Issues Affecting Climate Statistics and Trends

• Measurement Precision

• Sampling Frequency– Number of observations/day– Number of observations/month

• Long-term measurement stability

• Network size

• Locations of network stations

Tests with NCEP Reanalysis Data

• Start with 6-hourly data, at 6 pressure levels, at 15 locations, for 1948-2003

• Subsample, or introduce artificial changes

• Compare with unaltered data– Monthly means and variances– Multi-decadal trends

Precision Effects on Monthly MeansMeans are within 0.05K for precision <0.5 K

Estimated Minus Actual Monthly Mean Temperature (K)

Precision of Temperature Measurement (K)

0.01 0.10 0.50 1.00-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

Ratio of Estimated to Actual Monthly Standard Deviation

Precision of Temperature Measurement (K)

0.01 0.10 0.50 1.000.9

1.0

1.1

1.2

1.3

Effects of Reduced Measurement Precision onMonthly Means and Standard Deviations of Temperature

Full Temporal Sampling (4/day, every day)n=60480 (15 locations, 6 pressure levels, 672 months)

MAX

MIN

25%

75%50%

Reduced Diurnal Sampling:

• Effect varies with size and shape of diurnal cycle

• Going from 4 to 2 obs/day makes significant change in monthly means in only 2.3% of cases (mainly near surface and in stratosphere)

• For only 1/day, means change in 13-17% of cases

Conclusion: 2 obs/day may be enough

Number of Observations Per MonthEstimated Minus Actual Monthly Mean Temperature (K)

Sampling Frequency

1/2d 1/3d 1/7d-12

-8

-4

0

4

8

Ratio of Estimated to Actual Monthly Standard Deviation

Sampling Frequency

1/2d 1/3d 1/7d0.0

0.5

1.0

1.5

2.0

2.5

Effects of Subsampling the Month onMonthly Means and Standard Deviations of Temperature

Comparisons based on 2/day, every dayn=60840 (15 locations, 6 pressure levels, 672 months)

Observations every other day give • monthly means accurate to within 2 K or better • trends that are not statistically different in 90% of cases

Effects of Data Stability on Trends - 1 event

Reliable trend estimates require measurement stability within 0.5 K over 20-50 years.

Long-Term Data Stability

Effects of Random Interventions on Trends:Percent of Statistically Significantly Different Trends

Twice-Daily Sampling, Every Day, Full Precision

Segment Length (yrs)

20 25 30 35 40 45 50

Err

or R

ate

(%)

0

10

20

30

40

50

0.10K0.25K0.50K0.75K1.00K1.50K2.00K

Maximum Intervention

Effects of Multiple Random Interventions on Trends

Percent Statistically Significantly Different 25-Year Trends

Number of Interventions

0 1 2 3 4 5

Err

or R

ate

(%)

0

10

20

30

400.1K0.5K1.0K2.0K


Percent Statistically Significantly Different 50-Year Trends

Number of Interventions

0 1 2 3 4 5

Err

or R

ate

(%)

0

10

20

30

40 0.1K

0.5K

1.0K

2.0K


Effects of multiple changes = More errors

But the first event causes most of the error

Spatial sampling errors in trends from hypothetical networks from reanalysis– decrease with increasing size

Error = trend in subsampled minus trend in complete network

50

200

500 850

Spatial sampling errors in trends from actual radiosonde networksusing reanalysis- no decrease with increasing size

50

200

500 850

Take-Away Points• New Radiosonde Datasets – several,

unpublished• Comparison of Stratospheric Trends –

increasing cooling with height, with large uncertainties

• Evaluation of Sources of Differences – everything matters, but especially homogeneity adjustments in the stratosphere

• Linear Trends and Other Models – reasonable to consider nonmonotonic changes

• Upper-Air Measurement Requirements for Climate – input from this group would be very welcome!

























Documents

Stratospheric Temperature Variations and Trends: Recent Radiosonde Results Dian Seidel, Melissa Free NOAA Air Resources Laboratory Silver Spring, MD SPARC