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How Much Has the Atmospheric Temperature Really Changed? An Attempt to Resolve the Satellite Dataset Controversy. John Lanzante (GFDL) Carl Mears (RSS)

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Presentation on theme: "How Much Has the Atmospheric Temperature Really Changed? An Attempt to Resolve the Satellite Dataset Controversy. John Lanzante (GFDL) Carl Mears (RSS)"— Presentation transcript:

1 How Much Has the Atmospheric Temperature Really Changed? An Attempt to Resolve the Satellite Dataset Controversy. John Lanzante (GFDL) Carl Mears (RSS) Evaluate differences between temperatures from 2 leading satellite datasets using radiosonde (weather balloon) data WORK IN PROGRESS – RESULTS ARE PRELIMINARY! RESULTS ARE NOT FROM LATEST VERSIONS OF MSU

2 SATELLITE DATA USED Try to resolve trend differences between 2 satellite datasets UAH (Univ. Alabama/Huntsville) Christy & Spencer RSS (Remote Sensing Systems) Mears & Wentz Time period 1979-May 2005 (updating to present with more recent versions) Satellite data begins in 1979 Area averages Globe and Tropics (big picture)

3 SATELLITE DATA: MSU Three layers relevant to climate Channel 4 (T4) Lower stratosphere Channel 2 (T2) Mid/upper troposphere (some stratosphere) Artificial channel (TLT) Lower troposphere [no results yet!]

4 RADIOSONDE DATA USED LKS/RATPAC (global 85 station network) LKS= Lanzante/Klein/Seidel RATPAC = Radiosonde Atmospheric Temperature Products for Assessing Climate Direct satellite/radiosonde comparisons only at these stations Convert to layer averages using appropriate weighting functions

5 Purpose: Narrow the range of possibilities of real climate by critically comparing two satellite temperature datasets (UAH & RSS) that have been adjusted for homogeneity. Means: Use a third dataset (RATPAC radiosonde data), that is also subject to concerns about inhomogeneity, in a novel way to choose (between UAH & RSS). Caveat: Since all 3 datasets have concerns regarding homogeneity, we cant simply compare long-term trends, satellite vs radiosonde, (as others have done) to decide which satellite dataset is better! METHODOLOGY: OVERVIEW

6 METHODOLOGY: PROCEDURE Reduce the dimensionality of the problem Use global difference series (RSS-UAH) to identify events Chose these events in conjunction with satellite metadata Events are of 2 types: Step: Discrete transition (change in satellite or data use) Drift: Gradual change (satellite drift inhomogeneity) Note: A few events explain most of (RSS-UAH) trend difference

7 METHODOLOGY: T4 EVENTS

8 METHODOLOGY: T2 EVENTS

9 METHODOLOGY: PROCEDURE Compute step or drift metrics for pairs of differences (RSS-UAH, RSS-RATPAC & UAH-RATPAC) at radiosonde stations Step Difference in means; Drift Linear slope; Both t-test Compute summary statistics from station statistics For each event declare a winner (RSS or UAH) closest to sonde Vary the length of the step/drift segments to insure robustness Diagnose the events Examine maps of station statistics Suggest nature of satellite problems? Look for radiosonde inhomogeneities (using metadata) Adjust the satellite data to ameliorate inhomogeneities Adjust using winner takes all strategy, event by event (adjust loser to match winner) Vary adjustment stringency criterion to create different sets Compute trends from various adjustment sets Compare trends from different sets with original data

10 METHODOLOGY: STRENGTHS & WEAKNESSES Strength: Minimizes influence of sonde inhomogeneities/biases Use global network (not all stations change at same time) Examine sonde metadata & maps (look for inhomogeneities) Short segments minimize effect of sonde spurious cooling No direct adjustment of satellite data by sonde data Weakness: Winner takes all adjustment strategy Correct could be intermediate (between RSS & UAH) Correct could be outside of both (RSS & UAH both wrong) Strength: Sensitivity analyses Use of different segment lengths tests robustness Use of different adjustment criteria tests robustness Caveats: Any unmitigated sonde biases would likely impart a cool bias to the results (our strategy should minimize this) We assume no problems outside of events

11 RESULTS: T4 WINNERS

12 RESULTS: T2 WINNERS

13 RESULTS: LINEAR TRENDS (T4) Stratosphere (T4)GlobeTropics (30N/S) Adjustment TypeRSSUAHRSSUAH Unadjusted All Events Significant Only (1%) Significant & Robust Linear Trends 1 : Jan1979–May2005 [ (K/decade) X 100) ] 1 Based on median of pair-wise slopes (mps) nonparametric regression NOTE: Radiosonde trends ~ -65

14 RESULTS: LINEAR TRENDS (T2) Troposphere (T2)GlobeTropics (30N/S) Adjustment TypeRSSUAHRSSUAH Unadjusted All Events Significant Only (1%) Significant & Robust Linear Trends 1 : Jan1979–May2005 [ (K/decade) X 100) ] 1 Based on median of pair-wise slopes (mps) nonparametric regression NOTE: Radiosonde trends ~ 0-2

15 * THE END *

16 Supplementary Slides Follow

17 Chapter 3, Fig. 1 [CCSP SAP1.1] Figure Time series of globally averaged surface temperature (TS) for NOAA (violet), NASA (black), and HadCRUT2v (green) datasets. All time series are 7-month running averages (used as a smoother) of original monthly data, which were expressed as a departure (ºC) from the average.

18 Chapter 3, Fig. 2 [CCSP SAP1.1] Figure 3.2a - Bottom: Time series of globally averaged tropospheric temperature (T( )) for RATPAC (violet) and HadAT2 (green) radiosonde datasets. All time series are 7-month running averages (used as a smoother) of original monthly data, which were expressed as a departure (ºC) from the average. Figure 3.2b - Top: Time series of globally averaged stratospheric temperature (T( )) for RATPAC (violet) and HadAT2 (green) radiosonde datasets. All time series are 7- month running averages (used as a smoother) of original monthly data, which were expressed as a departure (ºC) from the average.

19 Chapter 3, Fig.3 [CCSP SAP1.1] Figure 3.3a- Bottom: Time series of globally averaged lower tropospheric temperature (T2LT) as follows: UAH (blue) and RSS (red) satellite datasets, and HadAT2 (green) radiosonde data. All time series are 7-month running averages (used as a smoother) of original monthly data, which were expressed as a departure (ºC) from the average. Figure 3.3b- Third: Time series of globally averaged middle tropospheric temperature (T*G) as follows: UAH (blue) and RSS (red) satellite datasets, and HadAT2 (green) radiosonde data. All time series are 7-month running averages (used as a smoother) of original monthly data, which were expressed as a departure (ºC) from the average. Figure 3.3c - Second: Time series of globally averaged upper middle tropospheric temperature (T2) as follows: UAH) (blue), RSS (red), and UMd (black) satellite datasets, and HadAT2 (green) radiosonde data. All time series are 7-month running averages (used as a smoother) of original monthly data, which were expressed as a departure (ºC) from the average. Figure 3.3d - Top: Time series of globally averaged lower stratospheric temperature (T4) as follows: UAH (blue) and RSS (red) satellite datasets, and HadAT2 (green) radiosonde data. All time series are 7-month running averages (used as a smoother) of original monthly data, which were expressed as a departure (ºC) from the average.

20 Chapter 3, Table 2 [CCSP SAP1.1] Table Global temperature trends in ºC per decade from 1958 through 2004 (except for ERA40 which terminates September 2001) calculated for the surface or atmospheric layers by data source. The trend is shown for each, with the approximate 95% confidence interval (2 sigma) below in parentheses. The levels/layers, from left to right, go from the lowest to the highest in the atmosphere. Bold values are estimated to be statistically significantly different from zero (at the 5% level). A Students t-test, using the lag-1 autocorrelation to account for the non-independence of residual values about the trend line, was used to assess significance (see Appendix A for discussion of confidence intervals and significance testing).

21 Chapter 3, Table 3 [CCSP SAP1.1] Table Global temperature trends in ºC per decade from 1979 through 2004 (except for ERA40 which terminates September 2001) calculated for the surface or atmospheric layers by data source. The trend is shown for each, with the approximate 95% confidence interval (2 sigma) below in parentheses. The levels/layers, from left to right, go from the lowest to the highest in the atmosphere. Bold values are estimated to be statistically significantly different from zero (at the 5% level). A Students t-test, using the lag-1 autocorrelation to account for the non- independence of residual values about the trend line, was used to assess significance (see Appendix A for discussion of confidence intervals and significance testing).

22 Chapter 3, Table 4 [CCSP SAP1.1] Table 3.4 – Tropical (20ºN-20ºS) temperature trends in ºC per decade from 1979 through 2004 (except for ERA40 which terminates September 2001) calculated for the surface or atmospheric layers by data source. The trend is shown for each, with the approximate 95% confidence interval (2 sigma) below in parentheses. The levels/layers, from left to right, go from the lowest to the highest in the atmosphere. Bold values are estimated to be statistically significantly different from zero (at the 5% level). A Students t-test, using the lag-1 autocorrelation to account for the non-independence of residual values about the trend line, was used to assess significance (see Appendix A for discussion of confidence intervals and significance testing).

23 RESULTS: ADJUSTED TIME SERIES (T4)

24 RESULTS: ADJUSTED TIME SERIES (T2)

25 TRENDS FROM SHERWOOD & MEYERS (2008)


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