1. Time, probe type and temperature variable bias corrections to historical eXpendable BathyThermograph observations 1. International Center for Climate and Environment Sciences (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. 2. University of Chinese Academy of Sciences, Beijing, China; 3. Wealth from Oceans Flagship, Centre for Australian Weather and Climate Research, CSIRO, Hobart, Tasmania, Australia. 4. National Oceanographic Data Center, NOAA, Silver Spring, Maryland, USA. Lijing Cheng 1,2, Jiang Zhu 1*, Rebecca Cowley 3, Tim Boyer 4, Susan Wijffels 3 Institute of Atmospheric Physics, Chinese Academy of Sciences

2. 1. Motivation Based on Cowley et al. 2013 (CW13)’s study of detecting XBT bias based on side-by-side XBT/CTD comparison database, we are thinking: Are the CW13 results (limited amount but high quality) consistent with those based on global-scale XBT data? Locations of XBT data in Side-by-side dataset Amount of XBT data in 1 o grid since 1966 (WOD2009) Examine the XBT bias in both datasets

3. 2. Data/Methods 1. Side-by-Side XBT/CTD comparison dataset 2. Global-scale XBT/CTD comparison dataset Fall rate equation ： Depth=A*time-B*time 2 -Offset Total XBT Bias = Fall rate error + Pure thermal bias Obtained based on WOD2009 dataset Pair: Within 1 by 1 degree and 1 month ~ 220,000 pairs ~4416 pairs, available at CSIRO website Method: Cheng et al. 2011

4. 3. Side-by-Side dataset (1) Fall rate ( A ) V.S. Temperature ( 0-100m ) Coefficient A increases with 0-100m averaged ocean temperature. A against 0-100m averaged temperature and linear regression Coefficient A represents an initial fall rate when probe gets to the stable falling in the upper ocean, this process is likely to happen within 30-100m.

5. 3. Side-by-Side dataset (2) Pure thermal bias V.S. Temperature Pure thermal bias increases with ocean temperature. Cowley et al. 2013

6. (3) Correlation between A/B and A/Offset Fall rate equation ： Depth=A*time-B*time 2 -Offset 3. Side-by-Side dataset The coefficients are not independent with each other A/B and linear regressionA/Offset and linear regression Some assumptions are presented in Cheng et al. 2014

7. 3. Side-by-Side dataset Summary 1.Correlation between A and B, A and offset 2.Relationship between Fall rate / Pure Thermal Bias and Ocean temperature 1. Only probes of T4/T6, T7/DB could be identified. Bias of other probe types could not be detected. 2. Could not sure whether ~4400 pairs could represent the time variation of the XBT bias over the global. Problems Global-scale XBT/CTD comparison dataset

8. Groups ： G1: T7/DB G2: DX: Deep-Unknown G3: T4/T6 G4: SX: Shallow-Unknown G5: T10 G6: T5 G7: TSK-T4/T6 G8: TSK-T5 G9: TSK-T7/DB G1 4. Global-scale pairs B and offset are calculated according to A by using the correlations: (1): reduce the freedom (2): reduce the CPU time Fall rate equation ： Depth=A*time-B*time 2 -Offset = A*time-(b1*A+b2)*time 2 -(c1*A+c2) ~22,0000 XBT/CTD pairs Four Unknown variables: A/B/Offset/Thermal bias Two: A/Thermal bias

9. G1 4. Global-scale pairs How many pairs do we need to get a satisfied statistics?? Bootstrap Test The standard errors for all depths decrease to 0.02 o C (2 times the XBT thermistor sensitivity) when the number of pairs is more than: 2200 (T7/DB), 2000 (DX), 2200 (T4/T6), and 3000 (SX) To 0.04 o C: 800 (T10), 700 (T5), 900 (TSK-T4/T6), 850 (TSK-T7/DB), 800 (TSK-T5) Determine how large a year window are used to get an estimate on annual mean bias

10.  Pure Thermal Bias (time) Red: This study,CH14 Black: Cowley et al, 2013 1.Different bias history for different probe types. 2.Larger bias pre-1985 3.DX and SX is different 4.Shift time for T7/DB is similar to side-by-side dataset. 5.Shift time for DX is earlier than side-by-side dataset. 6.Shift time for T4/T6 and SX is latter than side- by-side dataset.

11.  Fall rate coefficient A (time) Different bias history for different probe types. Difference of DX and SX Similar time variation of depth error for the two datasets Black: Cowley et al, 2013: Side-by-Side Red:This study,CH14: Global-scale

12. Side-By-Side Data 1. A(temperature), Pure thermal bias(temperature) 2. B(A), Offset(A) Global-Scale Data 3. Pure thermal bias(year), 4. A(year)  CH14 Scheme Bias(year) = Pure Thermal Bias (year) + Pure Thermal Bias (temperature) + A(year) + A(temperature) +B(A) +Offset(A) 5. Correction Scheme Thermal Bias Depth Error

13. 6. Evaluation-01 WOD2009 EN3 Removal of Total XBT-Bias Red: This study,CH14 Raw

14. 6. Evaluation-02 Removal of Latitude- dependency of XBT- Bias Red: Raw Blue: Only correct temperature dependent part Orange: Overall correct

15. 7. Summary A new XBT correction scheme is proposed, considering the time, probe-type and temperature dependency of both pure thermal bias and depth error,based on two XBT/CTD comparison datasets We confirmed that both fall rate and pure thermal bias is variable with temperature, time, probe-type. We found correlations among three coefficients, suggesting that they are not independent Thanks

16. 6. Evaluation-03 Partly-Removal of temperature-dependency of XBT-Bias EN3 Txbt-Tctd V.S. Temperature Maybe due to the depth- dependency