There seems to have been some interest in the sensitivity of Otto et al. (2013) lately. For instance, see Piers Forster’s comments about HadCRUT4 in Otto, or Trenberth and Fasullo (2014), which notes that using ORAS4 for the OHC dataset raises the estimate of ECS from 2.0 to 2.5 K. Anyhow, I ran a few of the tests, and thought I would share the results.
Otto et al (2013) uses five different intervals over which the differences of temperature, forcing, and TOA imbalance (for the ECS estimate) are calculated: 1) 2000s – Base, 2) 1990s – Base, 3) 1980s – Base, 4) 1970s – Base, and 5) the 40 year interval from 19702009. The base period used is 18601879. Here are the results when using the BEST or Cowtan and Way (2013) global temperature datasets (which infill undersampled regions to get more global coverage) to calculate TCR alongside HadCRUT4:
ΔF (W/m^2) 
HadCRUT4 ΔT (K) 
HadCRUT4 TCR (K) 
CW13 ΔT (K) 
CW13 TCR (K) 
BEST ΔT (K) 
BEST TCR (K) 

2000sBase 
1.94 
0.75 
1.33 
0.81 
1.44 
0.81 
1.44 
1990sBase 
1.25 
0.57 
1.57 
0.6 
1.65 
0.6 
1.65 
1980sBase 
0.99 
0.39 
1.36 
0.43 
1.49 
0.44 
1.53 
1970sBase 
0.75 
0.22 
1.01 
0.26 
1.19 
0.27 
1.24 
20091970 
1.28 
0.53 
1.42 
0.56 
1.51 
0.54 
1.45 
In most situations, it appears that the difference is relatively small, adding ~0.1K of TCR when using either Cowtan and Way (2013) or BEST global temperatures.
For ECS, I used Cowtan and Way (2013) for the temperature dataset and three different ocean heat content datasets: 1) Lyman and Johnson (2014), 2) ORAS4, the reanalysis product introduced in Balmaseda et al. (2013), and 3) Levitus et al (2012). For #1 and #2, I digitized the values from their respective papers. Since the calculation of ECS is slightly more complicated, I have included additional steps along the way. OHU represents the rate of ocean heat uptake as calculated over the period from the observed ocean heat content, and ΔQ represents the change in TOA imbalance over the interval (I assume 90% of the TOA imbalance goes into the ocean, and per Otto et al that the imbalance of the base period was 0.08 W/m^2). I also include a “most recent” period calculation, which occurs if we measure the OHC after the bulk of the ARGO deployment took place. For LJ14 they present this value from 20042011. For L12, they don’t present annual estimates until 2005, so I’ve used 20052013. For B13, I simply used 20042010. Here are the results:
ΔF (W/m^2) 
CW13 ΔT (K) 
LJ14 OHU (W/m^2) 
LJ14 ΔQ (W/m^2) 
LJ14 ECS (K) 
B13 OHU (W/m^2) 
B13 ΔQ (W/m^2) 
B13 ECS(K) 
L12 OHU (W/m^2) 
L12 ΔQ (W/m^2) 
L12 ECS (K) 

2000sBase 
1.94 
0.81 
0.50 
0.48 
1.90 
0.85 
0.86 
2.59 
0.48 
0.45 
1.87 
1990sBase 
1.25 
0.60 
0.25 
0.20 
1.96 
0.22 
0.32 
1.31 
0.25 
0.20 
1.96 
1980sBase 
0.99 
0.43 
0.26 
0.37 
1.09 
0.49 
0.46 
2.81 
0.13 
0.06 
1.60 
1970sBase 
0.75 
0.26 
0.32 
0.28 
1.89 
0.16 
0.10 
1.37 
0.49 
0.46 
3.13 
20091970 
1.28 
0.56 
0.20 
1.78 
0.77 
3.75 
0.01 
1.49 

Post_ARGO(2004 or 2005)Base 
1.94 
0.85 
0.29 
0.24 
1.72 
0.51 
0.49 
2.01 
0.54 
0.52 
2.06 
As you can see, the bulk of the estimates still seem to suggest an ECS < 2K, in line with the Otto et al (2013) calculations. For LJ14, the results are actually pretty tightly constrained (apart from the “lowball” 1980s estimate) between 1.7 and 2.0 K. Using ORAS4 (B13) does increase the ECS estimate when using the 2000s only, but it also largely decreases when using the 1990s or 1970s. When using only postARGO data, it is pretty much in line with the others, suggesting an ECS ~ 2K. L12 is also fairly tightly constrained, apart from the “highball” estimate of the 1970s. Overall,using ORAS4 produces the largest error margins and intervalspecific sensitivity. However, it is worth noting that using the postARGO OHC data seems to largely remove the dependence on which OHC dataset is chosen, producing best estimates between 1.7 and 2.1 for ECS.
Data for this post available here.
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