Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set

Abstract

[1] Recent developments in observational near-surface air temperature and sea-surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea-surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea-surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near-surface temperature analyses.

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations

“…First and foremost, such estimates are an emerging field and to date several distinct approaches have arisen (e.g., Kennedy et al 2011b;Morice et al 2012;Thorne et al 2011a;Mears et al 2011;Williams et al 2012). These reflect both the importance of such estimates, which allow users to assess the sensitivity of their analyses to observational uncertainties in a more informed manner, and also the real challenges in making such estimates.…”

“…This is unsurprising given the sequential nature of the processing as outlined in section 2. Indeed, based upon a tacitly stated assumption of such nonlinearity existing in dataset construction techniques more generally, many emergent parametric uncertainty estimates for both in situ (e.g., Kennedy et al 2011b;Morice et al 2012;Thorne et al 2011a) and satellite (Mears et al 2011) data products have used Monte Carlo estimation techniques to quantify their parametric uncertainties. To our knowledge, this is the first time that the need or otherwise for such a step has been formally quantified and proven, at least in an observational climate record reconstruction context for a given algorithmic approach.…”

Observations of Local Positive Low Cloud Feedback Patterns and Their Role in Internal Variability and Climate Sensitivity

“…In the instrumental record, the global mean warming rate, $\frac{\mathrm{normal\Delta }T}{\mathrm{normal\Delta }t}$, is not constant and undergoes periods of acceleration and deceleration (Figure 1a; Hansen et al, 2010; Morice et al, 2012). The warming rate is also spatially heterogeneous with characteristic large‐scale patterns (Figures 1b and 1c).…”

The reliability of global and hemispheric surface temperature records

“…The incorporation of these components is complicated by the fact that some issues cancel by the number of measurements (particularly those due to land-station homogeneity), while the biases tend to be consistent so do not cancel. In order for uncertainty errors to be widely used, Morice et al (2012) introduced the concept of multiple, but equally plausible, realizations of the past. The HadCRUT4 dataset has developed 100 such realizations with a best guess, the median value for each grid box, and the median of the 100 realizations of global and hemispheric averages.…”