Holly A. Titchner

Robust Tropospheric Warming Revealed by Iteratively Homogenized Radiosonde Data

Abstract Results are presented from a new homogenization of data since 1959 from 527 radiosonde stations. This effort differs from previous ones by employing an approach specifically designed to minimize systematic errors in adjustment, by including wind shear as well as temperature, by seasonally resolving adjustments, and by using neither satellite information nor station metadata. Relatively few artifacts were detected in wind shear, and associated adjustments were indistinguishable from random adjustments. Temperature artifacts were detected most often in the late 1980s–early 1990s. Uncertainty was characterized from variations within an ensemble of homogenizations and used to test goodness of fit with satellite data using reduced chi squared. The meridional variations of zonally aggregated temperature trend since 1979 moved significantly closer to those of the Microwave Sounding Unit (MSU) after data adjustment. Adjusted data from 5°S to 20°N continue to show relatively weak warming, but the error is...

The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2: 1. Sea ice concentrations

We present a new version of the sea ice concentration component of the Met Office Hadley Centre sea ice and sea surface temperature data set, HadISST.2.1.0.0. Passive microwave data are combined with historical sources, such as sea ice charts, to create global analyses on a 1° grid from 1850 to 2007. Climatology was used when no information about the sea ice was available. Our main aim was to create a homogenous data set by calculating and applying bias adjustments using periods of overlaps between the different data sources used. National Ice Center charts from 1995 to 2007 have been used as a reference to achieve this. In particular, large bias adjustments have been applied to the passive microwave data in both the Antarctic and Arctic summers. Overall, HadISST.2.1.0.0 contains more ice than HadISST1.1, with higher concentrations, shorter marginal ice zones, and larger extents and areas in some regions and periods. A new method for estimating the concentrations within the ice pack using the distance from the ice edge has been developed and evaluated. This was used when only the extents were known or the original concentration fields were heterogeneous. A number of discontinuities in the HadISST1.1 record are no longer found in HadISST.2.1.0.0.

A quantification of uncertainties in historical tropical tropospheric temperature trends from radiosondes

The consistency of tropical tropospheric temperature trends with climate model expectations remains contentious. A key limitation is that the uncertainties in observations from radiosondes are both substantial and poorly constrained. We present a thorough uncertainty analysis of radiosonde‐based temperature records. This uses an automated homogenization procedure and a previously developed set of complex error models where the answer is known a priori. We perform a number of homogenization experiments in which error models are used to provide uncertainty estimates of real‐world trends. These estimates are relatively insensitive to a variety of processing choices. Over 1979–2003, the satellite‐equivalent tropical lower tropospheric temperature trend has likely (5–95% confidence range) been between −0.01 K/decade and 0.19 K/decade (0.05–0.23 K/decade over 1958–2003) with a best estimate of 0.08 K/decade (0.14 K/decade). This range includes both available satellite data sets and estimates from models (based upon scaling their tropical amplification behavior by observed surface trends). On an individual pressure level basis, agreement between models, theory, and observations within the troposphere is uncertain over 1979 to 2003 and nonexistent above 300 hPa. Analysis of 1958–2003, however, shows consistent model‐data agreement in tropical lapse rate trends at all levels up to the tropical tropopause, so the disagreement in the more recent period is not necessarily evidence of a general problem in simulating long‐term global warming. Other possible reasons for the discrepancy since 1979 are: observational errors beyond those accounted for here, end‐point effects, inadequate decadal variability in model lapse rates, or neglected climate forcings.

Critically Reassessing Tropospheric Temperature Trends from Radiosondes Using Realistic Validation Experiments

Biases and uncertainties in large-scale radiosonde temperature trends in the troposphere are critically reassessed. Realistic validation experiments are performed on an automatic radiosonde homogenization system by applying it to climate model data with four distinct sets of simulated breakpoint profiles. Knowledge of the ‘‘truth’’ permits a critical assessment of the ability of the system to recover the large-scale trends and a reinterpretation of the results when applied to the real observations. The homogenization system consistently reduces the bias in the daytime tropical, global, and Northern Hemisphere (NH) extratropical trends but underestimates the full magnitude of the bias. Southern Hemisphere (SH) extratropical and all nighttime trends were less well adjusted owing to the sparsity of stations. The ability to recover the trends is dependent on the underlying error structure, and the true trend does not necessarily lie within the range of estimates. The implications are that tropical tropospheric trends in the unadjusted daytime radiosonde observations, and in many current upper-air datasets, are biased cold, but the degree of this bias cannot be robustly quantified. Therefore, remaining biases in the radiosonde temperature record may account for the apparent tropical lapse rate discrepancy between radiosonde data and climate models. Furthermore, the authors find that the unadjusted global and NH extratropical tropospheric trends are biased cold in the daytime radiosonde observations. Finally, observing system experiments show that, if the Global Climate Observing System (GCOS) Upper Air Network (GUAN) were to make climate quality observations adhering to the GCOS monitoring principles, then one would be able to constrain the uncertainties in trends at a more comprehensive set of stations. This reaffirms the importance of running GUAN under the GCOS monitoring principles.

Assessing Bias and Uncertainty in the HadAT-Adjusted Radiosonde Climate Record

Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from temperature records of radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quantifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combination of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade 1 )i n the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radiosondes.

An Analysis of Tropospheric Humidity Trends from Radiosondes

Abstract A new analysis of historical radiosonde humidity observations is described. An assessment of both known and unknown instrument and observing practice changes has been conducted to assess their impact on bias and uncertainty in long-term trends. The processing of the data includes interpolation of data to address known sampling bias from missing dry day and cold temperature events, a first-guess adjustment for known radiosonde model changes, and a more sophisticated ensemble of estimates based on 100 neighbor-based homogenizations. At each stage the impact and uncertainty of the process has been quantified. The adjustments remove an apparent drying over Europe and parts of Asia and introduce greater consistency between temperature and specific humidity trends from day and night observations. Interannual variability and trends at the surface are shown to be in good agreement with independent in situ datasets, although some steplike discrepancies are apparent between the time series of relative humi...

Observations for Reanalyses

AbstractGlobal dynamical reanalyses of the atmosphere and ocean fundamentally rely on observations, not just for the assimilation (i.e. for the definition of the state of the Earth-system components), but also in many other steps along the production chain. Observations are used to constrain the model boundary conditions, for the calibration or uncertainty determination of other observations, and for the evaluation of data products. This requires major efforts, including data rescue (for historical observations), data management (including metadatabases), compilation and quality control, and error estimation. The work on observations ideally occurs one cycle ahead of the generation cycle of reanalyses, allowing them to make full use of it. In this paper we describe the activities within ERA-CLIM2, which range from surface, upper-air and Southern Ocean data rescue to satellite data recalibration and from the generation of snow cover products to the development of a global station data metadatabase. The pro...

Accounting for Changing Temperature Patterns Increases Historical Estimates of Climate Sensitivity

Eight atmospheric general circulation models (AGCMs) are forced with observed historical (1871–2010) monthly sea surface temperature and sea ice variations using the Atmospheric Model Intercomparison Project II data set. The AGCMs therefore have a similar temperature pattern and trend to that of observed historical climate change. The AGCMs simulate a spread in climate feedback similar to that seen in coupled simulations of the response to CO2 quadrupling. However, the feedbacks are robustly more stabilizing and the effective climate sensitivity (EffCS) smaller. This is due to a pattern effect, whereby the pattern of observed historical sea surface temperature change gives rise to more negative cloud and longwave clear‐sky feedbacks. Assuming the patterns of long‐term temperature change simulated by models, and the radiative response to them, are credible; this implies that existing constraints on EffCS from historical energy budget variations give values that are too low and overly constrained, particularly at the upper end. For example, the pattern effect increases the long‐term Otto et al. (2013, https://doi.org/10.1038/ngeo1836) EffCS median and 5–95% confidence interval from 1.9 K (0.9–5.0 K) to 3.2 K (1.5–8.1 K).