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Mark P. McCarthy and Ralf Toumi

Abstract

Relative humidity fields from the High-Resolution Infrared Radiation Sounder (HIRS) flown on NOAA series satellites since 1979 have been used to study the seasonal aspects of the interannual variability of relative humidity in the tropical troposphere. The El Niño–Southern Oscillation (ENSO) is the only statistically identifiable physical mechanism of such variability. Boreal winter (December–February) relative humidity variations during an ENSO event follow patterns of anomalous convection and large-scale upper-level circulation. During El Niño (La Niña) regions of large negative (positive) relative humidity anomalies exist at subtropical latitudes over the Pacific Ocean. These are not always balanced by increases (decreases) in humidity near the equator. NCEP– NCAR reanalysis temperatures are used to separate observed changes in relative humidity into contributions from tropospheric temperature versus the contribution from changes in water vapor content. The authors find that at subtropical latitudes variations in temperature contribute between 50% and 70% of the observed change in relative humidity. It is also shown that large relative humidity anomalies exist over the equatorial Indian, Atlantic, and far east Pacific Oceans during the summer season (June–August) following an ENSO event. Ocean– atmosphere dynamics coupled with the seasonal cycle of relative humidity explain the existence of the long-lasting effects of ENSO in the atmosphere. The authors argue that observed linear trends in regional and tropical mean relative humidity are unlikely to be due solely to ENSO or a simple intensification of the hydrological cycle.

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Nikolaos Christidis, Mark McCarthy, Andrew Ciavarella, and Peter A. Stott
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Mark P. McCarthy, P. W. Thorne, and H. A. Titchner

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 humidity at the surface.

Adjusted trends, accounting for documented and undocumented break points and their uncertainty, across the extratropical Northern Hemisphere lower and midtroposphere show warming of 0.1–0.4 K decade−1 and moistening on the order of 1%–5% decade−1 since 1970. There is little or no change in the observed relative humidity in the same period, consistent with climate model expectation of a positive water vapor feedback in the extratropics with near-constant relative humidity.

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Mark P. McCarthy, H. A. Titchner, P. W. Thorne, S. F. B. Tett, L. Haimberger, and D. E. Parker

Abstract

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) in 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.

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