Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: A. J. Geer x
  • Refine by Access: All Content x
Clear All Modify Search
H. E. Brindley
,
A. J. Geer
, and
J. E. Harries

Abstract

Several recent studies have highlighted the potential of utilizing statistical techniques to pattern match observations and model simulations in order to establish a causal relationship between anthropogenic activity and climate change. Up to now these have tended to concentrate upon the spatial or vertical patterns of temperature change. Given the availability of contiguous, global-scale satellite observations over the past two decades, in this paper the authors seek to employ an analogous technique to spatially match model predictions to directly measured radiances. As part of the initial investigations, the technique to channel 1 of the Stratospheric Sounding Unit, sensitive to stratospheric temperature and carbon dioxide concentrations, is applied. Over the majority of the globe the observations show a negative trend in brightness temperature, with significant decreases occurring throughout the Tropics. The influence of the volcanic eruptions of El Chichón and Mount Pinatubo can also be clearly identified. Simulated brightness temperature fields, against which the satellite data are compared, are calculated using atmospheric temperature profiles from a transient climate change run of the Hadley Centre GCM. The modeled change pattern also indicates a global reduction in brightness temperature but with an altered spatial distribution relative to the observations. This tendency is reflected in the trends seen in the correlation statistics. One, dominated by the spatial mean change, shows a significant positive trend; while the other, influenced by patterns around this mean, exhibits a reducing correlation with time. Possible reasons for this behavior are discussed, and the importance of both improving model parameterizations and performing additional“unforced” simulations to assess the role of natural variability is stressed.

Full access
A. J. Geer
,
J. E. Harries
, and
H. E. Brindley

Abstract

The use of multivariate fingerprints and spatial pattern correlation in the detection and attribution of climate change has concentrated on radiosonde temperature fields. However, the large body of radiance data from satellite-borne instruments includes contiguous datasets of up to 17 yr in length and in future years will present the most well-calibrated and large-scale data archive available for climate change studies. Here the authors give an example of the spatial correlation technique used to analyze satellite radiance data. They examine yearly mean brightness temperatures from High Resolution Infrared Spectrometer (HIRS) channel 12, sensitive to upper-tropospheric water vapor and temperature. Atmospheric profiles from a climate change run of the Hadley Centre GCM (HADCM2) are used to simulate the pattern of brightness temperature change for comparison to the satellite data. Investigation shows that strong regional brightness temperature changes are predicted in the Tropics and are dominated by changes in relative humidity in the upper troposphere. At midlatitudes only small changes are predicted, partly due to a compensation between the effects of temperature and relative humidity. The observational data showed some significant regional changes, especially at 60°S, where there was a trend toward lower brightness temperatures. The pattern similarity statistics revealed a small trend between 1979 and 1995 toward the predicted climate change patterns but this was not significant. The detection of any trend is complicated by the high natural variability of HIRS-12 radiances, which is partly associated with the El Niño–Southern Oscillation.

Full access
G. A. Kelly
,
P. Bauer
,
A. J. Geer
,
P. Lopez
, and
J-N. Thépaut

Abstract

This paper presents the results from the Observing System Experiments (OSEs) with the current ECMWF data assimilation and modeling system for quantifying the impact on both analysis and forecast quality of Special Sensor Microwave Imager (SSM/I) observations sensitive to moisture and clouds as well as precipitation. SSM/I radiances have been assimilated operationally in clear-sky areas for 8 yr and in cloud- and rain-affected areas since June 2005. This paper examines experiments set up such that clear-sky and rain-affected observations were either added to a baseline with a restricted observing system configuration or withdrawn from the full system. The experiment duration was 10 weeks of which the first 14 days were excluded from the evaluation to allow the system to lose the memory of the initial conditions at day −1.

It is shown that both clear-sky and rain-affected observations account for the bulk correction of moisture in the ECMWF analysis. SSM/I data adds 1 day of forecast skill over the first 48 h when evaluated in addition to a baseline-observing system. In the tropics, the rain-affected data contributes more skill to the moisture forecast than the clear-sky data at 700 hPa and above. In the Northern and Southern Hemispheres, the effect is generally weaker and slightly in favor of clear-sky observations. A similar performance can be seen with respect to the wind vector forecast skill, which reflects the connection between the analysis of moisture and dynamics.

Full access
James A. Brey
,
Elizabeth W. Mills
,
Ira W. Geer
,
Robert S. Weinbeck
,
Kira A. Nugnes
,
Katie L. O’Neill
,
Bernard A. Blair
,
David R. Smith
, and
Edward J. Hopkins
Full access