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Roy W. Spencer and William D. Braswell

The humidity of the free troposphere is being increasingly scrutinized in climate research due to its central role in global warming theory through positive water vapor feedback. This feedback is the primary source of global warming in general circulation models (GCMs). Because the loss of infrared energy to space increases nonlinearly with decreases in relative humidity, the vast dry zones in the Tropics are of particular interest. These dry zones are nearly devoid of radiosonde stations, and most of those stations have, until recently, ignored the low humidity information from the sondes. This results in substantial uncertainty in GCM tuning and validation based on sonde data. While satellite infrared radiometers are now beginning to reveal some information about the aridity of the tropical free troposphere, the authors show that the latest microwave humidity sounder data suggests even drier conditions than have been previously reported. This underscores the importance of understanding how these low humidity levels are controlled in order to tune and validate GCMs, and to predict the magnitude of water vapor feedback and thus the magnitude of global warming.

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Roy W. Spencer and William D. Braswell

Abstract

The first Advanced Microwave Sounding Unit temperature sounder (AMSU-A) was launched on the NOAA-15 satellite on 13 May 1998. The AMSU-A’s higher spatial and radiometric resolutions provide more useful information on the strength of the middle- and upper-tropospheric warm cores associated with tropical cyclones than have previous microwave temperature sounders. The gradient wind relationship suggests that the temperature gradient near the core of tropical cyclones increases nonlinearly with wind speed. The gradient wind equation is recast to include AMSU-A-derived variables. Stepwise regression is used to determine which of these variables is most closely related to maximum sustained winds (V max). The satellite variables investigated include the radially averaged gradients at two spatial resolutions of AMSU-A channels 1–10 T b data (δ r T b), the squares of these gradients, a channel-15-based scattering index (SI89), and area-averaged T b. Calculations of T b and δ r T b from mesoscale model simulations of Andrew (1992) reveal the effects of the AMSU spatial sampling on the cyclone warm core presentation. Stepwise regression of 66 AMSU-A terms against National Hurricane Center V max estimates from the 1998 and 1999 Atlantic hurricane season confirms the existence of a nonlinear relationship between wind speed and radially averaged temperature gradients near the cyclone warm core. Of six regression terms, four are dominated by temperature information, and two are interpreted as correcting for hydrometeor contamination. Jackknifed regressions were performed to estimate the algorithm performance on independent data. For the 82 cases that had in situ measurements of V max, the average error standard deviation was 4.7 m s−1. For 108 cases without in situ wind data, the average error standard deviation was 7.5 m s−1. Operational considerations, including the detection of weak cyclones and false alarm reduction, are also discussed.

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Roy W. Spencer and William D. Braswell

Abstract

Feedbacks are widely considered to be the largest source of uncertainty in determining the sensitivity of the climate system to increasing anthropogenic greenhouse gas concentrations, yet the ability to diagnose them from observations has remained controversial. Here a simple model is used to demonstrate that any nonfeedback source of top-of-atmosphere radiative flux variations can cause temperature variability, which then results in a positive bias in diagnosed feedbacks. This effect is demonstrated with daily random flux variations, as might be caused by stochastic fluctuations in low cloud cover. The daily noise in radiative flux then causes interannual and decadal temperature variations in the model’s 50-m-deep swamp ocean. The amount of bias in the feedbacks diagnosed from time-averaged model output depends upon the size of the nonfeedback flux variability relative to the surface temperature variability, as well as the sign and magnitude of the specified (true) feedback. For model runs producing monthly shortwave flux anomaly and temperature anomaly statistics similar to those measured by satellites, the diagnosed feedbacks have positive biases generally in the range of −0.3 to −0.8 W m−2 K−1. These results suggest that current observational diagnoses of cloud feedback—and possibly other feedbacks—could be significantly biased in the positive direction.

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John R. Christy, Roy W. Spencer, and William D. Braswell

Abstract

Two deep-layer tropospheric temperature products, one for the lower troposphere (T2LT) and one for the midtroposphere (T2, which includes some stratospheric emissions), are based on the observations of channel 2 of the microwave sounding unit on National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Revisions to version C of these datasets have been explicitly applied to account for the effects of orbit decay (loss of satellite altitude) and orbit drift (east–west movement). Orbit decay introduces an artificial cooling in T2LT, while the effects of orbit drift introduce artificial warming in both T2LT and T2. The key issues for orbit drift are 1) accounting for the diurnal cycle and 2) the adjustment needed to correct for spurious effects related to the temperature of the instrument. In addition, new calibration coefficients for NOAA-12 have been applied. The net global effect of these revisions (version D) is small, having little impact on the year-to-year anomalies. The change in global trends from C to D for 1979–98 for T2LT is an increase from +0.03 to +0.06 K decade−1, and a decrease for T2 from +0.08 to +0.04 K decade−1.

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John R. Christy, Roy W. Spencer, William B. Norris, William D. Braswell, and David E. Parker

Abstract

Deep-layer temperatures derived from satellite-borne microwave sensors since 1979 are revised (version 5.0) to account for 1) a change from microwave sounding units (MSUs) to the advanced MSUs (AMSUs) and 2) an improved diurnal drift adjustment for tropospheric products. AMSU data, beginning in 1998, show characteristics indistinguishable from the earlier MSU products. MSU–AMSU error estimates are calculated through comparisons with radiosonde-simulated bulk temperatures for the low–middle troposphere (TLT), midtroposphere (TMT), and lower stratosphere (TLS.) Monthly (annual) standard errors for global mean anomalies of TLT satellite temperatures are estimated at 0.10°C (0.07°C). The TLT (TMT) trend for January 1979 to April 2002 is estimated as +0.06° (+0.02°) ±0.05°C decade–1 (95% confidence interval). Error estimates for TLS temperatures are less well characterized due to significant heterogeneities in the radiosonde data at high altitudes, though evidence is presented to suggest that since 1979 the trend is −0.51° ± 0.10°C decade–1.

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Roy W. Spencer, John R. Christy, William D. Braswell, and William B. Norris

Abstract

The problems inherent in the estimation of global tropospheric temperature trends from a combination of near-nadir Microwave Sounding Unit (MSU) channel-2 and -4 data are described. The authors show that insufficient overlap between those two channels’ weighting functions prevents a physical removal of the stratospheric influence on tropospheric channel 2 from the stratospheric channel 4. Instead, correlations between stratospheric and tropospheric temperature fluctuations based upon ancillary (e.g., radiosonde) information can be used to statistically estimate a correction for the stratospheric influence on MSU 2 from MSU 4. Fu et al. developed such a regression relationship from radiosonde data using the 850–300-hPa layer as the target predictand. There are large errors in the resulting fit of the two MSU channels to the tropospheric target layer, so the correlations from the ancillary data must be relied upon to provide a statistical minimization of the resulting errors. Such relationships depend upon the accuracy of the particular training dataset as well as the dataset time period and its global representativeness (i.e., temporal and spatial stationarity of the statistics). It is concluded that near-nadir MSU channels 2 and 4 cannot be combined to provide a tropospheric temperature measure without substantial uncertainty resulting from a necessary dependence on ancillary information regarding the vertical profile of temperature variations, which are, in general, not well known on a global basis.

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