Melissa Free

Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) : A new data set of large-area anomaly time series

[1] A new data set containing large-scale regional mean upper air temperatures based on adjusted global radiosonde data is now available up to the present. Starting with data from 85 of the 87 stations adjusted for homogeneity by Lanzante, Klein and Seidel, we extend the data beyond 1997 where available, using a first differencing method combined with guidance from station metadata. The data set consists of temperature anomaly time series for the globe, the hemispheres, tropics (30N–30S) and extratropics. Data provided include annual time series for 13 pressure levels from the surface to 30 mbar and seasonal time series for three broader layers (850–300, 300–100 and 100–50 mbar). The additional years of data increase trends to more than 0.1 K/decade for the global and tropical midtroposphere for 1979–2004. Trends in the stratosphere are approximately 0.5 to 0.9 K/decade and are more negative in the tropics than for the globe. Differences between trends at the surface and in the troposphere are generally reduced in the new time series as compared to raw data and are near zero in the global mean for 1979–2004. We estimate the uncertainty in global mean trends from 1979 to 2004 introduced by the use of first difference processing after 1995 at less than 0.02–0.04 K/decade in the troposphere and up to 0.15 K/decade in the stratosphere at individual pressure levels. Our reliance on metadata, which is often incomplete or unclear, adds further, unquantified uncertainty that could be comparable to the uncertainty from the FD processing. Because the first differencing method cannot be used for individual stations, we also provide updated station time series that are unadjusted after 1997. The Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) data set will be archived and updated at NOAA’s National Climatic Data Center as part of its climate monitoring program.

Reexamining the warming in the tropical upper troposphere: Models versus radiosonde observations

[1] A recent study of 1979–2010 tropical tropospheric temperature trends in climate model simulations and satellite microwave sounding unit (MSU) observations concluded that, although both showed greater warming in the upper than lower troposphere, the vertical amplification of warming was exaggerated in most models. We repeat that analysis of temperature trends, vertical difference trends, and trend ratios using five radiosonde datasets. Some, but not all, comparisons support the notion that vertical amplification in models exceeds that observed. However, larger ranges of radiosonde trends compared with those for MSU, and the sensitivity of results to the upper-tropospheric level analyzed, make it difficult to conclude unambiguously that models are inconsistent with radiosonde observations. The larger ranges are due to the availability of more radiosonde datasets with different approaches for adjusting measurement biases. Together these two studies highlight challenges of using imperfect observations of tropical tropospheric temperature over a few decades to assess climate model performance. Citation: Seidel, D. J., M. Free, and J. S. Wang (2012), Reexamining the warming in the tropical upper troposphere: Models versus radiosonde observations, Geophys. Res. Lett., 39, L22701,

Measurement Requirements for Climate Monitoring of Upper-Air Temperature Derived from Reanalysis Data

Abstract Using a reanalysis of the climate of the past half century as a model of temperature variations over the next half century, tests of various data collection protocols are made to develop recommendations for observing system requirements for monitoring upper-air temperature. The analysis focuses on accurately estimating monthly climatic data (specifically, monthly average temperature and its standard deviation) and multidecadal trends in monthly temperatures at specified locations, from the surface to 30 hPa. It does not address upper-air network size or station location issues. The effects of reducing the precision of temperature data, incomplete sampling of the diurnal cycle, incomplete sampling of the days of the month, imperfect long-term stability of the observations, and changes in observation schedule are assessed. To ensure accurate monthly climate statistics, observations with at least 0.5-K precision, made at least twice daily, at least once every two or three days are sufficient. Using ...