Publications Published in Review of Geophysics

1. Climate over past millenia. Jones, P D; Mann, M. E..
   Review of Geophysics: 2004
   Notes
   We review evidence for climate change over the past several millennia from instrumental and high-resolution climate ‘‘proxy’’ data sources and climate modeling studies. We focus on changes over the past 1 to 2 millennia. We assess reconstructions and modeling studies analyzing a number of different climate fields, including atmospheric circulation diagnostics, precipitation, and drought. We devote particular attention to proxy-based reconstructions of temperature patterns in past centuries, which place recent large-scale warming in an appropriate longer-term context. Our assessment affirms the conclusion that late 20th century warmth is unprecedented at hemispheric and, likely, global scales. There is more tentative evidence that particular modes of climate variability, such as the El Nino/Southern Oscillation and the North Atlantic Oscillation, may have exhibited late 20th century behavior that is anomalous in a long-term context. Regional conclusions, particularly for the Southern Hemisphere and parts of the tropics where highresolution proxy data are sparse, are more circumspect. The dramatic differences between regional and hemispheric/global past trends, and the distinction between changes in surface temperature and precipitation/drought fields, underscore the limited utility in the use of terms such as the "Little Ice Age’’ and ‘‘Medieval Warm Period’’ for describing past climate epochs during the last millennium. Comparison of empirical evidence with proxy-based reconstructions demonstrates that natural factors appear to explain relatively well the major surface temperature changes of the past millennium through the 19th century (including hemispheric means and some spatial patterns). Only anthropogenic forcing of climate, however, can explain the recent anomalous warming in the late 20th century. INDEX TERMS: 3344 Meteorology and Atmospheric Dynamics: Paleoclimatology; 3309 Meteorology and Atmospheric Dynamics: Climatology (1620); 1620 Global Change: Climate dynamics (3309); 3394 Meteorology and Atmospheric Dynamics: Instruments and techniques; 3354 Meteorology and Atmospheric Dynamics: Precipitation (1854);
   
   
   
2. Nonlinear multivariate and time series analysis by neural network methods. Hsieh, William W..
   Review of Geophysics: 2004
   Notes
   Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.
   
   
   
3. Nonlinear multivariate and time series analysis by nueral network methods. Hsieh, William W.
   Review of Geophysics: 2004
   Notes
   Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Nin˜o-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.
   
   
   
4. Present - day sea level change: observations and causes. Cazenave, A; Nerem, R S.
   Review of Geophysics: 2004
   Notes
   The determination of the present-day rate of sea level change is important for a variety of scientific and socioeconomic reasons. With over a decade of precision sea level measurements from satellite altimetry in hand and with the recent launch of new satellite missions addressing different aspects of sea level change, observationally, we have more information on sea level change than ever before. In fact, the geocentric rate of global mean sea level rise over the last decade (1993–2003) is now known to be very accurate, +2.8 ± 0.4 mm/yr, as determined from TOPEX/Poseidon and Jason altimeter measurements, 3.1 mm/yr if the effects of postglacial rebound are removed. This rate is significantly larger than the historical rate of sea level change measured by tide gauges during the past decades (in the range of 1–2 mm/yr). However, the altimetric rate could still be influenced by decadal variations of sea level unrelated to long-term climate change, such as the Pacific Decadal Oscillation, and thus a longer time series is needed to rule this out. There is evidence that the sea level rise observed over the last decade is largely due to thermal expansion, as opposed to the influx of freshwater mass from the continents. However, estimates of thermal expansion are still sufficiently uncertain to exclude some contribution of other sources, such as the melting of mountain glaciers and polar ice. Moreover, independent measurements of total ice melting during the 1990s suggest up to 0.8 mm/yr sea level rise, an amount that could eventually be canceled by change in land water storage caused by anthropogenic activities. Another important result of satellite altimetry concerns the nonuniform geographical distribution of sea level change, with some regions exhibiting trends about 10 times the global mean. Thermal expansion appears responsible for the observed regional variability. For the past 50 years, sea level trends caused by change in ocean heat storage also show high regional variability. The latter observation has led to questions about whether the rate of 20th century sea level rise, based on poorly distributed historical tide gauges, is really representative of the true global mean. Such a possibility has been the object of an active debate, and the discussion is far from being closed.
   
   
   
5. Review of mesospheric temperature trends. Beig, G; Keckhut, P; Lowe, R P; Roble, R G; Mlynczak, M G; Scheer, J; Fomichev, V I; Offermann, D; French, W J R; Sheperd, M G; Semenov, A I; Remsberg, E E; She, C Y; Lubken, F J; Bremer, J; Clemesha, B R; Stegman, J; Sigernes, F; Fadnavis, S.
   Review of Geophysics: 2003
   Notes
   In recent times it has become increasingly clear that releases of trace gases from human activity have a potential for causing change in the upper atmosphere. However, our knowledge of systematic changes and trends in the temperature of the mesosphere and lower thermosphere is relatively limited compared to the Earth's lower atmosphere, and not much effort as been made to synthesize these results so far. In this article, a comprehensive review of long-term trends in the temperature of the region from 50 to 100 km is made on the basis of the available up-to-date understanding of measurements and model calculations. An objective evaluation of the available data sets is attempted, and important uncertainly factors are discussed. Some natural variability factors, which are likely to play a role in modulating temperature trends, are also briefly touched upon. There are a growing number of experimental results centered on, or consistent with, zero temperature trend in the mesopause region (80–100 km). The most reliable data sets show no significant trend but an uncertainty of at least 2 K/decade. On the other hand, a majority of studies indicate negative trends in the lower and middle mesosphere with an amplitude of a few degrees (2–3 K) per decade. In tropical latitudes the cooling trend increases in the upper mesosphere. The most recent general circulation models indicate increased cooling closer to both poles in the middle mesosphere and a decrease in cooling toward the summer pole in the upper mesosphere. Quantitatively, the simulated cooling trend in the middle mesosphere produced only by CO2 increase is usually below the observed level. However, including other greenhouse ases and taking into account a “thermal shrinking” of the upper atmosphere result in a cooling of a few degrees per decade. This is close to the lower limit of the observed nonzero trends. In the mesopause region, recent model simulations produce trends, usually below 1 K/decade, that appear to be consistent with most observations in this region.