Multiforced statistical assessments of greenhouse-gas-induced surface air temperature change 1890–1985     

Christian-D Schönwiese  Ursula Stähler 《Climate Dynamics》1991,6(1):23-33

Based on univariate correlation and coherence analyses and considering the physical basis of the relationships, a simple multiforced (multiple) statistical concept is used which correlates observational climatic time series simultaneously with volcanic, solar, ENSO, and the anthropogenic greenhouse gases forcing. This is appropriate to remove some natural climate noise in the observed data and to evaluate the components (signals) possibly due to the anthropogenic greenhouse gas forcing (CO₂, or equivalent CO₂ implying additional gases) during industrial time. In this paper, we apply this technique to 100 global box data time series 1890–1985, of the surface air temperature, using observed data from Hansen and Lebedeff. The results are presented in terms of latitudinal-seasonal and regional trends, where the observed trend patterns are compared with the hypothetical signals (statistical assessments) possibly due to anthropogenic greenhouse forcing. These latter signals can be amplified to enable a comparison with corresponding results from general circulation model (GCM) CO₂ doubling experiments. These observed-statistical assessments lead to results which are, at least qualitatively and in respect to the zonal mean temperatures, very similar to some GCM experiments indicating the maximum CO₂ doubling signals (statistical assessment > 12 K) in the arctic winter. However, these signals are moderate in the tropics and in the Southern Hemisphere (global average 2.8–4.4 K). As far as the industrial signals are concerned (observed period) these signals are somewhat larger (maximum 7 K, global average 0.5–0.9 K) than the observed trends (maximum 5 K, global average 0.5 K). Phase shifts of cause and effect may amplify these signals but are very uncertain.This paper was presented at the International Conference on Modelling of Global Climate Change and Variability, held in Hamburg 11–15 September 1989 under the auspices of the Meteorological Institute of the University of Hamburg and the Max Planck Institute for Meteorology. Guest Editor for these papers is Dr. L. Dümenil  相似文献   

Compilation method for 1 km grid data of monthly mean air temperature for quantitative assessments of climate change impacts   总被引：1，自引：0，他引：1  

Hideki Ueyama  Sachiho Adachi  Fujio Kimura 《Theoretical and Applied Climatology》2010,101(3-4):421-431

A new method is proposed to compile 1 km grid data of monthly mean air temperature by dynamically downscaling general circulation model (GCM) data with a regional climate model (RCM). The downscaling method used is a technique referred to as the pseudoglobal warming method to reduce GCM bias. For the grid data, RCM data were corrected with data from an existing meteorological network. The correction model for the RCM bias was developed by stepwise multiple regression analysis using the difference in the monthly mean air temperatures between the observation and RCM output as a dependent variable and the geographical factors as independent variables. Our method corrected the RCM bias from 1.69°C to 0.58°C for the month of August in the 1990s (1990–1999).  相似文献   

A commentary on “how to interpret expert judgment assessments of twenty-first century sea-level rise” by Hylke de Vries and Roderik SW van de Wal     

JL Bamber  WP Aspinall  RM Cooke 《Climatic change》2016,137(3-4):321-328

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