CGD's Dr. Gerald Meehl

Abstract

Observations since the middle of the 19th century show that the decadal solar oscillation at its peaks strengthens the major convergence zones in the tropical Pacific (Intertropical Convergence Zone, ITCZ, and South Pacific Convergence Zone, SPCZ) during northern winter. Through an amplifying set of coupled feedbacks, a set of processes is described that link solar forcing and its response in the tropical Pacific with reductions in precipitation in the northwest U.S. The process begins with an increase in solar forcing which results in a strengthening of the major convergence zones in the tropical Pacific. This then increases the precipitation in those regions and increases the southeast trade winds. Stronger trades increase the upwelling of colder water in the eastern equatorial Pacific and extend the cold tongue westward, thus reducing precipitation in the western Pacific. This redistribution of diabatic heating and associated convective heating anomalies thus produces anomalies in the tropical Hadley (north-south) and Walker (east-west) circulations. Additionally, the resulting anomalous Rossby wave response in the atmosphere, and consequent positive sea level pressure anomalies in the eastern region of the Aleutian Low in the North Pacific that extends to western North America, is associated with reductions of precipitation in the northwest U.S.

Figure caption: a) The average anomalies of sea surface temperature in the 11 solar peak years from 1900 to the late 1990s (°C) from the Hadley SST dataset, December-January-February season averages. Yellow areas denote significance at and above the 95% level, indicating the relative magnitude of the anomalies compared to the noise. Negative values are dashed contours, positive are solid; b) same as in (a) but for additional solar peaks not included in (a) for the years 1860, 1870, 2000, using the NOAA Extended Reconstructed Sea Surface Temperature dataset, available from: http://www.cdc.noaa.gov/cdc/data.noaa.ersst.html with more details given in http://lwf.ncdc.noaa.gov/oa/climate/research/sst/sst.html.

 

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Meehl, G. A., T. F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory, A. Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper, I.G. Watterson, A.J. Weaver, and Z. -C. Zhao, 2007: Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 747--845.

The future climate change results assessed in this chapter are based on a hierarchy of models, ranging from Atmosphere-Ocean General Circulation Models (AOGCMs) and Earth System Models of Intermediate Complexity (EMICs) to Simple Climate Models (SCMs). These models are forced with concentrations of greenhouse gases and other constituents derived from various emissions scenarios ranging from non-mitigation scenarios to idealised long-term scenarios. In general, we assess non-mitigated projections of future climate change at scales from global to hundreds of kilometres. Further assessments of regional and local climate changes are provided in Chapter 11. Due to an unprecedented, joint effort by many modelling groups worldwide, climate change projections are now based on multi-model means, differences between models can be assessed quantitatively and in some instances, estimates of the probability of change of important climate system parameters complement expert judgement. New results corroborate those given in the Third Assessment Report (TAR). Continued greenhouse gas emissions at or above current rates will cause further warming and induce many changes in the global climate system during the 21st century that would very likely be larger than those observed during the 20th century.

Figure caption: Multi-model means of surface warming (relative to 1980-1999) for the scenarios A2, A1B and B1, shown as continuations of the 20th-century simulation. Values beyond 2100 are for the stabilisation scenarios (see Section 10.7). Linear trends from the corresponding control runs have been removed from these time series. Lines show the multi-model means, shading denotes the ±1 standard deviation range of individual model annual means. Discontinuities between different periods have no physical meaning and are caused by the fact that the number of models that have run a given scenario is different for each period and scenario, as indicated by the coloured numbers given for each period and scenario at the bottom of the panel.

 

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Furrer, R., R. Knutti, S.R. Sain, D.W. Nychka, and G. A. Meehl, 2007: Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophysical Research Letters, 34, L06711, doi:10.1029/2006GL027754.

Abstract

We present probabilistic projections for spatial patterns of future temperature change using a multivariate Bayesian analysis. The methodology is applied to the output from 21 global coupled climate models used for the Fourth Assessment Report of the IPCC. The statistical technique is based on the assumption that spatial patterns of climate change can be separated into a large scale signal related to the true forced climate change and a small scale signal due to model bias and variability. The different scales are represented via dimension reduction techniques in a hierarchical Bayesian model. Posterior probabilities are obtained with a Markov chain and Monte Carlo simulation. We show that with 66% (90%) probability 79% (48%) of the land areas warm by more than 2°C by the end of the century for the SRES A1B scenario.

Figure caption: (top) DJF and (bottom) JJA temperature change in C by 2080-2100 in the A1B scenario (relative to 1980-2000) that is exceeded with 80% probability.