I like old time rootsy music, Charlie Patton, Jimmie Rodgers, Bessie Smith, Blind Lemon Jefferson, Cannon’s Jug Stompers, and other, more obscure, artists.
But if you are familiar with the old 78 rpm discs from which such music is taken, you will know that they have more snap, crackle and pop than a bowl of breakfast cereal. Play the original vinyl discs on however exquisite a modern system and you still get more noise than music.
Digital remastering has changed this.
One of the basic techniques of digital remastering comprises getting a picture of what noise and scratches and pops look like, and simply removing them from the signal. That this technique works can be attested to by your ears. Sweet roots music piped to your earphones.
The case study described below employs analogous techniques to remove the noise of natural variations from the climate record, allowing us to see what the underlying human influence is. While the results are not sweet music to anybody sane, they are clearly pertinent.
The study described below is only a small part of the evidence available to us concerning the human contribution to climate change. It was cited by the Australian Academy of Science in their recent statement on the issue. (See http://www.science.org.au/policy/climatechange.html).
I am presenting this study as an example of how scientists approach the important problem of sorting through and making sense of climate data.
As a last word, I caution the reader that I am not a scientist, and that for a definitive reading she or he should rely on the original publication.
Separating human from natural influences in the 20th century climate record
The Earth’s climate is changing, but determining how much of the change humans are responsible for is made more difficult because of natural variation. The different parts of the atmosphere interact with each other, and interact with the underlying oceans, and this produces natural variations in global climate. Before we can clearly see the human contribution to climate change, we need to isolate these natural variations and remove them from the picture.
Climate variations occur on time scales ranging from years to decades. Seasons, because they occur on a well-known fixed time scale, are not too much of a problem. But in general, the shorter the span we are dealing with, the more difficult it is to separate out the effects of human activities—greenhouse gases, aerosols, and so on—and distinguish them from natural variation.
For instance, there’s El Niño.
Every two to four years, but not on any schedule scientists can yet predict, the waters in the tropical east-central Pacific get warmer. That is El Niño, also referred to as the El Niño-Southern Oscillation (ENSO). When El Niño happens, its effect on the global climate is widespread, but, depending on how the atmosphere circulates it, this effect can be delayed in some far corners of the Earth by 3 to 6 months.
Thus, unpredictability and delayed climate reaction times make it tricky to disentangle El Niño’s particular influence from the climate record.
Scientists at GISS and at the GSFC Global Modeling and Assimilation Office, however, have developed a technique for doing just that. The technique accounts for El Niño’s immediate effect when it first appears in the Pacific, and for its delayed effect elsewhere in the world.
Using this technique, El Niño’s contributions to climate variation can be accounted for, isolated and put aside.
Two different datasets are used, GISTEMP and ERSST.v2. The first is the NASA-GISS dataset of global surface temperature. The second is the sea surface temperature dataset from NOAA/National Climactic Data Center.
- In the diagram, the orange curve shows the GISTEMP data, all effects included.
- The blue dash-dot curve shows the contribution of El Niño (ENSO) to temperature fluctuations.
- The brown triangles show what the temperature variations in the GISTEMP data look like when the effect of El Niño (ENSO) is subtracted.
- The red squares show temperature changes associated with long-term global warming in the corrected GISTEMP dataset.
- The green circles show temperature changes associated with long-term global warming as shown in the ERSST dataset.
Figure 2. Spatial patterns of long-term global warming as found in the GISTEMP (upper panel) and ERSST (lower panel) datasets. Orange and red colors represent warming and blue colors represent cooling over the period 1900-2003. (Click for large GIF or PDF of figure.)
Chen, J., A.D. Del Genio, B.E. Carlson, and M.G. Bosilovich, 2008: The spatiotemporal structure of twentieth-century climate variations in observations and reanalyses. Part I: Long-term trend. J. Climate, 21, 2611-2633, doi:10.1175/2007JCLI2011.1.
The dominant interannual El Niño-Southern Oscillation (ENSO) phenomenon and the short length of climate observation records make it difficult to study long-term climate variations in the spatiotemporal domain. Based on the fact that the ENSO signal spreads to remote regions and induces delayed climate variation through atmospheric teleconnections, an ENSO-removal method is developed through which the ENSO signal can be approximately removed at the grid box level from the spatiotemporal field of a climate parameter. After this signal is removed, long-term climate variations are isolated at mid- and low latitudes in the climate parameter fields from observed and reanalysis datasets. This paper addresses the long-term global warming trend (GW); a companion paper concentrates on Pacific pan-decadal variability (PDV). The warming that occurs in the Pacific basin (approximately 0.4 K in the twentieth century) is much weaker than in surrounding regions and the other two ocean basins (approximately 0.8 K). The modest warming in the Pacific basin is likely due to its dynamic nature on the interannual and decadal time scales and/or the leakage of upper ocean water through the Indonesian Throughflow. Based on the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis (ERA-40), a comprehensive atmospheric structure associated with the GW trend is given. Significant discrepancies exist between the two datasets, especially in the tightly coupled dynamics and water vapor fields. The dynamics fields based on NCEP-NCAR, which show a change in the Walker Circulation, are consistent with the GW change in the surface temperature field. However, intensification in the Hadley Circulation is associated with GW trend in ERA-40 instead.