This is a companion to my article from 12-days ago, “Dramatic Confirmation of Temperature Change for 1980-2010”, which focused on a detailed geographical analysis of temperature trends in the past 30 years. These seemed to show clear signs of climate change. Rainfall is also a very important indicator of climate change, most importantly for agriculture. In this article, we use the same dataset, but focus on annual rainfall.
The dataset I use, AgMERRA, was recently developed by Alex Ruane of NASA and Richard Goldberg of the University of Chicago. The pixels are a half-degree in size, and the data spans the period of 1980 to 2010. As before, I run a regression for each pixel on annual rainfall using an intercept and the year as explanatory variables. The year parameter would be a measure of the year-to-year change in rainfall. I would be able to tell the statistical significance at each point by looking at the z-statistic for the parameter from the regression. Being somewhat a skeptic, my guess was that only a few pixels — maybe less than 10 percent of them — would have temperature changes that would be significant.
In the map we note changes in many parts of the world. Much of the Sahelian region of West Africa appears to be getting more rainfall, as well as typically dry parts of Southern Africa such as Namibia and Botswana. Very large increases seem to be happening in the Amazonian regions of Colombia and Peru, with declines in rainfall near the same area of Brazil where large temperature increases were noted, as well as in neighboring Bolivia. Much of the United States is untouched by changes in rainfall, except for areas of the West near where temperature increases were noted.
One word of caution to my readers: rainfall is notoriously difficult to interpolate, and using satellite data only helps a little bit. Nonetheless, noting statistically significant changes in rainfall may be helpful to farmers and policymakers alike, though one of the authors of the dataset, Alex Ruane, cautions against using AgMERRA for trend analysis, preferring, instead, to use weather station data.
– Timothy S. Thomas
References (as given by the authors who permitted me to use the data):
Ruane, A.C., and R. Goldberg, AgMIP Hybrid Baseline Climate Datasets: Shifted Reanalyses for Gap-filling and Historical Climate Series Estimation (in preparation).
(Tim’s note: I am trying to clarify this, but I believe an even more recent reference and site for downloading the data is at http://data.giss.nasa.gov/impacts/agmipcf/agmerra/).
Elliott, J., M. Glotter, N. Best, J. Chryssanthacopoulos, D. Kelly, M. Wilde, and I. Foster, (2013). The Parallel System for Integrating Impact Models and Sectors (pSIMS). Prepared for a special issue of Environmental Modeling and Software: Agricultural systems modeling & software (in review).
Elliott, J., M. Glotter, N. Best, D. Kelly, M. Wilde, and I. Foster, (2013). The Parallel System for Integrating Impact Models and Sectors (pSIMS). Proceedings of the 2013 XSEDE Conference, in press.