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.
This article is a follow up to my article from 12-days ago, “Dramatic Confirmation of Temperature Change for 1980-2010”, which presented a global map that showed using the AgMERRA dataset temperature trends between 1980 and 2010 that were statistically significant, in some sense showing climate change, or at least the areas that have experienced climate change over the last 30 years.
Jawoo Koo from the International Food Policy Research Institute (IFPRI) suggested that I examine another global dataset, the CRU Time Series, by Phil Jones and Ian Harris, from the University of East Anglia’s Climate Research Unit. This dataset spans the period 1901 to 2012, much longer than that of AgMERRA, which was only 1980 to 2010. CRU has wider geographic coverage, as well, reaching to the northern- and southern-most parts of the earth. Like the AgMERRA dataset, the CRU dataset has a half-degree resolution. However, unlike the AgMERRA dataset, the CRU only has monthly statistics rather than daily weather.
The map above shows the results. They are very similar to those of AgMERRA from the earlier article. This almost had to be the case, since they were using similar reference data to build the datasets. What is perhaps noteworthy is how AgMERRA’s use of satellite data produced differences. One might note, for example, cooling in southern India that was in AgMERRA but not in CRU.
The table shows the results of analysis for every 30-year period covered in the CRU dataset. The dataset has 67,420 land-based gridcells with data. We see that the 1980 to 2010 period had the highest number of gridcells that had statistically significant changes in temperature. We also see that of those with at least 10% statistical significance, almost 92% were positive changes (increases in temperature). This was second highest to the 1970 to 2000 period.
I analyzed a new global gridded daily weather dataset to see what it would tell us about what climate change, particularly temperature change, we may have already witnessed during my lifetime. What I discovered surprised me very much. I was expecting that some places would show a 1-degree Celsius temperature increases over a 30-year period, with a few additional pixels showing up to a 2-degree increase. Instead, we see a lot of places with 1- and 2-degree increases, and even others with 3- and 4-degree temperature changes.
The dataset I used, 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. The data includes solar radiation, minimum temperature, maximum temperature, rainfall, and wind speed. My primary interest is in the monthly mean daily maximum temperature for the warmest month of the year (for brevity, “tmax”), since this may be the critical limiting factor for agriculture in the future under climate change.
My idea was simply to run a regression for each pixel on tmax using an intercept and the year as explanatory variables. The year parameter would be a measure of the year-to-year change in tmax. 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.