forward from the late 20<sup>th</sup> Century to the early 21<sup>st</sup> . By 2031, the composition of Tanzanian poverty, as well as the household earnings sources and expenditure patterns will change in ways that cannot be fully anticipated. We resolve this complication by treating all economic changes as comparative static deviations from the 2001 economy, allowing us to attribute poverty changes solely to climate‐based agricultural productivity changes, and not any other event that may cause vulnerability to change between climates in two different periods. Since we are interested in the poverty impacts of interannual variability, we adopt a short run factor market closure in which land, capital, and natural resources are immobile across sectors. Thus we assume that a farmer has already made planting decisions before the climate outcome for that year is realized, and therefore cannot respond to a (e.g.) favorable climate outcome by expanding area under cultivation.

Tanzanian poverty vulnerability to climate volatility between 1971 and 2031 is determined by simulating the interannual climate‐instrumented productivity change for each year of the four (GCM‐based) predicted yield series. We thus generate a percentage point change in the poverty rate for each of those years by GCM‐based yield series. All simulations are perturbations from the 2001 base year, and the resulting poverty rate changes are thus solely those due to climate realizations. This has the essential property of rendering our results comparable across years. For each climate‐yield series that we consider, we then have a series of poverty impacts that are the result of simulating the climate‐instrumented productivity changes predicted to occur between 1971 and 2031.

We now analyze the distributions of these poverty changes. Based on the climate data that are the average across the 22 GCMs, we find that the median poverty rate change – measured as the percentage point difference from the national poverty rate in 2001 – is higher in the early 21<sup>st</sup> Century than in the late 20<sup>th</sup> Century (panel A, Figure 2). In the 20<sup>th</sup> Century, this median poverty rate change was ‐0.06 percent of the population – a poverty decrease. However, in the 21<sup>st</sup> Century, the median poverty change was ‐0.01 percent – a smaller decrease in the poverty rate. The 0.05 percentage point difference is equivalent to approximately 17.23 thousand people. There are fewer years in the future when climate outcomes would have been poverty decreasing than under current climate, as evidenced by a rightward (i.e. poverty increasing) shift of the mass of the interannual poverty change distribution.

The ensemble mean of the 22 GCMs (which is bias‐corrected to the historical mean and interannual standard deviation) thus suggests that changes in temperature and precipitation volatility could have the net effect of increasing poverty vulnerability, with the distribution shifting in the positive direction (panel A, Figure 2). However, near‐term, decadal‐scale climate prediction remains one of the most challenging problems in climate science (e.g. Keenlyside et al, 2008), and it is thus unclear exactly how Tanzanian climate volatility will actually change in the next two decades. Nonetheless, the CMIP3 GCM ensemble does provide some quantification of the envelope of potential change based on different representations of climate system processes and “initial conditions” (for a discussion of sources of uncertainty in regional climate change, see Giorgi et al, 2008). We therefore also analyze the pooled and individual GCM realizations, and by simulating climate predictions from these specific models, we are able to capture yield and poverty impacts that are at the upper and lower bounds of temperature and precipitation possibilities. Reality will surely falling somewhere in between.

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