Generating global high-resolution current and future weather timeseries, and the implications for India

Buildings are responsible for 39% of world carbon emissions, mostly from heating and cooling driven by the weather. Hence, successfully designing buildings for climate mitigation and adaptation is fundamentally dependent on good quality current and future weather timeseries. Unfortunately, in most of the world and especially in the Global South, where the impacts of climate change are predicted to be greatest, data with sufficient geographic or temporal resolution do not exist. Here we demonstrate a new globally-relevant method to produce high spatial-resolution, carefully calibrated, mutually consistent, hourly, current and future typical weather years using low-cost and high-quality synthetic data. The approach is then applied to India, which currently accounts for 18% of world population and is expected to add around 14% of all new buildings in the world by 2050. This results in an order of magnitude improvement in spatial resolution (∼400 → 4790 locations) for current (1981-2010) climate, with future (2060-2089) climate represented for the first time. Systematic comparison of several methods suggested multivariate kriging as ideal to scale spatio-temporally sparse calibration data to all 4790 locations. By moving a calibrated computer model of a typical home over all 4790 locations, we find a mean increase of 3 K in indoor summer temperatures and a 51% increase in cooling demand by 2060 compared to 2010, and the potential for severe heat stress. Finally, we make these files free-to-use, thus creating the first such large-scale public repository for anywhere in the Global South, with potential use in many other fields such as infrastructure and crop resilience. Practical Application: As already shown, when completing simulations, unless weather data is highly local, the results can be out by a factor of two. This not only risks incorrect design decisions, but potentially undermines the energy simulation industry. In the global south this has the potential to lead to buildings that are a health risk to occupants, or unnecessarily high air conditioning loads placing strain on unstable electricity grids. By demonstrating a mathematically rigorous, validated, low-cost approach, this work demonstrates for the first time the potential for the whole Global South to be represented by data that will allow accurate predictions of temperatures and energy demand.