The devastating 2022 summer flood in Pakistan displaced about 7 million people in the Sindh province alone. Up to one third of the country’s area, mostly the country’s south, was flooded. Effective response to intensifying and compounding climate change hazards requires impact assessments to include socio-economic components, as well as uncertainties arising from the dynamic interactions between impacts. Such quantitative evidence largely remains limited and fragmented, due to methodological challenges and data limitations. Using the open-source impact assessment platform CLIMADA, we study to what extent flood-related hazards can be used to quantify displacement outcomes in a data-limited region. Using flood depths, exposed population, and impact functions, we link flood vulnerability to displaced people. This allows us to estimate internal displacement resulting from the flood event, and to further assess how displacement varies across different areas. We find that a flood depth threshold of 0.67m (CI 0.35 - 1.10) provides a best fit to all data from Sindh province. We find a negative correlation between displacement and the degree of urbanisation. By testing the performance of our model in explaining differing displacement estimates reported across Pakistan, we show the limitations of existing impact assessment frameworks. We emphasise the importance of estimating potential displacement alongside other impacts to better characterise, communicate, and ultimately respond to the impacts of floods.

Large increases in joint heat-humidity extremes are a robust feature of climate projections, more so than increases in either variable independently. The wet-bulb temperature threshold of 35°C is considered particularly important, corresponding to the level at which it is impossible for the human body to maintain homeostasis. Our analysis of more than 7500 global weather stations from HadISD reveals that instances of 35°C have already been briefly but reliably recorded in some cities of the coastal Middle East. Additionally, South Asia and coastal areas throughout the subtropics regularly experience values perilously close to this threshold. Station and ERA-Interim reanalysis data both indicate that the most extreme heat-humidity combinations have increased significantly over the 1979-2017 period. We develop and employ a generalized-extreme-value model to estimate that the 30-year return value of global-maximum wet-bulb temperature at ERA-Interim resolution will exceed 35°C when global-mean temperature has risen between 1.5°C and 2.0°C above the pre-industrial, masking even higher values at smaller spatial scales. The occurrence in the coming decades of such severe heat and humidity over large populated regions represents a situation never before experienced. We also present observational evidence that these wet-bulb temperature extremes are fostered by locally high SSTs as well as modulated by regional dynamics such as monsoons. Large-scale climate modes of variability, such as ENSO, lead to highly correlated interannual variability between the number of global exceedances of different wet-bulb temperature thresholds. Overall, our results show that the wet-bulb temperature ‘safety margin’ between currently reported values and 35°C is both smaller than previously understood and rapidly shrinking, presenting a serious challenge to human survival in the hottest and most humid places on Earth.

Stochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for non-stationary conditions informed by the Global Climate Model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly Pressure Change Events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multi-variable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100-year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast US. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the northeast US. This SPG provides physically plausible weather ensembles for water resource studies involving climate change.