How Daily Temperature and Precipitation Distributions Evolve With Global Surface Temperature.

Abstract

The climate is an aggregate of the mean and variability of a range of meteorological variables, notably temperature (T) and precipitation (P). While the impacts of an increase in global mean surface temperature (GMST) are commonly quantified through changes in regional means and extreme value distributions, a concurrent shift in the shapes of the distributions of daily T and P is arguably equally important. Here, we employ a 30-member ensemble of coupled climate model simulations (CESM1 LENS) to consistently quantify the changes of regionally and seasonally resolved probability density functions of daily T and P as function of GMST. Focusing on aggregate regions covering both populated and rural zones, we identify large regional and seasonal diversity in the probability density functions and quantify where CESM1 projects the most noticeable changes compared to the preindustrial era. As global temperature increases, Europe and the United States are projected to see a rapid reduction in wintertime cold days, and East Asia to experience a strong increase in intense summertime precipitation. Southern Africa may see a shift to a more intrinsically variable climate but with little change in mean properties. The sensitivities of Arctic and African intrinsic variability to GMST are found to be particularly high. Our results highlight the need to further quantify future changes to daily temperature and precipitation distributions as an integral part of preparing for the societal and ecological impacts of climate change and show how large ensemble simulations can be a useful tool for such research.Plain Language Summary The weather varies naturally from day to day and between regions, seasons, and years. Ecosystems and our society are both adapted to the average weather conditions of a given place and to how variable the temperature and rainfall amounts are around that average. As the global surface temperature changes, whether through natural cycles or human interference, so may this variability. In this paper, we investigate changes to the distributions of daily temperature and rainfall for different levels of surface temperature increase. By using a large set of simulations from the same climate model, we estimate their means and shapes, currently and in the near future. We find that in parts of Europe and the United States, wintertime cold days will disappear more rapidly with global warming than hot days increase, leading to a less variable climate state. In Africa and the Arctic, however, climate conditions will rapidly transition out of the range of preindustrial variability and into a climate state not yet experienced by modern society. We emphasize that in order to prepare for the impacts of climate change, we need information about changes to average properties and to extreme events and about the potential changes to daily variability itself.

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

“…Whether variability changes matter for impacts needs to be assessed on a case-by-case basis. For example, changes in daily temperature variability can have a disproportionate effect on the tails and thus extreme events (Samset et al, 2019). However, there is a clear need to better validate model internal variability, as we found models to differ considerably in their magnitude of internal variability (consistent with Maher et al, 2020, andSchlunegger et al, 2020), a topic that has so far received less attention (Deser et al, 2018;Simpson et al, 2018).…”

The influence of variability on fire weather conditions in high latitude regions under present and future global warming

“…Building on methodology developed by Samset et al (2019), we quantify the PDFs of daily mean and maximum near-surface (2-meter) air temperature (SAT and SAT max ), precipitation (precip), minimum daily surface relative humidity (RH min ), and surface wind speed (wind). While other climatic and weather-related factors, such as soil moisture and snow, also influence wildfire regimes, we focus on the variables that pose the most direct risk factors and form the basis for the Canadian forest fire weather index (see below), which we use as an aggregate measure of the change in wildfire risk.…”

Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6

“…Another important source of uncertainty not explicitly addressable within the CMIP context is parameter uncertainty. Even within a single model structure, some response uncertainty can result from varying model parameters in a perturbed-physics ensemble (Murphy et al, 2004;Sanderson et al, 2008). Such parameter uncertainty is sampled inherently but non-systematically through a set of different models, such as CMIP.…”