Changes in temperature extremes for 21st century scenarios over South America derived from a multi-model ensemble of regional climate models

Abstract

This study examines a set of 4 temperature extreme indices (cold and warm nighttime and daytime indices) from an ensemble of 4 regional climate models (RCMs) for present and future periods in South America (SA). These models were integrated in the framework of the CLARIS-LPB 7FP-EU-project. We analyze the capability of RCMs to reproduce such indices and explore changes projected by the models under the scenario A1B for the end of the 21st century. The work also analyzes the role of cloudiness, surface radiative forcing and meridional wind components on temperature extremes. Future projections show that, during the austral summer, the increase in the frequency of warm nights is larger than that projected for warm days. This result agrees with the projections for the seasonal mean fields of minimum temperature (TN) versus the maximum temperature (TX) over most of SA. The analysis for the La Plata Basin suggests that this behavior is consistent with the cooling effect of cloud cover affecting TX, while TN is affected by nighttime greenhouse warming. The relationship between cloudiness and TN and TX anomalies shows a nonlinear behavior for near full cloud cover conditions. Although TX and TN anomalies are sensitive to strong cold air advection, TX is more sensitive than TN. Thus, RCMs are useful tools to analyze both the spatial pattern of temperature extremes and the climatic factors involved.

Observed and Projected Hydroclimate Changes in the Andes

“…The reliability of the projected changes from RCMs may also be built after assessing the extent to which models are able to represent present climate conditions. Several studies have focused on assessing the reliability of regional climate models over the Andes and most of them found that RCMs usually present systematic biases, such as an underestimation of temperature at different altitudes (López-Franca et al, 2016) and a overestimation of precipitation over the mountains, in particular over the eastern Andes slopes (Solman and Blazquez, 2019; see Figure 2a in Menéndez et al, 2016). The origin of these biases lies, at least partly, in unresolved topographical forcing which remains too smoothed over the complex terrain of the Andes, limiting the simulation of regional processes such as the interactions between local circulation and orography, a key factor in climate modulation at meso-scale levels.…”

Robust climate change research: a review on multi-model analysis

“…Third, how to choose the competent model from the intended model sets to successfully fulfill the research tasks is another key challenge. In addition to the wills of the modelers, applicability of such models to specific targeted problems is also a crucial consideration of model options, including solidifying model structures, data availability at the micro level, and solving feasibility, especially for IAM-based MMC frameworks and non-IAM multi-model ensemble analysis (Tavoni et al 2014, Zhang et al 2015, Lopez-Franca et al 2016, Romera et al 2017.…”

Capability of the SMHI‐RCA4 RCM driven by the ERA‐Interim reanalysis to simulate heat waves in Argentina

“…Other possible factors that influence the daily Tmax over LPB, such as cloud cover and surface radiative forcing, were discussed by López‐Franca et al . (). They showed that an ensemble of RCMs was able to capture the role of cloudiness in decreasing Tmax during the daytime by reflecting solar radiation.…”

“…() and Solman () for summer mean temperature and López‐Franca et al . () for seasonal mean of maximum temperature. Carril et al .…”

“…López‐Franca et al . () examined a set of four extreme temperature indices from an ensemble of four RCMs for present and future periods in South America. They showed that the RCM ensemble captured the regional spatial features of the seasonal mean of minimum and maximum temperature better than any of the individual models.…”