by Jonathan Wille from the Swiss Federal Institute of Technology in Zurich (ETH Zurich)

Temperature changes within a day (diurnal temperature range) and the day-to-day temperature changes (inter-diurnal temperature variability) have major impacts on agricultural practices and energy providers. Large swings in temperature can introduce heat and water stress for crop yields while creating sudden spikes in energy demand  (Lobell, 2007). Thus, properly simulating temperature variability in both the present and future climate is essential for projecting potential global warming impacts on daily heat stress. The enhanced resolution of the nextGEMS models offer a more detailed picture on local temperature variability thus potentially enabling communities to better prepare for future temperature variations. Before this can be realized, we must test the realism of these processes in the nextGEMS IFS and ICON models (cycle 3) in the present climate to ensure their future projections can be considered reliable. 

Temperature variability in mountainous regions 

Focusing first on the high-resolution capabilities of the IFS and ICON models, we see that possessing a horizontal resolution of ~5 km allows both models to capture the variations in the DTR (diurnal temperature range) across the complex topography of the European Alpes during the wintertime months of December, January, and February (Figure 1a and 1b). To verify if the values shown here can be considered realistic, we use the Copernicus European Regional ReAnalysis (CERRA) which uses past measurements and data assimilation to create a high-resolution depiction of temperature behavior. When compared with the nextGEMS IFS and ICON, we see that the ICON has a DTR that is much larger than observed in CERRA, while the IFS is closer to the reanalysis temperature behavior (Figure 1c and 1d). When looking at the IDTV (inter-diurnal temperature variability), similar patterns in the ICON and IFS biases are observed. These differences which are greatest in the winter months may be the result of the ICON having too few clouds thus creating greater daily temperature variability, but further testing is needed to verify this.

Figure 1: The average diurnal temperature range for DJF (December, January, and February) for a) ICON and b) IFS models along with their respective differences (depicted as difference in standard deviations) from the CERRA reanalysis (c, d).

A global look at temperature variability 

To see whether these patterns in temperature variability and associated biases in the European Alpes are isolated examples or representative of the broader globe, we repeated a similar analysis globally using the ERA5 reanalysis with the nextGEMS ICON and IFS interpolated to a lower resolution.  Within the ICON model, there is a great deal of spatial variability, but some patterns emerge. For instance, the high DTR bias in the European Alpes are observed again in the South American Andes and parts of the Himalayas (Figure 1a and 1c). There is also a strong gradient between overestimated and underestimated DTR separating the higher and lower latitudes respectively in the Northern hemisphere during winter. The DTR simulated in the IFS model is generally closer to the ERA5 reanalysis aside from notable areas of overestimation in equatorial regions of Southern America and Africa (Figure 1b and 1d).

Figure 2: The global differences in average diurnal temperature range for(a, c) ICON and (b, d) IFS for (a ,b) DJF (December, January, and February) and (c, d) JJA (June, July, and August) when compared with the ERA5 reanalysis.

Final thoughts

These preliminary results demonstrate the added benefits in resolving temperature variability in complex terrain using the nextGEMS storm-resolving Earth system models. The ability to resolve temperature variability within individual mountain valleys and peaks will prove beneficial to the communities residing in these areas when planning for future changes in temperature. This analysis also reveals areas where the nextGEMS models’ temperature behavior differs from observations, especially in the ICON model. While these biases are sometimes significant, similar biases also appear in coarser resolution CMIP5 simulations (Cattiaux et al., 2015), highlighting the challenge of accurately simulating daily temperature variability.  

References


Cattiaux, J., Douville, H., Schoetter, R., Parey, S., & Yiou, P. (2015). Projected increase in diurnal and interdiurnal variations of European summer temperatures. Geophysical Research Letters42(3), 899–907. https://doi.org/10.1002/2014GL062531
Lobell, D. B. (2007). Changes in diurnal temperature range and national cereal yields. Agricultural and Forest Meteorology145(3), 229–238. https://doi.org/10.1016/j.agrformet.2007.05.002

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