Abstract. In response to a warming climate, temperature extremes are changing in many regions of the world. Therefore, understanding how the fluxes of sensible heat, latent heat and net ecosystem exchange respond and contribute to these changes is important. We examined 216 sites from the open access Tier 1 FLUXNET2015 and Free-Fair-Use La Thuile datasets, focussing only on observed (non-gap filled) data periods. We examined the availability of sensible heat, latent heat and net ecosystem exchange observations coincident in time with measured temperature for all temperatures, and separately for the upper and lower tail of the temperature distribution and expressed this availability as a measurement ratio. We showed that the measurement ratios for both sensible and latent heat fluxes are generally lower (0.79 and 0.73 respectively) than for temperature, and the measurement ratio of net ecosystem exchange measurements are appreciably lower (0.42). However, sites do exist with a high proportion of measured sensible and latent heat fluxes, mostly over the United States, Europe and Australia. Few sites have a high proportion of measured fluxes at the lower tail of the temperature distribution over very cold regions (e.g. Alaska, Russia) and at the upper tail in many warm regions (e.g. Central America and the majority of the Mediterranean region), and many of the world’s coldest and hottest regions are not represented in the freely available FLUXNET data at all (e.g. India, the Gulf States, Greenland and Antarctica). However, some sites do provide measured fluxes at extreme temperatures suggesting an opportunity for the FLUXNET community to share strategies to increase measurement availability at the tails of the temperature distribution. We also highlight a wide discrepancy between the measurement ratios across FLUXNET sites that is not related to the actual temperature or rainfall regimes at the site, which we cannot explain. Our analysis provides guidance to help select eddy covariance sites for researchers interested in exploring responses to temperature extremes.
Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network
