Spatio-Temporal Variations in Carbon Isotope Discrimination Predicted by the JULES Land Surface Model.

Stable carbon isotopes in plants can help evaluate and improve the representation of carbon and water cycles in land-surface models, increasing confidence in projections of vegetation response to climate change. Here, we evaluated the predictive skills of the Joint UK Land Environmental Simulator (JULES) to capture spatio-temporal variations in carbon isotope discrimination (Δ13C) reconstructed by tree rings at 12 sites in the United Kingdom over the period 1979-2016. Modeled and measured Δ13C time series were compared at each site and their relationships with local climate investigated. Modeled Δ13C time series were significantly correlated (p < 0.05) with tree-ring Δ13C at eight sites, but JULES underestimated mean Δ13C values at all sites, by up to 2.6‰. Differences in mean Δ13C may result from post-photosynthetic isotopic fractionations that were not considered in JULES. Inter-annual variability in Δ13C was also underestimated by JULES at all sites. While modeled Δ13C typically increased over time across the UK, tree-ring Δ13C values increased only at five sites located in the northern regions but decreased at the southern-most sites. Considering all sites together, JULES captured the overall influence of environmental drivers on Δ13C but failed to capture the direction of change in Δ13C caused by air temperature, atmospheric CO2 and vapor pressure deficit at some sites. Results indicate that the representation of carbon-water coupling in JULES could be improved to reproduce both the trend and magnitude of interannual variability in isotopic records, the influence of local climate on Δ13C, and to reduce uncertainties in predicting vegetation-environment interactions.

A global climate niche for giant trees.

Rainforests are among the most charismatic as well as the most endangered ecosystems of the world. However, although the effects of climate change on tropical forests resilience is a focus of intense research, the conditions for their equally impressive temperate counterparts remain poorly understood, and it remains unclear whether tropical and temperate rainforests have fundamental similarities or not. Here we use new global data from high precision laser altimetry equipment on satellites to reveal for the first time that across climate zones 'giant forests' are a distinct and universal phenomenon, reflected in a separate mode of canopy height (~40 m) worldwide. Occurrence of these giant forests (cutoff height > 25 m) is negatively correlated with variability in rainfall and temperature. We also demonstrate that their distribution is sharply limited to situations with a mean annual precipitation above a threshold of 1,500 mm that is surprisingly universal across tropical and temperate climates. The total area with such precipitation levels is projected to increase by ~4 million km2 globally. Our results thus imply that strategic management could in principle facilitate the expansion of giant forests, securing critically endangered biodiversity as well as carbon storage in selected regions.

Computationally efficient method for retrieving aerosol optical depth from ATSR-2 and AATSR data.

We present a robust and computationally efficient method for retrieving aerosol optical depth (AOD) from top-of-atmosphere ATSR-2 (Along-Track Scanning Radiometer) and AATSR (Advanced ATSR) reflectance data that is formulated to allow retrieval of the AOD from the 11 year archive of (A)ATSR data on the global scale. The approach uses a physical model of light scattering that requires no a priori information on the land surface. Computational efficiency is achieved by using precalculated lookup tables (LUTs) for the numerical inversion of a radiative-transfer model of the atmosphere. Estimates of AOD retrieved by the LUT approach are tested on AATSR data for a range of global land surfaces and are shown to be highly correlated with sunphotometer measurements of the AOD at 550 nm. (Pearson's correlation coefficient r(2) is 0.71.).