Australia's Tinderbox Drought: An extreme natural event likely worsened by human-caused climate change.

We examine the characteristics and causes of southeast Australia's Tinderbox Drought (2017 to 2019) that preceded the Black Summer fire disaster. The Tinderbox Drought was characterized by cool season rainfall deficits of around -50% in three consecutive years, which was exceptionally unlikely in the context of natural variability alone. The precipitation deficits were initiated and sustained by an anomalous atmospheric circulation that diverted oceanic moisture away from the region, despite traditional indicators of drought risk in southeast Australia generally being in neutral states. Moisture deficits were intensified by unusually high temperatures, high vapor pressure deficits, and sustained reductions in terrestrial water availability. Anthropogenic forcing intensified the rainfall deficits of the Tinderbox Drought by around 18% with an interquartile range of 34.9 to -13.3% highlighting the considerable uncertainty in attributing droughts of this kind to human activity. Skillful predictability of this drought was possible by incorporating multiple remote and local predictors through machine learning, providing prospects for improving forecasting of droughts.

Plant profit maximization improves predictions of European forest responses to drought.

Knowledge of how water stress impacts the carbon and water cycles is a key uncertainty in terrestrial biosphere models. We tested a new profit maximization model, where photosynthetic uptake of CO2 is optimally traded against plant hydraulic function, as an alternative to the empirical functions commonly used in models to regulate gas exchange during periods of water stress. We conducted a multi-site evaluation of this model at the ecosystem scale, before and during major droughts in Europe. Additionally, we asked whether the maximum hydraulic conductance in the soil-plant continuum kmax (a key model parameter which is not commonly measured) could be predicted from long-term site climate. Compared with a control model with an empirical soil moisture function, the profit maximization model improved the simulation of evapotranspiration during the growing season, reducing the normalized mean square error by c. 63%, across mesic and xeric sites. We also showed that kmax could be estimated from long-term climate, with improvements in the simulation of evapotranspiration at eight out of the 10 forest sites during drought. Although the generalization of this approach is contingent upon determining kmax , it presents a mechanistic trait-based alternative to regulate canopy gas exchange in global models.

The plumbing of land surface models: is poor performance a result of methodology or data quality?

The PALS Land sUrface Model Benchmarking Evaluation pRoject (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave, surface air temperature and relative humidity. These results are explored here in greater detail and possible causes are investigated. We examine whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. We demonstrate that energy conservation in the observational data is not responsible for these results. We also show that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, we present evidence suggesting that the nature of this partitioning problem is likely shared among all contributing LSMs. While we do not find a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER, we do exclude multiple possible explanations and provide guidance on where future research should focus.

Which are the most important parameters for modelling carbon assimilation in boreal Norway spruce under elevated [CO(2)] and temperature conditions?

Photosynthesis is highly responsive to environmental and physiological variables, including phenology, foliage nitrogen (N) content, atmospheric CO2 concentration ([CO2]), irradiation (Q), air temperature (T) and vapour pressure deficit (D). Each of these responses is likely to be modified by long-term changes in climatic conditions such as rising air temperature and [CO2]. When modelling photosynthesis under climatic changes, which parameters are then most important to calibrate for future conditions? To assess this, we used measurements of shoot carbon assimilation rates and microclimate conditions collected at Flakaliden, northern Sweden. Twelve 40-year-old Norway spruce trees were enclosed in whole-tree chambers and exposed to elevated [CO2] and elevated air temperature, separately and in combination. The treatments imposed were elevated temperature, +2.8 °C in July/August and +5.6 °C in December above ambient, and [CO2] (ambient CO2 ∼370 µ mol mol(-1), elevated CO2 ∼700 µ mol mol(-1)). The relative importance of parameterization of Q, T and D responses for effects on the photosynthetic rate, expressed on a projected needle area, and the annual shoot carbon uptake was quantified using an empirical shoot photosynthesis model, which was developed and fitted to the measurements. The functional form of the response curves was established using an artificial neural network. The [CO2] treatment increased annual shoot carbon (C) uptake by 50%. Most important was effects on the light response curve, with a 67% increase in light-saturated photosynthetic rate, and a 52% increase in the initial slope of the light response curve. An interactive effect of light saturated photosynthetic rate was found with foliage N status, but no interactive effect for high temperature and high CO2. The air temperature treatment increased the annual shoot C uptake by 44%. The most important parameter was the seasonality, with an elongation of the growing season by almost 4 weeks. The temperature response curve was almost flat over much of the temperature range. A shift in temperature optimum had thus an insignificant effect on modelled annual shoot C uptake. The combined temperature and [CO2] treatment resulted in a 74% increase in annual shoot C uptake compared with ambient conditions, with no clear interactive effects on parameter values.