• Energy Research

SummaryAtmospheric carbon dioxide concentration ([CO2]) is increasing, which increases leaf‐scale photosynthesis and intrinsic water‐use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO2] increase and thus climate change. However, ecosystem CO2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO2]‐driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO2] (iCO2) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre‐industrial times. Established theory, supported by experiments, indicates that iCO2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO2 responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO2, albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

Abstract Surface Solar Irradiance (SSI) is required for solar energy planning and adoption, and is a fundamental parameter in modelling weather, climate, ecosystem and agricultural activities. Herein a time series based radiative transfer model was developed to simultaneously retrieve properties of clouds, aerosols and surface albedo, which were in turn used to calculate the components of SSI: i.e., global, direct and diffuse irradiance. The calculated results were calibrated across the Australian continent against in-situ measurements to account for minor factors (e.g., water vapor) not considered by the physics-based method. Detailed validation of the SSI components was performed against three years of in-situ measurements at 11 sites across Australia, at a range of time scales (i.e., instantaneous, hourly, daily and monthly) and under both all-sky and cloudy-sky conditions. The main advantage of the present method is the reliable separation of the direct and diffuse components with consistently low biases (~4 W/m2) at all four time scales, while still maintaining relatively low RMSE (root-mean-square-error) and MAE (mean-absolute-error). Once calibration has been performed the model does not require any ancillary data when implemented operationally: that is the model only requires geostationary satellite data. This model may be implemented across the globe using widely available next generation geostationary satellite data with a handful of ground-data for calibration.

Summary For instantaneous latent heat flux ( λE ) estimates from thermal remote sensing data to be useful in the hydrologic sciences, they require integration over longer time frames (e.g., months to years). This is not trivial because thermal remote sensing data acquired under cloud-free daytime conditions require upscaling to a monthly energy amount that is both relevant over cloudy periods and considers daytime and nighttime. Previous work has compared upscaling approaches, but as yet there is no authoritative comparison that does so under conditions relevant for thermal remote sensing. In this paper we describe, under the conditions relevant for thermal remote sensing, a generic framework for comparing any upscaling approach that assumes self-preservation. Then we use eddy-flux data from two sites in contrasting climates to systematically evaluate the accuracy of different upscaling proposals within the framework. We assumed that the instantaneous estimate of the latent heat flux measured by the eddy-flux technique would have been measured by a satellite sensor. We then scaled this estimate to a monthly period using four approaches and compared the result with the observed monthly integral. This design enabled us to isolate the accuracy of each upscaling method. The four methods upscaled λE by: (i) observed solar irradiance ( S ); (ii) modelled solar irradiance from a sine function ( S SIN ); (iii) modelled top-of-atmosphere solar irradiance ( S TOA ); and (iv) observed available energy ( A E ). We showed that upscaling λE using observed data ( S , A E ) resulted in underestimation of monthly evaporative energy, while the use of modelled data ( S SIN , S TOA ) led to overestimation, primarily due to the relationship between error and both the season (day-of-year) and cloud fraction. Of the two observed fluxes, upscaling with S resulted in lower overall errors than when using A E ( S bias: −1.11 M J m −2 d −1 or −16%; A E bias: −2.15 M J m −2 d −1 or −34%). Of the two modelled fluxes, upscaling with S TOA had lower errors than the widely used S SIN method ( S SIN bias: 1.03 M J m −2 d −1 or 14%; S TOA bias: 0.91 M J m −2 d −1 or 13%). We subsequently developed a simple procedure to minimise bias from all four upscaling approaches, and concluded that modelled data ( S TOA ) can be used to upscale λE to longer timescales for thermal remote sensing applications. This study developed the theory to minimise upscaling bias at two sites with contrasting climates, further work is needed to extend the approach to all global terrestrial climates.

AbstractVegetation is often viewed as a consequence of long‐term climate conditions. However, vegetation itself plays a fundamental role in shaping Earth's climate by regulating the energy, water, and biogeochemical cycles across terrestrial landscapes. It exerts influence by consuming water resources through transpiration and interception, lowering atmospheric CO2 concentration, altering surface roughness, and controlling net radiation and its partitioning into sensible and latent heat fluxes. This influence propagates through the atmosphere, from microclimate scales to the entire atmospheric boundary layer, subsequently impacting large‐scale circulation and the global transport of heat and moisture. Understanding the feedbacks between vegetation and atmosphere across multiple scales is crucial for predicting the influence of land use and land cover changes, and for accurately representing these processes in climate models. This review discusses the biophysical and biogeochemical mechanisms through which vegetation modulates climate across spatial and temporal scales. Particularly, we evaluate the influence of vegetation on circulation patterns, precipitation, and temperature, considering both long‐term trends and extreme events, such as droughts and heatwaves. Our goal is to highlight the state of science and review recent studies that may help advance our collective understanding of vegetation feedbacks and the role they play in climate.

Extra water loss from earlier spring greening dries soils locally in summer, but can moisten soils in areas downwind.