Search

Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development. [ABSTRACT FROM AUTHOR]

Semi‐arid ecosystems, common across the Australian continent, strongly influence the inter‐annual variability and trend in the global terrestrial net carbon sink. Here we explore the future Australian terrestrial carbon cycle using the CMIP6 ensemble, and the dynamic global vegetation model LPJ‐GUESS. Uncertainty in Australia's carbon storage in vegetation ranged between 6 and 49 PgC at the end of the century and was strongly linked to biases in the meteorological forcing. Using LPJ‐GUESS with bias‐corrected meteorological forcing reduced uncertainty in the vegetation carbon storage to between 14 and 20 PgC, with the remaining range linked to model sensitivities to rising atmospheric CO2 concentration, temperature, and precipitation variability. Reducing this uncertainty will require improved terrestrial biosphere models, but also major improvements in the simulation of regional precipitation by Global Climate Models. Plain Language Summary: Vegetation on land takes up about one‐third of greenhouse gas emissions associated with human activities and strongly influences the year‐to‐year variability in the atmospheric growth rate of CO2. Semi‐arid areas in the Southern Hemisphere, for example, in Australia, dominate the variability and trend in the net carbon sink. We use both coupled climate‐carbon cycle and uncoupled carbon cycle simulations to study future changes in the Australian carbon cycle and provide the first comprehensive regional analysis of future carbon cycle trends for Australia accounting for Global Climate Model uncertainty. Biases in the simulated climate lead to large uncertainty in the simulated carbon cycle but this uncertainty can be reduced when the biases are corrected. We identify areas of uncertainty in the process representation in the vegetation models, specifically carbon residence time, and a discrepancy in the sensitivity to three forcing variables (atmospheric CO2 concentration, temperature, and precipitation). Our study highlights that reducing precipitation biases in Global Climate Models would lead to a better understanding of future changes to the regional carbon cycle and help inform climate mitigation strategies. Key Points: A key area of future uncertainty in regional terrestrial carbon cycle projections is linked to biases in climate projectionsWe provide a comprehensive regional analysis of future carbon cycle trends for Australia accounting for uncertainty in climate projectionsApplying bias‐correction to the climate projections reduces the uncertainty in projected vegetation carbon from 6 – 49 PgC to 14–20 PgC [ABSTRACT FROM AUTHOR]

Global environmental change has implications for the spatial and temporal distribution of water resources, but quantifying its effects remains a challenge. The impact of vegetation responses to increasing atmospheric CO2 concentrations on the hydrologic cycle is particularly poorly constrained. Here we combine remotely sensed normalized difference vegetation index (NDVI) data and long-term water-balance evapotranspiration (ET) measurements from 190 unimpaired river basins across Australia during 1982-2010 to show that the precipitation threshold for water limitation of vegetation cover has significantly declined during the past three decades, whereas sub-humid and semi-arid basins are not only greening but also consuming more water, leading to significant (24-28%) reductions in streamflow. In contrast, wet and arid basins show nonsignificant changes in NDVI and reductions in ET. These observations are consistent with expected effects of elevated CO2 on vegetation. They suggest that projected future decreases in precipitation are likely to be compounded by increased vegetation water use, further reducing streamflow in water-stressed regions.

In drought-prone Australia, multi-year droughts have detrimental impacts on both the natural environment and human societies. For responsible water management, we need a thorough understanding of the full range of variability in multi-year droughts and how this might change in a warming world. But research into the long-term frequency, persistence, and severity of Australian droughts is limited. This is partly due to the length of the observational record, which is short relative to the timescales of hydroclimatic variability and hence may not capture the range of possible variability. Using simulations of Australian precipitation over the full past millennium (850–2000), we characterise the nature of multi-year meteorological droughts across Australia and include a particular focus on the Murray–Darling Basin (MDB), the largest agricultural region in Australia. We find that simulated Australian droughts in the 20th century (1900–2000) are within the bounds of pre-industrial natural variability in terms of drought intensity, severity, and frequency. A tendency for droughts to last longer in southwestern and eastern Australia (including the MDB) in the 20th century, compared with the pre-industrial period, suggests an emerging anthropogenic influence, consistent with projected rainfall changes in these regions. Large volcanic eruptions tend to promote drought-free intervals in the MDB. Model simulations of droughts over the last millennium suggest that future droughts across Australia could be much longer than what was experienced in the 20th century, even without any human influence. With the addition of anthropogenic climate change, which favours drought conditions across much of southern Australia due to reduced cool-season rainfall, it is likely that future droughts in Australia will exceed recent historical experience. [ABSTRACT FROM AUTHOR]

Climate projections from global circulation models (GCMs) part of the Coupled Model Intercomparison Project 6 (CMIP6) are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation, which hamper predictive capacity. In this study we examine different methods to constrain regional projections of the carbon cycle in Australia. We employ a dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias correct or calculate ensemble averages over the original forcing data to constrain the uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in carbon over time, highlighting the need to account for temporal properties in correction or ensemble averaging methods. Some bias correction methods reduce the ensemble uncertainty more than others. The vegetation distribution can depend on the bias correction method used. We further find that both the weighted ensemble averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. [ABSTRACT FROM AUTHOR]