Impact of Land Surface Initialization Approach on Subseasonal Forecast Skill: a Regional Analysis in the Southern Hemisphere

The authors use a sophisticated coupled land-atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%-20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia

Allowable CO2 emissions based on regional and impact-related climate targets

© 2016 Macmillan Publishers Limited. All rights reserved. Global temperature targets, such as the widely accepted limit of an increase above pre-industrial temperatures of two degrees Celsius, may fail to communicate the urgency of reducing carbon dioxide (CO2) emissions. The translation of CO2 emissions into regional- and impact-related climate targets could be more powerful because such targets are more directly aligned with individual national interests. We illustrate this approach using regional changes in extreme temperatures and precipitation. These scale robustly with global temperature across scenarios, and thus with cumulative CO2 emissions. This is particularly relevant for changes in regional extreme temperatures on land, which are much greater than changes in the associated global mean

Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

Leaf-area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. We investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI data-set is generated using the MODIS LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980-2008) are carried out at 25 km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly-varying LAI data-sets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from -90 to 60 %. PFTs with high absolute LAI and low inter-annual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, whilst those with lower absolute LAI and higher inter-annual variability, such as croplands, were more sensitive. We show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of the terrestrial carbon fluxes, especially for PFTs with high inter-annual variability. Our study highlights that the accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence this will become critical in quantifying the uncertainty in future changes in primary production

Thirty-eight years of CO<sub>2</sub> fertilization has outpaced growing aridity to drive greening of Australian woody ecosystems

Climate change is projected to increase the imbalance between the supply (precipitation) and atmospheric demand for water (i.e., increased potential evapotranspiration), stressing plants in water-limited environments. Plants may be able to offset increasing aridity because rising CO2 increases water use efficiency. CO2 fertilization has also been cited as one of the drivers of the widespread "greening" phenomenon. However, attributing the size of this CO2 fertilization effect is complicated, due in part to a lack of long-term vegetation monitoring and interannual- to decadalscale climate variability. In this study we asked the question of how much CO2 has contributed towards greening. We focused our analysis on a broad aridity gradient spanning eastern Australia's woody ecosystems. Next we analyzed 38 years of satellite remote sensing estimates of vegetation greenness (normalized difference vegetation index, NDVI) to examine the role of CO2 in ameliorating climate change impacts. Multiple statistical techniques were applied to separate the CO2-attributable effects on greening from the changes in water supply and atmospheric aridity. Widespread vegetation greening occurred despite a warming climate, increases in vapor pressure deficit, and repeated record-breaking droughts and heat waves. Between 1982-2019 we found that NDVI increased (median 11.3 %) across 90.5 % of the woody regions. After masking disturbance effects (e.g., fire), we statistically estimated an 11.7 % increase in NDVI attributable to CO2, broadly consistent with a hypothesized theoretical expectation of an 8.6 % increase in water use efficiency due to rising CO2. In contrast to reports of a weakening CO2 fertilization effect, we found no consistent temporal change in the CO2 effect. We conclude rising CO2 has mitigated the effects of increasing aridity, repeated record-breaking droughts, and record-breaking heat waves in eastern Australia. However, we were unable to determine whether trees or grasses were the primary beneficiary of the CO2-induced change in water use efficiency, which has implications for projecting future ecosystem resilience. A more complete understanding of how CO2-induced changes in water use efficiency affect trees and non-tree vegetation is needed

One Stomatal Model to Rule Them All?:Toward Improved Representation of Carbon and Water Exchange in Global Models

Stomatal conductance schemes that optimize with respect to photosynthetic and hydraulic functions have been proposed to address biases in land-surface model (LSM) simulations during drought. However, systematic evaluations of both optimality-based and alternative empirical formulations for coupling carbon and water fluxes are lacking. Here, we embed 12 empirical and optimization approaches within a LSM framework. We use theoretical model experiments to explore parameter identifiability and understand how model behaviors differ in response to abiotic changes. We also evaluate the models against leaf-level observations of gas-exchange and hydraulic variables, from xeric to wet forest/woody species spanning a mean annual precipitation range of 361–3,286 mm yr−1. We find that models differ in how easily parameterized they are, due to: (a) poorly constrained optimality criteria (i.e., resulting in multiple solutions), (b) low influence parameters, (c) sensitivities to environmental drivers. In both the idealized experiments and compared to observations, sensitivities to variability in environmental drivers do not agree among models. Marked differences arise in sensitivities to soil moisture (soil water potential) and vapor pressure deficit. For example, stomatal closure rates at high vapor pressure deficit range between −45% and +70% of those observed. Although over half the new generation of stomatal schemes perform to a similar standard compared to observations of leaf-gas exchange, two models do so through large biases in simulated leaf water potential (up to 11 MPa). Our results provide guidance for LSM development, by highlighting key areas in need for additional experimentation and theory, and by constraining currently viable stomatal hypotheses

Predicting resilience through the lens of competing adjustments to vegetation function

There is a pressing need to better understand ecosystem resilience to droughts and heatwaves. Eco-evolutionary optimization approaches have been proposed as means to build this understanding in land surface models and improve their predictive capability, but competing approaches are yet to be tested together. Here, we coupled approaches that optimize canopy gas exchange and leaf nitrogen investment, respectively, extending both approaches to account for hydraulic impairment. We assessed model predictions using observations from a native Eucalyptus woodland that experienced repeated droughts and heatwaves between 2013 and 2020, whilst exposed to an elevated [CO2] treatment. Our combined approaches improved predictions of transpiration and enhanced the simulated magnitude of the CO2 fertilization effect on gross primary productivity. The competing approaches also worked consistently along axes of change in soil moisture, leaf area, and [CO2]. Despite predictions of a significant percentage loss of hydraulic conductivity due to embolism (PLC) in 2013, 2014, 2016, and 2017 (99th percentile PLC > 45%), simulated hydraulic legacy effects were small and short-lived (2 months). Our analysis suggests that leaf shedding and/or suppressed foliage growth formed a strategy to mitigate drought risk. Accounting for foliage responses to water availability has the potential to improve model predictions of ecosystem resilience

Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems

The vegetation's response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate-carbon cycle feedbacks requires improving our understanding of both the immediate and long-term plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e. droughts and heatwaves) and structural lags in the systems (such as delays between rainfall and peak plant water content or between a precipitation deficit and down-regulation of productivity) have largely been overlooked in the development of terrestrial biosphere models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years. Using data from 12 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr-1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). By focussing our analysis on a single continent (and predominately on a single genus), we reduced the degrees of variation between each site, providing a novel chance to explore the unique characteristics that might drive the importance of memory. Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. λE was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.93. When considered at a continental scale, both fluxes were more predictable when memory effects (expressed as lagged climate predictors) were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. Consistent with prior studies, the importance of environmental memory in predicting fluxes increased as site water availability declined (ρ-0.73, p<0.01 for NEE, ρ-0.67, p<0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a model of k-means clustering plus regression to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in individual-site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems

Identification of Amazonian Trees with DNA Barcodes

International audienc